{"collections":[{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/3DEP/10m","title":"USGS 3DEP 10m National Map Seamless (1/3 Arc-Second) [deprecated]","description":"This is the seamless 3DEP DEM dataset for the U.S.\nwith full coverage of the 48 conterminous states, Hawaii, and\nU.S. territories. Alaska coverage is partially available now and is being\nexpanded to statewide coverage as part of the Alaska Mapping Initiative.\nGround spacing is approximately 10 meters north/south, but variable\neast/west due to convergence of meridians with latitude.\n\nSpatial metadata dataset is ingested as a separate asset\n[USGS_3DEP_10m_metadata](USGS_3DEP_10m_metadata).\n\nThe 1m dataset is ingested\nas [USGS_3DEP_1m](USGS_3DEP_1m).\n\nDataset uploaded by [Farmers Business Network](https://fbn.com).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-171,-16.6,164,76.9]]},"temporal":{"interval":[["1998-08-16T00:00:00Z","2020-05-06T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/3DEP/10m","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_3DEP_10m","rel":"related","title":"Run the example for USGS/3DEP/10m in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_3DEP_10m_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.usgs.gov/information-policies-and-instructions/crediting-usgs","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_3DEP_10m_collection.json","rel":"latest-version","title":"USGS/3DEP/10m_collection","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NED.json","rel":"predecessor-version","title":"USGS/NED","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_3DEP_10m_collection.json","rel":"successor-version","title":"USGS/3DEP/10m_collection","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/3DEP/10m/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/3DEP/1m","title":"USGS 3DEP 1m National Map","description":"This is a tiled collection of images with 1m pixel size from the 3D\nElevation Program (3DEP). The 3DEP data holdings serve as the elevation\nlayer of The National Map and provide foundational elevation information for\nearth science studies and mapping applications in the United States.\n\nThe elevations in this DEM represent the topographic bare-earth surface.\nUSGS standard 1m pixel size DEMs are produced exclusively from high\nresolution light detection and ranging (lidar) source data of images with 1m\npixel size or higher resolution. 1m pixel size DEM surfaces are seamless\nwithin collection projects but not necessarily seamless across projects.\nThe spatial reference used for tiles of the 1m pixel size DEM within the\nconterminous United States (CONUS) is Universal Transverse Mercator (UTM) in\nunits of meters and in conformance with the North American Datum of 1983\n(NAD83). All bare earth elevation values are in meters and are referenced to\nthe North American Vertical Datum of 1988 (NAVD88). Each tile is distributed\nin the UTM Zone in which it lies. If a tile crosses two UTM zones, it is\ndelivered in both zones.\nIn this and other cases of image overlaps, elevation values might be\nslightly different in different images covering the same area.\n\nThe 1m pixel size DEM is the highest resolution standard DEM offered in the\n3DEP product suite. The 10m 3DEP dataset is available at\n[USGS_3DEP_10m_collection](USGS_3DEP_10m_collection).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-171,-16.6,164,76.9]]},"temporal":{"interval":[["2003-07-23T00:00:00Z","2025-04-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/3DEP/1m","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_3DEP_1m","rel":"related","title":"Run the example for USGS/3DEP/1m in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_3DEP_1m_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_1m#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.usgs.gov/information-policies-and-instructions/crediting-usgs","rel":"license","type":"text/html"},{"href":"s3://prd-tnm/StagedProducts/Elevation/1m/Projects","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/3DEP/1m/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/3DEP/1m/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"AAFC/ACI","title":"Canada AAFC Annual Crop Inventory","description":"Starting in 2009, the Earth Observation Team of the Science and Technology\nBranch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process\nof generating annual crop type digital maps. Focusing on the Prairie\nProvinces in 2009 and 2010, a Decision Tree (DT) based methodology was\napplied using optical (Landsat-5, AWiFS, DMC) and radar (Radarsat-2) based\nsatellite images. Beginning with the 2011 growing season, this activity has\nbeen extended to other provinces in support of a national crop inventory.\nTo date this approach can consistently deliver a crop inventory that meets\nthe overall target accuracy of at least 85% at a final spatial resolution of\n30m (56m in 2009 and 2010).\n","license":"OGL-Canada-2.0","extent":{"spatial":{"bbox":[[-135.17,36.83,-51.24,62.25]]},"temporal":{"interval":[["2009-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/AAFC/ACI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AAFC/AAFC_ACI","rel":"related","title":"Run the example for AAFC/ACI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/AAFC/AAFC_ACI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/AAFC_ACI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/OGL-Canada-2.0.html","rel":"license","type":"text/html"},{"href":"https://www.agr.gc.ca/atlas/data_donnees/agr/annualCropInventory/tif/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/AAFC/ACI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/AAFC/ACI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ACA/reef_habitat/v1_0","title":"Allen Coral Atlas (ACA) - Geomorphic Zonation and Benthic Habitat - v1.0 [deprecated]","description":"The [Allen Coral Atlas](https://allencoralatlas.org/) dataset maps the\ngeomorphic zonation and benthic habitat for the world's shallow coral reefs at\n5m pixel resolution. The underlying satellite image data are temporal\ncomposites of [PlanetScope satellite](https://www.planet.com/products/basemap/)\nimagery spanning 2018-2020. The habitat maps are created via a machine learning\napproach with contextual editing, using a range of imagery, bathymetry, and\nderived products as input data, trained via a globally consistent reference\ndataset. A global mask layer is also included for use when generating global\nreporting statistics. A full description of the methods and approaches can be\nfound in the methods section of the [Allen Coral Atlas\nwebsite](https://allencoralatlas.org/methods/).\n\nThe Allen Coral Atlas was funded by [Vulcan Inc.](https://vulcan.com/) and\nis managed by the [Arizona State University Center for Global Discovery and\nConservation Science](https://gdcs.asu.edu/). Partners include\n[Planet](https://www.planet.com/), the [University of\nQueensland](https://www.uq.edu.au/), and the [National Geographic\nSociety](https://www.nationalgeographic.org/).\n\nScientific background publications:\n\n- Lyons, M. B., Roelfsema, C. M., Kennedy, E. V., Kovacs, E. M., Borrego-Acevedo, R., Markey, K., ... & Murray, N. J. (2020). Mapping the world's coral reefs using a global multiscale earth observation framework. Remote Sensing in Ecology and Conservation, 6(4), 557-568. [doi:10.1002/rse2.157](https://doi.org/10.1002/rse2.157)\n\n- Kennedy, E. V., Roelfsema, C. M., Lyons, M. B., Kovacs, E. M., Borrego-Acevedo, R., Roe, M., ... & Tudman, P. (2021). Reef Cover, a coral reef classification for global habitat mapping from remote sensing. Scientific Data, 8(1), 1-20. [doi:10.1038/s41597-021-00958-z](https://doi.org/10.1038/s41597-021-00958-z)\n\n- Roelfsema, C. M., Lyons, M., Murray, N., Kovacs, E. M., Kennedy, E., Markey, K., ... & Phinn, S. R. (2021). Workflow for the generation of expert-derived training and validation data: a view to global scale habitat mapping. Frontiers in Marine Science.  [doi:10.3389/fmars.2021.643381](https://doi.org/10.3389/fmars.2021.643381)\n\n- Li, J., Knapp, D. E., Lyons, M., Roelfsema, C., Phinn, S., Schill, S. R., & Asner, G. P. (2021). Automated global shallow water bathymetry mapping using Google Earth Engine. Remote Sensing, 13(8), 1469. [doi:10.3390/rs13081469](https://doi.org/10.3390/rs13081469)\n\n- Li, J., Knapp, D. E., Fabina, N. S., Kennedy, E. V., Larsen, K., Lyons, M. B., ... & Asner, G. P. (2020). A global coral reef probability map generated using convolutional neural networks. Coral Reefs, 39(6), 1805-1815. [doi:10.1007/s00338-020-02005-6](https://doi.org/10.1007/s00338-020-02005-6)\n\nAllen Coral Atlas maps, bathymetry and map statistics are &copy; 2020 Allen\nCoral Atlas Partnership and Vulcan, Inc.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-33,180,33]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ACA/reef_habitat/v1_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ACA/ACA_reef_habitat_v1_0","rel":"related","title":"Run the example for ACA/reef_habitat/v1_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ACA/ACA_reef_habitat_v1_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ACA_reef_habitat_v1_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/coral-atlas-user-downloads-prod/Global-Dataset-20211006223100.zip","rel":"source"},{"href":"https://doi.org/10.5281/zenodo.3833242","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/ACA/ACA_reef_habitat_v2_0.json","rel":"latest-version","title":"ACA/reef_habitat/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/ACA/ACA_reef_habitat_v2_0.json","rel":"successor-version","title":"ACA/reef_habitat/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ACA/reef_habitat/v1_0/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ACA/reef_habitat/v2_0","title":"Allen Coral Atlas (ACA) - Geomorphic Zonation and Benthic Habitat - v2.0","description":"The [Allen Coral Atlas](https://allencoralatlas.org/) dataset maps the geomorphic\nzonation and benthic habitat for the world's shallow coral reefs at 5 m pixel\nresolution. Also included is a global reef extent product that maps additional\nreef areas unable to be explicitly included in the geomorphic and benthic mapping.\nThe underlying satellite image data are temporal composites of [PlanetScope\nsatellite](https://www.planet.com/products/basemap/) imagery spanning 2018-2020.\nThe habitat maps are created via a machine learning approach with contextual\nediting, using a range of imagery, bathymetry and derived products as input data,\ntrained via a globally consistent reference data set. A full description of the\nmethods and approaches can be found in the methods section of the [Allen Coral\nAtlas website](https://allencoralatlas.org/methods/).\n\nThe first version\n([v1.0](https://developers.google.com/earth-engine/datasets/catalog/ACA_reef_habitat_v1_0))\nof the Allen Coral Atlas was completed in Q4 2021, and this new version (v2.0) has\na wide range of improvements across the globe that incorporated both user feedback\non v1.0 and new technical developments in mapping methodology. A brief overview of\nthe changes can be found\n[here](https://allencoralatlas.org/blog/geomorphic-and-benthic-maps-2022-update/)\nand a more comprehensive technical summary can be found\n[here](https://storage.googleapis.com/coral-atlas-static-files/resources-page-materials/Allen_Coral_Atlas_2022_habitat_map_revisions.pdf).\n\nThe Allen Coral Atlas was funded by [Vulcan Inc.](https://vulcan.com/) and is\nmanaged by the [Arizona State University Center for Global Discovery and\nConservation Science](https://gdcs.asu.edu/). Partners include\n[Planet](https://www.planet.com/), the\n[University of Queensland](https://www.uq.edu.au/), and the\n[Coral Reef Alliance](https://coral.org/en/).\n\nScientific background publications:\n\n- Lyons, M. B., Roelfsema, C. M., Kennedy, E. V., Kovacs, E. M., Borrego-Acevedo, R., Markey, K., ... & Murray, N. J. (2020). Mapping the world's coral reefs using a global multiscale earth observation framework. Remote Sensing in Ecology and Conservation, 6(4), 557-568. [doi:10.1002/rse2.157](https://doi.org/10.1002/rse2.157)\n\n- Kennedy, E. V., Roelfsema, C. M., Lyons, M. B., Kovacs, E. M., Borrego-Acevedo, R., Roe, M., ... & Tudman, P. (2021). Reef Cover, a coral reef classification for global habitat mapping from remote sensing. Scientific Data, 8(1), 1-20. [doi:10.1038/s41597-021-00958-z](https://doi.org/10.1038/s41597-021-00958-z)\n\n- Roelfsema, C. M., Lyons, M., Murray, N., Kovacs, E. M., Kennedy, E., Markey, K., ... & Phinn, S. R. (2021). Workflow for the generation of expert-derived training and validation data: a view to global scale habitat mapping. Frontiers in Marine Science.  [doi:10.3389/fmars.2021.643381](https://doi.org/10.3389/fmars.2021.643381)\n\n- Li, J., Knapp, D. E., Lyons, M., Roelfsema, C., Phinn, S., Schill, S. R., & Asner, G. P. (2021). Automated global shallow water bathymetry mapping using Google Earth Engine. Remote Sensing, 13(8), 1469. [doi:10.3390/rs13081469](https://doi.org/10.3390/rs13081469)\n\n- Li, J., Knapp, D. E., Fabina, N. S., Kennedy, E. V., Larsen, K., Lyons, M. B., ... & Asner, G. P. (2020). A global coral reef probability map generated using convolutional neural networks. Coral Reefs, 39(6), 1805-1815. [doi:10.1007/s00338-020-02005-6](https://doi.org/10.1007/s00338-020-02005-6)\n\nAllen Coral Atlas maps, bathymetry and map statistics are &copy; 2023 Allen\nCoral Atlas Partnership and Vulcan, Inc.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-33,180,33]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ACA/reef_habitat/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ACA/ACA_reef_habitat_v2_0","rel":"related","title":"Run the example for ACA/reef_habitat/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ACA/ACA_reef_habitat_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ACA_reef_habitat_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/coral-atlas-user-downloads-prod/Global-Dataset-20211006223100.zip","rel":"source"},{"href":"https://doi.org/10.5281/zenodo.3833242","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/ACA/ACA_reef_habitat_v2_0.json","rel":"latest-version","title":"ACA/reef_habitat/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/ACA/ACA_reef_habitat_v1_0.json","rel":"predecessor-version","title":"ACA/reef_habitat/v1_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ACA/reef_habitat/v2_0/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"AHN/AHN2_05M_INT","title":"AHN Netherlands 0.5m DEM, Interpolated","description":"The AHN DEM is a 0.5m DEM covering the Netherlands. It was generated from\nLIDAR data taken in the spring between 2007 and 2012.\n\nIt contains ground level samples with all other items above ground (such as\nbuildings, bridges, trees etc.) removed. This version is interpolated; the\nareas where objects have been removed are filled with interpolated values. The\npoint cloud was converted to a 0.5m grid using a squared inverse distance\nweighting method.\n\nNote: This dataset does not include a small number of tiles listed in the\nmanifest that are only available at a lower resolution.\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[3.35,50.74,7.24,53.55]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2012-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/AHN/AHN2_05M_INT","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AHN/AHN_AHN2_05M_INT","rel":"related","title":"Run the example for AHN/AHN2_05M_INT in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/AHN/AHN_AHN2_05M_INT_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/AHN_AHN2_05M_INT#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/AHN/AHN2_05M_INT/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"AHN/AHN2_05M_NON","title":"AHN Netherlands 0.5m DEM, Non-Interpolated","description":"The AHN DEM is a 0.5m DEM covering the Netherlands. It was generated from\nLIDAR data taken in the spring between 2007 and 2012.\n\nIt contains ground level samples with all other items above ground (such as\nbuildings, bridges, trees etc.) removed. This version is non-interpolated; the\nareas where objects have been removed are left blank and not filled with\ninterpolated values. The point cloud was converted to a 0.5m grid using a\nsquared inverse distance weighting method.\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[3.35,50.74,7.24,53.55]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2012-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/AHN/AHN2_05M_NON","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AHN/AHN_AHN2_05M_NON","rel":"related","title":"Run the example for AHN/AHN2_05M_NON in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/AHN/AHN_AHN2_05M_NON_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/AHN_AHN2_05M_NON#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/AHN/AHN2_05M_NON/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"AHN/AHN2_05M_RUW","title":"AHN Netherlands 0.5m DEM, Raw Samples","description":"The AHN DEM is a 0.5m DEM covering the Netherlands. It was generated from\nLIDAR data taken in the spring between 2007 and 2012.\n\nThis version contains both ground level samples and items above ground level\n(such as buildings, bridges, trees etc). The point cloud was converted to a\n0.5m grid using a squared inverse distance weighting method.\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[3.35,50.74,7.24,53.55]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2012-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/AHN/AHN2_05M_RUW","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AHN/AHN_AHN2_05M_RUW","rel":"related","title":"Run the example for AHN/AHN2_05M_RUW in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/AHN/AHN_AHN2_05M_RUW_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/AHN_AHN2_05M_RUW#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/AHN/AHN2_05M_RUW/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"AHN/AHN3","title":"AHN3: Netherlands AHN 0.5m","description":"The Actueel Hoogtebestand Nederland (AHN) is a dataset with detailed and\nprecise elevation data for the whole of the Netherlands. Elevation\ninformation was collected from helicopters and aircraft using laser\ntechnology with vertical accuracy of 5 cm.\n\nAHN3 Dataset contains the Netherlands AHN 0.5m DSM and DTM variables.\nThe data cover the period between 2014 and 2019.\n\nThe Digital Terrain Model (DTM) product represents the elevation of the\nground, while the Digital Surface Model (DSM) product represents the\nelevation of the tallest surfaces at that point.\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[3.35,50.74,7.24,53.55]]},"temporal":{"interval":[["2014-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/AHN/AHN3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AHN/AHN_AHN3","rel":"related","title":"Run the example for AHN/AHN3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/AHN/AHN_AHN3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/AHN_AHN3#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/AHN/AHN3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/AHN/AHN3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"AHN/AHN4","title":"AHN4: Netherlands AHN 0.5m","description":"The Actueel Hoogtebestand Nederland (AHN) is a dataset with detailed and\nprecise elevation data for the whole of the Netherlands. Elevation\ninformation was collected from helicopters and aircraft using laser\ntechnology with vertical accuracy of 5 cm.\n\nAHN4 Dataset contains the Netherlands AHN 0.5m DSM and DTM variables.\nThe data cover the period between 2020 and 2022.\n\nThe Digital Terrain Model (DTM) product represents the elevation of the\nground, while the Digital Surface Model (DSM) product represents the\nelevation of the tallest surfaces at that point.\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[3.35,50.74,7.24,53.55]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/AHN/AHN4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AHN/AHN_AHN4","rel":"related","title":"Run the example for AHN/AHN4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/AHN/AHN_AHN4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/AHN_AHN4#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/AHN/AHN4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/AHN/AHN4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/MS_NC/V1","title":"SPOT Multispectral Pseudo Natural Color Imagery 10-20m, Brazil","description":"This collection contains 2008-era \"pseudo natural color\" RGB images derived from the native multispectral bands of SPOT 2, 4, and 5 for Brazil. Because the HRV and HRG sensors did not include a native blue band, this product synthesizes an RGB representation to approximate a natural appearance for visual land use/land cover assessment. The spatial resolution remains at the native multispectral scale (10m for SPOT 5; 20m for SPOT 2/4). See also [Brazil Forest Imagery Dataset 2008: Visual Basemap](/earth-engine/datasets/catalog/GOOGLE_BRAZIL_FOREST_2008_V1_VISUAL).\n\n\n### Google modifications\n\nAddition of a cloud mask band. Google manually delineated polygons around clouds, wisps, and shadows for each image in this collection, converting the polygons into a mask image added as an additional band to the image.\n\n\n### Data access\n\nTo access this dataset, please fill out the [request form](https://forms.gle/jQmCbWgarjENQBYz6).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-74,-34,-34,6]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2009-11-26T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/MS_NC/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AIRBUS/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_MS_NC_V1","rel":"related","title":"Run the example for AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/MS_NC/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/AIRBUS/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_MS_NC_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_MS_NC_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Earth_Engine_SPOT_542_Datasets_License.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/MS_NC/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/MS_NC/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/MS/V1","title":"SPOT Multispectral Imagery 10-20m, Brazil","description":"This collection provides 2008-era raw multispectral imagery from the SPOT 2, 4, and 5 satellites over the country of Brazil. These missions, operated by CNES, were designed to provide high-resolution, wide-area optical imagery for managing Earth's resources. SPOT 2 and 4 utilized HRV and HRVIR \"pushbroom\" sensors with a 60 km swath. SPOT 5 introduced the High-Resolution Geometric (HRG) instrument, offering improved 10m multispectral resolution. This raw image collection can be used for historical environmental analysis such as in the context of the [Brazil Forest Code](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2012/lei/l12651.htm). See also [Brazil Forest Imagery Dataset 2008: Analytic Basemap](/earth-engine/datasets/catalog/GOOGLE_BRAZIL_FOREST_2008_V1_ANALYTIC).\n\nA cloud mask can be added to images in this collection by linking the [MS_NC collection](/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_MS_NC_V1). See the example below.\n\n\n### Data access\n\nTo access this dataset, please fill out the [request form](https://forms.gle/jQmCbWgarjENQBYz6).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-74,-34,-34,6]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2009-11-26T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/MS/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AIRBUS/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_MS_V1","rel":"related","title":"Run the example for AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/MS/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/AIRBUS/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_MS_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_MS_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Earth_Engine_SPOT_542_Datasets_License.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/MS/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/MS/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/PAN/V1","title":"SPOT Panchromatic Imagery 5-10m, Brazil","description":"This collection contains the raw panchromatic (PAN) bands from the SPOT 2, 4, and 5 satellites for Brazil circa 2008. The panchromatic sensor provides the highest native spatial resolution of the missions, capturing light across a broad visible spectrum (0.51-0.73 µm for SPOT 2, 0.61-0.68 µm for SPOT 4, and 0.48-0.71 µm for SPOT 5). These bands are intended for advanced photogrammetric analysis and custom fusion workflows. Assets from SPOT 5 have a pixel size of 5m and assets from SPOT 2 and 4 have a pixel size of 10m.\n\n\n### Data access\n\nTo access this dataset, please fill out the [request form](https://forms.gle/jQmCbWgarjENQBYz6).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-74,-34,-34,6]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2009-11-26T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/PAN/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AIRBUS/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_PAN_V1","rel":"related","title":"Run the example for AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/PAN/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/AIRBUS/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_PAN_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_PAN_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Earth_Engine_SPOT_542_Datasets_License.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/PAN/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/PAN/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/PMS_NC/V1","title":"SPOT Pansharpened Pseudo Natural Color Imagery 5-10m, Brazil","description":"This collection provides high-resolution pansharpened pseudo-natural color SPOT imagery satellites 2, 4, and 5 for Brazil circa 2008. Where a corresponding high-resolution panchromatic image was available, it was fused with [multispectral data](/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_MS_V1) to achieve enhanced spatial detail (up to 5m for SPOT 5 or 10m for SPOT 2/4). This raw image collection can be used for historical environmental visualization and visual interpretation. See also [Brazil Forest Imagery Dataset 2008: Visual Basemap](/earth-engine/datasets/catalog/GOOGLE_BRAZIL_FOREST_2008_V1_VISUAL).\n\n\n### Google modifications\n\nAddition of a cloud mask band. Google manually delineated polygons around clouds, wisps, and shadows for each image in this collection, converting the polygons into a mask image added as an additional band to the image.\n\n\n### Data access\n\nTo access this dataset, please fill out the [request form](https://forms.gle/jQmCbWgarjENQBYz6).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-74,-34,-34,6]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2009-11-26T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/PMS_NC/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AIRBUS/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_PMS_NC_V1","rel":"related","title":"Run the example for AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/PMS_NC/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/AIRBUS/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_PMS_NC_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_PMS_NC_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Earth_Engine_SPOT_542_Datasets_License.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/PMS_NC/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/AIRBUS/SPOT_2_4_5/BRAZIL/2007_2009/PMS_NC/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ASTER/AST_L1T_003","title":"ASTER L1T Radiance","description":"\n**Note:**\nThere is a gap in data collection between November 28, 2024 and January 16,\n2025 due to technical issues with the ASTER instrument. See the USGS\n[announcement](https://lpdaac.usgs.gov/news/aster-resumes-limited-operations/)\nfor more information.\n\nThe Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)\nis a multispectral imager that was launched on board NASA's Terra spacecraft\nin December, 1999. ASTER can collect data in 14 spectral bands from the\nvisible to the thermal infrared. Each scene covers an area of 60 x 60 km.\nThese scenes, produced by the USGS, contain calibrated at-sensor radiance,\northo-rectified and terrain corrected.\n\nNot all 14 bands were collected in each scene. An asset property named\nORIGINAL_BANDS_PRESENT contains the list of bands that are present in each\nscene.\n\nTo convert from Digital Numbers (DN) to radiance at the sensor, the unit\nconversion coefficients are available in the metadata. See\n[ASTER L1T Product Users' Guide](https://lpdaac.usgs.gov/documents/647/AST__L1T_User_Guide_V3.pdf)\nand [ASTER L1T Product Specification](https://lpdaac.usgs.gov/documents/300/ASTER_L1T_Product_Specification.pdf)\nfor more information.\n\nDocumentation:\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/72/AST_L1T_ATBD.pdf)\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/647/AST__L1T_User_Guide_V3.pdf)\n\n* [ASTER Level-1T Product Specification](https://lpdaac.usgs.gov/documents/300/ASTER_L1T_Product_Specification.pdf)\n\n* [ASTER L1T Quick Reference Guide](https://lpdaac.usgs.gov/documents/174/AST_L1T_Quick_Reference_Guide.pdf)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-04T00:00:00Z","2025-12-08T23:12:29Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ASTER/AST_L1T_003","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ASTER/ASTER_AST_L1T_003","rel":"related","title":"Run the example for ASTER/AST_L1T_003 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ASTER/ASTER_AST_L1T_003_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ASTER_AST_L1T_003#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/ASTER/AST_L1T.003","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ASTER/AST_L1T_003/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ASTER/AST_L1T_003/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/ASTER_GED/AG100_003","title":"AG100: ASTER Global Emissivity Dataset 100-meter V003","description":"The Advanced Spaceborne Thermal Emission and Reflection Radiometer Global\nEmissivity Database (ASTER-GED) was developed by the National Aeronautics\nand Space Administration's (NASA) Jet Propulsion Laboratory (JPL),\nCalifornia Institute of Technology. This product includes the mean\nemissivity and standard deviation for all 5 ASTER Thermal Infrared bands,\nmean land surface temperature (LST) and standard deviation, a re-sampled\nASTER GDEM, land-water mask, mean Normalized Difference Vegetation Index\n(NDVI) and standard deviation, and observation count.\n\nASTER-GED land surface temperature and emissivity (LST&E) are generated\nusing the ASTER Temperature Emissivity Separation (TES) algorithm in\ncombination with a Water Vapor Scaling (WVS) atmospheric correction method\nusing MODIS MOD07 atmospheric profiles and the MODTRAN 5.2 radiative\ntransfer model.\n\nThis product was derived from clear-sky pixels for all available ASTER data\n(2000-2008).\n\n[User's Guide] (https://lpdaac.usgs.gov/documents/120/ASTERGED_User_Guide_V3.pdf)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-59,180,80]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2008-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/ASTER_GED/AG100_003","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_ASTER_GED_AG100_003","rel":"related","title":"Run the example for NASA/ASTER_GED/AG100_003 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_ASTER_GED_AG100_003_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_ASTER_GED_AG100_003#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/COMMUNITY/ASTER_GED/AG100.003","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ASTER_GED/AG100_003/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"AU/GA/AUSTRALIA_5M_DEM","title":"Australian 5M DEM","description":"The Digital Elevation Model (DEM) 5 meter Grid of Australia\nderived from LiDAR model represents a National 5 meter (bare\nearth) DEM which has been derived from some 236 individual LiDAR\nsurveys between 2001 and 2015 covering an area in excess of 245,000\nsquare kilometers. These surveys cover Australia's populated\ncoastal zone; floodplain surveys within the Murray Darling Basin,\nand individual surveys of major and minor population centers.\nAll available 1 meter resolution LiDAR-derived DEMs have been\ncompiled and resampled using a neighborhood-mean method to 5\nmeter resolution datasets for each survey area, and then merged\ninto a single dataset for each State. Each state's dataset is\nprovided as a separate image within the image collection.\n\nThe acquisition of the individual LiDAR surveys and derivation\nof the 5m product has been part of a long-term collaboration\nbetween Geoscience Australia, the Cooperative Research Centre\nfor Spatial Information (CRCSI), the Departments of Climate Change\nand Environment, State and Territory jurisdictions, Local Government\nand the Murray Darling Basin Authority under the auspices of\nthe National Elevation Data Framework and Coastal and Urban DEM\nProgram. The source datasets have been captured to standards\nthat are generally consistent with the Australian ICSM LiDAR\nAcquisition Specifications with require a fundamental vertical\naccuracy of at least 0.30m (95% confidence) and horizontal accuracy\nof at least 0.80m (95% confidence).\n\nThere are several areas close to Perth with null (NaN) values around\n(115.85, -31.99), (115.72, -33.75), and (115.10, -33.43).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[114.09,-43.45,153.64,-9.88]]},"temporal":{"interval":[["2015-12-01T00:00:00Z","2015-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/AU/GA/AUSTRALIA_5M_DEM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AU/AU_GA_AUSTRALIA_5M_DEM","rel":"related","title":"Run the example for AU/GA/AUSTRALIA_5M_DEM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/AU/AU_GA_AUSTRALIA_5M_DEM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/AU_GA_AUSTRALIA_5M_DEM#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.4225/25/5652419862E23","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/AU/GA/AUSTRALIA_5M_DEM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/AU/GA/AUSTRALIA_5M_DEM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"AU/GA/DEM_1SEC/v10/DEM-H","title":"DEM-H: Australian SRTM Hydrologically Enforced Digital Elevation Model","description":"The Hydrologically Enforced Digital Elevation Model\n(DEM-H) was derived from the SRTM data acquired by NASA in February\n2000. The model has been hydrologically conditioned and drainage\nenforced. The DEM-H captures flow paths based on SRTM elevations\nand mapped stream lines, and supports delineation of catchments\nand related hydrological attributes. The dataset was derived from\nthe 1 second smoothed Digital Elevation Model (DEM-S; ANZCW0703014016)\nby enforcing hydrological connectivity with the ANUDEM software,\nusing selected AusHydro V1.6 (February 2010) 1:250,000 scale watercourse\nlines (ANZCW0503900101) and lines derived from DEM-S to define\nthe watercourses. The drainage enforcement has produced a consistent\nrepresentation of hydrological connectivity with some elevation\nartifacts resulting from the drainage enforcement. A full description\nof the methods is in preparation (Dowling et al., in prep).\n\nThis product provides a DEM suitable for use in hydrological analysis\nsuch as catchment definition and flow routing.\n\nThere are several areas with unexpected negative values: close to\nCanberra around (150.443044, -35.355281) with values of -55 and in Western\nAustralia around (124.84, -16.44) with -43.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[112.99,-44.06,154,-9.99]]},"temporal":{"interval":[["2010-02-01T00:00:00Z","2010-02-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/AU/GA/DEM_1SEC/v10/DEM-H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AU/AU_GA_DEM_1SEC_v10_DEM-H","rel":"related","title":"Run the example for AU/GA/DEM_1SEC/v10/DEM-H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/AU/AU_GA_DEM_1SEC_v10_DEM-H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/AU_GA_DEM_1SEC_v10_DEM-H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/AU/GA/DEM_1SEC/v10/DEM-H/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"AU/GA/DEM_1SEC/v10/DEM-S","title":"DEM-S: Australian Smoothed Digital Elevation Model","description":"The Smoothed Digital Elevation Model (DEM-S) was derived\nfrom the SRTM data acquired by NASA in February 2000. DEM-S represents\nground surface topography (excluding vegetation features) and has\nbeen smoothed to reduce noise and improve the representation of\nsurface shape. An adaptive process applied more smoothing in flatter\nareas than hilly areas, and more smoothing in noisier areas than\nin less noisy areas.\n\nThis DEM-S supports calculation of local terrain shape attributes\nsuch as slope, aspect, and curvature that could not be reliably\nderived from the unsmoothed 1 second DEM because of noise.\n\nThere are several areas with unexpected negative values: close to\nCanberra around (150.443044, -35.355281) with values of -55 and in Western\nAustralia around (124.84, -16.44) with -43.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[112.99,-44.06,154,-9.99]]},"temporal":{"interval":[["2010-02-01T00:00:00Z","2010-02-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/AU/GA/DEM_1SEC/v10/DEM-S","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AU/AU_GA_DEM_1SEC_v10_DEM-S","rel":"related","title":"Run the example for AU/GA/DEM_1SEC/v10/DEM-S in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/AU/AU_GA_DEM_1SEC_v10_DEM-S_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/AU_GA_DEM_1SEC_v10_DEM-S#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/AU/GA/DEM_1SEC/v10/DEM-S/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"BIOPAMA/GlobalOilPalm/v1","title":"Global Map of Oil Palm Plantations [deprecated]","description":"The dataset is a 10m global industrial and smallholder oil palm map for 2019.\nIt covers areas where oil palm plantations were detected. The classified images are the output\nof a convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites.\n\nSee [article](https://essd.copernicus.org/articles/13/1211/2021/) for additional information.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2019-01-01T00:00:00Z","2019-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/BIOPAMA/GlobalOilPalm/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/BIOPAMA/BIOPAMA_GlobalOilPalm_v1","rel":"related","title":"Run the example for BIOPAMA/GlobalOilPalm/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/BIOPAMA/BIOPAMA_GlobalOilPalm_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/BIOPAMA_GlobalOilPalm_v1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.4473715","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/BIOPAMA/BIOPAMA_GlobalOilPalm_v1_2.json","rel":"latest-version","title":"BIOPAMA/GlobalOilPalm/v1_2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/BIOPAMA/BIOPAMA_GlobalOilPalm_v1_2.json","rel":"successor-version","title":"BIOPAMA/GlobalOilPalm/v1_2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/BIOPAMA/GlobalOilPalm/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/BIOPAMA/GlobalOilPalm/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"BIOPAMA/GlobalOilPalm/v1_2","title":"Global Oil Palm Extent and Planting Year (1990-2021) v1.2","description":"Global oil palm extent for 2016-2021 at 10 m resolution and oil palm\nplanting year from 1990 to 2021 at 30 m resolution. The dataset consists of\n609 100x100 km grid tiles covering tropical and subtropical oil-palm-growing\nregions worldwide, mapping 23.98 Mha of closed-canopy oil palm (16.69 Mha\n[69.6%] industrial and 7.29 Mha [30.4%] smallholder), with a sample-based\narea estimate of 16.82 ± 0.19 Mha industrial and 7.37 ± 0.25 Mha\nsmallholder. Note that although the catalog time range spans 1990-2021 to\nreflect the planting year range, this collection is a single static mosaic\nrather than a multi-temporal time series.\n\n### Methodology\n\n* **Oil Palm Extent (`classification`, 10 m):** Annual closed-canopy oil\n  palm maps for 2016-2021 were generated from Sentinel-1 C-band SAR GRD\n  dual-polarization (VV and VH) annual composites using a DeepLabv3+\n  (MobileNetV2 backbone) convolutional neural network (CNN) trained on 296\n  10x10 km regions. Daily Sentinel-1 scenes were corrected for local\n  incident angle using the SRTM v4 DEM, aggregated into annual median\n  composites separately for ascending and descending orbits, and averaged\n  across both orbits to avoid terrain artifacts when one orbit had more\n  passes. Unlike v1 (2019), Sentinel-2 red reflectance (Band 4) was excluded\n  because systematic Sentinel-2 Level-2A coverage of oil palm regions was\n  unavailable prior to 2019; to reuse the 3-channel DeepLabv3+ architecture\n  from v1, the VV and VH composites were stacked with a zero-filled third\n  channel.\n* To reduce false positives (such as coconut plantations and paddy fields),\n  overlapping cropland, built-up, waterbody, herbaceous wetland, and\n  mangrove classes from ESA WorldCover 10 m v200 were masked out, followed\n  by manual visual screening. In post-processing, 82 of the 100x100 km grid\n  cells from the 2019 v1 dataset were discarded (mostly false-positive\n  coconut regions in Mexico, India, the Philippines, and Indonesia) while\n  new hotspot grid cells (Andhra Pradesh in India, Congo Basin, Thailand,\n  and Central/South America) were added to arrive at the 609 tiles.\n* The six annual maps (2016-2021) were merged into a single static layer\n  prioritizing oil palm presence: a pixel is labeled industrial (`1`) if\n  classified as industrial in any year during 2016-2021; otherwise\n  smallholder (`2`) if classified as smallholder in any year during\n  2016-2021; and other land cover (`0`) otherwise.\n* **Planting Year (`planting_year`, 30 m):** Derived for all pixels mapped\n  as oil palm (`1` or `2`) using 30 m Landsat-5, Landsat-7, and Landsat-8\n  Collection 2 Tier 1 Level-2 surface reflectance time series from 1990\n  to 2021. A Random Forest classifier labeled each clear Landsat observation\n  as vegetation, bare land, or cloud to identify the Last Date of Land\n  Preparation (LDLP)—the final bare-land observation corresponding to land\n  clearing before canopy closure—smoothed with a spatial mode filter. The\n  planting year is defined as the calendar year with the minimum `(NIR -\n  SWIR2) / (NIR + SWIR2)` within the 5-year window preceding the LDLP\n  (referred to as Normalized Difference Water Index, `NDWI (Gao, 1996)`, in\n  the paper, though computed with `SWIR2` such as Landsat-8 Bands 5 and 7,\n  which is the standard Normalized Burn Ratio [NBR] formula and named\n  `minNBR` in the source GeoTIFF band description). In the `planting_year`\n  band, values span `1989` to `2022` (rather than `1990` to `2021`): because\n  the algorithm looks back up to 5 years before the LDLP, plantations\n  established before 1990 (see the note below in limitations about\n  left-censoring) are recorded as `1989` (~7.5% of oil palm pixels globally,\n  the single most frequent value in the dataset), and a small fraction of\n  pixels (~0.04%) have value `2022`.\n\n### Limitations\n\n* **Closed-canopy detection lag and 2019-2021 omission:** Sentinel-1 SAR\n  reliably detects oil palm only after stands reach full canopy closure\n  (typically ~3 years after planting). Consequently, newly established young\n  plantations planted during 2019-2021 are largely omitted from the extent\n  layer. Pixels with `planting_year` in 2019-2021 primarily represent\n  **replanting (rotation) of older plantations** that already had a closed\n  canopy during 2016-2018 before being clear-cut, and **must not** be used\n  to measure new oil palm expansion or deforestation between 2019 and 2021.\n* **Last rotation vs. initial establishment and 1989 left-censoring:**\n  Because the algorithm detects the *last* bare-land clearing event prior to\n  2021, `planting_year` records the establishment year of the current stand\n  as of 2021. In plantations that have undergone rotation (typically every\n  ~25 years), this reflects the most recent replanting year rather than\n  initial conversion to oil palm. Additionally, `planting_year` is\n  *left-censored* at `1989`; this means pixels with `planting_year == 1989`\n  (~7.5% of oil palm pixels globally) represent plantations established in\n  or before 1989 and should not be treated as literally planted in 1989 when\n  calculating plantation age.\n* **Post-clearing land-use conversion:** Because the 2016-2021 merge rule\n  retains any pixel detected as oil palm in at least one year, plantations\n  clear-cut between 2016 and 2021 and subsequently converted to a different\n  land use remain classified as oil palm.\n* **Omission of open-canopy and semi-wild oil palm:** The CNN only maps\n  closed-canopy stands and largely omits traditional, scattered, or\n  subsistence semi-wild oil palm groves (particularly in West Africa, such\n  as Nigeria) as well as stands with open canopies caused by fire, flooding,\n  disease, or agroforestry intercropping.\n* **Lower accuracy in smallholder plantations:** Across all 6,843\n  ground-truth planting-year observations, overall planting-year RMSE is\n  2.65 years (Mean Error = -0.24 years, R^2 = 0.86). Against visual\n  interpretation of Landsat time series (n = 841 points), exact-year\n  agreement of the Random Forest LDLP was only 59.6% overall (64.8%\n  industrial, 44.0% smallholder; 76.3% within ±3 years). Smallholder\n  plantations have lower classification accuracy (producer's accuracy 71.7%,\n  user's accuracy 72.4%) and higher planting-year error (RMSE = 4.89 years,\n  reaching 7.01 years in Cameroon) than industrial plantations (producer's\n  accuracy 91.0%, user's accuracy 91.8%, planting-year RMSE = 2.02 years).\n  Smallholder planting-year accuracy is degraded by mixed 30 m pixels along\n  irregular plot boundaries, multi-year sequential planting, and\n  \"under-planting\" (planting seedlings beneath old palms prior to felling,\n  which avoids a bare-soil stage).\n* **Narrow geographic coverage of field validation:** Field validation of\n  `planting_year` had narrow geographic coverage: only 3 countries for\n  industrial plantations (Brazil, Gabon, Indonesia) and 2 countries for\n  smallholders (Cameroon and Indonesia, 93.5% collected by a single company\n  via owner interviews, which are also subject to owner recall error on\n  older stands), with no field validation in Malaysia, Thailand, India, or\n  other producing regions.\n* **Resolution mismatch and grid offset between bands:** Because\n  `classification` is at 10 m and `planting_year` is at 30 m (with a 20 m\n  West-edge origin offset between the source GeoTIFF grids), ~5% of 10 m\n  oil palm pixels along plantation edges lack a `planting_year` value, and\n  30 m `planting_year` pixels along boundaries can spill onto adjacent 10 m\n  non-oil-palm (`0`) pixels.\n* **Regional artifacts from clouds and seasonality:** Persistent cloud cover\n  and sparse pre-2013 Landsat archive coverage in Equatorial and West Africa\n  (e.g., Cameroon, Gabon, Congo Basin) increase planting-year uncertainty.\n  In regions with strong dry seasons (notably Andhra Pradesh in India,\n  Thailand, and Sulawesi in Indonesia), seasonal drops in NDWI can trigger\n  false bare-land detections, biasing `planting_year` toward overly recent\n  years.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","2021-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/BIOPAMA/GlobalOilPalm/v1_2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/BIOPAMA/BIOPAMA_GlobalOilPalm_v1_2","rel":"related","title":"Run the example for BIOPAMA/GlobalOilPalm/v1_2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/BIOPAMA/BIOPAMA_GlobalOilPalm_v1_2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/BIOPAMA_GlobalOilPalm_v1_2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.13379129","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/BIOPAMA/BIOPAMA_GlobalOilPalm_v1_2.json","rel":"latest-version","title":"BIOPAMA/GlobalOilPalm/v1_2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/BIOPAMA/BIOPAMA_GlobalOilPalm_v1.json","rel":"predecessor-version","title":"BIOPAMA/GlobalOilPalm/v1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/BIOPAMA/GlobalOilPalm/v1_2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/BIOPAMA/GlobalOilPalm/v1_2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"BNETD/land_cover/v1","title":"Cote d'Ivoire BNETD 2020 Land Cover Map","description":"The Cote d'Ivoire BNETD 2020 Land Cover Map was produced by the Ivorian\nGovernment through a national institution, the Center for Geographic\nInformation and Digital from the National Study Office Techniques and\nDevelopment (BNETD-CIGN), with technical and financial support from the\nEuropean Union. The methodology used to produce the map was transparent,\nparticipatory and in line with international standards.\n\nTo develop this map, a mosaic of satellite images (Sentinel 2) from 2020 was\nprocessed via Google Earth Engine and supplemented with data collected in the\nfield, to train a supervised classification algorithm (Random Forest).\nTwo field campaigns were conducted, from 10 November to 9 December 2022 and\nfrom 26 January to 13 February 2023, throughout the country. These missions\ninvolved 33 people from multiple partner organizations because the data\ncollection methods and definitions of certain land use classes\nadopted by stakeholders may sometimes differ.\n\nAs part of the EUDR due diligence process, geolocation data for plots of\nland producing EUDR-relevant products could be overlaid with 2020 forest\ncover data in order to assess the risk that the plot is located in an area\nthat was forested before the 2020 cut-off date. To do this, forest cover\ndata aligned with the FAO definition of forests and the 2020 cut-off date\nis required. The 2020 land cover map of Cote d'Ivoire meets these needs.\nIndeed, the classes in the land cover map can be combined to create a\nforest/non-forest map that is aligned with the FAO definition of forests.\n\nA platform for accessing 2020 land cover data, metadata and the methodology\nhas been developed using ESRI solutions, from Africa GeoPortal, for data\nanalysis and visualization:\n\nThe address is: [https://bit.ly/carte-ci-2020](https://bit.ly/carte-ci-2020)\n\nDocumentation:\n\n* [Detailed documentation](https://africageoportal.maps.arcgis.com/sharing/rest/content/items/26a717d4c13f4f3db2c6056f7e5c0bab/data)\n\n* [Methodology in French](https://africageoportal.maps.arcgis.com/sharing/rest/content/items/76dc18767b89472eb89e8aa54e08a6c9/data)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-8.602,4.3603,-2.493,10.74]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/BNETD/land_cover/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/BNETD/BNETD_land_cover_v1","rel":"related","title":"Run the example for BNETD/land_cover/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/BNETD/BNETD_land_cover_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/BNETD_land_cover_v1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://africageoportal.maps.arcgis.com/home/item.html?id=46348aa12a3d46e592584737de64f72a","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/BNETD/land_cover/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/BNETD/land_cover/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"BNU/FGS/CCNL/v1","title":"CCNL: Consistent and Corrected Nighttime Light Dataset from DMSP-OLS (1992-2013) v1","description":"The Consistent and Corrected Nighttime Lights (CCNL) dataset is\na reprocessed version of the [Defense Meteorological Program (DMSP)\nOperational Line-Scan System (OLS) Version 4](NOAA_DMSP-OLS_NIGHTTIME_LIGHTS).\nA series of methods was used to mitigate the impact of inter-annual\ninconsistency, saturation, and blooming effects and to improve data quality.\n\nCCNL Version 1 spans the globe from 75N to 65S and has 1 km pixel size.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-65,180,75]]},"temporal":{"interval":[["1992-01-01T00:00:00Z","2014-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/BNU/FGS/CCNL/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/BNU/BNU_FGS_CCNL_v1","rel":"related","title":"Run the example for BNU/FGS/CCNL/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/BNU/BNU_FGS_CCNL_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/BNU_FGS_CCNL_v1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/BNU/FGS/CCNL/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/BNU/FGS/CCNL/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CANADA/NFIS/NTEMS/CA_FOREST_AGE","title":"Landsat-derived forest age for Canada 2019","description":"Canadian primary forest dataset is a satellite-based forest age map for\n2019 across Canada's forested ecozones at a 30-m spatial resolution.\nRemotely-sensed data from Landsat (disturbances, surface\nreflectance composites, forest structure) and MODIS (Gross Primary\nProduction) are utilized to determine age.\n\nForest age can be determined where disturbance can be identified directly\n(disturbance approach) or inferred using spectral information (recovery\napproach) or using inverted allometric equations to model age where there\nis no evidence of disturbance (allometric approach). The disturbance\napproach is based upon satellite data and mapped changes and is the most\naccurate. The recovery approach also relies upon satellite data plus logic\nregarding forest succession, with an accuracy that is greater than pure\nmodeling.\n\nGiven the lack of widespread recent disturbance over Canada's forests, the\nallometric approach is required over the greatest area (86.6%). Using\ninformation regarding realized heights and growth and yield modeling, ages\nare estimated where none are otherwise possible. Trees of all ages are\nmapped, with trees >150 years old combined in an \"old tree\" category.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2019-01-01T01:00:00Z","2020-01-01T01:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CANADA/NFIS/NTEMS/CA_FOREST_AGE","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CANADA/CANADA_NFIS_NTEMS_CA_FOREST_AGE","rel":"related","title":"Run the example for CANADA/NFIS/NTEMS/CA_FOREST_AGE in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CANADA/CANADA_NFIS_NTEMS_CA_FOREST_AGE_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CANADA_NFIS_NTEMS_CA_FOREST_AGE#terms-of-use","rel":"license","type":"text/html"},{"href":"https://opendata.nfis.org/downloads/forest_change/CA_forest_age_2019.zip","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CANADA/NFIS/NTEMS/CA_FOREST_AGE/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CANADA/NFIS/NTEMS/CA_FOREST_AGE/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CAS/IGSNRR/PML/V2","title":"PML_V2 0.1.4: Coupled Evapotranspiration and Gross Primary Production (GPP) [deprecated]","description":"Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2) products include\nevapotranspiration (ET), its three components, and\ngross primary product (GPP) at 500m and 8-day resolution during 2002-2017\nand with spatial range from -60°S to 90°N. The major advantages of the\nPML_V2 products are:\n\n  1. coupled estimates of transpiration and GPP via canopy\n  conductance (Gan et al., 2018; Zhang et al., 2019)\n  2. partitioning ET into three components: transpiration from vegetation,\n  direct evaporation from the soil and vaporization of intercepted\n   rainfall from vegetation (Zhang et al., 2016).\n\nThe PML_V2 products perform well against observations\nat 95 flux sites across the globe, and are similar to or noticeably better than\nmajor state-of-the-art ET and GPP products widely used by water and ecology\nresearch communities (Zhang et al., 2019).\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2017-12-27T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CAS/IGSNRR/PML/V2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CAS/CAS_IGSNRR_PML_V2","rel":"related","title":"Run the example for CAS/IGSNRR/PML/V2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CAS/CAS_IGSNRR_PML_V2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PML_V2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/pml_evapotranspiration/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a.json","rel":"latest-version","title":"projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CAS/CAS_IGSNRR_PML_V2_v017.json","rel":"successor-version","title":"CAS/IGSNRR/PML/V2_v017","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CAS/IGSNRR/PML/V2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CAS/IGSNRR/PML/V2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CAS/IGSNRR/PML/V2_v017","title":"PML_V2.1.7: Coupled Evapotranspiration and Gross Primary Product (GPP) [deprecated]","description":"Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2) products include\nevapotranspiration (ET), its three components, and\ngross primary production (GPP) at 500m and 8-day resolution during 2000-2017\nand with spatial range from -60&deg;S to 90&deg;N. The major advantages of the\nPML_V2 products are:\n\n  1. Coupled estimates of transpiration and GPP via canopy\n  conductance (Gan et al., 2018; Zhang et al., 2019)\n  2. Partitioning ET into three components: transpiration from vegetation,\n  direct evaporation from the soil and vaporization of intercepted\n   rainfall from vegetation (Zhang et al., 2016).\n\nThe PML_V2 products perform well against observations\nat 95 flux sites across the globe, and are similar to or noticeably better than\nmajor state-of-the-art ET and GPP products widely used by water and ecology\nresearch communities (Zhang et al., 2019).\n\nNote on versioning: For consistency in naming conventions, PML-V2.1.7 replaces the previous PML-V2 0.1.7.\n\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["2000-02-26T00:00:00Z","2020-12-26T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CAS/IGSNRR/PML/V2_v017","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CAS/CAS_IGSNRR_PML_V2_v017","rel":"related","title":"Run the example for CAS/IGSNRR/PML/V2_v017 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CAS/CAS_IGSNRR_PML_V2_v017_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PML_V2_v017#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/pml_evapotranspiration/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a.json","rel":"latest-version","title":"projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CAS/CAS_IGSNRR_PML_V2.json","rel":"predecessor-version","title":"CAS/IGSNRR/PML/V2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CAS/CAS_IGSNRR_PML_V2_v018.json","rel":"successor-version","title":"CAS/IGSNRR/PML/V2_v018","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CAS/IGSNRR/PML/V2_v017/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CAS/IGSNRR/PML/V2_v017/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CAS/IGSNRR/PML/V2_v018","title":"PML_V2.1.8: Coupled Evapotranspiration and Gross Primary Product (GPP) [deprecated]","description":"Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2) products include\nevapotranspiration (ET), its three components, and\ngross primary production (GPP) at 500m and 8-day resolution during 2000-2023\nand with spatial range from -60&deg;S to 90&deg;N. The major advantages of the\nPML_V2 products are:\n\n  1. Coupled estimates of transpiration and GPP via canopy\n  conductance (Gan et al., 2018; Zhang et al., 2019)\n  2. Partitioning ET into three components: transpiration from vegetation,\n  direct evaporation from the soil, and vaporization of intercepted\n   rainfall from vegetation (Zhang et al., 2016).\n\nThe PML_V2 products perform well against observations\nat 95 flux sites across the globe, and are similar to or noticeably better than\nmajor state-of-the-art ET and GPP products widely used by water and ecology\nresearch communities (Zhang et al., 2019).\n\nNote on versioning: For consistency in naming conventions, PML-V2.1.8 replaces the previous PML-V2 0.1.8.\n\nKey changes in V2.1.8 compared with the original V2.0 (Zhang et al., 2019):\n1. Temporal coverage is lengthened to the latest (may update annually) with the MODIS C6.1 data.\n2. MODIS Terra LAI (MOD15A2H) is used rather than the composite LAI (MCD15A3H).\n3. Parameters are recalibrated with the change in LAI, while other forcings remain the same.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["2000-02-26T00:00:00Z","2023-12-27T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CAS/IGSNRR/PML/V2_v018","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CAS/CAS_IGSNRR_PML_V2_v018","rel":"related","title":"Run the example for CAS/IGSNRR/PML/V2_v018 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CAS/CAS_IGSNRR_PML_V2_v018_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PML_V2_v018#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/pml_evapotranspiration/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a.json","rel":"latest-version","title":"projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CAS/CAS_IGSNRR_PML_V2_v017.json","rel":"predecessor-version","title":"CAS/IGSNRR/PML/V2_v017","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/pml_evapotranspiration/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a.json","rel":"successor-version","title":"projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CAS/IGSNRR/PML/V2_v018/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CAS/IGSNRR/PML/V2_v018/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/ATMOS_NEAR_SURFACE/V2","title":"NOAA CDR: Ocean Near-Surface Atmospheric Properties, Version 2","description":"The Ocean Near-Surface Atmospheric Properties dataset is part of the NOAA\nOcean Surface Bundle (OSB) and provides a high quality Climate Data Record\n(CDR) of air temperature, wind speed, and specific humidity over ice-free\nocean surfaces.\n\nThese atmospheric properties are calculated based on brightness temperature\ndata from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor\nMicrowave/Imager Sounder (SSMIS) on the Defense Meteorological Satellite\nProgram (DMSP) spacecraft, using a neural network.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1988-01-01T00:00:00Z","2021-08-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/ATMOS_NEAR_SURFACE/V2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_ATMOS_NEAR_SURFACE_V2","rel":"related","title":"Run the example for NOAA/CDR/ATMOS_NEAR_SURFACE/V2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_ATMOS_NEAR_SURFACE_V2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_ATMOS_NEAR_SURFACE_V2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V55T3HH0","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/ATMOS_NEAR_SURFACE/V2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/ATMOS_NEAR_SURFACE/V2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/AVHRR/AOT/V3","title":"NOAA CDR AVHRR AOT: Daily Aerosol Optical Thickness Over Global Oceans, v03 [deprecated]","description":"The NOAA Climate Data Record (CDR) of Aerosol Optical Thickness (AOT) is a\ncollection of global daily 0.1 degree derived data from the PATMOS-x AVHRR\nlevel-2b channel 1 (0.63 micron) orbital clear-sky radiance. 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For\nmore details, see the [Algorithm Description](https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Aerosol_Optical_Thickness/AlgorithmDescription_01B-04.pdf).\n\nImage and data processing by NOAA's National Climatic Data Center.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-01-01T00:00:00Z","2022-03-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/AOT/V3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_AVHRR_AOT_V3","rel":"related","title":"Run the example for NOAA/CDR/AVHRR/AOT/V3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_AVHRR_AOT_V3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_AOT_V3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V5BZ642P","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_AOT_V4.json","rel":"latest-version","title":"NOAA/CDR/AVHRR/AOT/V4","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_AOT_V4.json","rel":"successor-version","title":"NOAA/CDR/AVHRR/AOT/V4","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/AOT/V3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/AOT/V3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/AVHRR/LAI_FAPAR/V4","title":"NOAA CDR AVHRR LAI FAPAR: Leaf Area Index and Fraction of Absorbed Photosynthetically Active Radiation, Version 4 [deprecated]","description":"The NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and\nFraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset\ncontains derived values that characterize the canopy and photosynthetic\nactivity of plants. This dataset is derived from the NOAA AVHRR Surface\nReflectance product and is gridded at a resolution of 0.05° on a daily\nbasis. The values are computed globally over land surfaces, but not over\nbare or very sparsely vegetated areas, permanent ice or snow, permanent\nwetland, urban areas, or water bodies.\n\nKnown issues with this dataset include:\n\n- TIMEOFDAY variable contains values that are too large by 1 day\n\n- Latitude values are not correctly associated with the center of\n  the grid cell, error is < 0.002 degrees\n\n- Longitude values are not correctly associated with the center of\n  the grid cell, error is < 0.02 degrees\n\nSee [technical note from the data provider](https://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/AVHRR%20Surface%20Reflectance/TechNote.pdf).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-06-24T00:00:00Z","2019-05-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/LAI_FAPAR/V4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_AVHRR_LAI_FAPAR_V4","rel":"related","title":"Run the example for NOAA/CDR/AVHRR/LAI_FAPAR/V4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_AVHRR_LAI_FAPAR_V4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_LAI_FAPAR_V4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V5M043BX","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_LAI_FAPAR_V5.json","rel":"latest-version","title":"NOAA/CDR/AVHRR/LAI_FAPAR/V5","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_LAI_FAPAR_V5.json","rel":"successor-version","title":"NOAA/CDR/AVHRR/LAI_FAPAR/V5","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/LAI_FAPAR/V4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/LAI_FAPAR/V4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/AVHRR/LAI_FAPAR/V5","title":"NOAA CDR AVHRR LAI FAPAR: Leaf Area Index and Fraction of Absorbed Photosynthetically Active Radiation, Version 5","description":"The NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and\nFraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset\ncontains derived values that characterize the canopy and photosynthetic\nactivity of plants. This dataset is derived from the NOAA AVHRR Surface\nReflectance product and is gridded at a resolution of 0.05° on a daily\nbasis. The values are computed globally over land surfaces, but not over\nbare or very sparsely vegetated areas, permanent ice or snow, permanent\nwetland, urban areas, or water bodies.\n\nKnown issues with this dataset include:\n\n- TIMEOFDAY variable contains values that are too large by 1 day\n\n- Latitude values are not correctly associated with the center of\n  the grid cell, error is < 0.002 degrees\n\n- Longitude values are not correctly associated with the center of\n  the grid cell, error is < 0.02 degrees\n\nSee [technical note from the data provider](https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Leaf_Area_Index_and_FAPAR/AVHRR/AlgorithmDescriptionAVHRR_01B-20c.pdf).\n\nProvider's note: the orbital drift of N-19 (the last NOAA satellite\ncarrying the AVHRR sensor) began to severely degrade the retrieved\nproduct quality. Therefore, VIIRS is now the primary sensor being used for\nthese products from 2014-present.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-06-24T00:00:00Z","2013-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/LAI_FAPAR/V5","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_AVHRR_LAI_FAPAR_V5","rel":"related","title":"Run the example for NOAA/CDR/AVHRR/LAI_FAPAR/V5 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_AVHRR_LAI_FAPAR_V5_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_LAI_FAPAR_V5#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V5M043BX","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_LAI_FAPAR_V5.json","rel":"latest-version","title":"NOAA/CDR/AVHRR/LAI_FAPAR/V5","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_LAI_FAPAR_V4.json","rel":"predecessor-version","title":"NOAA/CDR/AVHRR/LAI_FAPAR/V4","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/LAI_FAPAR/V5/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/LAI_FAPAR/V5/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/AVHRR/NDVI/V4","title":"NOAA CDR AVHRR NDVI: Normalized Difference Vegetation Index, Version 4 [deprecated]","description":"The NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation\nIndex (NDVI) contains gridded daily NDVI derived from the NOAA AVHRR Surface\nReflectance product. It provides a measurement of surface vegetation\ncoverage activity, gridded at a resolution of 0.05° and computed globally\nover land surfaces.\n\nKnown issues with this dataset include:\n\n- TIMEOFDAY variable contains values that are too large by 1 day\n\n- Latitude values are not correctly associated with the center of\n  the grid cell, error is < 0.002 degrees\n\n- Longitude values are not correctly associated with the center of\n  the grid cell, error is < 0.02 degrees\n\nSee [technical note from the data provider](https://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/AVHRR%20Surface%20Reflectance/TechNote.pdf).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-06-24T00:00:00Z","2019-05-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/NDVI/V4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_AVHRR_NDVI_V4","rel":"related","title":"Run the example for NOAA/CDR/AVHRR/NDVI/V4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_AVHRR_NDVI_V4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_NDVI_V4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V5PZ56R6","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_NDVI_V5.json","rel":"latest-version","title":"NOAA/CDR/AVHRR/NDVI/V5","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_NDVI_V5.json","rel":"successor-version","title":"NOAA/CDR/AVHRR/NDVI/V5","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/NDVI/V4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/NDVI/V4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/AVHRR/NDVI/V5","title":"NOAA CDR AVHRR NDVI: Normalized Difference Vegetation Index, Version 5","description":"The NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation\nIndex (NDVI) contains gridded daily NDVI derived from the NOAA AVHRR Surface\nReflectance product. It provides a measurement of surface vegetation\ncoverage activity, gridded at a resolution of 0.05° and computed globally\nover land surfaces.\n\nKnown issues with this dataset include:\n\n- TIMEOFDAY variable contains values that are too large by 1 day\n\n- Latitude values are not correctly associated with the center of\n  the grid cell, error is < 0.002 degrees\n\n- Longitude values are not correctly associated with the center of\n  the grid cell, error is < 0.02 degrees\n\nSee [technical note from the data provider](https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Normalized_Difference_Vegetation_Index/AVHRR/AlgorithmDescriptionAVHRR_01B-20b.pdf).\n\nProvider's note: the orbital drift of N-19 (the last NOAA satellite\ncarrying the AVHRR sensor) began to severely degrade the retrieved\nproduct quality. Therefore, VIIRS is now the primary sensor being used for\nthese products from 2014-present.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-06-24T00:00:00Z","2013-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/NDVI/V5","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_AVHRR_NDVI_V5","rel":"related","title":"Run the example for NOAA/CDR/AVHRR/NDVI/V5 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_AVHRR_NDVI_V5_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_NDVI_V5#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V5PZ56R6","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_NDVI_V5.json","rel":"latest-version","title":"NOAA/CDR/AVHRR/NDVI/V5","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_NDVI_V4.json","rel":"predecessor-version","title":"NOAA/CDR/AVHRR/NDVI/V4","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/NDVI/V5/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/NDVI/V5/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/AVHRR/SR/V4","title":"NOAA CDR AVHRR: Surface Reflectance, Version 4 [deprecated]","description":"The NOAA Climate Data Record (CDR) of AVHRR Surface Reflectance contains\ngridded daily surface reflectance and brightness temperatures derived from\nthe Advanced Very High Resolution Radiometer (AVHRR) sensors onboard seven\nNOAA polar orbiting satellites. The data are gridded at a\nresolution of 0.05° and computed globally over land surfaces.\n\nKnown issues with this dataset include:\n\n- TIMEOFDAY variable contains values that are too large by 1 day\n\n- Latitude values are not correctly associated with the center of\n  the grid cell, error is < 0.002 degrees\n\n- Longitude values are not correctly associated with the center of\n  the grid cell, error is < 0.02 degrees\n\nSee [technical note from the data provider](https://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/AVHRR%20Surface%20Reflectance/TechNote.pdf).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-06-24T00:00:00Z","2019-05-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/SR/V4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_AVHRR_SR_V4","rel":"related","title":"Run the example for NOAA/CDR/AVHRR/SR/V4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_AVHRR_SR_V4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_SR_V4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V5TM782M","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_SR_V5.json","rel":"latest-version","title":"NOAA/CDR/AVHRR/SR/V5","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_SR_V5.json","rel":"successor-version","title":"NOAA/CDR/AVHRR/SR/V5","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/SR/V4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/SR/V4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/AVHRR/SR/V5","title":"NOAA CDR AVHRR: Surface Reflectance, Version 5","description":"The NOAA Climate Data Record (CDR) of AVHRR Surface Reflectance contains\ngridded daily surface reflectance and brightness temperatures derived from\nthe Advanced Very High Resolution Radiometer (AVHRR) sensors onboard seven\nNOAA polar orbiting satellites. The data are gridded at a\nresolution of 0.05° and computed globally over land surfaces.\n\nKnown issues with this dataset include:\n\n- TIMEOFDAY variable contains values that are too large by 1 day\n\n- Latitude values are not correctly associated with the center of\n  the grid cell, error is < 0.002 degrees\n\n- Longitude values are not correctly associated with the center of\n  the grid cell, error is < 0.02 degrees\n\nSee [technical note from the data provider](https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Surface_Reflectance/AVHRR/AlgorithmDescriptionAVHRR_01B-20a.pdf).\n\nProvider's note: the orbital drift of N-19 (the last NOAA satellite\ncarrying the AVHRR sensor) began to severely degrade the retrieved\nproduct quality. Therefore, VIIRS is now the primary sensor being used for\nthese products from 2014-present.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-06-24T00:00:00Z","2013-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/SR/V5","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_AVHRR_SR_V5","rel":"related","title":"Run the example for NOAA/CDR/AVHRR/SR/V5 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_AVHRR_SR_V5_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_SR_V5#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V53776Z4","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_SR_V5.json","rel":"latest-version","title":"NOAA/CDR/AVHRR/SR/V5","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_SR_V4.json","rel":"predecessor-version","title":"NOAA/CDR/AVHRR/SR/V4","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/SR/V5/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/SR/V5/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/GRIDSAT-B1/V2","title":"NOAA CDR GRIDSAT-B1: Geostationary IR Channel Brightness Temperature","description":"\nNote: This dataset has not been updated by the provider since 2024-03-31\ndue to ongoing infrastructure updates. There is no current timeline for when\ndataset updates will resume.\n\nThis dataset provides a high quality Climate Data\nRecord (CDR) of global infrared measurements from geostationary satellites.\n\nThe geostationary satellite data (GridSat-B1) provides data from 3\nchannels: the CDR-quality infrared window (IRWIN) channel (near 11&micro;m),\nthe visible channel (near 0.6&micro;m) and the infrared water vapor (IRWVP)\nchannel (near 6.7&micro;m). The GridSat-B1 data is projected onto a global\n0.07 degree latitude equal-angle grid with date coverage spanning from\n1980-present. This data is sourced from the 3-hourly International Satellite\nCloud Climatology Project (ISCCP) B1 data. The satellites included in this\ndataset with their longitudinal coverage over time can be seen [here](https://www.ncdc.noaa.gov/gridsat/images/isccp_coverage_VZA60_nolegend.png).\nIn regions of overlap the CDR methodology merges satellites by selecting\nthe nadir-most observations for each grid point.\n\nNotes:\n\n- Mappings from satid to satellite name are contained in the image's\n  properties as satid_number: \\\"satellite_name\\\", e.g. satid_0: GOES-13,\n  satid_1: GOES-15, and satid_2: GOES-16.\n\n- IRWIN data has been corrected for view zenith angle, but this correction\n  is not perfect. It also treats all satellites the same way, whereas the\n  view zenith angle dependence will vary by satellite. Some VZA residual\n  will be apparent.\n\n- IRWVP data has no view zenith angle correction and is not CDR quality.\n\n- VSCHN data has no view zenith angle corrections and is not CDR quality.\n\n- Removing the View Zenigth Angle correction for the IRWIN channels can be\n  done with the following:\n  Original_temperature_observed = irwin_cdr - irwin_vza_adj\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1980-01-01T00:00:00Z","2024-09-30T21:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/GRIDSAT-B1/V2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_GRIDSAT-B1_V2","rel":"related","title":"Run the example for NOAA/CDR/GRIDSAT-B1/V2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_GRIDSAT-B1_V2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_GRIDSAT-B1_V2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V59P2ZKR","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/GRIDSAT-B1/V2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/GRIDSAT-B1/V2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/HEAT_FLUXES/V2","title":"NOAA CDR: Ocean Heat Fluxes, Version 2","description":"The Ocean Heat Fluxes dataset is part of the NOAA Ocean Surface Bundle (OSB)\nand provides a high quality Climate Data Record (CDR) of the air/ocean heat\nfluxes over ice-free oceans.\n\nThis dataset is calculated from the OSB CDR parameters of near-surface\natmospheric and sea surface temperature using a neural-network\nemulator of the TOGA-COARE Bulk Air-Sea Flux Algorithm.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1988-01-01T00:00:00Z","2021-08-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/HEAT_FLUXES/V2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_HEAT_FLUXES_V2","rel":"related","title":"Run the example for NOAA/CDR/HEAT_FLUXES/V2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_HEAT_FLUXES_V2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_HEAT_FLUXES_V2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V59K4885","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/HEAT_FLUXES/V2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/HEAT_FLUXES/V2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/OISST/V2","title":"NOAA CDR OISST v2: Optimum Interpolation Sea Surface Temperature [deprecated]","description":"The NOAA 1/4 degree daily Optimum Interpolation Sea Surface Temperature\n(OISST) provides complete ocean temperature fields constructed by combining\nbias-adjusted observations from different platforms (satellite, ships,\nbuoys) on a regular global grid, with gaps filled in by interpolation.\nSatellite data from the Advanced Very High Resolution Radiometer (AVHRR)\nprovides the main input which permits the high temporal-spatial coverage\nbeginning in late 1981 to the present.\n\nThe OISST dataset has a single day's data processed twice. First a near\nreal-time preliminary version is released with a lag of 1 day, and a final\nversion with a lag of 14 days. The final version uses extra days for\nsmoothing, and zonal bias correction in addition to replacing the\npreliminary version.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-09-01T00:00:00Z","2020-04-26T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/OISST/V2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_OISST_V2","rel":"related","title":"Run the example for NOAA/CDR/OISST/V2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_OISST_V2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V5SQ8XB5","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2_1.json","rel":"latest-version","title":"NOAA/CDR/OISST/V2_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2_1.json","rel":"successor-version","title":"NOAA/CDR/OISST/V2_1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/OISST/V2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/OISST/V2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/OISST/V2_1","title":"NOAA CDR OISST v02r01: Optimum Interpolation Sea Surface Temperature","description":"The NOAA 1/4 degree daily Optimum Interpolation Sea Surface Temperature\n(OISST) provides complete ocean temperature fields constructed by combining\nbias-adjusted observations from different platforms (satellite, ships,\nbuoys) on a regular global grid, with gaps filled in by interpolation.\nSatellite data from the Advanced Very High Resolution Radiometer (AVHRR)\nprovides the main input which permits the high temporal-spatial coverage\nbeginning in late 1981 to the present.\n\nThe OISST dataset has a single day's data processed twice. First a near\nreal-time preliminary version is released with a lag of 1 day, and a final\nversion with a lag of 14 days. The final version uses extra days for\nsmoothing, and zonal bias correction in addition to replacing the\npreliminary version.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-09-01T00:00:00Z","2026-11-07T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/OISST/V2_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_OISST_V2_1","rel":"related","title":"Run the example for NOAA/CDR/OISST/V2_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_OISST_V2_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2_1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V5SQ8XB5","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2_1.json","rel":"latest-version","title":"NOAA/CDR/OISST/V2_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2.json","rel":"predecessor-version","title":"NOAA/CDR/OISST/V2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/OISST/V2_1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/OISST/V2_1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/PATMOSX/V53","title":"NOAA CDR PATMOSX: Cloud Properties, Reflectance, and Brightness Temperatures, Version 5.3","description":"This dataset provides high quality Climate Data Record (CDR) of multiple\ncloud properties along with Advanced Very High Resolution Radiometer (AVHRR)\nPathfinder Atmospheres Extended (PATMOS-x) brightness temperatures and\nreflectances. These data have been fitted to a 0.1 x 0.1 equal angle-grid\nwith both ascending and descending assets generated daily from two to ten\nNOAA and MetOp satellite passes per day.\n\nThis dataset includes 48 bands, 11 of which are deemed CDR quality\n(marked with \"CDR variable\" in the band list).\nThe cloud products are derived using the ABI (Advanced Baseline Imager)\nCloud Height Algorithm (ACHA), and the Daytime Cloud Optical Properties\n(DCOMP) algorithm. For more detail on the processing see the\n[Climate Algorithm Theoretical Basis Document (C-ATBD)](\nhttps://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/AVHRR-HIRS_Reflectance_PATMOS-x/AlgorithmDescription%20_01B-1c.pdf).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/PATMOSX/V53","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_PATMOSX_V53","rel":"related","title":"Run the example for NOAA/CDR/PATMOSX/V53 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_PATMOSX_V53_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_PATMOSX_V53#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V5348HCK","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/PATMOSX/V53/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/PATMOSX/V53/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/SST_PATHFINDER/V53","title":"NOAA AVHRR Pathfinder Version 5.3 Collated Global 4km Sea Surface Temperature","description":"The AVHRR Pathfinder Version 5.3 Sea Surface Temperature dataset (PFV53)\nis a collection of global, twice-daily 4km sea surface temperature data\nproduced in a partnership by the NOAA National Oceanographic Data Center\nand the University of Miami's Rosenstiel School of Marine and Atmospheric\nScience. PFV53 was computed from data from the AVHRR instruments\non board NOAA's polar orbiting satellite series using an entirely\nmodernized system based on SeaDAS. PFV53 data are nearly 100%\ncompliant with the GHRSST Data Specification Version 2.0 for L3C\nproducts and only deviate from that standard in that 'sses_bias',\n'sses_standard_deviation', and 'sst_dtime' variables are empty and hence\nnot included into EE assets. PFV53 data were collected through the\noperational periods of the NOAA-7 through NOAA-19 Polar Operational\nEnvironmental Satellites (POES), and are available from 1981 to 2014.\nAdditional information is available at the\n[NOAA Pathfinder site](https://www.nodc.noaa.gov/satellitedata/pathfinder4km53/).\n\nAdditional band details can be found in the\n[Tech Specs](https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Sea_Surface_Temperature_Pathfinder/AlgorithmDescription_01B-08.pdf)\npage.\n\nThese data were provided by GHRSST and the US NOAA National\nCenters for Environmental Information (NCEI). This project was\nsupported in part by a grant from the NOAA Climate Data Record (CDR)\nProgram for satellites.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-08-24T00:00:00Z","2023-12-30T21:35:08Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/SST_PATHFINDER/V53","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_SST_PATHFINDER_V53","rel":"related","title":"Run the example for NOAA/CDR/SST_PATHFINDER/V53 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_SST_PATHFINDER_V53_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_PATHFINDER_V53#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V52J68XX","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/SST_PATHFINDER/V53/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/SST_PATHFINDER/V53/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/SST_WHOI/V2","title":"NOAA CDR WHOI: Sea Surface Temperature, Version 2","description":"The Sea Surface Temperature - WHOI dataset is part of the NOAA Ocean Surface\nBundle (OSB) and provides a high quality Climate Data Record (CDR) of sea\nsurface temperature over ice-free oceans.\n\nThe SST values are found through modeling the diurnal variability in\ncombination with AVHRR observations of sea surface temperature.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1988-01-01T00:00:00Z","2021-08-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/SST_WHOI/V2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_SST_WHOI_V2","rel":"related","title":"Run the example for NOAA/CDR/SST_WHOI/V2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_SST_WHOI_V2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_WHOI_V2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7289/V5FB510W","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/SST_WHOI/V2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/SST_WHOI/V2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CFSV2/FOR6H","title":"CFSV2: NCEP Climate Forecast System Version 2, 6-Hourly Products [deprecated]","description":"\nNote: Starting October 20, 2025, this dataset switched to using Celsius\ninstead of Kelvin units for temperature bands. Please use\n[NOAA/CFSV2/FOR6H_HARMONIZED](https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSV2_FOR6H_HARMONIZED)\nfor consistent temperature units (Kelvin).\n\nThe National Centers for Environmental Prediction (NCEP) Climate Forecast\nSystem (CFS) is a fully coupled model representing the interaction between\nthe Earth's atmosphere, oceans, land, and sea ice. CFS was developed at the\nEnvironmental Modeling Center (EMC) at NCEP. The operational CFS was\nupgraded to version 2 (CFSv2) on March 30, 2011.\n\nForecasts are initialized four times per day (0000, 0600, 1200, and 1800\nUTC). This is the same model that was used to create the NCEP Climate\nForecast System Reanalysis (CFSR), and the purpose of the CFSv2 dataset is\nto extend CFSR. We ingest only a subset of bands from files matching\ncdas1.t??z.sfluxgrbf**06**.grib2.\n\nFor more information about CFS, please see the [CFS NOAA site](https://cfs.ncep.noaa.gov/).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z","2026-11-09T06:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSV2/FOR6H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CFSV2_FOR6H","rel":"related","title":"Run the example for NOAA/CFSV2/FOR6H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CFSV2_FOR6H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSV2_FOR6H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5065/D61C1TXF","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSV2_FOR6H_HARMONIZED.json","rel":"latest-version","title":"NOAA/CFSV2/FOR6H_HARMONIZED","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSV2_FOR6H_HARMONIZED.json","rel":"successor-version","title":"NOAA/CFSV2/FOR6H_HARMONIZED","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSV2/FOR6H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSV2/FOR6H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CGIAR/SRTM90_V4","title":"SRTM Digital Elevation Data Version 4","description":"The Shuttle Radar Topography Mission (SRTM) digital\nelevation dataset was originally produced to provide consistent,\nhigh-quality elevation data at near global scope. This version\nof the SRTM digital elevation data has been processed to fill data\nvoids, and to facilitate its ease of use.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-56,180,60]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CGIAR/SRTM90_V4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CGIAR/CGIAR_SRTM90_V4","rel":"related","title":"Run the example for CGIAR/SRTM90_V4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CGIAR/CGIAR_SRTM90_V4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CGIAR_SRTM90_V4#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CGIAR/SRTM90_V4/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CIESIN/GPWv411/GPW_Basic_Demographic_Characteristics","title":"GPWv411: Basic Demographic Characteristics (Gridded Population of the World Version 4.11)","description":"This dataset contains population estimates, by age and sex, per\n30 arc-second grid cell consistent with national censuses and population\nregisters. There is one image for each modeled age and sex category based\non the 2010 round of Census.\n\n[General Documentation](https://sedac.ciesin.columbia.edu/data/set/gpw-v4-basic-demographic-characteristics-rev11/docs)\n\nThe Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution\nof global human population for the years 2000, 2005, 2010, 2015, and 2020\non 30 arc-second (approximately 1 km) grid cells. Population is distributed\nto cells using proportional allocation of population from census and\nadministrative units. Population input data are collected at the most\ndetailed spatial resolution available from the results of the 2010 round of\ncensuses, which occurred between 2005 and 2014. 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Population is distributed\nto cells using proportional allocation of population from census and\nadministrative units. Population input data are collected at the most\ndetailed spatial resolution available from the results of the 2010 round of\ncensuses, which occurred between 2005 and 2014. 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Population is distributed\nto cells using proportional allocation of population from census and\nadministrative units. Population input data are collected at the most\ndetailed spatial resolution available from the results of the 2010 round of\ncensuses, which occurred between 2005 and 2014. The input data are\nextrapolated to produce population estimates for each modeled year.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_Mean_Administrative_Unit_Area","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CIESIN/CIESIN_GPWv411_GPW_Mean_Administrative_Unit_Area","rel":"related","title":"Run the example for CIESIN/GPWv411/GPW_Mean_Administrative_Unit_Area in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CIESIN/CIESIN_GPWv411_GPW_Mean_Administrative_Unit_Area_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Mean_Administrative_Unit_Area#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7927/H42Z13KG","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_Mean_Administrative_Unit_Area/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_Mean_Administrative_Unit_Area/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CIESIN/GPWv411/GPW_National_Identifier_Grid","title":"GPWv411: National Identifier Grid (Gridded Population of the World Version 4.11)","description":"This dataset represents the Census data source used to produce\nthe GPW v4.11 populations estimates. Pixels that have the same value reflect the same\ndata source, most often a country or territory.\n\n[General Documentation](https://sedac.ciesin.columbia.edu/data/set/gpw-v4-national-identifier-grid-rev11/docs)\n\nThe Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution\nof global human population for the years 2000, 2005, 2010, 2015, and 2020\non 30 arc-second (approximately 1 km) grid cells. Population is distributed\nto cells using proportional allocation of population from census and\nadministrative units. Population input data are collected at the most\ndetailed spatial resolution available from the results of the 2010 round of\ncensuses, which occurred between 2005 and 2014. 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There is one image for each modeled year.\n\n[General Documentation](https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11/docs)\n\nNote: Because this collection has a pyramid policy of MEAN, zooming out\nresults in information loss. Calculations need to be performed at native\nresolution.\n\nThe Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution\nof global human population for the years 2000, 2005, 2010, 2015, and 2020\non 30 arc-second (approximately 1 km) grid cells. Population is distributed\nto cells using proportional allocation of population from census and\nadministrative units. Population input data are collected at the most\ndetailed spatial resolution available from the results of the 2010 round of\ncensuses, which occurred between 2005 and 2014. The input data are\nextrapolated to produce population estimates for each modeled year.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_Population_Count","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CIESIN/CIESIN_GPWv411_GPW_Population_Count","rel":"related","title":"Run the example for CIESIN/GPWv411/GPW_Population_Count in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CIESIN/CIESIN_GPWv411_GPW_Population_Count_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Population_Count#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7927/H4JW8BX5","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CIESIN/CIESIN_GPWv411_GPW_Population_Count.json","rel":"latest-version","title":"CIESIN/GPWv411/GPW_Population_Count","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CIESIN/CIESIN_GPWv4_population-count.json","rel":"predecessor-version","title":"CIESIN/GPWv4/population-count","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_Population_Count/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv411/GPW_Population_Count/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CIESIN/GPWv411/GPW_Population_Density","title":"GPWv411: Population Density (Gridded Population of the World Version 4.11)","description":"This dataset contains estimates of the number of persons\nper square kilometer consistent with national censuses and population\nregisters. There is one image for each modeled year.\n\n[General Documentation](https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/docs)\n\nThe Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution\nof global human population for the years 2000, 2005, 2010, 2015, and 2020\non 30 arc-second (approximately 1 km) grid cells. Population is distributed\nto cells using proportional allocation of population from census and\nadministrative units. Population input data are collected at the most\ndetailed spatial resolution available from the results of the 2010 round of\ncensuses, which occurred between 2005 and 2014. 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There is one image for each modeled year.\n\n[General Documentation](https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11/docs)\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv4/population-density","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CIESIN/CIESIN_GPWv4_population-density","rel":"related","title":"Run the example for CIESIN/GPWv4/population-density in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CIESIN/CIESIN_GPWv4_population-density_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv4_population-density#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7927/H4NP22DQ","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CIESIN/CIESIN_GPWv411_GPW_Population_Density.json","rel":"latest-version","title":"CIESIN/GPWv411/GPW_Population_Density","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CIESIN/CIESIN_GPWv411_GPW_Population_Density.json","rel":"successor-version","title":"CIESIN/GPWv411/GPW_Population_Density","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv4/population-density/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv4/population-density/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CIESIN/GPWv4/unwpp-adjusted-population-count","title":"GPWv4: Gridded Population of the World Version 4, UN-Adjusted Population Count [deprecated]","description":"The Gridded Population of World Version 4 (GPWv4) models the distribution\nof global human population for the years 2000, 2005, 2010, 2015, and 2020\non 30 arc-second (approximately 1 km) grid cells. 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Population is distributed\nto cells using proportional allocation of population from census and\nadministrative units. Population input data are collected at the most\ndetailed spatial resolution available from the results of the 2010 round of\ncensuses, which occurred between 2005 and 2014. The input data are\nextrapolated to produce population estimates for each modeled year.\n\nThese population density grids contain estimates of the number of persons\nper 30 arc-second grid cell, consistent with national censuses and\npopulation registers with respect to relative spatial distribution but\nadjusted to match the 2015 Revision of UN World Population Prospects\ncountry totals. There is one image for each modeled year.\n\n[General Documentation](https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-adjusted-to-2015-unwpp-country-totals/docs)\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv4/unwpp-adjusted-population-density","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CIESIN/CIESIN_GPWv4_unwpp-adjusted-population-density","rel":"related","title":"Run the example for CIESIN/GPWv4/unwpp-adjusted-population-density in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CIESIN/CIESIN_GPWv4_unwpp-adjusted-population-density_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv4_unwpp-adjusted-population-density#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7927/H4HX19NJ","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CIESIN/CIESIN_GPWv411_GPW_UNWPP-Adjusted_Population_Density.json","rel":"latest-version","title":"CIESIN/GPWv411/GPW_UNWPP-Adjusted_Population_Density","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CIESIN/CIESIN_GPWv411_GPW_UNWPP-Adjusted_Population_Density.json","rel":"successor-version","title":"CIESIN/GPWv411/GPW_UNWPP-Adjusted_Population_Density","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv4/unwpp-adjusted-population-density/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CIESIN/GPWv4/unwpp-adjusted-population-density/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/CORINE/V18_5_1/100m","title":"Copernicus CORINE Land Cover [deprecated]","description":"The CORINE (coordination of information on the environment) Land Cover\n(CLC) inventory was initiated in 1985 to standardize data collection on land\nin Europe to support environmental policy development. The project is\ncoordinated by the European Environment Agency (EEA) in the frame of the EU\nCopernicus programme and implemented by national teams. The number of\nparticipating countries has increased over time currently including 33 (EEA)\nmember countries and six cooperating countries (EEA39) with a total area of\nover 5.8M km2.\n\nThe reference year of the first CLC inventory was 1990 and the first update\ncreated in 2000. Later, the update cycle has become 6 years. Satellite\nimagery provides the geometrical and thematic basis for mapping with in-situ\ndata as essential ancillary information. The basic technical parameters of\nCLC (i.e. 44 classes in nomenclature, 25 hectares minimum mapping unit\n(MMU), and 100 meters minimum mapping width) have not changed since the\nbeginning, therefore the results of the different inventories are\ncomparable.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-81.77,-29.2,94.13,73.81]]},"temporal":{"interval":[["1986-01-01T00:00:00Z","2012-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/CORINE/V18_5_1/100m","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_CORINE_V18_5_1_100m","rel":"related","title":"Run the example for COPERNICUS/CORINE/V18_5_1/100m in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_CORINE_V18_5_1_100m_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_CORINE_V18_5_1_100m#terms-of-use","rel":"license","type":"text/html"},{"href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc-1990?tab=download","rel":"source"},{"href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc-2000?tab=download","rel":"source"},{"href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc-2006?tab=download","rel":"source"},{"href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=download","rel":"source"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_CORINE_V20_100m.json","rel":"latest-version","title":"COPERNICUS/CORINE/V20/100m","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_CORINE_V20_100m.json","rel":"successor-version","title":"COPERNICUS/CORINE/V20/100m","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/CORINE/V18_5_1/100m/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/CORINE/V18_5_1/100m/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/CORINE/V20/100m","title":"Copernicus CORINE Land Cover","description":"The CORINE (coordination of information on the environment) Land Cover\n(CLC) inventory was initiated in 1985 to standardize data collection on land\nin Europe to support environmental policy development. The project is\ncoordinated by the European Environment Agency (EEA) in the frame of the EU\nCopernicus programme and implemented by national teams. The number of\nparticipating countries has increased over time currently including 33 (EEA)\nmember countries and six cooperating countries (EEA39) with a total area of\nover 5.8M km2.\n\nCLC2018 is one of the datasets produced within the frame the Corine Land Cover\nprogramme referring to land cover / land use status of year 2018.\nThe reference year of the first CLC inventory was 1990 and the first update\ncreated in 2000. Later, the update cycle has become 6 years. Satellite\nimagery provides the geometrical and thematic basis for mapping with in-situ\ndata as essential ancillary information. The basic technical parameters of\nCLC (i.e. 44 classes in nomenclature, 25 hectares minimum mapping unit\n(MMU), and 100 meters minimum mapping width) have not changed since the\nbeginning, therefore the results of the different inventories are\ncomparable.\n\nThe time period covered by each asset is:\n\n* 1990 asset: 1989 to 1998\n* 2000 asset: 1999 to 2001\n* 2006 asset: 2005 to 2007\n* 2012 asset: 2011 to 2012\n* 2018 asset: 2017 to 2018\n","license":"proprietary","extent":{"spatial":{"bbox":[[-81.77,-29.2,94.13,73.81]]},"temporal":{"interval":[["1986-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/CORINE/V20/100m","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_CORINE_V20_100m","rel":"related","title":"Run the example for COPERNICUS/CORINE/V20/100m in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_CORINE_V20_100m_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_CORINE_V20_100m#terms-of-use","rel":"license","type":"text/html"},{"href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc-1990?tab=download","rel":"source"},{"href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc-2000?tab=download","rel":"source"},{"href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc-2006?tab=download","rel":"source"},{"href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=download","rel":"source"},{"href":"https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download","rel":"source"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_CORINE_V20_100m.json","rel":"latest-version","title":"COPERNICUS/CORINE/V20/100m","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_CORINE_V18_5_1_100m.json","rel":"predecessor-version","title":"COPERNICUS/CORINE/V18_5_1/100m","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/CORINE/V20/100m/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/CORINE/V20/100m/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/DEM/GLO30","title":"Copernicus DEM GLO-30: Global 30m Digital Elevation Model [deprecated]","description":"The Copernicus DEM is a Digital Surface Model (DSM) which represents the\nsurface of the Earth including buildings, infrastructure and vegetation.\nThis DEM is derived from an edited DSM named WorldDEM&trade;, i.e., flattening of\nwater bodies and consistent flow of rivers has been included. 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This means a 0\nelevation at a location does not imply the location is at a mean sea level.\n\nEarth Engine asset has been ingested from the DGED files.\n\nNote: See the code example for the recommended way of computing slope.\nUnlike most DEMs in Earth Engine, this is an image collection due to\nmultiple resolutions of source files that make it impossible to mosaic them\ninto a single asset, so the slope computations need a reprojection.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2010-12-01T00:00:00Z","2015-01-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/DEM/GLO30","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_DEM_GLO30","rel":"related","title":"Run the example for COPERNICUS/DEM/GLO30 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_DEM_GLO30_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_DEM_GLO30#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_DEM_GLO30_2024_1.json","rel":"latest-version","title":"COPERNICUS/DEM/GLO30_2024_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_DEM_GLO30_2024_1.json","rel":"successor-version","title":"COPERNICUS/DEM/GLO30_2024_1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/DEM/GLO30/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/DEM/GLO30/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/DEM/GLO30_2024_1","title":"Copernicus DEM GLO-30 (2024_1): Global 30m Digital Elevation Model","description":"The Copernicus DEM is a Digital Surface Model (DSM) which represents the\nsurface of the Earth including buildings, infrastructure and vegetation.\nThis DEM is derived from an edited DSM named WorldDEM&trade;, i.e., flattening of\nwater bodies and consistent flow of rivers has been included. 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This product is generated\nby blending observations from multiple satellite missions\n(like SMOS and SMAP) with in-situ measurements\n(such as those from Argo floats and TSGs) using a sophisticated\nmultivariate optimal interpolation algorithm, thereby creating a long and\nconsistent time series that includes both\nhistorical (reprocessed) data and near-real-time updates.\nBy delivering these climate variables, the dataset is useful\nfor monitoring the global water cycle, studying changes in ocean circulation\ndriven by density variations, and serving as an input for climate\nmodeling and ocean forecasting systems.\n\nThis dataset provides the Near-Real-Time (NRT) version of the daily,\nglobal, Level-4 analysis for Sea Surface Salinity (SSS) and Sea Surface\nDensity (SSD).\n\nDocumentation:\n\n * [User's Guide](https://documentation.marine.copernicus.eu/PUM/CMEMS-MOB-PUM-015-013.pdf)\n\n * [Quality Information Document](https://documentation.marine.copernicus.eu/QUID/CMEMS-MOB-QUID-015-013.pdf)\n\n * [Copernicus Marine Datastore](https://data.marine.copernicus.eu/products)\n\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-01-01T00:00:00Z","2026-09-08T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/GLOBAL_SEA_SURFACE/NRT_DAILY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_MARINE_GLOBAL_SEA_SURFACE_NRT_DAILY","rel":"related","title":"Run the example for COPERNICUS/MARINE/GLOBAL_SEA_SURFACE/NRT_DAILY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_MARINE_GLOBAL_SEA_SURFACE_NRT_DAILY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_MARINE_GLOBAL_SEA_SURFACE_NRT_DAILY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/GLOBAL_SEA_SURFACE/NRT_DAILY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/GLOBAL_SEA_SURFACE/NRT_DAILY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/MARINE/GLOBAL_SEA_SURFACE/REP_DAILY","title":"Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density : Daily, Multi-Year Reprocessed","description":"Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density\nis an oceanographic product that provides daily, global, and gap-free\nLevel-4 (L4) analyses of Sea Surface Salinity (SSS) and Sea Surface Density\n(SSD) at a high resolution of 1/8 degree. 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The dataset is used to monitor phytoplankton blooms, assess ocean\nproductivity, and study marine ecosystems.\n\nDocumentation:\n\n * [User's Guide](https://documentation.marine.copernicus.eu/PUM/CMEMS-OC-PUM.pdf)\n\n * [Quality Information Document](https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-101to104-111-113-116-118.pdf)\n\n * [Copernicus Marine Datastore](https://data.marine.copernicus.eu/products)\n\nNOTE: The provider may remove Copernicus Marine data (especially Near\n Real-Time, NRT, observations) from their repository after some time,\n possibly aggregating that data into different (e.g., monthly) products.\n However, the corresponding assets remain available in the Earth Engine\n collection.\n\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-02-10T00:00:00Z","2026-11-07T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/PLANKTON_MULTI_4KM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_PLANKTON_MULTI_4KM","rel":"related","title":"Run the example for COPERNICUS/MARINE/OC_GLO_BGC/PLANKTON_MULTI_4KM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_PLANKTON_MULTI_4KM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_MARINE_OC_GLO_BGC_PLANKTON_MULTI_4KM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/PLANKTON_MULTI_4KM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/PLANKTON_MULTI_4KM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/MARINE/OC_GLO_BGC/PLANKTON_OLCI_300M","title":"Global Ocean Colour: Bio-Geo-Chemical, L4, from Satellite Observations, Plankton, OLCI, 300M","description":"The Global Ocean Colour (Copernicus-GlobColour) dataset is a Bio-Geo-Chemical\n(BGC) product developed by ACRI-ST. Derived from multiple satellite sources\nlike SeaWiFS, MODIS, and OLCI. It provides a comprehensive range of\noceanographic variables, including: Chlorophyll (CHL),\nPhytoplankton Functional types and sizes (PFT), Primary Production (PP),\nSuspended Matter (SPM), Secchi Transparency Depth (ZSD),\nDiffuse Attenuation (KD490), Particulate Backscattering (BBP),\nAbsorption Coefficient (CDM), Reflectance (RRS) and more.\n\nThis dataset is a global product from the Copernicus Marine Service (CMEMS)\nfocused on ocean plankton. This dataset is derived specifically from the\nOcean and Land Colour Instrument (OLCI) on the Sentinel-3 satellites and\nprovides a very high spatial resolution of 300 meters, making it ideal for\ncoastal and regional-scale analysis.The primary layer is chlorophyll-a\nconcentration (CHL), which serves as a proxy for the biomass of phytoplankton,\na crucial component of the marine food web.\n\nDocumentation:\n\n * [User's Guide](https://documentation.marine.copernicus.eu/PUM/CMEMS-OC-PUM.pdf)\n\n * [Quality Information Document](https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-101to104-111-113-116-118.pdf)\n\n * [Copernicus Marine Datastore](https://data.marine.copernicus.eu/products)\n\nNOTE: The provider may remove Copernicus Marine data (especially Near\n Real-Time, NRT, observations) from their repository after some time,\n possibly aggregating that data into different (e.g., monthly) products.\n However, the corresponding assets remain available in the Earth Engine\n collection.\n\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-03-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/PLANKTON_OLCI_300M","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_PLANKTON_OLCI_300M","rel":"related","title":"Run the example for COPERNICUS/MARINE/OC_GLO_BGC/PLANKTON_OLCI_300M in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_PLANKTON_OLCI_300M_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_MARINE_OC_GLO_BGC_PLANKTON_OLCI_300M#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/PLANKTON_OLCI_300M/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/PLANKTON_OLCI_300M/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/MARINE/OC_GLO_BGC/PRIMARY_PRODUCTION_MULTI_4KM","title":"Global Ocean Colour: Bio-Geo-Chemical, L4, from Satellite Observations, Primary Production, Multi-Sensor 4KM","description":"The Global Ocean Colour (Copernicus-GlobColour) dataset is a Bio-Geo-Chemical\n(BGC) product developed by ACRI-ST. Derived from multiple satellite sources\nlike SeaWiFS, MODIS, and OLCI. It provides a comprehensive range of\noceanographic variables, including: Chlorophyll (CHL),\nPhytoplankton Functional types and sizes (PFT), Primary Production (PP),\nSuspended Matter (SPM), Secchi Transparency Depth (ZSD),\nDiffuse Attenuation (KD490), Particulate Backscattering (BBP),\nAbsorption Coefficient (CDM), Reflectance (RRS) and more.\n\nThis dataset provides a global estimate of marine primary production.\nIt uses multi-sensor satellite data to measure the rate of carbon fixation by\nphytoplankton, which is a key indicator of ocean health.\nThe primary production values are expressed in units of mg/m^2/day, making\nit a valuable tool for monitoring global carbon cycles and marine ecosystem\ndynamics.\n\nDocumentation:\n\n * [User's Guide](https://documentation.marine.copernicus.eu/PUM/CMEMS-OC-PUM.pdf)\n\n * [Quality Information Document](https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-101to104-111-113-116-118.pdf)\n\n * [Copernicus Marine Datastore](https://data.marine.copernicus.eu/products)\n\nNOTE: The provider may remove Copernicus Marine data (especially Near\n Real-Time, NRT, observations) from their repository after some time,\n possibly aggregating that data into different (e.g., monthly) products.\n However, the corresponding assets remain available in the Earth Engine\n collection.\n\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-03-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/PRIMARY_PRODUCTION_MULTI_4KM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_PRIMARY_PRODUCTION_MULTI_4KM","rel":"related","title":"Run the example for COPERNICUS/MARINE/OC_GLO_BGC/PRIMARY_PRODUCTION_MULTI_4KM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_PRIMARY_PRODUCTION_MULTI_4KM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_MARINE_OC_GLO_BGC_PRIMARY_PRODUCTION_MULTI_4KM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/PRIMARY_PRODUCTION_MULTI_4KM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/PRIMARY_PRODUCTION_MULTI_4KM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/MARINE/OC_GLO_BGC/REFLECTANCE_OLCI_300M","title":"Global Ocean Colour: Bio-Geo-Chemical, L4, from Satellite Observations, Reflectance, OLCI 300M","description":"The Global Ocean Colour (Copernicus-GlobColour) dataset is a Bio-Geo-Chemical\n(BGC) product developed by ACRI-ST. Derived from multiple satellite sources\nlike SeaWiFS, MODIS, and OLCI. It provides a comprehensive range of\noceanographic variables, including: Chlorophyll (CHL),\nPhytoplankton Functional types and sizes (PFT), Primary Production (PP),\nSuspended Matter (SPM), Secchi Transparency Depth (ZSD),\nDiffuse Attenuation (KD490), Particulate Backscattering (BBP),\nAbsorption Coefficient (CDM), Reflectance (RRS) and more.\n\nThis dataset is a global product from the Copernicus Marine Service (CMEMS).\nIt provides Remote-Sensing Reflectance (Rrs), a fundamental optical property\nof the water that represents the light leaving the ocean surface.\nDerived from the Ocean and Land Colour Instrument (OLCI), this L4 dataset is\nprocessed into a ready-to-use format. With an exceptionally high spatial\nresolution of 300 meters, this dataset is particularly valuable for detailed\ncoastal and regional studies of water quality and marine constituents.\n\nDocumentation:\n\n * [User's Guide](https://documentation.marine.copernicus.eu/PUM/CMEMS-OC-PUM.pdf)\n\n * [Quality Information Document](https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-101to104-111-113-116-118.pdf)\n\n * [Copernicus Marine Datastore](https://data.marine.copernicus.eu/products)\n\nNOTE: The provider may remove Copernicus Marine data (especially Near\n Real-Time, NRT, observations) from their repository after some time,\n possibly aggregating that data into different (e.g., monthly) products.\n However, the corresponding assets remain available in the Earth Engine\n collection.\n\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-03-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/REFLECTANCE_OLCI_300M","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_REFLECTANCE_OLCI_300M","rel":"related","title":"Run the example for COPERNICUS/MARINE/OC_GLO_BGC/REFLECTANCE_OLCI_300M in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_REFLECTANCE_OLCI_300M_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_MARINE_OC_GLO_BGC_REFLECTANCE_OLCI_300M#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/REFLECTANCE_OLCI_300M/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/REFLECTANCE_OLCI_300M/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/MARINE/SATELLITE_OCEAN_COLOR/V6","title":"Copernicus Satellite Ocean Color Daily Data","description":"This dataset provides global daily estimates of ocean surface chlorophyll-a\nconcentration and remote sensing reflectance derived from multiple satellite\nsensors.\n\nRemote-sensing reflectance (or Rrs) is defined as the ratio of water-leaving\nradiance to downwelling irradiance and serves as the main input to\nalgorithms used to derive other ocean colour products. Chlorophyll-a (Chl-a)\nis the main photosynthetic pigment found in phytoplankton, which form the\nbase of the marine food-web and are responsible for approximately half of\nthe global photosynthesis. Chl-a can be estimated from Rrs data using\ndifferent algorithms (see details in the Documentation). Here, we provide a\nblended Chl-a estimate from multiple algorithms, where blending is based on\nthe suitability of each candidate algorithm to the optical typology of a\ngiven pixel. This approach provides the best estimates of global Chl-a\nacross a range of water types.\n\nThe files from this dataset contain global daily composites of merged sensor\nproducts: SeaWiFS, MERIS, MODIS Aqua, VIIRS, and (from version 5.0 onward)\nOLCI. Note that Rrs and Chl-a data are only available over cloud- and\nice-free areas. As a result, more complete spatial coverage (as shown in the\nmap in the upper-right corner) can be achieved by aggregating data over\nlonger time periods.\n\nThis dataset is produced using the processing chain software developed by\nthe Ocean Colour component of the European Space Agency Climate Change\nInitiative project (ESA OC-CCI). More details are available in the dataset\n[documentation](https://dast.copernicus-climate.eu/documents/satellite-ocean-colour/v6.0/WP2-FDDP-2022-04_C3S2-Lot3_PUGS-of-v6.0-OceanColour-product_v1.1_FINAL.pdf).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1997-01-01T00:00:00Z","2026-09-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/SATELLITE_OCEAN_COLOR/V6","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_MARINE_SATELLITE_OCEAN_COLOR_V6","rel":"related","title":"Run the example for COPERNICUS/MARINE/SATELLITE_OCEAN_COLOR/V6 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_MARINE_SATELLITE_OCEAN_COLOR_V6_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_MARINE_SATELLITE_OCEAN_COLOR_V6#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/SATELLITE_OCEAN_COLOR/V6/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/SATELLITE_OCEAN_COLOR/V6/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/MARINE/WAV/ANFC_0_083DEG_PT3H","title":"Global Ocean Waves Analysis and Forecast ","description":"The operational global ocean analysis and forecast system of Meteo-France\nwith a resolution of 1/12 degree is providing daily analyses and 10 days\nforecasts for the global ocean sea surface waves. This product includes\n3-hourly instantaneous fields of integrated wave parameters from the\ntotal spectrum (significant height, period, direction, Stokes\ndrift,...etc), as well as the following partitions: the wind wave,\nthe primary and secondary swell waves.\n\nThe global wave system of Meteo-France is based on the wave model MFWAM\nwhich is a third generation wave model. MFWAM uses the computing code\nECWAM-IFS-38R2 with dissipation terms developed by Ardhuin et al. (2010).\nThe model MFWAM was upgraded on November 2014 thanks to improvements\nobtained from the European research project 'my wave' ,(Janssen et al.\n2014). The model mean bathymetry is generated by using 2-minute gridded\nglobal topography data ETOPO2/NOAA. Native model grid is irregular with\ndecreasing distance in the latitudinal direction close to the poles. At\nthe equator the distance in the latitudinal direction is more or less\nfixed with grid size 1/10 degrees. The operational model MFWAM is driven\nby 6-hourly analysis and 3-hourly forecasted winds from the IFS-ECMWF\natmospheric system. The wave spectrum is discretized in 24 directions and\n30 frequencies starting from 0.035 Hz to 0.58 Hz. The model MFWAM uses\nthe assimilation of altimeters with a time step of 6 hours. The global\nwave system provides analysis 4 times a day, and a forecast of 10 days at\n0:00 UTC. The wave model MFWAM uses the partitioning to split the swell\nspectrum in primary and secondary swells.\n\nGLOBAL_ANALYSISFORECAST_WAV_001_027, implemented by Mercator Ocean\nInternational. It provides a comprehensive overview of the global ocean\nwave analysis and forecasting system produced by Meteo-France. The system\ndelivers aggregated analyses and a 10-day forecast, updated twice-daily,\nincluding various integrated wave parameters such as significant wave\nheight, period, direction, and Stokes drift for both the total wave\nspectrum and partitioned waves (wind wave, primary and secondary swell).\n\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-06-01T00:00:00Z","2026-11-18T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/WAV/ANFC_0_083DEG_PT3H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_MARINE_WAV_ANFC_0_083DEG_PT3H","rel":"related","title":"Run the example for COPERNICUS/MARINE/WAV/ANFC_0_083DEG_PT3H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_MARINE_WAV_ANFC_0_083DEG_PT3H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_MARINE_WAV_ANFC_0_083DEG_PT3H#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/WAV/ANFC_0_083DEG_PT3H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/WAV/ANFC_0_083DEG_PT3H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/MARINE/WAV/ANFC_0_083DEG_STATIC","title":"Global Ocean Waves Static Bathymetry","description":"Static bathymetry for the Global Ocean Waves Analysis and Forecast\nsystem. This dataset provides the sea floor depth in meters.\n\nThe global wave system of Meteo-France is based on the wave model\nMFWAM which is a third generation wave model. MFWAM uses the\ncomputing code ECWAM-IFS-38R2 with dissipation terms developed by\nArdhuin et al. (2010). The model MFWAM was upgraded on November 2014\nthanks to improvements obtained from the European research project\n'my wave' (Janssen et al. 2014).\n\nThe model mean bathymetry is generated by using 2-minute gridded\nglobal topography data ETOPO2/NOAA. Native model grid is irregular\nwith decreasing distance in the latitudinal direction close to the\npoles. At the equator the distance in the latitudinal direction is\nmore or less fixed with grid size 1/10 degrees. The operational\nmodel MFWAM is driven by 6-hourly analysis and 3-hourly forecasted\nwinds from the IFS-ECMWF atmospheric system. The wave spectrum is\ndiscretized in 24 directions and 30 frequencies starting from 0.035\nHz to 0.58 Hz. The model MFWAM uses the assimilation of altimeters\nwith a time step of 6 hours. The global wave system provides\nanalysis 4 times a day, and a forecast of 10 days at 0:00 UTC. The\nwave model MFWAM uses the partitioning to split the swell spectrum in\nprimary and secondary swells.\n\nGLOBAL_ANALYSISFORECAST_WAV_001_027, implemented by Mercator Ocean\nInternational. It provides a comprehensive overview of the global\nocean wave analysis and forecasting system produced by Meteo-France.\nThe system delivers aggregated analyses and a 10-day forecast,\nupdated twice-daily, including various integrated wave parameters\nsuch as significant wave height, period, direction, and Stokes drift\nfor both the total wave spectrum and partitioned waves (wind wave,\nprimary and secondary swell).\n\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-06-01T00:00:00Z","2001-09-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/WAV/ANFC_0_083DEG_STATIC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_MARINE_WAV_ANFC_0_083DEG_STATIC","rel":"related","title":"Run the example for COPERNICUS/MARINE/WAV/ANFC_0_083DEG_STATIC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_MARINE_WAV_ANFC_0_083DEG_STATIC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_MARINE_WAV_ANFC_0_083DEG_STATIC#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/WAV/ANFC_0_083DEG_STATIC/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/WAV/ANFC_0_083DEG_STATIC/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S1_GRD","title":"Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling","description":"The Sentinel-1 mission provides data from a dual-polarization\nC-band Synthetic Aperture Radar (SAR) instrument at 5.405GHz (C band).  This collection\nincludes the S1 Ground Range Detected (GRD) scenes, processed\nusing the Sentinel-1 Toolbox to generate a calibrated, ortho-corrected\nproduct. The collection is updated daily. New assets are ingested\nwithin two days after they become available.\n\nThis collection\ncontains all of the GRD scenes.  Each scene has one of 3 resolutions\n(10, 25 or 40 meters), 4 band combinations (corresponding to\nscene polarization) and 3 instrument modes.  Use of the collection\nin a mosaic context will likely require filtering down to a homogeneous\nset of bands and parameters.  See\n[this article](https://developers.google.com/earth-engine/sentinel1)\nfor details of collection use and preprocessing. Each scene contains\neither 1 or 2 out of 4 possible polarization bands, depending\non the instrument's polarization settings.  The possible combinations\nare single band VV, single band HH, dual band VV+VH, and dual band HH+HV:\n\n  1. VV: single co-polarization, vertical transmit/vertical receive\n  2. HH: single co-polarization, horizontal transmit/horizontal receive\n  3. VV + VH: dual-band cross-polarization, vertical transmit/horizontal\n  receive\n  4. HH + HV: dual-band cross-polarization, horizontal transmit/vertical\n  receive\n\nEach scene also includes an additional 'angle' band that contains\nthe approximate incidence angle from ellipsoid in degrees at every point.\nThis band is generated by interpolating the 'incidenceAngle' property\nof the 'geolocationGridPoint' gridded field provided with each asset.\n\nEach scene was pre-processed with\n[Sentinel-1 Toolbox](https://earth.esa.int/eogateway/tools/sentinel-1-toolbox)\nusing the following steps:\n\n  1. Thermal noise removal\n  2. Radiometric calibration\n  3. Terrain correction using SRTM 30 or ASTER DEM for areas greater than\n     60 degrees latitude, where SRTM is not available.\nThe final terrain-corrected values are converted to decibels via log scaling\n(10*log10(x)).\n\nFor more information about these pre-processing steps, please refer to the\n[Sentinel-1 Pre-processing\narticle](https://developers.google.com/earth-engine/sentinel1). For further\nadvice on working with Sentinel-1 imagery, see [Guido Lemoine's tutorial on SAR\nbasics](https://developers.google.com/earth-engine/tutorials/community/sar-basics)\nand [Mort Canty's tutorial on SAR change\ndetection](https://developers.google.com/earth-engine/tutorials/community/detecting-changes-in-sentinel-1-imagery-pt-1).\n\nThis collection is computed on-the-fly. If you want to use the underlying\ncollection with raw power values (which is updated faster), see\nCOPERNICUS/S1_GRD_FLOAT.\n\nFor those interested in doing their own processing, the unprocessed S1 GRD\nscenes, as downloaded from ESA, are available in\n[gs://earth-engine-public-requester-pays/sentinel1/GRD](https://console.cloud.google.com/storage/browser/earth-engine-public-requester-pays/sentinel1/GRD),\nalong with corresponding orbit files (restituted and precise) in\n[gs://earth-engine-public-requester-pays/sentinel1/AUX](https://console.cloud.google.com/storage/browser/earth-engine-public-requester-pays/sentinel1/AUX).\nThe unprocessed data is also indexed in BigQuery table\n`earth-engine-public-data.geo_index.sentinel1_index`. Each row in the table\ncontains information about one S1 GRD product, including the product\nidentifier, datatake identifier, acquisition time, lat-lng bounds, and the\npath to the product's data in\n`gs://earth-engine-public-requester-pays/sentinel1/GRD`.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2014-10-03T00:00:00Z","2026-11-10T19:39:54Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S1_GRD","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S1_GRD","rel":"related","title":"Run the example for COPERNICUS/S1_GRD in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S1_GRD_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S1_GRD/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S1_GRD/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S2","title":"Sentinel-2 MSI: MultiSpectral Instrument, Level-1C (TOA) [deprecated]","description":"See also collection COPERNICUS/S2_HARMONIZED that shifts data with\nPROCESSING_BASELINE '04.00' or above (after 2022-01-25) to be in the same range\nas in older scenes.\n\nSentinel-2 is a wide-swath, high-resolution, multi-spectral\nimaging mission supporting Copernicus Land Monitoring studies,\nincluding the monitoring of vegetation, soil and water cover,\nas well as observation of inland waterways and coastal areas.\n\nThe Sentinel-2 data contain 13 UINT16 spectral bands representing\nTOA reflectance scaled by 10000. See the [Sentinel-2 User Handbook](https://sentinel.esa.int/documents/247904/685211/Sentinel-2_User_Handbook)\nfor details. QA60 is a bitmask band that contained rasterized cloud mask\npolygons until Feb 2022, when these polygons stopped being produced.\nStarting in February 2024, legacy-consistent QA60 bands are constructed from the MSK_CLASSI\ncloud classification bands.\nFor more details,\n[see the full explanation of how cloud masks are computed.](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks).\n\nEach Sentinel-2 product (zip archive) may contain multiple\ngranules. Each granule becomes a separate Earth Engine asset.\nEE asset ids for Sentinel-2 assets have the following format:\nCOPERNICUS/S2/20151128T002653_20151128T102149_T56MNN. Here the\nfirst numeric part represents the sensing date and time, the\nsecond numeric part represents the product generation date and\ntime, and the final 6-character string is a unique granule identifier\nindicating its UTM grid reference (see [MGRS](https://en.wikipedia.org/wiki/Military_Grid_Reference_System)).\n\nThe Level-2 data produced by ESA can be found in the collection\n[COPERNICUS/S2_SR](COPERNICUS_S2_SR).\n\nFor datasets to assist with cloud and/or cloud shadow detection, see [COPERNICUS/S2_CLOUD_PROBABILITY](COPERNICUS_S2_CLOUD_PROBABILITY)\nand [GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED](GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED).\n\nFor more details on Sentinel-2 radiometric resolution, [see this page](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/radiometric).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-56,180,83]]},"temporal":{"interval":[["2015-06-27T00:00:00Z","2026-11-10T19:09:09.986000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S2","rel":"related","title":"Run the example for COPERNICUS/S2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_HARMONIZED.json","rel":"latest-version","title":"COPERNICUS/S2_HARMONIZED","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_HARMONIZED.json","rel":"successor-version","title":"COPERNICUS/S2_HARMONIZED","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S2_CLOUD_PROBABILITY","title":"Sentinel-2: Cloud Probability","description":"The S2 cloud probability is created with the\n[sentinel2-cloud-detector](https://github.com/sentinel-hub/sentinel2-cloud-detector)\nlibrary (using [LightGBM](https://github.com/microsoft/LightGBM)).\nAll bands are upsampled using bilinear interpolation to 10m resolution\nbefore the gradient boost base algorithm is applied.  The resulting\n`0..1` floating point probability is scaled to `0..100` and stored as\nan UINT8.  Areas missing any or all of the bands are masked out.\nHigher values are more likely to be clouds or highly reflective surfaces\n(e.g. roof tops or snow).\n\nSentinel-2 is a wide-swath, high-resolution, multi-spectral\nimaging mission supporting Copernicus Land Monitoring studies,\nincluding the monitoring of vegetation, soil and water cover,\nas well as observation of inland waterways and coastal areas.\n\nThe Level-2 data can be found in the collection\n[COPERNICUS/S2_SR_HARMONIZED](COPERNICUS_S2_SR_HARMONIZED).  The Level-1B data can be found in the collection\n[COPERNICUS/S2_HARMONIZED](COPERNICUS_S2_HARMONIZED).  Additional metadata is available on assets in those\ncollections.\n\nSee\n[this tutorial](https://developers.google.com/earth-engine/tutorials/community/sentinel-2-s2cloudless)\nexplaining how to apply the cloud mask.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-56,180,83]]},"temporal":{"interval":[["2015-06-27T00:00:00Z","2026-11-10T18:26:01Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_CLOUD_PROBABILITY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY","rel":"related","title":"Run the example for COPERNICUS/S2_CLOUD_PROBABILITY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_CLOUD_PROBABILITY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_CLOUD_PROBABILITY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S2_HARMONIZED","title":"Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-1C (TOA)","description":"After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above\nhave their DN (value) range shifted by 1000. The HARMONIZED collection\nshifts data in newer scenes to be in the same range as in older scenes.\n\nSentinel-2 is a wide-swath, high-resolution, multi-spectral\nimaging mission supporting Copernicus Land Monitoring studies,\nincluding the monitoring of vegetation, soil and water cover,\nas well as observation of inland waterways and coastal areas.\n\nThe Sentinel-2 data contain 13 UINT16 spectral bands representing\nTOA reflectance scaled by 10000. See the [Sentinel-2 User Handbook](https://sentinel.esa.int/documents/247904/685211/Sentinel-2_User_Handbook)\nfor details. QA60 is a bitmask band that contained rasterized cloud mask\npolygons until Feb 2022, when these polygons stopped being produced.\nStarting in February 2024, legacy-consistent QA60 bands are constructed from the MSK_CLASSI\ncloud classification bands.\nFor more details,\n[see the full explanation of how cloud masks are computed.](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks).\n\nEach Sentinel-2 product (zip archive) may contain multiple\ngranules. Each granule becomes a separate Earth Engine asset.\nEE asset ids for Sentinel-2 assets have the following format:\nCOPERNICUS/S2/20151128T002653_20151128T102149_T56MNN. Here the\nfirst numeric part represents the sensing date and time, the\nsecond numeric part represents the product generation date and\ntime, and the final 6-character string is a unique granule identifier\nindicating its UTM grid reference (see [MGRS](https://en.wikipedia.org/wiki/Military_Grid_Reference_System)).\n\nThe Level-2 data produced by ESA can be found in the collection\n[COPERNICUS/S2_SR](COPERNICUS_S2_SR).\n\nFor datasets to assist with cloud and/or cloud shadow detection, see [COPERNICUS/S2_CLOUD_PROBABILITY](COPERNICUS_S2_CLOUD_PROBABILITY)\nand [GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED](GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED).\n\nFor more details on Sentinel-2 radiometric resolution, [see this page](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/radiometric).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-56,180,83]]},"temporal":{"interval":[["2015-06-27T00:00:00Z","2026-11-10T18:27:52.625000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_HARMONIZED","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S2_HARMONIZED","rel":"related","title":"Run the example for COPERNICUS/S2_HARMONIZED in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S2_HARMONIZED_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_HARMONIZED.json","rel":"latest-version","title":"COPERNICUS/S2_HARMONIZED","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2.json","rel":"predecessor-version","title":"COPERNICUS/S2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_HARMONIZED/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_HARMONIZED/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S2_SR","title":"Sentinel-2 MSI: MultiSpectral Instrument, Level-2A (SR) [deprecated]","description":"See also collection COPERNICUS/S2_SR_HARMONIZED that shifts data with\nPROCESSING_BASELINE '04.00' or above (after 2022-01-25) to be in the same range\nas in older scenes.\n\nSentinel-2 is a wide-swath, high-resolution, multi-spectral\nimaging mission supporting Copernicus Land Monitoring studies,\nincluding the monitoring of vegetation, soil and water cover,\nas well as observation of inland waterways and coastal areas.\n\nThe Sentinel-2 L2 data are downloaded from CDSE. They were\ncomputed by running sen2cor. WARNING: 2017-2018 L2 coverage\nin the EE collection is not yet global.\n\nThe assets contain\n12 UINT16 spectral bands representing SR scaled by 10000 (unlike in L1 data,\nthere is no B10). There are also several more L2-specific bands (see band\nlist for details). See the\n[Sentinel-2 User Handbook](https://sentinel.esa.int/documents/247904/685211/Sentinel-2_User_Handbook)\nfor details.\n\nQA60 is a bitmask band that contained rasterized cloud mask\npolygons until 2022-01-25, when these polygons stopped being produced.\nStarting 2024-02-28, legacy-consistent QA60 bands are constructed from\nthe MSK_CLASSI cloud classification bands.\nFor more details,\n[see the full explanation of how cloud masks are computed.](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks)\n\nEE asset ids for Sentinel-2 L2 assets have the following format:\nCOPERNICUS/S2_SR/20151128T002653_20151128T102149_T56MNN. Here the\nfirst numeric part represents the sensing date and time, the\nsecond numeric part represents the product generation date and\ntime, and the final 6-character string is a unique granule identifier\nindicating its UTM grid reference (see [MGRS](https://en.wikipedia.org/wiki/Military_Grid_Reference_System)).\n\nFor datasets to assist with cloud and/or cloud shadow detection, see [COPERNICUS/S2_CLOUD_PROBABILITY](COPERNICUS_S2_CLOUD_PROBABILITY)\nand [GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED](GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED).\n\nFor more details on Sentinel-2 radiometric resolution, [see this page](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/radiometric).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-56,180,83]]},"temporal":{"interval":[["2017-03-28T00:00:00Z","2026-11-10T18:27:52.625000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_SR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S2_SR","rel":"related","title":"Run the example for COPERNICUS/S2_SR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S2_SR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR_HARMONIZED.json","rel":"latest-version","title":"COPERNICUS/S2_SR_HARMONIZED","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR_HARMONIZED.json","rel":"successor-version","title":"COPERNICUS/S2_SR_HARMONIZED","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_SR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_SR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S2_SR_HARMONIZED","title":"Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A (SR)","description":"After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above\nhave their DN (value) range shifted by 1000. The HARMONIZED collection\nshifts data in newer scenes to be in the same range as in older scenes.\n\nSentinel-2 is a wide-swath, high-resolution, multi-spectral\nimaging mission supporting Copernicus Land Monitoring studies,\nincluding the monitoring of vegetation, soil and water cover,\nas well as observation of inland waterways and coastal areas.\n\nThe Sentinel-2 L2 data are downloaded from CDSE. They were\ncomputed by running sen2cor. WARNING: 2017-2018 L2 coverage\nin the EE collection is not yet global.\n\nThe assets contain\n12 UINT16 spectral bands representing SR scaled by 10000 (unlike in L1 data,\nthere is no B10). There are also several more L2-specific bands (see band\nlist for details). See the\n[Sentinel-2 User Handbook](https://sentinel.esa.int/documents/247904/685211/Sentinel-2_User_Handbook)\nfor details.\n\nQA60 is a bitmask band that contained rasterized cloud mask\npolygons until 2022-01-25, when these polygons stopped being produced.\nStarting 2024-02-28, legacy-consistent QA60 bands are constructed from\nthe MSK_CLASSI cloud classification bands.\nFor more details,\n[see the full explanation of how cloud masks are computed.](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks)\n\nEE asset ids for Sentinel-2 L2 assets have the following format:\nCOPERNICUS/S2_SR/20151128T002653_20151128T102149_T56MNN. Here the\nfirst numeric part represents the sensing date and time, the\nsecond numeric part represents the product generation date and\ntime, and the final 6-character string is a unique granule identifier\nindicating its UTM grid reference (see [MGRS](https://en.wikipedia.org/wiki/Military_Grid_Reference_System)).\n\nFor datasets to assist with cloud and/or cloud shadow detection, see [COPERNICUS/S2_CLOUD_PROBABILITY](COPERNICUS_S2_CLOUD_PROBABILITY)\nand [GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED](GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED).\n\nFor more details on Sentinel-2 radiometric resolution, [see this page](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/radiometric).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-56,180,83]]},"temporal":{"interval":[["2017-03-28T00:00:00Z","2026-11-10T18:27:52.625000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_SR_HARMONIZED","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S2_SR_HARMONIZED","rel":"related","title":"Run the example for COPERNICUS/S2_SR_HARMONIZED in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S2_SR_HARMONIZED_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR_HARMONIZED.json","rel":"latest-version","title":"COPERNICUS/S2_SR_HARMONIZED","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR.json","rel":"predecessor-version","title":"COPERNICUS/S2_SR","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_SR_HARMONIZED/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S2_SR_HARMONIZED/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S3/OLCI","title":"Sentinel-3 OLCI EFR: Ocean and Land Color Instrument Earth Observation Full Resolution","description":"The Ocean and Land Color Instrument (OLCI) Earth Observation\nFull Resolution (EFR) dataset contains top of atmosphere radiances\nat 21 spectral bands with center wavelengths ranging between\n0.4&micro;m and 1.02&micro;m at spatial resolution of 300m with\nworldwide coverage every ~2 days.\n\nOLCI is one of the instruments\nin the ESA/EUMETSAT Sentinel-3 mission for measuring sea-surface\ntopography, sea- and land-surface temperature, ocean color and\nland color with high-end accuracy and reliability to support\nocean forecasting systems, as well as environmental and climate\nmonitoring.\n\nThe Sentinel-3 OLCI instrument is based on the\noptomechanical and imaging design of ENVISAT's MERIS. It is designed\nto retrieve the spectral distribution of upwelling radiance just\nabove the sea surface (the water-leaving radiance).\n\nOLCI observation is performed simultaneously in 21 spectral bands\nranging from the visible to the near-infrared (400 to 1029 nm).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-10-18T19:25:42Z","2026-11-08T08:36:26Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S3/OLCI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S3_OLCI","rel":"related","title":"Run the example for COPERNICUS/S3/OLCI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S3_OLCI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S3_OLCI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S3/OLCI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S3/OLCI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/NRTI/L3_AER_AI","title":"Sentinel-5P NRTI AER AI: Near Real-Time UV Aerosol Index","description":"### NRTI/L3_AER_AI\n\nThis dataset provides near real-time high-resolution imagery of the UV Aerosol Index\n(UVAI), also called the Absorbing Aerosol Index (AAI).\n\nThe AAI is based on wavelength-dependent changes in Rayleigh scattering in\nthe UV spectral range for a pair of wavelengths.  The difference between\nobserved and modelled reflectance results in the AAI.  When the AAI is\npositive, it indicates the presence of UV-absorbing aerosols like dust and\nsmoke. It is useful for tracking the evolution of episodic aerosol plumes from\ndust outbreaks, volcanic ash, and biomass burning.\n\nThe wavelengths used have very low ozone absorption, so unlike aerosol optical\nthickness measurements, AAI can be calculated in the presence of clouds.\nDaily global coverage is therefore possible.\n\nFor this L3 AER_AI product, the absorbing_aerosol_index is calculated with a\npair of measurements at the 354 nm and 388 nm wavelengths.\n\n### NRTI L3 Product\n\nTo make our NRTI L3 products, we use\n[harpconvert](https://stcorp.github.io/harp/doc/html/harpconvert.html) to grid the data.\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'absorbing_aerosol_index_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(absorbing_aerosol_index,sensor_altitude,sensor_azimuth_angle,\n     sensor_zenith_angle,solar_azimuth_angle,solar_zenith_angle)'\nS5P_NRTI_L2__AER_AI_20181113T080042_20181113T080542_05618_01_010200_20181113T083707.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-10T11:17:44Z","2026-11-10T19:36:04Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_AER_AI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_AI","rel":"related","title":"Run the example for COPERNICUS/S5P/NRTI/L3_AER_AI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_AI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_AI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_AER_AI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_AER_AI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/NRTI/L3_AER_LH","title":"Sentinel-5P NRTI AER LH: Near Real-Time UV Aerosol Layer Height","description":"### NRTI/L3_AER_LH\n\nThis dataset provides offline high-resolution imagery of the UV Aerosol Index\n(UVAI), also called the Absorbing Layer Height (ALH).\n\nThe ALH is very sensitive to cloud contamination. However, aerosols and clouds can\nbe difficult to distinguish, and ALH is computed for all FRESCO effective cloud\nfractions smaller than 0.05. Cloud masks are available from FRESCO and VIIRS, and\nare strongly recommended to filter for residual clouds. A sunglint mask is also\navailable to screen sunglint regions, which are not filtered beforehand.\n\nIt is known that high surface albedos negatively influence the ALH, biasing the ALH\ntowards the surface. In general, the ALH over (dark) oceans is considered reliable\nto within the requirement of 1000 m or 100 hPa. Over land, especially bright surfaces,\nthe accuracy may be lower, and the use of the ALH product over bright surfaces like\ndeserts is not advisable.\n\nFor this L3 AER_LH product, the aerosol_mid_pressure is calculated with a\npair of measurements at the 354 nm and 388 nm wavelengths.  The\n[COPERNICUS/S5P/OFFL/L3_SO2](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2)\nproduct has the absorbing_aerosol_index calculated using the 340 nm and 380\nnm wavelengths.\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'aerosol_height_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(aerosol_height,aerosol_pressure,aerosol_optical_depth,\n     sensor_zenith_angle,sensor_azimuth_angle,solar_azimuth_angle,solar_zenith_angle)'\nS5P_NRTI_L2__AER_LH_20191202T233055_20191202T233555_11074_01_010302_20191203T012120.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-10T11:17:44Z","2026-11-10T09:31:04Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_AER_LH","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_LH","rel":"related","title":"Run the example for COPERNICUS/S5P/NRTI/L3_AER_LH in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_LH_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_LH#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_AER_LH/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_AER_LH/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/NRTI/L3_CLOUD","title":"Sentinel-5P NRTI CLOUD: Near Real-Time Cloud Properties","description":"### NRTI/L3_CLOUD\n\nThis dataset provides near real-time high-resolution imagery of cloud parameters.\n\nThe TROPOMI/S5P cloud properties retrieval is based on the OCRA and ROCINN\nalgorithms currently being used in the operational GOME and GOME-2 products.\nOCRA retrieves the cloud fraction using measurements in the UV/VIS spectral\nregions and ROCINN retrieves the cloud height (pressure) and optical thickness\n(albedo) using measurements in and around the oxygen A-band at 760 nm. Version 3.0\nof the algorithms are used, which are based on a more realistic\ntreatment of clouds as optically uniform layers of light-scattering particles.\nAdditionally, the cloud parameters are also provided for a cloud model which\nassumes the cloud to be a Lambertian reflecting boundary.\n[More information.](http://www.tropomi.eu/data-products/cloud)\n\n### NRTI L3 Product\n\nTo make our NRTI L3 products, we use [harpconvert](https://stcorp.github.io/harp/doc/html/harpconvert.html)\nto grid the data.\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'cloud_fraction>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(cloud_fraction,cloud_top_pressure,cloud_top_height,\ncloud_base_pressure,cloud_base_height,cloud_optical_depth,surface_albedo,\nsensor_azimuth_angle,sensor_zenith_angle,solar_azimuth_angle,\nsolar_zenith_angle)'\nS5P_NRTI_L2__CLOUD__20190208T230503_20190208T231003_06860_01_010105_20190209T005255.nc\noutput.h5\n```\n\nAssets between the dates 2018-07-10 and 2018-07-18 are missing due to non-standard structure\nof product files.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-05T23:24:16Z","2026-11-10T09:31:04Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_CLOUD","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CLOUD","rel":"related","title":"Run the example for COPERNICUS/S5P/NRTI/L3_CLOUD in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CLOUD_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CLOUD#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_CLOUD/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_CLOUD/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/NRTI/L3_CO","title":"Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide","description":"### NRTI/L3_CO\n\nThis dataset provides near real-time high-resolution imagery of CO\nconcentrations.\n\nCarbon monoxide (CO) is an important atmospheric trace gas for understanding\ntropospheric chemistry. In certain urban areas, it is a major atmospheric\npollutant. Main sources of CO are combustion of fossil fuels, biomass burning,\nand atmospheric oxidation of methane and other hydrocarbons. Whereas fossil\nfuel combustion is the main source of CO at northern mid-latitudes, the oxidation\nof isoprene and biomass burning play an important role in the tropics. TROPOMI\non the Sentinel 5 Precursor (S5P) satellite observes the CO global abundance\nexploiting clear-sky and cloudy-sky Earth radiance measurements in the 2.3 &mu;m\nspectral range of the shortwave infrared (SWIR) part of the solar spectrum.\nTROPOMI clear sky observations provide CO total columns with sensitivity to the\ntropospheric boundary layer. For cloudy atmospheres, the column sensitivity\nchanges according to the light path.\n[More information.](http://www.tropomi.eu/data-products/carbon-monoxide)\n\n### NRTI L3 Product\n\nTo make our NRTI L3 products, we use [harpconvert](https://stcorp.github.io/harp/doc/html/harpconvert.html)\nto grid the data.\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'CO_column_number_density_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(CO_column_number_density,H2O_column_number_density,cloud_height,\nsensor_altitude,sensor_azimuth_angle, sensor_zenith_angle,\nsolar_azimuth_angle,solar_zenith_angle)'\nS5P_NRTI_L2__CO_____20181122T000018_20181122T000518_05741_01_010200_20181122T004844.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-11-22T12:00:13Z","2026-11-10T19:36:04Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_CO","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CO","rel":"related","title":"Run the example for COPERNICUS/S5P/NRTI/L3_CO in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CO_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CO#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_CO/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_CO/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/NRTI/L3_HCHO","title":"Sentinel-5P NRTI HCHO: Near Real-Time Formaldehyde","description":"### NRTI/L3_HCHO\n\nThis dataset provides near real-time high-resolution imagery of atmospheric\nformaldehyde (HCHO) concentrations.\n\nFormaldehyde is an intermediate gas in almost all oxidation chains of non-methane\nvolatile organic compounds (NMVOC), leading eventually to CO<sub>2</sub>. Non-Methane Volatile\nOrganic Compounds (NMVOCs) are, together with NO<sub>x</sub>, CO and CH<sub>4</sub>, among the\nmost important precursors of tropospheric O<sub>3</sub>. The major HCHO source in the remote\natmosphere is CH<sub>4</sub> oxidation. Over the continents, the oxidation of higher NMVOCs\nemitted from vegetation, fires, traffic and industrial sources results in important\nand localized enhancements of the HCHO levels. The seasonal and inter-annual\nvariations of the formaldehyde distribution are principally related to temperature\nchanges and fire events, but also to changes in anthropogenic activities. HCHO concentrations\nin the boundary layer can be directly related to the release of short-lived hydrocarbons, which\nmostly cannot be observed directly from space.\n[More information.](http://www.tropomi.eu/data-products/formaldehyde)\n\n### NRTI L3 Product\n\nTo make our NRTI L3 products, we use [harpconvert](https://stcorp.github.io/harp/doc/html/harpconvert.html)\nto grid the data.\n\nExample harpconvert invocation for one tile:\n\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'tropospheric_HCHO_column_number_density_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(tropospheric_HCHO_column_number_density,\n     tropospheric_HCHO_column_number_density_amf,\n     HCHO_slant_column_number_density,cloud_fraction,sensor_altitude,\n     sensor_azimuth_angle, sensor_zenith_angle,solar_azimuth_angle,\n     solar_zenith_angle)'\nS5P_NRTI_L2__HCHO___20181017T181013_20181017T181513_05241_01_010102_20181017T185718.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-10-02T07:58:03Z","2026-11-10T09:26:04Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_HCHO","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_NRTI_L3_HCHO","rel":"related","title":"Run the example for COPERNICUS/S5P/NRTI/L3_HCHO in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_NRTI_L3_HCHO_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_HCHO#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_HCHO/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_HCHO/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/NRTI/L3_NO2","title":"Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide","description":"### NRTI/L3_NO2\n\nThis dataset provides near real-time high-resolution imagery of NO<sub>2</sub> concentrations.\n\nNitrogen oxides (NO<sub>2</sub> and NO) are important trace gases in the Earth's atmosphere,\npresent in both the troposphere and the stratosphere. They enter the atmosphere\nas a result of anthropogenic activities (notably fossil fuel combustion and\nbiomass burning) and natural processes (wildfires, lightning, and microbiological processes in\nsoils). Here, NO<sub>2</sub> is used to represent concentrations\nof collective nitrogen oxides because during daytime, i.e. in the presence of\nsunlight, a photochemical cycle involving ozone (O<sub>3</sub>) converts NO into NO<sub>2</sub>\nand vice versa on a timescale of minutes. The TROPOMI NO<sub>2</sub> processing system is based\non the algorithm developments for the DOMINO-2 product and for the EU QA4ECV NO<sub>2</sub>\nreprocessed dataset for OMI, and has been adapted for TROPOMI. This\nretrieval-assimilation-modelling system uses the 3-dimensional global TM5-MP\nchemistry transport model at a resolution of 1x1 degree as an essential element.\n[More information.](http://www.tropomi.eu/data-products/nitrogen-dioxide)\n\n### NRTI L3 Product\n\nTo make our NRTI L3 products, we use [harpconvert](https://stcorp.github.io/harp/doc/html/harpconvert.html)\nto grid the data.\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'tropospheric_NO2_column_number_density_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(NO2_column_number_density,tropospheric_NO2_column_number_density,\n     stratospheric_NO2_column_number_density,NO2_slant_column_number_density,\n     tropopause_pressure,absorbing_aerosol_index,cloud_fraction,\n     sensor_altitude,sensor_azimuth_angle,\n     sensor_zenith_angle,solar_azimuth_angle,solar_zenith_angle)'\nS5P_NRTI_L2__NO2____20181107T013042_20181107T013542_05529_01_010200_20181107T021824.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-10T10:05:44Z","2026-11-10T19:36:04Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_NO2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2","rel":"related","title":"Run the example for COPERNICUS/S5P/NRTI/L3_NO2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_NO2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_NO2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_NO2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/NRTI/L3_O3","title":"Sentinel-5P NRTI O3: Near Real-Time Ozone","description":"### NRTI/L3_O3\n\nThis dataset provides near-real-time high-resolution imagery of total\ncolumn ozone concentrations. See also `COPERNICUS/S5P/OFFL/L3_O3_TCL`\nfor the tropospheric column data.\n\nIn the stratosphere, the ozone layer shields the biosphere from dangerous\nsolar ultraviolet radiation. In the troposphere, it acts as an efficient\ncleansing agent, but at high concentration it also becomes harmful to the\nhealth of humans, animals, and vegetation. Ozone is also an important\ngreenhouse-gas contributor to ongoing climate change. Since the discovery\nof the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol\nregulating the production of chlorine-containing ozone-depleting substances,\nozone has been routinely monitored from the ground and from space.\n\nFor this product, there are two algorithms that deliver total ozone: GDP for\nthe near real-time and GODFIT for the offline products. GDP is currently\nbeing used for generating the operational total ozone products from GOME,\nSCIAMACHY and GOME-2; while GODFIT is being used in the ESA CCI and the\nCopernicus C3S projects.\n[More information.](https://www.tropomi.eu/data-products/ozone-total-column)\n[Product user manual.](https://sentinels.copernicus.eu/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Ozone-Total-Column)\n\n### NRTI L3 Product\n\nTo make our NRTI L3 products, we use [harpconvert](https://stcorp.github.io/harp/doc/html/harpconvert.html)\nto grid the data.\n\nThe qa value is adjusted before running harpconvert to satisfy all of the\nfollowing criteria:\n\n* ozone_total_vertical_column in [0, 0.45]\n* ozone_effective_temperature in [180, 260]\n* fitted_root_mean_square <= 0.01\n\nExample harpconvert invocation:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'O3_column_number_density_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(O3_column_number_density,O3_column_number_density_amf,\nO3_slant_column_number_density,O3_effective_temperature,cloud_fraction,\nsensor_azimuth_angle,sensor_zenith_angle,solar_azimuth_angle,\nsolar_zenith_angle)'\nS5P_NRTI_L2__O3_____20180710T230038_20180710T230538_03840_01_010000_20180711T005227.nc\noutput.h5\n```\n\n- Assets between the dates 2018-07-10 and 2018-07-18 are missing due to non-standard structure\nof product files.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-10T11:02:44Z","2026-11-10T09:31:04Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_O3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_NRTI_L3_O3","rel":"related","title":"Run the example for COPERNICUS/S5P/NRTI/L3_O3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_NRTI_L3_O3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_O3#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_O3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_O3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/NRTI/L3_SO2","title":"Sentinel-5P NRTI SO2: Near Real-Time Sulfur Dioxide","description":"### NRTI/L3_SO2\n\nThis dataset provides near real-time high-resolution imagery of atmospheric\nsulfur dioxide (SO<sub>2</sub>) concentrations.\n\nSulfur dioxide (SO<sub>2</sub>) enters the Earth's atmosphere through both natural and\nanthropogenic processes. It plays a role in chemistry on a local and global\nscale and its impact ranges from short-term pollution to effects on climate.\nOnly about 30% of the emitted SO<sub>2</sub> comes from natural sources; the majority\nis of anthropogenic origin. SO<sub>2</sub> emissions adversely affect human health and\nair quality. SO<sub>2</sub> has an effect on climate through radiative forcing, via\nthe formation of sulfate aerosols. Volcanic SO<sub>2</sub> emissions can also pose a\nthreat to aviation, along with volcanic ash. S5P/TROPOMI samples the Earth's\nsurface with a revisit time of one day with unprecedented spatial resolution\nof 3.5 x 7 km which allows the resolution of fine details including the\ndetection of much smaller SO<sub>2</sub> plumes.\n[More information.](https://www.tropomi.eu/data-products/sulfur-dioxide)\n\n### NRTI L3 Product\n\nTo make our NRTI L3 products, we use [harpconvert](https://stcorp.github.io/harp/doc/html/harpconvert.html)\nto grid the data.\n\nThe qa value is adjusted before running harpconvert to satisfy all of the\nfollowing criteria:\n\n* snow_ice < 0.5\n* sulfurdioxide_total_air_mass_factor_polluted > 0.1\n* sulfurdioxide_total_vertical_column > -0.001\n* qa_value > 0.5\n* cloud_fraction_crb < 0.3\n* solar_zenith_angle < 60\n\nThe 15km SO2 band is ingested only when solar_zenith_angle < 70.\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'SO2_column_number_density_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(SO2_column_number_density,SO2_column_number_density_amf,\n     SO2_slant_column_number_density,cloud_fraction, sensor_altitude,\n     sensor_azimuth_angle, sensor_zenith_angle,solar_azimuth_angle,\n     solar_zenith_angle)'\nS5P_NRTI_L2__SO2____20190129T101503_20190129T102003_06711_01_010105_20190129T111328.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-10T11:17:44Z","2026-11-10T09:31:04Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_SO2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_NRTI_L3_SO2","rel":"related","title":"Run the example for COPERNICUS/S5P/NRTI/L3_SO2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_NRTI_L3_SO2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_SO2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_SO2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/NRTI/L3_SO2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/OFFL/L3_AER_AI","title":"Sentinel-5P OFFL AER AI: Offline UV Aerosol Index","description":"### OFFL/L3_AER_AI\n\nThis dataset provides offline high-resolution imagery of the UV Aerosol Index\n(UVAI), also called the Absorbing Aerosol Index (AAI).\n\nThe AAI is based on wavelength-dependent changes in Rayleigh scattering in\nthe UV spectral range for a pair of wavelengths.  The difference between\nobserved and modelled reflectance results in the AAI.  When the AAI is\npositive, it indicates the presence of UV-absorbing aerosols like dust and\nsmoke. It is useful for tracking the evolution of episodic aerosol plumes from\ndust outbreaks, volcanic ash, and biomass burning.\n\nThe wavelengths used have very low ozone absorption, so unlike aerosol optical\nthickness measurements, AAI can be calculated in the presence of clouds.\nDaily global coverage is therefore possible.\n\nFor this L3 AER_AI product, the absorbing_aerosol_index is calculated with a\npair of measurements at the 354 nm and 388 nm wavelengths.  The\n[COPERNICUS/S5P/OFFL/L3_SO2](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2)\nproduct has the absorbing_aerosol_index calculated using the 340 nm and 380\nnm wavelengths.\n\n### OFFL L3 Product\n\nTo make our OFFL L3 products, we find areas within the product's bounding\nbox with data using a command like this:\n\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'absorbing_aerosol_index_validity>50;derive(datetime_stop {time})'\nS5P_OFFL_L2__AER_AI_20181030T213916_20181030T232046_05427_01_010200_20181105T210529.nc\ngrid_info.h5\n```\n\nWe then merge all the data into one large mosaic (area-averaging values for\npixels that may have different values for different times).  From the\nmosaic, we create a set of tiles containing orthorectified raster data.\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'absorbing_aerosol_index_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(absorbing_aerosol_index,sensor_altitude,sensor_azimuth_angle,\n     sensor_zenith_angle,solar_azimuth_angle,solar_zenith_angle)'\nS5P_OFFL_L2__AER_AI_20181030T213916_20181030T232046_05427_01_010200_20181105T210529.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-04T13:34:21Z","2026-11-07T18:12:28Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_AER_AI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI","rel":"related","title":"Run the example for COPERNICUS/S5P/OFFL/L3_AER_AI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_AI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_AER_AI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_AER_AI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/OFFL/L3_AER_LH","title":"Sentinel-5P OFFL AER LH: Offline UV Aerosol Layer Height","description":"### OFFL/L3_AER_LH\n\nThis dataset provides offline high-resolution imagery of the UV Aerosol Index\n(UVAI), also called the Absorbing Layer Height (ALH).\n\nThe ALH is very sensitive to cloud contamination. However, aerosols and clouds can\nbe difficult to distinguish, and ALH is computed for all FRESCO effective cloud\nfractions smaller than 0.05. Cloud masks are available from FRESCO and VIIRS, and\nare strongly recommended to filter for residual clouds. A sunglint mask is also\navailable to screen sunglint regions, which are not filtered beforehand.\n\nIt is known that high surface albedos negatively influence the ALH, biasing the ALH\ntowards the surface. In general, the ALH over (dark) oceans is considered reliable\nto within the requirement of 1000 m or 100 hPa. Over land, especially bright surfaces,\nthe accuracy may be lower, and the use of the ALH product over bright surfaces like\ndeserts is not advisable.\n\nFor this L3 AER_LH product, the aerosol_mid_pressure is calculated with a\npair of measurements at the 354 nm and 388 nm wavelengths.  The\n[COPERNICUS/S5P/OFFL/L3_SO2](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2)\nproduct has the absorbing_aerosol_index calculated using the 340 nm and 380\nnm wavelengths.\n\n### OFFL L3 Product\n\nTo make our OFFL L3 products, we find areas within the product's bounding\nbox with data using a command like this:\n\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'aerosol_height_validity>50;derive(datetime_stop {time})'\nS5P_OFFL_L2__AER_LH_20190404T042423_20190404T060554_07630_01_010300_20190410T062552.nc\ngrid_info.h5\n```\n\nWe then merge all the data into one large mosaic (area-averaging values for\npixels that may have different values for different times).  From the\nmosaic, we create a set of tiles containing orthorectified raster data.\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'aerosol_height_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(aerosol_height,aerosol_pressure,aerosol_optical_depth,\n     sensor_zenith_angle,sensor_azimuth_angle,solar_azimuth_angle,solar_zenith_angle)'\nS5P_OFFL_L2__AER_LH_20190404T042423_20190404T060554_07630_01_010300_20190410T062552.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-04T13:34:21Z","2026-11-08T04:21:25Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_AER_LH","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_LH","rel":"related","title":"Run the example for COPERNICUS/S5P/OFFL/L3_AER_LH in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_LH_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_LH#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_AER_LH/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_AER_LH/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/OFFL/L3_CH4","title":"Sentinel-5P OFFL CH4: Offline Methane","description":"### OFFL/L3_CH4\n\nThis dataset provides offline high-resolution imagery of methane\nconcentrations.\n\nMethane (CH<sub>4</sub>) is, after carbon dioxide (CO<sub>2</sub>), the most\nimportant contributor to the anthropogenically enhanced greenhouse effect.\nRoughly three-quarters of methane emissions are anthropogenic and as such, it\nis important to continue the record of satellite based measurements. TROPOMI\naims at providing CH<sub>4</sub> column concentrations with high sensitivity\nto the Earth's surface, good spatiotemporal coverage, and sufficient\naccuracy to facilitate inverse modeling of sources and sinks. TROPOMI uses\nabsorption information from the Oxygen-A Band (760nm) and the SWIR spectral\nrange to monitor CH<sub>4</sub> abundances in the Earth's atmosphere.\n[More information.](http://www.tropomi.eu/data-products/methane)\n\nCurrently, the following data quality issues are known, are not covered by\nthe quality flags, and should be kept in mind when looking at the methane\nproduct and also at preliminary validation results. For more details, see\nthe [MPC VDAF website](http://mpc-vdaf.tropomi.eu).\n\nFiltering on qa_value &lt; 0.5 does not remove all pixels considered bad.\nSome pixels with too low methane concentrations are still present:\n\n - Single TROPOMI overpasses show stripes of erroneous CH<sub>4</sub> values\n   in the flight direction.\n\n - Not all pixels above inland water are filtered.\n\n - Uncertainties for the XCH<sub>4</sub> is only based on the single\n   sounding precision due to measurement noise. For applications requiring\n   an overall uncertainty estimate, we propose to multiply the provided\n   error by a factor 2, which reflects the scatter of single sounding errors\n   in the TCCON validation.\n\n - Data prior to November 2021 only provides XCH<sub>4</sub> over land,\n   after which glint ocean observations were added.\n\n - No data are present between 2022-07-26 and 2022-08-31 due to a\n   [provider outage](https://scihub.copernicus.eu/news/News01082).\n\n### OFFL L3 Product\n\nTo make our OFFL L3 products, we find which areas within the product's\nbounding box contain data by using a command like this:\n\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'CH4_column_volume_mixing_ratio_dry_air_validity>50;derive(datetime_stop {time})'\nS5P_OFFL_L2__CH4____20190223T202409_20190223T220540_07072_01_010202_20190301T221106.nc\ngrid_info.h5\n```\n\nWe then merge all the data into one large mosaic (area-averaging values for\npixels that may have different values for different times).  From the\nmosaic, we create a set of tiles containing orthorectified raster data.\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'CH4_column_volume_mixing_ratio_dry_air_validity>50;\nderive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(CH4_column_volume_mixing_ratio_dry_air, aerosol_height,\naerosol_optical_depth, sensor_azimuth_angle, sensor_zenith_angle,\nsolar_azimuth_angle, solar_zenith_angle)'\nS5P_OFFL_L2__CH4____20190223T202409_20190223T220540_07072_01_010202_20190301T221106.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2019-02-08T08:13:16Z","2026-11-07T23:16:56Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_CH4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CH4","rel":"related","title":"Run the example for COPERNICUS/S5P/OFFL/L3_CH4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CH4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CH4#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_CH4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_CH4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/OFFL/L3_CLOUD","title":"Sentinel-5P OFFL CLOUD: Offline Cloud Properties","description":"### OFFL/L3_CLOUD\n\nThis dataset provides offline high-resolution imagery of cloud parameters.\n\nThe TROPOMI/S5P cloud properties retrieval is based on the OCRA and ROCINN\nalgorithms currently being used in the operational GOME and GOME-2 products.\nOCRA retrieves the cloud fraction using measurements in the UV/VIS spectral\nregions and ROCINN retrieves the cloud height (pressure) and optical thickness\n(albedo) using measurements in and around the oxygen A-band at 760 nm. Version 3.0\nof the algorithms are used, which are based on a more realistic\ntreatment of clouds as optically uniform layers of light-scattering particles.\nAdditionally, the cloud parameters are also provided for a cloud model which\nassumes the cloud to be a Lambertian reflecting boundary.\n[More information.](http://www.tropomi.eu/data-products/cloud)\n\n### OFFL L3 Product\n\nTo make our OFFL L3 products, we find which areas within the product's\nbounding box contain data by using a command like this:\n\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'cloud_fraction>50;derive(datetime_stop {time})'\nS5P_OFFL_L2__CLOUD__20180705T095218_20180705T113348_03760_01_010000_20180712T082510.nc\ngrid_info.h5\n```\n\nWe then merge all the data into one large mosaic (area-averaging values for\npixels that may have different values for different times).  From the\nmosaic, we create a set of tiles containing orthorectified raster data.\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'cloud_fraction>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(cloud_fraction,cloud_top_pressure,cloud_top_height,\ncloud_base_pressure,cloud_base_height,cloud_optical_depth,surface_albedo,\nsensor_azimuth_angle,sensor_zenith_angle,solar_azimuth_angle,\nsolar_zenith_angle)'\nS5P_OFFL_L2__CLOUD__20180705T095218_20180705T113348_03760_01_010000_20180712T082510.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-04T11:34:21Z","2026-11-07T16:30:58Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_CLOUD","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CLOUD","rel":"related","title":"Run the example for COPERNICUS/S5P/OFFL/L3_CLOUD in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CLOUD_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CLOUD#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_CLOUD/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_CLOUD/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/OFFL/L3_CO","title":"Sentinel-5P OFFL CO: Offline Carbon Monoxide","description":"### OFFL/L3_CO\n\nThis dataset provides offline high-resolution imagery of CO concentrations.\n\nCarbon monoxide (CO) is an important atmospheric trace gas for understanding\ntropospheric chemistry. In certain urban areas, it is a major atmospheric\npollutant. Main sources of CO are combustion of fossil fuels, biomass burning,\nand atmospheric oxidation of methane and other hydrocarbons. Whereas fossil\nfuel combustion is the main source of CO at northern mid-latitudes, the oxidation\nof isoprene and biomass burning play an important role in the tropics. TROPOMI\non the Sentinel 5 Precursor (S5P) satellite observes the CO global abundance\nexploiting clear-sky and cloudy-sky Earth radiance measurements in the 2.3 &mu;m\nspectral range of the shortwave infrared (SWIR) part of the solar spectrum.\nTROPOMI clear sky observations provide CO total columns with sensitivity to the\ntropospheric boundary layer. For cloudy atmospheres, the column sensitivity\nchanges according to the light path.\n[More information.](http://www.tropomi.eu/data-products/carbon-monoxide)\n\n### OFFL L3 Product\n\nTo make our OFFL L3 products, we find areas within the product's bounding\nbox with data using a command like this:\n\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'CO_column_number_density_validity>50;derive(datetime_stop {time})'\nS5P_OFFL_L2__CO_____20181031T060643_20181031T074813_05432_01_010200_20181106T052542.nc\ngrid_info.h5\n```\n\nWe then merge all the data into one large mosaic (area-averaging values for\npixels that may have different values for different times).  From the\nmosaic, we create a set of tiles containing orthorectified raster data.\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'CO_column_number_density_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(CO_column_number_density,H2O_column_number_density,cloud_height,\nsensor_altitude,sensor_azimuth_angle, sensor_zenith_angle,\nsolar_azimuth_angle,solar_zenith_angle)'\nS5P_OFFL_L2__CO_____20181031T060643_20181031T074813_05432_01_010200_20181106T052542.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-06-28T10:24:07Z","2026-11-07T18:12:28Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_CO","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CO","rel":"related","title":"Run the example for COPERNICUS/S5P/OFFL/L3_CO in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CO_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_CO/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_CO/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/OFFL/L3_HCHO","title":"Sentinel-5P OFFL HCHO: Offline Formaldehyde","description":"### OFFL/L3_HCHO\n\nThis dataset provides offline high-resolution imagery of atmospheric\nformaldehyde (HCHO) concentrations.\n\nFormaldehyde is an intermediate gas in almost all oxidation chains of non-methane\nvolatile organic compounds (NMVOC), leading eventually to CO<sub>2</sub>. Non-Methane Volatile\nOrganic Compounds (NMVOCs) are, together with NO<sub>x</sub>, CO and CH<sub>4</sub>, among the\nmost important precursors of tropospheric O<sub>3</sub>. The major HCHO source in the remote\natmosphere is CH<sub>4</sub> oxidation. Over the continents, the oxidation of higher NMVOCs\nemitted from vegetation, fires, traffic and industrial sources results in important\nand localized enhancements of the HCHO levels. The seasonal and inter-annual\nvariations of the formaldehyde distribution are principally related to temperature\nchanges and fire events, but also to changes in anthropogenic activities. HCHO concentrations\nin the boundary layer can be directly related to the release of short-lived hydrocarbons, which\nmostly cannot be observed directly from space.\n[More information.](http://www.tropomi.eu/data-products/formaldehyde)\n\n### OFFL L3 Product\n\nTo make our OFFL L3 products, we find areas within the product's bounding\nbox with data using a command like this:\n\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'tropospheric_HCHO_column_number_density_validity>50;derive(datetime_stop {time})'\nS5P_OFFL_L2__HCHO___20190116T171037_20190116T185207_06531_01_010105_20190123T104749.nc\ngrid_info.h5\n```\n\nWe then merge all the data into one large mosaic (area-averaging values for\npixels that may have different values for different times).  From the\nmosaic, we create a set of tiles containing orthorectified raster data.\n\nExample harpconvert invocation for one tile:\n\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'tropospheric_HCHO_column_number_density_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(tropospheric_HCHO_column_number_density,\n     tropospheric_HCHO_column_number_density_amf,\n     HCHO_slant_column_number_density,cloud_fraction,sensor_altitude,\n     sensor_azimuth_angle, sensor_zenith_angle,solar_azimuth_angle,\n     solar_zenith_angle)'\nS5P_OFFL_L2__HCHO___20190116T171037_20190116T185207_06531_01_010105_20190123T104749.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-12-05T12:14:36Z","2026-11-07T11:26:30Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_HCHO","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_OFFL_L3_HCHO","rel":"related","title":"Run the example for COPERNICUS/S5P/OFFL/L3_HCHO in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_OFFL_L3_HCHO_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_HCHO/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_HCHO/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/OFFL/L3_NO2","title":"Sentinel-5P OFFL NO2: Offline Nitrogen Dioxide","description":"### OFFL/L3_NO2\n\nThis dataset provides offline high-resolution imagery of NO<sub>2</sub> concentrations.\n\nNitrogen oxides (NO<sub>2</sub> and NO) are important trace gases in the Earth's atmosphere,\npresent in both the troposphere and the stratosphere. They enter the atmosphere\nas a result of anthropogenic activities (notably fossil fuel combustion and\nbiomass burning) and natural processes (wildfires, lightning, and microbiological processes in\nsoils). Here, NO<sub>2</sub> is used to represent concentrations\nof collective nitrogen oxides because during daytime, i.e. in the presence of\nsunlight, a photochemical cycle involving ozone (O<sub>3</sub>) converts NO into NO<sub>2</sub>\nand vice versa on a timescale of minutes. The TROPOMI NO<sub>2</sub> processing system is based\non the algorithm developments for the DOMINO-2 product and for the EU QA4ECV NO<sub>2</sub>\nreprocessed dataset for OMI, and has been adapted for TROPOMI. This\nretrieval-assimilation-modelling system uses the 3-dimensional global TM5-MP\nchemistry transport model at a resolution of 1x1 degree as an essential element.\n[More information.](http://www.tropomi.eu/data-products/nitrogen-dioxide)\n\n### OFFL L3 Product\n\nTo make our OFFL L3 products, we find areas within the product's bounding\nbox with data using a command like this:\n\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'tropospheric_NO2_column_number_density_validity>50;derive(datetime_stop {time})'\nS5P_OFFL_L2__NO2____20181010T074409_20181010T092539_05135_01_010100_20181016T092316.nc\ngrid_info.h5\n```\n\nWe then merge all the data into one large mosaic (area-averaging values for\npixels that may have different values for different times).  From the\nmosaic, we create a set of tiles containing orthorectified raster data.\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'tropospheric_NO2_column_number_density_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(NO2_column_number_density,tropospheric_NO2_column_number_density,\n     stratospheric_NO2_column_number_density,NO2_slant_column_number_density,\n     tropopause_pressure,absorbing_aerosol_index,cloud_fraction,\n     sensor_altitude,sensor_azimuth_angle,\n     sensor_zenith_angle,solar_azimuth_angle,solar_zenith_angle)'\nS5P_OFFL_L2__NO2____20181010T074409_20181010T092539_05135_01_010100_20181016T092316.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-06-28T10:24:07Z","2026-10-31T22:07:32Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_NO2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2","rel":"related","title":"Run the example for COPERNICUS/S5P/OFFL/L3_NO2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_NO2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_NO2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/OFFL/L3_O3","title":"Sentinel-5P OFFL O3: Offline Ozone","description":"### OFFL/L3_O3\n\nThis dataset provides offline high-resolution imagery of total column ozone\nconcentrations. See also `COPERNICUS/S5P/OFFL/L3_O3_TCL` for the tropospheric\ncolumn data.\n\nIn the stratosphere, the ozone layer shields the biosphere from dangerous\nsolar ultraviolet radiation. In the troposphere, it acts as an efficient\ncleansing agent, but at high concentration it also becomes harmful to the\nhealth of humans, animals, and vegetation. Ozone is also an important\ngreenhouse-gas contributor to ongoing climate change. Since the discovery\nof the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol\nregulating the production of chlorine-containing ozone-depleting substances,\nozone has been routinely monitored from the ground and from space.\n\nFor this product, there are two algorithms that deliver total ozone: GDP for\nthe near real-time and GODFIT for the offline products. GDP is currently\nbeing used for generating the operational total ozone products from GOME,\nSCIAMACHY and GOME-2; while GODFIT is being used in the ESA CCI and the\nCopernicus C3S projects.\n[More information.](https://www.tropomi.eu/data-products/ozone-total-column)\n[Product user manual](https://sentinels.copernicus.eu/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Ozone-Total-Column)\n\n### OFFL L3 Product\n\nTo make our OFFL L3 products, we find areas within the product's bounding\nbox with data using a command like this:\n\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'O3_column_number_density_validity>50;derive(datetime_stop {time})'\nS5P_OFFL_L2__O3_____20181005T225147_20181006T003317_05073_01_010102_20181012T001415.nc\ngrid_info.h5\n```\n\nWe then merge all the data into one large mosaic (area-averaging values for\npixels that may have different values for different times).  From the\nmosaic, we create a set of tiles containing orthorectified raster data.\n\nThe qa value is adjusted before running harpconvert to satisfy all of the\nfollowing criteria:\n\n* ozone_total_vertical_column in [0, 0.45]\n* ozone_effective_temperature in [180, 260]\n* ring_scale_factor in [0, 0.15]\n* effective_albedo in [-0.5, 1.5]\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'O3_column_number_density_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(O3_column_number_density,O3_effective_temperature,cloud_fraction,\nsensor_altitude,sensor_azimuth_angle, sensor_zenith_angle,\nsolar_azimuth_angle,solar_zenith_angle)'\nS5P_OFFL_L2__O3_____20181005T225147_20181006T003317_05073_01_010102_20181012T001415.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-09-08T21:19:29Z","2026-11-07T11:26:30Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_O3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3","rel":"related","title":"Run the example for COPERNICUS/S5P/OFFL/L3_O3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_O3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_O3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/OFFL/L3_O3_TCL","title":"Sentinel-5P OFFL O3 TCL: Offline Tropospheric Ozone","description":"### OFFL/L3_O3_TCL\n\nThis dataset provides offline tropospheric high-resolution imagery of ozone\nconcentrations between 20N and 20S. See also `COPERNICUS/S5P/OFFL/L3_O3` and\n`COPERNICUS/S5P/NRTI/L3_O3` for the total column data.\n\nIn the stratosphere, the ozone layer shields the biosphere from dangerous\nsolar ultraviolet radiation. In the troposphere, it acts as an efficient\ncleansing agent, but at high concentration it also becomes harmful to the\nhealth of humans, animals, and vegetation. Ozone is also an important\ngreenhouse-gas contributor to ongoing climate change. Since the discovery\nof the Antarctic ozone hole in the 1980s and the subsequent Montreal Protocol\nregulating the production of chlorine-containing ozone-depleting substances,\nozone has been routinely monitored from the ground and from space.\n\nFor this product, the convective cloud differential (ccd) and\ncloud slicing (csa) algorithm were used.\n[Product user manual.](https://sentinels.copernicus.eu/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Ozone-Tropospheric-Column).\n[More information.](http://www.tropomi.eu/data-products/tropospheric-ozone-column).\n\nUnlike all other S5P products, this product was ingested directly without using\n`harpconvert`, as it is already a gridded product. Pixels with qa_value<=70\nare masked out.\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-04-30T10:50:26Z","2026-10-26T11:52:31Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_O3_TCL","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3_TCL","rel":"related","title":"Run the example for COPERNICUS/S5P/OFFL/L3_O3_TCL in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3_TCL_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3_TCL#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_O3_TCL/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_O3_TCL/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/S5P/OFFL/L3_SO2","title":"Sentinel-5P OFFL SO2: Offline Sulfur Dioxide","description":"### OFFL/L3_SO2\n\nThis dataset provides offline high-resolution imagery of atmospheric sulfur\ndioxide (SO<sub>2</sub>) concentrations.\n\nSulfur dioxide (SO<sub>2</sub>) enters the Earth's atmosphere through both natural and\nanthropogenic processes. It plays a role in chemistry on a local and global\nscale and its impact ranges from short-term pollution to effects on climate.\nOnly about 30% of the emitted SO<sub>2</sub> comes from natural sources; the majority\nis of anthropogenic origin. SO<sub>2</sub> emissions adversely affect human health and\nair quality. SO<sub>2</sub> has an effect on climate through radiative forcing, via\nthe formation of sulfate aerosols. Volcanic SO<sub>2</sub> emissions can also pose a\nthreat to aviation, along with volcanic ash. S5P/TROPOMI samples the Earth's\nsurface with a revisit time of one day with unprecedented spatial resolution\nof 3.5 x 7 km which allows the resolution of fine details including the\ndetection of much smaller SO<sub>2</sub> plumes.\n[More information.](https://www.tropomi.eu/data-products/sulfur-dioxide)\n\nFor this L3 SO<sub>2</sub> product, the absorbing_aerosol_index is calculated with a\npair of measurements at the 340 nm and 380 nm wavelengths.  The\n[COPERNICUS/S5P/OFFL/L3_AER_AI](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_AI)\nproduct has the absorbing_aerosol_index calculated using the 354 nm and 388\nnm wavelengths.\n\n### OFFL L3 Product\n\nTo make our OFFL L3 products, we find areas within the product's bounding\nbox with data using a command like this:\n\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'SO2_column_number_density_validity>50;derive(datetime_stop {time})'\nS5P_OFFL_L2__SO2____20181228T231131_20181229T005301_06265_01_010105_20190104T083244.nc\ngrid_info.h5\n```\n\nWe then merge all the data into one large mosaic (area-averaging values for\npixels that may have different values for different times).  From the\nmosaic, we create a set of tiles containing orthorectified raster data.\n\nThe qa value is adjusted before running harpconvert to satisfy all of the\nfollowing criteria:\n\n* snow_ice < 0.5\n* sulfurdioxide_total_air_mass_factor_polluted > 0.1\n* sulfurdioxide_total_vertical_column > -0.001\n* qa_value > 0.5\n* cloud_fraction_crb < 0.3\n* solar_zenith_angle < 60\n\nThe 15km SO2 band is ingested only when solar_zenith_angle < 70.\n\nExample harpconvert invocation for one tile:\n```\nharpconvert --format hdf5 --hdf5-compression 9\n-a 'SO2_column_number_density_validity>50;derive(datetime_stop {time});\nbin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01);\nkeep(SO2_column_number_density,SO2_column_number_density_amf,\n     SO2_slant_column_number_density,absorbing_aerosol_index,cloud_fraction, sensor_altitude,\n     sensor_azimuth_angle, sensor_zenith_angle,solar_azimuth_angle,\n     solar_zenith_angle)'\nS5P_OFFL_L2__SO2____20181228T231131_20181229T005301_06265_01_010105_20190104T083244.nc\noutput.h5\n```\n\n### Sentinel-5 Precursor\n\nSentinel-5 Precursor is a satellite launched on 13 October 2017\nby the European Space Agency to monitor air pollution.  The onboard sensor\nis frequently referred to as Tropomi (TROPOspheric Monitoring Instrument).\n\nAll of the S5P datasets, except CH<sub>4</sub>, have two versions: Near \nReal-Time (NRTI) and Offline (OFFL). CH<sub>4</sub> is available as OFFL only. \nThe NRTI assets cover a smaller area than the OFFL assets, but appear more \nquickly after acquisition.  The OFFL assets contain data from a single orbit \n(which, due to half the earth being dark, contains data only for a single \nhemisphere).\n\nBecause of noise in the data, negative vertical column values are often\nobserved in particular over clean regions or for low SO2 emissions. It is\nrecommended not to filter these values except for outliers, i.e.\nfor vertical columns lower than -0.001 mol/m^2.\n\nThe original Sentinel 5P Level 2 (L2) data is binned\nby time, not by latitude/longitude.  To make it possible to ingest the data\ninto Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping\na single grid per orbit (that is, no aggregation across products\nis performed).\n\nSource products spanning the antimeridian are ingested as two Earth Engine\nassets, with suffixes _1 and _2.\n\nThe conversion to L3 is done by the [harpconvert](https://cdn.rawgit.com/stcorp/harp/master/doc/html/harpconvert.html)\ntool using the `bin_spatial` operation.  The source data is filtered to\nremove pixels with QA values less than:\n\n*  80% for AER_AI\n*  75% for the tropospheric_NO2_column_number_density band of NO2\n*  50% for all other datasets except for O3 and SO2\n\nThe O3_TCL product is ingested directly (without running harpconvert).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-12-05T11:53:01Z","2026-11-07T18:12:28Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_SO2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_S5P_OFFL_L3_SO2","rel":"related","title":"Run the example for COPERNICUS/S5P/OFFL/L3_SO2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_S5P_OFFL_L3_SO2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_SO2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/S5P/OFFL/L3_SO2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CPOM/CryoSat2/ANTARCTICA_DEM","title":"CryoSat-2 Antarctica 1km DEM","description":"This dataset is a digital elevation model (DEM) of the\nAntarctic ice sheet and ice shelves based on\nobservations recorded by the CryoSat-2 satellite\nradar altimeter between July 2010 and July 2016.\n\nThe DEM is formed from spatio-temporal fits to elevation\nmeasurements accumulated within 1, 2, and 5 km grid cells,\nand is posted at the modal resolution of 1 km. The median\nand root mean square difference between the DEM and 2.3*10<sup>7</sup>;\nairborne laser altimeter measurements acquired during\nNASA Operation IceBridge campaigns are -0.30 and 13.50 m,\nrespectively.\n\nThe DEM uncertainty rises in regions of high slope,\nespecially where elevation measurements were acquired in\nlow-resolution mode; taking this into account, we estimate\nthe average accuracy to be 9.5 m.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-88,180,-60]]},"temporal":{"interval":[["2010-07-01T00:00:00Z","2016-07-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CPOM/CryoSat2/ANTARCTICA_DEM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CPOM/CPOM_CryoSat2_ANTARCTICA_DEM","rel":"related","title":"Run the example for CPOM/CryoSat2/ANTARCTICA_DEM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CPOM/CPOM_CryoSat2_ANTARCTICA_DEM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CPOM_CryoSat2_ANTARCTICA_DEM#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5194/tc-2017-223","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CPOM/CryoSat2/ANTARCTICA_DEM/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSIC/SPEI/2_10","title":"SPEIbase: Standardised Precipitation-Evapotranspiration Index database, Version 2.10 [deprecated]","description":"The Global SPEI database (SPEIbase) offers long-time robust information\nabout drought conditions at the global scale, with a 0.5 degree pixel size\nand monthly cadence. It provides SPEI time scales from 1 to 48 months.\n\nThe Standardized Precipitation-Evapotranspiration Index (SPEI) expresses,\nas a standardized variate (mean zero and unit variance), the deviations of\nthe current climatic balance (precipitation minus evapotranspiration\npotential) with respect to the long-term balance. The reference period for\nthe calculation in the SPEIbase corresponds to the whole study period.\nBeing a standardized variate means that the SPEI condition can be compared\nacross space and time.\n\nThe SPEIbase is based on the\n[FAO-56 Penman-Monteith estimation](https://www.fao.org/3/x0490e/x0490e06.htm)\nestimation of potential evapotranspiration.\nThis is a major difference with respect to the SPEI Global Drought Monitor,\nthat uses the Thornthwaite PET estimation.\nThe Penman-Monteith method is considered a superior method, so the\nSPEIbase is recommended for most uses including long-term climatological\nanalysis.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1901-01-01T00:00:00Z","2023-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSIC/SPEI/2_10","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSIC/CSIC_SPEI_2_10","rel":"related","title":"Run the example for CSIC/SPEI/2_10 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSIC/CSIC_SPEI_2_10_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSIC_SPEI_2_10#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.20350/digitalCSIC/15121","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_11.json","rel":"latest-version","title":"CSIC/SPEI/2_11","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_9.json","rel":"predecessor-version","title":"CSIC/SPEI/2_9","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_11.json","rel":"successor-version","title":"CSIC/SPEI/2_11","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSIC/SPEI/2_10/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CSIC/SPEI/2_10/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSIC/SPEI/2_11","title":"SPEIbase: Standardised Precipitation-Evapotranspiration Index database, Version 2.11","description":"The Global SPEI database (SPEIbase) offers long-time robust information\nabout drought conditions at the global scale, with a 0.5 degree pixel size\nand monthly cadence. It provides SPEI time scales from 1 to 48 months.\n\nThe Standardized Precipitation-Evapotranspiration Index (SPEI) expresses,\nas a standardized variate (mean zero and unit variance), the deviations of\nthe current climatic balance (precipitation minus evapotranspiration\npotential) with respect to the long-term balance. The reference period for\nthe calculation in the SPEIbase corresponds to the whole study period.\nBeing a standardized variate means that the SPEI condition can be compared\nacross space and time.\n\nThe SPEIbase is based on the\n[FAO-56 Penman-Monteith](https://www.fao.org/3/x0490e/x0490e06.htm)\nestimation of potential evapotranspiration.\nThis is a major difference with respect to the SPEI Global Drought Monitor,\nthat uses the Thornthwaite PET estimation.\nThe Penman-Monteith method is considered a superior method, so the\nSPEIbase is recommended for most uses including long-term climatological\nanalysis.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1901-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSIC/SPEI/2_11","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSIC/CSIC_SPEI_2_11","rel":"related","title":"Run the example for CSIC/SPEI/2_11 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSIC/CSIC_SPEI_2_11_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSIC_SPEI_2_11#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.20350/digitalCSIC/15121","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_11.json","rel":"latest-version","title":"CSIC/SPEI/2_11","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_10.json","rel":"predecessor-version","title":"CSIC/SPEI/2_10","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSIC/SPEI/2_11/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CSIC/SPEI/2_11/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSIC/SPEI/2_8","title":"SPEIbase: Standardised Precipitation-Evapotranspiration Index database, Version 2.8 [deprecated]","description":"The Global SPEI database (SPEIbase) offers long-time robust information\nabout drought conditions at the global scale, with a 0.5 degree pixel size\nand monthly cadence. It provides SPEI time scales from 1 to 48 months.\n\nThe Standardized Precipitation-Evapotranspiration Index (SPEI) expresses,\nas a standardized variate (mean zero and unit variance), the deviations of\nthe current climatic balance (precipitation minus evapotranspiration\npotential) with respect to the long-term balance. The reference period for\nthe calculation in the SPEIbase corresponds to the whole study period.\nBeing a standardized variate means that the SPEI condition can be compared\nacross space and time.\n\nThe SPEIbase is based on the\n[FAO-56 Penman-Monteith estimation](https://www.fao.org/3/x0490e/x0490e06.htm)\nestimation of potential evapotranspiration.\nThis is a major difference with respect to the SPEI Global Drought Monitor,\nthat uses the Thornthwaite PET estimation.\nThe Penman-Monteith method is considered a superior method, so the\nSPEIbase is recommended for most uses including long-term climatological\nanalysis.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1901-01-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSIC/SPEI/2_8","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSIC/CSIC_SPEI_2_8","rel":"related","title":"Run the example for CSIC/SPEI/2_8 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSIC/CSIC_SPEI_2_8_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSIC_SPEI_2_8#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.20350/digitalCSIC/15121","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_11.json","rel":"latest-version","title":"CSIC/SPEI/2_11","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_9.json","rel":"successor-version","title":"CSIC/SPEI/2_9","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSIC/SPEI/2_8/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CSIC/SPEI/2_8/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSIC/SPEI/2_9","title":"SPEIbase: Standardised Precipitation-Evapotranspiration Index database, Version 2.9 [deprecated]","description":"The Global SPEI database (SPEIbase) offers long-time robust information\nabout drought conditions at the global scale, with a 0.5 degree pixel size\nand monthly cadence. It provides SPEI time scales from 1 to 48 months.\n\nThe Standardized Precipitation-Evapotranspiration Index (SPEI) expresses,\nas a standardized variate (mean zero and unit variance), the deviations of\nthe current climatic balance (precipitation minus evapotranspiration\npotential) with respect to the long-term balance. The reference period for\nthe calculation in the SPEIbase corresponds to the whole study period.\nBeing a standardized variate means that the SPEI condition can be compared\nacross space and time.\n\nThe SPEIbase is based on the\n[FAO-56 Penman-Monteith estimation](https://www.fao.org/3/x0490e/x0490e06.htm)\nestimation of potential evapotranspiration.\nThis is a major difference with respect to the SPEI Global Drought Monitor,\nthat uses the Thornthwaite PET estimation.\nThe Penman-Monteith method is considered a superior method, so the\nSPEIbase is recommended for most uses including long-term climatological\nanalysis.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1901-01-01T00:00:00Z","2023-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSIC/SPEI/2_9","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSIC/CSIC_SPEI_2_9","rel":"related","title":"Run the example for CSIC/SPEI/2_9 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSIC/CSIC_SPEI_2_9_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSIC_SPEI_2_9#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.20350/digitalCSIC/15121","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_11.json","rel":"latest-version","title":"CSIC/SPEI/2_11","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_8.json","rel":"predecessor-version","title":"CSIC/SPEI/2_8","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_10.json","rel":"successor-version","title":"CSIC/SPEI/2_10","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSIC/SPEI/2_9/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CSIC/SPEI/2_9/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSIRO/BHI/v4","title":"CSIRO Global Biodiversity Indicators","description":"The CSIRO biodiversity indicators v4 dataset, produced by CSIRO using the BILBI\n(Biogeographic Infrastructure for Large-scale Biodiversity Indicators) framework,\nmodels global terrestrial biodiversity indicators at 30 arcsecond (~1 km) resolution\nannually from 2000 to 2024. These indicators provide a system-level view of change\nin biodiversity protection, expected persistence, and resilience in the face of\nclimate change.\n\nA complementary static global weighting surface is available as a standalone image\nat CSIRO/BHI/v4_weights. It represents summed compositional similarity across\nterrestrial pixels, which is used for calculating regional and global geometric\naggregations of the biodiversity indicators.\n\nFor more detailed explanations of the indicators and how they are derived, see\n[CSIRO Biodiversity Modelling](https://research.csiro.au/biodiversitymodelling/indicators-tracking-progress-for-biodiversity/).\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSIRO/BHI/v4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSIRO/CSIRO_BHI_v4","rel":"related","title":"Run the example for CSIRO/BHI/v4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSIRO/CSIRO_BHI_v4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSIRO_BHI_v4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.25919/3aka-y730","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSIRO/BHI/v4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CSIRO/BHI/v4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSIRO/SLGA","title":"SLGA: Soil and Landscape Grid of Australia (Soil Attributes)","description":"The Soil and Landscape Grid of Australia (SLGA) is a comprehensive dataset\nof soil attributes across Australia at 3 arc-second resolution (~90m\npixels). The surfaces are the outcomes from modelling that describe the\nspatial distribution of the soil attributes using existing soil data and\nenvironmental covariates. See [Viscarra Rossel et al (2015)](https://www.publish.csiro.au/sr/SR14366)\nfor further details. The SLGA can be used in studies of vadose zone\nprocesses, including solute transport, groundwater and nutrient fluxes\nbeyond the root zone, as well as a wide spectrum of ecological,\nhydrological, and broader environmental applications.\n\nEach product contains six digital soil attribute maps and their upper and\nlower confidence limits, representing the soil attribute at six depths:\n0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, and 100-200cm. These depths and\nsoil attributes are consistent with the specifications of\n[GlobalSoilMap](https://www.isric.org/projects/globalsoilmapnet).\n\nThis collection has 12 images. Ten of them contain data for GSM primary\nsoil attributes; the other two contain regolith depth and soil depth GSM\nattributes.\n\n| Attribute                              | Description                                                                                                                   | Code | # of Bands |\n|----------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|------|------------|\n| Bulk Density (whole earth)             | Bulk density of the whole soil (including coarse fragments) in mass per unit volume by a method equivalent to the core method | BDW  | 18         |\n| Organic Carbon                         | Mass fraction of carbon by weight in the < 2mm soil material as determined by dry combustion at 900 Celsius                   | SOC  | 18         |\n| Clay                                   | < 2&micro;m mass fraction of the <2mm soil material determined using the pipette method                                       | CLY  | 18         |\n| Silt                                   | 2-20&micro;m mass fraction of the < 2mm soil material determined using the pipette method                                     | SLT  | 18         |\n| Sand                                   | 20&micro;m - 2mm mass fraction of the < 2mm soil material determined using the pipette method                                 | SND  | 18         |\n| pH (CaCl2)                             | pH of 1:5 soil/0.01M calcium chloride extract                                                                                 | pHc  | 18         |\n| Available Water Capacity               | Available water capacity computed for each of the specified depth increments                                                  | AWC  | 18         |\n| Total Nitrogen                         | Mass fraction of total nitrogen in the soil by weight                                                                         | NTO  | 18         |\n| Total Phosphorus                       | Mass fraction of total phosphorus in the soil by weight                                                                       | PTO  | 18         |\n| Effective Cation Exchange Capacity     | Cations extracted using barium chloride (BaCl2) plus exchangeable H + Al                                                      | ECE  | 18         |\n| Depth of Regolith                      | Depth to hard rock. Depth is inclusive of all regolith.                                                                       | DER  | 3          |\n| Depth of Soil                          | Depth of soil profile (A & B horizons)                                                                                        | DES  | 3          |\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[113,-44.15,154,-9.97]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2013-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSIRO/SLGA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSIRO/CSIRO_SLGA","rel":"related","title":"Run the example for CSIRO/SLGA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSIRO/CSIRO_SLGA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSIRO_SLGA#terms-of-use","rel":"license","type":"text/html"},{"href":"https://creativecommons.org/licenses/by/4.0/","rel":"license","type":"text/html"},{"href":"https://www.csiro.au/en/about/Policies/Legal/Legal-notice","rel":"license","type":"text/html"},{"href":"https://doi.org/10.4225/08/546ED604ADD8A","rel":"cite-as"},{"href":"https://doi.org/10.4225/08/546EE212B0048","rel":"cite-as"},{"href":"https://doi.org/10.4225/08/546EEE35164BF","rel":"cite-as"},{"href":"https://doi.org/10.4225/08/546F091C11777","rel":"cite-as"},{"href":"https://doi.org/10.4225/08/546F17EC6AB6E","rel":"cite-as"},{"href":"https://doi.org/10.4225/08/546F29646877E","rel":"cite-as"},{"href":"https://doi.org/10.4225/08/546F48D6A6D48","rel":"cite-as"},{"href":"https://doi.org/10.4225/08/546F540FE10AA","rel":"cite-as"},{"href":"https://doi.org/10.4225/08/546F564AE11F9","rel":"cite-as"},{"href":"https://doi.org/10.4225/08/546F617719CAF","rel":"cite-as"},{"href":"https://doi.org/10.4225/08/547523BB0801A","rel":"cite-as"},{"href":"https://doi.org/10.4225/08/55C9472F05295","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSIRO/SLGA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CSIRO/SLGA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/Global/ALOS_CHILI","title":"Global ALOS CHILI (Continuous Heat-Insolation Load Index)","description":"CHILI is a surrogate for effects of insolation and topographic shading on\nevapotranspiration represented by calculating insolation at early afternoon,\nsun altitude equivalent to equinox. It is based on the 30m \\\"AVE\\\" band of\nJAXA's ALOS DEM (available in EE as JAXA/ALOS/AW3D30_V1_1).\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/ALOS_CHILI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_Global_ALOS_CHILI","rel":"related","title":"Run the example for CSP/ERGo/1_0/Global/ALOS_CHILI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_Global_ALOS_CHILI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_Global_ALOS_CHILI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/ALOS_CHILI/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/Global/ALOS_landforms","title":"Global ALOS Landforms","description":"The ALOS Landform dataset provides landform classes created by combining\nthe Continuous Heat-Insolation Load Index (ALOS CHILI) and the\nmulti-scale Topographic Position Index (ALOS mTPI) datasets. It is\nbased on the 30m \\\"AVE\\\" band of JAXA's ALOS DEM (available in EE as\nJAXA/ALOS/AW3D30_V1_1).\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/ALOS_landforms","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_Global_ALOS_landforms","rel":"related","title":"Run the example for CSP/ERGo/1_0/Global/ALOS_landforms in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_Global_ALOS_landforms_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_Global_ALOS_landforms#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/ALOS_landforms/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/Global/ALOS_mTPI","title":"Global ALOS mTPI (Multi-Scale Topographic Position Index)","description":"The mTPI distinguishes ridge from valley forms. It is calculated using\nelevation data for each location subtracted by the mean elevation within a\nneighborhood. mTPI uses moving windows of radius (km): 115.8, 89.9, 35.5,\n13.1, 5.6, 2.8, and 1.2. It is based on the 30m \\\"AVE\\\" band of JAXA's ALOS\nDEM (available in EE as JAXA/ALOS/AW3D30_V1_1).\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/ALOS_mTPI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_Global_ALOS_mTPI","rel":"related","title":"Run the example for CSP/ERGo/1_0/Global/ALOS_mTPI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_Global_ALOS_mTPI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_Global_ALOS_mTPI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/ALOS_mTPI/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/Global/ALOS_topoDiversity","title":"Global ALOS Topographic Diversity","description":"Topographic diversity (D) is a surrogate variable that represents the\nvariety of temperature and moisture conditions available to species as local\nhabitats. It expresses the logic that a higher variety of topo-climate\nniches should support higher diversity (especially plant) and support\nspecies persistence given climatic change.\n\nTo calculate D, the multi-scale Topographic Position Index (mTPI), being a\ndominant control of soil moisture (T), was used for measuring hillslope\nposition. The mTPI was combined with the square-root transform for\nmTPI>0 (T') and with the standard deviation of the Continuous\nHeat-Insolation Load Index (CHILI), calculated at multiple scales (C') as:\nD = 1 - ((1-T') * (1-C'). It is based on the 30m \\\"AVE\\\" band of JAXA's ALOS\nDEM (available in EE as JAXA/ALOS/AW3D30_V1_1).\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/ALOS_topoDiversity","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_Global_ALOS_topoDiversity","rel":"related","title":"Run the example for CSP/ERGo/1_0/Global/ALOS_topoDiversity in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_Global_ALOS_topoDiversity_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_Global_ALOS_topoDiversity#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/ALOS_topoDiversity/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/Global/SRTM_CHILI","title":"Global SRTM CHILI (Continuous Heat-Insolation Load Index)","description":"CHILI is a surrogate for effects of insolation and topographic shading on\nevapotranspiration represented by calculating insolation at early afternoon,\nsun altitude equivalent to equinox. It is based on the 30m SRTM DEM\n(available in EE as USGS/SRTMGL1_003).\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/SRTM_CHILI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_Global_SRTM_CHILI","rel":"related","title":"Run the example for CSP/ERGo/1_0/Global/SRTM_CHILI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_Global_SRTM_CHILI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_Global_SRTM_CHILI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/SRTM_CHILI/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/Global/SRTM_landforms","title":"Global SRTM Landforms","description":"The SRTM Landform dataset provides landform classes created by combining\nthe Continuous Heat-Insolation Load Index (SRTM CHILI) and the\nmulti-scale Topographic Position Index (SRTM mTPI) datasets. It is\nbased on the 30m SRTM DEM (available in EE as USGS/SRTMGL1_003).\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/SRTM_landforms","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_Global_SRTM_landforms","rel":"related","title":"Run the example for CSP/ERGo/1_0/Global/SRTM_landforms in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_Global_SRTM_landforms_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_Global_SRTM_landforms#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/SRTM_landforms/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/Global/SRTM_mTPI","title":"Global SRTM mTPI (Multi-Scale Topographic Position Index)","description":"The mTPI distinguishes ridge from valley forms. It is calculated using\nelevation data for each location subtracted by the mean elevation within a\nneighborhood. mTPI uses moving windows of radius (km): 115.8, 89.9, 35.5,\n13.1, 5.6, 2.8, and 1.2. It is based on the 30m SRTM DEM (available in EE\nas USGS/SRTMGL1_003).\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/SRTM_mTPI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_Global_SRTM_mTPI","rel":"related","title":"Run the example for CSP/ERGo/1_0/Global/SRTM_mTPI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_Global_SRTM_mTPI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_Global_SRTM_mTPI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/SRTM_mTPI/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/Global/SRTM_topoDiversity","title":"Global SRTM Topographic Diversity","description":"Topographic diversity (D) is a surrogate variable that represents the\nvariety of temperature and moisture conditions available to species as local\nhabitats. It expresses the logic that a higher variety of topo-climate\nniches should support higher diversity (especially plant) and support\nspecies persistence given climatic change.\n\nTo calculate D, the multi-scale Topographic Position Index (mTPI), being a\ndominant control of soil moisture (T), was used for measuring hillslope\nposition. The mTPI was combined with the square-root transform for\nmTPI>0 (T') and with the standard deviation of the Continuous\nHeat-Insolation Load Index (CHILI), calculated at multiple scales (C') as:\nD = 1 - ((1-T') * (1-C'). It is based on the 30m SRTM DEM (available in EE\nas USGS/SRTMGL1_003).\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/SRTM_topoDiversity","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_Global_SRTM_topoDiversity","rel":"related","title":"Run the example for CSP/ERGo/1_0/Global/SRTM_topoDiversity in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_Global_SRTM_topoDiversity_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_Global_SRTM_topoDiversity#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/Global/SRTM_topoDiversity/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/US/CHILI","title":"US NED CHILI (Continuous Heat-Insolation Load Index)","description":"CHILI is a surrogate for effects of insolation and topographic shading on\nevapotranspiration represented by calculating insolation at early afternoon,\nsun altitude equivalent to equinox. It is based on the USGS's 10m NED DEM\n(available in EE as USGS/NED).\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-132.09,12.54,-60.35,56.21]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/CHILI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_US_CHILI","rel":"related","title":"Run the example for CSP/ERGo/1_0/US/CHILI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_US_CHILI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_US_CHILI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/CHILI/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/US/landforms","title":"US NED Landforms","description":"The ALOS Landform dataset provides landform classes created by combining\nthe Continuous Heat-Insolation Load Index (CHILI) and the\nmulti-scale Topographic Position Index (mTPI) datasets. It is\nbased on the USGS's 10m NED DEM (available in EE as USGS/NED).\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-132.09,12.54,-60.35,56.21]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/landforms","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_US_landforms","rel":"related","title":"Run the example for CSP/ERGo/1_0/US/landforms in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_US_landforms_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_US_landforms#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/landforms/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/US/lithology","title":"US Lithology","description":"The Lithology dataset provides classes of the general types of parent\nmaterial of soil on the surface. It is not derived from any DEM.\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-132.09,12.54,-60.35,56.21]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/lithology","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_US_lithology","rel":"related","title":"Run the example for CSP/ERGo/1_0/US/lithology in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_US_lithology_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_US_lithology#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/lithology/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/US/mTPI","title":"US NED mTPI (Multi-Scale Topographic Position Index)","description":"The mTPI distinguishes ridge from valley forms. It is calculated using\nelevation data for each location subtracted by the mean elevation within a\nneighborhood. mTPI uses moving windows of radius (km): 115.8, 89.9, 35.5,\n13.1, 5.6, 2.8, and 1.2. It is based on the USGS's 10m NED DEM (available\nin EE as USGS/NED).\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-132.09,12.54,-60.35,56.21]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/mTPI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_US_mTPI","rel":"related","title":"Run the example for CSP/ERGo/1_0/US/mTPI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_US_mTPI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_US_mTPI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/mTPI/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/US/physioDiversity","title":"US NED Physiographic Diversity","description":"The Physiographic Diversity dataset provides an index of the diversity of\nphysiographic types. It was calculated using the Shannon diversity index at\nmultiple scales (km): 115.8, 89.9, 35.5, 13.1, 5.6, 2.8, and 1.2. It is\nbased on the USGS's 10m NED DEM (available in EE as USGS/NED).\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-132.09,12.54,-60.35,56.21]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/physioDiversity","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_US_physioDiversity","rel":"related","title":"Run the example for CSP/ERGo/1_0/US/physioDiversity in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_US_physioDiversity_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_US_physioDiversity#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/physioDiversity/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/US/physiography","title":"US Physiography","description":"The Physiography dataset represents the spatial intersection of landforms\n(available in EE as ERGo/1_0/US/landforms) and lithology (available in EE\nas ERGo/1_0/US/lithology) data layers. It provides 247 unique combinations\nout of a possible 270. The values for each type are formed by concatenating\nthe landform and lithology types (e.g., 1101 is \\\"Peak/ridge\\\" landform on\n\\\"carbonate\\\" lithology). This data layer is sometimes referred to as\ncharacterizing \\\"land facets\\\".\n\nThe landforms layer is based on the USGS's 10m NED DEM (available in EE\nas USGS/NED). The lithology layer is not basen on any DEM.\n\nThis dataset is provided just for the US, because of the availability\nof the lithology data layer, though these data are likely available for\nother countries.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-132.09,12.54,-60.35,56.21]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/physiography","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_US_physiography","rel":"related","title":"Run the example for CSP/ERGo/1_0/US/physiography in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_US_physiography_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_US_physiography#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/physiography/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/ERGo/1_0/US/topoDiversity","title":"US NED Topographic Diversity","description":"Topographic diversity (D) is a surrogate variable that represents the\nvariety of temperature and moisture conditions available to species as local\nhabitats. It expresses the logic that a higher variety of topo-climate\nniches should support higher diversity (especially plant) and support\nspecies persistence given climatic change.\n\nTo calculate D, the multi-scale Topographic Position Index (mTPI), being a\ndominant control of soil moisture (T), was used for measuring hillslope\nposition. The mTPI was combined with the square-root transform for\nmTPI>0 (T') and with the standard deviation of the Continuous\nHeat-Insolation Load Index (CHILI), calculated at multiple scales (C') as:\nD = 1 - ((1-T') * (1-C'). It is based on the USGS's 10m NED DEM (available\nin EE as USGS/NED).\n\nThe Conservation Science Partners (CSP) Ecologically Relevant Geomorphology\n(ERGo) Datasets, Landforms and Physiography contain detailed, multi-scale\ndata on landforms and physiographic (aka land facet) patterns. Although\nthere are many potential uses of these data, the original purpose for these\ndata was to develop an ecologically relevant classification and map of\nlandforms and physiographic classes that are suitable for climate adaptation\nplanning. Because there is large uncertainty associated with future climate\nconditions and even more uncertainty around ecological responses, providing\ninformation about what is unlikely to change offers a strong foundation for\nmanagers to build robust climate adaptation plans. The quantification of\nthese features of the landscape is sensitive to the resolution, so we\nprovide the highest resolution possible given the extent and characteristics\nof a given index.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-132.09,12.54,-60.35,56.21]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/topoDiversity","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_ERGo_1_0_US_topoDiversity","rel":"related","title":"Run the example for CSP/ERGo/1_0/US/topoDiversity in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_ERGo_1_0_US_topoDiversity_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_US_topoDiversity#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/ERGo/1_0/US/topoDiversity/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"CSP/HM/GlobalHumanModification","title":"CSP gHM: Global Human Modification","description":"The global Human Modification dataset (gHM) provides a cumulative measure of human modification\nof terrestrial lands globally at 1 square-kilometer resolution. The gHM\nvalues range from 0.0-1.0 and are calculated by estimating the proportion of a given location\n(pixel) that is modified, the estimated intensity of modification\nassociated with a given type of human modification or \"stressor\".\n5 major anthropogenic stressors circa 2016 were mapped using 13\nindividual datasets:\n\n* human settlement (population density, built-up areas)\n* agriculture (cropland, livestock)\n* transportation (major, minor, and two-track roads; railroads)\n* mining and energy production\n* electrical infrastructure (power lines, nighttime lights)\n\nPlease see the paper for additional methodological details.\nThis asset was re-projected to WGS84 for use in Earth Engine.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-01-01T00:00:00Z","2016-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/CSP/HM/GlobalHumanModification","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/CSP/CSP_HM_GlobalHumanModification","rel":"related","title":"Run the example for CSP/HM/GlobalHumanModification in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/CSP/CSP_HM_GlobalHumanModification_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/CSP_HM_GlobalHumanModification#terms-of-use","rel":"license","type":"text/html"},{"href":"https://gdra-tnc.org/current/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/CSP/HM/GlobalHumanModification/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/CSP/HM/GlobalHumanModification/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"DLR/WSF/WSF2015/v1","title":"World Settlement Footprint 2015","description":"The World Settlement Footprint (WSF) 2015 is a 10m resolution binary mask\noutlining the extent of human settlements globally derived by means of\n2014-2015 multitemporal Landsat-8 and Sentinel-1 imagery (of which ~217,000 and\n~107,000 scenes have been processed, respectively).\n\nThe temporal dynamics of human settlements over time are\nsensibly different than those of all other non-settlement information classes.\nHence, given all the multitemporal images available over a region of interest\nin the selected time interval, key temporal statistics (i.e., temporal mean,\nminimum, maximum, etc.) are extracted for:\n\n* the original backscattering value\nin the case of radar data; and\n*  different spectral indices (e.g., vegetation\nindex, built-up index, etc.) derived after performing cloud masking in the case\nof optical imagery.\n\nNext, different classification schemes based on Support\nVector Machines (SVMs) are separately applied to the optical and radar temporal\nfeatures, respectively, and, finally, the two outputs are properly combined\ntogether.\n\nTo quantitatively assess the high accuracy and reliability of the\nlayer, an extensive validation exercise has been carried out in collaboration\nwith Google based on a huge amount of ground-truth samples (i.e., 900,000)\nlabeled by crowd-sourcing photo-interpretation. A statistically\nrobust and transparent protocol has been defined following the state-of-the-art\npractices currently recommended in the literature.\n\nFor all technical details, please refer to\n[the publication](https://www.nature.com/articles/s41597-020-00580-5)\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2016-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/DLR/WSF/WSF2015/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/DLR/DLR_WSF_WSF2015_v1","rel":"related","title":"Run the example for DLR/WSF/WSF2015/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/DLR/DLR_WSF_WSF2015_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/DLR_WSF_WSF2015_v1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC0-1.0.html","rel":"license","type":"text/html"},{"href":"https://springernature.figshare.com/articles/dataset/World_Settlement_Footprint_WSF_2015/10048412","rel":"source"},{"href":"https://doi.org/10.1038/s41597-020-00580-5","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/DLR/WSF/WSF2015/v1/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4","title":"DMSP OLS: Global Radiance-Calibrated Nighttime Lights Version 4, Defense Meteorological Program Operational Linescan System","description":"The Defense Meteorological Program (DMSP) Operational Line-Scan System\n(OLS) has a unique capability to detect visible and near-infrared (VNIR)\nemission sources at night.\n\nThis collection contains global nighttime lights images with no sensor\nsaturation. The sensor is typically operated at a high-gain setting to\nenable the detection of moonlit clouds. However, with six bit quantization\nand limited dynamic range, the recorded data are saturated in the bright\ncores of urban centers. A limited set of observations at low lunar\nillumination were obtained where the gain of the detector was set\nsignificantly lower than its typical operational setting (sometimes by a\nfactor of 100). Sparse data acquired at low-gain settings were combined\nwith the operational data acquired at high-gain settings to produce the set\nof global nighttime lights images with no sensor saturation. Data from\ndifferent satellites were merged and blended into the final product in order\nto gain maximum coverage. For more information, see this\n[readme](https://eogdata.mines.edu/dmsp/radcal_readme.txt)\nfile from the provider.\n\nImage and data processing by NOAA's National Geophysical Data Center.\nDMSP data collected by US Air Force Weather Agency.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-65,180,75]]},"temporal":{"interval":[["1996-03-16T00:00:00Z","2011-07-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_DMSP-OLS_CALIBRATED_LIGHTS_V4","rel":"related","title":"Run the example for NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_DMSP-OLS_CALIBRATED_LIGHTS_V4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_CALIBRATED_LIGHTS_V4#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/DMSP-OLS/NIGHTTIME_LIGHTS","title":"DMSP OLS: Nighttime Lights Time Series Version 4, Defense Meteorological Program Operational Linescan System","description":"The Defense Meteorological Program (DMSP) Operational Line-Scan System\n(OLS) has a unique capability to detect visible and near-infrared (VNIR)\nemission sources at night.\n\nVersion 4 of the DMSP-OLS Nighttime Lights Time Series consists of\ncloud-free composites made using all the available archived DMSP-OLS smooth\nresolution data for calendar years. In cases where two satellites were\ncollecting data, two composites were produced.\n\nImage and data processing by NOAA's National Geophysical Data Center.\nDMSP data collected by US Air Force Weather Agency.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-65,180,75]]},"temporal":{"interval":[["1992-01-01T00:00:00Z","2014-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/DMSP-OLS/NIGHTTIME_LIGHTS","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS","rel":"related","title":"Run the example for NOAA/DMSP-OLS/NIGHTTIME_LIGHTS in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"DOE/ORNL/LandScan_HD/Ukraine_202201","title":"LandScan High Definition Data for Ukraine, January 2022","description":"[LandScan High Definition (HD)](https://landscan.ornl.gov)\nprovides gridded population estimates at 3\narc-second (~100m) resolution. Values for each LandScan HD cell represent an\nambient (i.e. 24 hour average) population count estimate.  In this way, the\ndata capture the full potential activity space of people throughout the\ncourse of the day and night rather than just a residential location. The\nLandScan HD model incorporates current land use and infrastructure data from\na variety of sources, applies facility occupancy estimates from ORNL's\nPopulation Density Tables (PDT) project, and leverages novel image\nprocessing algorithms developed at ORNL to rapidly map building\nstructures and neighborhood areas using high-performance computing\nenvironments.\n\nThe source for subnational population counts used in the development of this\ndata comes from\n[State Statistics Service of Ukraine](https://ukrstat.org/en/operativ/operativ2021/ds/kn/arh_kn2021_e.html).\n\nThese subnational estimates were adjusted to the country total population\nprovided by the\n[CIA World Factbook](https://www.cia.gov/the-world-factbook/countries/ukraine/#people-and-society).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[22.125,44.175,40.225,52.4]]},"temporal":{"interval":[["2022-01-01T00:00:00Z","2022-02-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/DOE/ORNL/LandScan_HD/Ukraine_202201","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/DOE/DOE_ORNL_LandScan_HD_Ukraine_202201","rel":"related","title":"Run the example for DOE/ORNL/LandScan_HD/Ukraine_202201 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/DOE/DOE_ORNL_LandScan_HD_Ukraine_202201_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/DOE_ORNL_LandScan_HD_Ukraine_202201#terms-of-use","rel":"license","type":"text/html"},{"href":"https://landscan.ornl.gov/sites/default/files/ukraine2022/LandScanHDUkraine.zip","rel":"source"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/DOE/ORNL/LandScan_HD/Ukraine_202201/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/CAMS/NRT","title":"Copernicus Atmosphere Monitoring Service (CAMS) Global Near-Real-Time","description":"The Copernicus Atmosphere Monitoring Service provides the capacity to\ncontinuously monitor the composition of the Earth's atmosphere at global and\nregional scales. The main global near-real-time production system is a data\nassimilation and forecasting suite providing two 5-day forecasts per day for\naerosols and chemical compounds that are part of the chemical scheme.\n\nPrior to 2021-07-01 only two parameters were available,\n1. Total Aerosol Optical Depth at 550 nm surface\n2. Particulate matter d < 25 um surface\nNote that system:time_start refers to forecast time.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-06-22T12:00:00Z","2026-11-14T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/CAMS/NRT","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_CAMS_NRT","rel":"related","title":"Run the example for ECMWF/CAMS/NRT in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_CAMS_NRT_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/CAMS/NRT/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/CAMS/NRT/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/ERA5/DAILY","title":"ERA5 Daily Aggregates - Latest Climate Reanalysis Produced by ECMWF / Copernicus Climate Change Service","description":"ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate.\nReanalysis combines model data with observations from across the world into\na globally complete and consistent dataset. ERA5 replaces its predecessor,\nthe ERA-Interim reanalysis.\n\nERA5 DAILY provides aggregated values for each day for seven ERA5 climate\nreanalysis parameters: 2m air temperature, 2m dewpoint temperature, total\nprecipitation, mean sea level pressure, surface pressure, 10m u-component\nof wind and 10m v-component of wind. Additionally, daily minimum and maximum\nair temperature at 2m has been calculated based on the hourly 2m air\ntemperature data. Daily total precipitation values are given as daily sums.\nAll other parameters are provided as daily averages.\n\nERA5 data is available from 1979 to three months from real-time. More information\nand more ERA5 atmospheric parameters can be found at the\n[Copernicus Climate Data Store](https://cds.climate.copernicus.eu).\n\nProvider's Note: Daily aggregates have been calculated based on the ERA5 hourly values\nof each parameter.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-02T00:00:00Z","2020-07-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/DAILY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_ERA5_DAILY","rel":"related","title":"Run the example for ECMWF/ERA5/DAILY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_ERA5_DAILY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_DAILY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/DAILY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/DAILY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/ERA5/HOURLY","title":"ERA5 Hourly - ECMWF Climate Reanalysis","description":"ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global\nclimate. It is produced by the Copernicus Climate Change Service (C3S)\nat ECMWF. Reanalysis combines model data with observations from across the\nworld into a globally complete and consistent dataset using the laws of\nphysics. ERA5 provides hourly estimates for a large number of\natmospheric, ocean-wave and land-surface quantities. The data\ncover the Earth on a roughly 31 km grid and resolve the atmosphere using\n137 levels from the surface up to a height of 80km. This dataset\nrepresents the 'single levels' data, containing 2D parameters.\nData is available from 1940 to the present.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1940-01-01T00:00:00Z","2026-11-04T19:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/HOURLY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_ERA5_HOURLY","rel":"related","title":"Run the example for ECMWF/ERA5/HOURLY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_ERA5_HOURLY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_HOURLY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/HOURLY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/HOURLY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/ERA5/HOURLY_PRESSURE_LEVELS","title":"ERA5 Hourly Pressure Levels - ECMWF Climate Reanalysis","description":"ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global\nclimate. Reanalysis combines model data with observations from across the\nworld into a globally complete and consistent dataset. ERA5 replaces its\npredecessor, the ERA-Interim reanalysis.\n\nThis dataset contains 3D parameters at 37 pressure levels.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1940-01-01T00:00:00Z","2026-11-03T23:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/HOURLY_PRESSURE_LEVELS","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_ERA5_HOURLY_PRESSURE_LEVELS","rel":"related","title":"Run the example for ECMWF/ERA5/HOURLY_PRESSURE_LEVELS in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_ERA5_HOURLY_PRESSURE_LEVELS_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_HOURLY_PRESSURE_LEVELS#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/HOURLY_PRESSURE_LEVELS/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/HOURLY_PRESSURE_LEVELS/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/ERA5_LAND/DAILY_AGGR","title":"ERA5-Land Daily Aggregated - ECMWF Climate Reanalysis","description":"ERA5-Land is a reanalysis dataset providing a consistent view of the\nevolution of land variables over several decades at an enhanced resolution\ncompared to ERA5. ERA5-Land has been produced by replaying the land\ncomponent of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\ndata with observations from across the world into a globally complete and\nconsistent dataset using the laws of physics. Reanalysis produces data that\ngoes several decades back in time, providing an accurate description of the\nclimate of the past. This dataset includes all 50 variables as available on\nCDS.\n\nERA5-Land data is available from 1950 to three months from real-time.\n\nPlease consult [the ERA5-Land \"Known Issues\" section](https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation). In particular, note that three components of the total evapotranspiration have values swapped as follows:\n\n* variable \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)) has the values corresponding to the \"Evaporation from vegetation transpiration\" (mars parameter 228103 (evavt)),\n* variable \"Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)),\n* variable \"Evaporation from vegetation transpiration\" (mars parameter code 228103 (evavt)) has the values corresponding to the \"Evaporation from open water surfaces excluding oceans\" (mars parameter code 228102 (evaow)).\n\n\nThe asset is a daily aggregate of ECMWF ERA5 Land hourly assets which\nincludes both flow and non-flow bands. Flow bands are formed by collecting\nthe first hour's data of the following day which holds aggregated sum of\nprevious day and while the non-flow bands are created by averaging all\nhourly data of the day. The flow bands are labeled with the \"_sum\"\nidentifier, which approach is different from the daily data produced by\nCopernicus Climate Data Store, where flow bands are averaged\ntoo.\n\nDaily aggregates have been pre-calculated to facilitate many applications\nrequiring easy and fast access to the data.\n\nPrecipitation and other flow (accumulated) bands might occasionally have\nnegative values, which doesn't make physical sense. At other times their values\nmight be excessively high.\n\nThis problem is due to how the GRIB format saves data: it simplifies or \"packs\"\nthe data into smaller, less precise numbers, which can introduce errors. These\nerrors get worse when the data varies a lot.\n\nBecause of this, when we look at the data for a whole day to compute daily\ntotals, sometimes the highest amount of rainfall recorded at one time can seem\nlarger than the total rainfall measured for the entire day.\n\nTo learn more, Please see:\n[\"Why are there sometimes small negative precipitation accumulations\"](https://confluence.ecmwf.int/display/UDOC/Why+are+there+sometimes+small+negative+precipitation+accumulations+-+ecCodes+GRIB+FAQ)","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-02T00:00:00Z","2026-11-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/DAILY_AGGR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_ERA5_LAND_DAILY_AGGR","rel":"related","title":"Run the example for ECMWF/ERA5_LAND/DAILY_AGGR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_ERA5_LAND_DAILY_AGGR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_DAILY_AGGR#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/DAILY_AGGR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/DAILY_AGGR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/ERA5_LAND/HOURLY","title":"ERA5-Land Hourly - ECMWF Climate Reanalysis","description":"ERA5-Land is a reanalysis dataset providing a consistent view of the\nevolution of land variables over several decades at an enhanced resolution\ncompared to ERA5. ERA5-Land has been produced by replaying the land\ncomponent of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\ndata with observations from across the world into a globally complete and\nconsistent dataset using the laws of physics. Reanalysis produces data that\ngoes several decades back in time, providing an accurate description of the\nclimate of the past. This dataset includes all 50 variables as available on\nCDS.\n\nERA5-Land data is available from 1950 to three months from real-time.\n\nPlease consult [the ERA5-Land \"Known Issues\" section](https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation). In particular, note that three components of the total evapotranspiration have values swapped as follows:\n\n* variable \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)) has the values corresponding to the \"Evaporation from vegetation transpiration\" (mars parameter 228103 (evavt)),\n* variable \"Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)),\n* variable \"Evaporation from vegetation transpiration\" (mars parameter code 228103 (evavt)) has the values corresponding to the \"Evaporation from open water surfaces excluding oceans\" (mars parameter code 228102 (evaow)).\n\n\nPlease note that the convention for accumulations used in ERA5-Land differs\nwith that for ERA5. The accumulations are treated the same as those in\nERA-Interim or ERA-Interim/Land, i.e., they are accumulated from the\nbeginning of the forecast to the end of the forecast step. This happens\nwithin every day and gets reset on midnight.\n[See this page for more information](https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation#ERA5Land:datadocumentation-accumulationsAccumulations).\nThe Earth Engine Data team added 19 additional bands,\none for each of the accumulation bands, with the hourly values computed as\nthe difference between two consecutive forecast steps.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T01:00:00Z","2026-11-04T21:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/HOURLY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_ERA5_LAND_HOURLY","rel":"related","title":"Run the example for ECMWF/ERA5_LAND/HOURLY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_ERA5_LAND_HOURLY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/HOURLY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/HOURLY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/ERA5_LAND/MONTHLY","title":"ERA5-Land Monthly Averaged - ECMWF Climate Reanalysis [deprecated]","description":"ERA5-Land is a reanalysis dataset providing a consistent view of the\nevolution of land variables over several decades at an enhanced resolution\ncompared to ERA5. ERA5-Land has been produced by replaying the land\ncomponent of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\ndata with observations from across the world into a globally complete and\nconsistent dataset using the laws of physics. Reanalysis produces data that\ngoes several decades back in time, providing an accurate description of the\nclimate of the past. This dataset includes all 50 variables as available on\nCDS.\n\nERA5-Land data is available from 1950 to three months from real-time.\n\nPlease consult [the ERA5-Land \"Known Issues\" section](https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation). In particular, note that three components of the total evapotranspiration have values swapped as follows:\n\n* variable \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)) has the values corresponding to the \"Evaporation from vegetation transpiration\" (mars parameter 228103 (evavt)),\n* variable \"Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)),\n* variable \"Evaporation from vegetation transpiration\" (mars parameter code 228103 (evavt)) has the values corresponding to the \"Evaporation from open water surfaces excluding oceans\" (mars parameter code 228102 (evaow)).\n\n\nThe data presented here is a subset of the full ERA5-Land dataset\npost-processed by ECMWF. Monthly-mean averages have been pre-calculated to\nfacilitate many applications requiring easy and fast access to the data,\nwhen sub-monthly fields are not required.\n\nERA5-Land data is available from 1950 to three months from real-time. More\ninformation can be found at the\n[Copernicus Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-02-01T00:00:00Z","2023-04-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/MONTHLY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_ERA5_LAND_MONTHLY","rel":"related","title":"Run the example for ECMWF/ERA5_LAND/MONTHLY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_ERA5_LAND_MONTHLY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_MONTHLY_AGGR.json","rel":"latest-version","title":"ECMWF/ERA5_LAND/MONTHLY_AGGR","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_MONTHLY_AGGR.json","rel":"successor-version","title":"ECMWF/ERA5_LAND/MONTHLY_AGGR","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/MONTHLY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/MONTHLY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/ERA5_LAND/MONTHLY_AGGR","title":"ERA5-Land Monthly Aggregated - ECMWF Climate Reanalysis","description":"ERA5-Land is a reanalysis dataset providing a consistent view of the\nevolution of land variables over several decades at an enhanced resolution\ncompared to ERA5. ERA5-Land has been produced by replaying the land\ncomponent of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\ndata with observations from across the world into a globally complete and\nconsistent dataset using the laws of physics. Reanalysis produces data that\ngoes several decades back in time, providing an accurate description of the\nclimate of the past. This dataset includes all 50 variables as available on\nCDS.\n\nERA5-Land data is available from 1950 to three months from real-time.\n\nPlease consult [the ERA5-Land \"Known Issues\" section](https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation). In particular, note that three components of the total evapotranspiration have values swapped as follows:\n\n* variable \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)) has the values corresponding to the \"Evaporation from vegetation transpiration\" (mars parameter 228103 (evavt)),\n* variable \"Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)),\n* variable \"Evaporation from vegetation transpiration\" (mars parameter code 228103 (evavt)) has the values corresponding to the \"Evaporation from open water surfaces excluding oceans\" (mars parameter code 228102 (evaow)).\n\n\nThe asset is a monthly aggregate of ECMWF ERA5 Land hourly assets which\nincludes both flow and non-flow bands. Flow bands are formed by collecting\nthe first hour's data of the following day for each day of the month and\nthen adding them together, while the non-flow bands are created by\naveraging all hourly data of the month. The flow bands are labeled with\nthe \"_sum\" identifier, which approach is different from the monthly data\nproduced by Copernicus Climate Data Store, where flow bands are averaged\ntoo.\n\nMonthly aggregates have been pre-calculated to facilitate many applications\nrequiring easy and fast access to the data, when sub-monthly fields are not\nrequired.\n\nPrecipitation and other flow (accumulated) bands might occasionally have\nnegative values, which doesn't make physical sense. At other times their values\nmight be excessively high.\n\nThis problem is due to how the GRIB format saves data: it simplifies or \"packs\"\nthe data into smaller, less precise numbers, which can introduce errors. These\nerrors get worse when the data varies a lot.\n\nBecause of this, when we look at the data for a whole day to compute daily\ntotals, sometimes the highest amount of rainfall recorded at one time can seem\nlarger than the total rainfall measured for the entire day.\n\nTo learn more, Please see:\n[\"Why are there sometimes small negative precipitation accumulations\"](https://confluence.ecmwf.int/display/UDOC/Why+are+there+sometimes+small+negative+precipitation+accumulations+-+ecCodes+GRIB+FAQ)","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-02-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/MONTHLY_AGGR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_ERA5_LAND_MONTHLY_AGGR","rel":"related","title":"Run the example for ECMWF/ERA5_LAND/MONTHLY_AGGR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_ERA5_LAND_MONTHLY_AGGR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_AGGR#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_MONTHLY_AGGR.json","rel":"latest-version","title":"ECMWF/ERA5_LAND/MONTHLY_AGGR","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_MONTHLY.json","rel":"predecessor-version","title":"ECMWF/ERA5_LAND/MONTHLY","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/MONTHLY_AGGR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/MONTHLY_AGGR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/ERA5_LAND/MONTHLY_BY_HOUR","title":"ERA5-Land Monthly Averaged by Hour of Day - ECMWF Climate Reanalysis","description":"ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables\nover several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by\nreplaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\ndata with observations from across the world into a globally complete and consistent dataset\nusing the laws of physics. Reanalysis produces data that goes several decades back in time,\nproviding an accurate description of the climate of the past. This dataset includes all 50\nvariables as available on CDS.\n\nThe data presented here is a subset of the full ERA5-Land dataset post-processed by ECMWF.\nMonthly-mean averages have been pre-calculated to facilitate many applications requiring easy and\nfast access to the data, when sub-monthly fields are not required.\n\nPlease note that the convention for accumulations used in ERA5-Land differs with that for ERA5.\nThe accumulations are treated the same as those in ERA-Interim or ERA-Interim/Land, i.e., they\nare accumulated from the beginning of the forecast to the end of the forecast step. This happens\nwithin every day and gets reset on midnight. The Earth Engine Data team added 19 additional bands,\none for each of the accumulation bands, with the hourly values computed as the difference\nbetween two consecutive forecast steps.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T01:00:00Z","2026-10-01T23:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/MONTHLY_BY_HOUR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_ERA5_LAND_MONTHLY_BY_HOUR","rel":"related","title":"Run the example for ECMWF/ERA5_LAND/MONTHLY_BY_HOUR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_ERA5_LAND_MONTHLY_BY_HOUR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_BY_HOUR#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/MONTHLY_BY_HOUR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/MONTHLY_BY_HOUR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/ERA5_LAND/STATIC","title":"ERA5-Land Static - ECMWF Climate Reanalysis","description":"ERA5-Land is a reanalysis dataset providing a consistent view of the\nevolution of land variables over several decades at an enhanced resolution\ncompared to ERA5. ERA5-Land has been produced by replaying the land\ncomponent of the ECMWF ERA5 climate reanalysis. Reanalysis combines model\ndata with observations from across the world into a globally complete and\nconsistent dataset using the laws of physics. Reanalysis produces data that\ngoes several decades back in time, providing an accurate description of the\nclimate of the past. This dataset includes all 50 variables as available on\nCDS.\n\nERA5-Land data is available from 1950 to three months from real-time.\n\nPlease consult [the ERA5-Land \"Known Issues\" section](https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation). In particular, note that three components of the total evapotranspiration have values swapped as follows:\n\n* variable \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)) has the values corresponding to the \"Evaporation from vegetation transpiration\" (mars parameter 228103 (evavt)),\n* variable \"Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the \"Evaporation from bare soil\" (mars parameter code 228101 (evabs)),\n* variable \"Evaporation from vegetation transpiration\" (mars parameter code 228103 (evavt)) has the values corresponding to the \"Evaporation from open water surfaces excluding oceans\" (mars parameter code 228102 (evaow)).\n\nThis dataset contains the static variables of ERA5-Land.\nThese variables do not vary in time.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z",null]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/STATIC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_ERA5_LAND_STATIC","rel":"related","title":"Run the example for ECMWF/ERA5_LAND/STATIC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_ERA5_LAND_STATIC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_STATIC#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5_LAND/STATIC/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/ERA5/MONTHLY","title":"ERA5 Monthly Aggregates - Latest Climate Reanalysis Produced by ECMWF / Copernicus Climate Change Service","description":"ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate.\nReanalysis combines model data with observations from across the world into\na globally complete and consistent dataset. ERA5 replaces its predecessor,\nthe ERA-Interim reanalysis.\n\nERA5 MONTHLY provides aggregated values for each month for seven ERA5\nclimate reanalysis  parameters: 2m air temperature, 2m dewpoint temperature,\ntotal precipitation,\nmean sea level pressure, surface pressure, 10m u-component of wind and\n10m v-component of wind. Additionally, monthly minimum and maximum air\ntemperature at 2m has been calculated based on the hourly 2m air temperature\ndata. Monthly total precipitation values are given as monthly sums.\nAll other parameters are provided as monthly averages.\n\nERA5 data is available from 1940 to three months from real-time, the version in the EE Data Catalog is available from 1979. More information\nand more ERA5 atmospheric parameters can be found at the\n[Copernicus Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview).\n\nProvider's Note: Monthly aggregates have been calculated based on the ERA5 hourly values\nof each parameter.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z","2020-06-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/MONTHLY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_ERA5_MONTHLY","rel":"related","title":"Run the example for ECMWF/ERA5/MONTHLY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_ERA5_MONTHLY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_MONTHLY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/MONTHLY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/ERA5/MONTHLY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/NRT_FORECAST/IFS/OPER","title":"ECMWF Near-Realtime IFS Atmospheric Forecasts","description":"This dataset contains 15-day forecasts of the atmospheric model variables\ngenerated by the ECMWF Integrated Forecasting System (IFS) at 0.25 degree\nresolution. We refer to these as Near-Realtime (NRT) because new products\nare released twice a day after the release of the ECMWF realtime forecast\ndata, of which this is a subset. Data may be distributed and used\ncommercially with\n[proper attribution](https://apps.ecmwf.int/datasets/licences/general/).\n\nProducts are available in Earth Engine starting with the implementation of\n[Cycle 49r1](https://confluence.ecmwf.int/display/FCST/Implementation+of+IFS+Cycle+49r1)\non 2024/11/12; earlier products are not included. For general information\nabout how to use ECMWF NRT datasets, see their\n[user documentation](https://confluence.ecmwf.int/display/DAC/ECMWF+open+data%3A+real-time+forecasts+from+IFS+and+AIFS).\nSources files are available in the\n[Google Cloud marketplace](https://console.cloud.google.com/marketplace/product/bigquery-public-data/open-data-ecmwf).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2024-11-12T12:00:00Z","2026-11-10T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/NRT_FORECAST/IFS/OPER","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_NRT_FORECAST_IFS_OPER","rel":"related","title":"Run the example for ECMWF/NRT_FORECAST/IFS/OPER in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_NRT_FORECAST_IFS_OPER_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_NRT_FORECAST_IFS_OPER#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/NRT_FORECAST/IFS/OPER/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/NRT_FORECAST/IFS/OPER/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/NRT_FORECAST/IFS/SCWV","title":"ECMWF Near-Realtime IFS Wave (Short-Cutoff) Forecasts","description":"This dataset contains 6-day forecasts of the wave model fields generated by\nthe ECMWF Integrated Forecasting System (IFS) at 0.25 degree resolution.\nWe refer to these as Near-Realtime (NRT) because new products are released\ntwice a day after the release of the ECMWF realtime forecast data, of which\nthis is a subset. This is a \"Short-Cutoff\" stream, meaning the forecasts\nhave a shorter range than the standard IFS WAVE forecasts. Data may be\ndistributed and used commercially with\n[proper attribution](https://apps.ecmwf.int/datasets/licences/general/).\n\nProducts are available in Earth Engine starting with the implementation of\n[Cycle 49r1](https://confluence.ecmwf.int/display/FCST/Implementation+of+IFS+Cycle+49r1)\non 2024-11-12; earlier products are not included. For general information\nabout how to use ECMWF NRT datasets, see their\n[user documentation](https://confluence.ecmwf.int/display/DAC/ECMWF+open+data%3A+real-time+forecasts+from+IFS+and+AIFS).\nSources files are available in the\n[Google Cloud marketplace](https://console.cloud.google.com/marketplace/product/bigquery-public-data/open-data-ecmwf).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2024-11-12T18:00:00Z","2026-11-10T06:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/NRT_FORECAST/IFS/SCWV","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_NRT_FORECAST_IFS_SCWV","rel":"related","title":"Run the example for ECMWF/NRT_FORECAST/IFS/SCWV in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_NRT_FORECAST_IFS_SCWV_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_NRT_FORECAST_IFS_SCWV#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/NRT_FORECAST/IFS/SCWV/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/NRT_FORECAST/IFS/SCWV/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ECMWF/NRT_FORECAST/IFS/WAVE","title":"ECMWF Near-Realtime IFS Wave Forecasts","description":"This dataset contains 15-day forecasts of the wave model fields\ngenerated by the ECMWF Integrated Forecasting System (IFS) at 0.25 degree\nresolution. We refer to these as Near-Realtime (NRT) because new products\nare released twice a day after the release of the ECMWF realtime forecast\ndata, of which this is a subset. Data may be distributed and used\ncommercially with\n[proper attribution](https://apps.ecmwf.int/datasets/licences/general/).\n\nProducts are available in Earth Engine starting with the implementation of\n[Cycle 49r1](https://confluence.ecmwf.int/display/FCST/Implementation+of+IFS+Cycle+49r1)\non 2024-11-12; earlier products are not included. For general information\nabout how to use ECMWF NRT datasets, see their\n[user documentation](https://confluence.ecmwf.int/display/DAC/ECMWF+open+data%3A+real-time+forecasts+from+IFS+and+AIFS).\nSources files are available in the\n[Google Cloud marketplace](https://console.cloud.google.com/marketplace/product/bigquery-public-data/open-data-ecmwf).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2024-11-12T12:00:00Z","2026-11-10T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/NRT_FORECAST/IFS/WAVE","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ECMWF/ECMWF_NRT_FORECAST_IFS_WAVE","rel":"related","title":"Run the example for ECMWF/NRT_FORECAST/IFS/WAVE in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ECMWF/ECMWF_NRT_FORECAST_IFS_WAVE_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ECMWF_NRT_FORECAST_IFS_WAVE#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/NRT_FORECAST/IFS/WAVE/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ECMWF/NRT_FORECAST/IFS/WAVE/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"EDF/MethaneSAT/MethaneAIR/L3concentration","title":"MethaneAIR L3 Concentration v1","description":"This dataset provides geospatial data for the total column dry air mole\nfraction of methane in the atmosphere, \"XCH4\", as observed by the MethaneAIR\nimaging spectrometer. XCH4 is defined as the total column amount (number of\nmolecules) of methane (\"CH4\") divided by the total amount of air (number of\nmolecules, water vapor amount removed) along the line of sight from the\nairborne spectrometer to the earth's surface and back up to the sun.\nAdditional data layers are provided for reference: observed albedo at 1622\nnm, surface pressure, and terrain height.\n\n\nMethane is a potent greenhouse gas that has more than 80 times the warming power\nof carbon dioxide over the first 20 years after it reaches the atmosphere. At\nleast 30% of today's global warming is driven by methane from human actions.\nCutting methane emissions associated with human activities - including avoidable\nemissions from oil and gas operations, agriculture, and waste management - is\nthe single fastest way to slow the rate of global warming.\n\nThe aircraft was flown at 11.5 - 13 km altitude (37,000 - 43,000 feet),\nacquiring 10 frames per second with a swath of 4.5 - 4.8 km with a 25 degree\nfield of view and 896 pixels across track, giving a native pixel size at\nnadir of approximately 25m along track and 5 m across track. The\nspectroscopic data are aggregated by 5 across track, giving a mean pixel\nsize of 25m x 25m. Concentrations (XCH4) are retrieved from these high\nresolution (0.1 nm sampling, 0.3nm resolution), spatially distributed\nspectra ([Chan Miller et al. (2024)](https://doi.org/10.5194/egusphere-2023-1962)),\nthen gridded on a 0.0001 x 0.0001 degree grid (approximately 10m x 8m over\nthe US). The precision of the gridded data is approximately 25 parts per\nbillion (1s, about 1.3%), and a spatial correlation length (1/e) of about\n70m. There are some variations from flight-to-flight according to operating\nconditions, look angle and surface albedo.\n\nThe flight pattern was typically a series of oval swaths in a Zamboni\npattern to cover a region, typically about 70 km x 100 km, over a 2 - 3 hour\ninterval. The data presented here are averaged where flight tracks overlap,\ntypically at the edges of each swath and in the urns at the top and bottom\nof the Zamboni pattern.\n\n\nThis dataset was generated using MethaneAIR measurements taken on flights\nbetween 30 July 2021 and 13 October 2023. MethaneAIR is an airborne precursor of\nthe MethaneSAT satellite mission, managed by [MethaneSAT LLC](https://www.methanesat.org/),\na wholly owned subsidiary of Environmental Defense Fund. Methane emission fluxes were produced using a geostatistical inverse\nmodeling framework on each of these Zamboni swaths.\n\n\nFor additional information about the MethaneAIR instrument, instrument\ncalibration and emission detections, please refer to recent publications by\n[Loughner et al. (2021)](https://doi.org/10.1175/JAMC-D-20-0158.1),\n[Staebell et al. (2021)](https://doi.org/10.5194/amt-14-3737-2021),\n[Conway et al. (2023)](https://doi.org/10.5194/amt-2023-111),\n[Chulakadabba et al. (2023)](https://doi.org/10.5194/egusphere-2023-822),\n[Abbadi et al. (2023)](https://doi.org/10.31223/X51D4C),\n[Omara et al. (2023)](https://doi.org/10.5194/essd-15-3761-2023),\nand [Miller et al. (2023)](https://doi.org/10.5194/egusphere-2023-1962).\n\nContact the data provider for more information about the project at this\nlink: [https://www.methanesat.org/contact/](https://www.methanesat.org/contact/)","license":"proprietary","extent":{"spatial":{"bbox":[[-118.42,27.62,-73.44,54.6]]},"temporal":{"interval":[["2021-08-06T00:00:00Z","2023-10-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/EDF/MethaneSAT/MethaneAIR/L3concentration","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/EDF/EDF_MethaneSAT_MethaneAIR_L3concentration","rel":"related","title":"Run the example for EDF/MethaneSAT/MethaneAIR/L3concentration in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/EDF/EDF_MethaneSAT_MethaneAIR_L3concentration_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/EDF_MethaneSAT_MethaneAIR_L3concentration#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.methanesat.org/sites/default/files/2025-02/MethaneSAT%20-%20Content%20License%20Terms%20of%20Use%20%28Revised%202-12-2025%29%5B25%5D.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/EDF/MethaneSAT/MethaneAIR/L3concentration/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/EDF/MethaneSAT/MethaneAIR/L3concentration/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"EDF/MethaneSAT/MethaneAIR/L4area","title":"MethaneAIR L4 Area Sources v1","description":"**The area emissions model is still in development and not representative of\na final product.**\n\nThis dataset provides spatially disaggregated methane emission fluxes\n(kg/hr) using measurements from MethaneAIR flights focused on oil and gas\nproducing regions throughout the United States. Total emissions for the\nsurvey area are obtained by summing the pixel values.\n\n\nMethane is a potent greenhouse gas that has more than 80 times the warming power\nof carbon dioxide over the first 20 years after it reaches the atmosphere. At\nleast 30% of today's global warming is driven by methane from human actions.\nCutting methane emissions associated with human activities - including avoidable\nemissions from oil and gas operations, agriculture, and waste management - is\nthe single fastest way to slow the rate of global warming.\n\nArea emissions are estimated from observed XCH4 using a geostatistical inverse\nmodel framework (see [\"MethaneAIR L4 Area Sources\"](EDF_MethaneSAT_MethaneAIR_L4area) dataset). An atmospheric transport model - \nthe Stochastic Time-Inverted Lagrangian Transport model, \"STILT\";\n[Lin et al. (2003)](https://doi.org/10.1029/2002JD003161),\n[Fasoli et al. (2018)](https://doi.org/10.5194/gmd-11-2813-2018); driven by\nmeteorological data from the NOAA High-Resolution Rapid Refresh Model \"HRRR\" -\nis used to link variations in observed XCH4 to potential upwind sources. A\nhierarchical approach is used to separate XCH4 variations due to area emissions\nfrom those due to point source emissions or inflow across the domain boundary\n(the \"background\" concentration). Point source emissions are determined\nindividually (see [\"MethaneAIR L4 Point Sources\"](EDF_MethaneSAT_MethaneAIR_L4point) dataset) and pre-subtracted from the observed XCH4. An inverse model is then\nused to estimate XCH4 inflow across the boundary domain. Finally, area emissions\nare estimated using a geostatistical inverse model with an enforced non-negative solution. Total emissions are the sum of area and point source emissions.\n\nThis dataset was generated using MethaneAIR measurements taken on flights\nbetween 30 July 2021 and 13 October 2023. MethaneAIR is an airborne precursor of\nthe MethaneSAT satellite mission, managed by [MethaneSAT LLC](https://www.methanesat.org/),\na wholly owned subsidiary of Environmental Defense Fund. The methane emission fluxes were produced using a geostatistical inverse\nmodeling framework specialized to exploit the high spatial resolution, wide\nspatial coverage, and high precision of MethaneAIR data. Not all data\nproducts are available for all flights.\n\n\nFor additional information about the MethaneAIR instrument, instrument\ncalibration and emission detections, please refer to recent publications by\n[Loughner et al. (2021)](https://doi.org/10.1175/JAMC-D-20-0158.1),\n[Staebell et al. (2021)](https://doi.org/10.5194/amt-14-3737-2021),\n[Conway et al. (2023)](https://doi.org/10.5194/amt-2023-111),\n[Chulakadabba et al. (2023)](https://doi.org/10.5194/egusphere-2023-822),\n[Abbadi et al. (2023)](https://doi.org/10.31223/X51D4C),\n[Omara et al. (2023)](https://doi.org/10.5194/essd-15-3761-2023),\nand [Miller et al. (2023)](https://doi.org/10.5194/egusphere-2023-1962).\n\nContact the data provider for more information about the project at this\nlink: [https://www.methanesat.org/contact/](https://www.methanesat.org/contact/)","license":"proprietary","extent":{"spatial":{"bbox":[[-112.56,27.8,-73.45,43.14]]},"temporal":{"interval":[["2021-07-30T00:00:00Z","2023-10-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/EDF/MethaneSAT/MethaneAIR/L4area","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/EDF/EDF_MethaneSAT_MethaneAIR_L4area","rel":"related","title":"Run the example for EDF/MethaneSAT/MethaneAIR/L4area in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/EDF/EDF_MethaneSAT_MethaneAIR_L4area_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/EDF_MethaneSAT_MethaneAIR_L4area#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/EDF/EDF_MethaneSAT_MethaneAIR_L4area.json","rel":"latest-version","title":"EDF/MethaneSAT/MethaneAIR/L4area","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/EDF/EDF_MethaneSAT_MethaneAIR_methaneair-L4area-2021.json","rel":"predecessor-version","title":"EDF/MethaneSAT/MethaneAIR/methaneair-L4area-2021","type":"application/json"},{"href":"https://www.methanesat.org/sites/default/files/2025-02/MethaneSAT%20-%20Content%20License%20Terms%20of%20Use%20%28Revised%202-12-2025%29%5B25%5D.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/EDF/MethaneSAT/MethaneAIR/L4area/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/EDF/MethaneSAT/MethaneAIR/L4area/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"EDF/MethaneSAT/MethaneAIR/methaneair-L4area-2021","title":"MethaneAIR L4 Area Sources 2021 [deprecated]","description":"**The area emissions model is still in development and not representative of\na final product.**\n\nThis dataset provides high-resolution, spatially disaggregated methane\nemission fluxes (kg/hr) of the Permian Delaware sub-basin in southern New\nMexico and western Texas as well as for the Uinta basin in Utah.\n\nMethane is a potent greenhouse gas that has more than 80 times the warming\npower of carbon dioxide over the first 20 years after it reaches the\natmosphere. At least 30% of today's global warming is driven by methane from\nhuman actions. Cutting methane emissions associated with human activities\n- including avoidable emissions from oil and gas operations, agriculture,\nand waste management - is the single fastest way to slow the rate of global\nwarming.\n\nThis dataset was generated using MethaneAIR measurements taken on 8 August\n2021 (research flight RF06) over the Permian and 11 August 2021 (RF08) over\nUinta. MethaneAIR is an airborne precursor of the MethaneSAT satellite\nmission, managed by [MethaneSAT LLC](https://www.methanesat.org/), a wholly\nowned subsidiary of Environmental Defense Fund. The methane emission fluxes\nwere produced using a geostatistical inverse modeling framework specialized\nto exploit the high spatial resolution, wide spatial coverage, and high\nprecision of MethaneAIR data.\n\nFor additional information about the MethaneAIR instrument, instrument\ncalibration and emission detections, please refer to recent publications\nby [Staebell et al. (2021)](https://doi.org/10.5194/amt-14-3737-2021),\n[Conway et al. (2023)](https://doi.org/10.5194/amt-2023-111), [Chulakadabba\net al. (2023)](https://doi.org/10.5194/egusphere-2023-822), [Abbadi et al.\n(2023)](https://doi.org/10.31223/X51D4C), [Omara et al. (2023)](https://doi.org/10.5194/essd-15-3761-2023),\nand [Miller et al. (2023)](https://doi.org/10.5194/egusphere-2023-1962).\n\nContact the data provider for more information about the project at this link:\n[https://www.methanesat.org/contact/](https://www.methanesat.org/contact/)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-110.3,31.4,-103.1,40.6]]},"temporal":{"interval":[["2021-08-07T00:00:00Z","2021-08-11T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/EDF/MethaneSAT/MethaneAIR/methaneair-L4area-2021","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/EDF/EDF_MethaneSAT_MethaneAIR_methaneair-L4area-2021","rel":"related","title":"Run the example for EDF/MethaneSAT/MethaneAIR/methaneair-L4area-2021 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/EDF/EDF_MethaneSAT_MethaneAIR_methaneair-L4area-2021_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/EDF_MethaneSAT_MethaneAIR_methaneair-L4area-2021#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/EDF/EDF_MethaneSAT_MethaneAIR_L4area.json","rel":"latest-version","title":"EDF/MethaneSAT/MethaneAIR/L4area","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/EDF/EDF_MethaneSAT_MethaneAIR_L4area.json","rel":"successor-version","title":"EDF/MethaneSAT/MethaneAIR/L4area","type":"application/json"},{"href":"https://www.methanesat.org/sites/default/files/2025-02/MethaneSAT%20-%20Content%20License%20Terms%20of%20Use%20%28Revised%202-12-2025%29%5B25%5D.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/EDF/MethaneSAT/MethaneAIR/methaneair-L4area-2021/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"EO1/HYPERION","title":"EO-1 Hyperion Hyperspectral Imager","description":"Hyperion is a high resolution hyperspectral imager producing 220 unique\nspectral channels ranging from 0.357 to 2.576 micrometers with a 10-nm\nbandwidth. The instrument operates in a pushbroom fashion, with a spatial\nresolution of 30 meters for all bands and a standard scene width of 7.7\nkilometers.\n\nThis dataset contains level 1A radiance images, radiometrically calibrated\nand orthorectified. The SWIR bands have a scaling factor of 80 and the VNIR\nbands have a scaling factor of 40 applied.\n\n- VNIR bands (B008-B057, 426.82nm - 925.41nm): L = Digital Number / 40\n\n- SWIR bands (B077-B224, 912.45nm - 2395.50nm): L = Digital Number / 80\n\nNote that bands B001-B007, B058-B076, and B225-242 are not calibrated,\nhave no valid values and are not included into EE assets. See the\n[detailed spectral coverage information](https://doi.org/10.5066/P9JXHMO2).\n\nThis is a preview dataset; only a portion of the data from the\noriginal source have been downloaded.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-05-01T00:00:00Z","2017-03-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/EO1/HYPERION","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/EO1/EO1_HYPERION","rel":"related","title":"Run the example for EO1/HYPERION in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/EO1/EO1_HYPERION_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/EO1_HYPERION#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/EO1/HYPERION/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/EO1/HYPERION/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ESA/CCI/Above_Ground_Biomass/V6_0","title":"ESA CCI Above-Ground Biomass for 2007, 2010, 2015-2022 (v6.0)","description":"This dataset updated to v6.0 provides estimates of forest above-ground\nbiomass for the years 2007, 2010, 2015, 2016, 2017, 2018, 2019, 2020,\n2021, and 2022. These estimates are derived from a combination of Earth\nobservation data, depending on the year, obtained from the Copernicus\nSentinel-1 mission, Envisat’s ASAR (Advanced Synthetic Aperture Radar)\ninstrument, and JAXA's Advanced Land Observing Satellite (ALOS-1 and\nALOS-2), along with additional information from other Earth observation\nsources. The dataset has been generated as part of the European Space\nAgency's (ESA's) Climate Change Initiative (CCI) program by the Biomass\nCCI team.\n\nCompared to version 5, version 6 consists of updated AGB maps for the\nyears 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and new AGB maps\nfor 2007 and 2022. The dataset includes multi-temporal observations\nat L-band for all biomes and for each year. The above-ground biomass\n(AGB) maps utilize revised allometries, which are now based on a more\nextensive collection of spaceborne LiDAR data from the GEDI and ICESat-2\nmissions. The retrieval algorithm incorporates extended ICESat-2\nobservations to calibrate retrieval models and a refined cost function\nthat preserves temporal features as expressed in the remote sensing\ndata, limiting biases between the 2007-2010 and the 2015+ maps.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2023-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ESA/CCI/Above_Ground_Biomass/V6_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ESA/ESA_CCI_Above_Ground_Biomass_V6_0","rel":"related","title":"Run the example for ESA/CCI/Above_Ground_Biomass/V6_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ESA/ESA_CCI_Above_Ground_Biomass_V6_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ESA_CCI_Above_Ground_Biomass_V6_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5285/95913ffb6467447ca72c4e9d8cf30501","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ESA/CCI/Above_Ground_Biomass/V6_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ESA/CCI/Above_Ground_Biomass/V6_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ESA/CCI/FireCCI/5_1","title":"FireCCI51: MODIS Fire_cci Burned Area Pixel Product, Version 5.1","description":"The MODIS Fire_cci Burned Area pixel product version 5.1 (FireCCI51)\nis a monthly global ~250m spatial resolution dataset\ncontaining information on burned area as well as ancillary data. It is based\non surface reflectance in the Near Infrared (NIR) band from the MODIS\ninstrument onboard the Terra satellite, as well as active fire information from\nthe same sensor of the Terra and Aqua satellites.\n\nThe burned area algorithm uses a two-phase hybrid approach. In a first step\npixels with a high probability of being burned (called \"seeds\") are detected\nbased on the active fires. In a second one, a contextual growing is applied to\ncompletely detect the fire patch. This growing phase is controlled by an\nadaptive thresholding, where thresholds are computed based on the specific\ncharacteristics of the area surrounding each seed. The variable used to guide\nthe whole detection process is the NIR drop between pre- and post-fire images.\n\nThe dataset includes for each pixel the estimated day of the first detection of\nthe fire, the confidence level of that detection, and the land cover that has\nbeen burned (extracted from the ESA CCI Land Cover dataset v2.0.7). In addition,\nan observation flag is provided to identify the pixels that were not processed due\nto the lack of valid observations or because they belong to a non-burnable land\ncover.\n\nFireCCI51 was developed as part of the ESA Climate Change Initiative (CCI)\nProgramme, and it is also part of the Copernicus Climate Change Service (C3S).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2020-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ESA/CCI/FireCCI/5_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ESA/ESA_CCI_FireCCI_5_1","rel":"related","title":"Run the example for ESA/CCI/FireCCI/5_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ESA/ESA_CCI_FireCCI_5_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ESA_CCI_FireCCI_5_1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5285/58f00d8814064b79a0c49662ad3af537","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ESA/CCI/FireCCI/5_1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ESA/CCI/FireCCI/5_1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ESA/GLOBCOVER_L4_200901_200912_V2_3","title":"GlobCover: Global Land Cover Map","description":"GlobCover 2009 is a global land cover map based on ENVISAT's Medium\nResolution Imaging Spectrometer (MERIS) Level 1B data acquired in full\nresolution mode with a spatial resolution of approximately 300 meters.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2009-01-01T00:00:00Z","2010-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ESA/GLOBCOVER_L4_200901_200912_V2_3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ESA/ESA_GLOBCOVER_L4_200901_200912_V2_3","rel":"related","title":"Run the example for ESA/GLOBCOVER_L4_200901_200912_V2_3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ESA/ESA_GLOBCOVER_L4_200901_200912_V2_3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ESA_GLOBCOVER_L4_200901_200912_V2_3#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ESA/GLOBCOVER_L4_200901_200912_V2_3/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ESA/WorldCereal/2021/MARKERS/v100","title":"ESA WorldCereal Active Cropland 10 m v100","description":"The European Space Agency (ESA) WorldCereal Active Cropland 10 m 2021\nproduct suite contains global-scale seasonal active cropland markers. They\nwere generated as part of the\n[ESA-WorldCereal project](https://esa-worldcereal.org/). The active cropland\nproducts indicate whether or not a pixel identified as temporary crops has\nbeen actively cultivated during a specific growing season. In order for a\npixel to be labeled as “active” during a particular growing season, a full\ncrop growth cycle (sowing, growing, senescence and harvesting) needs to take\nplace within the designated time period. Note that this active marker is not\ncrop-type specific. Any crop grown (slightly) outside the predefined growing\nseasons will not be flagged as active cropland in any of the seasons covered\nby the WorldCereal system. The active marker results from running a growing\nseason detection algorithm [1] on the Sentinel-2 derived enhanced vegetation\nindex (EVI). More information on the methodology used to generate these\nproducts is described in [2].\n\nThis collection contains up to 106 agro-ecological zone (AEZ) images per\ncrop season, where each AEZ has its own seasonality. The seasons for which\nthe active cropland marker is available are described in the list below and\nwere developed in [3] as part of the project.\n\nWorldCereal seasons description:\n\n- tc-wintercereals: the main cereals season defined in an AEZ\n- tc-springcereals: optional springcereals season, only defined in certain\nAEZ\n- tc-maize-main: the main maize season defined in an AEZ\n- tc-maize-second: optional second maize season, only defined in certain AEZ\n\nEach product (image) has a binary classification band where value 0\ncorresponds to inactive cropland and value 100 corresponds to active\ncropland.\n\nThe collection should be filtered using one or more of the following image\nproperties:\n\n- aez_id, holding the ID of the AEZ to which the image belongs\n- season, describing the season for which the image is valid.\n\nReferences:\n\n- [1] Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., and\nFriedl, M. A.: Continental-scale land surface phenology from harmonized\nLandsat 8 and Sentinel-2 imagery, Remote Sens. Environ., 240, 111685,\nhttps://doi.org/10.1016/j.rse.2020.111685, 2020.\n- [2] [WorldCereal methodology and products paper](https://doi.org/10.5194/essd-2023-184)\n- [3] [WorldCereal global seasonality paper](https://doi.org/10.1080/15481603.2022.2079273)\n\nWorldCereal datasets:\n\n-  Version 100 for year 2021\n   -  [ESA/WorldCereal/AEZ/v100](ESA_WorldCereal_AEZ_v100)\n   -  [ESA/WorldCereal/2021/MODELS/v100](ESA_WorldCereal_2021_MODELS_v100)\n   -  [ESA/WorldCereal/2021/MARKERS/v100](ESA_WorldCereal_2021_MARKERS_v100)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2021-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ESA/WorldCereal/2021/MARKERS/v100","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ESA/ESA_WorldCereal_2021_MARKERS_v100","rel":"related","title":"Run the example for ESA/WorldCereal/2021/MARKERS/v100 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ESA/ESA_WorldCereal_2021_MARKERS_v100_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCereal_2021_MARKERS_v100#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ESA/WorldCereal/2021/MARKERS/v100/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ESA/WorldCereal/2021/MARKERS/v100/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ESA/WorldCereal/2021/MODELS/v100","title":"ESA WorldCereal 10 m v100","description":"The European Space Agency (ESA) WorldCereal 10 m 2021 product suite consists\nof global-scale annual and seasonal crop maps and their related confidence.\nThey were generated as part of the\n[ESA-WorldCereal project](https://esa-worldcereal.org/). More information on\nthe content of these products and the methodology used to generate them is\ndescribed in [1].\n\nThis collection contains up to 106 agro-ecological zone (AEZ) images for\neach product which were all processed with respect to their own regional\nseasonality and should be considered as independent products. These seasons\nare described in the list below and were developed in [2] as part of the\nproject. Note that cereals as described by WorldCereal include wheat,\nbarley, and rye, which belong to the *Triticeae* tribe.\n\nWorldCereal seasons description:\n\n- tc-annual: a one-year cycle being defined in an AEZ by the end of the last\nconsidered growing season\n- tc-wintercereals: the main cereals season defined in an AEZ\n- tc-springcereals: optional springcereals season, only defined in certain\nAEZ\n- tc-maize-main: the main maize season defined in an AEZ\n- tc-maize-second: optional second maize season, only defined in certain AEZ\n\nThe available products in this collection are:\n\n- temporarycrops\n- maize\n- wintercereals\n- springcereals\n- irrigation\n\nEach product (image) has a binary classification (0 or 100) and a confidence\n(0-100) band. Note that AEZs for which no irrigation product is available\nwere not processed because of the unavailability of thermal Landsat data.\n\nThe collection should be filtered using one or more of the following image\nproperties:\n\n- aez_id, holding the ID of the AEZ to which the image belongs\n- product, describing the WorldCereal product name of the image\n- season, describing the season for which the image is valid.\n\nReferences:\n\n- [1] [WorldCereal methodology and products paper](https://doi.org/10.5194/essd-2023-184)\n- [2] [WorldCereal global seasonality paper](https://doi.org/10.1080/15481603.2022.2079273)\n\nWorldCereal datasets:\n\n-  Version 100 for year 2021\n   -  [ESA/WorldCereal/AEZ/v100](ESA_WorldCereal_AEZ_v100)\n   -  [ESA/WorldCereal/2021/MODELS/v100](ESA_WorldCereal_2021_MODELS_v100)\n   -  [ESA/WorldCereal/2021/MARKERS/v100](ESA_WorldCereal_2021_MARKERS_v100)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2021-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ESA/WorldCereal/2021/MODELS/v100","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ESA/ESA_WorldCereal_2021_MODELS_v100","rel":"related","title":"Run the example for ESA/WorldCereal/2021/MODELS/v100 in the Earth Engine Code 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land cover map for 2020 at 10 m resolution based on Sentinel-1 and\nSentinel-2 data.\n The WorldCover product comes with 11 land cover classes and has been\ngenerated in the framework of the ESA WorldCover project, part of the\n5th Earth Observation Envelope Programme (EOEP-5) of the European Space\nAgency.\n\nSee also:\n\n* [ESA WorldCover website](https://esa-worldcover.org)\n* [User Manual and Validation Report](https://esa-worldcover.org/en/data-access)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ESA/WorldCover/v100","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ESA/ESA_WorldCover_v100","rel":"related","title":"Run the example for ESA/WorldCover/v100 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ESA/ESA_WorldCover_v100_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://esa-worldcover.org/en/data-access","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ESA/WorldCover/v100/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ESA/WorldCover/v100/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ESA/WorldCover/v200","title":"ESA WorldCover 10m v200","description":"The European Space Agency (ESA) WorldCover 10 m 2021 product provides a\nglobal land cover map for 2021 at 10 m resolution based on Sentinel-1 and\nSentinel-2 data.\n The WorldCover product comes with 11 land cover classes and has been\ngenerated in the framework of the ESA WorldCover project, part of the\n5th Earth Observation Envelope Programme (EOEP-5) of the European Space\nAgency.\n\nSee also:\n\n* [ESA WorldCover website](https://esa-worldcover.org)\n* [User Manual and Validation Report](https://esa-worldcover.org/en/data-access)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ESA/WorldCover/v200","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ESA/ESA_WorldCover_v200","rel":"related","title":"Run the example for ESA/WorldCover/v200 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ESA/ESA_WorldCover_v200_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v200#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://esa-worldcover.org/en/data-access","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ESA/WorldCover/v200/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ESA/WorldCover/v200/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/BACKSCATTER","title":"Global Seasonal Sentinel-1 Interferometric Coherence and Backscatter V2019 Backscatter","description":"This data set is the first-of-its-kind spatial representation of\nmulti-seasonal, global C-band Synthetic Aperture Radar (SAR)\ninterferometric repeat-pass coherence and backscatter signatures.\nCoverage comprises land masses and ice sheets from 82 degrees\nNorthern to 79 degrees Southern latitudes. The data set is derived\nfrom multi-temporal repeat-pass interferometric processing of about\n205,000 Sentinel-1 C-band SAR images acquired in Interferometric\nWide-Swath Mode from 1-Dec-2019 to 30-Nov-2020.\n\nThis dataset contains mean Radiometrically Terrain Corrected (RTC)\nbackscatter. The raw signal intensity has been adjusted to account for the\ninfluence of local topography (like hills and slopes) on the measured radar\ncross-section. This correction is performed to produce a\nterrain-flattened backscatter value that is more representative of the\nintrinsic scattering properties of the surface itself.\n\nIf you would like to do calculations using the digital numbers from the\noriginal rasters, load\n*Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/BACKSCATTER_RAW* instead.\n\nOther datasets derived from Sentinel-1 observations over the same time\nperiod include:\n\n* [Coherence](Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_COHERENCE)\n* [Decay Model Parameters](Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_DECAY_MODEL_PARAMETERS)\n* [Incidence Layover Shadow](Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_INCIDENCE_LAYOVER_SHADOW)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-79,180,82]]},"temporal":{"interval":[["2019-12-01T00:00:00Z","2020-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/BACKSCATTER","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Earth_Big_Data/Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_BACKSCATTER","rel":"related","title":"Run the example for Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/BACKSCATTER in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Earth_Big_Data/Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_BACKSCATTER_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_BACKSCATTER#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/BACKSCATTER/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/BACKSCATTER/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/COHERENCE","title":"Global Seasonal Sentinel-1 Interferometric Coherence and Backscatter V2019 Coherence","description":"This data set is the first-of-its-kind spatial representation of\nmulti-seasonal, global C-band Synthetic Aperture Radar (SAR)\ninterferometric repeat-pass coherence and backscatter signatures.\nCoverage comprises land masses and ice sheets from 82 degrees\nNorthern to 79 degrees Southern latitudes. The data set is derived\nfrom multi-temporal repeat-pass interferometric processing of about\n205,000 Sentinel-1 C-band SAR images acquired in Interferometric\nWide-Swath Mode from 1-Dec-2019 to 30-Nov-2020.\n\nCoherence is a measure of the similarity, or correlation, between two\ncomplex SAR images acquired over the same area at different times, ranging\nfrom 0 (no correlation/high noise) to 1 (perfect correlation). The dataset\nwas generated by analyzing every possible image pair up to 48 days apart.\nIt reports the median coherence for specific, standardized temporal\nbaselines (repeat intervals) aggregated over each season.\n\nThe scale factor has already been applied to each of the bands. If you\nwould like to do calculations using the digital numbers from the original\nrasters, load\n*Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/COHERENCE_RAW* instead.\n\nOther datasets derived from Sentinel-1 observations over the same time\nperiod include:\n\n* [Backscatter](Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_BACKSCATTER)\n* [Decay Model Parameters](Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_DECAY_MODEL_PARAMETERS)\n* [Incidence Layover Shadow](Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_INCIDENCE_LAYOVER_SHADOW)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-79,180,82]]},"temporal":{"interval":[["2019-12-01T00:00:00Z","2020-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/COHERENCE","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Earth_Big_Data/Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_COHERENCE","rel":"related","title":"Run the example for Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/COHERENCE in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Earth_Big_Data/Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_COHERENCE_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_COHERENCE#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/COHERENCE/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/COHERENCE/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/DECAY_MODEL_PARAMETERS","title":"Global Seasonal Sentinel-1 Interferometric Coherence and Backscatter V2019 Decay Model Parameters","description":"This data set is the first-of-its-kind spatial representation of\nmulti-seasonal, global C-band Synthetic Aperture Radar (SAR)\ninterferometric repeat-pass coherence and backscatter signatures.\nCoverage comprises land masses and ice sheets from 82 degrees\nNorthern to 79 degrees Southern latitudes. The data set is derived\nfrom multi-temporal repeat-pass interferometric processing of about\n205,000 Sentinel-1 C-band SAR images acquired in Interferometric\nWide-Swath Mode from 1-Dec-2019 to 30-Nov-2020.\n\nThis dataset includes parameters for the coherence decay model, an\nexponential function fitted to the seasonal median coherence values as a\nfunction of the repeat interval to quantify how quickly coherence is lost\nover time. The parameters are:\n\n* **rho**: Represents the long-term coherence, the value that the model\n  predicts as the repeat interval increases to infinity. It is a measure of\n  the temporal stability of the scattering elements in the target area.\n\n* **tau**: Represents the rate of coherence decay, a measure of how quickly\n the coherence drops from its initial high value towards *rho*. Lower\n values indicate faster decorrelation.\n\n* **rmse**: The root mean square error, a quality indicator of the model\n  fit. It quantifies the difference between the modeled coherence curve and\n  the original, non-aggregated coherence observations used to fit the model.\n\nThe scale factor has already been applied to each of the bands. If you\nwould like to do calculations using the digital numbers from the original\nrasters, load\n*Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/DECAY_MODEL_PARAMETERS_RAW*\ninstead.\n\nOther datasets derived from Sentinel-1 observations over the same time\nperiod include:\n\n* [Backscatter](Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_BACKSCATTER)\n* [Coherence](Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_COHERENCE)\n* [Incidence Layover Shadow](Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_INCIDENCE_LAYOVER_SHADOW)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-79,180,82]]},"temporal":{"interval":[["2019-12-01T00:00:00Z","2020-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/DECAY_MODEL_PARAMETERS","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Earth_Big_Data/Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_DECAY_MODEL_PARAMETERS","rel":"related","title":"Run the example for Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/DECAY_MODEL_PARAMETERS in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Earth_Big_Data/Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_DECAY_MODEL_PARAMETERS_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_DECAY_MODEL_PARAMETERS#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/DECAY_MODEL_PARAMETERS/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/DECAY_MODEL_PARAMETERS/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/INCIDENCE_LAYOVER_SHADOW","title":"Global Seasonal Sentinel-1 Interferometric Coherence and Backscatter V2019 Incidence Layover Shadow","description":"This data set is the first-of-its-kind spatial representation of\nmulti-seasonal, global C-band Synthetic Aperture Radar (SAR)\ninterferometric repeat-pass coherence and backscatter signatures.\nCoverage comprises land masses and ice sheets from 82 degrees\nNorthern to 79 degrees Southern latitudes. The data set is derived\nfrom multi-temporal repeat-pass interferometric processing of about\n205,000 Sentinel-1 C-band SAR images acquired in Interferometric\nWide-Swath Mode from 1-Dec-2019 to 30-Nov-2020.\n\nThis dataset contains ancillary data to aid in the interpretation and\nprocessing of the backscatter and coherence products.\n\nOther datasets derived from Sentinel-1 observations over the same time\nperiod include:\n\n* [Backscatter](Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_BACKSCATTER)\n* [Coherence](Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_COHERENCE)\n* [Decay Model Parameters](Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_DECAY_MODEL_PARAMETERS)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-79,180,82]]},"temporal":{"interval":[["2019-12-01T00:00:00Z","2020-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/INCIDENCE_LAYOVER_SHADOW","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Earth_Big_Data/Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_INCIDENCE_LAYOVER_SHADOW","rel":"related","title":"Run the example for Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/INCIDENCE_LAYOVER_SHADOW in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Earth_Big_Data/Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_INCIDENCE_LAYOVER_SHADOW_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Earth_Big_Data_GLOBAL_SEASONAL_S1_V2019_INCIDENCE_LAYOVER_SHADOW#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/INCIDENCE_LAYOVER_SHADOW/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Earth_Big_Data/GLOBAL_SEASONAL_S1/V2019/INCIDENCE_LAYOVER_SHADOW/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Estonia/Maamet/orthos/mono","title":"Estonia mono orthophotos","description":"Orthophotos are an aerial photo dataset covering Estonia.\n\nAn orthophoto is a processed aerial photo from which distortions caused\nby terrain relief, camera tilt relative to the ground at the moment of\nexposure and camera central projection are removed. A digital orthophoto\nhas a certain pixel size or resolution which shows the smallest\nindivisible exposed area on the ground (Ground Sampling Distance, GSD).\n\nOrthophotos have a nationwide coverage and correspond to the scale of\n1:5000-1:10000 (pixel size 20-40 cm). Orthophotos for densely-populated\nareas are produced with the pixel size of 10-16 cm.\n\nThe mono dataset has a single grayscale band with nationwide coverage.\n\nFor more information, please see the\n[Estonia orthophotos documentation](https://geoportaal.maaamet.ee/eng/Spatial-Data/Orthophotos-p309.html)\n","license":"proprietary","extent":{"spatial":{"bbox":[[21.5,57.3,28.1,59.5]]},"temporal":{"interval":[["1993-01-01T00:00:00Z","2021-06-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Estonia/Maamet/orthos/mono","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Estonia/Estonia_Maamet_orthos_mono","rel":"related","title":"Run the example for Estonia/Maamet/orthos/mono in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Estonia/Estonia_Maamet_orthos_mono_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Estonia_Maamet_orthos_mono#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Estonia/Maamet/orthos/mono/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Estonia/Maamet/orthos/mono/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Estonia/Maamet/orthos/mono_low_flying","title":"Estonia mono low flying orthophotos","description":"Orthophotos are an aerial photo dataset covering Estonia.\n\nAn orthophoto is a processed aerial photo from which distortions caused\nby terrain relief, camera tilt relative to the ground at the moment of\nexposure and camera central projection are removed. A digital orthophoto\nhas a certain pixel size or resolution which shows the smallest\nindivisible exposed area on the ground (Ground Sampling Distance, GSD).\n\nOrthophotos have a nationwide coverage and correspond to the scale of\n1:5000-1:10000 (pixel size 20-40 cm). Orthophotos for densely-populated\nareas are produced with the pixel size of 10-16 cm.\n\nThe mono_low_flying dataset has a single grayscale band for\ndensely-populated areas.\n\nFor more information, please see the\n[Estonia orthophotos documentation](https://geoportaal.maaamet.ee/eng/Spatial-Data/Orthophotos-p309.html)\n","license":"proprietary","extent":{"spatial":{"bbox":[[21.5,57.3,28.1,59.5]]},"temporal":{"interval":[["1993-01-01T00:00:00Z","2021-06-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Estonia/Maamet/orthos/mono_low_flying","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Estonia/Estonia_Maamet_orthos_mono_low_flying","rel":"related","title":"Run the example for Estonia/Maamet/orthos/mono_low_flying in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Estonia/Estonia_Maamet_orthos_mono_low_flying_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Estonia_Maamet_orthos_mono_low_flying#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Estonia/Maamet/orthos/mono_low_flying/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Estonia/Maamet/orthos/mono_low_flying/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Estonia/Maamet/orthos/rgb","title":"Estonia RGB orthophotos","description":"Orthophotos are an aerial photo dataset covering Estonia.\n\nAn orthophoto is a processed aerial photo from which distortions caused\nby terrain relief, camera tilt relative to the ground at the moment of\nexposure and camera central projection are removed. A digital orthophoto\nhas a certain pixel size or resolution which shows the smallest\nindivisible exposed area on the ground (Ground Sampling Distance, GSD).\n\nOrthophotos have a nationwide coverage and correspond to the scale of\n1:5000-1:10000 (pixel size 20-40 cm). Orthophotos for densely-populated\nareas are produced with the pixel size of 10-16 cm.\n\nThe RGB dataset has three bands with nationwide coverage: red, green, and\nblue.\n\nFor more information, please see the\n[Estonia orthophotos documentation](https://geoportaal.maaamet.ee/eng/Spatial-Data/Orthophotos-p309.html)\n","license":"proprietary","extent":{"spatial":{"bbox":[[21.5,57.3,28.1,59.5]]},"temporal":{"interval":[["1993-01-01T00:00:00Z","2021-06-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Estonia/Maamet/orthos/rgb","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Estonia/Estonia_Maamet_orthos_rgb","rel":"related","title":"Run the example for Estonia/Maamet/orthos/rgb in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Estonia/Estonia_Maamet_orthos_rgb_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Estonia_Maamet_orthos_rgb#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Estonia/Maamet/orthos/rgb/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Estonia/Maamet/orthos/rgb/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Estonia/Maamet/orthos/rgb_low_flying","title":"Estonia RGB low flying orthophotos","description":"Orthophotos are an aerial photo dataset covering Estonia.\n\nAn orthophoto is a processed aerial photo from which distortions caused\nby terrain relief, camera tilt relative to the ground at the moment of\nexposure and camera central projection are removed. A digital orthophoto\nhas a certain pixel size or resolution which shows the smallest\nindivisible exposed area on the ground (Ground Sampling Distance, GSD).\n\nOrthophotos have a nationwide coverage and correspond to the scale of\n1:5000-1:10000 (pixel size 20-40 cm). Orthophotos for densely-populated\nareas are produced with the pixel size of 10-16 cm.\n\nThe rgb_low_flying dataset has three bands covering densely-populated\nareas: red, green, and blue.\n\nFor more information, please see the\n[Estonia orthophotos documentation](https://geoportaal.maaamet.ee/eng/Spatial-Data/Orthophotos-p309.html)\n","license":"proprietary","extent":{"spatial":{"bbox":[[21.5,57.3,28.1,59.5]]},"temporal":{"interval":[["1993-01-01T00:00:00Z","2021-06-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Estonia/Maamet/orthos/rgb_low_flying","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Estonia/Estonia_Maamet_orthos_rgb_low_flying","rel":"related","title":"Run the example for Estonia/Maamet/orthos/rgb_low_flying in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Estonia/Estonia_Maamet_orthos_rgb_low_flying_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Estonia_Maamet_orthos_rgb_low_flying#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Estonia/Maamet/orthos/rgb_low_flying/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Estonia/Maamet/orthos/rgb_low_flying/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"FAO/GHG/1/DROSA_A","title":"UN FAO Drained Organic Soils Area (Annual) 1.0","description":"\nThe two related FAO datasets on Drained Organic Soils provide estimates of:\n\n1. DROSA-A: area of Organic Soils (in hectares) drained for agricultural\nactivities (cropland and grazed grassland)\n\n2. DROSE-A: carbon (C) and nitrous oxide (N2O) estimates (in gigagrams) from\nthe agricultural drainage of organic soils under these land uses.\n\nAnnual data are available at 0.0083333 X 0.0083333 resolution (~1 km at the\nequator), with global coverage for the period 1992 - 2018.\n\nFAOSTAT estimates follow the Intergovernmental Panel on Climate Change\nGuidelines (IPCC) and use histosols as proxy for the presence of organic\nsoils and annual land cover maps as time- dependent component. Additionally,\nsoils characteristics, land use, and climate information are applied in the\nanalysis. The carbon emissions can be converted to CO2, multiplying pixel\nvalues by the ratio of the molecular weight of carbon dioxide (CO2) to that\nof C (44/12).\n\nOrganic soils develop in wet soil ecosystems. They include tropical and\nboreal peatlands, high-latitude bogs, ferns, and mires. Organic soils cover\nglobally a mere 3 percent of the terrestrial land area but represent up to\n30 percent of the total soil carbon, thus playing an important role in\nmaintaining the earth's carbon balance. Agriculture is a major cause of\ndrainage of organic soils around the world.  Drainage exposes to aerobic\nconditions the organic matter of organic soils that oxidizes releasing large\namounts of harmful greenhouse gases (GHG) to the atmosphere.\n\nDROSA-A and DROSE-A are the basis for country and regional statistics on\ndrained organic soils disseminated in three FAOSTAT datasets (Cultivation of\nOrganic Soils; Cropland; and Grassland).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1992-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/FAO/GHG/1/DROSA_A","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/FAO/FAO_GHG_1_DROSA_A","rel":"related","title":"Run the example for FAO/GHG/1/DROSA_A in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/FAO/FAO_GHG_1_DROSA_A_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/FAO_GHG_1_DROSA_A#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/FAO/GHG/1/DROSA_A/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/FAO/GHG/1/DROSA_A/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"FAO/GHG/1/DROSE_A","title":"Drained Organic Soils Emissions (Annual) 1.0","description":"The two related FAO datasets on Drained Organic Soils provide estimates of:\n\n1. DROSA-A: area of Organic Soils (in hectares) drained for agricultural\nactivities (cropland and grazed grassland)\n\n2. DROSE-A: carbon (C) and nitrous oxide (N2O) estimates (in gigagrams) from\nthe agricultural drainage of organic soils under these land uses.\n\nAnnual data are available at 0.0083333 X 0.0083333 resolution (~1 km at the\nequator), with global coverage for the period 1992 - 2018.\n\nFAOSTAT estimates follow the Intergovernmental Panel on Climate Change\nGuidelines (IPCC) and use histosols as proxy for the presence of organic\nsoils and annual land cover maps as time- dependent component. Additionally,\nsoils characteristics, land use, and climate information are applied in the\nanalysis. The carbon emissions can be converted to CO2, multiplying pixel\nvalues by the ratio of the molecular weight of carbon dioxide (CO2) to that\nof C (44/12).\n\nOrganic soils develop in wet soil ecosystems. They include tropical and\nboreal peatlands, high-latitude bogs, ferns, and mires. Organic soils cover\nglobally a mere 3 percent of the terrestrial land area but represent up to\n30 percent of the total soil carbon, thus playing an important role in\nmaintaining the earth's carbon balance. Agriculture is a major cause of\ndrainage of organic soils around the world.  Drainage exposes to aerobic\nconditions the organic matter of organic soils that oxidizes releasing large\namounts of harmful greenhouse gases (GHG) to the atmosphere.\n\nDROSA-A and DROSE-A are the basis for country and regional statistics on\ndrained organic soils disseminated in three FAOSTAT datasets (Cultivation of\nOrganic Soils; Cropland; and Grassland).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1992-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/FAO/GHG/1/DROSE_A","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/FAO/FAO_GHG_1_DROSE_A","rel":"related","title":"Run the example for FAO/GHG/1/DROSE_A in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/FAO/FAO_GHG_1_DROSE_A_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/FAO_GHG_1_DROSE_A#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/FAO/GHG/1/DROSE_A/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/FAO/GHG/1/DROSE_A/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"FAO/SOFO/1/FPP","title":"Forest proximate people (FPP) 1.0","description":"The \"Forest Proximate People\" (FPP) dataset is one of the data layers\ncontributing to the development of indicator #13, \"number of\nforest-dependent people in extreme poverty,\" of the Collaborative\nPartnership on Forests (CPF) Global Core Set of forest-related\nindicators (GCS). The FPP dataset provides an estimate of the number of\npeople living in or within 1 kilometer of forests\n(forest-proximate people) for the year 2019 with a pixel size of 100 meters\nat a global level.\n[Find out more about the dataset.](\n  https://data.apps.fao.org/catalog/dcat/forest-proximate-people)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2019-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/FAO/SOFO/1/FPP","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/FAO/FAO_SOFO_1_FPP","rel":"related","title":"Run the example for FAO/SOFO/1/FPP in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/FAO/FAO_SOFO_1_FPP_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/FAO_SOFO_1_FPP#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/FAO/SOFO/1/FPP/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/FAO/SOFO/1/FPP/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"FAO/SOFO/1/TPP","title":"Tree proximate people (TPP) 1.0","description":"The \"Tree Proximate People\" (TPP) is one of the datasets contributing to the\ndevelopment of indicator #13, number of forest-dependent people in extreme\npoverty, of the Collaborative Partnership on Forests (CPF) Global Core Set\nof forest-related indicators (GCS). The TPP dataset provides 4 different\nestimates of tree proximate people (trees outside forests), all of them for\nthe year 2019 with a pixel size of 100 meters at a global level.\n[Find out more about the dataset.](\n  https://data.apps.fao.org/catalog/dcat/tree-proximate-people)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2019-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/FAO/SOFO/1/TPP","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/FAO/FAO_SOFO_1_TPP","rel":"related","title":"Run the example for FAO/SOFO/1/TPP in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/FAO/FAO_SOFO_1_TPP_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/FAO_SOFO_1_TPP#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/FAO/SOFO/1/TPP/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/FAO/SOFO/1/TPP/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"FAO/WAPOR/2/L1_AETI_D","title":"WAPOR Actual Evapotranspiration and Interception 2.0","description":"The actual evapotranspiration and interception (ETIa) (dekadal, in mm/day)\nis the sum of the soil evaporation (E), canopy transpiration (T), and\nevaporation from rainfall intercepted by leaves (I). The value of each pixel\nrepresents the average daily ETIa in a given dekad.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-30.0044643,-40.0044644,65.0044644,40.0044643]]},"temporal":{"interval":[["2009-01-01T00:00:00Z","2023-03-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/FAO/WAPOR/2/L1_AETI_D","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/FAO/FAO_WAPOR_2_L1_AETI_D","rel":"related","title":"Run the example for FAO/WAPOR/2/L1_AETI_D in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/FAO/FAO_WAPOR_2_L1_AETI_D_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_AETI_D#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/FAO/WAPOR/2/L1_AETI_D/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/FAO/WAPOR/2/L1_AETI_D/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"FAO/WAPOR/2/L1_E_D","title":"WAPOR Dekadal Evaporation 2.0","description":"The evaporation (E) data component (dekadal, in mm/day) is the actual\nevaporation of the soil surface. 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The value of each pixel represents\nthe average daily actual transpiration for that specific dekad.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-30.0044643,-40.0044644,65.0044644,40.0044643]]},"temporal":{"interval":[["2009-01-01T00:00:00Z","2026-10-21T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/FAO/WAPOR/3/L1_T_D","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/FAO/FAO_WAPOR_3_L1_T_D","rel":"related","title":"Run the example for FAO/WAPOR/3/L1_T_D in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/FAO/FAO_WAPOR_3_L1_T_D_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_3_L1_T_D#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/FAO/WAPOR/3/L1_T_D/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/FAO/WAPOR/3/L1_T_D/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"FIRMS","title":"FIRMS: Fire Information for Resource Management System","description":"The Earth Engine version of the Fire Information for Resource Management\nSystem (FIRMS) dataset contains the LANCE fire detection product in\nrasterized form. The near real-time (NRT) active fire locations are\nprocessed by LANCE using the standard MODIS MOD14/MYD14 Fire and Thermal\nAnomalies product. Each active fire location represents the centroid of a\n1km pixel that is flagged by the algorithm as containing one or more fires\nwithin the pixel. The data are rasterized as follows: for each FIRMS active\nfire point, a 1km bounding box (BB) is defined; pixels in the MODIS\nsinusoidal projection that intersect the FIRMS BB are identified; if\nmultiple FIRMS BBs intersect the same pixel, the one with higher confidence\nis retained; in case of a tie, the brighter one is retained.\n\nThe data in the near-real-time dataset are not considered to be of science\nquality.\n\nAdditional information can be found [here](https://firms.modaps.eosdis.nasa.gov/).\n\nNOTE: VIIRS FIRMS datasets from NOAA20 and SUOMI are also available:\n\n* [NASA/LANCE/NOAA20_VIIRS/C2](NASA_LANCE_NOAA20_VIIRS_C2)\n* [NASA/LANCE/SNPP_VIIRS/C2](NASA_LANCE_SNPP_VIIRS_C2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-11-01T00:00:00Z","2026-11-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/FIRMS","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/FIRMS/FIRMS","rel":"related","title":"Run the example for FIRMS in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/FIRMS/FIRMS_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/FIRMS#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/FIRMS/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/FIRMS/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/FLDAS/NOAH01/C/GL/M/V001","title":"FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System","description":"The FLDAS dataset (McNally et al. 2017), was designed to assist with food\nsecurity assessments in data-sparse, developing country settings. It\nincludes information on many climate-related variables including moisture\ncontent, humidity, evapotranspiration, average soil temperature, total\nprecipitation rate, etc.\n\nThere are multiple different FLDAS datasets; this one uses Noah version\n3.6.1 surface model with CHIRPS-6 hourly rainfall that has been\ndownscaled using the [NASA Land Surface Data Toolkit](https://lis.gsfc.nasa.gov/software/ldt).\nwhich is part of the [Land Information System\nframework](LIS; [https://lis.gsfc.nasa.gov/](https://lis.gsfc.nasa.gov/)). Temporal desegregation is required so that daily\nrainfall inputs can be used in both energy and water balance calculations\n\nFor forcing data, this simulation uses a combination of the new version of Modern-Era\nRetrospective analysis for Research and Applications version 2 (MERRA-2) data and Climate\nHazards Group InfraRed Precipitation with Station data (CHIRPS), a quasi-global rainfall\ndataset designed for seasonal drought monitoring and trend analysis (Funk et al., 2015).\n\nDocumentation:\n\n* [Readme](https://hydro1.gesdisc.eosdis.nasa.gov/data/FLDAS/FLDAS_NOAH01_C_GL_M.001/doc/README_FLDAS.pdf)\n\n* [How-to](https://disc.gsfc.nasa.gov/information/howto?tags=hydrology)\n\n* [GES DISC Hydrology Documentation](https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%20Documentation)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["1982-01-01T00:00:00Z","2026-09-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/FLDAS/NOAH01/C/GL/M/V001","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_FLDAS_NOAH01_C_GL_M_V001","rel":"related","title":"Run the example for NASA/FLDAS/NOAH01/C/GL/M/V001 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_FLDAS_NOAH01_C_GL_M_V001_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001#terms-of-use","rel":"license","type":"text/html"},{"href":"https://hydro1.gesdisc.eosdis.nasa.gov/data/FLDAS/FLDAS_NOAH01_C_GL_M.001/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/FLDAS/NOAH01/C/GL/M/V001/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/FLDAS/NOAH01/C/GL/M/V001/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"FORMA/FORMA_500m","title":"FORMA Global Forest Watch Deforestation Alerts, 500m [deprecated]","description":"FORMA is a MODIS-based deforestation alerting system for the humid tropical\nforests. FORMA is designed for quick identification of new areas of tree\ncover loss. The system analyzes data gathered daily by the MODIS sensor,\nwhich operates on NASA's Terra and Aqua satellites. The FORMA alerts system\nthen detects pronounced changes in vegetation cover over time, as measured\nby the Normalized Difference Vegetation Index (NDVI), a measure of\nvegetation greenness. These pronounced changes in vegetation cover are\nlikely to indicate forest being cleared, burned, or defoliated. An alert is\nadded to the dataset by setting the pixel value to the date at which a\nchange was detected. FORMA alerts only appear in areas where the probability\nof tree cover loss is greater than or equal to 50%. FORMA data has 500-meter\nspatial resolution and twice-monthly update interval. FORMA alerts start in\nJanuary 2006.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-01T00:00:00Z","2015-06-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/FORMA/FORMA_500m","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/FORMA/FORMA_FORMA_500m","rel":"related","title":"Run the example for FORMA/FORMA_500m in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/FORMA/FORMA_FORMA_500m_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/FORMA_FORMA_500m#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/WRI/WRI_GFW_FORMA_alerts.json","rel":"latest-version","title":"WRI/GFW/FORMA/alerts","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/WRI/WRI_GFW_FORMA_alerts.json","rel":"successor-version","title":"WRI/GFW/FORMA/alerts","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/FORMA/FORMA_500m/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/MARINE/OC_GLO_BGC/REFLECTANCE_MULTI_4KM","title":"Global Ocean Colour: Bio-Geo-Chemical, L4, from Satellite Observations, Reflectance, Multi-Sensor 4KM","description":"The Global Ocean Colour (Copernicus-GlobColour) dataset is a Bio-Geo-Chemical\n(BGC) product developed by ACRI-ST. Derived from multiple satellite sources\nlike SeaWiFS, MODIS, and OLCI. It provides a comprehensive range of\noceanographic variables, including: Chlorophyll (CHL),\nPhytoplankton Functional types and sizes (PFT), Primary Production (PP),\nSuspended Matter (SPM), Secchi Transparency Depth (ZSD),\nDiffuse Attenuation (KD490), Particulate Backscattering (BBP),\nAbsorption Coefficient (CDM), Reflectance (RRS) and more.\n\nThis dataset provides a global record of marine remote sensing reflectance.\nThis L4 product integrates data from multiple satellite sensors to provide a\nconsistent and high-quality dataset with a 4 km spatial resolution. The marine\nreflectance data, which measures the light leaving the ocean surface at\nvarious wavelengths, is a fundamental ocean color parameter used to derive\nother biogeochemical properties like chlorophyll-a concentration and total\nsuspended matter.\n\nDocumentation:\n\n * [User's Guide](https://documentation.marine.copernicus.eu/PUM/CMEMS-OC-PUM.pdf)\n\n * [Quality Information Document](https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-101to104-111-113-116-118.pdf)\n\n * [Copernicus Marine Datastore](https://data.marine.copernicus.eu/products)\n\nNOTE: The provider may remove Copernicus Marine data (especially Near\n Real-Time, NRT, observations) from their repository after some time,\n possibly aggregating that data into different (e.g., monthly) products.\n However, the corresponding assets remain available in the Earth Engine\n collection.\n\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-03-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/REFLECTANCE_MULTI_4KM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_REFLECTANCE_MULTI_4KM","rel":"related","title":"Run the example for COPERNICUS/MARINE/OC_GLO_BGC/REFLECTANCE_MULTI_4KM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_REFLECTANCE_MULTI_4KM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_MARINE_OC_GLO_BGC_REFLECTANCE_MULTI_4KM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/REFLECTANCE_MULTI_4KM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/REFLECTANCE_MULTI_4KM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/MARINE/OC_GLO_BGC/TRANSPARENCY_MULTI_4KM","title":"Global Ocean Colour: Bio-Geo-Chemical, L4, from Satellite Observations, Transparency, Multi-Sensor, 4KM","description":"The Global Ocean Colour (Copernicus-GlobColour) dataset is a Bio-Geo-Chemical\n(BGC) product developed by ACRI-ST. Derived from multiple satellite sources\nlike SeaWiFS, MODIS, and OLCI. It provides a comprehensive range of\noceanographic variables, including: Chlorophyll (CHL),\nPhytoplankton Functional types and sizes (PFT), Primary Production (PP),\nSuspended Matter (SPM), Secchi Transparency Depth (ZSD),\nDiffuse Attenuation (KD490), Particulate Backscattering (BBP),\nAbsorption Coefficient (CDM), Reflectance (RRS) and more.\n\nThis dataset is a global product from the Copernicus Marine Service that\nprovides key information on ocean transparency. It's a gap-free multi-sensor\ndataset that includes two primary layers: KD490, which measures light\nabsorption, and ZSD, a traditional measure of water clarity.\nThis comprehensive, daily product is vital for monitoring marine ecosystems,\nwater quality, and light availability.\n\nDocumentation:\n\n * [User's Guide](https://documentation.marine.copernicus.eu/PUM/CMEMS-OC-PUM.pdf)\n\n * [Quality Information Document](https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-101to104-111-113-116-118.pdf)\n\n * [Copernicus Marine Datastore](https://data.marine.copernicus.eu/products)\n\nNOTE: The provider may remove Copernicus Marine data (especially Near\n Real-Time, NRT, observations) from their repository after some time,\n possibly aggregating that data into different (e.g., monthly) products.\n However, the corresponding assets remain available in the Earth Engine\n collection.\n\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-02-10T00:00:00Z","2026-11-07T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/TRANSPARENCY_MULTI_4KM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_TRANSPARENCY_MULTI_4KM","rel":"related","title":"Run the example for COPERNICUS/MARINE/OC_GLO_BGC/TRANSPARENCY_MULTI_4KM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_TRANSPARENCY_MULTI_4KM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_MARINE_OC_GLO_BGC_TRANSPARENCY_MULTI_4KM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/TRANSPARENCY_MULTI_4KM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/TRANSPARENCY_MULTI_4KM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Finland/SMK/V/50cm","title":"Finland RGB NLS orthophotos 50 cm by SMK","description":"NLS Orthophotos are an aerial photo dataset covering the whole of Finland.\nThis data is provided by SMK([The Energy Agency](https://energiavirasto.fi/etusivu),\nformerly abbreviated SMK). An orthophoto is a combination of several\nindividual aerial photos. The aerial photo data set in orthophoto format is\navailable as the most recent data set consisting of the most recent aerial\nphotos available. The most recent data is usually 1-3 years old. NLS\nOrthophotos are updated every 3 years (in Northern Lapland 12 years).\n\n(In Dataset id, V stands for \"v&auml;ri\", which means \"color\" as opposed to\n\"false color\")\nFor more information, please see the\n[NLS orthophotos documentation](https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/product-descriptions/orthophotos)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[18,59,29.2,69.4]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2023-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Finland/SMK/V/50cm","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Finland/Finland_SMK_V_50cm","rel":"related","title":"Run the example for Finland/SMK/V/50cm in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Finland/Finland_SMK_V_50cm_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Finland_SMK_V_50cm#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Finland/SMK/V/50cm/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Finland/SMK/V/50cm/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/GAP/AK/2001","title":"USGS GAP Alaska 2001","description":"The GAP/LANDFIRE National Terrestrial Ecosystems data represents a detailed\nvegetation and land cover classification for the Conterminous U.S., Alaska,\nHawaii, and Puerto Rico.GAP/LF 2011 Ecosystems for the Conterminous U.S. is\nan update of the National Gap Analysis Program Land Cover Data - Version 2.2.\nAlaska ecosystems have been updated by LANDFIRE to 2012 conditions (LANDFIRE 2012).\nHawaii and Puerto Rico data represent the 2001 time-frame (Gon et al. 2006, Gould et al. 2008).\nThe classification scheme used for the Alaska and the lower 48 states is based\non NatureServe's Ecological System Classification (Comer et al. 2003), while\nPuerto Rico and Hawaii's map legend are based on island specific classification\nsystems (Gon et al. 2006, Gould et al. 2008).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,49.09,180,71.84]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2002-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/GAP/AK/2001","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_GAP_AK_2001","rel":"related","title":"Run the example for USGS/GAP/AK/2001 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_GAP_AK_2001_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GAP_AK_2001#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/GAP/AK/2001/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/GAP/CONUS/2011","title":"USGS GAP CONUS 2011","description":"The GAP/LANDFIRE National Terrestrial Ecosystems data represents a detailed\nvegetation and land cover classification for the Conterminous U.S., Alaska,\nHawaii, and Puerto Rico.GAP/LF 2011 Ecosystems for the Conterminous U.S. is\nan update of the National Gap Analysis Program Land Cover Data - Version 2.2.\nAlaska ecosystems have been updated by LANDFIRE to 2012 conditions (LANDFIRE 2012).\nHawaii and Puerto Rico data represent the 2001 time-frame (Gon et al. 2006, Gould et al. 2008).\nThe classification scheme used for the Alaska and the lower 48 states is based\non NatureServe's Ecological System Classification (Comer et al. 2003), while\nPuerto Rico and Hawaii's map legend are based on island specific classification\nsystems (Gon et al. 2006, Gould et al. 2008).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-127.98,22.79,-65.27,52.57]]},"temporal":{"interval":[["2011-01-01T00:00:00Z","2012-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/GAP/CONUS/2011","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_GAP_CONUS_2011","rel":"related","title":"Run the example for USGS/GAP/CONUS/2011 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_GAP_CONUS_2011_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GAP_CONUS_2011#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/GAP/CONUS/2011/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/GAP/HI/2001","title":"USGS GAP Hawaii 2001","description":"The GAP/LANDFIRE National Terrestrial Ecosystems data represents a detailed\nvegetation and land cover classification for the Conterminous U.S., Alaska,\nHawaii, and Puerto Rico.GAP/LF 2011 Ecosystems for the Conterminous U.S. is\nan update of the National Gap Analysis Program Land Cover Data - Version 2.2.\nAlaska ecosystems have been updated by LANDFIRE to 2012 conditions (LANDFIRE 2012).\nHawaii and Puerto Rico data represent the 2001 time-frame (Gon et al. 2006, Gould et al. 2008).\nThe classification scheme used for the Alaska and the lower 48 states is based\non NatureServe's Ecological System Classification (Comer et al. 2003), while\nPuerto Rico and Hawaii's map legend are based on island specific classification\nsystems (Gon et al. 2006, Gould et al. 2008).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-160.2665484663998,18.851697692492277,-154.669588436752,22.295218441899475]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2002-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/GAP/HI/2001","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_GAP_HI_2001","rel":"related","title":"Run the example for USGS/GAP/HI/2001 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_GAP_HI_2001_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GAP_HI_2001#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/GAP/HI/2001/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/GAP/PR/2001","title":"USGS GAP Puerto Rico 2001","description":"The GAP/LANDFIRE National Terrestrial Ecosystems data represents a detailed\nvegetation and land cover classification for the Conterminous U.S., Alaska,\nHawaii, and Puerto Rico.GAP/LF 2011 Ecosystems for the Conterminous U.S. is\nan update of the National Gap Analysis Program Land Cover Data - Version 2.2.\nAlaska ecosystems have been updated by LANDFIRE to 2012 conditions (LANDFIRE 2012).\nHawaii and Puerto Rico data represent the 2001 time-frame (Gon et al. 2006, Gould et al. 2008).\nThe classification scheme used for the Alaska and the lower 48 states is based\non NatureServe's Ecological System Classification (Comer et al. 2003), while\nPuerto Rico and Hawaii's map legend are based on island specific classification\nsystems (Gon et al. 2006, Gould et al. 2008).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-67.96,17.87,-65.22,18.53]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2002-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/GAP/PR/2001","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_GAP_PR_2001","rel":"related","title":"Run the example for USGS/GAP/PR/2001 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_GAP_PR_2001_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GAP_PR_2001#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/GAP/PR/2001/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LARSE/GEDI/GEDI02_A_002_MONTHLY","title":"GEDI L2A Raster Canopy Top Height (Version 2)","description":"GEDI's Level 2A Geolocated Elevation and Height Metrics Product (GEDI02_A)\nis primarily composed of 100 Relative Height (RH) metrics, which\ncollectively describe the waveform collected by GEDI.\n\nThe original GEDI02_A product is a table of point with a spatial resolution\n(average footprint) of 25 meters.\nThe dataset LARSE/GEDI/GEDI02_A_002_MONTHLY is a raster version\nof the original GEDI02_A product. The raster images are organized as monthly\ncomposites of individual orbits in the corresponding month. Only root-level\nRH values and their associated quality flags and metadata are preserved\nas raster bands. Each GEDI02_A_002 raster has 136 bands.\n\nSee [User Guide](https://lpdaac.usgs.gov/documents/986/GEDI02_UserGuide_V2.pdf)\nfor more information.\n\nThe Global Ecosystem Dynamics Investigation [GEDI](https://gedi.umd.edu/)\nmission aims to characterize ecosystem structure and dynamics to enable\nradically improved quantification and understanding of the Earth's carbon cycle\nand biodiversity. The GEDI instrument, attached to the International Space\nStation (ISS), collects data globally between 51.6&deg; N and 51.6&deg; S\nlatitudes at the highest resolution and densest sampling of the\n3-dimensional structure of the Earth. The GEDI instrument consists of three\nlasers producing a total of eight beam ground transects, which instantaneously\nsample eight ~25 m footprints spaced approximately every 60 m along-track.\n\nProduct                 | Description\n:---------------------  | :----------------------------------------------\nL2A Vector         | [LARSE/GEDI/GEDI02_A_002](LARSE_GEDI_GEDI02_A_002)\nL2A Monthly raster | [LARSE/GEDI/GEDI02_A_002_MONTHLY](LARSE_GEDI_GEDI02_A_002_MONTHLY)\nL2A table index    | [LARSE/GEDI/GEDI02_A_002_INDEX](LARSE_GEDI_GEDI02_A_002_INDEX)\nL2B Vector         | [LARSE/GEDI/GEDI02_B_002](LARSE_GEDI_GEDI02_B_002)\nL2B Monthly raster | [LARSE/GEDI/GEDI02_B_002_MONTHLY](LARSE_GEDI_GEDI02_B_002_MONTHLY)\nL2B table index    | [LARSE/GEDI/GEDI02_B_002_INDEX](LARSE_GEDI_GEDI02_B_002_INDEX)\nL4A Biomass Vector | [LARSE/GEDI/GEDI04_A_002](LARSE_GEDI_GEDI04_A_002)\nL4A Monthly raster | [LARSE/GEDI/GEDI04_A_002_MONTHLY](LARSE_GEDI_GEDI04_A_002_MONTHLY)\nL4A table index    | [LARSE/GEDI/GEDI04_A_002_INDEX](LARSE_GEDI_GEDI04_A_002_INDEX)\nL4B Biomass        | [LARSE/GEDI/GEDI04_B_002](LARSE_GEDI_GEDI04_B_002)\nL4D Imputed Waveforms | [LARSE/GEDI/GEDI04_D_002](LARSE_GEDI_GEDI04_D_002)","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-51.6,180,51.6]]},"temporal":{"interval":[["2019-03-25T00:00:00Z","2025-07-01T08:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI02_A_002_MONTHLY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LARSE/LARSE_GEDI_GEDI02_A_002_MONTHLY","rel":"related","title":"Run the example for LARSE/GEDI/GEDI02_A_002_MONTHLY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LARSE/LARSE_GEDI_GEDI02_A_002_MONTHLY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LARSE_GEDI_GEDI02_A_002_MONTHLY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI02_A_002_MONTHLY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI02_A_002_MONTHLY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GEOS-CF/v1/fcst/htf","title":"GEOS-CF fcst htf v1: Goddard Earth Observing System Composition Forecast","description":"This dataset contains meteorological forecast (fcst) of high-temporal\nfrequency data (htf). Use the 'creation_time' and 'forecast_time' properties\nto select data of interest.\nThe Goddard Earth Observing System Composition Forecast (GEOS-CF) system is\na global constituent prediction system from NASA's [Global Modeling and Assimilation Office (GMAO)](https://gmao.gsfc.nasa.gov/).\n\nGEOS-CF offers a new tool for atmospheric chemistry research, with the goal\nto supplement NASA's broad range of space-based and in-situ observations.\nGEOS-CF expands on the GEOS weather and aerosol modeling system by\nintroducing the [GEOS-Chem](http://wiki.seas.harvard.edu/geos-chem/)\nchemistry module to provide hindcasts and 5-days forecasts of atmospheric\nconstituents including ozone (O<sub>3</sub>), carbon monoxide (CO), nitrogen dioxide\n(NO<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), and fine particulate matter (PM<sub>2.5</sub>). The\nchemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem\nmodel and readily benefits from the innovations provided by the GEOS-Chem\ncommunity. Model outputs are provided on a 0.25&deg; latitude-longitude grid.\nPlease refer to the [GEOS-CF Product Page](https://gmao.gsfc.nasa.gov/gmao-products/geos-cf/) for more details.\n\n[Evaluation of GEOS-CF](https://doi.org/10.1029/2020MS002413) against satellite, ozonesonde, and surface\nobservations for years 2018&ndash;2019 shows realistic simulated\nconcentrations of O<sub>3</sub>, NO<sub>2</sub>, and CO, with normalized mean biases of &minus;0.1\nto 0.3, normalized root mean square errors between 0.1&ndash;0.4, and\ncorrelations between 0.3&ndash;0.8. Comparisons against surface observations\nhighlight the successful representation of air pollutants in many regions of\nthe world and during all seasons, yet also highlight current limitations,\nsuch as a global high bias in SO<sub>2</sub> and an overprediction of summertime O<sub>3</sub>\nover the Southeast United States.\nGEOS-CF v1 generally overestimates aerosols by 20%&ndash;50% due to known\nissues in GEOS-Chem v12.0.1 that have been addressed in later versions. The\n5-days forecasts have skill scores comparable to the 1-day hindcast. Model\nskills can be improved significantly by applying a bias-correction to the\nsurface model output using a machine-learning approach.\n\n**Please Note:** A new version of GEOS-CF, v2, was released in January 2026. The updates \nto the GEOS-CF system include updates to the GEOS atmospheric general \ncirculation model and the GEOS-Chem chemical model, replaying meteorology \nto [GEOS IT](https://gmao.gsfc.nasa.gov/gmao-products/geos-it/), direct assimilation of stratospheric profiles and column ozone, \nand updates to the anthropogenic emission source. Many of the same products \nare available, and some new products have been added, but other products have \nbeen discontinued. Furthermore, GEOS-CF v2 data will not be back-processed to\ncover the same period as GEOS-CF v1. For this reason, we consider GEOS-CF v2 \nto be a continuation and supplement to the GEOS-CF v data record, but not a \nreplacement for GEOS-CF v1 data. Please see the [GEOS-CF system description](https://gmao.gsfc.nasa.gov/gmao-products/geos-cf/system-description_geos-cf/) \nfor a summary of the updates, and consider the \n[preliminary GEOS-CF v2 evaluation results](https://gmao.gsfc.nasa.gov/science-snapshots/improved-atmospheric-composition-forecasting-in-geos-cf-version-2/) \nwhen deciding which version to use in your analysis.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-10-01T00:00:00Z","2026-01-02T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v1/fcst/htf","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GEOS-CF_v1_fcst_htf","rel":"related","title":"Run the example for NASA/GEOS-CF/v1/fcst/htf in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GEOS-CF_v1_fcst_htf_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_htf#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v1/fcst/htf/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v1/fcst/htf/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GEOS-CF/v1/rpl/htf","title":"GEOS-CF rpl htf v1: Goddard Earth Observing System Composition Forecast","description":"This dataset contains meteorological replay (rpl) of high-temporal\nfrequency data (htf).\nThe Goddard Earth Observing System Composition Forecast (GEOS-CF) system is\na global constituent prediction system from NASA's [Global Modeling and Assimilation Office (GMAO)](https://gmao.gsfc.nasa.gov/).\n\nGEOS-CF offers a new tool for atmospheric chemistry research, with the goal\nto supplement NASA's broad range of space-based and in-situ observations.\nGEOS-CF expands on the GEOS weather and aerosol modeling system by\nintroducing the [GEOS-Chem](http://wiki.seas.harvard.edu/geos-chem/)\nchemistry module to provide hindcasts and 5-days forecasts of atmospheric\nconstituents including ozone (O<sub>3</sub>), carbon monoxide (CO), nitrogen dioxide\n(NO<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), and fine particulate matter (PM<sub>2.5</sub>). The\nchemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem\nmodel and readily benefits from the innovations provided by the GEOS-Chem\ncommunity. Model outputs are provided on a 0.25&deg; latitude-longitude grid.\nPlease refer to the [GEOS-CF Product Page](https://gmao.gsfc.nasa.gov/gmao-products/geos-cf/) for more details.\n\n[Evaluation of GEOS-CF](https://doi.org/10.1029/2020MS002413) against satellite, ozonesonde, and surface\nobservations for years 2018&ndash;2019 shows realistic simulated\nconcentrations of O<sub>3</sub>, NO<sub>2</sub>, and CO, with normalized mean biases of &minus;0.1\nto 0.3, normalized root mean square errors between 0.1&ndash;0.4, and\ncorrelations between 0.3&ndash;0.8. Comparisons against surface observations\nhighlight the successful representation of air pollutants in many regions of\nthe world and during all seasons, yet also highlight current limitations,\nsuch as a global high bias in SO<sub>2</sub> and an overprediction of summertime O<sub>3</sub>\nover the Southeast United States.\nGEOS-CF v1 generally overestimates aerosols by 20%&ndash;50% due to known\nissues in GEOS-Chem v12.0.1 that have been addressed in later versions. The\n5-days forecasts have skill scores comparable to the 1-day hindcast. Model\nskills can be improved significantly by applying a bias-correction to the\nsurface model output using a machine-learning approach.\n\n**Please Note:** A new version of GEOS-CF, v2, was released in January 2026. The updates \nto the GEOS-CF system include updates to the GEOS atmospheric general \ncirculation model and the GEOS-Chem chemical model, replaying meteorology \nto [GEOS IT](https://gmao.gsfc.nasa.gov/gmao-products/geos-it/), direct assimilation of stratospheric profiles and column ozone, \nand updates to the anthropogenic emission source. Many of the same products \nare available, and some new products have been added, but other products have \nbeen discontinued. Furthermore, GEOS-CF v2 data will not be back-processed to\ncover the same period as GEOS-CF v1. For this reason, we consider GEOS-CF v2 \nto be a continuation and supplement to the GEOS-CF v data record, but not a \nreplacement for GEOS-CF v1 data. Please see the [GEOS-CF system description](https://gmao.gsfc.nasa.gov/gmao-products/geos-cf/system-description_geos-cf/) \nfor a summary of the updates, and consider the \n[preliminary GEOS-CF v2 evaluation results](https://gmao.gsfc.nasa.gov/science-snapshots/improved-atmospheric-composition-forecasting-in-geos-cf-version-2/) \nwhen deciding which version to use in your analysis.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2026-01-02T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v1/rpl/htf","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GEOS-CF_v1_rpl_htf","rel":"related","title":"Run the example for NASA/GEOS-CF/v1/rpl/htf in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GEOS-CF_v1_rpl_htf_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_htf#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v1/rpl/htf/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v1/rpl/htf/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GEOS-CF/v1/rpl/tavg1hr","title":"GEOS-CF rpl tavg1hr v1: Goddard Earth Observing System Composition Forecast","description":"This dataset contains meteorological replay (rpl) of time-average one hour\ndata (tavg1hr). It is built by merging the original GEOS-CF collections\nchm_tavg_1hr_g1440x721_v1, met_tavg_1hr_g1440x721_x1, and\nxgc_tavg_1hr_g1440x721_x1.\nThe Goddard Earth Observing System Composition Forecast (GEOS-CF) system is\na global constituent prediction system from NASA's [Global Modeling and Assimilation Office (GMAO)](https://gmao.gsfc.nasa.gov/).\n\nGEOS-CF offers a new tool for atmospheric chemistry research, with the goal\nto supplement NASA's broad range of space-based and in-situ observations.\nGEOS-CF expands on the GEOS weather and aerosol modeling system by\nintroducing the [GEOS-Chem](http://wiki.seas.harvard.edu/geos-chem/)\nchemistry module to provide hindcasts and 5-days forecasts of atmospheric\nconstituents including ozone (O<sub>3</sub>), carbon monoxide (CO), nitrogen dioxide\n(NO<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), and fine particulate matter (PM<sub>2.5</sub>). The\nchemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem\nmodel and readily benefits from the innovations provided by the GEOS-Chem\ncommunity. Model outputs are provided on a 0.25&deg; latitude-longitude grid.\nPlease refer to the [GEOS-CF Product Page](https://gmao.gsfc.nasa.gov/gmao-products/geos-cf/) for more details.\n\n[Evaluation of GEOS-CF](https://doi.org/10.1029/2020MS002413) against satellite, ozonesonde, and surface\nobservations for years 2018&ndash;2019 shows realistic simulated\nconcentrations of O<sub>3</sub>, NO<sub>2</sub>, and CO, with normalized mean biases of &minus;0.1\nto 0.3, normalized root mean square errors between 0.1&ndash;0.4, and\ncorrelations between 0.3&ndash;0.8. Comparisons against surface observations\nhighlight the successful representation of air pollutants in many regions of\nthe world and during all seasons, yet also highlight current limitations,\nsuch as a global high bias in SO<sub>2</sub> and an overprediction of summertime O<sub>3</sub>\nover the Southeast United States.\nGEOS-CF v1 generally overestimates aerosols by 20%&ndash;50% due to known\nissues in GEOS-Chem v12.0.1 that have been addressed in later versions. The\n5-days forecasts have skill scores comparable to the 1-day hindcast. Model\nskills can be improved significantly by applying a bias-correction to the\nsurface model output using a machine-learning approach.\n\n**Please Note:** A new version of GEOS-CF, v2, was released in January 2026. The updates \nto the GEOS-CF system include updates to the GEOS atmospheric general \ncirculation model and the GEOS-Chem chemical model, replaying meteorology \nto [GEOS IT](https://gmao.gsfc.nasa.gov/gmao-products/geos-it/), direct assimilation of stratospheric profiles and column ozone, \nand updates to the anthropogenic emission source. Many of the same products \nare available, and some new products have been added, but other products have \nbeen discontinued. Furthermore, GEOS-CF v2 data will not be back-processed to\ncover the same period as GEOS-CF v1. For this reason, we consider GEOS-CF v2 \nto be a continuation and supplement to the GEOS-CF v data record, but not a \nreplacement for GEOS-CF v1 data. Please see the [GEOS-CF system description](https://gmao.gsfc.nasa.gov/gmao-products/geos-cf/system-description_geos-cf/) \nfor a summary of the updates, and consider the \n[preliminary GEOS-CF v2 evaluation results](https://gmao.gsfc.nasa.gov/science-snapshots/improved-atmospheric-composition-forecasting-in-geos-cf-version-2/) \nwhen deciding which version to use in your analysis.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2026-01-02T11:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v1/rpl/tavg1hr","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GEOS-CF_v1_rpl_tavg1hr","rel":"related","title":"Run the example for NASA/GEOS-CF/v1/rpl/tavg1hr in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GEOS-CF_v1_rpl_tavg1hr_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_tavg1hr#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v1/rpl/tavg1hr/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v1/rpl/tavg1hr/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/GFSAD1000_V0","title":"GFSAD1000: Cropland Extent 1km Crop Dominance, Global Food-Support Analysis Data [deprecated]","description":"The GFSAD is a NASA-funded project to provide high-resolution global\ncropland data and their water use that contributes towards global food\nsecurity in the twenty-first century. The GFSAD products are derived\nthrough multi-sensor remote sensing data (e.g., Landsat, MODIS, AVHRR),\nsecondary data, and field-plot data and aims at documenting cropland\ndynamics.\n\nAt a nominal 1km scale, V0.0 provides the spatial distribution of the\nfive major global cropland types (wheat, rice, corn, barley, and soybeans)\nwhich occupy 60% of all global cropland areas. The map is produced by\noverlaying these crops over the remote sensing derived global irrigated and\nrainfed cropland area map of the International Water Management Institute.\nV0.0 an 8-class product that provides information on global: cropland\nextent, crop dominance, irrigated versus rainfed cropping, and cropping\nintensity (single, double, triple, and continuous crops). The GFSAD1000\nnominal 2010 product was created with data from 2007 to 2012.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2011-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/GFSAD1000_V0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_GFSAD1000_V0","rel":"related","title":"Run the example for USGS/GFSAD1000_V0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_GFSAD1000_V0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GFSAD1000_V0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_GFSAD1000_V1.json","rel":"latest-version","title":"USGS/GFSAD1000_V1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_GFSAD1000_V1.json","rel":"successor-version","title":"USGS/GFSAD1000_V1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/GFSAD1000_V0/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/GFSAD1000_V1","title":"GFSAD1000: Cropland Extent 1km Multi-Study Crop Mask, Global Food-Support Analysis Data","description":"The GFSAD is a NASA-funded project to provide high-resolution global\ncropland data and their water use that contributes towards global food\nsecurity in the twenty-first century. The GFSAD products are derived\nthrough multi-sensor remote sensing data (e.g., Landsat, MODIS, AVHRR),\nsecondary data, and field-plot data and aims at documenting cropland\ndynamics.\n\nAt a nominal 1km scale, V0.1 provides the spatial distribution of a\ndisaggregated five-class global cropland extent map derived from four\nmajor studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010),\nYu et al. (2013), and Friedl et al. (2010). V1.0 is a 5-class product that\nprovides information on global cropland extent and irrigated versus rainfed\ncropping. There is no crop type or crop type dominance information. Cropping\nintensity (single, double, triple, and continuous crops) can be obtained for\nevery pixel using time-series remote sensing data. The GFSAD1000 nominal\n2010 product was created with data from 2007 to 2012.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/GFSAD1000_V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_GFSAD1000_V1","rel":"related","title":"Run the example for USGS/GFSAD1000_V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_GFSAD1000_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GFSAD1000_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_GFSAD1000_V1.json","rel":"latest-version","title":"USGS/GFSAD1000_V1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_GFSAD1000_V0.json","rel":"predecessor-version","title":"USGS/GFSAD1000_V0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/GFSAD1000_V1/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"GFW/GFF/V1/fishing_hours","title":"GFW (Global Fishing Watch) Daily Fishing Hours","description":"Fishing effort, measured in hours of inferred fishing activity. Each asset\nis the effort for a given flag state and day, with one band for the\nfishing activity of each gear type.\n\nSee [sample Earth Engine\nscripts](https://globalfishingwatch.org/research/global-footprint-of-fisheries/).\nAlso see [the main GFW site](https://GlobalFishingWatch.org) for program\ninformation, fully interactive visualization maps, and impacts.\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/GFW/GFF/V1/fishing_hours","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/GFW/GFW_GFF_V1_fishing_hours","rel":"related","title":"Run the example for GFW/GFF/V1/fishing_hours in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/GFW/GFW_GFF_V1_fishing_hours_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/GFW_GFF_V1_fishing_hours#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/GFW/GFF/V1/fishing_hours/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/GFW/GFF/V1/fishing_hours/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"GFW/GFF/V1/vessel_hours","title":"GFW (Global Fishing Watch) Daily Vessel Hours","description":"Fishing vessel presence, measured in hours per square km. Each asset\nis the vessel presence for a given flag state and day, with one band for the\npresence of each gear type.\n\nSee [sample Earth Engine\nscripts](https://globalfishingwatch.org/research/global-footprint-of-fisheries/).\nAlso see [the main GFW site](https://GlobalFishingWatch.org) for program\ninformation, fully interactive visualization maps, and impacts.\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/GFW/GFF/V1/vessel_hours","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/GFW/GFW_GFF_V1_vessel_hours","rel":"related","title":"Run the example for GFW/GFF/V1/vessel_hours in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/GFW/GFW_GFF_V1_vessel_hours_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/GFW_GFF_V1_vessel_hours#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/GFW/GFF/V1/vessel_hours/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/GFW/GFF/V1/vessel_hours/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GIMMS/3GV0","title":"GIMMS NDVI From AVHRR Sensors (3rd Generation)","description":"GIMMS NDVI is generated from several NOAA's AVHRR sensors for a global\n1/12-degree lat/lon grid. The latest version of the GIMMS NDVI dataset\nis named NDVI3g (third generation GIMMS NDVI from AVHRR sensors).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-07-01T00:00:00Z","2013-12-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GIMMS/3GV0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GIMMS_3GV0","rel":"related","title":"Run the example for NASA/GIMMS/3GV0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GIMMS_3GV0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GIMMS_3GV0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GIMMS/3GV0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GIMMS/3GV0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"GLCF/GLS_TCC","title":"GLCF: Landsat Tree Cover Continuous Fields [deprecated]","description":"The Landsat Vegetation Continuous Fields (VCF) tree cover layers contain\nestimates of the percentage of horizontal ground in each 30-m pixel covered\nby woody vegetation greater than 5 meters in height. The data represent\nthree nominal epochs, 2000, 2005 and 2010, compiled from the NASA/USGS\nGlobal Land Survey (GLS) collection of Landsat data. The product is derived\nfrom all seven bands of Landsat-5 Thematic Mapper (TM) and/or Landsat-7\nEnhanced Thematic Mapper Plus (ETM+), depending on the GLS image selection.\n\nTree cover, the proportional, vertically projected area of vegetation\n(including leaves, stems, branches, etc.) of woody plants above a given\nheight, affects terrestrial energy and water exchanges, photosynthesis and\ntranspiration, net primary production, and carbon and nutrient fluxes. Tree\ncover also affects habitat quality and movements of wildlife, residential\nproperty value for humans, and other ecosystem services. The continuous\nclassification scheme of the VCF product enables better depiction of land\ncover gradients than traditional discrete classification schemes.\nImportantly for detection and monitoring of forest changes (e.g.,\ndeforestation and degradation), tree cover provides a measurable attribute\nupon which to define forest cover and its changes. Changes in tree cover\nover time can be used to monitor and retrieve site-specific histories of\nforest change.\n\nThe dataset has been produced for three year epochs: 2000, 2005, 2010, with\nan image in the collection for each available WRS2 path/row.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/GLCF/GLS_TCC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/GLCF/GLCF_GLS_TCC","rel":"related","title":"Run the example for GLCF/GLS_TCC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/GLCF/GLCF_GLS_TCC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/GLCF_GLS_TCC#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_MEASURES_GFCC_TC_v3.json","rel":"latest-version","title":"NASA/MEASURES/GFCC/TC/v3","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_MEASURES_GFCC_TC_v3.json","rel":"successor-version","title":"NASA/MEASURES/GFCC/TC/v3","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/GLCF/GLS_TCC/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/GLCF/GLS_TCC/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"GLCF/GLS_WATER","title":"GLCF: Landsat Global Inland Water","description":"The Global Inland Water dataset shows inland surface water bodies, including\nfresh and saline lakes, rivers, and reservoirs.\n\nFrom the GLS 2000 epoch, 3,650,723 km2 of inland water were identified,\naround three quarters of which were in North America and Asia. Boreal\nforests and tundra hold the largest portion of inland water, about 40% of\nthe global total. The data exhibits strong linear correlation with both the\nMODIS dataset as well as 30-m resolution datasets over the United States\nand Canada. Residual errors were due primarily to the seasonality of water\ncover, snow and ice, and residual clouds.\n\nThe dataset contains one or more image for each available Landsat WRS2\npath/row.\n\nDocumentation:\n\n* [User's guide](https://lpdaac.usgs.gov/documents/1371/GFCC_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1370/GFCC_ATBD.pdf)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2000-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/GLCF/GLS_WATER","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/GLCF/GLCF_GLS_WATER","rel":"related","title":"Run the example for GLCF/GLS_WATER in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/GLCF/GLCF_GLS_WATER_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/GLCF_GLS_WATER#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/GLCF/GLS_WATER/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/GLCF/GLS_WATER/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GLDAS/V021/NOAH/G025/T3H","title":"GLDAS-2.1: Global Land Data Assimilation System","description":"NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three\ncomponents: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely\nwith the Princeton meteorological forcing input data and provides a temporally\nconsistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination\nof model and observation data from 2000 to present. GLDAS-2.2 product suites use\ndata assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are\n\"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as\nDA observation source, variable, and scheme, vary for different GLDAS-2.2\nproducts.GLDAS-2.1 is one of two components of the GLDAS Version 2 (GLDAS-2) dataset,\nthe second being GLDAS-2.0. GLDAS-2.1 is analogous to GLDAS-1 product stream,\nwith upgraded models forced by a combination of [GDAS, disaggregated GPCP,\nand AGRMET radiation data sets](https://ldas.gsfc.nasa.gov/gldas/GLDASforcing.php).\n\nThe GLDAS-2.1 simulation started on January 1, 2000 using the\nconditions from the GLDAS-2.0 simulation. This simulation was\nforced with National Oceanic and Atmospheric Administration\n(NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis\nfields (Derber et al., 1991), the disaggregated Global Precipitation\nClimatology Project (GPCP) precipitation fields (Adler et al., 2003),\nand the Air Force Weather Agency's AGRicultural METeorological\nmodeling system (AGRMET) radiation fields which became available\nfor March 1, 2001 onwards.\n\nDocumentation:\n\n* [Readme](https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/GLDAS_NOAH025_3H.2.1/doc/README_GLDAS2.pdf)\n\n* [How-to](https://disc.gsfc.nasa.gov/information/howto?tags=hydrology)\n\n* [GES DISC Hydrology Documentation](https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%20Documentation)\n\n* [GES DISC Data Rods Documentation](https://disc.gsfc.nasa.gov/information/tools?title=Hydrology%20Data%20Rods)\n\nProvider's Note: the names with extension _tavg are variables\naveraged over the past 3-hours, the names with extension '_acc' are\nvariables accumulated over the past 3-hours, the names with extension\n'_inst' are instantaneous variables, and the names with '_f' are\nforcing variables.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T03:00:00Z","2026-10-15T21:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GLDAS/V021/NOAH/G025/T3H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GLDAS_V021_NOAH_G025_T3H","rel":"related","title":"Run the example for NASA/GLDAS/V021/NOAH/G025/T3H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GLDAS_V021_NOAH_G025_T3H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V021_NOAH_G025_T3H#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GLDAS/V021/NOAH/G025/T3H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GLDAS/V021/NOAH/G025/T3H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GLDAS/V022/CLSM/G025/DA1D","title":"GLDAS-2.2: Global Land Data Assimilation System","description":"NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three\ncomponents: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely\nwith the Princeton meteorological forcing input data and provides a temporally\nconsistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination\nof model and observation data from 2000 to present. GLDAS-2.2 product suites use\ndata assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are\n\"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as\nDA observation source, variable, and scheme, vary for different GLDAS-2.2\nproducts.GLDAS-2.2 is new to the GES DISC archive and currently includes a main\nproduct from CLSM-F2.5 with Data Assimilation for the Gravity Recovery and\nClimate Experiment (GRACE-DA) from February 2003 to present. The GLDAS-2.2\ndata are available in two production streams: main and Early, only main one\nis ingested.\n\nThe GLDAS-2.2 GRACE-DA product was simulated with Catchment-F2.5 in Land\nInformation System (LIS) Version 7. The data product contains 24 land\nsurface fields from February 1, 2003 to present.\n\nThe simulation started on February 1, 2003 using the conditions from the\nGLDAS-2.0 Daily Catchment model simulation, forced with the meteorological\nanalysis fields from the operational European Centre for Medium-Range\nWeather Forecasts (ECMWF) Integrated Forecasting System. The total\nterrestrial water anomaly observation from GRACE satellite was assimilated\n(Li et al, 2019). Due to the data agreement with ECMWF, this GLDAS-2.2 daily\nproduct does not include the meteorological forcing fields.\n\nDocumentation:\n\n* [Readme](https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/GLDAS_CLSM025_DA1_D.2.2/doc/README_GLDAS2.pdf)\n\n* [How-to](https://disc.gsfc.nasa.gov/information/howto?tags=hydrology)\n\n* [GES DISC Hydrology Documentation](https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%20Documentation)\n\nProvider's Note: the names with extension _tavg are variables\naveraged over the past 3-hours, the names with extension '_acc' are\nvariables accumulated over the past 3-hours, the names with extension\n'_inst' are instantaneous variables, and the names with '_f' are\nforcing variables.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T03:00:00Z","2026-08-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GLDAS/V022/CLSM/G025/DA1D","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GLDAS_V022_CLSM_G025_DA1D","rel":"related","title":"Run the example for NASA/GLDAS/V022/CLSM/G025/DA1D in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GLDAS_V022_CLSM_G025_DA1D_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V022_CLSM_G025_DA1D#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GLDAS/V022/CLSM/G025/DA1D/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GLDAS/V022/CLSM/G025/DA1D/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GLDAS/V20/NOAH/G025/T3H","title":"Reprocessed GLDAS-2.0: Global Land Data Assimilation System","description":"NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three\ncomponents: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely\nwith the Princeton meteorological forcing input data and provides a temporally\nconsistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination\nof model and observation data from 2000 to present. GLDAS-2.2 product suites use\ndata assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are\n\"open-loop\" (i.e., no data assimilation). The choice of forcing data, as well as\nDA observation source, variable, and scheme, vary for different GLDAS-2.2\nproducts.GLDAS-2.0 is one of two components of the GLDAS Version 2 (GLDAS-2)\ndataset, the second being GLDAS-2.1. GLDAS-2.0 is reprocessed with\nthe updated Princeton Global Meteorological Forcing Dataset\n(Sheffield et al., 2006) and upgraded Land Information System\nVersion 7 (LIS-7). It covers the period 1948-2010, and will be extended\nto more recent years as corresponding forcing data become available.\n\nThe model simulation was initialized on January 1, 1948, using soil\nmoisture and other state fields from the LSM climatology for\nthat day of the year. The simulation used the common GLDAS datasets\nfor land cover (MCD12Q1: Friedl et al., 2010), land water mask\n(MOD44W: Carroll et al., 2009), soil texture (Reynolds, 1999), and\nelevation (GTOPO30). The MODIS based land surface parameters are used in\nthe current GLDAS-2.x products while the AVHRR base parameters were used\nin GLDAS-1 and previous GLDAS-2 products (prior to October 2012).\n\nDocumentation:\n\n* [Readme](https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/GLDAS_NOAH025_3H.2.0/doc/README_GLDAS2.pdf)\n\n* [How-to](https://disc.gsfc.nasa.gov/information/howto?tags=hydrology)\n\n* [GES DISC Hydrology Documentation](https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%20Documentation)\n\nProvider's Note: the names with extension _tavg are variables\naveraged over the past 3-hours, the names with extension '_acc' are\nvariables accumulated over the past 3-hours, the names with extension\n'_inst' are instantaneous variables, and the names with '_f' are\nforcing variables.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1948-01-01T03:00:00Z","2014-12-31T21:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GLDAS/V20/NOAH/G025/T3H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GLDAS_V20_NOAH_G025_T3H","rel":"related","title":"Run the example for NASA/GLDAS/V20/NOAH/G025/T3H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GLDAS_V20_NOAH_G025_T3H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V20_NOAH_G025_T3H#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GLDAS/V20/NOAH/G025/T3H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GLDAS/V20/NOAH/G025/T3H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"GLOBAL_FLOOD_DB/MODIS_EVENTS/V1","title":"Global Flood Database v1 (2000-2018)","description":"The Global Flood Database contains maps of the extent and\ntemporal distribution of 913 flood events occurring between 2000-2018. For more\ninformation, see\n[the associated journal article](https://doi.org/10.1038/s41586-021-03695-w).\n\nFlood events were collected from\nthe [Dartmouth Flood Observatory](https://floodobservatory.colorado.edu/)\nand used to collect MODIS imagery. The selected\n913 events are those that were successfully mapped (passed quality control as\nhaving significant inundation beyond permanent water) using 12,719 scenes from\nTerra and Aqua MODIS sensors. Each pixel was classified as water or non-water\nat 250-meter resolution during the full date range of each flood event and\nsubsequent data products were generated including maximum flood extent\n(\"flooded\" band) and the duration of inundation in days (\"duration\" band).\nWater and non-water classifications during a flood event include permanent\nwater (here resampling the 30-meter\n[JRC Global Surface Water dataset](https://global-surface-water.appspot.com/)\nrepresenting permanent water to 250-meter resolution), which can be\nmasked out to isolate flood water using the \"jrc_perm_water\" band.\nExtra data quality\nbands were added representing cloud conditions during the flood event (e.g.,\n\"clear_views\" representing the number of clear days the flood was observed\nbetween its start and end dates and \"clear_perc\" representing the percentage of\nclear day observation of the total event duration in days).\n\nEach image in the ImageCollection represents the map of an individual flood.\nThe collection can be filtered\nby date, country, or Dartmouth Flood Observatory original ID.\n","license":"CC-BY-NC-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-17T00:00:00Z","2018-12-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/GLOBAL_FLOOD_DB/MODIS_EVENTS/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/GLOBAL_FLOOD_DB/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1","rel":"related","title":"Run the example for GLOBAL_FLOOD_DB/MODIS_EVENTS/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/GLOBAL_FLOOD_DB/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-NC-4.0.html","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gfd_v3","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/GLOBAL_FLOOD_DB/MODIS_EVENTS/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/GLOBAL_FLOOD_DB/MODIS_EVENTS/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/16/FDCC","title":"GOES-16 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Fire (HSC) product contains four images: one in the form\nof a fire mask and the other three with pixel values identifying fire temperature, fire area,\nand fire radiative power.\n\nThe ABI L2+ FHS metadata mask assigns a flag to every earth-navigated pixel\nthat indicates its disposition with respect to the FHS algorithm. Operational users who have\nthe lowest tolerance for false alarms should focus on the \"processed\" and\n\"saturated\" categories (mask codes 10, 11, 30, and 31), but within these categories there\ncan still be false alarms.\n\nFormerly known as \"GOES East\"; replaced by\n[GOES-19](https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_19_FDCC)\nas of April 7, 2025.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r)\n\nNOAA provides the following scripts for suggested categories,\ncolor maps, and visualizations:\n\n - [GOES-16-17_FireDetection.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireDetection.js)\n - [GOES-16-17_FireReclassification.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireReclassification.js)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-152.11,14,-49.18,56.77]]},"temporal":{"interval":[["2017-05-24T00:00:00Z","2025-04-07T18:31:17.400000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/FDCC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_16_FDCC","rel":"related","title":"Run the example for NOAA/GOES/16/FDCC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_16_FDCC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCC#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-16/ABI-L2-FDCC/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/FDCC/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/FDCC/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/16/FDCF","title":"GOES-16 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Fire (HSC) product contains four images: one in the form\nof a fire mask and the other three with pixel values identifying fire temperature, fire area,\nand fire radiative power.\n\nThe ABI L2+ FHS metadata mask assigns a flag to every earth-navigated pixel\nthat indicates its disposition with respect to the FHS algorithm. Operational users who have\nthe lowest tolerance for false alarms should focus on the \"processed\" and\n\"saturated\" categories (mask codes 10, 11, 30, and 31), but within these categories there\ncan still be false alarms.\n\nFormerly known as \"GOES East\"; replaced by\n[GOES-19](https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_19_FDCF)\nas of April 7, 2025.\n\n[README](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\n\nNOAA provides the following scripts for suggested categories,\ncolor maps, and visualizations:\n\n - [GOES-16-17_FireDetection.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireDetection.js)\n - [GOES-16-17_FireReclassification.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireReclassification.js)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-05-24T00:00:00Z","2025-04-07T18:30:20.600000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/FDCF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_16_FDCF","rel":"related","title":"Run the example for NOAA/GOES/16/FDCF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_16_FDCF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-16/ABI-L2-FDCF/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/FDCF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/FDCF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/16/MCMIPC","title":"GOES-16 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Cloud and Moisture Imagery products are all at 2km\nresolution. Bands 1-6 are reflective. The dimensionless \"reflectance factor\" quantity is\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\n\nFormerly known as \"GOES East\"; replaced by\n[GOES-19](https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_19_MCMIPC)\nas of April 7, 2025.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r-series)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-152.11,14,-49.18,56.77]]},"temporal":{"interval":[["2017-07-10T00:00:00Z","2025-04-07T18:31:17.400000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/MCMIPC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_16_MCMIPC","rel":"related","title":"Run the example for NOAA/GOES/16/MCMIPC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_16_MCMIPC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPC#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-16/ABI-L2-MCMIPC/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/MCMIPC/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/MCMIPC/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/16/MCMIPF","title":"GOES-16 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Cloud and Moisture Imagery products are all at 2km\nresolution. Bands 1-6 are reflective. The dimensionless \"reflectance factor\" quantity is\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\n\nFormerly known as \"GOES East\"; replaced by\n[GOES-19](https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_19_MCMIPF)\nas of April 7, 2025.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r-series)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-07-10T00:00:00Z","2025-04-07T18:30:20.600000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/MCMIPF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_16_MCMIPF","rel":"related","title":"Run the example for NOAA/GOES/16/MCMIPF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_16_MCMIPF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-16/ABI-L2-MCMIPF/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/MCMIPF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/MCMIPF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/16/MCMIPM","title":"GOES-16 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Mesoscale","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Cloud and Moisture Imagery products are all at 2km\nresolution. Bands 1-6 are reflective. The dimensionless \"reflectance factor\" quantity is\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\n\nThe locations of domains 1 and 2 change over time.\n\nFormerly known as \"GOES East\"; replaced by\n[GOES-19](https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_19_MCMIPM)\nas of April 7, 2025.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r-series)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-07-10T00:00:00Z","2025-04-07T18:32:55.300000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/MCMIPM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_16_MCMIPM","rel":"related","title":"Run the example for NOAA/GOES/16/MCMIPM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_16_MCMIPM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPM#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-16/ABI-L2-MCMIPM/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/MCMIPM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/16/MCMIPM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/17/FDCC","title":"GOES-17 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Fire (HSC) product contains four images: one in the form\nof a fire mask and the other three with pixel values identifying fire temperature, fire area,\nand fire radiative power.\n\nThe ABI L2+ FHS metadata mask assigns a flag to every earth-navigated pixel\nthat indicates its disposition with respect to the FHS algorithm. Operational users who have\nthe lowest tolerance for false alarms should focus on the \"processed\" and\n\"saturated\" categories (mask codes 10, 11, 30, and 31), but within these categories there\ncan still be false alarms.\n\n[README](https://www.ncei.noaa.gov/sites/default/files/2021-08/GOES-17_ABI_L2_FireHotSpot_Beta_ReadMe.pdf)\n\nNOAA provides the following scripts for suggested categories,\ncolor maps, and visualizations:\n\n - [GOES-16-17_FireDetection.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireDetection.js)\n - [GOES-16-17_FireReclassification.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireReclassification.js)\n\nFormerly known as \"GOES West.\"  Satellite is in storage.\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,14.57,180,53.51]]},"temporal":{"interval":[["2018-08-27T00:00:00Z","2023-01-10T16:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/FDCC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_17_FDCC","rel":"related","title":"Run the example for NOAA/GOES/17/FDCC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_17_FDCC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_FDCC#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-17/ABI-L2-FDCC/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/FDCC/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/FDCC/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/17/FDCF","title":"GOES-17 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Fire (HSC) product contains four images: one in the form\nof a fire mask and the other three with pixel values identifying fire temperature, fire area,\nand fire radiative power.\n\nThe ABI L2+ FHS metadata mask assigns a flag to every earth-navigated pixel\nthat indicates its disposition with respect to the FHS algorithm. Operational users who have\nthe lowest tolerance for false alarms should focus on the \"processed\" and\n\"saturated\" categories (mask codes 10, 11, 30, and 31), but within these categories there\ncan still be false alarms.\n\n[README](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C01520)\n\nNOAA provides the following scripts for suggested categories,\ncolor maps, and visualizations:\n\n - [GOES-16-17_FireDetection.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireDetection.js)\n - [GOES-16-17_FireReclassification.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireReclassification.js)\n\nFormerly known as \"GOES West.\"  Satellite is in storage.\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-08-27T00:00:00Z","2023-01-10T16:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/FDCF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_17_FDCF","rel":"related","title":"Run the example for NOAA/GOES/17/FDCF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_17_FDCF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_FDCF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-17/ABI-L2-FDCF/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/FDCF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/FDCF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/17/MCMIPC","title":"GOES-17 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Cloud and Moisture Imagery products are all at 2km\nresolution. Bands 1-6 are reflective. The dimensionless \"reflectance factor\" quantity is\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r-series)\n\nFormerly known as \"GOES West.\"  Satellite is in storage.\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,14.57,180,53.51]]},"temporal":{"interval":[["2018-12-04T16:30:38Z","2023-01-10T16:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/MCMIPC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_17_MCMIPC","rel":"related","title":"Run the example for NOAA/GOES/17/MCMIPC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_17_MCMIPC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPC#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-17/ABI-L2-MCMIPC/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/MCMIPC/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/MCMIPC/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/17/MCMIPF","title":"GOES-17 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Cloud and Moisture Imagery products are all at 2km\nresolution. Bands 1-6 are reflective. The dimensionless \"reflectance factor\" quantity is\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r-series)\n\nFormerly known as \"GOES West.\"  Satellite is in storage.\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-12-04T16:30:38Z","2023-01-10T16:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/MCMIPF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_17_MCMIPF","rel":"related","title":"Run the example for NOAA/GOES/17/MCMIPF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_17_MCMIPF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-17/ABI-L2-MCMIPF/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/MCMIPF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/MCMIPF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/17/MCMIPM","title":"GOES-17 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Mesoscale","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Cloud and Moisture Imagery products are all at 2km\nresolution. Bands 1-6 are reflective. The dimensionless \"reflectance factor\" quantity is\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\n\nThe locations of domains 1 and 2 change over time.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r-series)\n\nFormerly known as \"GOES West.\"  Satellite is in storage.\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-12-04T16:30:38Z","2023-01-10T16:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/MCMIPM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_17_MCMIPM","rel":"related","title":"Run the example for NOAA/GOES/17/MCMIPM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_17_MCMIPM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPM#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-17/ABI-L2-MCMIPM/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/MCMIPM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/17/MCMIPM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/18/FDCC","title":"GOES-18 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Fire (HSC) product contains four images: one in the form\nof a fire mask and the other three with pixel values identifying fire temperature, fire area,\nand fire radiative power.\n\nThe ABI L2+ FHS metadata mask assigns a flag to every earth-navigated pixel\nthat indicates its disposition with respect to the FHS algorithm. Operational users who have\nthe lowest tolerance for false alarms should focus on the \"processed\" and\n\"saturated\" categories (mask codes 10, 11, 30, and 31), but within these categories there\ncan still be false alarms.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r)\n\nNOAA provides the following scripts for suggested categories,\ncolor maps, and visualizations:\n\n - [GOES-16-17_FireDetection.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireDetection.js)\n - [GOES-16-17_FireReclassification.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireReclassification.js)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,14.57,180,53.51]]},"temporal":{"interval":[["2022-10-13T16:01:17Z","2026-11-10T21:41:17.200000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/FDCC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_18_FDCC","rel":"related","title":"Run the example for NOAA/GOES/18/FDCC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_18_FDCC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCC#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-18/ABI-L2-FDCC/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/FDCC/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/FDCC/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/18/FDCF","title":"GOES-18 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Fire (HSC) product contains four images: one in the form\nof a fire mask and the other three with pixel values identifying fire temperature, fire area,\nand fire radiative power.\n\nThe ABI L2+ FHS metadata mask assigns a flag to every earth-navigated pixel\nthat indicates its disposition with respect to the FHS algorithm. Operational users who have\nthe lowest tolerance for false alarms should focus on the \"processed\" and\n\"saturated\" categories (mask codes 10, 11, 30, and 31), but within these categories there\ncan still be false alarms.\n\n[README](https://www.ncei.noaa.gov/products/goes-terrestrial-weather-abi-glm)\n\nNOAA provides the following scripts for suggested categories,\ncolor maps, and visualizations:\n\n - [GOES-16-17_FireDetection.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireDetection.js)\n - [GOES-16-17_FireReclassification.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireReclassification.js)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-10-13T16:10:21Z","2026-11-10T21:30:20.400000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/FDCF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_18_FDCF","rel":"related","title":"Run the example for NOAA/GOES/18/FDCF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_18_FDCF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-18/ABI-L2-FDCF/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/FDCF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/FDCF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/18/MCMIPC","title":"GOES-18 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Cloud and Moisture Imagery products are all at 2km\nresolution. Bands 1-6 are reflective. The dimensionless \"reflectance factor\" quantity is\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r-series)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,14.57,180,53.51]]},"temporal":{"interval":[["2018-12-04T16:30:38Z","2026-11-10T21:36:17.200000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/MCMIPC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_18_MCMIPC","rel":"related","title":"Run the example for NOAA/GOES/18/MCMIPC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_18_MCMIPC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPC#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-18/ABI-L2-MCMIPC/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/MCMIPC/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/MCMIPC/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/18/MCMIPF","title":"GOES-18 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Cloud and Moisture Imagery products are all at 2km\nresolution. Bands 1-6 are reflective. The dimensionless \"reflectance factor\" quantity is\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r-series)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-12-04T16:30:38Z","2026-11-10T21:10:20.400000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/MCMIPF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_18_MCMIPF","rel":"related","title":"Run the example for NOAA/GOES/18/MCMIPF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_18_MCMIPF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-18/ABI-L2-MCMIPF/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/MCMIPF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/MCMIPF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/18/MCMIPM","title":"GOES-18 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Mesoscale","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Cloud and Moisture Imagery products are all at 2km\nresolution. Bands 1-6 are reflective. The dimensionless \"reflectance factor\" quantity is\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\n\nThe locations of domains 1 and 2 change over time.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r-series)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-05-11T20:42:54Z","2026-11-10T21:44:55Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/MCMIPM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_18_MCMIPM","rel":"related","title":"Run the example for NOAA/GOES/18/MCMIPM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_18_MCMIPM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPM#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-18/ABI-L2-MCMIPM/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/MCMIPM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/18/MCMIPM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"GOOGLE/BRAZIL_FOREST_2008/V1/ANALYTIC","title":"Brazil Forest Imagery Dataset 2008: Analytic Basemap","description":"This dataset provides up to a 10-meter resolution multispectral (green, red, and near-infrared) basemap of Brazil, primarily captured in 2008, to support implementation of the [Brazil Forest Code](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2012/lei/L12651compilado.htm). The mosaic is synthesized from SPOT 2, 4, and 5 satellite data. It serves as a higher-resolution alternative to Landsat for identifying consolidated areas, Permanent Protection Areas (APPs), and legal reserves as they existed on the July 22, 2008 regulatory deadline. Gaps in SPOT coverage are represented by a transparent mask to maintain data integrity. This multispectral product is designed to automate Brazil Forest Code related analysis such as identification of forest through vegetation indices like NDVI.\n\n### Why Google created this basemap\n\nWhile raw image collections are also available (see [MS](/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_MS_V1), [MS_NC](/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_MS_NC_V1), [PMS_NC](/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_PMS_NC_V1), [PAN](/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_PAN_V1)), this 2008-era multispectral mosaic was synthesized to provide a seamless, ready-to-use product for analysis. By performing radiometric and geometric corrections at scale, raw, disparate satellite scenes were transformed into a unified dataset that supports scientific analysis across Brazil in the context of the Brazil Forest Code, such as historic land cover classification relevant to Brazil's Rural Environmental Registry.\n\n\n### Geographic prioritization and coverage\n\nTo maximize immediate environmental impact, image selection prioritized achieving near-complete, high-resolution coverage for privately held lands registered in the **Rural Environmental Registry (CAR)**. Specific focus was given to the **Amazon biome** and five states in the **\"Arc of Deforestation\"** - Maranhão, Mato Grosso, Pará, Rondônia, and Tocantins - where there is an average of 93% state-wide coverage. The average coverage across Brazil is 68% in v1. Gaps in coverage are represented by a transparent mask to maintain data integrity where high-quality 2008-era imagery was unavailable.\n\n\n### Processing methodology\n\nPlease see the [methodology guide](/earth-engine/guides/spot_brazil_forest_imagery_dataset_2008_v1) for an in-depth description of the processing, including:\n\n* **Radiometric calibration:** Raw digital numbers were converted to TOA reflectance using band-specific physical gain and bias metadata, normalized by solar irradiance and Earth-Sun distance.\n* **Radiometric normalization:** To minimize inter-image radiometric discontinuities pixel values were adjusted via histogram matching to a consistent Landsat 2008 mosaic target.\n* **Edge refinement:** Google applied a 2.5-pixel focal minimum erosion to image masks to remove lossy compression artifacts present in the source data.\n* **Cloud masking:** Trained surveyors manually delineated polygons around clouds and shadows, which were converted to a mask to ensure that only the most useful pixels are included in the composite.\n* **Automated misregistration correction:** Select scenes were coregistered using the `ee.Image.register` algorithm against a 2008-era Landsat reference composite.\n* **Composition:** The final mosaic prioritizes the highest resolution pixels available, layering SPOT 5 imagery (10 m) on top of SPOT 4 and 2\\.\n\n\n### Limitations and known issues\n\n* **Spatial completeness:** Gaps exist where no imagery met the project’s strict 2008-era temporal window (2007–2009), cloud-cover thresholds, or quality standards.\n* **Variable native resolution:**Boundaries between different nominal resolutions of source imagery may be visible because nearest neighbor resampling was used during processing..\n* **Residual misregistration:** Spatial shifts may persist in some areas, particularly in regions with extreme terrain relief or dense, homogeneous forest areas.\n* **Atmospheric and cloud artifacts:** Manual cloud masking is not exhaustive; users may encounter occasional residual artifacts like thin cirrus haze or small cloud shadows.\n* **Radiometric inconsistency:** Residual spectral variation remains between adjacent images, which may increase spectral variance and reduce the precision of class separation during machine learning inference.\n* **Spectral saturation:** In areas of extreme brightness, pixels may reach the sensor’s maximum detectable limit, resulting in a loss of texture and detail.\n\n\n### Data access\n\nTo access this dataset, please fill out the [request form](https://forms.gle/jQmCbWgarjENQBYz6).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-74,-34,-34,6]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2009-11-26T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/BRAZIL_FOREST_2008/V1/ANALYTIC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/GOOGLE/GOOGLE_BRAZIL_FOREST_2008_V1_ANALYTIC","rel":"related","title":"Run the example for GOOGLE/BRAZIL_FOREST_2008/V1/ANALYTIC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/GOOGLE/GOOGLE_BRAZIL_FOREST_2008_V1_ANALYTIC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_BRAZIL_FOREST_2008_V1_ANALYTIC#terms-of-use","rel":"license","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Earth_Engine_SPOT_542_Datasets_License.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/BRAZIL_FOREST_2008/V1/ANALYTIC/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"GOOGLE/BRAZIL_FOREST_2008/V1/VISUAL","title":"Brazil Forest Imagery Dataset 2008: Visual Basemap","description":"This dataset provides a visual imagery basemap of Brazil with up to 5-meter resolution, primarily captured in 2008, to support implementation of the [Brazil Forest Code](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2012/lei/L12651compilado.htm). The mosaic is synthesized from SPOT 2, 4, and 5 satellite data. It serves as a high-resolution alternative to Landsat for identifying consolidated areas, Permanent Protection Areas (APPs), and legal reserves as they existed on the July 22, 2008 regulatory deadline. Gaps in SPOT coverage are represented by a transparent mask to maintain data integrity.\n\n### Why Google created this basemap\n\nWhile raw image collections are also available (see [MS](/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_MS_V1), [MS_NC](/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_MS_NC_V1), [PMS_NC](/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_PMS_NC_V1), [PAN](/earth-engine/datasets/catalog/AIRBUS_SPOT_2_4_5_BRAZIL_2007_2009_PAN_V1)), Google synthesized this mosaic to provide a seamless, ready-to-use product. By performing radiometric and geometric corrections at scale, Google transformed raw, disparate satellite scenes into a unified layer that allows for immediate visualization within applications like [SICAR](https://car.gov.br) (the Rural Environmental Registry System of Brazil).\n\n\n### Geographic prioritization and coverage\n\nTo maximize immediate environmental impact, image selection prioritized achieving near-complete, high-resolution coverage for privately held lands registered in the **Rural Environmental Registry (CAR)**. Specific focus was given to the **Amazon biome** and five states in the **\"Arc of Deforestation\"** - Maranhão, Mato Grosso, Pará, Rondônia, and Tocantins - where there is an average of 93% state-wide coverage. The average coverage across Brazil is 68% in v1. Gaps in coverage are represented by a transparent mask to maintain data integrity where high-quality 2008-era imagery was unavailable.\n\n\n### Processing methodology\n\nPlease see the [methodology guide](/earth-engine/guides/spot_brazil_forest_imagery_dataset_2008_v1) for an in-depth description of the processing, including:\n\n* **Radiometric calibration:** Raw digital numbers were converted to TOA reflectance using band-specific physical gain and bias metadata, normalized by solar irradiance and Earth-Sun distance.\n* **Radiometric normalization:** To minimize inter-image radiometric discontinuities pixel values were adjusted via histogram matching to a consistent Landsat 2008 mosaic target.\n* **Edge refinement:** Google applied a 2.5-pixel focal minimum erosion to image masks to remove lossy compression artifacts present in the source data.\n* **Cloud masking:** Trained surveyors manually delineated polygons around clouds and shadows, which were converted to a mask to ensure that only the most useful pixels are included in the composite.\n* **Automated misregistration correction:** Select scenes were coregistered using the `ee.Image.register` algorithm against a 2008-era Landsat reference composite.\n* **Composition:** The final mosaic prioritizes the highest resolution pixels available, layering SPOT 5 imagery (10 m) on top of SPOT 4 and 2\\.\n\n\n### Limitations and known issues\n\n* **Spatial completeness:** Gaps exist where no imagery met the project’s strict 2008-era temporal window (2007–2009), cloud-cover thresholds, or quality standards.\n* **Variable native resolution:**Boundaries between different nominal resolutions of source imagery may be visible because nearest neighbor resampling was used during processing..\n* **Residual misregistration:** Spatial shifts may persist in some areas, particularly in regions with extreme terrain relief or dense, homogeneous forest areas.\n* **Atmospheric and cloud artifacts:** Manual cloud masking is not exhaustive; users may encounter occasional residual artifacts like thin cirrus haze or small cloud shadows.\n* **Radiometric inconsistency:** Residual spectral variation remains between adjacent images, which may increase spectral variance and reduce the precision of class separation during machine learning inference.\n* **Spectral saturation:** In areas of extreme brightness, pixels may reach the sensor’s maximum detectable limit, resulting in a loss of texture and detail.\n\n\n### Data access\n\nTo access this dataset, please fill out the [request form](https://forms.gle/jQmCbWgarjENQBYz6).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-74,-34,-34,6]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2009-11-26T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/BRAZIL_FOREST_2008/V1/VISUAL","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/GOOGLE/GOOGLE_BRAZIL_FOREST_2008_V1_VISUAL","rel":"related","title":"Run the example for GOOGLE/BRAZIL_FOREST_2008/V1/VISUAL in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/GOOGLE/GOOGLE_BRAZIL_FOREST_2008_V1_VISUAL_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_BRAZIL_FOREST_2008_V1_VISUAL#terms-of-use","rel":"license","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Earth_Engine_SPOT_542_Datasets_License.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/BRAZIL_FOREST_2008/V1/VISUAL/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED","title":"Cloud Score+ S2_HARMONIZED V1","description":"Cloud Score+ is a quality assessment (QA) processor for medium-to-high\nresolution optical satellite imagery. The Cloud Score+ S2_HARMONIZED\ndataset is being operationally produced from the\n[harmonized Sentinel-2 L1C collection](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED),\nand Cloud Score+ outputs can be used to identify relatively clear pixels and\neffectively remove clouds and cloud shadows from [L1C (Top-of-Atmosphere)](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED)\nor [L2A (Surface Reflectance)](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED)\nimagery.\n\nThe Cloud Score+ S2_HARMONIZED dataset includes two QA bands, `cs` and\n`cs_cdf`, that both grade the usability of individual pixels with respect to\nsurface visibility on a continuous scale between 0 and 1, where 0 represents\n\"not clear\" (occluded), while 1 represents \"clear\" (unoccluded) observations.\nThe `cs` band scores QA based on a spectral distance between the observed\npixel and a (theoretical) clear reference observation, while the `cs_cdf` band\nrepresents the likelihood an observed pixel is clear based on an estimated\ncumulative distribution of scores for a given location through time. In\nother words, `cs` can be thought of as a more instantaneous atmospheric\nsimilarity score (i.e., how similar is this pixel to what we'd expect to\nsee in a perfectly a clear reference), while `cs_cdf` captures an expectation\nof the estimated score through time (i.e., if we had all the scores for this\npixel through time, how would this score rank?).\n\nImages in the Cloud Score+ S2_HARMONIZED collection have the same id and\n`system:index` properties as the individual [Sentinel-2 L1C](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED)\nassets from which they were produced such that Cloud Score+ bands can be\nlinked to source images based on their shared `system:index`.\n\nCloud Score+ backfill for the entire Sentinel-2 archive is currently in\nprogress and Dataset Availability dates will be updated periodically as new\nresults are added to the Cloud Score+ collection.\n\nFor more information about the Cloud Score+ dataset and modelling\napproach, see\n[this Medium post](https://medium.com/google-earth/all-clear-with-cloud-score-bd6ee2e2235e).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-06-27T00:00:00Z","2026-11-10T15:07:55.069000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/GOOGLE/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED","rel":"related","title":"Run the example for GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/GOOGLE/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"GOOGLE/DYNAMICWORLD/V1","title":"Dynamic World V1","description":"Dynamic World is a 10m near-real-time (NRT) Land Use/Land Cover (LULC)\ndataset that includes class probabilities and label information for nine\nclasses.\n\nDynamic World predictions are available for the Sentinel-2 L1C collection\nfrom 2015-06-27 to present. The revisit frequency of Sentinel-2 is between\n2-5 days depending on latitude. Dynamic World predictions are generated for\nSentinel-2 L1C images with CLOUDY_PIXEL_PERCENTAGE <= 35%.\nPredictions are masked to remove clouds and cloud shadows using\na combination of S2 Cloud Probability, Cloud Displacement Index, and\nDirectional Distance Transform.\n\nImages in the Dynamic World collection have names matching the individual\nSentinel-2 L1C asset names from which they were derived, e.g:\n\n  ee.Image('COPERNICUS/S2/20160711T084022_20160711T084751_T35PKT')\n\nhas a matching Dynamic World image named:\n  ee.Image('GOOGLE/DYNAMICWORLD/V1/20160711T084022_20160711T084751_T35PKT').\n\nAll probability bands except the \"label\" band collectively sum to 1.\n\nTo learn more about the Dynamic World dataset and see examples for\ngenerating composites, calculating regional statistics, and working with the\ntime series, see the [Introduction to Dynamic World](https://developers.google.com/earth-engine/tutorials/community/introduction-to-dynamic-world-pt-1) tutorial series.\n\nGiven Dynamic World class estimations are derived from single images using a\nspatial context from a small moving window, top-1 \"probabilities\" for\npredicted land covers that are in-part defined by cover over time, like\ncrops, can be comparatively low in the absence of obvious distinguishing\nfeatures. High-return surfaces in arid climates, sand, sunglint, etc may\nalso exhibit this phenomenon.\n\nTo select only pixels that confidently belong to a Dynamic World class,\nit is recommended to mask Dynamic World outputs by thresholding the\nestimated \"probability\" of the top-1 prediction.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-06-27T00:00:00Z","2026-11-10T17:39:28.248000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/DYNAMICWORLD/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/GOOGLE/GOOGLE_DYNAMICWORLD_V1","rel":"related","title":"Run the example for GOOGLE/DYNAMICWORLD/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/GOOGLE/GOOGLE_DYNAMICWORLD_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/DYNAMICWORLD/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/DYNAMICWORLD/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"GOOGLE/GLOBAL_CCDC/V1","title":"Google Global Landsat-based CCDC Segments (1999-2019)","description":"This collection contains precomputed results from running the\nContinuous Change Detection and Classification (CCDC) algorithm on\n20 years of Landsat surface reflectance data. CCDC is a break-point\nfinding algorithm that uses harmonic fitting with a dynamic RMSE\nthreshold to detect breakpoints in time-series data.\n\nThe dataset was created from the Landsat 5, 7, and 8 Collection-1, Tier-1,\nsurface reflectance time series, using all daytime images between 1999-01-01\nand 2019-12-31. Each image was preprocessed to mask pixels identified as\ncloud, shadow, or snow (according to the 'pixel_qa' band), saturated pixels,\nand pixels with an atmospheric opacity > 300 (as identified by the\n'sr_atmos_opacity' and 'sr_aerosol' bands). Pixels repeated in\nnorth/south scene overlap were deduplicated. The results were\noutput in 2-degree tiles for all landmasses between -60&deg; and +85&deg; latitude.\nThe images are suitable to simply mosaic() into one global image.\n\nThe CCDC algorithm was run with the default algorithm parameters except for\nthe dateFormat:\n\n* tmaskBands: ['green', 'swir']\n* minObservations: 6\n* chiSquareProbability: 0.99\n* minNumOfYearsScaler: 1.33\n* dateFormat: 1 (fractional year)\n* lambda: 20\n* maxIterations: 25000\n\nEach pixel in the output is encoded using variable length arrays.  The outer\nlength of each array (axis 0) corresponds to the number of breakpoints\nfound at that location. The coefs bands contain 2-D arrays, where each inner\narray contains the scaling factors for the 8 terms in the linear harmonic\nmodel, in the order: [offset, t, cos(&omega;t), sin(&omega;t), cos(2&omega;t),\nsin(2&omega;t), cos(3&omega;t), sin(3&omega;t)], where &omega; = 2&Pi;.  The\nmodels are scale to produce refelectance units (0.0 - 1.0) for the optical\nbands and degrees (K) / 100.0 for the thermal band.\n\nNote that since the output bands are arrays and can only be downsampled\nusing a SAMPLE pyramiding policy. At lower zoom levels, the\nresults are usually no longer representative of the full-resolution data,\nand, for instance, tile boundaries can be seen due to the downsampled masks.\nIt's therefore not recommended to use this dataset at resolutions less than\n240m/pixel.\n\nThere are no current plans to add post-2019 assets to this dataset.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,72]]},"temporal":{"interval":[["1999-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/GLOBAL_CCDC/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/GOOGLE/GOOGLE_GLOBAL_CCDC_V1","rel":"related","title":"Run the example for GOOGLE/GLOBAL_CCDC/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/GOOGLE/GOOGLE_GLOBAL_CCDC_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_GLOBAL_CCDC_V1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/GLOBAL_CCDC/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/GLOBAL_CCDC/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"GOOGLE/Research/open-buildings-temporal/v1","title":"Open Buildings Temporal V1","description":"The Open Buildings 2.5D Temporal Dataset contains data about building\npresence, fractional building counts, and building heights at an effective<sup>1</sup>\nspatial resolution of 4m (rasters are provided at 0.5m resolution) at an\nannual cadence from 2016-2023. It is produced from open-source,\nlow-resolution imagery from the [Sentinel-2](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2)\ncollection.\n\nThe dataset is available across Africa, South Asia, South-East Asia, Latin\nAmerica and the Caribbean. The goal of this data is to support organizations\n(e.g., governmental, non-profits, commercial) focusing on a range of\napplications for social good.\n\nExplore the data interactively with the demo Earth Engine [app](https://goo.gle/open-buildings-temporal-demo-app). (In case you encounter performance issues with the Earth Engine app please try [this](https://goo.gle/open-buildings-temporal-demo) Earth Engine script instead.)\n\nAlternatively, if you are not an Earth Engine user, you can download the\ndata directly from Google Cloud Storage using [this notebook](https://colab.research.google.com/github/google-research/google-research/blob/master/building_detection/open_buildings_temporal_download_region_geotiffs.ipynb).\n\nFor more details on the project and FAQs about the data checkout the project\n[website](https://sites.research.google/gr/open-buildings/temporal/).\n\nExample scripts:\n\n* <code>[How to compute building count for a given AOI](https://code.earthengine.google.com/31ca8ccee65887ed2a152c8ea920bbd2)</code>\n* <code>[How to compute built-up area for a given AOI](https://code.earthengine.google.com/46468628f66eb57c79753ae4c1c09f54)</code>\n* <code>[How to see two years side-by-side and compare](https://code.earthengine.google.com/e9e5a84d279d4b5669872a560e8329a2?hideCode=true)</code>\n\n<small><sup>1</sup>equivalent to what could be achieved by a high-resolution model\nusing a single frame of 4 m resolution imagery.</small>\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-06-30T07:00:00Z","2023-06-30T07:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/Research/open-buildings-temporal/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/GOOGLE/GOOGLE_Research_open-buildings-temporal_v1","rel":"related","title":"Run the example for GOOGLE/Research/open-buildings-temporal/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/GOOGLE/GOOGLE_Research_open-buildings-temporal_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_Research_open-buildings-temporal_v1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/ODbL-1.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/Research/open-buildings-temporal/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/Research/open-buildings-temporal/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL","title":"Satellite Embedding V1","description":"The Google Satellite Embedding dataset is a global, analysis-ready\ncollection of learned geospatial [embeddings](https://developers.google.com/machine-learning/crash-course/embeddings/embedding-space).\nEach 10-meter pixel in this dataset is a 64-dimensional representation, or\n\"[embedding vector](https://developers.google.com/machine-learning/glossary#embedding-vector),\"\nthat encodes temporal trajectories of surface conditions at and around that\npixel as measured by various Earth observation instruments and datasets,\nover a single calendar year. Unlike conventional spectral inputs and\nindices, where bands correspond to physical measurements, embeddings are\nfeature vectors that summarize relationships across multi-source,\nmulti-modal observations in a less directly interpretable, but more powerful\nway.\n[See usage examples and more detailed explanations](https://medium.com/google-earth/ai-powered-pixels-introducing-googles-satellite-embedding-dataset-31744c1f4650).\n\nThe dataset covers terrestrial land surfaces and shallow waters, including\nintertidal and reef zones, inland waterways, and coastal waterways.\nCoverage at the poles is limited by satellite orbits and instrument\ncoverage.\n\nThe collection is composed of images covering approximately 163,840 meters\nby 163,840 meters, and each image has 64 bands `{A00, A01, …, A63}`, one for\neach axis of the 64D embedding space. All bands should be used for\ndownstream analysis as they collectively refer to a 64D coordinate in\nthe embedding space and are not independently interpretable.\n\nAll images are generated in their local Universal Transverse Mercator\nprojection as indicated by the UTM_ZONE property, and have\n`system:time_start` and `system:time_end` properties that reflect the\ncalendar year summarized by the embeddings; for example, an embedding image\nfor 2021 will have a `system:start_time` equal to\n`ee.Date('2021-01-01 00:00:00')` and a `system:end_time` equal to\n`ee.Date('2022-01-01 00:00:00')`.\n\nThe embeddings are unit-length, meaning they have a magnitude of 1 and\ndo not require any additional normalization, and are distributed across the\n[unit sphere](https://en.wikipedia.org/wiki/Unit_sphere), making them\nwell-suited for use with clustering algorithms and tree-based classifiers.\nThe embedding space is also consistent across years, and embeddings from\ndifferent years can be used for condition change detection by considering\nthe dot product or angle between two embedding vectors. Furthermore, the\nembeddings are designed to be linearly composable, i.e., they can be\naggregated to produce embeddings at coarser spatial resolutions or\ntransformed with vector arithmetic, and still retain their semantic meaning\nand distance relationships.\n\nThe Satellite Embedding dataset was produced by [AlphaEarth Foundations](https://deepmind.google/discover/blog/alphaearth-foundations-helps-map-our-planet-in-unprecedented-detail/),\na geospatial embedding model that assimilates multiple datastreams including\noptical, radar, LiDAR, and other sources (Brown, Kazmierski, Pasquarella et\nal., 2025; preprint available [here](https://arxiv.org/abs/2507.22291)).\n\nBecause representations are learned across many sensors and images,\nembedding representations generally overcome common issues such as\nclouds, scan lines, sensor artifacts, or missing data, providing seamless\nanalysis-ready features that can be directly substituted for other Earth\nObservation image sources in classification, regression, and change\ndetection analyses.\n\nThe embeddings in this image collection were generated using v2.1 of the\nAlphaEarth Foundations model, which includes a number of improvements over\nthe v2.0 model evaluated in the AlphaEarth Foundations paper. Namely, the\ntraining dataset was re-generated to include a large number of samples from\nAntarctica that had previously been dropped due to limited sensor coverage,\neffectively increasing the count of training video sequences from over 8.4\nmillion to over 10.1 million sequences; and the USDA NASS Cropland Data\nLayer was included as an additional target during training; loss weights for\nNLCD and CDL were lowered from 0.50 to 0.25; and several other minor changes\nwere implemented to better mitigate visual artifacts associated with input\nsensor swaths, tiling, and multi-resolution pixel targets. These changes did\nnot notably affect the model's performance in terms of evaluation metrics,\nbut generally improved the quality of the resulting embeddings.\n\nWhile some large scale swath and data availability artifacts remain, these\ntypically represent minor vector offsets and generally do not significantly\naffect downstream processing or results.\n\nThe dataset is also available on Google Cloud Storage (GCS) in the bucket\n`gs://alphaearth_foundations`. You can\nbrowse the bucket contents via the [Google Cloud\nConsole](https://console.cloud.google.com/storage/browser/alphaearth_foundations).\nand refer to the bucket\n[README](https://developers.google.com/earth-engine/guides/aef_on_gcs_readme)\nfor full details on the data structure. As of July 2026, this bucket is set\nup as \"provider pays\".\n\nUpdates:\n\n* As of 2025-11-17, embedding layers have DATASET_VERSION 1.1. This\nincludes a regenerated 2017 layer that incorporates additional Sentinel-1\nacquisitions.\n\nGoogle is committed to ongoing production of annual Satellite Embedding\nlayers and will provide at least one year of advance notice of any\nanticipated change in delivery, subject to the ongoing availability of\ninput data streams from USGS and ESA that production of the dataset relies\non.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/GOOGLE/GOOGLE_SATELLITE_EMBEDDING_V1_ANNUAL","rel":"related","title":"Run the example for GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/GOOGLE/GOOGLE_SATELLITE_EMBEDDING_V1_ANNUAL_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_SATELLITE_EMBEDDING_V1_ANNUAL#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GPM_L3/IMERG_MONTHLY_V06","title":"GPM: Monthly Global Precipitation Measurement (GPM) v6","description":"IMERG-Final version \"06\" stopped being produced in September, 2021.\nVersion \"07\" is expected to be released in September 2022\n\nGlobal Precipitation Measurement (GPM) is an international\nsatellite mission to provide next-generation observations of\nrain and snow worldwide every three hours. The Integrated Multi-satellitE\nRetrievals for GPM (IMERG) is the unified algorithm that provides\nrainfall estimates combining data from all passive-microwave\ninstruments in the GPM Constellation.\n\nThis algorithm is intended to intercalibrate, merge, and interpolate\nall satellite microwave precipitation estimates, together with\nmicrowave-calibrated infrared (IR) satellite estimates, precipitation\ngauge analyses, and potentially other precipitation estimators at fine\ntime and space scales for the TRMM and GPM eras over the entire globe.\nThe system is run several times for each observation time, first\ngiving a quick estimate and successively providing better estimates\nas more data arrive. The final step uses monthly gauge data to\ncreate research-level products. See [IMERG Technical Documentation](https://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf)\nfor more details on the algorithm.\n\nDocumentation:\n\n* [Algorithm Theoretical Basis Document](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_ATBD_V06.pdf)\n\n* [IMERG Quality Index](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_QI.pdf)\n\n* [Caveats for IMERG extension into TRMM era](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_TRMMera-caveats.pdf)\n\n* [IMERG Technical Documentation](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_doc.06.pdf)\n\n* [Release notes; New Morphing algorithm](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/MorphingInV06IMERG.pdf)\n\n* [Remote-Sensing Reflectance](https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/doc/README.GPM.pdf)\n\n* [Anomalies](https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-06-01T00:00:00Z","2021-09-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_MONTHLY_V06","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GPM_L3_IMERG_MONTHLY_V06","rel":"related","title":"Run the example for NASA/GPM_L3/IMERG_MONTHLY_V06 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GPM_L3_IMERG_MONTHLY_V06_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_MONTHLY_V06#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/GPM/IMERG/3B-MONTH/06","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_MONTHLY_V06/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_MONTHLY_V06/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GPM_L3/IMERG_V06","title":"GPM: Global Precipitation Measurement (GPM) Release 06 [deprecated]","description":"Global Precipitation Measurement (GPM) is an international\nsatellite mission to provide next-generation observations of\nrain and snow worldwide every three hours. The Integrated Multi-satellitE\nRetrievals for GPM (IMERG) is the unified algorithm that provides\nrainfall estimates combining data from all passive-microwave\ninstruments in the GPM Constellation.\n\nThis algorithm is intended to intercalibrate, merge, and interpolate\nall satellite microwave precipitation estimates, together with\nmicrowave-calibrated infrared (IR) satellite estimates, precipitation\ngauge analyses, and potentially other precipitation estimators at fine\ntime and space scales for the TRMM and GPM eras over the entire globe.\nThe system is run several times for each observation time, first\ngiving a quick estimate and successively providing better estimates\nas more data arrive. The final step uses monthly gauge data to\ncreate research-level products. See [IMERG Technical Documentation](https://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf)\nfor more details on the algorithm.\n\nDocumentation:\n\n* [Algorithm Theoretical Basis Document](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_ATBD_V06.pdf)\n\n* [IMERG Quality Index](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_QI.pdf)\n\n* [Caveats for IMERG extension into TRMM era](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_TRMMera-caveats.pdf)\n\n* [IMERG Technical Documentation](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_doc.06.pdf)\n\n* [Release notes; New Morphing algorithm](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/MorphingInV06IMERG.pdf)\n\n* [Remote-Sensing Reflectance](https://gpm1.gesdisc.eosdis.nasa.gov/data/doc/README.GPM.pdf)\n\n* [Anomalies](https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html)\n\nThis collection contains provisional\nproducts that are regularly replaced with updated versions when\nthe data become available. The products are marked with a metadata\nproperty called 'status'. When a product is initially made available,\nthe property value is 'provisional'. Once a provisional product\nhas been updated with the final version, this value is updated\nto 'permanent'.\n\nThis collection contains data from:\n\n* GPM_3IMERGHH_V06 [doi:10.5067/GPM/IMERG/3B-HH-L/06](https://doi.org/10.5067/GPM/IMERG/3B-HH-L/06)\n* GPM_3IMERGHH_06 [doi:10.5067/GPM/IMERG/3B-HH/06](https://doi.org/10.5067/GPM/IMERG/3B-HH/06)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-06-01T00:00:00Z","2024-06-02T18:30:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_V06","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GPM_L3_IMERG_V06","rel":"related","title":"Run the example for NASA/GPM_L3/IMERG_V06 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GPM_L3_IMERG_V06_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_V06#terms-of-use","rel":"license","type":"text/html"},{"href":"ftp://198.118.195.100/gpmallversions/V06","rel":"source"},{"href":"ftp://198.118.195.61/data/imerg/late/","rel":"source"},{"href":"https://doi.org/10.5067/GPM/IMERG/3B-HH-L/06","rel":"cite-as"},{"href":"https://doi.org/10.5067/GPM/IMERG/3B-HH/06","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_V07.json","rel":"latest-version","title":"NASA/GPM_L3/IMERG_V07","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_V07.json","rel":"successor-version","title":"NASA/GPM_L3/IMERG_V07","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_V06/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_V06/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GRACE/MASS_GRIDS/LAND","title":"GRACE Monthly Mass Grids - Land [deprecated]","description":"GRACE Tellus Monthly Mass Grids provides monthly\ngravitational anomalies relative to a 2004-2010 time-mean baseline.\nThe data contained in this dataset are units of \\\"Equivalent Water Thickness\\\"\nwhich represent the deviations of mass in terms of vertical extent\nof water in centimeters. See the provider's [Monthly Mass\nGrids Overview](https://grace.jpl.nasa.gov/data/monthly-mass-grids/)\nfor more details.\n\nThe GRACE Tellus (GRCTellus) Monthly Mass Grids dataset is produced\nby three centers: CSR (U. Texas / Center for Space Research),\nGFZ (GeoForschungsZentrum Potsdam), and JPL\n(NASA Jet Propulsion Laboratory). Each center is a part\nof the GRACE Ground System and generates Level-2 data\n(spherical harmonic fields) used in this dataset.\nThe output includes spherical harmonic coefficients\nof the gravity field and of the dealiasing fields used to compute them.\nSince each center independently produces the coefficients, the results\nmay be slightly different. It is recommended for most users to\nuse the mean of all three datasets. See the provider's [choosing a\nsolution](https://grace.jpl.nasa.gov/data/choosing-a-solution/) page\nfor more details.\n\n**Note**\n\n* Due to the sampling and post-processing of GRACE observations,\n  surface mass variations at small spatial scales tend to be attenuated.\n  Therefore, users should multiply the GRCTellus Land data by the\n  scaling grid available at\n  [NASA/GRACE/MASS_GRIDS/LAND_AUX_2014](https://code.earthengine.google.com/?asset=NASA/GRACE/MASS_GRIDS/LAND_AUX_2014).\n\n* The GRCTellus Land grids processed from the Spherical Harmonic\n  Level-2 data are not suited to accurately quantify ice mass\n  changes over Greenland or Antarctica, or glaciers and ice caps.\n  For those areas it is recommended to use JPL's mascon\n  solution available as the following image collection:\n  [NASA/GRACE/MASS_GRIDS/MASCON](https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-04-01T00:00:00Z","2017-01-07T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/LAND","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GRACE_MASS_GRIDS_LAND","rel":"related","title":"Run the example for NASA/GRACE/MASS_GRIDS/LAND in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GRACE_MASS_GRIDS_LAND_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_LAND#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_LAND.json","rel":"latest-version","title":"NASA/GRACE/MASS_GRIDS_V04/LAND","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_LAND.json","rel":"successor-version","title":"NASA/GRACE/MASS_GRIDS_V04/LAND","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/LAND/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/LAND/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GRACE/MASS_GRIDS/MASCON","title":"GRACE Monthly Mass Grids - Global Mascons [deprecated]","description":"GRACE Tellus Monthly Mass Grids provides monthly\ngravitational anomalies relative to a 2004-2010 time-mean baseline.\nThe data contained in this dataset are units of \\\"Equivalent Water Thickness\\\"\nwhich represent the deviations of mass in terms of vertical extent\nof water in centimeters. See the provider's [Monthly Mass\nGrids Overview](https://grace.jpl.nasa.gov/data/monthly-mass-grids/)\nfor more details.\n\nThe GRACE Tellus (GRCTellus) Global Mascon dataset\nis based on Level-1 GRACE observations and processed\nat NASA Jet Propulsion Laboratory (JPL).\n\nThis dataset uses a-priori constraints in space and time to\nestimate global, monthly gravity fields in terms of equal-area\n3&deg;x3&deg; spherical cap mass concentration (mascon) functions to minimize the\neffect of measurement errors. No additional empirical destriping\nfilter has been applied to the data. This results in better S/N\nratios of the mascon fields compared to the conventional\nspherical-harmonic solutions.\n\n**Note**\n\n* Mascons which lie on coastlines contain mixed land and ocean signals.\n  A version of this dataset with the Coastline Resolution Improvement\n  (CRI) filter applied to separate the land and ocean portions of mass\n  within each land/ocean mascon in a post-processing step. This\n  dataset is available at\n  [NASA/GRACE/MASS_GRIDS/MASCON_CRI](https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON_CRI).\n\n* The data are represented on a 1/2 degree lon-lat grid, but they\n  represent the 3x3 degree equal-area caps, which is the current\n  native resolution of JPL-RL05M.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-03-31T00:00:00Z","2017-05-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/MASCON","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GRACE_MASS_GRIDS_MASCON","rel":"related","title":"Run the example for NASA/GRACE/MASS_GRIDS/MASCON in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GRACE_MASS_GRIDS_MASCON_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_MASCON.json","rel":"latest-version","title":"NASA/GRACE/MASS_GRIDS_V04/MASCON","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V03_MASCON.json","rel":"successor-version","title":"NASA/GRACE/MASS_GRIDS_V03/MASCON","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/MASCON/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/MASCON/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GRACE/MASS_GRIDS/MASCON_CRI","title":"GRACE Monthly Mass Grids - Global Mascon (CRI Filtered) [deprecated]","description":"GRACE Tellus Monthly Mass Grids provides monthly\ngravitational anomalies relative to a 2004-2010 time-mean baseline.\nThe data contained in this dataset are units of \\\"Equivalent Water Thickness\\\"\nwhich represent the deviations of mass in terms of vertical extent\nof water in centimeters. See the provider's [Monthly Mass\nGrids Overview](https://grace.jpl.nasa.gov/data/monthly-mass-grids/)\nfor more details.\n\nThe GRACE Tellus (GRCTellus) Global Mascon dataset\nis based on Level-1 GRACE observations and processed\nat NASA Jet Propulsion Laboratory (JPL).\n\nThis dataset uses a-priori constraints in space and time to\nestimate global, monthly gravity fields in terms of equal-area\n3&deg;x3&deg; spherical cap mass concentration (mascon) functions to minimize the\neffect of measurement errors. This results in better S/N\nratios of the mascon fields compared to the conventional\nspherical-harmonic solutions.\n\nThis version of the Global Mascon dataset has a Coastline Resolution\nImprovement (CRI) filter applied to separate the land and ocean\nportions of mass within each land/ocean mascon in a post-processing step.\nFor users who wish to do their own separation of land and ocean mass\nsignals, a version of the data without the CRI filter is available\nat [NASA/GRACE/MASS_GRIDS/MASCON](https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON).\n\n**Note**\n\n* A set of global gain factors to aid in the interpretation of signals\n  at sub-mascon resolution is available at\n  [NASA/GRACE/MASS_GRIDS/MASCON_SCALE_CRI_2015](https://code.earthengine.google.com/?asset=NASA/GRACE/MASS_GRIDS/MASCON_SCALE_CRI_2015).\n  **Please note**: the gain factors can be used for hydrology-related\n  signals, but not for mountain glaciers or ice sheets. For more\n  information on scaling the mascon solutions, please see [Wiese et al.\n  (2016)](https://doi.org/10.1002/2016WR019344).\n\n* The data are represented on a 1/2 degree lon-lat grid, but they\n  represent the 3x3 degree equal-area caps, which is the current\n  native resolution of JPL-RL05M.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-03-31T00:00:00Z","2017-05-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/MASCON_CRI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GRACE_MASS_GRIDS_MASCON_CRI","rel":"related","title":"Run the example for NASA/GRACE/MASS_GRIDS/MASCON_CRI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GRACE_MASS_GRIDS_MASCON_CRI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_MASCON_CRI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_MASCON_CRI.json","rel":"latest-version","title":"NASA/GRACE/MASS_GRIDS_V04/MASCON_CRI","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V03_MASCON_CRI.json","rel":"successor-version","title":"NASA/GRACE/MASS_GRIDS_V03/MASCON_CRI","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/MASCON_CRI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/MASCON_CRI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GRACE/MASS_GRIDS/OCEAN","title":"GRACE Monthly Mass Grids - Ocean [deprecated]","description":"GRACE Tellus Monthly Mass Grids provides monthly\ngravitational anomalies relative to a 2004-2010 time-mean baseline.\nThe data contained in this dataset are units of \\\"Equivalent Water Thickness\\\"\nwhich represent the deviations of mass in terms of vertical extent\nof water in centimeters. See the provider's [Monthly Mass\nGrids Overview](https://grace.jpl.nasa.gov/data/monthly-mass-grids/)\nfor more details.\n\nThe GRACE Tellus (GRCTellus) Monthly Mass Grids Ocean dataset is produced\nby three centers: CSR (U. Texas / Center for Space Research),\nGFZ (GeoForschungsZentrum Potsdam), and JPL\n(NASA Jet Propulsion Laboratory). Each center is a part\nof the GRACE Ground System and generates Level-2 data\n(spherical harmonic fields) used in this dataset.\nThe output includes spherical harmonic coefficients\nof the gravity field and of the dealiasing fields used to compute them.\nSince each center independently produces the coefficients, the results\nmay be slightly different. It is recommended for most users to\nuse the mean of all three datasets. See the provider's [choosing a\nsolution](https://grace.jpl.nasa.gov/data/choosing-a-solution/) page\nfor more details.\n\n**Note**\n\n* Land leakage correction: Ocean signals are typically weaker\n  than land signals, by factors of 2 and more, on seasonal and\n  interannual time scales. To minimize leakage from high land\n  signals onto ocean signals, a destriping filter has been applied\n  which may cause cause correlations over much larger distances.\n\n* The GRCTellus Ocean datasets are optimized to examine regional\n  ocean bottom pressure, but NOT intended to be spatially\n  averaged to determine global mean ocean mass.\n\n* A filtered version of the  GRCTellus Ocean dataset is also available\n  which uses an alternative filtering method which may reduce leakage\n  errors, and have results which agree better with altimetric sea surface\n  height. [NASA/GRACE/MASS_GRIDS/OCEAN_EOFR](https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_OCEAN_EOFR).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-31T00:00:00Z","2016-12-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/OCEAN","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GRACE_MASS_GRIDS_OCEAN","rel":"related","title":"Run the example for NASA/GRACE/MASS_GRIDS/OCEAN in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GRACE_MASS_GRIDS_OCEAN_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_OCEAN#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_OCEAN.json","rel":"latest-version","title":"NASA/GRACE/MASS_GRIDS_V04/OCEAN","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_OCEAN.json","rel":"successor-version","title":"NASA/GRACE/MASS_GRIDS_V04/OCEAN","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/OCEAN/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/OCEAN/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GRACE/MASS_GRIDS/OCEAN_EOFR","title":"GRACE Monthly Mass Grids - Ocean EOFR","description":"GRACE Tellus Monthly Mass Grids provides monthly\ngravitational anomalies relative to a 2004-2010 time-mean baseline.\nThe data contained in this dataset are units of \\\"Equivalent Water Thickness\\\"\nwhich represent the deviations of mass in terms of vertical extent\nof water in centimeters. See the provider's [Monthly Mass\nGrids Overview](https://grace.jpl.nasa.gov/data/monthly-mass-grids/)\nfor more details.\n\nThis dataset is a filtered version of the GRACE Tellus (GRCTellus)\nOcean dataset. The 'EOFR' bottom pressure (OBP) grids are obtained by\nprojecting the data from the regular GRC Ocean grids product\nonto the Empirical Orthogonal Functions (EOFs) of the\nOcean Model for Circulation and Tides (OMCT). This effectively\nfilters out signals in the GRACE data that are inconsistent\nwith the physics and OBP variations in the OMCT ocean model.\n\nThe EOFR filtered reconstructed OBP fields agree\nbetter with radar altimetric sea surface height,\nreduce leakage around ice sheets and glaciers, and reduce\nnoise in the mid latitudes where OBP variability is lower.\n[(Chambers and Willis, 2010)](https://doi.org/10.1175/2010JTECHO738.1)\n\n**Note**\n\n* The GRCTellus Ocean datasets are optimized to examine regional\n  ocean bottom pressure, but NOT intended to be spatially\n  averaged to determine global mean ocean mass.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-12-31T00:00:00Z","2016-12-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/OCEAN_EOFR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GRACE_MASS_GRIDS_OCEAN_EOFR","rel":"related","title":"Run the example for NASA/GRACE/MASS_GRIDS/OCEAN_EOFR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GRACE_MASS_GRIDS_OCEAN_EOFR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_OCEAN_EOFR#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/OCEAN_EOFR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS/OCEAN_EOFR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"GRIDMET/DROUGHT","title":"GRIDMET DROUGHT: CONUS Drought Indices","description":"This dataset contains drought indices derived from the 4-km daily Gridded\nSurface Meteorological (GRIDMET) dataset. The drought indices provided include\nthe standardized precipitation index (SPI), the evaporative drought demand\nindex (EDDI), the standardized precipitation evapotranspiration index (SPEI),\nthe Palmer Drought Severity Index (PDSI) and Palmer Z Index (Z).\n\nSPI, EDDI, and SPEI are supplied on different time scales corresponding to the\ntime aggregation of precipitation, reference evapotranspiration, and\nprecipitation minus reference evapotranspiration, respectively. The time scales\ninclude 14 day, 30 day, 90 day, 180 day, 270 day, 1 year, 2 years and 5 years.\nThe standardization is done by using a non-parametric standardized probability\nbased method where plotting positions are used to obtain probabilities which\nare transformed to indices assuming an inverse-normal distribution. All data\nare standardized over a common time period of 1981-2016.\n\nPDSI and Z are calculated using a modified version of the Palmer formula which\nuses reference evapotranspiration and precipitation from GRIDMET, and a static\nsoil water holding capacity layer (top 1500mm) from STATSGO. Modifications to\nthe coefficients of the original Palmer formula are applied to calculate PDSI.\nThe baseline period for PDSI and Z calculations is 1979-2018.\n\nInterpretation of the drought indices in this dataset is different for PDSI and\nZ than for SPI, SPEI and EDDI. Utilizing the interpretation from the US Drought\nmonitor, values of these drought indices have the following meaning:\n\nPDSI and z:\n\n  * 5.0 or more (extremely wet)\n  * 4.0 to 4.99 (very wet)\n  * 3.0 to 3.99 (moderately wet),\n  * 2.0 to 2.99 (slightly wet)\n  * 1.0 to 1.99 (incipient wet spell)\n  * -0.99 to 0.99(near normal)\n  * -1.99 to -1.00 (incipient dry spell)\n  * -2.99 to -2.00 (mild drought)\n  * -3.99 to -3.00 (moderate drought)\n  * -4.99 to -4.00 (severe drought)\n  * -5.0 or less (extreme drought)\n\nSPI/SPEI/EDDI:\n\n  * 2.0 or more (extremely wet)\n  * 1.6 to 1.99 (very wet)\n  * 1.3 to 1.59 (moderately wet),\n  * 0.8 to 1.29 (slightly wet)\n  * 0.5 to 0.79 (incipient wet spell)\n  * -0.49 to 0.49(near normal),\n  * -0.79 to -0.5 (incipient dry spell)\n  * -1.29 to -0.8 (mild drought)\n  * -1.59 to -1.3 (moderate drought)\n  * -1.99 to -1.6 (severe drought)\n  * -2.0 or less (extreme drought).\n\nThis dataset contains provisional products that are replaced with\nupdated versions when the complete source data become available.\nProducts can be distinguished by the value of the 'status' property.\nAt first, assets are ingested with status='early'. After several\ndays, they are replaced by assets with status='provisional'.\nAfter about 2 months, they are replaced by the final assets with\nstatus='permanent'.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-124.9,24.9,-66.8,49.6]]},"temporal":{"interval":[["1980-01-05T00:00:00Z","2026-11-01T06:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/GRIDMET/DROUGHT","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/GRIDMET/GRIDMET_DROUGHT","rel":"related","title":"Run the example for GRIDMET/DROUGHT in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/GRIDMET/GRIDMET_DROUGHT_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/GRIDMET_DROUGHT#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/GRIDMET/GRIDMET_DROUGHT.json","rel":"latest-version","title":"GRIDMET/DROUGHT","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_PDSI.json","rel":"predecessor-version","title":"IDAHO_EPSCOR/PDSI","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/GRIDMET/DROUGHT/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/GRIDMET/DROUGHT/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GSFC/MERRA/aer/2","title":"MERRA-2 M2T1NXAER: Aerosol Diagnostics V5.12.4","description":"M2T1NXAER (or tavg1_2d_aer_Nx) is an hourly time-averaged 2-dimensional data\ncollection in Modern-Era Retrospective analysis for Research and\nApplications version 2 (MERRA-2). This collection consists of assimilated\naerosol diagnostics, such as column mass density of aerosol components\n(black carbon, dust, sea-salt, sulfate, and organic carbon), surface mass\nconcentration of aerosol components, and total extinction (and scattering)\naerosol optical thickness (AOT) at 550 nm. The total PM1.0, PM2.5, and PM10\nmay be derived with the formula described in the\n[in the FAQ] (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/FAQ/)\n\nThe data field is time-stamped with the\ncentral time of an hour starting from 00:30 UTC,\ne.g.: 00:30, 01:30, ... , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the\nsatellite era produced by NASA Global Modeling and Assimilation Office\n(GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4.\nThe dataset covers the period of 1980-present with the latency of ~3 weeks\nafter the end of a month.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1980-01-01T00:00:00Z","2026-10-01T23:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GSFC/MERRA/aer/2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GSFC_MERRA_aer_2","rel":"related","title":"Run the example for NASA/GSFC/MERRA/aer/2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GSFC_MERRA_aer_2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_aer_2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://earthdata.nasa.gov/collaborate/open-data-services-and-software/data-information-policy","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GSFC/MERRA/aer/2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GSFC/MERRA/aer/2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GSFC/MERRA/flx/2","title":"MERRA-2 M2T1NXFLX: Surface Flux Diagnostics V5.12.4","description":"M2T1NXFLX (or tavg1_2d_flx_Nx) is an hourly time-averaged data\ncollection in Modern-Era Retrospective analysis for Research and\nApplications version 2 (MERRA-2). This collection consists of\nassimilated surface flux diagnostics, such as total precipitation, bias\ncorrected total precipitation, surface air temperature, surface specific\nhumidity, surface wind speed, and evaporation from turbulence. The\n\"surface\" in this data collection is the model surface layer. The\nheights of the model surface layer (HLML) vary with time and location,\nwith the value of ~60 meters above ground. The data field is time-stamped\nwith the central time of an hour starting from 00:30 UTC, e.g.: 00:30,\n01:30, ... , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the\nsatellite era produced by [NASA Global Modeling and Assimilation Office\n(GMAO)](https://gmao.gsfc.nasa.gov/) using the [Goddard Earth Observing\nSystem Model (GEOS)](https://gmao.gsfc.nasa.gov/GEOS_systems/) version\n5.12.4. The dataset covers the period of 1980-present with the latency\nof ~3 weeks after the end of a month.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1980-01-01T00:00:00Z","2026-10-01T23:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GSFC/MERRA/flx/2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GSFC_MERRA_flx_2","rel":"related","title":"Run the example for NASA/GSFC/MERRA/flx/2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GSFC_MERRA_flx_2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_flx_2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://earthdata.nasa.gov/collaborate/open-data-services-and-software/data-information-policy","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GSFC/MERRA/flx/2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GSFC/MERRA/flx/2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GSFC/MERRA/lnd/2","title":"MERRA-2 M2T1NXLND: Land Surface Diagnostics V5.12.4","description":"M2T1NXLND (or tavg1_2d_lnd_Nx) is an hourly time-averaged data\ncollection in Modern-Era Retrospective analysis for Research and\nApplications version 2 (MERRA-2). 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This collection consists of meteorology\ndiagnostics at popularly used vertical levels, such as air temperature at\n2-meter (or at 10-meter, 850hPa, 500 hPa, 250hPa), wind components at\n50-meter (or at 2-meter, 10-meter, 850 hPa, 500hPa, 250 hPa), sea level\npressure, surface pressure, and total precipitable water vapor (or ice\nwater, liquid water). The data field is time-stamped with the central time\nof an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, ... , 23:30 UTC.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for\nthe satellite era produced by NASA Global Modeling and Assimilation\nOffice (GMAO) using the Goddard Earth Observing System Model (GEOS)\nversion 5.12.4. The dataset covers the period of 1980-present with the\nlatency of ~3 weeks after the end of a month.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1980-01-01T00:00:00Z","2026-10-01T23:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GSFC/MERRA/slv/2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GSFC_MERRA_slv_2","rel":"related","title":"Run the example for NASA/GSFC/MERRA/slv/2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GSFC_MERRA_slv_2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_slv_2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://earthdata.nasa.gov/collaborate/open-data-services-and-software/data-information-policy","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GSFC/MERRA/slv/2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GSFC/MERRA/slv/2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Germany/Brandenburg/orthos/20cm","title":"Brandenburg (Germany) RGBN orthophotos 20 cm","description":"Orthophotos are an aerial photo dataset covering the Brandenburg state of\nGermany.\nThis data is provided by State government of\nBrandenburg ([LGB](https://geobasis-bb.de/lgb/de/)).\nDigital orthophotos are digitally corrected aerial photos and show all\nobjects that are visible from the air at the time of recording in a parallel\nperspective. They have a high density of information on ecological,\nphenological, geographical and other topics.\n\nFor more information, please see the\n[Brandenburg orthophotos documentation](https://geobasis-bb.de/lgb/de/geodaten/luftbilder/luftbilder-aktuell/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[11,51.28,15,53.7]]},"temporal":{"interval":[["2021-08-23T00:00:00Z","2023-01-20T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Germany/Brandenburg/orthos/20cm","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Germany/Germany_Brandenburg_orthos_20cm","rel":"related","title":"Run the example for Germany/Brandenburg/orthos/20cm in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Germany/Germany_Brandenburg_orthos_20cm_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Germany_Brandenburg_orthos_20cm#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Germany/Brandenburg/orthos/20cm/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Germany/Brandenburg/orthos/20cm/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"HYCOM/GLBu0_08/sea_surface_elevation","title":"HYCOM: Hybrid Coordinate Ocean Model, Sea Surface Elevation [deprecated]","description":"The Hybrid Coordinate Ocean Model (HYCOM) is a data-assimilative hybrid\nisopycnal-sigma-pressure (generalized) coordinate ocean model. 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The salinity,\ntemperature, and velocity variables have been interpolated to 40\nstandard z-levels.\n\nThe HYCOM Consortium, which includes the National Ocean Partnership\nProgram (NOPP), is part of the U.S. Global Ocean Data Assimilation\nExperiment (GODAE).\n\nFunded by the National Ocean Partnership Program, the Office of Naval\nResearch (ONR), and DoD High Performance Computing Modernization Program.\n\nFor more information, see:\n\n- [hycom.org](https://www.hycom.org/)\n- [GIS StackExchange hycom](https://gis.stackexchange.com/questions/tagged/hycom)\n- Wikipedia [HyCOM](https://en.wikipedia.org/wiki/HyCOM)\n- Wikipedia [List of ocean circulation models](https://en.wikipedia.org/wiki/List_of_ocean_circulation_models)\n- Wikipedia [Ocean general circulation model (OGCM)](https://en.wikipedia.org/wiki/Ocean_general_circulation_model)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-80.48,180,80.48]]},"temporal":{"interval":[["1992-10-02T00:00:00Z","2018-12-09T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/HYCOM/GLBu0_08/sea_surface_elevation","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/HYCOM/HYCOM_GLBu0_08_sea_surface_elevation","rel":"related","title":"Run the example for HYCOM/GLBu0_08/sea_surface_elevation in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/HYCOM/HYCOM_GLBu0_08_sea_surface_elevation_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/HYCOM_GLBu0_08_sea_surface_elevation#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_surface_elevation.json","rel":"latest-version","title":"HYCOM/sea_surface_elevation","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_surface_elevation.json","rel":"successor-version","title":"HYCOM/sea_surface_elevation","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/HYCOM/GLBu0_08/sea_surface_elevation/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/HYCOM/GLBu0_08/sea_surface_elevation/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"HYCOM/GLBu0_08/sea_temp_salinity","title":"HYCOM: Hybrid Coordinate Ocean Model, Water Temperature and Salinity [deprecated]","description":"The Hybrid Coordinate Ocean Model (HYCOM) is a data-assimilative hybrid\nisopycnal-sigma-pressure (generalized) coordinate ocean model. The subset\nof HYCOM data hosted in EE contains the variables salinity, temperature,\nvelocity, and elevation. They have been interpolated to a uniform\n0.08 degree lat/long grid between 80.48°S and 80.48°N. 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The salinity,\ntemperature, and velocity variables have been interpolated to 40\nstandard z-levels.\n\nThe HYCOM Consortium, which includes the National Ocean Partnership\nProgram (NOPP), is part of the U.S. Global Ocean Data Assimilation\nExperiment (GODAE).\n\nFunded by the National Ocean Partnership Program, the Office of Naval\nResearch (ONR), and DoD High Performance Computing Modernization Program.\n\nFor more information, see:\n\n- [hycom.org](https://www.hycom.org/)\n- [GIS StackExchange hycom](https://gis.stackexchange.com/questions/tagged/hycom)\n- Wikipedia [HyCOM](https://en.wikipedia.org/wiki/HyCOM)\n- Wikipedia [List of ocean circulation models](https://en.wikipedia.org/wiki/List_of_ocean_circulation_models)\n- Wikipedia [Ocean general circulation model (OGCM)](https://en.wikipedia.org/wiki/Ocean_general_circulation_model)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-80.48,180,80.48]]},"temporal":{"interval":[["1992-10-02T00:00:00Z","2024-09-05T09:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/HYCOM/sea_water_velocity","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/HYCOM/HYCOM_sea_water_velocity","rel":"related","title":"Run the example for HYCOM/sea_water_velocity in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/HYCOM/HYCOM_sea_water_velocity_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_water_velocity#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_water_velocity.json","rel":"latest-version","title":"HYCOM/sea_water_velocity","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_GLBu0_08_sea_water_velocity.json","rel":"predecessor-version","title":"HYCOM/GLBu0_08/sea_water_velocity","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/HYCOM/sea_water_velocity/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/HYCOM/sea_water_velocity/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"IAHS/GFPLAIN250/v0","title":"GFPLAIN250m: Global 250m Floodplain Dataset","description":"The GFPLAIN250m includes raster data of Earth's floodplains identified\nusing a geomorphic approach presented in Nardi et al. 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After several\ndays, they are replaced by assets with status='provisional'.\nAfter about 2 months, they are replaced by the final assets with\nstatus='permanent'.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-124.9,24.9,-66.8,49.6]]},"temporal":{"interval":[["1979-01-01T00:00:00Z","2026-11-07T06:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/GRIDMET","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/IDAHO_EPSCOR/IDAHO_EPSCOR_GRIDMET","rel":"related","title":"Run the example for IDAHO_EPSCOR/GRIDMET in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/IDAHO_EPSCOR/IDAHO_EPSCOR_GRIDMET_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/GRIDMET/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/GRIDMET/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"IDAHO_EPSCOR/MACAv2_METDATA","title":"MACAv2-METDATA: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models","description":"The MACAv2-METDATA dataset is a collection of 20 global\nclimate models covering the conterminous USA. The Multivariate Adaptive\nConstructed Analogs (MACA) method is a statistical downscaling\nmethod which utilizes a training dataset (i.e. a meteorological\nobservation dataset) to remove historical biases and match spatial\npatterns in climate model output.\n\nThe MACA method was used to downscale the model output from 20\nglobal climate models (GCMs) of the Coupled Model Inter-Comparison\nProject 5 (CMIP5) for the historical GCM forcings (1950-2005) and\nthe future Representative Concentration Pathways (RCPs) RCP 4.5\nand RCP 8.5 scenarios (2006-2100) from the native resolution of\nthe GCMS to 4km.\n\nThe full list of models can be found at:\n[https://climate.northwestknowledge.net/MACA/GCMs.php](https://climate.northwestknowledge.net/MACA/GCMs.php)\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-124.9,24.9,-67,49.6]]},"temporal":{"interval":[["1900-01-01T00:00:00Z","2100-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/MACAv2_METDATA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/IDAHO_EPSCOR/IDAHO_EPSCOR_MACAv2_METDATA","rel":"related","title":"Run the example for IDAHO_EPSCOR/MACAv2_METDATA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/IDAHO_EPSCOR/IDAHO_EPSCOR_MACAv2_METDATA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/MACAv2_METDATA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/MACAv2_METDATA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY","title":"MACAv2-METDATA Monthly Summaries: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global  Climate Models","description":"The MACAv2-METDATA dataset is a collection of 20 global\nclimate models covering the conterminous USA. The Multivariate Adaptive\nConstructed Analogs (MACA) method is a statistical downscaling\nmethod which utilizes a training dataset (i.e. a meteorological\nobservation dataset) to remove historical biases and match spatial\npatterns in climate model output.\n\nThe MACA method was used to downscale the model output from 20\nglobal climate models (GCMs) of the Coupled Model Inter-Comparison\nProject 5 (CMIP5) for the historical GCM forcings (1950-2005) and\nthe future Representative Concentration Pathways (RCPs) RCP 4.5\nand RCP 8.5 scenarios (2006-2100) from the native resolution of\nthe GCMS to 4km.\n\nThis version contains monthly summaries.\n\nThe full list of models can be found at:\n[https://climate.northwestknowledge.net/MACA/GCMs.php](https://climate.northwestknowledge.net/MACA/GCMs.php)\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-124.9,24.9,-67,49.6]]},"temporal":{"interval":[["1900-01-01T00:00:00Z","2099-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/IDAHO_EPSCOR/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY","rel":"related","title":"Run the example for IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/IDAHO_EPSCOR/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"IDAHO_EPSCOR/PDSI","title":"PDSI: University of Idaho Palmer Drought Severity Index [deprecated]","description":"The Palmer Drought Severity Index dataset provides\nhigh spatial resolution (~4-km) thrice-monthly estimates of this\nwidely used measure of integrated water supply and demand anomalies\nacross the contiguous United States from 1979-present. The PDSI\nis calculated using precipitation and potential evapotranspiration\nderived from the gridded meteorological dataset of Abatzoglou (2013).\n\nPotential evapotranspiration is computed using the Penman-Montieth\nequation for a reference grass surface. Available soil water holding\ncapacity in the top 2.5m of the soil was derived from the STATSGO\nsoils database and used in the computations. Whereas PDSI has typically\nbeen computed on monthly timescales, we compute these data three-times\na month to provide more timely updates. Due to the spin-up of PDSI\ncalculations, data for the first year of record should be used\nsparingly.\n\nThis dataset contains provisional products that are replaced with\nupdated versions when the complete source data become available.\nProducts can be distinguished by the value of the 'status' property.\nAt first, assets are ingested with status='early'. After several\ndays, they are replaced by assets with status='provisional'.\nAfter about 2 months, they are replaced by the final assets with\nstatus='permanent'.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-124.9,24.9,-66.8,49.6]]},"temporal":{"interval":[["1979-03-01T00:00:00Z","2020-06-20T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/PDSI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/IDAHO_EPSCOR/IDAHO_EPSCOR_PDSI","rel":"related","title":"Run the example for IDAHO_EPSCOR/PDSI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/IDAHO_EPSCOR/IDAHO_EPSCOR_PDSI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_PDSI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/GRIDMET/GRIDMET_DROUGHT.json","rel":"latest-version","title":"GRIDMET/DROUGHT","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/GRIDMET/GRIDMET_DROUGHT.json","rel":"successor-version","title":"GRIDMET/DROUGHT","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/PDSI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/PDSI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"IDAHO_EPSCOR/TERRACLIMATE","title":"TerraClimate:  Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces, University of Idaho","description":"TerraClimate is a dataset of monthly climate and climatic water balance for\nglobal terrestrial surfaces. It uses climatically aided interpolation,\ncombining high-spatial resolution climatological normals from the\n[WorldClim dataset](https://www.worldclim.org/), with coarser spatial\nresolution, but time-varying data from\n[CRU Ts4.0](https://data.ceda.ac.uk/badc/cru/data/cru_ts/) and the\n[Japanese 55-year Reanalysis (JRA55)](https://jra.kishou.go.jp/JRA-55/index_en.html).\nConceptually, the procedure applies interpolated time-varying anomalies\nfrom CRU Ts4.0/JRA55 to the high-spatial resolution climatology of\nWorldClim to create a high-spatial resolution dataset that covers a broader\ntemporal record.\n\nTemporal information is inherited from CRU Ts4.0 for most global land\nsurfaces for temperature, precipitation, and vapor pressure. However,\nJRA55 data is used for regions where CRU data had zero climate stations\ncontributing (including all of Antarctica, and parts of Africa,\nSouth America, and scattered islands). For primary climate variables of\ntemperature, vapor pressure, and precipitation, the University of Idaho\nprovides additional data on the number of stations (between 0 and 8) that\ncontributed to the CRU Ts4.0 data used by TerraClimate. JRA55 was used\nexclusively for solar radiation and wind speeds.\n\nTerraClimate additionally produces monthly surface water balance datasets\nusing a water balance model that incorporates reference evapotranspiration,\nprecipitation, temperature, and interpolated plant extractable soil water\ncapacity. A modified Thornthwaite-Mather climatic water-balance model and\nextractable soil water storage capacity data was used at a 0.5° grid from\nWang-Erlandsson et al. (2016).\n\nData Limitations:\n\n 1. Long-term trends in data are inherited from parent datasets.\n    TerraClimate should not be used directly for independent assessments of\n    trends.\n\n 2. TerraClimate will not capture temporal variability at finer scales than\n    parent datasets and thus is not able to capture variability in\n    orographic precipitation ratios and inversions.\n\n 3. The water balance model is very simple and does not account for\n    heterogeneity in vegetation types or their physiological response to\n    changing environmental conditions.\n\n 4. Limited validation in data-sparse regions (e.g., Antarctica).\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1958-01-01T00:00:00Z","2024-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/TERRACLIMATE","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/IDAHO_EPSCOR/IDAHO_EPSCOR_TERRACLIMATE","rel":"related","title":"Run the example for IDAHO_EPSCOR/TERRACLIMATE in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/IDAHO_EPSCOR/IDAHO_EPSCOR_TERRACLIMATE_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/TERRACLIMATE/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/IDAHO_EPSCOR/TERRACLIMATE/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"IGN/RGE_ALTI/1M/2_0","title":"RGE ALTI: IGN RGE ALTI Digital Elevation 1m","description":"The RGE ALTI dataset describes the digital elevation model (DEM) of France\nwith the pixel size of 1m.  It was created from surveys obtained by airborne\nlidar or by correlation of aerial images.\n\nLidar was deployed for flood-prone, coastal, and large forest\nareas. The vertical accuracy of the DEM in these areas is between\n0.2m and 0.5m. Radar was used in mountain areas (Alps, Pyrenees,\nCevennes, Corsica). Caution: in areas with steep slopes, the average\nvertical accuracy is the order of 7m.\n\nThe accuracy of these fields has been checked against various sources:\nthe road and hydrographic networks of the\n[BD TOPO](https://geoservices.ign.fr/bdtopo), geodetic terminals and\npoints calculated on the ground.\n\nAerial photo correlation techniques are used in the rest of the territory.\nOn certain zones treated by correlation, ground measurements are absent\nover large extents due to the presence of vegetation\n(wooded areas, for example). 1987-2001 altimetry data (BD Alti) are used\nto fill these gaps. The vertical accuracy of the DEM on these areas\nis between 0.5m and 0.7m.\n\nCurrently the collection includes a single image IGN/RGE_ALTI/1M/2_0/FXX\nshowing metropolitan France.\n\nThis dataset was prepared and ingested with the support of Guillaume Attard\nand Julien Bardonnet ([AGEOCE](https://www.ageoce.com)).\nThe preparation process is\n[described here](https://medium.com/@gui.attard/pre-processing-the-dem-of-france-rge-alti-5m-for-implementation-into-earth-engine-de9a0778e0d9).\n\nSee also:\n[user's guide](https://geoservices.ign.fr/sites/default/files/2021-07/DC_RGEALTI_2-0.pdf).\n\n","license":"etalab-2.0","extent":{"spatial":{"bbox":[[-5.2,41.35,9.85,51.14]]},"temporal":{"interval":[["2009-01-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/IGN/RGE_ALTI/1M/2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/IGN/IGN_RGE_ALTI_1M_2_0","rel":"related","title":"Run the example for IGN/RGE_ALTI/1M/2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/IGN/IGN_RGE_ALTI_1M_2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/IGN_RGE_ALTI_1M_2_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/IGN/RGE_ALTI/1M/2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/IGN/RGE_ALTI/1M/2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"IPCC/AR6/SLP","title":"IPCC AR6 Sea Level Projections Regional (Medium Confidence)","description":"The dataset provided by the IPCC features comprehensive global and\nregional sea level projections from the IPCC 6th Assessment Report (AR6).\nThis collection contains assets for the medium confidence sea level\nrise projections. The dataset spans from 2020 to 2150 and includes\nprojections for various future scenarios outlined in the AR6. It offers a\ndetailed breakdown of individual processes contributing to sea level\nchanges, alongside globally averaged projections, regional projections on a\nregular global grid. These projections align with those in the IPCC AR6\nreport, ensuring consistency and reliability. (Local projections at specific\ntide gauge locations are not included because they are not on a regular\ngrid.)\n\nThe following are summaries of the future scenarios included in the dataset:\n\n- SSP1-1.9 holds warming to approximately 1.5&deg;C above 1850-1900 in 2100\n  after slight overshoot (median) and implies net zero CO2 emissions around\n  the middle of the century.\n- SSP1-2.6 stays below 2.0&deg;C warming relative to 1850-1900 (median) with\n  implied net zero emissions in the second half of the century.\n- SSP2-4.5 is approximately in line with the upper end of aggregate\n  Nationally Determined Contribution emission levels by 2030. SR1.5 assessed\n  temperature projections for NDCs to be between 2.7 and 3.4&deg;C by 2100,\n  corresponding to the upper half of projected warming under SSP2-4.5.\n- SSP3-7.0 is a medium to high reference scenario resulting from no\n  additional climate policy under the SSP3 socioeconomic development\n  narrative. SSP3-7.0 has particularly high non-CO2 emissions, including\n  high aerosols emissions.\n- SSP5-8.5 is a high reference scenario with no additional climate policy.\n  Emission levels as high as SSP5-8.5 are not obtained by Integrated\n  Assessment Models (IAMs) under any of the SSPs other than the fossil\n  fueled SSP5 socioeconomic development pathway.\n\nEach component of the sea level projection scenarios is provided as a\nseparate band, as detailed in the table below.\n\nComponent               | Description\n:---------------------  | :----------------------------------------------\nTotal                   | Total integrated over all components\nAIS                     | Antarctic Ice Sheet\nGIS                     | Greenland Ice Sheet\nglaciers                | Glaciers\nlandwaterstorage        | Land Water Storage\noceandynamics           | Ocean Dynamics (includes Thermal Expansion)\nverticallandmotion      | Vertical Land Motion (non-climatic processes)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2120-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/IPCC/AR6/SLP","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/IPCC/IPCC_AR6_SLP","rel":"related","title":"Run the example for IPCC/AR6/SLP in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/IPCC/IPCC_AR6_SLP_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/IPCC_AR6_SLP#terms-of-use","rel":"license","type":"text/html"},{"href":"https://creativecommons.org/licenses/by/4.0/legalcode.en","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.5914709","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/IPCC/AR6/SLP/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/IPCC/AR6/SLP/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/aluminium_extractable","title":"iSDAsoil extractable Aluminium","description":"Extractable aluminium at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/aluminium_extractable","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_aluminium_extractable","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/aluminium_extractable in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_aluminium_extractable_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_aluminium_extractable#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/aluminium_extractable/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/bedrock_depth","title":"iSDAsoil Depth to Bedrock","description":"Depth to bedrock at 0-200 cm depth, predicted mean and standard deviation.\n\nDue to the potential cropland mask that was used for generating the data,\nmany areas of exposed rock (where depth to bedrock would be 0 cm) have been\nmasked out and therefore appear as nodata values. The maximum depth of this\nlayer is 200 cm, but this does not represent the maximum possible soil depth,\ntherefore values of 200 should be interpreted as >= 200.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/bedrock_depth","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_bedrock_depth","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/bedrock_depth in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_bedrock_depth_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_bedrock_depth#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/bedrock_depth/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/bulk_density","title":"iSDAsoil Bulk Density, <2mm Fraction","description":"Bulk density, <2mm fraction at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `x/100`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/bulk_density","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_bulk_density","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/bulk_density in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_bulk_density_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_bulk_density#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/bulk_density/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/calcium_extractable","title":"iSDAsoil Extractable Calcium","description":"Extractable calcium at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/calcium_extractable","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_calcium_extractable","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/calcium_extractable in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_calcium_extractable_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_calcium_extractable#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/calcium_extractable/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/carbon_organic","title":"iSDAsoil Organic Carbon","description":"Organic carbon at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/carbon_organic","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_carbon_organic","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/carbon_organic in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_carbon_organic_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_carbon_organic#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/carbon_organic/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/carbon_total","title":"iSDAsoil Total Carbon","description":"Total carbon at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/carbon_total","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_carbon_total","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/carbon_total in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_carbon_total_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_carbon_total#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/carbon_total/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/cation_exchange_capacity","title":"iSDAsoil Effective Cation Exchange Capacity","description":"Effective Cation Exchange Capacity predicted mean and standard deviation\nat soil depths of 0-20 cm and 20-50 cm,\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/cation_exchange_capacity","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_cation_exchange_capacity","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/cation_exchange_capacity in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_cation_exchange_capacity_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_cation_exchange_capacity#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/cation_exchange_capacity/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/clay_content","title":"iSDAsoil Clay Content","description":"Clay content at soil depths of 0-20 cm and 20-50 cm,\\npredicted mean and standard deviation.\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/clay_content","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_clay_content","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/clay_content in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_clay_content_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_clay_content#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/clay_content/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/fcc","title":"iSDAsoil Fertility Capability Classification","description":"Soil fertility capability classification derived using slope, chemical,\nand physical soil properties.  For more information about this layer, please\nvisit [this page](https://www.isda-africa.com/isdasoil/faq/#faq7).\n\nThe classes for the 'fcc' band apply to pixel values that must be\nback-transformed with `x modulo 3000`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/fcc","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_fcc","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/fcc in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_fcc_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_fcc#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/fcc/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/iron_extractable","title":"iSDAsoil Extractable Iron","description":"Extractable iron at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/iron_extractable","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_iron_extractable","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/iron_extractable in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_iron_extractable_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_iron_extractable#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/iron_extractable/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/magnesium_extractable","title":"iSDAsoil Extractable Magnesium","description":"Extractable magnesium at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/magnesium_extractable","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_magnesium_extractable","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/magnesium_extractable in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_magnesium_extractable_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_magnesium_extractable#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/magnesium_extractable/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/nitrogen_total","title":"iSDAsoil Total Nitrogen","description":"Total nitrogen at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/100)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/nitrogen_total","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_nitrogen_total","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/nitrogen_total in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_nitrogen_total_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_nitrogen_total#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/nitrogen_total/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/ph","title":"iSDAsoil pH","description":"pH at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `x/10`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/ph","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_ph","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/ph in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_ph_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_ph#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/ph/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/phosphorus_extractable","title":"iSDAsoil Extractable Phosphorus","description":"Extractable phosphorus at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/phosphorus_extractable","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_phosphorus_extractable","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/phosphorus_extractable in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_phosphorus_extractable_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_phosphorus_extractable#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/phosphorus_extractable/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/potassium_extractable","title":"iSDAsoil Extractable Potassium","description":"Extractable potassium at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/potassium_extractable","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_potassium_extractable","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/potassium_extractable in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_potassium_extractable_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_potassium_extractable#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/potassium_extractable/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/sand_content","title":"iSDAsoil Sand Content","description":"Sand content at soil depths of 0-20 cm and 20-50 cm,\\npredicted mean and standard deviation.\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/sand_content","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_sand_content","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/sand_content in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_sand_content_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_sand_content#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/sand_content/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/silt_content","title":"iSDAsoil Silt Content","description":"Silt content at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/silt_content","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_silt_content","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/silt_content in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_silt_content_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_silt_content#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/silt_content/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/stone_content","title":"iSDAsoil Stone Content","description":"Stone content at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/stone_content","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_stone_content","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/stone_content in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_stone_content_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_stone_content#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/stone_content/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/sulphur_extractable","title":"iSDAsoil Extractable Sulfur","description":"Extractable sulfur at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/sulphur_extractable","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_sulphur_extractable","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/sulphur_extractable in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_sulphur_extractable_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_sulphur_extractable#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/sulphur_extractable/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/texture_class","title":"iSDAsoil USDA Texture Class","description":"USDA Texture Class at soil depths of 0-20 cm and 20-50 cm.\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/texture_class","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_texture_class","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/texture_class in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_texture_class_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_texture_class#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/texture_class/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISDASOIL/Africa/v1/zinc_extractable","title":"iSDAsoil Extractable Zinc","description":"Extractable zinc at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-31.46,-35.22,57.08,37.98]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/zinc_extractable","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_zinc_extractable","rel":"related","title":"Run the example for ISDASOIL/Africa/v1/zinc_extractable in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_zinc_extractable_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_zinc_extractable#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISDASOIL/Africa/v1/zinc_extractable/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISRIC/SoilGrids250m/v20","title":"SoilGrids250m 2.0 - Volumetric Water Content","description":"Volumetric Water Content at 10kPa, 33kPa, and 1500kPa suction in\n10^-3 cm^3/cm^3 (0.1 v% or 1 mm/m) at 6 standard depths (0-5cm, 5-15cm,\n15-30cm, 30-60cm, 60-100cm, 100-200cm). Predictions were derived using a\ndigital soil mapping approach based on Quantile Random Forest, drawing on a\nglobal compilation of soil profile data and environmental layers.\nThis dataset includes predictions for three different suction levels,\nproviding insights into soil water availability.\n\nDocumentation:\n\n* [Scientific Paper](https://www.sciencedirect.com/science/article/pii/S2095633922000636?via%3Dihub)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-56,180,84]]},"temporal":{"interval":[["1905-04-01T00:00:00Z","2016-07-05T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISRIC/SoilGrids250m/v20","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISRIC/ISRIC_SoilGrids250m_v20","rel":"related","title":"Run the example for ISRIC/SoilGrids250m/v20 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISRIC/ISRIC_SoilGrids250m_v20_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISRIC_SoilGrids250m_v20#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.isric.org/explore/soilgrids","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISRIC/SoilGrids250m/v20/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ISRIC/SoilGrids250m/v20/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"ISRIC/SoilGrids250m/v2_0","title":"SoilGrids250m 2.0 - Volumetric Water Content","description":"Volumetric Water Content at 10kPa, 33kPa, and 1500kPa suction in\n10^-3 cm^3/cm^3 (0.1 v% or 1 mm/m) at 6 standard depths (0-5cm, 5-15cm,\n15-30cm, 30-60cm, 60-100cm, 100-200cm). Predictions were derived using a\ndigital soil mapping approach based on Quantile Random Forest, drawing on a\nglobal compilation of soil profile data and environmental layers.\nThis dataset includes predictions for three different suction levels,\nproviding insights into soil water availability.\n\nThe dataset is organized into three main assets: `/wv0010`, `/wv0033`,\nand `/wv1500`. Each of these assets contains bands representing soil\nproperties at different depths and quantiles. The band names follow the\npattern `val_<depth>_<quantile>`, where `depth` represents a soil depth\nrange (e.g., 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm) and\n`quantile` represents a statistical measure (e.g., mean, Q0.05, Q0.5,\nQ0.95).\n\nThe uncertainty band is not yet included. It is possible to calculate\nthe uncertainty from the ratio between the inter-quantile range\n(90% prediction interval width) and the median: (Q0.95-Q0.05)/Q0.50.\n\nDocumentation:\n\n* [Scientific Paper](https://www.sciencedirect.com/science/article/pii/S2095633922000636?via%3Dihub)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-56,180,84]]},"temporal":{"interval":[["1905-04-01T00:00:00Z","2016-07-05T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/ISRIC/SoilGrids250m/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISRIC/ISRIC_SoilGrids250m_v2_0","rel":"related","title":"Run the example for ISRIC/SoilGrids250m/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ISRIC/ISRIC_SoilGrids250m_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/ISRIC_SoilGrids250m_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.isric.org/explore/soilgrids","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/ISRIC/SoilGrids250m/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/ISRIC/SoilGrids250m/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/ALOS/AVNIR-2/ORI","title":"ALOS/AVNIR-2 ORI","description":"This dataset is contains orthorectified imagery from\nthe Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2)\nsensor on-board the Advanced Land Observing Satellite (ALOS) \\\"DAICHI\\\".\n\nThe AVNIR-2 ORI product was created from AVNIR-2 1B1 data after stereo\nmatching with reference to ALOS's Panchromatic Remote-sensing Instrument\nfor Stereo Mapping (PRISM)-derived DSM AW3D30. The orthorectification\nprocess used AW3D30 DSM data when available and SRTM (The Shuttle\nRadar Topography Mission) DSM data otherwise.\n","license":"proprietary","extent":{"spatial":{"bbox":[[127.05,23.81,154.41,46.04]]},"temporal":{"interval":[["2006-04-26T00:00:00Z","2011-04-18T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AVNIR-2/ORI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_ALOS_AVNIR-2_ORI","rel":"related","title":"Run the example for JAXA/ALOS/AVNIR-2/ORI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_ALOS_AVNIR-2_ORI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AVNIR-2_ORI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AVNIR-2/ORI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AVNIR-2/ORI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/ALOS/AW3D30/V1_1","title":"ALOS DSM: Global 30m v1.1 [deprecated]","description":"ALOS World 3D - 30m (AW3D30) is a global digital surface\nmodel (DSM) dataset with a horizontal resolution of approximately\n30 meters (1 arcsec mesh). The dataset is based on the DSM dataset\n(5-meter mesh version) of the [World 3D Topographic Data](https://www.aw3d.jp/en/).\nMore details are available in the dataset [documentation](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v11_format_e.pdf).\n\nIn the version 1.1, released in March 2017, void values in the cloud and snow\npixels between 60 degrees North and 60 degrees South were filled with existing\nDEMs using the Delta Surface Fill method.\n\nThe AW3D DSM elevation is calculated by an image matching process\nthat uses a stereo pair of optical images. Clouds, snow, and ice are\nautomatically identified during processing and applied the\nmask information. However, mismatched points sometimes\nremain especially surrounding (or at the edges of) clouds, snow,\nand ice areas, which cause some height errors in the final DSM.\n\nHere are some example areas with data values outside of valid elevation\nrange. Impossibly low negative values are concentrated in Antarctica around\n(-63.77, -61.660), (-77.22, -150.27), and (-73.29, 168.14); in Indonesia\naround (-5.36, 134.55); in Brazil around (-1.667113844, -50.6269684);\nand in Peru around (-10.45048137, -75.39459876) with approximate values of\n-1013, -998, -635, and -610 respectively. Impossibly high\npositive values are found in several locations in the Arctic around\n(79.83, -77.67) and (69.54, -75.42); in Fiji around (-16.58, 179.44)\nand (-18.96, 178.39); and in Nepal around (28.50, 84.56) with\napproximate values of 15369, 15213, and 10900 respectively.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-12T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AW3D30/V1_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_ALOS_AW3D30_V1_1","rel":"related","title":"Run the example for JAXA/ALOS/AW3D30/V1_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_ALOS_AW3D30_V1_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AW3D30_V1_1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://earth.jaxa.jp/en/data/policy/","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_AW3D30_V4_1.json","rel":"latest-version","title":"JAXA/ALOS/AW3D30/V4_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_AW3D30_V2_1.json","rel":"successor-version","title":"JAXA/ALOS/AW3D30/V2_1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AW3D30/V1_1/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/ALOS/AW3D30/V2_1","title":"ALOS DSM: Global 30m v2.1 [deprecated]","description":"ALOS World 3D - 30m (AW3D30) is a global digital surface\nmodel (DSM) dataset with a horizontal resolution of approximately\n30 meters (1 arcsec mesh). The dataset is based on the DSM dataset\n(5-meter mesh version) of the [World 3D Topographic Data](https://www.aw3d.jp/en/).\nMore details are available in the dataset [documentation](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v11_format_e.pdf).\n\nIn the version 2.1, released in April 2018, the source AW3D DSM has been upgraded\nto version 2. Masks of the land water and low correlation pixels were also filled\nwith existing DEMs in addition to the cloud and snow pixels between 60 degrees\nNorth and 60 degrees South. In Japan area, filling was carried out after updating\ncoastline information.\n\nThe AW3D DSM elevation is calculated by an image matching process\nthat uses a stereo pair of optical images. Clouds, snow, and ice are\nautomatically identified during processing and applied the\nmask information. However, mismatched points sometimes\nremain especially surrounding (or at the edges of) clouds, snow,\nand ice areas, which cause some height errors in the final DSM.\n\nHere are some example areas with data values outside of valid elevation\nrange. Impossibly low negative values are concentrated in Antarctica around\n(-63.77, -61.660), (-77.22, -150.27), and (-73.29, 168.14); in Indonesia\naround (-5.36, 134.55); in Brazil around (-1.667113844, -50.6269684);\nand in Peru around (-10.45048137, -75.39459876) with approximate values of\n-1013, -998, -635, and -610 respectively. Impossibly high\npositive values are found in several locations in the Arctic around\n(79.83, -77.67) and (69.54, -75.42); in Fiji around (-16.58, 179.44)\nand (-18.96, 178.39); and in Nepal around (28.50, 84.56) with\napproximate values of 15369, 15213, and 10900 respectively.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-12T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AW3D30/V2_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_ALOS_AW3D30_V2_1","rel":"related","title":"Run the example for JAXA/ALOS/AW3D30/V2_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_ALOS_AW3D30_V2_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AW3D30_V2_1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://earth.jaxa.jp/en/data/policy/","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_AW3D30_V4_1.json","rel":"latest-version","title":"JAXA/ALOS/AW3D30/V4_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_AW3D30_V1_1.json","rel":"predecessor-version","title":"JAXA/ALOS/AW3D30/V1_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_AW3D30_V2_2.json","rel":"successor-version","title":"JAXA/ALOS/AW3D30/V2_2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AW3D30/V2_1/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/ALOS/AW3D30/V2_2","title":"ALOS DSM: Global 30m v2.2 [deprecated]","description":"ALOS World 3D - 30m (AW3D30) is a global digital surface\nmodel (DSM) dataset with a horizontal resolution of approximately\n30 meters (1 arcsec mesh). The dataset is based on the DSM dataset\n(5-meter mesh version) of the [World 3D Topographic Data](https://www.aw3d.jp/en/).\nMore details are available in the dataset [documentation](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v11_format_e.pdf).\n\nVersion 2.2, released in April 2019 is an improved version of the\nnorthern region over 60 degrees north. Along with the complement of no-data or\nlow-quality area, updating of coastline was also performed.\n\nThe AW3D DSM elevation is calculated by an image matching process\nthat uses a stereo pair of optical images. Clouds, snow, and ice are\nautomatically identified during processing and applied the\nmask information. However, mismatched points sometimes\nremain especially surrounding (or at the edges of) clouds, snow,\nand ice areas, which cause some height errors in the final DSM.\n\nHere are some example areas with data values outside of valid elevation\nrange. Impossibly low negative values are concentrated in Antarctica around\n(-63.77, -61.660), (-77.22, -150.27), and (-73.29, 168.14); in Indonesia\naround (-5.36, 134.55); in Brazil around (-1.667113844, -50.6269684);\nand in Peru around (-10.45048137, -75.39459876) with approximate values of\n-1013, -998, -635, and -610 respectively. Impossibly high\npositive values are found in several locations in the Arctic around\n(79.83, -77.67) and (69.54, -75.42); in Fiji around (-16.58, 179.44)\nand (-18.96, 178.39); and in Nepal around (28.50, 84.56) with\napproximate values of 15369, 15213, and 10900 respectively.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-12T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AW3D30/V2_2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_ALOS_AW3D30_V2_2","rel":"related","title":"Run the example for JAXA/ALOS/AW3D30/V2_2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_ALOS_AW3D30_V2_2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AW3D30_V2_2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://earth.jaxa.jp/en/data/policy/","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_AW3D30_V4_1.json","rel":"latest-version","title":"JAXA/ALOS/AW3D30/V4_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_AW3D30_V2_1.json","rel":"predecessor-version","title":"JAXA/ALOS/AW3D30/V2_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_AW3D30_V3_2.json","rel":"successor-version","title":"JAXA/ALOS/AW3D30/V3_2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AW3D30/V2_2/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/ALOS/AW3D30/V3_2","title":"ALOS DSM: Global 30m  v3.2 [deprecated]","description":"ALOS World 3D - 30m (AW3D30) is a global digital surface\nmodel (DSM) dataset with a horizontal resolution of approximately\n30 meters (1 arcsec mesh). The dataset is based on the DSM dataset\n(5-meter mesh version) of the [World 3D Topographic Data](https://www.aw3d.jp/en/).\nMore details are available in the dataset [documentation](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v11_format_e.pdf).\n\nVersion 3.2, released in January 2021, is an improved version created by reconsidering\nthe format in the high latitude area, auxiliary data, and processing method. Different pixel\nspacing for each latitude zone was adopted at high latitude area. Coastline data, which is one\nof the auxiliary datasets, was changed, and new supplementary data was used. In addition, as a\nsource data for Japan, AW3D version 3 was also used. Furthermore, the method of detecting\nanomalous values in the process was improved.\n\nNote: See the code example for the recommended way of computing slope.\nUnlike most DEMs in Earth Engine, this is an image collection due to\nmultiple resolutions of source files that make it impossible to mosaic them\ninto a single asset, so the slope computations need a reprojection.\n\nThe AW3D DSM elevation is calculated by an image matching process\nthat uses a stereo pair of optical images. Clouds, snow, and ice are\nautomatically identified during processing and applied the\nmask information. However, mismatched points sometimes\nremain especially surrounding (or at the edges of) clouds, snow,\nand ice areas, which cause some height errors in the final DSM.\n\nHere are some example areas with data values outside of valid elevation\nrange. Impossibly low negative values are concentrated in Antarctica around\n(-63.77, -61.660), (-77.22, -150.27), and (-73.29, 168.14); in Indonesia\naround (-5.36, 134.55); in Brazil around (-1.667113844, -50.6269684);\nand in Peru around (-10.45048137, -75.39459876) with approximate values of\n-1013, -998, -635, and -610 respectively. Impossibly high\npositive values are found in several locations in the Arctic around\n(79.83, -77.67) and (69.54, -75.42); in Fiji around (-16.58, 179.44)\nand (-18.96, 178.39); and in Nepal around (28.50, 84.56) with\napproximate values of 15369, 15213, and 10900 respectively.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-12T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AW3D30/V3_2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_ALOS_AW3D30_V3_2","rel":"related","title":"Run the example for JAXA/ALOS/AW3D30/V3_2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_ALOS_AW3D30_V3_2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AW3D30_V3_2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://earth.jaxa.jp/en/data/policy/","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_AW3D30_V4_1.json","rel":"latest-version","title":"JAXA/ALOS/AW3D30/V4_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_AW3D30_V2_2.json","rel":"predecessor-version","title":"JAXA/ALOS/AW3D30/V2_2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_AW3D30_V4_1.json","rel":"successor-version","title":"JAXA/ALOS/AW3D30/V4_1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AW3D30/V3_2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AW3D30/V3_2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/ALOS/AW3D30/V4_1","title":"ALOS DSM: Global 30m  v4.1","description":"ALOS World 3D - 30m (AW3D30) is a global digital surface\nmodel (DSM) dataset with a horizontal resolution of approximately\n30 meters (1 arcsec mesh). The dataset is based on the DSM dataset\n(5-meter mesh version) of the [World 3D Topographic Data](https://www.aw3d.jp/en/).\nMore details are available in the dataset [documentation](https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/data/aw3d30v4.1_product_e_1.0.pdf).\n\nThis ingested dataset combines data from versions 3.1, 4.0, and 4.1.\n\nVersion 4.1 (April 2024): This major update released 19,051 tiles covering\nglobal regions (excluding Antarctica and Japan). It incorporates new\nsupplementary data for void filling and corrects partial anomalies found in\nversions 3.1 and 3.2, along with re-filling voids. For specific tile updates\nin v4.1, please use the v4.1 filter on map tiles or consult the latest\nformat description.\n\nVersion 4.0 (April 2023): This update released 1,886 tiles, improving low\nand mid-latitude regions and areas south of 60 degrees latitude.\n\nKey changes include:\n1. New supplementary data for void filling.\n2. Correction of partial anomalies and re-filling of voids (2 tiles).\n3. Updated coastlines for regions south of 60 degrees latitude (44 tiles).\n4. Disabled Caspian Sea water mask and supplemented with elevation data\n  (54 tiles).\n5. Extracted and corrected new partial anomaly areas in South America\n  (1,786 tiles).\n6. For detailed tile information for v4.0, please use the v4.0 filter on\n  map tiles or refer to the format description.\n\nVersion 3.2, released in January 2021, is an improved version created by\nreconsidering the format in the high latitude area, auxiliary data, and\nprocessing method. Different pixel spacing for each latitude zone was\nadopted at high latitude area. Coastline data, which is one of the\nauxiliary datasets, was changed, and new supplementary data was used. In\naddition, as a source data for Japan, AW3D version 3 was also used.\nFurthermore, the method of detecting anomalous values in the process was\nimproved.\n\nNote: See the code example for the recommended way of computing slope.\nUnlike most DEMs in Earth Engine, this is an image collection due to\nmultiple resolutions of source files that make it impossible to mosaic them\ninto a single asset, so the slope computations need a reprojection.\n\nThe AW3D DSM elevation is calculated by an image matching process\nthat uses a stereo pair of optical images. Clouds, snow, and ice are\nautomatically identified during processing and applied the\nmask information. However, mismatched points sometimes\nremain especially surrounding (or at the edges of) clouds, snow,\nand ice areas, which cause some height errors in the final DSM.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-12T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AW3D30/V4_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_ALOS_AW3D30_V4_1","rel":"related","title":"Run the example for JAXA/ALOS/AW3D30/V4_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_ALOS_AW3D30_V4_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AW3D30_V4_1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://earth.jaxa.jp/en/data/policy/","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_AW3D30_V4_1.json","rel":"latest-version","title":"JAXA/ALOS/AW3D30/V4_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_AW3D30_V3_2.json","rel":"predecessor-version","title":"JAXA/ALOS/AW3D30/V3_2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AW3D30/V4_1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/AW3D30/V4_1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/ALOS/PALSAR-2/Level2_1/StripMap_202401","title":"ALOS-2 PALSAR-2 StripMap Level 2.1","description":"Starting from the night of January 1st, 2024, based on the request from\nJapanese ministries and related organization, JAXA implemented ALOS-2\nPALSAR-2 emergency observation. Since JAXA expects these emergency\nobservation data to be extremely useful for disaster\nmanagement, JAXA decided to open these data on Google Earth Engine for\npublic and research usage.\n\nJAXA released PALSAR-2 Level 2.1 strip map mode observation data with 3m\nsingle polarization for January 1-3 and January 8 2024, together with\nsome archive\ndata. PALSAR-2 Level 2.1 data is orthorectified from level 1.1 data by using\ndigital elevation model. The DN values can be converted to sigma naught\nvalues in decibel unit (dB) using the following equation:\n\n* &sigma;<sub>0</sub> = 10*log<sub>10</sub>(DN<sup>2</sup>) - 83.0 dB\n\nBelow are the dates for which the dataset has data.\n\nObs date                   | Frame     |  Beam     | A/D\n:--------------------------|:----------|:----------|:---------------\n2024/01/01 and 2022/09/26  | 0750, 0760, 0770 | U2-6L | Asc.\n2024/01/02 and 2023/06/06  | 2820, 2830, 2840 | U2-8L | Desc.\n2024/01/08, 2024/01/03 and 2023/12/06  | 0720, 0730 | U2-9R | Asc.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-09-26T00:00:00Z","2024-01-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR-2/Level2_1/StripMap_202401","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_ALOS_PALSAR-2_Level2_1_StripMap_202401","rel":"related","title":"Run the example for JAXA/ALOS/PALSAR-2/Level2_1/StripMap_202401 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_ALOS_PALSAR-2_Level2_1_StripMap_202401_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR-2_Level2_1_StripMap_202401#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR-2/Level2_1/StripMap_202401/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR-2/Level2_1/StripMap_202401/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/ALOS/PALSAR-2/Level2_2/ScanSAR","title":"PALSAR-2 ScanSAR Level 2.2","description":"The 25 m PALSAR-2 ScanSAR is normalized backscatter data\nof PALSAR-2 broad area observation mode with observation width of 350 km.\nThe SAR imagery was ortho-rectificatied and slope corrected\nusing the ALOS World 3D - 30 m (AW3D30) Digital Surface Model.\nPolarization data are stored as 16-bit digital numbers (DN).\nThe DN values can be converted to gamma naught values in decibel unit (dB)\nusing the following equation:\n\n* &gamma;<sub>0</sub> = 10*log<sub>10</sub>(DN<sup>2</sup>) - 83.0 dB\n\nLevel 2.2 data are ortho-rectified and radiometrically terrain-corrected.\n\nThis dataset is compatible with the\n[Committee on Earth Observation (CEOS)](https://ceos.org/)\n[Analysis Ready Data for LAND (CARD4L)](https://ceos.org/ard/files/PFS/NRB/v5.5/CARD4L-PFS_NRB_v5.5.pdf) standard.\n\nNote that this collection is heterogeneous and contains a mixture of\nsingle-polarization (HH) and dual-polarization (HH+HV, and rarely VV+VH)\nimages. Users should filter the collection by the `Polarizations` property\nbefore selecting polarization bands (e.g.,\n`collection.filter(ee.Filter.listContains('Polarizations', 'HV'))`).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2014-08-04T00:00:00Z","2026-10-18T05:58:27.346000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR-2/Level2_2/ScanSAR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_ALOS_PALSAR-2_Level2_2_ScanSAR","rel":"related","title":"Run the example for JAXA/ALOS/PALSAR-2/Level2_2/ScanSAR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_ALOS_PALSAR-2_Level2_2_ScanSAR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR-2_Level2_2_ScanSAR#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR-2/Level2_2/ScanSAR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR-2/Level2_2/ScanSAR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/ALOS/PALSAR/YEARLY/FNF","title":"Global 3-class PALSAR-2/PALSAR Forest/Non-Forest Map","description":"A newer version of this dataset with 4 classes for 2017-2020 can be found in\n[JAXA/ALOS/PALSAR/YEARLY/FNF4](JAXA_ALOS_PALSAR_YEARLY_FNF4.html)\n\nThe global forest/non-forest map (FNF) is generated by\nclassifying the SAR image (backscattering coefficient) in the\nglobal 25m resolution PALSAR-2/PALSAR SAR mosaic so that strong and\nlow backscatter pixels are assigned as \\\"forest\\\" and \\\"non-forest\\\",\nrespectively. Here, \\\"forest\\\" is defined as the natural forest\nwith the area larger than 0.5 ha and forest cover over 10%. This\ndefinition is the same as the Food and Agriculture Organization\n(FAO) definition. Since the radar backscatter from the forest\ndepends on the region (climate zone), the classification of\nForest/Non-Forest is conducted by using a region-dependent\nthreshold of backscatter. The classification accuracy is\nchecked by using in-situ photos and high-resolution optical\nsatellite images. Detailed information is available in the provider's\n[Dataset Description](https://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/DatasetDescription_PALSAR2_Mosaic_FNF_revE.pdf).\n\nAttention:\n\n*   Backscatter values may vary significantly from path to path\n    over high latitude forest areas. This is due to the change of\n    backscattering intensity caused by freezing trees in winter.\n    Please note that this may affect the classification of\n    forest/non-forest.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR/YEARLY/FNF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_ALOS_PALSAR_YEARLY_FNF","rel":"related","title":"Run the example for JAXA/ALOS/PALSAR/YEARLY/FNF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_ALOS_PALSAR_YEARLY_FNF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_FNF#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR/YEARLY/FNF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR/YEARLY/FNF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/ALOS/PALSAR/YEARLY/FNF4","title":"Global 4-class PALSAR-2/PALSAR Forest/Non-Forest Map","description":"The global forest/non-forest map (FNF) is generated by\nclassifying the SAR image (backscattering coefficient) in the\nglobal 25m resolution PALSAR-2/PALSAR SAR mosaic so that strong and\nlow backscatter pixels are assigned as \\\"forest\\\" and \\\"non-forest\\\",\nrespectively. Here, \\\"forest\\\" is defined as the natural forest\nwith the area larger than 0.5 ha and forest cover over 10%. This\ndefinition is the same as the Food and Agriculture Organization\n(FAO) definition. Since the radar backscatter from the forest\ndepends on the region (climate zone), the classification of\nForest/Non-Forest is conducted by using a region-dependent\nthreshold of backscatter. The classification accuracy is\nchecked by using in-situ photos and high-resolution optical\nsatellite images.\n\nDetailed information:\n\n*   [2017 - 2020](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_v200a.pdf)\n\nAttention:\n\n*   Backscatter values may vary significantly from path to path\n    over high latitude forest areas. This is due to the change of\n    backscattering intensity caused by freezing trees in winter.\n    Please note that this may affect the classification of\n    forest/non-forest.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR/YEARLY/FNF4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_ALOS_PALSAR_YEARLY_FNF4","rel":"related","title":"Run the example for JAXA/ALOS/PALSAR/YEARLY/FNF4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_ALOS_PALSAR_YEARLY_FNF4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_FNF4#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR/YEARLY/FNF4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR/YEARLY/FNF4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/ALOS/PALSAR/YEARLY/SAR","title":"Global PALSAR-2/PALSAR Yearly Mosaic, version 1","description":"A newer version of this dataset with data for 2015-2021 can be found in\n[JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH](JAXA_ALOS_PALSAR_YEARLY_SAR_EPOCH.html)\n\nThe global 25m PALSAR/PALSAR-2 mosaic is a seamless global\nSAR image created by mosaicking strips of SAR imagery\nfrom PALSAR/PALSAR-2. For each year and location,\nthe strip data were selected through visual inspection of the\nbrowse mosaics available over the period, with those showing minimum\nresponse to surface moisture preferentially used. In cases\nwhere the availability was limited (e.g., because of the requirement\nfor observations during specific emergencies), data were necessarily\nselected from the year before or after, including from 2006.\n[Shimada et al. 2014](https://doi.org/10.1016/j.rse.2014.04.014)\n\nThere is no data for 2011-2014 due to the gap between ALOS and ALOS-2\ntemporal coverage.\n\nThe SAR imagery was ortho-rectificatied and slope corrected\nusing the 90m SRTM Digital Elevation Model.\nA destriping process (Shimada & Isoguchi, 2002, 2010) was applied\nto equalize the intensity differences between neighboring strips,\noccurring largely due to seasonal and daily differences\nin surface moisture conditions.\n\nPolarization data are stored as 16-bit digital numbers (DN).\nThe DN values can be converted to gamma naught values\nin decibel unit (dB) using the following equation:\n\n  *    &#947;&#8320; = 10log&#8321;&#8320;(DN&#178;) - 83.0 dB\n\nAttention:\n\n*   Backscatter values may vary significantly from path to path\n    over high latitude forest areas. This is due to the change of\n    backscattering intensity caused by freezing trees in winter.\n\nMore information is available in the provider's\n[Dataset Description](https://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/DatasetDescription_PALSAR2_Mosaic_FNF_revH.pdf).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR/YEARLY/SAR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_ALOS_PALSAR_YEARLY_SAR","rel":"related","title":"Run the example for JAXA/ALOS/PALSAR/YEARLY/SAR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_ALOS_PALSAR_YEARLY_SAR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_SAR#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR/YEARLY/SAR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR/YEARLY/SAR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH","title":"Global PALSAR-2/PALSAR Yearly Mosaic, version 2.5.0","description":"The global 25m PALSAR/PALSAR-2 mosaic is a seamless global\nSAR image created by mosaicking strips of SAR imagery\nfrom PALSAR/PALSAR-2. For each year and location,\nthe strip data were selected through visual inspection of the\nbrowse mosaics available over the period, with those showing minimum\nresponse to surface moisture preferentially used.\nOnly data from the target year have been used for each annual mosaic,\nand hence no gap-filling using data from previous years in case of gaps\nin the annual global coverage.\n\nThere is no data for 2011-2014 due to the gap between ALOS and ALOS-2\ntemporal coverage.\n\nThe SAR imagery was ortho-rectificatied and slope corrected using the\ndigital surface model ALOS World 3D - 30m (AW3D30).\n\nA destriping process (Shimada & Isoguchi, 2002, 2010) was applied\nto equalize the intensity differences between neighboring strips,\noccurring largely due to seasonal and daily differences\nin surface moisture conditions.\n\nPolarization data are stored as 16-bit digital numbers (DN).\nThe DN values can be converted to gamma naught values\nin decibel unit (dB) using the following equation:\n\n  *    &#947;&#8320; = 10log&#8321;&#8320;(DN&#178;) - 83.0 dB\n\nAttention:\n\n*   Backscatter values may vary significantly from path to path\n    over high latitude forest areas. This is due to the change of\n    backscattering intensity caused by freezing trees in winter.\n\nMore information is available in the provider's\n[Dataset Description](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_Mosaic_ver250.pdf).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2024-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_ALOS_PALSAR_YEARLY_SAR_EPOCH","rel":"related","title":"Run the example for JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_ALOS_PALSAR_YEARLY_SAR_EPOCH_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_SAR_EPOCH#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/GCOM-C/L3/LAND/LAI/V1","title":"GCOM-C/SGLI L3 Leaf Area Index (V1)","description":"This product is the sum of the one-sided green leaf area per unit ground area.\n\nA newer version [JAXA/GCOM-C/L3/LAND/LAI/V3](JAXA_GCOM-C_L3_LAND_LAI_V3) is also available\nfor this dataset which uses this [algorithm](https://suzaku.eorc.jaxa.jp/GCOM_C/data/product_std.html)\nfor processing.\n\nGCOM-C conducts long-term and continuous global observation and data collection to elucidate the\nmechanism behind fluctuations in radiation budget and carbon cycle needed to make accurate\nprojections regarding future temperature rise. At the same time, cooperating with research\ninstitutions having a climate numerical model, it contributes to reduction of errors in\ntemperature rise prediction derived from the climate numerical model and improvement of accuracy\nof prediction of various environmental changes. SGLI mounted on GCOM-C is the succession sensor\nof the Global Imager (GLI) mounted on ADEOS-II (MIDORI II) and is the Imaging Radiometer which\nmeasures the radiation from near-ultraviolet to thermal infrared region (380 nm-12 um) in 19\nchannels. Global observation of once for approximately every two days is possible at\nmid-latitude near Japan by observation width at ground greater than 1,000 km. In addition, SGLI\nrealizes high resolution than the similar global sensor and has a polarized observation function\nand a multi-angle observation function.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2020-06-28T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/LAND/LAI/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V1","rel":"related","title":"Run the example for JAXA/GCOM-C/L3/LAND/LAI/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://gportal.jaxa.jp/gpr/index/eula?lang=en","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/LAND/LAI/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/LAND/LAI/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/GCOM-C/L3/LAND/LAI/V2","title":"GCOM-C/SGLI L3 Leaf Area Index (V2)","description":"This product is the sum of the one-sided green leaf area per unit ground area.\n\nFor data after 2021-11-28, see [the V3 dataset](JAXA/GCOM-C/L3/LAND/LAI/V3).\n\nGCOM-C conducts long-term and continuous global observation and data collection to elucidate the\nmechanism behind fluctuations in radiation budget and carbon cycle needed to make accurate\nprojections regarding future temperature rise. At the same time, cooperating with research\ninstitutions having a climate numerical model, it contributes to reduction of errors in\ntemperature rise prediction derived from the climate numerical model and improvement of accuracy\nof prediction of various environmental changes. SGLI mounted on GCOM-C is the succession sensor\nof the Global Imager (GLI) mounted on ADEOS-II (MIDORI II) and is the Imaging Radiometer which\nmeasures the radiation from near-ultraviolet to thermal infrared region (380 nm-12 um) in 19\nchannels. Global observation of once for approximately every two days is possible at\nmid-latitude near Japan by observation width at ground greater than 1,000 km. In addition, SGLI\nrealizes high resolution than the similar global sensor and has a polarized observation function\nand a multi-angle observation function.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2021-11-28T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/LAND/LAI/V2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V2","rel":"related","title":"Run the example for JAXA/GCOM-C/L3/LAND/LAI/V2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://gportal.jaxa.jp/gpr/index/eula?lang=en","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/LAND/LAI/V2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/LAND/LAI/V2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/GCOM-C/L3/LAND/LAI/V3","title":"GCOM-C/SGLI L3 Leaf Area Index (V3)","description":"This product is the sum of the one-sided green leaf area per unit ground area.\n\nThis is an ongoing dataset with a latency of 3-4 days.\n\nGCOM-C conducts long-term and continuous global observation and data collection to elucidate the\nmechanism behind fluctuations in radiation budget and carbon cycle needed to make accurate\nprojections regarding future temperature rise. At the same time, cooperating with research\ninstitutions having a climate numerical model, it contributes to reduction of errors in\ntemperature rise prediction derived from the climate numerical model and improvement of accuracy\nof prediction of various environmental changes. SGLI mounted on GCOM-C is the succession sensor\nof the Global Imager (GLI) mounted on ADEOS-II (MIDORI II) and is the Imaging Radiometer which\nmeasures the radiation from near-ultraviolet to thermal infrared region (380 nm-12 um) in 19\nchannels. Global observation of once for approximately every two days is possible at\nmid-latitude near Japan by observation width at ground greater than 1,000 km. In addition, SGLI\nrealizes high resolution than the similar global sensor and has a polarized observation function\nand a multi-angle observation function.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-11-29T00:00:00Z","2026-10-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/LAND/LAI/V3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V3","rel":"related","title":"Run the example for JAXA/GCOM-C/L3/LAND/LAI/V3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://gportal.jaxa.jp/gpr/index/eula?lang=en","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/LAND/LAI/V3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/LAND/LAI/V3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/GCOM-C/L3/LAND/LST/V1","title":"GCOM-C/SGLI L3 Land Surface Temperature (V1)","description":"This product is the temperature of terrestrial land surface.\n\nA newer version [JAXA/GCOM-C/L3/LAND/LST/V3](JAXA_GCOM-C_L3_LAND_LST_V3) is also available\nfor this dataset which uses this [algorithm](https://suzaku.eorc.jaxa.jp/GCOM_C/data/product_std.html)\nfor processing.\n\nGCOM-C conducts long-term and continuous global observation and data collection to elucidate the\nmechanism behind fluctuations in radiation budget and carbon cycle needed to make accurate\nprojections regarding future temperature rise. At the same time, cooperating with research\ninstitutions having a climate numerical model, it contributes to reduction of errors in\ntemperature rise prediction derived from the climate numerical model and improvement of accuracy\nof prediction of various environmental changes. SGLI mounted on GCOM-C is the succession sensor\nof the Global Imager (GLI) mounted on ADEOS-II (MIDORI II) and is the Imaging Radiometer which\nmeasures the radiation from near-ultraviolet to thermal infrared region (380 nm-12 um) in 19\nchannels. Global observation of once for approximately every two days is possible at\nmid-latitude near Japan by observation width at ground greater than 1,000 km. In addition, SGLI\nrealizes high resolution than the similar global sensor and has a polarized observation function\nand a multi-angle observation function.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2020-06-28T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/LAND/LST/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_GCOM-C_L3_LAND_LST_V1","rel":"related","title":"Run the example for JAXA/GCOM-C/L3/LAND/LST/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_GCOM-C_L3_LAND_LST_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LST_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://gportal.jaxa.jp/gpr/index/eula?lang=en","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/LAND/LST/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/LAND/LST/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/GCOM-C/L3/LAND/LST/V2","title":"GCOM-C/SGLI L3 Land Surface Temperature (V2)","description":"This product is the temperature of terrestrial land surface.\n\nFor data after 2021-11-28, see [the V3 dataset](JAXA_GCOM-C_L3_LAND_LST_V3).\n\nGCOM-C conducts long-term and continuous global observation and data collection to elucidate the\nmechanism behind fluctuations in radiation budget and carbon cycle needed to make accurate\nprojections regarding future temperature rise. At the same time, cooperating with research\ninstitutions having a climate numerical model, it contributes to reduction of errors in\ntemperature rise prediction derived from the climate numerical model and improvement of accuracy\nof prediction of various environmental changes. SGLI mounted on GCOM-C is the succession sensor\nof the Global Imager (GLI) mounted on ADEOS-II (MIDORI II) and is the Imaging Radiometer which\nmeasures the radiation from near-ultraviolet to thermal infrared region (380 nm-12 um) in 19\nchannels. Global observation of once for approximately every two days is possible at\nmid-latitude near Japan by observation width at ground greater than 1,000 km. 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At the same time, cooperating with research\ninstitutions having a climate numerical model, it contributes to reduction of errors in\ntemperature rise prediction derived from the climate numerical model and improvement of accuracy\nof prediction of various environmental changes. SGLI mounted on GCOM-C is the succession sensor\nof the Global Imager (GLI) mounted on ADEOS-II (MIDORI II) and is the Imaging Radiometer which\nmeasures the radiation from near-ultraviolet to thermal infrared region (380 nm-12 um) in 19\nchannels. Global observation of once for approximately every two days is possible at\nmid-latitude near Japan by observation width at ground greater than 1,000 km. 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At the same time, cooperating with research\ninstitutions having a climate numerical model, it contributes to reduction of errors in\ntemperature rise prediction derived from the climate numerical model and improvement of accuracy\nof prediction of various environmental changes. SGLI mounted on GCOM-C is the succession sensor\nof the Global Imager (GLI) mounted on ADEOS-II (MIDORI II) and is the Imaging Radiometer which\nmeasures the radiation from near-ultraviolet to thermal infrared region (380 nm-12 um) in 19\nchannels. Global observation of once for approximately every two days is possible at\nmid-latitude near Japan by observation width at ground greater than 1,000 km. 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At the same time, cooperating with research\ninstitutions having a climate numerical model, it contributes to reduction of errors in\ntemperature rise prediction derived from the climate numerical model and improvement of accuracy\nof prediction of various environmental changes. SGLI mounted on GCOM-C is the succession sensor\nof the Global Imager (GLI) mounted on ADEOS-II (MIDORI II) and is the Imaging Radiometer which\nmeasures the radiation from near-ultraviolet to thermal infrared region (380 nm-12 um) in 19\nchannels. Global observation of once for approximately every two days is possible at\nmid-latitude near Japan by observation width at ground greater than 1,000 km. In addition, SGLI\nrealizes high resolution than the similar global sensor and has a polarized observation function\nand a multi-angle observation function.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2021-11-28T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/CHLA/V2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V2","rel":"related","title":"Run the example for JAXA/GCOM-C/L3/OCEAN/CHLA/V2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://gportal.jaxa.jp/gpr/index/eula?lang=en","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/CHLA/V2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/CHLA/V2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/GCOM-C/L3/OCEAN/CHLA/V3","title":"GCOM-C/SGLI L3 Chlorophyll-a Concentration (V3)","description":"This product is the concentration of the photosynthetic pigment (chlorophyll-a) in phytoplankton\nin the sea surface layer.\n\nThis is an ongoing dataset with a latency of 3-4 days.\n\nGCOM-C conducts long-term and continuous global observation and data collection to elucidate the\nmechanism behind fluctuations in radiation budget and carbon cycle needed to make accurate\nprojections regarding future temperature rise. At the same time, cooperating with research\ninstitutions having a climate numerical model, it contributes to reduction of errors in\ntemperature rise prediction derived from the climate numerical model and improvement of accuracy\nof prediction of various environmental changes. SGLI mounted on GCOM-C is the succession sensor\nof the Global Imager (GLI) mounted on ADEOS-II (MIDORI II) and is the Imaging Radiometer which\nmeasures the radiation from near-ultraviolet to thermal infrared region (380 nm-12 um) in 19\nchannels. Global observation of once for approximately every two days is possible at\nmid-latitude near Japan by observation width at ground greater than 1,000 km. In addition, SGLI\nrealizes high resolution than the similar global sensor and has a polarized observation function\nand a multi-angle observation function.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-11-29T00:00:00Z","2026-10-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/CHLA/V3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V3","rel":"related","title":"Run the example for JAXA/GCOM-C/L3/OCEAN/CHLA/V3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://gportal.jaxa.jp/gpr/index/eula?lang=en","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/CHLA/V3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/CHLA/V3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/GCOM-C/L3/OCEAN/SST/V1","title":"GCOM-C/SGLI L3 Sea Surface Temperature (V1)","description":"This product is the temperature of sea surface.\n\nA newer version [JAXA/GCOM-C/L3/OCEAN/SST/V3](JAXA_GCOM-C_L3_OCEAN_SST_V3) is also available\nfor this dataset which uses this [algorithm](https://suzaku.eorc.jaxa.jp/GCOM_C/data/product_std.html)\nfor processing.\n\nGCOM-C conducts long-term and continuous global observation and data collection to elucidate the\nmechanism behind fluctuations in radiation budget and carbon cycle needed to make accurate\nprojections regarding future temperature rise. At the same time, cooperating with research\ninstitutions having a climate numerical model, it contributes to reduction of errors in\ntemperature rise prediction derived from the climate numerical model and improvement of accuracy\nof prediction of various environmental changes. SGLI mounted on GCOM-C is the succession sensor\nof the Global Imager (GLI) mounted on ADEOS-II (MIDORI II) and is the Imaging Radiometer which\nmeasures the radiation from near-ultraviolet to thermal infrared region (380 nm-12 um) in 19\nchannels. Global observation of once for approximately every two days is possible at\nmid-latitude near Japan by observation width at ground greater than 1,000 km. In addition, SGLI\nrealizes high resolution than the similar global sensor and has a polarized observation function\nand a multi-angle observation function.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2020-06-28T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/SST/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V1","rel":"related","title":"Run the example for JAXA/GCOM-C/L3/OCEAN/SST/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://gportal.jaxa.jp/gpr/index/eula?lang=en","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/SST/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/SST/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/GCOM-C/L3/OCEAN/SST/V2","title":"GCOM-C/SGLI L3 Sea Surface Temperature (V2)","description":"This product is the temperature of sea surface.\n\nFor data after 2021-11-28, see [the V3 dataset](JAXA/GCOM-C/L3/LAND/SST/V3).\n\nGCOM-C conducts long-term and continuous global observation and data collection to elucidate the\nmechanism behind fluctuations in radiation budget and carbon cycle needed to make accurate\nprojections regarding future temperature rise. At the same time, cooperating with research\ninstitutions having a climate numerical model, it contributes to reduction of errors in\ntemperature rise prediction derived from the climate numerical model and improvement of accuracy\nof prediction of various environmental changes. SGLI mounted on GCOM-C is the succession sensor\nof the Global Imager (GLI) mounted on ADEOS-II (MIDORI II) and is the Imaging Radiometer which\nmeasures the radiation from near-ultraviolet to thermal infrared region (380 nm-12 um) in 19\nchannels. Global observation of once for approximately every two days is possible at\nmid-latitude near Japan by observation width at ground greater than 1,000 km. In addition, SGLI\nrealizes high resolution than the similar global sensor and has a polarized observation function\nand a multi-angle observation function.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2021-11-28T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/SST/V2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V2","rel":"related","title":"Run the example for JAXA/GCOM-C/L3/OCEAN/SST/V2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://gportal.jaxa.jp/gpr/index/eula?lang=en","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/SST/V2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/SST/V2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/GCOM-C/L3/OCEAN/SST/V3","title":"GCOM-C/SGLI L3 Sea Surface Temperature (V3)","description":"This product is the temperature of sea surface.\n\nThis is an ongoing dataset with a latency of 3-4 days.\n\nGCOM-C conducts long-term and continuous global observation and data collection to elucidate the\nmechanism behind fluctuations in radiation budget and carbon cycle needed to make accurate\nprojections regarding future temperature rise. At the same time, cooperating with research\ninstitutions having a climate numerical model, it contributes to reduction of errors in\ntemperature rise prediction derived from the climate numerical model and improvement of accuracy\nof prediction of various environmental changes. SGLI mounted on GCOM-C is the succession sensor\nof the Global Imager (GLI) mounted on ADEOS-II (MIDORI II) and is the Imaging Radiometer which\nmeasures the radiation from near-ultraviolet to thermal infrared region (380 nm-12 um) in 19\nchannels. Global observation of once for approximately every two days is possible at\nmid-latitude near Japan by observation width at ground greater than 1,000 km. In addition, SGLI\nrealizes high resolution than the similar global sensor and has a polarized observation function\nand a multi-angle observation function.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-22T00:00:00Z","2026-10-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/SST/V3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V3","rel":"related","title":"Run the example for JAXA/GCOM-C/L3/OCEAN/SST/V3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://gportal.jaxa.jp/gpr/index/eula?lang=en","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/SST/V3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GCOM-C/L3/OCEAN/SST/V3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/GPM_L3/GSMaP/v6/operational","title":"GSMaP Operational: Global Satellite Mapping of Precipitation - V6","description":"Global Satellite Mapping of Precipitation (GSMaP) provides\na global hourly rain rate with a 0.1 x 0.1 degree resolution. GSMaP\nis a product of the Global Precipitation Measurement (GPM) mission,\nwhich provides global precipitation observations at three hour\nintervals.  Values are estimated using multi-band passive microwave\nand infrared radiometers from the GPM Core Observatory satellite\nand with the assistance of a constellation of other satellites.  GPM's\nprecipitation rate retrieval algorithm is based on a radiative\ntransfer model. The gauge-adjusted rate is calculated based on\nthe optimization of the 24h accumulation of GSMaP hourly rain rate\nto daily precipitation by NOAA/CPC gauge measurement.\nThis dataset is processed by GSMaP algorithm version 6 (product version 3).\nSee [GSMaP Technical Documentation](https://www.eorc.jaxa.jp/GPM/doc/product/format/en/07.GPM_GSMaP_Product_Format_V5_E.pdf)\nfor more details.\n\nThis dataset contains provisional products GSMaP_NRT that are\nregularly replaced with updated versions when the GSMaP_MVK data\nbecome available.\nThe products are marked with a metadata property called ''status''.\nWhen a product is initially made available, the property value\nis ''provisional''. Once a provisional product has been updated\nwith the final version, this value is updated to ''permanent''.\nFor more information please refer\n[General Documentation](https://eolp.jaxa.jp/GSMaP_Hourly.html).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["2014-03-01T00:00:00Z","2026-11-09T14:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v6/operational","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_GPM_L3_GSMaP_v6_operational","rel":"related","title":"Run the example for JAXA/GPM_L3/GSMaP/v6/operational in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_GPM_L3_GSMaP_v6_operational_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_operational#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v6/operational/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v6/operational/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/GPM_L3/GSMaP/v6/reanalysis","title":"GSMaP Reanalysis: Global Satellite Mapping of Precipitation","description":"Global Satellite Mapping of Precipitation (GSMaP) provides\na global hourly rain rate with a 0.1 x 0.1 degree resolution. GSMaP\nis a product of the Global Precipitation Measurement (GPM) mission,\nwhich provides global precipitation observations at three hour\nintervals.  Values are estimated using multi-band passive microwave\nand infrared radiometers from the GPM Core Observatory satellite\nand with the assistance of a constellation of other satellites.  GPM's\nprecipitation rate retrieval algorithm is based on a radiative\ntransfer model. The gauge-adjusted rate is calculated based on\nthe optimization of the 24h accumulation of GSMaP hourly rain rate\nto daily precipitation by NOAA/CPC gauge measurement. This dataset\nis processed by GSMaP algorithm version 6 (product version 3).\nSee [GSMaP Technical Documentation](https://www.eorc.jaxa.jp/GPM/doc/algorithm/GSMaPforGPM_20140902_E.pdf)\nfor more details.\n\nThe operational GSMaP dataset (from 2014 to present)\nis also [available](JAXA_GPM_L3_GSMaP_v6_operational].\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["2000-03-01T00:00:00Z","2014-03-12T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v6/reanalysis","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_GPM_L3_GSMaP_v6_reanalysis","rel":"related","title":"Run the example for JAXA/GPM_L3/GSMaP/v6/reanalysis in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_GPM_L3_GSMaP_v6_reanalysis_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_reanalysis#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v6/reanalysis/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v6/reanalysis/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/GPM_L3/GSMaP/v7/operational","title":"GSMaP Operational: Global Satellite Mapping of Precipitation - V7","description":"Global Satellite Mapping of Precipitation (GSMaP) provides\na global hourly rain rate with a 0.1 x 0.1 degree resolution. GSMaP\nis a product of the Global Precipitation Measurement (GPM) mission,\nwhich provides global precipitation observations at three hour\nintervals.  Values are estimated using multi-band passive microwave\nand infrared radiometers from the GPM Core Observatory satellite\nand with the assistance of a constellation of other satellites.  GPM's\nprecipitation rate retrieval algorithm is based on a radiative\ntransfer model. The gauge-adjusted rate is calculated based on\nthe optimization of the 24h accumulation of GSMaP hourly rain rate\nto daily precipitation by NOAA/CPC gauge measurement.\nThis dataset is processed by GSMaP algorithm version 7 (product version 4).\nSee [GSMaP Technical Documentation](https://www.eorc.jaxa.jp/GPM/doc/product/format/en/07.GPM_GSMaP_Product_Format_V5_E.pdf)\nfor more details.\n\nThis dataset contains provisional products GSMaP_NRT that are\nregularly replaced with updated versions when the GSMaP_MVK data\nbecome available.\nThe products are marked with a metadata property called ''status''.\nWhen a product is initially made available, the property value\nis ''provisional''. Once a provisional product has been updated\nwith the final version, this value is updated to ''permanent''.\nFor more information please refer\n[General Documentation](https://eolp.jaxa.jp/GSMaP_Hourly.html).\n(Note: No 'permanent' products are available beyond May 31, 2026 and the\nproduction of 'provisional' products will cease in June 2026.)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["2014-03-01T00:00:00Z","2026-06-29T22:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v7/operational","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_GPM_L3_GSMaP_v7_operational","rel":"related","title":"Run the example for JAXA/GPM_L3/GSMaP/v7/operational in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_GPM_L3_GSMaP_v7_operational_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v7_operational#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v7/operational/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v7/operational/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JAXA/GPM_L3/GSMaP/v8/operational","title":"GSMaP Operational: Global Satellite Mapping of Precipitation - V8","description":"Global Satellite Mapping of Precipitation (GSMaP) provides\na global hourly rain rate with a 0.1 x 0.1 degree resolution. GSMaP\nis a product of the Global Precipitation Measurement (GPM) mission,\nwhich provides global precipitation observations at three hour\nintervals.  Values are estimated using multi-band passive microwave\nand infrared radiometers from the GPM Core Observatory satellite\nand with the assistance of a constellation of other satellites.  GPM's\nprecipitation rate retrieval algorithm is based on a radiative\ntransfer model. The gauge-adjusted rate is calculated based on\nthe optimization of the 24h accumulation of GSMaP hourly rain rate\nto daily precipitation by NOAA/CPC gauge measurement.\nThis dataset is processed by GSMaP algorithm version 8 (product version 5).\nSee [GSMaP Technical Documentation](https://www.eorc.jaxa.jp/GPM/doc/product/format/en/07.GPM_GSMaP_Product_Format_V5_E.pdf)\nfor more details.\n\nThis dataset contains provisional products GSMaP_NRT that are\nregularly replaced with updated versions when the GSMaP_MVK data\nbecome available.\nThe products are marked with a metadata property called ''status''.\nWhen a product is initially made available, the property value\nis ''provisional''. Once a provisional product has been updated\nwith the final version, this value is updated to ''permanent''.\nFor more information please refer\n[General Documentation](https://eolp.jaxa.jp/GSMaP_Hourly.html).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["1998-01-01T00:00:00Z","2026-11-09T14:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v8/operational","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JAXA/JAXA_GPM_L3_GSMaP_v8_operational","rel":"related","title":"Run the example for JAXA/GPM_L3/GSMaP/v8/operational in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JAXA/JAXA_GPM_L3_GSMaP_v8_operational_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v8_operational#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v8/operational/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JAXA/GPM_L3/GSMaP/v8/operational/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JCU/Murray/GIC/global_tidal_wetland_change/2019","title":"Murray Global Tidal Wetland Change v1.0 (1999-2019)","description":"The Murray Global Tidal Wetland Change Dataset contains maps of the global\nextent of tidal wetlands and their change. The maps were developed from a three\nstage classification that sought to (i) estimate the global distribution of\ntidal wetlands (defined as either tidal marsh, tidal flat or mangrove\necosystems), (ii) detect their change over the study period, and (iii) estimate\nthe ecosystem type and timing of tidal wetland change events.\n\nThe dataset was produced by combining observations from 1,166,385 satellite\nimages acquired by Landsat 5 to 8 with environmental data of variables known\nto influence the distributions of each ecosystem type, including temperature,\nslope, and elevation. The image contains bands for a tidal wetland extent\nproduct (random forest probability of tidal wetland occurrence) for the\nstart and end time-steps of the study period and a tidal wetland change product\nover the full study period (loss and gain of tidal wetlands).\n\nPlease see the [usage notes](https://www.globalintertidalchange.org/data-usage)\non the [project website](https://www.globalintertidalchange.org/). A full\ndescription of the methods, validation, and limitations of the data produced by\nthis software is available in the associated scientific paper.\n\nSee also\n[UQ/murray/Intertidal/v1_1/global_intertidal](UQ_murray_Intertidal_v1_1_global_intertidal)\nfor global maps of the distribution of tidal flat ecosystems.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z","2019-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JCU/Murray/GIC/global_tidal_wetland_change/2019","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JCU/JCU_Murray_GIC_global_tidal_wetland_change_2019","rel":"related","title":"Run the example for JCU/Murray/GIC/global_tidal_wetland_change/2019 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JCU/JCU_Murray_GIC_global_tidal_wetland_change_2019_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JCU_Murray_GIC_global_tidal_wetland_change_2019#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JCU/Murray/GIC/global_tidal_wetland_change/2019/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/JPL/global_forest_canopy_height_2005","title":"Global Forest Canopy Height, 2005","description":"This dataset represents global tree heights based on a fusion of\nspaceborne-lidar data (2005) from the Geoscience Laser Altimeter\nSystem (GLAS) and ancillary geospatial data. See\n[Simard et al. (2011)](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2011JG001708)\nfor details.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2005-05-20T00:00:00Z","2005-06-23T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/JPL/global_forest_canopy_height_2005","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_JPL_global_forest_canopy_height_2005","rel":"related","title":"Run the example for NASA/JPL/global_forest_canopy_height_2005 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_JPL_global_forest_canopy_height_2005_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_JPL_global_forest_canopy_height_2005#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/JPL/global_forest_canopy_height_2005/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/CEMS_GLOFAS/FloodHazard/v1","title":"JRC Global River Flood Hazard Maps Version 1 [deprecated]","description":"The global river flood hazard maps are a gridded data set representing\ninundation along the river network, for seven different flood return periods\n(from 1-in-10-years to 1-in-500-years). The input river flow data for the\nnew maps are produced by means of the open-source hydrological model\nLISFLOOD, while inundation simulations are performed with the hydrodynamic\nmodel LISFLOOD-FP. The extent comprises the entire world with the exception\nof Greenland and Antarctica and small islands with river basins smaller than\n500 km^2.\n\nCell values indicate water depth (in meters). The maps can be used to assess\nthe exposure of population and economic assets to river floods, and to\nperform flood risk assessments. The dataset is created as part of the\nCopernicus Emergency Management Service.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2024-03-16T00:00:00Z","2024-03-16T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/CEMS_GLOFAS/FloodHazard/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_CEMS_GLOFAS_FloodHazard_v1","rel":"related","title":"Run the example for JRC/CEMS_GLOFAS/FloodHazard/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_CEMS_GLOFAS_FloodHazard_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_CEMS_GLOFAS_FloodHazard_v1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_CEMS_GLOFAS_FloodHazard_v2_1.json","rel":"latest-version","title":"JRC/CEMS_GLOFAS/FloodHazard/v2_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_CEMS_GLOFAS_FloodHazard_v2_1.json","rel":"successor-version","title":"JRC/CEMS_GLOFAS/FloodHazard/v2_1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/CEMS_GLOFAS/FloodHazard/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JRC/CEMS_GLOFAS/FloodHazard/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/CEMS_GLOFAS/FloodHazard/v2","title":"JRC Global River Flood Hazard Maps Version 2","description":"The global river flood hazard maps are a gridded data set representing\ninundation along the river network, for seven different flood return periods\n(from 1-in-10-years to 1-in-500-years). The input river flow data for the\nnew maps are produced by means of the open-source hydrological model\nLISFLOOD, while inundation simulations are performed with the hydrodynamic\nmodel LISFLOOD-FP. The extent comprises the entire world with the exception\nof Greenland and Antarctica and small islands with river basins smaller than\n500 km^2.\n\nCell values indicate water depth (in meters). The maps can be used to assess\nthe exposure of population and economic assets to river floods, and to\nperform flood risk assessments. The dataset is created as part of the\nCopernicus Emergency Management Service.\n\nThis version also includes additional datasets. For each tile, a flood\nhazard map is available with water depth categorized according to the GloFAS\n\"Flood hazard 100 year return period\" static layer. Furthermore, the\n\"spurious_depth_category\" band identifies areas where depths greater than\n10m are predicted in small channels (less than 3,000km^2) for the 10-year\nreturn period, including a 2km buffer.\n\nNote: The band structure has been updated in this version (v2). Each return\nperiod now has separate bands for depth and depth category. Additionally,\nbands for permanent water class and spurious depth category are included.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2024-03-16T00:00:00Z","2024-03-16T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/CEMS_GLOFAS/FloodHazard/v2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_CEMS_GLOFAS_FloodHazard_v2","rel":"related","title":"Run the example for JRC/CEMS_GLOFAS/FloodHazard/v2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_CEMS_GLOFAS_FloodHazard_v2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_CEMS_GLOFAS_FloodHazard_v2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/CEMS_GLOFAS/FloodHazard/v2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JRC/CEMS_GLOFAS/FloodHazard/v2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/CEMS_GLOFAS/FloodHazard/v2_1","title":"JRC Global River Flood Hazard Maps Version 2.1","description":"The global river flood hazard maps are a gridded data set representing\ninundation along the river network, for seven different flood return periods\n(from 1-in-10-years to 1-in-500-years). The input river flow data for the\nnew maps are produced by means of the open-source hydrological model\nLISFLOOD, while inundation simulations are performed with the hydrodynamic\nmodel LISFLOOD-FP. The extent comprises the entire world with the exception\nof Greenland and Antarctica and small islands with river basins smaller than\n500 km^2.\n\nCell values indicate water depth (in meters). The maps can be used to assess\nthe exposure of population and economic assets to river floods, and to\nperform flood risk assessments. The dataset is created as part of the\nCopernicus Emergency Management Service.\n\nThis version also includes additional datasets. For each tile, a flood\nhazard map is available with water depth categorized according to the GloFAS\n\"Flood hazard 100 year return period\" static layer. Furthermore, the\n\"spurious_depth_category\" band identifies areas where depths greater than\n10m are predicted in small channels (less than 3,000km^2) for the 10-year\nreturn period, including a 2km buffer.\n\nNote: The band structure has been updated in this version (v2). Each return\nperiod now has separate bands for depth and depth category. Additionally,\nbands for permanent water class and spurious depth category are included.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2024-03-16T00:00:00Z","2024-03-16T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/CEMS_GLOFAS/FloodHazard/v2_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_CEMS_GLOFAS_FloodHazard_v2_1","rel":"related","title":"Run the example for JRC/CEMS_GLOFAS/FloodHazard/v2_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_CEMS_GLOFAS_FloodHazard_v2_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_CEMS_GLOFAS_FloodHazard_v2_1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_CEMS_GLOFAS_FloodHazard_v2_1.json","rel":"latest-version","title":"JRC/CEMS_GLOFAS/FloodHazard/v2_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_CEMS_GLOFAS_FloodHazard_v1.json","rel":"predecessor-version","title":"JRC/CEMS_GLOFAS/FloodHazard/v1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/CEMS_GLOFAS/FloodHazard/v2_1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JRC/CEMS_GLOFAS/FloodHazard/v2_1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/D5/EUCROPMAP/V1","title":"EUCROPMAP","description":"European crop type maps based on Sentinel-1 and LUCAS Copernicus 2018 in-situ \nobservations for 2018, and a combination of Sentinel-1, Sentinel-2, \nand auxiliary data with LUCAS Copernicus 2022 observations for 2022.\n\nBased on the unique [LUCAS 2018 Copernicus in-situ survey](JRC_LUCAS_HARMO_THLOC_V1), this dataset \nrepresents the first EU-scale crop type map at 10m resolution, \nwhich was derived exclusively from S1A and S1B synthetic aperture radar observations. \nThe 2022 dataset continues this effort using a multi-source approach that combines \nSentinel-1 and Sentinel-2 imagery with temperature and elevation data, supported by LUCAS 2022 observations.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-16.171875,34.313433,36.386719,72.182526]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/D5/EUCROPMAP/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_D5_EUCROPMAP_V1","rel":"related","title":"Run the example for JRC/D5/EUCROPMAP/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_D5_EUCROPMAP_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_D5_EUCROPMAP_V1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/D5/EUCROPMAP/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JRC/D5/EUCROPMAP/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GFC2020/V1","title":"EC JRC global map of forest cover 2020, V1 [deprecated]","description":"The EC JRC global map of forest cover provides a spatially explicit representation\nof forest presence and absence for the year 2020 at 10m spatial resolution.\n\nThe year 2020 corresponds to the cut-off date of the Regulation from the\nEuropean Union \"on the making available on the Union market and the export from\nthe Union of certain commodities and products associated with deforestation and\nforest degradation\" (EUDR, [Regulation (EU) 2023/1115](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32023R1115)). In the context of the\nEUDR, the global forest cover map can be used as a non-mandatory, non-exclusive,\nand not legally binding source of information. Further information about the\nmap and its use can be found on the [EU Observatory on Deforestation and Forest\nDegradation](https://forest-observatory.ec.europa.eu/forest/) (EUFO) in the section on Frequently Asked Questions.\n\nForest means land spanning more than 0.5 hectares with trees higher than 5\nmeters and a canopy cover of more than 10%, or trees able to reach those\nthresholds in situ, excluding land that is predominantly under agricultural or\nurban land use. Agricultural use means the use of land for the purpose of\nagriculture, including for agricultural plantations (i.e. tree stands in\nagricultural production systems such as fruit tree plantations, oil palm\nplantations, olive orchards and agroforestry systems) and set- aside\nagricultural areas, and for rearing livestock. All plantations of relevant\ncommodities other than wood, that is cattle, cocoa, coffee, oil palm, rubber,\nsoya, are excluded from the forest definition.\n\nThe global map of forest cover was created by combining available global\ndatasets (wall-to-wall or global in their scope) on tree cover, tree height,\nland cover and land use into a single harmonized globally-consistent\nrepresentation of where forests existed in 2020.\n\nThe workflow consisted in first mapping the global maximum extent of tree\ncover circa the year 2020 from the combination of ESA World Cover 2020 and\n2021, WRI Tropical Tree Cover 2020, UMD Global land cover and land use 2019,\nGlobal Mangrove Watch 2020, and JRC Tropical Moist Forest 2020 datasets. In the\nsecond step, a series of overlays and decision rules were applied to reduce this\nmaximum extent of tree cover and align it with the Forest definition using\ndatasets covering cropland and commodity expansion (ESA World Cereal, UMD\nGlobal land cover and land use 2019, UMD Global Cropland Expansion,\nHigh-resolution global map of smallholder and industrial oil palm plantations,\nand WRI Spatial Database of Planted Trees), land use change (UMD global forest\ncover loss, JRC Tropical Moist Forest, IIASA Global Forest Management),\nbuilt-up (JRC Global Human Settlement), and water (JRC Global Surface Water).\n\nThe global input layers, mapping approach, and a preliminary accuracy\nassessment are described in a [JRC science for policy report](https://op.europa.eu/en/publication-detail/-/publication/f9baaa45-e73f-11ee-9ea8-01aa75ed71a1).\nThe JRC will undertake a formal statistically and thematically robust\naccuracy assessment in 2024.\n\nPlease also refer to the [list of known issues](https://forobs.jrc.ec.europa.eu/GFC) and to the [JRC Data Catalogue entry](https://data.jrc.ec.europa.eu/dataset/10d1b337-b7d1-4938-a048-686c8185b290).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-12-31T00:00:00Z","2020-12-31T00:00:01Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GFC2020_V1","rel":"related","title":"Run the example for JRC/GFC2020/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GFC2020_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GFC2020_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_V4.json","rel":"latest-version","title":"JRC/GFC2020/V4","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_V2.json","rel":"successor-version","title":"JRC/GFC2020/V2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GFC2020/V2","title":"EC JRC global map of forest cover 2020, V2 [deprecated]","description":"The global map of forest cover provides a spatially explicit representation\nof forest presence and absence for the year 2020 at 10m spatial resolution.\n\nThe year 2020 corresponds to the cut-off date of the Regulation from the\nEuropean Union \"on the making available on the Union market and the export\nfrom the Union of certain commodities and products associated with\ndeforestation and forest degradation\" (EUDR, Regulation (EU) 2023/1115).\nIn the context of the EUDR, the global forest cover map can be used as a\nnon-mandatory, non-exclusive and not legally binding source of information.\nFurther information about the map and its use can be found on the [EU\nObservatory on Deforestation and Forest Degradation](https://forest-observatory.ec.europa.eu/forest/) (EUFO) and namely in\nthe section on Frequently Asked Questions.\n\nForest means land spanning more than 0.5 hectares with trees higher than\n5 meters and a canopy cover of more than 10%, or trees able to reach those\nthresholds in situ, excluding land that is predominantly under agricultural\nor urban land use. Agricultural use means the use of land for the purpose of\nagriculture, including for agricultural plantations (i.e. tree stands in\nagricultural production systems such as fruit tree plantations, oil palm\nplantations, olive orchards and agroforestry systems) and set- aside\nagricultural areas, and for rearing livestock. All plantations of relevant\ncommodities other than wood, that is cattle, cocoa, coffee, oil palm,\nrubber, soya are excluded from the forest definition.\n\nThe global map of forest cover was created by combining existing global\nspatial layers (wall-to-wall or global in their scope), e.g. on land cover,\nland use, and tree height. The map aims to represent the state of forest\ncover by 31 December 2020. The global land cover from the ESA World Cover\nproject serves as one baseline layer to define the extent of tree cover for\nyear 2020 at 10 m spatial resolution. In 2024, the global map of forest\ncover 2020 was improved by integrating user feedback, and new or revised\nspatial data layers.\n\nIt now better captures temporally unstocked forests, low-density\ntropical forests, and secondary tropical forests that have regrown for at\nleast five years. Additionally, the exclusion criteria have been enhanced\nto more effectively exclude trees in urban areas, mining sites, wetlands,\nareas with shifting cultivation, and tree plantations. This is achieved by\nutilizing multiple global maps of canopy height, crop area, and specific\ncrop commodity maps to more accurately distinguish forests from trees\nunder agricultural use.\n\nFor direct access and metadata, please consult the JRC\ndata catalogue ([JRC 2024](https://data.jrc.ec.europa.eu/dataset/e554d6fb-6340-45d5-9309-332337e5bc26)). A technical report ([Bourgoin et al 2025](https://op.europa.eu/en/publication-detail/-/publication/e2c286ac-14e9-11f0-b1a3-01aa75ed71a1/language-en))\ndescribes the mapping approach for the second version. The accuracy\nassessment of the Global Forest Cover map is described in a\n[separate report](https://op.europa.eu/en/publication-detail/-/publication/e86f56dd-15b5-11f0-b1a3-01aa75ed71a1/language-en).\n\nThe global map of forest cover may be revised if\nnew information, additional large-scale data layers, or revised global\nspatial data layers will be made available for year 2020.\n\nFor a list of known issues please refer to [this website](https://forobs.jrc.ec.europa.eu/GFC).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-12-31T00:00:00Z","2020-12-31T00:00:01Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020/V2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GFC2020_V2","rel":"related","title":"Run the example for JRC/GFC2020/V2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GFC2020_V2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GFC2020_V2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_V4.json","rel":"latest-version","title":"JRC/GFC2020/V4","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_V1.json","rel":"predecessor-version","title":"JRC/GFC2020/V1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_V3.json","rel":"successor-version","title":"JRC/GFC2020/V3","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020/V2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020/V2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GFC2020/V3","title":"EC JRC global map of forest cover 2020, V3 [deprecated]","description":"The global map of forest cover provides a spatially explicit representation\nof forest presence and absence for the year 2020 at 10m spatial resolution.\n\nThe year 2020 corresponds to the cut-off date of the Regulation from the\nEuropean Union \"on the making available on the Union market and the export\nfrom the Union of certain commodities and products associated with\ndeforestation and forest degradation\" (EUDR, Regulation (EU) 2023/1115).\nIn the context of the EUDR, the global forest cover map can be used as a\nnon-mandatory, non-exclusive and not legally binding source of information.\nFurther information about the map and its use can be found on the [EU\nObservatory on Deforestation and Forest Degradation](https://forest-observatory.ec.europa.eu/forest/) (EUFO) and namely in\nthe section on Frequently Asked Questions.\n\nForest means land spanning more than 0.5 hectares with trees higher than\n5 meters and a canopy cover of more than 10%, or trees able to reach those\nthresholds in situ, excluding land that is predominantly under agricultural\nor urban land use. Agricultural use means the use of land for the purpose of\nagriculture, including for agricultural plantations (i.e. tree stands in\nagricultural production systems such as fruit tree plantations, oil palm\nplantations, olive orchards and agroforestry systems) and set- aside\nagricultural areas, and for rearing livestock. All plantations of relevant\ncommodities other than wood, that is cattle, cocoa, coffee, oil palm,\nrubber, soya are excluded from the forest definition.\n\nIn a first step, the workflow identifies the maximum potential extent of forest\ncover circa 2020 by integrating global tree- and mangrove cover data for the year 2020 \n(e.g. [Copernicus global land cover map](https://land.copernicus.eu/en/products/global-dynamic-land-cover/land-cover-2020-raster-10-m-global-annual)).\nIn addition, this delineation uses historical time-series of tree cover and loss to\nidentify areas of potentially unstocked forest due to e.g., natural disturbances or\nforest management practices. In a second step, this maximum potential forest extent\nis intersected with various other data layers on tree height, deforestation, cropland,\nagroforestry, and other land uses. The application of a series of complex Boolean decision\nrules excludes areas that are not forest according to the definition of the FAO-FRA and EUDR.\n\nThe global input layers, mapping approach, and accuracy of GFC 2020 version 3 are\ndescribed in a Science for Policy report released in May 2026 and available from the JRC Publications Repository: [Bourgoin et al. 2026](https://publications.jrc.ec.europa.eu/repository/handle/JRC146622). A technical\nreport ([Bourgoin et al. 2024](https://op.europa.eu/en/publication-detail/-/publication/f9baaa45-e73f-11ee-9ea8-01aa75ed71a1/language-en)) describes the mapping approach for the first version; methodological\nchanges in version 2 and comparisons with other maps are described in [Bourgoin et al. (2025)](https://op.europa.eu/en/publication-detail/-/publication/e2c286ac-14e9-11f0-b1a3-01aa75ed71a1/language-en).\n[Colditz et al. (2025)](https://op.europa.eu/en/publication-detail/-/publication/e86f56dd-15b5-11f0-b1a3-01aa75ed71a1/language-en) describe the accuracy assessment protocol and results for GFC 2020 version 2.\n[Bourgoin et al. 2025](https://essd.copernicus.org/preprints/essd-2025-351/) present the mapping methodology and accuracy and compare the map to other\nglobal products. A comparative study between GFC 2020 version 1 and a national map was carried\nout over Ivory Coast ([Verhegghen et al, 2024](https://op.europa.eu/en/publication-detail/-/publication/ff3a48df-7957-11ef-bbbe-01aa75ed71a1/language-en)). A user guide explains how to visualize and interact\nwith GFC2020 to support the EUDR risk assessment ([Simonetti et al. 2025](https://op.europa.eu/en/publication-detail/-/publication/a9ec1c5d-3780-11f0-8a44-01aa75ed71a1/language-en)). \n\nThe GFC 2020 map may be revised if new information, additional large-scale data layers\nor revised global spatial data layers for year 2020 will be made available.\n\nFor a list of known issues please refer to [this website](https://forobs.jrc.ec.europa.eu/GFC).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-12-31T00:00:00Z","2020-12-31T00:00:01Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020/V3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GFC2020_V3","rel":"related","title":"Run the example for JRC/GFC2020/V3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GFC2020_V3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GFC2020_V3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_V4.json","rel":"latest-version","title":"JRC/GFC2020/V4","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_V2.json","rel":"predecessor-version","title":"JRC/GFC2020/V2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_V4.json","rel":"successor-version","title":"JRC/GFC2020/V4","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020/V3/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GFC2020/V4","title":"EC JRC global map of forest cover 2020, V4","description":"The global map of forest cover provides a spatially explicit representation\nof forest presence and absence for the year 2020 at 10m spatial resolution.\n\nThe year 2020 corresponds to the cut-off date of the Regulation from the\nEuropean Union \"on the making available on the Union market and the export\nfrom the Union of certain commodities and products associated with\ndeforestation and forest degradation\" (EUDR, [Regulation (EU) 2023/1115](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32023R1115)).\nIn the context of the EUDR, the global forest cover map can be used as a\nnon-mandatory, non-exclusive and not legally binding source of information.\nFurther information about the map and its use can be found on the [EU\nObservatory on Deforestation and Forest Degradation](https://forest-observatory.ec.europa.eu/forest/)\n(EUFO) and namely in the section on Frequently Asked Questions.\n\nForest means land spanning more than 0.5 hectares with trees higher than\n5 meters and a canopy cover of more than 10%, or trees able to reach those\nthresholds in situ, excluding land that is predominantly under agricultural\nor urban land use. Agricultural use means the use of land for the purpose of\nagriculture, including for agricultural plantations (i.e. tree stands in\nagricultural production systems such as fruit tree plantations, oil palm\nplantations, olive orchards and agroforestry systems) and set-aside\nagricultural areas, and for rearing livestock. All plantations of relevant\ncommodities other than wood, that is cattle, cocoa, coffee, oil palm,\nrubber, soya are excluded from the forest definition.\n\nIn a first step, the workflow identifies the maximum potential extent of\nforest cover circa 2020 by integrating global tree- and mangrove cover data\nfor the year 2020 (e.g. [Copernicus global land cover map](https://land.copernicus.eu/en/products/global-dynamic-land-cover/land-cover-2020-raster-10-m-global-annual)).\nIn addition, this delineation uses historical time-series of tree cover and\nloss to identify areas of potentially unstocked forest due to e.g., natural\ndisturbances or forest management practices. In a second step, this maximum\npotential forest extent is intersected with various other data layers on\ntree height, deforestation, cropland, agroforestry, and other land uses. The\napplication of a series of complex Boolean decision rules excludes areas\nthat are not forest according to the definition of the FAO-FRA and EUDR.\n\nThe global input layers, mapping approach, and accuracy of GFC 2020 version\n4 will be described in a separate technical report, expected to be released\nby December 2026. The mapping approaches of versions 1, 2, and 3 are\ndescribed in [Bourgoin et al. 2024](https://op.europa.eu/en/publication-detail/-/publication/f9baaa45-e73f-11ee-9ea8-01aa75ed71a1/language-en),\n[2025](https://op.europa.eu/en/publication-detail/-/publication/e2c286ac-14e9-11f0-b1a3-01aa75ed71a1/language-en),\nand\n[2026](https://publications.jrc.ec.europa.eu/repository/handle/JRC146622),\nrespectively, and the overall approach for mapping forests, incorporating\nboth physical characteristics and land-use components, is presented in\n[Bourgoin et al. 2026](https://essd.copernicus.org/articles/18/1331/2026/).\n[Colditz et al. 2025](https://op.europa.eu/en/publication-detail/-/publication/e86f56dd-15b5-11f0-b1a3-01aa75ed71a1/language-en)\ndescribe the accuracy assessment protocol and results for GFC 2020 version\n2. Comparative studies between GFC 2020 and national maps were carried out\nover Ivory Coast ([Verhegghen et al. 2024](https://op.europa.eu/en/publication-detail/-/publication/ff3a48df-7957-11ef-bbbe-01aa75ed71a1/language-en))\nand Thailand ([Khiripet et al. 2026](https://op.europa.eu/en/publication-detail/-/publication/6d5ed8fd-42a7-11f1-8095-01aa75ed71a1/language-en)).\nA user guide explains how to visualize and interact with GFC2020 to support\nthe EUDR risk assessment ([Simonetti et al. 2025](https://op.europa.eu/en/publication-detail/-/publication/a9ec1c5d-3780-11f0-8a44-01aa75ed71a1/language-en)).\n\nThe GFC 2020 map may be revised if new information, additional large-scale\ndata layers or revised global spatial data layers for year 2020 will be made\navailable.\n\nFor a list of known issues please refer to [this website](https://forobs.jrc.ec.europa.eu/GFC).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-12-31T00:00:00Z","2020-12-31T00:00:01Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020/V4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GFC2020_V4","rel":"related","title":"Run the example for JRC/GFC2020/V4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GFC2020_V4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GFC2020_V4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_V4.json","rel":"latest-version","title":"JRC/GFC2020/V4","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_V3.json","rel":"predecessor-version","title":"JRC/GFC2020/V3","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020/V4/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GFC2020_subtypes/V0","title":"Global map of forest types 2020 V0 [deprecated]","description":"The global map of forest types provides a spatially explicit representation\nof primary forest, naturally regenerating forest and planted forest (including\nplantation forest) for the year 2020 at 10m spatial resolution. The base layer\nfor mapping these forest types is the extent of forest cover of version 1 of\nthe Global Forest Cover map for year 2020 (JRC GFC 2020). The definitions of\nthe forest types follow the definitions of the Regulation from the European\nUnion \"on the making available on the Union market and the export from the\nUnion of certain commodities and products associated with deforestation and\nforest degradation\" (EUDR, Regulation (EU) 2023/1115), which are similar to\ncharacteristics and specific forest categories from the FAO Global Forest\nResources Assessment. The year 2020 corresponds to the cut-off date of the\nEUDR.\n\nIn the context of the EUDR, the global forest types map can be used as a\nnon-mandatory, non-exclusive and not legally binding source of information,\nnamely in the phase of risk assessment by operators and traders. Further\ninformation about the map and its use can be found on the EU Observatory on\nDeforestation and Forest Degradation. The definition of forest degradation\nunder the EUDR implies that for year 2020 it is only necessary to assess the\npresence of primary forests and naturally regenerating forests; all other\nforests, including plantation forests, were mapped under class \"planted\nforests\". Class \"other wooded land\" exists only outside the extent of forest\ncover in the Global Forest Cover map for year 2020 and is therefore not\nmapped in GFT 2020.\n\nData for deriving a harmonized, globally consistent representation of forest\ntypes are scarce, and mapping of the respective forest types from Earth\nObservation data is challenging. This map of global forest types is released\nas a preliminary version (version 0) for feedback by the user community that\nis concerned or interested in the issue of forest degradation under the\nEUDR. Based on expected feedback and future additional or improved data\nsets, the JRC aims to produce a consolidated map during year 2025.\n\nThe global map of forest types v0 combines available global datasets\n(wall-to-wall or global in their scope) that indicate or are proxies for the\nfour main forest types The main data layers that are used to delineate primary\nforests in GFT 2020 are:\n\n1. The Forest Landscape Integrity Index in 2019\n1. Map of undisturbed tropical mangroves in 2020 and degradation and\ndeforestation in tropical moist forests from 1990 to 2020 (from JRC-TMF)\n1. Map of Intact Forest Landscapes 2020\n1. World Database on Protected Areas\n1. The European Primary Forest Dataset from Sabatini et al. 2021\n1. Global tree cover loss from 2001 to 2020 in combination with the map of\ndrivers of global forest loss\n1. A map on global mining land use.\n\nThe main layers to map planted forests (including plantation forests) are:\n\n1. The WRI Spatial Database on Planted Trees (version 2.1)\n1. IIASA Forest Management classes on planted/plantation forest\n1. The Global Forest Canopy Height dataset in 2019\n1. global tree cover in 2000 and loss from 2001 to 2020.\n\nThe global input layers and mapping approach are described in\n[this technical report](https://op.europa.eu/en/publication-detail/-/publication/e2c286ac-14e9-11f0-b1a3-01aa75ed71a1/language-en).\n\nFor a list of known issues please refer to\n[this website](https://forobs.jrc.ec.europa.eu/GFC).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020_subtypes/V0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GFC2020_subtypes_V0","rel":"related","title":"Run the example for JRC/GFC2020_subtypes/V0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GFC2020_subtypes_V0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GFC2020_subtypes_V0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_subtypes_V1.json","rel":"latest-version","title":"JRC/GFC2020_subtypes/V1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_subtypes_V1.json","rel":"successor-version","title":"JRC/GFC2020_subtypes/V1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020_subtypes/V0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020_subtypes/V0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GFC2020_subtypes/V1","title":"EC JRC global map of forest types 2020, V1","description":"The global map of forest types provides a spatially explicit representation\nof primary forest, naturally regenerating forest and planted forest (including\nplantation forest) for the year 2020 at 10m spatial resolution. The base layer\nfor mapping these forest types is the extent of forest cover of version 3 of\nthe Global Forest Cover map for year 2020 (JRC GFC 2020). The definitions of\nthe forest types follow the definitions of the Regulation from the European\nUnion \"on the making available on the Union market and the export from the\nUnion of certain commodities and products associated with deforestation and\nforest degradation\" (EUDR, Regulation (EU) 2023/1115), which are similar to\ncharacteristics and specific forest categories from the FAO Global Forest\nResources Assessment. The year 2020 corresponds to the cut-off date of the\nEUDR.\n\nIn the context of the EUDR, the global forest type map can be used as a\nnon-mandatory, non-exclusive and not legally binding source of information,\nnamely in the phase of risk assessment by operators and traders. Further\ninformation about the map and its use can be found on the EU Observatory on\nDeforestation and Forest Degradation. The definition of forest degradation\nunder the EUDR implies that for year 2020 it is only necessary to assess the\npresence of primary forests and naturally regenerating forests; all other\nforests, including plantation forests, were mapped under class \"planted\nforests\". Class \"other wooded land\" exists only outside the extent of forest\ncover in the Global Forest Cover map for year 2020 and is therefore not\nmapped in GFT 2020.\n\nThe global map of forest types v1 combines available global datasets\n(wall-to-wall or global in their scope) that indicate or are proxies for the\nfour main forest types. The main data layers that are used to delineate primary\nforests in GFT 2020 are:\n\n1. The Forest Landscape Integrity Index in 2019\n1. Map of undisturbed tropical mangroves in 2020 and degradation and\ndeforestation in tropical moist forests from 1990 to 2020 (from JRC-TMF)\n1. Deforestation alerts (RADD) up to end of 2020\n1. Map of Intact Forest Landscapes 2020\n1. World Database on Protected Areas\n1. The European Primary Forest Dataset from Sabatini et al. 2021\n1. Global tree cover loss from 2001 to 2020 in combination with the map of\ndrivers of global forest loss\n1. A map on global mining land use.\n\nThe main layers to map planted forests (including plantation forests) are:\n\n1. The WRI Spatial Database on Planted Trees (version 3)\n1. IIASA Forest Management classes on planted/plantation forest\n1. The Global Forest Canopy Height dataset in 2019\n1. global tree cover in 2000 and loss from 2001 to 2020.\n1. global drivers of forest loss.\n1. Global natural forests 2020.\n\nThe global input layers and mapping approach of GFT 2020 version 1 are\ndescribed in a separate technical report, expected to be released by March 2026 and available from\nthe JRC Publications Repository: [Bourgoin et al. 2026](https://publications.jrc.ec.europa.eu/repository/handle/JRC146622).\nA technical report [Bourgoin et al. 2025](https://op.europa.eu/en/publication-detail/-/publication/e2c286ac-14e9-11f0-b1a3-01aa75ed71a1/language-en)\ndescribes the mapping approach for the preliminary version (version 0) of GFT 2020.\n\nFor a list of known issues please refer to\n[this website](https://forobs.jrc.ec.europa.eu/GFT).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020_subtypes/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GFC2020_subtypes_V1","rel":"related","title":"Run the example for JRC/GFC2020_subtypes/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GFC2020_subtypes_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GFC2020_subtypes_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_subtypes_V1.json","rel":"latest-version","title":"JRC/GFC2020_subtypes/V1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GFC2020_subtypes_V0.json","rel":"predecessor-version","title":"JRC/GFC2020_subtypes/V0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GFC2020_subtypes/V1/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GHSL/P2016/BUILT_LDSMT_GLOBE_V1","title":"GHSL: Global Human Settlement Layers, Built-Up Grid 1975-1990-2000-2015 (P2016) [deprecated]","description":"The GHSL relies on the design and implementation of new spatial data mining\ntechnologies allowing to automatically process and extract analytics and\nknowledge from large amount of heterogeneous data including: global,\nfine-scale satellite image data streams, census data, and crowd sources or\nvolunteered geographic information sources.\n\nThese data contain a multitemporal information layer on built-up presence as\nderived from Landsat image collections (GLS1975, GLS1990, GLS2000, and\nad-hoc Landsat 8 collection 2013/2014).\n\nThe data have been produced by means of [Global Human Settlement Layer\nmethodology](https://publications.jrc.ec.europa.eu/repository/handle/JRC97705)\nin 2015.\n\nFor more information visit: [https://ghsl.jrc.ec.europa.eu/ghs_bu.php](https://ghsl.jrc.ec.europa.eu/ghs_bu.php)\nand [https://ghsl.jrc.ec.europa.eu/documents/GHSL_data_access.pdf](https://ghsl.jrc.ec.europa.eu/documents/GHSL_data_access.pdf).\n\nThe Global Human Settlement Layer (GHSL) project is supported by the\nEuropean Commission, Joint Research Center, and Directorate-General for\nRegional and Urban Policy. The GHSL produces new global spatial information,\nevidence-based analytics, and knowledge describing the human presence in\nthe planet.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-01T00:00:00Z","2014-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2016/BUILT_LDSMT_GLOBE_V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GHSL_P2016_BUILT_LDSMT_GLOBE_V1","rel":"related","title":"Run the example for JRC/GHSL/P2016/BUILT_LDSMT_GLOBE_V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GHSL_P2016_BUILT_LDSMT_GLOBE_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_BUILT_LDSMT_GLOBE_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GHSL_P2023A_GHS_BUILT_S.json","rel":"latest-version","title":"JRC/GHSL/P2023A/GHS_BUILT_S","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GHSL_P2023A_GHS_BUILT_S.json","rel":"successor-version","title":"JRC/GHSL/P2023A/GHS_BUILT_S","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2016/BUILT_LDSMT_GLOBE_V1/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GHSL/P2016/POP_GPW_GLOBE_V1","title":"GHSL: Global Human Settlement Layers, Population Grid 1975-1990-2000-2015 (P2016) [deprecated]","description":"The GHSL relies on the design and implementation of new spatial data mining\ntechnologies allowing to automatically process and extract analytics and\nknowledge from large amount of heterogeneous data including: global,\nfine-scale satellite image data streams, census data, and crowd sources or\nvolunteered geographic information sources.\n\nThis dataset depicts the distribution and density of population, expressed\nas the number of people per cell, for reference epochs: 1975, 1990, 2000,\n2015.\n\nResidential population estimates were provided by CIESIN GPW v4. These were\ndisaggregated from census or administrative units to grid cells, informed by\nthe distribution and density of built-up as mapped in the GHSL global layer\nper corresponding epoch. (See [\"Development of New Open and Free\nMulti-temporal Global Population Grids at 250 m\nResolution.\"](https://publications.jrc.ec.europa.eu/repository/handle/JRC100523))\n\nThis dataset was produced in the World Mollweide projection (EPSG:54009).\n\nFor more information visit: [https://ghsl.jrc.ec.europa.eu/ghs_pop.php](https://ghsl.jrc.ec.europa.eu/ghs_pop.php).\n\nThe Global Human Settlement Layer (GHSL) project is supported by the\nEuropean Commission, Joint Research Center, and Directorate-General for\nRegional and Urban Policy. The GHSL produces new global spatial information,\nevidence-based analytics, and knowledge describing the human presence in\nthe planet.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-01T00:00:00Z","2015-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2016/POP_GPW_GLOBE_V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GHSL_P2016_POP_GPW_GLOBE_V1","rel":"related","title":"Run the example for JRC/GHSL/P2016/POP_GPW_GLOBE_V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GHSL_P2016_POP_GPW_GLOBE_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GHSL_P2023A_GHS_POP.json","rel":"latest-version","title":"JRC/GHSL/P2023A/GHS_POP","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GHSL_P2023A_GHS_POP.json","rel":"successor-version","title":"JRC/GHSL/P2023A/GHS_POP","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2016/POP_GPW_GLOBE_V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2016/POP_GPW_GLOBE_V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GHSL/P2016/SMOD_POP_GLOBE_V1","title":"GHSL: Global Human Settlement Layers, Settlement Grid 1975-1990-2000-2014 (P2016) [deprecated]","description":"The GHSL relies on the design and implementation of new spatial data mining\ntechnologies allowing to automatically process and extract analytics and\nknowledge from large amount of heterogeneous data including: global,\nfine-scale satellite image data streams, census data, and crowd sources or\nvolunteered geographic information sources.\n\nThe GHS-SMOD is the rural-urban Settlement classification MODel adopted by\nthe GHSL. It is the representation of the degree of urbanization\n([DEGURBA](https://ec.europa.eu/eurostat/web/degree-of-urbanisation/background))\nconcept into the GHSL data scenario. Each grid in the GHS-SMOD has been\ngenerated by integrating the GHSL built-up areas and GHSL population grids\ndata for reference epochs: 1975, 1990, 2000, 2015.\n\nThe DEGURBA classification schema is a people-based definition of cities and\nsettlements: it operates using as main input a 1 km² grid cell accounting\nfor population at a given point in time. The DEGURBA discriminates the\npopulation grid cells in three main classes: '[urban centers](https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Urban_centre)'\n(cities), '[urban clusters](https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Urban_cluster)'\n(towns and suburbs), and '[rural grid cells](https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Rural_grid_cell)'.\n(base). These class abstractions translate to 'high density clusters (HDC)',\n'low density clusters (LDC)', and 'rural grid cells (RUR)', respectively,\nin the GHS-SMOD implementation.\n\nThe 'HDC' differ from the DEGURBA '[urban centers](https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Urban_centre)'\nin that they account for the over-fragmentation of cities in regions\nwith large low-density residential development by integrating the\nbuilt-up layer. In the GHS-SMOD representation, the 'HDC' are the spatial\ngeneralization of contiguous population grid cells (4-connectivity,\ngap-filling) with a density of at least 1500 inhabitants per km² or a\ndensity of built-up surface > 50%, and a minimum total resident population\nof 50000. The 'LDC' are continuous grid cells with a density of at least 300\ninhabitants per km² and a minimum total population of 5000. The 'RUR' are\ngrid cells outside 'HDC' and 'LDC' with population > 0 and < 300. Everything\nelse is classified as inhabited areas where population = 0.\n\nThis dataset was produced in the World Mollweide projection (EPSG:54009).\n\nFor more information visit: [http://ghsl.jrc.ec.europa.eu/ghs_smod.php](http://ghsl.jrc.ec.europa.eu/ghs_smod.php).\n\nThe Global Human Settlement Layer (GHSL) project is supported by the\nEuropean Commission, Joint Research Center, and Directorate-General for\nRegional and Urban Policy. The GHSL produces new global spatial information,\nevidence-based analytics, and knowledge describing the human presence in\nthe planet.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-01T00:00:00Z","2015-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2016/SMOD_POP_GLOBE_V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GHSL_P2016_SMOD_POP_GLOBE_V1","rel":"related","title":"Run the example for JRC/GHSL/P2016/SMOD_POP_GLOBE_V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GHSL_P2016_SMOD_POP_GLOBE_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_SMOD_POP_GLOBE_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GHSL_P2023A_GHS_SMOD_V2-0.json","rel":"latest-version","title":"JRC/GHSL/P2023A/GHS_SMOD_V2-0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GHSL_P2023A_GHS_SMOD.json","rel":"successor-version","title":"JRC/GHSL/P2023A/GHS_SMOD","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2016/SMOD_POP_GLOBE_V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2016/SMOD_POP_GLOBE_V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GHSL/P2023A/GHS_BUILT_C","title":"GHSL: Global settlement characteristics (10 m) 2018 (P2023A)","description":"This spatial raster dataset delineates human settlements at 10 m resolution,\nand describes their inner characteristics in terms of the functional and\nheight-related components of the built environment.\n\nMore information about the GHSL data products can be found in the [GHSL Data\nPackage 2023 report]\n(https://ghsl.jrc.ec.europa.eu/documents/GHSL_Data_Package_2023.pdf?t=1683540422)\n\nThe Global Human Settlement Layer (GHSL) project is supported by the European\nCommission, Joint Research Centre, and Directorate-General for Regional and\nUrban Policy.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2023A/GHS_BUILT_C","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GHSL_P2023A_GHS_BUILT_C","rel":"related","title":"Run the example for JRC/GHSL/P2023A/GHS_BUILT_C in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GHSL_P2023A_GHS_BUILT_C_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_C#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2023A/GHS_BUILT_C/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2023A/GHS_BUILT_C/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GHSL/P2023A/GHS_BUILT_H","title":"GHSL: Global building height 2018 (P2023A)","description":"This spatial raster dataset depicts the global distribution of building\nheights at a resolution of 100 m, referred to the year 2018. The input data\nused to predict building heights are the ALOS Global Digital Surface Model\n(30 m), the NASA Shuttle Radar Topographic Mission data (30 m), and a global\nSentinel-2 image composite from L1C data for the period 2017-2018.\n\nMore information about the GHSL data products can be found in the\n[GHSL Data Package 2023 report]\n(https://ghsl.jrc.ec.europa.eu/documents/GHSL_Data_Package_2023.pdf?t=1683540422),\nwhere the building height layer is referred to as the Average Net Building\nHeight (ANBH).\n\nThe Global Human Settlement Layer (GHSL) project is supported by the\nEuropean Commission, Joint Research Centre, and Directorate-General for\nRegional and Urban Policy.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2023A/GHS_BUILT_H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GHSL_P2023A_GHS_BUILT_H","rel":"related","title":"Run the example for JRC/GHSL/P2023A/GHS_BUILT_H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GHSL_P2023A_GHS_BUILT_H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_H#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2023A/GHS_BUILT_H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2023A/GHS_BUILT_H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GHSL/P2023A/GHS_BUILT_S","title":"GHSL: Global built-up surface 1975-2030 (P2023A)","description":"This raster dataset depicts the distribution of built-up surfaces,\nexpressed in square metres per 100 m grid cell.\nThe dataset measures:\na) the total built-up surface, and b) the built-up surface allocated to grid\ncells of predominant non-residential (NRES) use. Data are\nspatially-temporally interpolated or extrapolated from 1975 to 2030 in 5\nyear intervals.\n\nThe complete information about the GHSL main products can be\nfound in the [GHSL Data Package 2023 report]\n(https://ghsl.jrc.ec.europa.eu/documents/GHSL_Data_Package_2023.pdf?t=1683540422)\n\nThe Global Human Settlement Layer (GHSL) project is supported by the\nEuropean Commission, Joint Research Centre, and Directorate-General for\nRegional and Urban Policy.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-01T00:00:00Z","2030-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2023A/GHS_BUILT_S","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GHSL_P2023A_GHS_BUILT_S","rel":"related","title":"Run the example for JRC/GHSL/P2023A/GHS_BUILT_S in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GHSL_P2023A_GHS_BUILT_S_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_S#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GHSL_P2023A_GHS_BUILT_S.json","rel":"latest-version","title":"JRC/GHSL/P2023A/GHS_BUILT_S","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GHSL_P2016_BUILT_LDSMT_GLOBE_V1.json","rel":"predecessor-version","title":"JRC/GHSL/P2016/BUILT_LDSMT_GLOBE_V1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2023A/GHS_BUILT_S/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2023A/GHS_BUILT_S/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GHSL/P2023A/GHS_BUILT_S_10m","title":"GHSL: Global built-up surface 10m (P2023A)","description":"This raster dataset depicts the distribution of built-up surfaces,\nexpressed in square metres per 10 m grid cell, for 2018\nas observed from the S2 image data.\n\nThe datasets measure:\na) the total built-up surface, and b) the built-up surface allocated to grid\ncells of predominant non-residential (NRES) use.\n\nThe complete information about the GHSL main products can be\nfound in the [GHSL Data Package 2023 report]\n(https://ghsl.jrc.ec.europa.eu/documents/GHSL_Data_Package_2023.pdf?t=1683540422)\n\nThe Global Human Settlement Layer (GHSL) project is supported by the\nEuropean Commission, Joint Research Centre, and Directorate-General for\nRegional and Urban Policy.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2023A/GHS_BUILT_S_10m","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GHSL_P2023A_GHS_BUILT_S_10m","rel":"related","title":"Run the example for JRC/GHSL/P2023A/GHS_BUILT_S_10m in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GHSL_P2023A_GHS_BUILT_S_10m_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_S_10m#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2023A/GHS_BUILT_S_10m/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2023A/GHS_BUILT_S_10m/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GHSL/P2023A/GHS_BUILT_V","title":"GHSL: Global building volume 1975-2030 (P2023A)","description":"This raster dataset depicts the global distribution of building volume,\nexpressed in cubic metres per 100 m grid cell. The dataset measures the\ntotal building volume and the building volume allocated to grid cells of\npredominant non-residential (NRES) use. Estimates are based on the [built-up\nsurface](https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_S)\nand [building height](https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_H)\nproducts.\n\nMore information about the GHSL data products can be found in the [GHSL Data\nPackage 2023 report](https://ghsl.jrc.ec.europa.eu/documents/GHSL_Data_Package_2023.pdf?t=1683540422)\n\nThe Global Human Settlement Layer (GHSL) project is supported by the\nEuropean Commission, Joint Research Centre, and Directorate-General for\nRegional and Urban Policy.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-01T00:00:00Z","2030-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GHSL/P2023A/GHS_BUILT_V","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GHSL_P2023A_GHS_BUILT_V","rel":"related","title":"Run the example for JRC/GHSL/P2023A/GHS_BUILT_V in the Earth Engine Code 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population estimates between 1975 and 2020 in 5-year intervals\nand projections to 2025 and 2030 derived from CIESIN GPWv4.11 were\ndisaggregated from census or administrative units to grid cells, informed by\nthe distribution, volume, and classification of built-up area as mapped in\nthe [global GHSL built-up surface layers](https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_S)\nper epoch.\n\nMore information about the GHSL main products can be found in the\n[GHSL Data Package 2023 report]\n(https://ghsl.jrc.ec.europa.eu/documents/GHSL_Data_Package_2023.pdf?t=1683540422)\n\nThe Global Human Settlement Layer (GHSL) project is supported by the\nEuropean Commission, Joint Research Center, and Directorate-General for\nRegional and Urban 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images: monthly measures of\nthe seasonality of water based on the occurrence values detected in that\nmonth over all years.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-03-16T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GSW1_1/MonthlyRecurrence","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GSW1_1_MonthlyRecurrence","rel":"related","title":"Run the example for JRC/GSW1_1/MonthlyRecurrence in the Earth Engine Code 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Classification collection contains a year-by-year\nclassification of the seasonality of water based on the occurrence values\ndetected throughout the year.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-59,180,78]]},"temporal":{"interval":[["1984-03-16T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GSW1_3/YearlyHistory","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GSW1_3_YearlyHistory","rel":"related","title":"Run the example for JRC/GSW1_3/YearlyHistory in the Earth Engine Code 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Areas where water has never\nbeen detected are masked.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-03-16T00:00:00Z","2022-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GSW1_4/Metadata","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GSW1_4_Metadata","rel":"related","title":"Run the example for JRC/GSW1_4/Metadata in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/JRC/JRC_GSW1_4_Metadata_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_4_Metadata#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GSW1_4_Metadata.json","rel":"latest-version","title":"JRC/GSW1_4/Metadata","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_GSW1_3_Metadata.json","rel":"predecessor-version","title":"JRC/GSW1_3/Metadata","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/JRC/GSW1_4/Metadata/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"JRC/GSW1_4/MonthlyHistory","title":"JRC Monthly Water History, v1.4","description":"This dataset contains maps of the location and temporal\ndistribution of surface water from 1984 to 2021 and provides\nstatistics on the extent and change of those water surfaces.\nFor more information see the associated journal article: [High-resolution\nmapping of global surface water and its long-term changes](https://www.nature.com/nature/journal/v540/n7633/full/nature20584.html)\n(Nature, 2016) and the online\n[Data Users Guide](https://storage.googleapis.com/global-surface-water/downloads_ancillary/DataUsersGuidev2021.pdf).\n\nThese data were generated using 4,716,475 scenes from Landsat\n5, 7, and 8 acquired between 16 March 1984 and 31 December 2021.\nEach pixel was individually classified into water / non-water\nusing an expert system and the results were collated into a monthly\nhistory for the entire time period and two epochs (1984-1999,\n2000-2021) for change detection.\n\nThis Monthly History collection holds the entire history of water detection\non a month-by-month basis. The collection contains 454 images, one for each\nmonth between March 1984 and December 2021.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-59,180,78]]},"temporal":{"interval":[["1984-03-16T00:00:00Z","2022-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GSW1_4/MonthlyHistory","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GSW1_4_MonthlyHistory","rel":"related","title":"Run the example for JRC/GSW1_4/MonthlyHistory in the Earth Engine Code 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monthly measures of\nthe seasonality of water based on the occurrence values detected in that\nmonth over all years.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-03-16T00:00:00Z","2022-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/JRC/GSW1_4/MonthlyRecurrence","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/JRC/JRC_GSW1_4_MonthlyRecurrence","rel":"related","title":"Run the example for JRC/GSW1_4/MonthlyRecurrence in the Earth Engine Code 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For this purpose, over 2,500\nSentinel-1 and over 11,000 Sentinel-2 images were processed to produce a single\nmosaic dataset for the year 2017. Then, an object-based Random Forest\nclassification method was trained by a large number of reference samples for 13\nclasses to generate the Iran-wide land cover map.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[43.46,24.62,65.58,39.95]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/KNTU/LiDARLab/IranLandCover/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/KNTU/KNTU_LiDARLab_IranLandCover_V1","rel":"related","title":"Run the example for KNTU/LiDARLab/IranLandCover/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/KNTU/KNTU_LiDARLab_IranLandCover_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/KNTU_LiDARLab_IranLandCover_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"ftp://94.184.181.78:1399/Iran_LC/Iran_LC.rar","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/KNTU/LiDARLab/IranLandCover/V1/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Fire/FRG/v1_2_0","title":"LANDFIRE FRG (Fire Regime Groups) v1.2.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions\nare mapped using the Vegetation Dynamics Development Tool (VDDT).\nThese data support fire and landscape management planning goals in\nthe National Cohesive Wildland Fire Management Strategy, the Federal Wildland\nFire Management Policy, and the Healthy Forests Restoration Act.\n\nFire Regime Groups (FRG) are created by linking the Biophysical Settings (BPS) Group\nattribute in the BpS layer with the Refresh Model Tracker (RMT) data and assigning\nthe FRG attribute.\nThis geospatial product should display a reasonable approximation of FRG,\nas documented in the RMT. (LF 1.0.0 CONUS only used the vegetation and disturbance\ndynamics model LANDSUM.)\nFRG can be used in landscape assessments.\n\nThe LANDIFRE Fire datasets include:\n\n* Fire Regime Groups (FRG) is intended to characterize presumed historical\n  fire regimes within landscapes based on interactions between vegetation\n  dynamics, fire spread, fire effects, and spatial context\n* Mean Fire Return Interval (MFRI) quantifies the average period between\n  fires under the presumed historical fire regime\n* Percent of Low-severity Fire (PLS) image quantifies the amount of\n  low-severity fires relative to mixed- and replacement-severity fires\n  under the presumed historical fire regime and is defined as less than 25\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Mixed-severity Fire (PMS) layer quantifies the amount of\n  mixed-severity fires relative to low- and replacement-severity fires under\n  the presumed historical fire regime, and is defined as between 25 and 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Replacement-severity Fire (PRS) layer quantifies the amount of\n  replacement-severity fires relative to low- and mixed-severity fires under\n  the presumed historical fire regime, and is defined as greater than 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Succession Classes (SClass) layer characterizes current vegetation conditions\n  with respect to the vegetation species composition, cover, and height ranges\n  of successional states that occur within each biophysical setting\n* Vegetation Condition Class (VCC) represents a simple categorization of the\n  associated Vegetation Departure (VDEP) layer and indicates the general level\n  to which current vegetation is different from the simulated historical\n  vegetation reference conditions\n* Vegetation Departure (VDep) indicates how different current vegetation on a\n  landscape is from estimated historical conditions. VDep is based on changes\n  to species composition, structural stage, and canopy closure.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/FRG/v1_2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Fire_FRG_v1_2_0","rel":"related","title":"Run the example for LANDFIRE/Fire/FRG/v1_2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Fire_FRG_v1_2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_FRG_v1_2_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/FRG/v1_2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/FRG/v1_2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Fire/MFRI/v1_2_0","title":"LANDFIRE MFRI (Mean Fire Return Interval) v1.2.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions\nare mapped using the Vegetation Dynamics Development Tool (VDDT).\nThese data support fire and landscape management planning goals in\nthe National Cohesive Wildland Fire Management Strategy, the Federal Wildland\nFire Management Policy, and the Healthy Forests Restoration Act.\n\nThe Mean Fire Return Interval (MFRI) layer quantifies the average period between fires under the presumed historical fire regime.\nMFRI is intended to describe one component of historical fire regime characteristics in the context of the broader historical time period represented by the LANDFIRE (LF) Biophysical Settings (BPS) layer and BPS Model documentation.\nMFRI is derived from the vegetation and disturbance dynamics model VDDT (Vegetation Dynamics Development Tool) (LF 1.0.0 CONUS only used the vegetation and disturbance dynamics model LANDSUM).\nThis layer is created by linking the BpS Group attribute in the BpS layer with the Refresh Model Tracker (RMT) data and assigning the MFRI attribute.\nThis geospatial product should display a reasonable approximation of MFRI, as documented in the RMT.\nMFRI is used in landscape assessments.\n\nThe LANDIFRE Fire datasets include:\n\n* Fire Regime Groups (FRG) is intended to characterize presumed historical\n  fire regimes within landscapes based on interactions between vegetation\n  dynamics, fire spread, fire effects, and spatial context\n* Mean Fire Return Interval (MFRI) quantifies the average period between\n  fires under the presumed historical fire regime\n* Percent of Low-severity Fire (PLS) image quantifies the amount of\n  low-severity fires relative to mixed- and replacement-severity fires\n  under the presumed historical fire regime and is defined as less than 25\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Mixed-severity Fire (PMS) layer quantifies the amount of\n  mixed-severity fires relative to low- and replacement-severity fires under\n  the presumed historical fire regime, and is defined as between 25 and 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Replacement-severity Fire (PRS) layer quantifies the amount of\n  replacement-severity fires relative to low- and mixed-severity fires under\n  the presumed historical fire regime, and is defined as greater than 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Succession Classes (SClass) layer characterizes current vegetation conditions\n  with respect to the vegetation species composition, cover, and height ranges\n  of successional states that occur within each biophysical setting\n* Vegetation Condition Class (VCC) represents a simple categorization of the\n  associated Vegetation Departure (VDEP) layer and indicates the general level\n  to which current vegetation is different from the simulated historical\n  vegetation reference conditions\n* Vegetation Departure (VDep) indicates how different current vegetation on a\n  landscape is from estimated historical conditions. VDep is based on changes\n  to species composition, structural stage, and canopy closure.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/MFRI/v1_2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Fire_MFRI_v1_2_0","rel":"related","title":"Run the example for LANDFIRE/Fire/MFRI/v1_2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Fire_MFRI_v1_2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_MFRI_v1_2_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/MFRI/v1_2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/MFRI/v1_2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Fire/PLS/v1_2_0","title":"LANDFIRE PLS (Percent Low-severity Fire) v1.2.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions\nare mapped using the Vegetation Dynamics Development Tool (VDDT).\nThese data support fire and landscape management planning goals in\nthe National Cohesive Wildland Fire Management Strategy, the Federal Wildland\nFire Management Policy, and the Healthy Forests Restoration Act.\n\nThe Percent of Low-severity Fire (PLS) layer quantifies the amount of low-severity fires relative to mixed- and replacement-severity fires under the presumed historical fire regime.\nLow severity is defined as less than 25 percent average top-kill within a typical fire perimeter for a given vegetation type.\nPLS was derived from the vegetation and disturbance dynamics model VDDT (Vegetation Dynamics Development Tool) (LF 1.0.0 CONUS only used the vegetation and disturbance dynamics model LANDSUM).\nThis layer is intended to describe one component of historical fire regime characteristics in the context of the broader historical time period represented by the LANDFIRE (LF) Biophysical Settings (BPS) layer and BPS Model documentation.\nThis layer is created by linking the BPS Group attribute in the BPS layer with the Refresh Model Tracker (RMT) data and assigning the PLS attribute.\nThis geospatial product should display a reasonable approximation of PLS, as documented in the RMT.\nPLS is used in landscape assessments.\n\nThe LANDIFRE Fire datasets include:\n\n* Fire Regime Groups (FRG) is intended to characterize presumed historical\n  fire regimes within landscapes based on interactions between vegetation\n  dynamics, fire spread, fire effects, and spatial context\n* Mean Fire Return Interval (MFRI) quantifies the average period between\n  fires under the presumed historical fire regime\n* Percent of Low-severity Fire (PLS) image quantifies the amount of\n  low-severity fires relative to mixed- and replacement-severity fires\n  under the presumed historical fire regime and is defined as less than 25\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Mixed-severity Fire (PMS) layer quantifies the amount of\n  mixed-severity fires relative to low- and replacement-severity fires under\n  the presumed historical fire regime, and is defined as between 25 and 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Replacement-severity Fire (PRS) layer quantifies the amount of\n  replacement-severity fires relative to low- and mixed-severity fires under\n  the presumed historical fire regime, and is defined as greater than 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Succession Classes (SClass) layer characterizes current vegetation conditions\n  with respect to the vegetation species composition, cover, and height ranges\n  of successional states that occur within each biophysical setting\n* Vegetation Condition Class (VCC) represents a simple categorization of the\n  associated Vegetation Departure (VDEP) layer and indicates the general level\n  to which current vegetation is different from the simulated historical\n  vegetation reference conditions\n* Vegetation Departure (VDep) indicates how different current vegetation on a\n  landscape is from estimated historical conditions. VDep is based on changes\n  to species composition, structural stage, and canopy closure.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/PLS/v1_2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Fire_PLS_v1_2_0","rel":"related","title":"Run the example for LANDFIRE/Fire/PLS/v1_2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Fire_PLS_v1_2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_PLS_v1_2_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/PLS/v1_2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/PLS/v1_2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Fire/PMS/v1_2_0","title":"LANDFIRE PMS (Percent of Mixed-severity Fire) v1.2.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions\nare mapped using the Vegetation Dynamics Development Tool (VDDT).\nThese data support fire and landscape management planning goals in\nthe National Cohesive Wildland Fire Management Strategy, the Federal Wildland\nFire Management Policy, and the Healthy Forests Restoration Act.\n\nThe Percent of Mixed-severity Fire (PMS) layer quantifies the amount of mixed-severity fires relative to low- and replacement-severity fires under the presumed historical fire regime.\nMixed severity is defined as between 25 and 75 percent average top-kill within a typical fire perimeter for a given vegetation type.\nPMS was derived from the vegetation and disturbance dynamics model VDDT (Vegetation Dynamics Development Tool) (LF 1.0.0 CONUS only used the vegetation and disturbance dynamics model LANDSUM).\nThis layer is intended to describe one component of historical fire regime characteristics in the context of the broader historical time period represented by the LANDFIRE (LF) Biophysical Settings (BPS) layer and BPS Model documentation.\nThis layer is created by linking the BPS Group attribute in the BPS layer with the Refresh Model Tracker (RMT) data and assigning the PMS attribute.\nThis geospatial product should display a reasonable approximation of PMS, as documented in the RMT.\nPMS is used in landscape assessments.\n\nThe LANDIFRE Fire datasets include:\n\n* Fire Regime Groups (FRG) is intended to characterize presumed historical\n  fire regimes within landscapes based on interactions between vegetation\n  dynamics, fire spread, fire effects, and spatial context\n* Mean Fire Return Interval (MFRI) quantifies the average period between\n  fires under the presumed historical fire regime\n* Percent of Low-severity Fire (PLS) image quantifies the amount of\n  low-severity fires relative to mixed- and replacement-severity fires\n  under the presumed historical fire regime and is defined as less than 25\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Mixed-severity Fire (PMS) layer quantifies the amount of\n  mixed-severity fires relative to low- and replacement-severity fires under\n  the presumed historical fire regime, and is defined as between 25 and 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Replacement-severity Fire (PRS) layer quantifies the amount of\n  replacement-severity fires relative to low- and mixed-severity fires under\n  the presumed historical fire regime, and is defined as greater than 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Succession Classes (SClass) layer characterizes current vegetation conditions\n  with respect to the vegetation species composition, cover, and height ranges\n  of successional states that occur within each biophysical setting\n* Vegetation Condition Class (VCC) represents a simple categorization of the\n  associated Vegetation Departure (VDEP) layer and indicates the general level\n  to which current vegetation is different from the simulated historical\n  vegetation reference conditions\n* Vegetation Departure (VDep) indicates how different current vegetation on a\n  landscape is from estimated historical conditions. VDep is based on changes\n  to species composition, structural stage, and canopy closure.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/PMS/v1_2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Fire_PMS_v1_2_0","rel":"related","title":"Run the example for LANDFIRE/Fire/PMS/v1_2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Fire_PMS_v1_2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_PMS_v1_2_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/PMS/v1_2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/PMS/v1_2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Fire/PRS/v1_2_0","title":"LANDFIRE PRS (Percent of Replacement-severity Fire) v1.2.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions\nare mapped using the Vegetation Dynamics Development Tool (VDDT).\nThese data support fire and landscape management planning goals in\nthe National Cohesive Wildland Fire Management Strategy, the Federal Wildland\nFire Management Policy, and the Healthy Forests Restoration Act.\n\nThe Percent of Replacement-severity Fire (PRS) layer quantifies the amount of replacement-severity fires relative to low- and mixed-severity fires under the presumed historical fire regime.\nReplacement severity is defined as greater than 75 percent average top-kill within a typical fire perimeter for a given vegetation type.\nPRS was derived from the vegetation and disturbance dynamics model VDDT (Vegetation Dynamics Development Tool) (LF 1.0.0 CONUS only used the vegetation and disturbance dynamics model LANDSUM).\nThis layer is intended to describe one component of historical fire regime characteristics in the context of the broader historical time period represented by the LANDFIRE(LF) Biophysical Settings (BPS) layer and BPS Model documentation.\nThis layer is created by linking the BPS Group attribute in the BPS layer with the Refresh Model Tracker (RMT) data and assigning the PRS attribute.\nThis geospatial product should display a reasonable approximation of PRS, as documented in the RMT.\nPRS is used in landscape assessments.\n\nThe LANDIFRE Fire datasets include:\n\n* Fire Regime Groups (FRG) is intended to characterize presumed historical\n  fire regimes within landscapes based on interactions between vegetation\n  dynamics, fire spread, fire effects, and spatial context\n* Mean Fire Return Interval (MFRI) quantifies the average period between\n  fires under the presumed historical fire regime\n* Percent of Low-severity Fire (PLS) image quantifies the amount of\n  low-severity fires relative to mixed- and replacement-severity fires\n  under the presumed historical fire regime and is defined as less than 25\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Mixed-severity Fire (PMS) layer quantifies the amount of\n  mixed-severity fires relative to low- and replacement-severity fires under\n  the presumed historical fire regime, and is defined as between 25 and 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Replacement-severity Fire (PRS) layer quantifies the amount of\n  replacement-severity fires relative to low- and mixed-severity fires under\n  the presumed historical fire regime, and is defined as greater than 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Succession Classes (SClass) layer characterizes current vegetation conditions\n  with respect to the vegetation species composition, cover, and height ranges\n  of successional states that occur within each biophysical setting\n* Vegetation Condition Class (VCC) represents a simple categorization of the\n  associated Vegetation Departure (VDEP) layer and indicates the general level\n  to which current vegetation is different from the simulated historical\n  vegetation reference conditions\n* Vegetation Departure (VDep) indicates how different current vegetation on a\n  landscape is from estimated historical conditions. VDep is based on changes\n  to species composition, structural stage, and canopy closure.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/PRS/v1_2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Fire_PRS_v1_2_0","rel":"related","title":"Run the example for LANDFIRE/Fire/PRS/v1_2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Fire_PRS_v1_2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_PRS_v1_2_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/PRS/v1_2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/PRS/v1_2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Fire/SClass/v1_4_0","title":"LANDFIRE SClass (Succession Classes) v1.4.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions\nare mapped using the Vegetation Dynamics Development Tool (VDDT).\nThese data support fire and landscape management planning goals in\nthe National Cohesive Wildland Fire Management Strategy, the Federal Wildland\nFire Management Policy, and the Healthy Forests Restoration Act.\n\nThe Succession Classes (SClass) layer characterizes current vegetation conditions with respect to the vegetation species composition, cover, and height ranges of successional states that occur within each biophysical setting.\nSClass can also represent uncharacteristic vegetation components, such as exotic species, that are not found within the compositional or structural variability of successional states defined for a biophysical setting.\nSuccession classes do not directly quantify fuel characteristics of the current vegetation, but rather represent vegetative states with unique succession or disturbance-related dynamics, such as structural development or fire frequency.\nTo produce SClass, the historical reference conditions of these successional states were derived from the vegetation and disturbance dynamics model VDDT (Vegetation Dynamics Development Tool) (LF_1.0.0 CONUS only used the vegetation and disturbance dynamics model LANDSUM).\nThe area contained in succession classes is compared to the simulated historical reference conditions to calculate measurements of vegetation departure, such as fire regime condition class.\nSClass is used in landscape assessments.\n\nThe LANDIFRE Fire datasets include:\n\n* Fire Regime Groups (FRG) is intended to characterize presumed historical\n  fire regimes within landscapes based on interactions between vegetation\n  dynamics, fire spread, fire effects, and spatial context\n* Mean Fire Return Interval (MFRI) quantifies the average period between\n  fires under the presumed historical fire regime\n* Percent of Low-severity Fire (PLS) image quantifies the amount of\n  low-severity fires relative to mixed- and replacement-severity fires\n  under the presumed historical fire regime and is defined as less than 25\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Mixed-severity Fire (PMS) layer quantifies the amount of\n  mixed-severity fires relative to low- and replacement-severity fires under\n  the presumed historical fire regime, and is defined as between 25 and 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Replacement-severity Fire (PRS) layer quantifies the amount of\n  replacement-severity fires relative to low- and mixed-severity fires under\n  the presumed historical fire regime, and is defined as greater than 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Succession Classes (SClass) layer characterizes current vegetation conditions\n  with respect to the vegetation species composition, cover, and height ranges\n  of successional states that occur within each biophysical setting\n* Vegetation Condition Class (VCC) represents a simple categorization of the\n  associated Vegetation Departure (VDEP) layer and indicates the general level\n  to which current vegetation is different from the simulated historical\n  vegetation reference conditions\n* Vegetation Departure (VDep) indicates how different current vegetation on a\n  landscape is from estimated historical conditions. VDep is based on changes\n  to species composition, structural stage, and canopy closure.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/SClass/v1_4_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Fire_SClass_v1_4_0","rel":"related","title":"Run the example for LANDFIRE/Fire/SClass/v1_4_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Fire_SClass_v1_4_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_SClass_v1_4_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/SClass/v1_4_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/SClass/v1_4_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Fire/VCC/v1_4_0","title":"LANDFIRE VCC (Vegetation Condition Class) v1.4.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions\nare mapped using the Vegetation Dynamics Development Tool (VDDT).\nThese data support fire and landscape management planning goals in\nthe National Cohesive Wildland Fire Management Strategy, the Federal Wildland\nFire Management Policy, and the Healthy Forests Restoration Act.\n\nVegetation Condition Class (VCC) represents a simple categorization of the associated Vegetation Departure (VDEP) layer and indicates the general level to which current vegetation is different from the simulated historical vegetation reference conditions.\nVDEP and VCC are based upon methods originally described in Interagency Fire Regime Condition Class Guidebook, but are not identical to those methods.\nFull descriptions of the methods used can be found in the VDEP product description.\nUsers should review those methods before comparing VDEP or VCC values across LANDFIRE (LF) versions.\nVCC is a derivative of the VDEP layer.\nIt is important to read and understand the characteristics of the VDEP spatial product, particularly if VCC values are compared across versions as the VDEP methods varied which directly impacts across-version VDEP and VCC comparability.\nIn LF 2012, the original three VCC classes were divided in half to create six VCC classes to provide additional precision.\n\nThe LANDIFRE Fire datasets include:\n\n* Fire Regime Groups (FRG) is intended to characterize presumed historical\n  fire regimes within landscapes based on interactions between vegetation\n  dynamics, fire spread, fire effects, and spatial context\n* Mean Fire Return Interval (MFRI) quantifies the average period between\n  fires under the presumed historical fire regime\n* Percent of Low-severity Fire (PLS) image quantifies the amount of\n  low-severity fires relative to mixed- and replacement-severity fires\n  under the presumed historical fire regime and is defined as less than 25\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Mixed-severity Fire (PMS) layer quantifies the amount of\n  mixed-severity fires relative to low- and replacement-severity fires under\n  the presumed historical fire regime, and is defined as between 25 and 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Replacement-severity Fire (PRS) layer quantifies the amount of\n  replacement-severity fires relative to low- and mixed-severity fires under\n  the presumed historical fire regime, and is defined as greater than 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Succession Classes (SClass) layer characterizes current vegetation conditions\n  with respect to the vegetation species composition, cover, and height ranges\n  of successional states that occur within each biophysical setting\n* Vegetation Condition Class (VCC) represents a simple categorization of the\n  associated Vegetation Departure (VDEP) layer and indicates the general level\n  to which current vegetation is different from the simulated historical\n  vegetation reference conditions\n* Vegetation Departure (VDep) indicates how different current vegetation on a\n  landscape is from estimated historical conditions. VDep is based on changes\n  to species composition, structural stage, and canopy closure.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/VCC/v1_4_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Fire_VCC_v1_4_0","rel":"related","title":"Run the example for LANDFIRE/Fire/VCC/v1_4_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Fire_VCC_v1_4_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_VCC_v1_4_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/VCC/v1_4_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/VCC/v1_4_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Fire/VDep/v1_4_0","title":"LANDFIRE VDep (Vegetation Departure) v1.4.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLandfire (LF) Historical fire regimes, intervals, and vegetation conditions\nare mapped using the Vegetation Dynamics Development Tool (VDDT).\nThese data support fire and landscape management planning goals in\nthe National Cohesive Wildland Fire Management Strategy, the Federal Wildland\nFire Management Policy, and the Healthy Forests Restoration Act.\n\nVegetation Departure (VDep) indicates how different current vegetation on a landscape is from estimated historical conditions.\nVDep is based on changes to species composition, structural stage, and canopy closure using methods originally described in the Interagency Fire Regime Condition Class Guidebook, but is not identical to those methods.\nLANDFIRE (LF) VDep is based only on departure of current vegetation conditions from reference vegetation conditions, whereas the Guidebook approach includes departure of current fire regimes from those of the reference period.\nVDep is a landscape metric and is scale dependent. Every pixel in a unique biophysical settings (BpS) in a summary unit has the same VDep value.\nThese large landscape values may not represent smaller areas within a summary unit.\nThe VDep metric ranges from 0 - 100, and is based on four factors. These inputs are held constant within a single version of LF, but can be different across LF versions, which directly impacts VDep comparability across versions.\nVDep can be compared across versions but caution is advised.\n\nThe LANDIFRE Fire datasets include:\n\n* Fire Regime Groups (FRG) is intended to characterize presumed historical\n  fire regimes within landscapes based on interactions between vegetation\n  dynamics, fire spread, fire effects, and spatial context\n* Mean Fire Return Interval (MFRI) quantifies the average period between\n  fires under the presumed historical fire regime\n* Percent of Low-severity Fire (PLS) image quantifies the amount of\n  low-severity fires relative to mixed- and replacement-severity fires\n  under the presumed historical fire regime and is defined as less than 25\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Mixed-severity Fire (PMS) layer quantifies the amount of\n  mixed-severity fires relative to low- and replacement-severity fires under\n  the presumed historical fire regime, and is defined as between 25 and 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Percent of Replacement-severity Fire (PRS) layer quantifies the amount of\n  replacement-severity fires relative to low- and mixed-severity fires under\n  the presumed historical fire regime, and is defined as greater than 75\n  percent average top-kill within a typical fire perimeter for a given\n  vegetation type\n* Succession Classes (SClass) layer characterizes current vegetation conditions\n  with respect to the vegetation species composition, cover, and height ranges\n  of successional states that occur within each biophysical setting\n* Vegetation Condition Class (VCC) represents a simple categorization of the\n  associated Vegetation Departure (VDEP) layer and indicates the general level\n  to which current vegetation is different from the simulated historical\n  vegetation reference conditions\n* Vegetation Departure (VDep) indicates how different current vegetation on a\n  landscape is from estimated historical conditions. VDep is based on changes\n  to species composition, structural stage, and canopy closure.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/VDep/v1_4_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Fire_VDep_v1_4_0","rel":"related","title":"Run the example for LANDFIRE/Fire/VDep/v1_4_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Fire_VDep_v1_4_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Fire_VDep_v1_4_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/VDep/v1_4_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Fire/VDep/v1_4_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Vegetation/BPS/v1_4_0","title":"LANDFIRE BPS (Biophysical Settings) v1.4.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLANDFIRE (LF) layers are created using predictive landscape models based on\nextensive field-referenced data, satellite imagery and biophysical gradient\nlayers using classification and regression trees.\n\nLANDFIRE's (LF) Biophysical Settings (BPS) represents the vegetation that may have been dominant on the landscape prior to Euro-American settlement and is based on both the current biophysical environment and an approximation of the historical disturbance regime.\nMap units are based on NatureServe's Ecological Systems classification and represent the natural plant communities that may have been present during the reference period. Each BPS map unit is matched with a model of vegetation succession, and both serve as key inputs to the LANDSUM landscape succession model.\nThe actual time period for this data set is a composite of both the historical context provided by the fire regime and vegetation dynamics models and the more recent field and geospatial inputs used to create it.\nLF's current BPS is unchanged from LF National's BPS except for updates made to water, barren, and snow classes (additions or removal), so that non-vegetated cover types within the BPS product matches LF existing vegetation and fuel products.\nWith the final release of LF Remap for CONUS in mid to late 2020, the LF Remap BPS product will include the following as attributes: Mean Fire Return Interval (MFRI), Percent of Low-severity Fire (PLS), Percent of Mixed-severity Fire (PMS), Percent of Replacement-severity Fire (PRS), and Fire Regime Groups (FRG), so that the linkage of these characteristics to BPS is maintained.\nLF uses BPS to depict reference conditions of vegetation across landscapes.\n\nThe LANDIFRE Vegetation datasets include:\n\n* Biophysical Settings (BPS)\n* Environmental Site Potential (ESP)\n* Existing Vegetation Canopy Cover (EVC)\n* Existing Vegetation Height (EVH).\n* Existing Vegetation Type (EVT)\nThese layers are created using predictive landscape models based on extensive\nfield-referenced data, satellite imagery and biophysical gradient layers using\nclassification and regression trees.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/BPS/v1_4_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Vegetation_BPS_v1_4_0","rel":"related","title":"Run the example for LANDFIRE/Vegetation/BPS/v1_4_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Vegetation_BPS_v1_4_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_BPS_v1_4_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/BPS/v1_4_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/BPS/v1_4_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Vegetation/EVC/v1_4_0","title":"LANDFIRE EVC (Existing Vegetation Cover) v1.4.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLANDFIRE (LF) layers are created using predictive landscape models based on\nextensive field-referenced data, satellite imagery and biophysical gradient\nlayers using classification and regression trees.\n\nLANDFIRE's (LF) Existing Vegetation Cover (EVC) represents the vertically projected percent cover of the live canopy layer for a 30-m cell.\nEVC is generated separately for tree, shrub, and herbaceous cover lifeforms using training data and other geospatial layers. Percentage tree, shrub, and herbaceous canopy cover training data are generated using plot-level ground-based visual assessments and lidar observations.\n* Once the training data are developed, relationships are then established separately for each lifeform between the training data and combination of Landsat and ancillary data. Each of the derived data layers (tree, shrub, herbaceous) has a potential range from 0-100 percent which are merged into a single composite EVC layer.\n* Disturbance data were used to develop LF Remap products for LFRDB plot filtering and to ensure 2015 and 2016 disturbances were included that were not visible in the source imagery.\n* The EVC product is then reconciled through QA/QC measures to ensure life-form is synchronized with both Existing Vegetation Height and Existing Vegetation Type products.\nLF uses EVC in several subsequent layers, including the development of the fuel layers.\n\nThe LANDIFRE Vegetation datasets include:\n\n* Biophysical Settings (BPS)\n* Environmental Site Potential (ESP)\n* Existing Vegetation Canopy Cover (EVC)\n* Existing Vegetation Height (EVH).\n* Existing Vegetation Type (EVT)\nThese layers are created using predictive landscape models based on extensive\nfield-referenced data, satellite imagery and biophysical gradient layers using\nclassification and regression trees.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/EVC/v1_4_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Vegetation_EVC_v1_4_0","rel":"related","title":"Run the example for LANDFIRE/Vegetation/EVC/v1_4_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Vegetation_EVC_v1_4_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_EVC_v1_4_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/EVC/v1_4_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/EVC/v1_4_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Vegetation/EVH/v1_4_0","title":"LANDFIRE EVH (Existing Vegetation Height) v1.4.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLANDFIRE (LF) layers are created using predictive landscape models based on\nextensive field-referenced data, satellite imagery and biophysical gradient\nlayers using classification and regression trees.\n\nExisting Vegetation Height (EVH) represents the average height of the dominant vegetation for a 30-m cell.\nCanopy height is generated separately for tree, shrub, and herbaceous lifeforms using training data and other geospatial layers. EVH is determined by the average height weighted by species cover and based on the Existing Vegetation Type (EVT) lifeform.\n\n* Decision tree models using field reference data, lidar, Landsat, and ancillary data are developed separately for each lifeform. Decision tree relationships are used to generate lifeform specific height class layers, which are merged into a single composite EVH layer.\n\n* Disturbance data were used to develop LF Remap products for LFRDB plot filtering and to ensure 2015 and 2016 disturbances were included that were not visible in the source imagery.\n\n* The EVC product is then reconciled through QA/QC measures to ensure life-form is synchronized with both Existing Vegetation Cover and EVT products.\n\nLF uses EVH in several subsequent layers, including the development of the fuel products.\n\nThe LANDIFRE Vegetation datasets include:\n\n* Biophysical Settings (BPS)\n* Environmental Site Potential (ESP)\n* Existing Vegetation Canopy Cover (EVC)\n* Existing Vegetation Height (EVH).\n* Existing Vegetation Type (EVT)\nThese layers are created using predictive landscape models based on extensive\nfield-referenced data, satellite imagery and biophysical gradient layers using\nclassification and regression trees.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/EVH/v1_4_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Vegetation_EVH_v1_4_0","rel":"related","title":"Run the example for LANDFIRE/Vegetation/EVH/v1_4_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Vegetation_EVH_v1_4_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_EVH_v1_4_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/EVH/v1_4_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/EVH/v1_4_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Vegetation/EVT/v1_4_0","title":"LANDFIRE EVT (Existing Vegetation Type) v1.4.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLANDFIRE (LF) layers are created using predictive landscape models based on\nextensive field-referenced data, satellite imagery and biophysical gradient\nlayers using classification and regression trees.\n\nLANDFIRE's (LF) Existing Vegetation Type (EVT) represents the current distribution of the terrestrial ecological systems classification, developed by NatureServe for the western hemisphere, through 2016. A terrestrial ecological system is defined as a group of plant community types (associations) that tend to co-occur within landscapes with similar ecological processes, substrates, and/or environmental gradients.\n\n*The LF Ecological Systems Descriptions for CONUS provides descriptions for each Ecological System including species, distribution and classification information.\n\nEVT also includes ruderal or semi-natural vegetation types within the U.S. National Vegetation Classification.\nThe LF Ruderal NVC Groups Descriptions for CONUS provides descriptions for each ruderal NVC Group including species, distribution, and classification information.\n\nEVT is mapped using decision tree models, field data, Landsat imagery, elevation, and biophysical gradient data.\n\n* Decision tree models are developed separately for each of the three lifeforms-tree, shrub, and herbaceous and are then used to generate lifeform specific EVT layers.\n\n* Disturbance products are included in LF Remap products to describe areas on the landscape that have experienced change within the previous 10-year period.\n\n* The EVT product is reconciled through QA/QC measures to ensure life-form is synchronized with both Existing Vegetation Cover and Existing Vegetation Height.\n\nThe LANDIFRE Vegetation datasets include:\n\n* Biophysical Settings (BPS)\n* Environmental Site Potential (ESP)\n* Existing Vegetation Canopy Cover (EVC)\n* Existing Vegetation Height (EVH).\n* Existing Vegetation Type (EVT)\nThese layers are created using predictive landscape models based on extensive\nfield-referenced data, satellite imagery and biophysical gradient layers using\nclassification and regression trees.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2014-09-01T00:00:00Z","2014-09-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/EVT/v1_4_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Vegetation_EVT_v1_4_0","rel":"related","title":"Run the example for LANDFIRE/Vegetation/EVT/v1_4_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Vegetation_EVT_v1_4_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_EVT_v1_4_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/EVT/v1_4_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/EVT/v1_4_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_32DAY","title":"Landsat Collection 2 Tier 1 Level 2 32-Day Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1\nLevel 2 orthorectified scenes, and include the SR bands:\nblue, green, red, nir, swir1, swir2, and thermal.\n\nThese composites are created from\nall the scenes in\neach 32-day period beginning from the first day of the year and\ncontinuing to the 352nd day of the year.  The last composite of the\nyear, beginning on day 353, will overlap the first composite of the\nfollowing year by 20 days.  All the images from each 32-day period are\nincluded in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-10-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_32DAY","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_32DAY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_32DAY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_32DAY_BAI","title":"Landsat Collection 2 Tier 1 Level 2 32-Day BAI Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Burn Area Index (BAI) is generated from the Red and\nNear-IR bands, and measures the spectral distance of each pixel from a\nreference spectral point (the measured reflectance of charcoal).  This\nindex is intended to emphasize the charcoal signal in post-fire\nimages. See\n[Chuvieco et al. (2002)](https://www.tandfonline.com/doi/abs/10.1080/01431160210153129) for details.\n\nThese composites are created from\nall the scenes in\neach 32-day period beginning from the first day of the year and\ncontinuing to the 352nd day of the year.  The last composite of the\nyear, beginning on day 353, will overlap the first composite of the\nfollowing year by 20 days.  All the images from each 32-day period are\nincluded in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-10-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_BAI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_BAI","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_32DAY_BAI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_BAI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_BAI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_BAI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_BAI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_32DAY_EVI","title":"Landsat Collection 2 Tier 1 Level 2 32-Day EVI Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Enhanced Vegetation Index (EVI) is generated from the\nNear-IR, Red and Blue bands of each scene, and ranges in value from\n-1.0 to 1.0. See\n[Huete et al. (2002)](https://www.sciencedirect.com/science/article/pii/S0034425702000962)\nfor details.\n\nThese composites are created from\nall the scenes in\neach 32-day period beginning from the first day of the year and\ncontinuing to the 352nd day of the year.  The last composite of the\nyear, beginning on day 353, will overlap the first composite of the\nfollowing year by 20 days.  All the images from each 32-day period are\nincluded in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-10-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_EVI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_EVI","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_32DAY_EVI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_EVI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_EVI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_EVI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_EVI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NBR","title":"Landsat Collection 2 Tier 1 Level 2 32-Day NBR Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Normalized Burn Ratio Thermal (NBRT) index is\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\nhas a range from -1.0 to 1.0. See\n[Holden et al. (2005)](https://www.tandfonline.com/doi/abs/10.1080/01431160500239008) for details.\n\nThese composites are created from\nall the scenes in\neach 32-day period beginning from the first day of the year and\ncontinuing to the 352nd day of the year.  The last composite of the\nyear, beginning on day 353, will overlap the first composite of the\nfollowing year by 20 days.  All the images from each 32-day period are\nincluded in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-10-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NBR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_NBR","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NBR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_NBR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_NBR#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NBR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NBR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NDVI","title":"Landsat Collection 2 Tier 1 Level 2 32-Day NDVI Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Normalized Difference Vegetation Index is\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\n(NIR + Red), and ranges in value from -1.0 to 1.0.\n\nThese composites are created from\nall the scenes in\neach 32-day period beginning from the first day of the year and\ncontinuing to the 352nd day of the year.  The last composite of the\nyear, beginning on day 353, will overlap the first composite of the\nfollowing year by 20 days.  All the images from each 32-day period are\nincluded in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-10-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NDVI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_NDVI","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NDVI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_NDVI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_NDVI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NDVI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NDVI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NDWI","title":"Landsat Collection 2 Tier 1 Level 2 32-Day NDWI Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Normalized Difference Water Index (NDWI) is designed to delineate open\nwater features and enhance their presence in imagery while eliminating soil\nand terrestrial vegetation features. It is derived from the Green band and\nthe Near-Infrared (NIR) band. It ranges in value from -1.0 to 1.0. See\n[McFeeters (1996)](https://doi.org/10.1080/01431169608948714)\nfor details.\n\nThese composites are created from\nall the scenes in\neach 32-day period beginning from the first day of the year and\ncontinuing to the 352nd day of the year.  The last composite of the\nyear, beginning on day 353, will overlap the first composite of the\nfollowing year by 20 days.  All the images from each 32-day period are\nincluded in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-10-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NDWI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_NDWI","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NDWI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_NDWI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_32DAY_NDWI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NDWI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_32DAY_NDWI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_8DAY","title":"Landsat Collection 2 Tier 1 Level 2 8-Day Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1\nLevel 2 orthorectified scenes, and include the SR bands:\nblue, green, red, nir, swir1, swir2, and thermal.\n\nThese composites are created from\nall the scenes in each\n8-day period beginning from the first day of the year and continuing\nto the 360th day of the year.  The last composite of the year,\nbeginning on day 361, will overlap the first composite of the\nfollowing year by 3 days.  All the images from each 8-day period are\nincluded in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-11-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_8DAY","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_8DAY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_8DAY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_8DAY_BAI","title":"Landsat Collection 2 Tier 1 Level 2 8-Day BAI Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Burn Area Index (BAI) is generated from the Red and\nNear-IR bands, and measures the spectral distance of each pixel from a\nreference spectral point (the measured reflectance of charcoal).  This\nindex is intended to emphasize the charcoal signal in post-fire\nimages. See\n[Chuvieco et al. (2002)](https://www.tandfonline.com/doi/abs/10.1080/01431160210153129) for details.\n\nThese composites are created from\nall the scenes in each\n8-day period beginning from the first day of the year and continuing\nto the 360th day of the year.  The last composite of the year,\nbeginning on day 361, will overlap the first composite of the\nfollowing year by 3 days.  All the images from each 8-day period are\nincluded in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-11-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_BAI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_BAI","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_8DAY_BAI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_BAI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_BAI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_BAI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_BAI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_8DAY_EVI","title":"Landsat Collection 2 Tier 1 Level 2 8-Day EVI Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Enhanced Vegetation Index (EVI) is generated from the\nNear-IR, Red and Blue bands of each scene, and ranges in value from\n-1.0 to 1.0. See\n[Huete et al. (2002)](https://www.sciencedirect.com/science/article/pii/S0034425702000962)\nfor details.\n\nThese composites are created from\nall the scenes in each\n8-day period beginning from the first day of the year and continuing\nto the 360th day of the year.  The last composite of the year,\nbeginning on day 361, will overlap the first composite of the\nfollowing year by 3 days.  All the images from each 8-day period are\nincluded in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-11-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_EVI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_EVI","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_8DAY_EVI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_EVI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_EVI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_EVI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_EVI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NBR","title":"Landsat Collection 2 Tier 1 Level 2 8-Day NBR Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Normalized Burn Ratio Thermal (NBRT) index is\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\nhas a range from -1.0 to 1.0. See\n[Holden et al. (2005)](https://www.tandfonline.com/doi/abs/10.1080/01431160500239008)\nfor details.\n\nThese composites are created from\nall the scenes in each\n8-day period beginning from the first day of the year and continuing\nto the 360th day of the year.  The last composite of the year,\nbeginning on day 361, will overlap the first composite of the\nfollowing year by 3 days.  All the images from each 8-day period are\nincluded in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-11-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NBR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_NBR","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NBR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_NBR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_NBR#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NBR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NBR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NDVI","title":"Landsat Collection 2 Tier 1 Level 2 8-Day NDVI Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Normalized Difference Vegetation Index is\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\n(NIR + Red), and ranges in value from -1.0 to 1.0.\n\nThese composites are created from\nall the scenes in each\n8-day period beginning from the first day of the year and continuing\nto the 360th day of the year.  The last composite of the year,\nbeginning on day 361, will overlap the first composite of the\nfollowing year by 3 days.  All the images from each 8-day period are\nincluded in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-11-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NDVI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_NDVI","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NDVI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_NDVI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_NDVI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NDVI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NDVI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NDWI","title":"Landsat Collection 2 Tier 1 Level 2 8-Day NDWI Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Normalized Difference Water Index (NDWI) is designed to delineate open\nwater features and enhance their presence in imagery while eliminating soil\nand terrestrial vegetation features. It is derived from the Green band and \nthe Near-Infrared (NIR) band. It ranges in value from -1.0 to 1.0. See\n[McFeeters (1996)](https://doi.org/10.1080/01431169608948714)\nfor details.\n\nThese composites are created from\nall the scenes in each\n8-day period beginning from the first day of the year and continuing\nto the 360th day of the year.  The last composite of the year,\nbeginning on day 361, will overlap the first composite of the\nfollowing year by 3 days.  All the images from each 8-day period are\nincluded in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-11-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NDWI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_NDWI","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NDWI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_NDWI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_8DAY_NDWI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NDWI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_8DAY_NDWI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL","title":"Landsat Collection 2 Tier 1 Level 2 Annual Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1\nLevel 2 orthorectified scenes, and include the SR bands:\nblue, green, red, nir, swir1, swir2, and thermal.\n\nThese composites are created from\nall the scenes in\neach annual period beginning from the first day of the year and\ncontinuing to the last day of the year.  All the images from each year\nare included in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_BAI","title":"Landsat Collection 2 Tier 1 Level 2 Annual BAI Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Burn Area Index (BAI) is generated from the Red and\nNear-IR bands, and measures the spectral distance of each pixel from a\nreference spectral point (the measured reflectance of charcoal).  This\nindex is intended to emphasize the charcoal signal in post-fire\nimages. See\n[Chuvieco et al. (2002)](https://www.tandfonline.com/doi/abs/10.1080/01431160210153129)\nfor details.\n\nThese composites are created from\nall the scenes in\neach annual period beginning from the first day of the year and\ncontinuing to the last day of the year.  All the images from each year\nare included in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_BAI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_BAI","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_BAI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_BAI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_BAI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_BAI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_BAI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_EVI","title":"Landsat Collection 2 Tier 1 Level 2 Annual EVI Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Enhanced Vegetation Index (EVI) is generated from the\nNear-IR, Red and Blue bands of each scene, and ranges in value from\n-1.0 to 1.0. See\n[Huete et al. (2002)](https://www.sciencedirect.com/science/article/pii/S0034425702000962)\nfor details.\n\nThese composites are created from\nall the scenes in\neach annual period beginning from the first day of the year and\ncontinuing to the last day of the year.  All the images from each year\nare included in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_EVI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_EVI","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_EVI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_EVI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_EVI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_EVI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_EVI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NBR","title":"Landsat Collection 2 Tier 1 Level 2 Annual NBR Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Normalized Burn Ratio Thermal (NBRT) index is\ngenerated from the Near-IR, Mid-IR (2215 nm), and Thermal bands, and\nhas a range from -1.0 to 1.0. See\n[Holden et al. (2005)](https://www.tandfonline.com/doi/abs/10.1080/01431160500239008)\nfor details.\n\nThese composites are created from\nall the scenes in\neach annual period beginning from the first day of the year and\ncontinuing to the last day of the year.  All the images from each year\nare included in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NBR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_NBR","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NBR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_NBR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_NBR#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NBR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NBR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NDVI","title":"Landsat Collection 2 Tier 1 Level 2 Annual NDVI Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Normalized Difference Vegetation Index is\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\n(NIR + Red), and ranges in value from -1.0 to 1.0.\n\nThese composites are created from\nall the scenes in\neach annual period beginning from the first day of the year and\ncontinuing to the last day of the year.  All the images from each year\nare included in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NDVI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_NDVI","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NDVI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_NDVI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_NDVI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NDVI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NDVI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NDWI","title":"Landsat Collection 2 Tier 1 Level 2 Annual NDWI Composite","description":"These Landsat Collection 2 Tier 1 Level 2 composites are made from Tier 1 Level 2 orthorectified scenes.\n\nThe Normalized Difference Water Index (NDWI) is designed to delineate open\nwater features and enhance their presence in imagery while eliminating soil\nand terrestrial vegetation features. It is derived from the Green band and \nthe Near-Infrared (NIR) band. It ranges in value from -1.0 to 1.0. See\n[McFeeters (1996)](https://doi.org/10.1080/01431169608948714)\nfor details.\n\nThese composites are created from\nall the scenes in\neach annual period beginning from the first day of the year and\ncontinuing to the last day of the year.  All the images from each year\nare included in the composite, with the most recent pixel as the composite value.\n\nNotes:\n\n* The code used to create the composites\n  [can be seen here](https://github.com/google/earthengine-catalog/blob/main/pipelines/landsat.py).\n\n* Only daytime images with WRS_ROW < 122 are included.\n\n* For Landsat 7 , images after 2017-01-01 are excluded due to orbital drift.\n\n* For Landsat 8, images before 2013-05-01 are excluded due to pointing issues.\n\n**Caution:**  These composites are computed on the fly and count towards\nthe requesting project's\n[EECU](https://developers.google.com/earth-engine/guides/computation_overview#eecus)\nusage.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2026-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NDWI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_NDWI","rel":"related","title":"Run the example for LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NDWI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_NDWI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_COMPOSITES_C02_T1_L2_ANNUAL_NDWI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NDWI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/COMPOSITES/C02/T1_L2_ANNUAL_NDWI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/GLS1975","title":"Landsat Global Land Survey 1975","description":"The Global Land Survey (GLS) 1975 is a\nglobal collection of imagery from the Landsat Multispectral\nScanner (MSS).  Most scenes were acquired by Landsat 1-3\nin 1972-1983.  A few gaps in the Landsat 1-3 data\nhave been filled with scenes acquired by Landsat 4-5 during the\nyears 1982-1987.  These data contain 4 spectral bands:\nGreen, Red, an NIR band, and a SWIR band.  In the typical\nFalse-color presentation, the images appear red because\nthe NIR band, displayed as red, highlights vegetation.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1972-07-25T00:00:00Z","1983-02-20T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS1975","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_GLS1975","rel":"related","title":"Run the example for LANDSAT/GLS1975 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_GLS1975_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS1975#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS1975/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS1975/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/GLS1975_MOSAIC","title":"Landsat Global Land Survey 1975 Mosaic","description":"The Global Land Survey (GLS) 1975 is a\nglobal collection of imagery from the Landsat Multispectral Scanner\n(MSS).  Most scenes were acquired by Landsat 1&ndash;3 during the\nyears from 1972&ndash;1983.  A few gaps in the Landsat 1&ndash;3 data\nhave been filled with scenes acquired by Landsat 4&ndash;5 during the\nyears 1982&ndash;1987.\n\nThese data contain 4 spectral bands:\nGreen, Red, an NIR band, and a SWIR band.  In the typical false-color\npresentation the images appear red because the NIR band, displayed as\nred, highlights vegetation.\n\nAll scenes in the collection are included in this composite image.\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-01T00:00:00Z","1976-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS1975_MOSAIC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_GLS1975_MOSAIC","rel":"related","title":"Run the example for LANDSAT/GLS1975_MOSAIC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_GLS1975_MOSAIC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS1975_MOSAIC#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS1975_MOSAIC/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS1975_MOSAIC/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/GLS2005","title":"Landsat Global Land Survey 2005, Landsat 5+7 scenes","description":"The GLS2005 data set is a collection of 9500\northorectified leaf-on medium-resolution satellite images collected between\n2004 and 2007 and covering the Earth's land masses. GLS2005 uses mainly Landsat\n5 and gap-filled Landsat 7 data with EO-1 ALI and Terra ASTER data filling in\nany data holes.\n\nThis dataset contains images from just the L5 TM and L7 ETM+ sensors, and only the 6 bands that those two sensors have in common: 10, 20, 30, 40, 50, and 70.\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-07-29T00:00:00Z","2008-07-29T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS2005","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_GLS2005","rel":"related","title":"Run the example for LANDSAT/GLS2005 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_GLS2005_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS2005/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS2005/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/GLS2005_L5","title":"Landsat Global Land Survey 2005, Landsat 5 scenes","description":"The GLS2005 data set is a collection of 9500\northorectified leaf-on medium-resolution satellite images collected between\n2004 and 2007 and covering the Earth's land masses. GLS2005 uses mainly Landsat\n5 and gap-filled Landsat 7 data with EO-1 ALI and Terra ASTER data filling in\nany data holes.\n\nThis collection contains just the subset of the GLS2005 images\n      from the L5 ETM sensor.\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-08-14T00:00:00Z","2008-05-29T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS2005_L5","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_GLS2005_L5","rel":"related","title":"Run the example for LANDSAT/GLS2005_L5 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_GLS2005_L5_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005_L5#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS2005_L5/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS2005_L5/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/GLS2005_L7","title":"Landsat Global Land Survey 2005, Landsat 7 scenes","description":"The GLS2005 data set is a collection of 9500\northorectified leaf-on medium-resolution satellite images collected between\n2004 and 2007 and covering the Earth's land masses. GLS2005 uses mainly Landsat\n5 and gap-filled Landsat 7 data with EO-1 ALI and Terra ASTER data filling in\nany data holes.\n\nThis collection contains just the subset of the GLS2005 images\n      from the L7 ETM+ sensor.\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-07-29T00:00:00Z","2008-07-29T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS2005_L7","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_GLS2005_L7","rel":"related","title":"Run the example for LANDSAT/GLS2005_L7 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_GLS2005_L7_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005_L7#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS2005_L7/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/GLS2005_L7/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC08/C02/T1","title":"USGS Landsat 8 Collection 2 Tier 1 Raw Scenes","description":"Landsat 8 Collection 2 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-03-18T15:58:14Z","2026-11-03T14:01:44.275000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC08_C02_T1","rel":"related","title":"Run the example for LANDSAT/LC08/C02/T1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC08_C02_T1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC08/C02/T1_L2","title":"USGS Landsat 8 Level 2, Collection 2, Tier 1","description":"This dataset contains atmospherically corrected\nsurface reflectance and land surface temperature derived from the data\nproduced by the Landsat 8 OLI/TIRS sensors.\nThese images contain 5 visible and near-infrared (VNIR) bands and\n2 short-wave infrared (SWIR) bands processed to orthorectified surface\nreflectance, and one thermal infrared (TIR) band processed to orthorectified\nsurface temperature. They also contain intermediate bands used in\ncalculation of the ST products, as well as QA bands.\n\nLandsat 8 SR products are created with the Land Surface Reflectance Code\n(LaSRC). All Collection 2 ST products are created with a single-channel\nalgorithm jointly created by the Rochester Institute of Technology (RIT) and\nNational Aeronautics and Space Administration (NASA)\nJet Propulsion Laboratory (JPL).\n\nStrips of collected data are packaged into overlapping \\\"scenes\\\" covering approximately\n170km x 183km using a [standardized reference grid](https://landsat.gsfc.nasa.gov/about/worldwide-reference-system).\n\nSome assets have only SR data, in which case ST bands are present but empty.\nFor assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'.\nFor assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'.\n\n[Additional documentation and usage examples.](/earth-engine/guides/landsat)\n\nLandsat Collection 2 files are publicly available in a [Google Cloud Storage\nbucket](https://console.cloud.google.com/storage/browser/gee-public-data-landsat)\non requester-pays basis. The files are indexed in a regularly updated\nBigQuery table for ease of analysis:\n[earth-engine-public-data.geo_index.landsat_c2_index](https://console.cloud.google.com/bigquery?project=earth-engine-public-data&earth-engine-public-data!2sgeo_index=&inv=1&invt=Ab4V5A&ws=!1m5!1m4!4m3!1searth-engine-public-data!2sgeo_index!3slandsat_c2_index).\n\nData provider notes:\n\n* Data products must contain both optical and thermal data to be\n  successfully processed to surface temperature, as ASTER NDVI is\n  required to temporally adjust the ASTER GED product to the target Landsat\n  scene. Therefore, night time acquisitions cannot be processed to\n  surface temperature.\n\n* A known error exists in the surface temperature retrievals relative\n  to clouds and possibly cloud shadows. The characterization of these\n  issues has been documented by\n  [Cook et al., (2014)](https://doi.org/10.3390/rs61111244).\n\n* ASTER GED contains areas of missing mean emissivity data required for\n  successful ST product generation. If there is missing ASTER GED\n  information, there will be missing ST data in those areas.\n\n* The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes\n  acquired from 2000 through 2008. While this dataset has a global spatial\n  extent, there are areas missing mean emissivity information due to\n  persistent cloud contamination in the ASTER measurements.\n\n* The USGS further screens unphysical values (emissivity < 0.6) in ASTER\n  GED to remove any emissivity underestimation due to undetected clouds. For\n  any given pixel with no ASTER GED input or unphysical emissivity value,\n  the resulting Landsat ST products have missing pixels. The missing Landsat\n  ST pixels will be consistent through time (1982-present) given the static\n  nature of ASTER GED mean climatology data. For more information refer to\n  [landsat-collection-2-surface-temperature-data-gaps-due-missing](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature-data-gaps-due-missing-aster-ged)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-03-18T15:58:14Z","2026-10-26T13:44:06.709000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1_L2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC08_C02_T1_L2","rel":"related","title":"Run the example for LANDSAT/LC08/C02/T1_L2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC08_C02_T1_L2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.usgs.gov/centers/eros/data-citation","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1_L2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1_L2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC08/C02/T1_RT","title":"USGS Landsat 8 Collection 2 Tier 1 and Real-Time data Raw Scenes","description":"Landsat 8 Collection 2 Tier 1 and Real-Time data DN values, representing scaled, calibrated at-sensor radiance.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n\nThe T1_RT collection contains both Tier 1 and Real-Time (RT) assets.\nNewly-acquired Landsat 7 ETM+ and Landsat 8 OLI/TIRS data are processed\nupon downlink but use predicted ephemeris, initial bumper mode parameters,\nor initial TIRS line of sight model parameters. The data is placed in the\nReal-Time tier and made available for immediate download. Once the data\nhave been reprocessed with definitive ephemeris, updated bumper mode\nparameters and refined TIRS parameters, the products are transitioned to\neither Tier 1 or Tier 2 and removed from the Real-Time tier. The transition\ndelay from Real-Time to Tier 1 or Tier 2 is between 14 and 26 days.\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-03-18T15:58:14Z","2026-11-03T14:01:44.275000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1_RT","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC08_C02_T1_RT","rel":"related","title":"Run the example for LANDSAT/LC08/C02/T1_RT in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC08_C02_T1_RT_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1_RT/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1_RT/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC08/C02/T1_RT_TOA","title":"USGS Landsat 8 Collection 2 Tier 1 and Real-Time data TOA Reflectance","description":"Landsat 8 Collection 2 Tier 1 and Real-Time data\n  calibrated top-of-atmosphere (TOA) reflectance.\n  Calibration coefficients are extracted from the image metadata.\n  See [Chander et al. (2009)](https://www.sciencedirect.com/science/article/pii/S0034425709000169)\n  for details on the TOA computation.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-03-18T15:58:14Z","2026-11-03T14:01:44.275000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1_RT_TOA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC08_C02_T1_RT_TOA","rel":"related","title":"Run the example for LANDSAT/LC08/C02/T1_RT_TOA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC08_C02_T1_RT_TOA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT_TOA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1_RT_TOA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1_RT_TOA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC08/C02/T1_TOA","title":"USGS Landsat 8 Collection 2 Tier 1 TOA Reflectance","description":"Landsat 8 Collection 2 Tier 1\n  calibrated top-of-atmosphere (TOA) reflectance.\n  Calibration coefficients are extracted from the image metadata.\n  See [Chander et al. (2009)](https://www.sciencedirect.com/science/article/pii/S0034425709000169)\n  for details on the TOA computation.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n\nThe T1_RT collection contains both Tier 1 and Real-Time (RT) assets.\nNewly-acquired Landsat 7 ETM+ and Landsat 8 OLI/TIRS data are processed\nupon downlink but use predicted ephemeris, initial bumper mode parameters,\nor initial TIRS line of sight model parameters. The data is placed in the\nReal-Time tier and made available for immediate download. Once the data\nhave been reprocessed with definitive ephemeris, updated bumper mode\nparameters and refined TIRS parameters, the products are transitioned to\neither Tier 1 or Tier 2 and removed from the Real-Time tier. The transition\ndelay from Real-Time to Tier 1 or Tier 2 is between 14 and 26 days.\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-03-18T15:58:14Z","2026-11-03T14:01:44.275000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1_TOA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC08_C02_T1_TOA","rel":"related","title":"Run the example for LANDSAT/LC08/C02/T1_TOA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC08_C02_T1_TOA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_TOA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1_TOA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T1_TOA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC08/C02/T2","title":"USGS Landsat 8 Collection 2 Tier 2 Raw Scenes","description":"Landsat 8 Collection 2 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.\nScenes not meeting Tier 1 criteria during\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\nRMSE and other properties to determine the suitability for use in individual\napplications and studies. See more information [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-10-28T23:17:26Z","2026-11-03T15:27:53.677000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC08_C02_T2","rel":"related","title":"Run the example for LANDSAT/LC08/C02/T2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC08_C02_T2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC08/C02/T2_L2","title":"USGS Landsat 8 Level 2, Collection 2, Tier 2","description":"This dataset contains atmospherically corrected\nsurface reflectance and land surface temperature derived from the data\nproduced by the Landsat 8 OLI/TIRS sensors.\nThese images contain 5 visible and near-infrared (VNIR) bands and\n2 short-wave infrared (SWIR) bands processed to orthorectified surface\nreflectance, and one thermal infrared (TIR) band processed to orthorectified\nsurface temperature. They also contain intermediate bands used in\ncalculation of the ST products, as well as QA bands.\n\nLandsat 8 SR products are created with the Land Surface Reflectance Code\n(LaSRC). All Collection 2 ST products are created with a single-channel\nalgorithm jointly created by the Rochester Institute of Technology (RIT) and\nNational Aeronautics and Space Administration (NASA)\nJet Propulsion Laboratory (JPL).\n\nStrips of collected data are packaged into overlapping \\\"scenes\\\" covering approximately\n170km x 183km using a [standardized reference grid](https://landsat.gsfc.nasa.gov/about/worldwide-reference-system).\n\nSome assets have only SR data, in which case ST bands are present but empty.\nFor assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'.\nFor assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'.\n\n[Additional documentation and usage examples.](/earth-engine/guides/landsat)\n\nLandsat Collection 2 files are publicly available in a [Google Cloud Storage\nbucket](https://console.cloud.google.com/storage/browser/gee-public-data-landsat)\non requester-pays basis. The files are indexed in a regularly updated\nBigQuery table for ease of analysis:\n[earth-engine-public-data.geo_index.landsat_c2_index](https://console.cloud.google.com/bigquery?project=earth-engine-public-data&earth-engine-public-data!2sgeo_index=&inv=1&invt=Ab4V5A&ws=!1m5!1m4!4m3!1searth-engine-public-data!2sgeo_index!3slandsat_c2_index).\n\nData provider notes:\n\n* Data products must contain both optical and thermal data to be\n  successfully processed to surface temperature, as ASTER NDVI is\n  required to temporally adjust the ASTER GED product to the target Landsat\n  scene. Therefore, night time acquisitions cannot be processed to\n  surface temperature.\n\n* A known error exists in the surface temperature retrievals relative\n  to clouds and possibly cloud shadows. The characterization of these\n  issues has been documented by\n  [Cook et al., (2014)](https://doi.org/10.3390/rs61111244).\n\n* ASTER GED contains areas of missing mean emissivity data required for\n  successful ST product generation. If there is missing ASTER GED\n  information, there will be missing ST data in those areas.\n\n* The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes\n  acquired from 2000 through 2008. While this dataset has a global spatial\n  extent, there are areas missing mean emissivity information due to\n  persistent cloud contamination in the ASTER measurements.\n\n* The USGS further screens unphysical values (emissivity < 0.6) in ASTER\n  GED to remove any emissivity underestimation due to undetected clouds. For\n  any given pixel with no ASTER GED input or unphysical emissivity value,\n  the resulting Landsat ST products have missing pixels. The missing Landsat\n  ST pixels will be consistent through time (1982-present) given the static\n  nature of ASTER GED mean climatology data. For more information refer to\n  [landsat-collection-2-surface-temperature-data-gaps-due-missing](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature-data-gaps-due-missing-aster-ged)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-03-18T15:58:38Z","2026-10-26T13:52:06.326000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T2_L2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC08_C02_T2_L2","rel":"related","title":"Run the example for LANDSAT/LC08/C02/T2_L2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC08_C02_T2_L2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2_L2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.usgs.gov/centers/eros/data-citation","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T2_L2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T2_L2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC08/C02/T2_TOA","title":"USGS Landsat 8 Collection 2 Tier 2 TOA Reflectance","description":"Landsat 8 Collection 2 Tier 2\n  calibrated top-of-atmosphere (TOA) reflectance.\n  Calibration coefficients are extracted from the image metadata.\n  See [Chander et al. (2009)](https://www.sciencedirect.com/science/article/pii/S0034425709000169)\n  for details on the TOA computation.\nScenes not meeting Tier 1 criteria during\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\nRMSE and other properties to determine the suitability for use in individual\napplications and studies. See more information [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-10-28T23:17:26Z","2026-11-03T15:27:53.677000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T2_TOA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC08_C02_T2_TOA","rel":"related","title":"Run the example for LANDSAT/LC08/C02/T2_TOA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC08_C02_T2_TOA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2_TOA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T2_TOA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC08/C02/T2_TOA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC09/C02/T1","title":"USGS Landsat 9 Collection 2 Tier 1 Raw Scenes","description":"Landsat 9 Collection 2 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-10-31T00:00:00Z","2026-11-03T13:39:45.760000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC09_C02_T1","rel":"related","title":"Run the example for LANDSAT/LC09/C02/T1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC09_C02_T1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC09/C02/T1_L2","title":"USGS Landsat 9 Level 2, Collection 2, Tier 1","description":"This dataset contains atmospherically corrected\nsurface reflectance and land surface temperature derived from the data\nproduced by the Landsat 9 OLI/TIRS sensors.\nThese images contain 5 visible and near-infrared (VNIR) bands and\n2 short-wave infrared (SWIR) bands processed to orthorectified surface\nreflectance, and one thermal infrared (TIR) band processed to orthorectified\nsurface temperature. They also contain intermediate bands used in\ncalculation of the ST products, as well as QA bands.\n\nLandsat 9 SR products are created with the Land Surface Reflectance Code\n(LaSRC). All Collection 2 ST products are created with a single-channel\nalgorithm jointly created by the Rochester Institute of Technology (RIT) and\nNational Aeronautics and Space Administration (NASA)\nJet Propulsion Laboratory (JPL).\n\nStrips of collected data are packaged into overlapping \\\"scenes\\\" covering approximately\n170km x 183km using a [standardized reference grid](https://landsat.gsfc.nasa.gov/about/worldwide-reference-system).\n\nSome assets have only SR data, in which case ST bands are present but empty.\nFor assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'.\nFor assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'.\n\n[Additional documentation and usage examples.](/earth-engine/guides/landsat)\n\nLandsat Collection 2 files are publicly available in a [Google Cloud Storage\nbucket](https://console.cloud.google.com/storage/browser/gee-public-data-landsat)\non requester-pays basis. The files are indexed in a regularly updated\nBigQuery table for ease of analysis:\n[earth-engine-public-data.geo_index.landsat_c2_index](https://console.cloud.google.com/bigquery?project=earth-engine-public-data&earth-engine-public-data!2sgeo_index=&inv=1&invt=Ab4V5A&ws=!1m5!1m4!4m3!1searth-engine-public-data!2sgeo_index!3slandsat_c2_index).\n\nData provider notes:\n\n* Data products must contain both optical and thermal data to be\n  successfully processed to surface temperature, as ASTER NDVI is\n  required to temporally adjust the ASTER GED product to the target Landsat\n  scene. Therefore, night time acquisitions cannot be processed to\n  surface temperature.\n\n* A known error exists in the surface temperature retrievals relative\n  to clouds and possibly cloud shadows. The characterization of these\n  issues has been documented by\n  [Cook et al., (2014)](https://doi.org/10.3390/rs61111244).\n\n* ASTER GED contains areas of missing mean emissivity data required for\n  successful ST product generation. If there is missing ASTER GED\n  information, there will be missing ST data in those areas.\n\n* The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes\n  acquired from 2000 through 2008. While this dataset has a global spatial\n  extent, there are areas missing mean emissivity information due to\n  persistent cloud contamination in the ASTER measurements.\n\n* The USGS further screens unphysical values (emissivity < 0.6) in ASTER\n  GED to remove any emissivity underestimation due to undetected clouds. For\n  any given pixel with no ASTER GED input or unphysical emissivity value,\n  the resulting Landsat ST products have missing pixels. The missing Landsat\n  ST pixels will be consistent through time (1982-present) given the static\n  nature of ASTER GED mean climatology data. For more information refer to\n  [landsat-collection-2-surface-temperature-data-gaps-due-missing](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature-data-gaps-due-missing-aster-ged)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-10-31T00:00:00Z","2026-11-02T22:45:24.701000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T1_L2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC09_C02_T1_L2","rel":"related","title":"Run the example for LANDSAT/LC09/C02/T1_L2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC09_C02_T1_L2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.usgs.gov/centers/eros/data-citation","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T1_L2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T1_L2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC09/C02/T1_TOA","title":"USGS Landsat 9 Collection 2 Tier 1 TOA Reflectance","description":"Landsat 9 Collection 2 Tier 1\n  calibrated top-of-atmosphere (TOA) reflectance.\n  Calibration coefficients are extracted from the image metadata.\n  See [Chander et al. (2009)](https://www.sciencedirect.com/science/article/pii/S0034425709000169)\n  for details on the TOA computation.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n\nThe T1_RT collection contains both Tier 1 and Real-Time (RT) assets.\nNewly-acquired Landsat 7 ETM+ and Landsat 8 OLI/TIRS data are processed\nupon downlink but use predicted ephemeris, initial bumper mode parameters,\nor initial TIRS line of sight model parameters. The data is placed in the\nReal-Time tier and made available for immediate download. Once the data\nhave been reprocessed with definitive ephemeris, updated bumper mode\nparameters and refined TIRS parameters, the products are transitioned to\neither Tier 1 or Tier 2 and removed from the Real-Time tier. The transition\ndelay from Real-Time to Tier 1 or Tier 2 is between 14 and 26 days.\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-10-31T00:00:00Z","2026-11-03T13:39:45.760000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T1_TOA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC09_C02_T1_TOA","rel":"related","title":"Run the example for LANDSAT/LC09/C02/T1_TOA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC09_C02_T1_TOA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_TOA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T1_TOA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T1_TOA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC09/C02/T2","title":"USGS Landsat 9 Collection 2 Tier 2 Raw Scenes","description":"Landsat 9 Collection 2 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.\nScenes not meeting Tier 1 criteria during\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\nRMSE and other properties to determine the suitability for use in individual\napplications and studies. See more information [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-11-02T01:06:10Z","2026-11-03T14:47:10.098000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC09_C02_T2","rel":"related","title":"Run the example for LANDSAT/LC09/C02/T2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC09_C02_T2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC09/C02/T2_L2","title":"USGS Landsat 9 Level 2, Collection 2, Tier 2","description":"This dataset contains atmospherically corrected\nsurface reflectance and land surface temperature derived from the data\nproduced by the Landsat 9 OLI/TIRS sensors.\nThese images contain 5 visible and near-infrared (VNIR) bands and\n2 short-wave infrared (SWIR) bands processed to orthorectified surface\nreflectance, and one thermal infrared (TIR) band processed to orthorectified\nsurface temperature. They also contain intermediate bands used in\ncalculation of the ST products, as well as QA bands.\n\nLandsat 9 SR products are created with the Land Surface Reflectance Code\n(LaSRC). All Collection 2 ST products are created with a single-channel\nalgorithm jointly created by the Rochester Institute of Technology (RIT) and\nNational Aeronautics and Space Administration (NASA)\nJet Propulsion Laboratory (JPL).\n\nStrips of collected data are packaged into overlapping \\\"scenes\\\" covering approximately\n170km x 183km using a [standardized reference grid](https://landsat.gsfc.nasa.gov/about/worldwide-reference-system).\n\nSome assets have only SR data, in which case ST bands are present but empty.\nFor assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'.\nFor assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'.\n\n[Additional documentation and usage examples.](/earth-engine/guides/landsat)\n\nLandsat Collection 2 files are publicly available in a [Google Cloud Storage\nbucket](https://console.cloud.google.com/storage/browser/gee-public-data-landsat)\non requester-pays basis. The files are indexed in a regularly updated\nBigQuery table for ease of analysis:\n[earth-engine-public-data.geo_index.landsat_c2_index](https://console.cloud.google.com/bigquery?project=earth-engine-public-data&earth-engine-public-data!2sgeo_index=&inv=1&invt=Ab4V5A&ws=!1m5!1m4!4m3!1searth-engine-public-data!2sgeo_index!3slandsat_c2_index).\n\nData provider notes:\n\n* Data products must contain both optical and thermal data to be\n  successfully processed to surface temperature, as ASTER NDVI is\n  required to temporally adjust the ASTER GED product to the target Landsat\n  scene. Therefore, night time acquisitions cannot be processed to\n  surface temperature.\n\n* A known error exists in the surface temperature retrievals relative\n  to clouds and possibly cloud shadows. The characterization of these\n  issues has been documented by\n  [Cook et al., (2014)](https://doi.org/10.3390/rs61111244).\n\n* ASTER GED contains areas of missing mean emissivity data required for\n  successful ST product generation. If there is missing ASTER GED\n  information, there will be missing ST data in those areas.\n\n* The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes\n  acquired from 2000 through 2008. While this dataset has a global spatial\n  extent, there are areas missing mean emissivity information due to\n  persistent cloud contamination in the ASTER measurements.\n\n* The USGS further screens unphysical values (emissivity < 0.6) in ASTER\n  GED to remove any emissivity underestimation due to undetected clouds. For\n  any given pixel with no ASTER GED input or unphysical emissivity value,\n  the resulting Landsat ST products have missing pixels. The missing Landsat\n  ST pixels will be consistent through time (1982-present) given the static\n  nature of ASTER GED mean climatology data. For more information refer to\n  [landsat-collection-2-surface-temperature-data-gaps-due-missing](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature-data-gaps-due-missing-aster-ged)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-10-31T00:00:00Z","2026-11-02T22:59:23.559000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T2_L2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC09_C02_T2_L2","rel":"related","title":"Run the example for LANDSAT/LC09/C02/T2_L2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC09_C02_T2_L2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_L2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.usgs.gov/centers/eros/data-citation","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T2_L2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T2_L2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LC09/C02/T2_TOA","title":"USGS Landsat 9 Collection 2 Tier 2 TOA Reflectance","description":"Landsat 9 Collection 2 Tier 2\n  calibrated top-of-atmosphere (TOA) reflectance.\n  Calibration coefficients are extracted from the image metadata.\n  See [Chander et al. (2009)](https://www.sciencedirect.com/science/article/pii/S0034425709000169)\n  for details on the TOA computation.\nScenes not meeting Tier 1 criteria during\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\nRMSE and other properties to determine the suitability for use in individual\napplications and studies. See more information [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-11-02T01:06:10Z","2026-11-03T14:47:10.098000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T2_TOA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LC09_C02_T2_TOA","rel":"related","title":"Run the example for LANDSAT/LC09/C02/T2_TOA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LC09_C02_T2_TOA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_TOA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T2_TOA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LC09/C02/T2_TOA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LE07/C02/T1","title":"USGS Landsat 7 Collection 2 Tier 1 Raw Scenes","description":"Landsat 7 Collection 2 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n\nNote that [Landsat 7's orbit has been drifting to an earlier acquisition time since\n2017](https://www.sciencedirect.com/science/article/pii/S2666017221000134?via%3Dihub).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-05-28T01:02:17Z","2024-01-19T01:39:58.042000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LE07_C02_T1","rel":"related","title":"Run the example for LANDSAT/LE07/C02/T1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LE07_C02_T1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LE07/C02/T1_L2","title":"USGS Landsat 7 Level 2, Collection 2, Tier 1","description":"This dataset contains atmospherically corrected\nsurface reflectance and land surface temperature derived from the data\nproduced by the Landsat 7 ETM+ sensor.\nThese images contain 4 visible and near-infrared (VNIR) bands and\n2 short-wave infrared (SWIR) bands processed to orthorectified surface\nreflectance, and one thermal infrared (TIR) band processed to orthorectified\nsurface temperature. They also contain intermediate bands used in\ncalculation of the ST products, as well as QA bands.\n\nLandsat 7 SR products are created with the Landsat Ecosystem Disturbance\nAdaptive Processing System (LEDAPS) algorithm (version 3.4.0).\nAll Collection 2 ST products are created with a single-channel algorithm\njointly created by the Rochester Institute of Technology (RIT) and\nNational Aeronautics and Space Administration (NASA)\nJet Propulsion Laboratory (JPL).\n\nStrips of collected data are packaged into overlapping \\\"scenes\\\" covering approximately\n170km x 183km using a [standardized reference grid](https://landsat.gsfc.nasa.gov/about/worldwide-reference-system).\n\nSome assets have only SR data, in which case ST bands are present but empty.\nFor assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'.\nFor assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'.\n\n[Additional documentation and usage examples.](/earth-engine/guides/landsat)\n\nLandsat Collection 2 files are publicly available in a [Google Cloud Storage\nbucket](https://console.cloud.google.com/storage/browser/gee-public-data-landsat)\non requester-pays basis. The files are indexed in a regularly updated\nBigQuery table for ease of analysis:\n[earth-engine-public-data.geo_index.landsat_c2_index](https://console.cloud.google.com/bigquery?project=earth-engine-public-data&earth-engine-public-data!2sgeo_index=&inv=1&invt=Ab4V5A&ws=!1m5!1m4!4m3!1searth-engine-public-data!2sgeo_index!3slandsat_c2_index).\n\nData provider notes:\n\n* Data products must contain both optical and thermal data to be\n  successfully processed to surface temperature, as ASTER NDVI is\n  required to temporally adjust the ASTER GED product to the target Landsat\n  scene. Therefore, night time acquisitions cannot be processed to\n  surface temperature.\n\n* A known error exists in the surface temperature retrievals relative\n  to clouds and possibly cloud shadows. The characterization of these\n  issues has been documented by\n  [Cook et al., (2014)](https://doi.org/10.3390/rs61111244).\n\n* ASTER GED contains areas of missing mean emissivity data required for\n  successful ST product generation. If there is missing ASTER GED\n  information, there will be missing ST data in those areas.\n\n* The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes\n  acquired from 2000 through 2008. While this dataset has a global spatial\n  extent, there are areas missing mean emissivity information due to\n  persistent cloud contamination in the ASTER measurements.\n\n* The USGS further screens unphysical values (emissivity < 0.6) in ASTER\n  GED to remove any emissivity underestimation due to undetected clouds. For\n  any given pixel with no ASTER GED input or unphysical emissivity value,\n  the resulting Landsat ST products have missing pixels. The missing Landsat\n  ST pixels will be consistent through time (1982-present) given the static\n  nature of ASTER GED mean climatology data. For more information refer to\n  [landsat-collection-2-surface-temperature-data-gaps-due-missing](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature-data-gaps-due-missing-aster-ged)\n\nNote that [Landsat 7's orbit has been drifting to an earlier acquisition time since\n2017](https://www.sciencedirect.com/science/article/pii/S2666017221000134?via%3Dihub).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-05-28T01:02:17Z","2024-01-19T01:39:58.042000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1_L2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LE07_C02_T1_L2","rel":"related","title":"Run the example for LANDSAT/LE07/C02/T1_L2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LE07_C02_T1_L2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_L2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1_L2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1_L2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LE07/C02/T1_RT","title":"USGS Landsat 7 Collection 2 Tier 1 and Real-Time data Raw Scenes","description":"Landsat 7 Collection 2 Tier 1 and Real-Time data DN values, representing scaled, calibrated at-sensor radiance.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n\nThe T1_RT collection contains both Tier 1 and Real-Time (RT) assets.\nNewly-acquired Landsat 7 ETM+ and Landsat 8 OLI/TIRS data are processed\nupon downlink but use predicted ephemeris, initial bumper mode parameters,\nor initial TIRS line of sight model parameters. The data is placed in the\nReal-Time tier and made available for immediate download. Once the data\nhave been reprocessed with definitive ephemeris, updated bumper mode\nparameters and refined TIRS parameters, the products are transitioned to\neither Tier 1 or Tier 2 and removed from the Real-Time tier. The transition\ndelay from Real-Time to Tier 1 or Tier 2 is between 14 and 26 days.\n\nNote that [Landsat 7's orbit has been drifting to an earlier acquisition time since\n2017](https://www.sciencedirect.com/science/article/pii/S2666017221000134?via%3Dihub).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-05-28T01:02:17Z","2024-01-19T01:39:58.042000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1_RT","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LE07_C02_T1_RT","rel":"related","title":"Run the example for LANDSAT/LE07/C02/T1_RT in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LE07_C02_T1_RT_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_RT#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1_RT/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1_RT/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LE07/C02/T1_RT_TOA","title":"USGS Landsat 7 Collection 2 Tier 1 and Real-Time data TOA Reflectance","description":"Landsat 7 Collection 2 Tier 1 and Real-Time data\n  calibrated top-of-atmosphere (TOA) reflectance.\n  Calibration coefficients are extracted from the image metadata.\n  See [Chander et al. (2009)](https://www.sciencedirect.com/science/article/pii/S0034425709000169)\n  for details on the TOA computation.\n\nNote that [Landsat 7's orbit has been drifting to an earlier acquisition time since\n2017](https://www.sciencedirect.com/science/article/pii/S2666017221000134?via%3Dihub).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-05-28T01:02:17Z","2024-01-19T01:39:58.042000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1_RT_TOA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LE07_C02_T1_RT_TOA","rel":"related","title":"Run the example for LANDSAT/LE07/C02/T1_RT_TOA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LE07_C02_T1_RT_TOA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_RT_TOA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1_RT_TOA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1_RT_TOA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LE07/C02/T1_TOA","title":"USGS Landsat 7 Collection 2 Tier 1 TOA Reflectance","description":"Landsat 7 Collection 2 Tier 1\n  calibrated top-of-atmosphere (TOA) reflectance.\n  Calibration coefficients are extracted from the image metadata.\n  See [Chander et al. (2009)](https://www.sciencedirect.com/science/article/pii/S0034425709000169)\n  for details on the TOA computation.\n\nNote that [Landsat 7's orbit has been drifting to an earlier acquisition time since\n2017](https://www.sciencedirect.com/science/article/pii/S2666017221000134?via%3Dihub).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-05-28T01:02:17Z","2024-01-19T01:39:58.042000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1_TOA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LE07_C02_T1_TOA","rel":"related","title":"Run the example for LANDSAT/LE07/C02/T1_TOA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LE07_C02_T1_TOA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_TOA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1_TOA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T1_TOA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Finland/MAVI/VV/50cm","title":"Finland NRG NLS orthophotos 50 cm by Mavi","description":"NLS Orthophotos are an aerial photo dataset covering the whole of Finland.\nThis data is provided by [Mavi(Agency for Rural Affairs)](https://maaseutuverkosto.fi/en/).\nAn orthophoto is a combination of several individual aerial photos. The\naerial photo dataset in orthophoto format is available as the most recent\ndataset consisting of the most recent aerial photos available. The most\nrecent data is usually 1-3 years old. NLS Orthophotos are updated every\n3 years (in Northern Lapland 12 years).\n\nIn these images, the bands are near-infrared, red, and green.\n\n(In Dataset id, VV stands for \"v&auml;&auml;r&auml;v&auml;ri\", false color)\nFor more information, please see the\n[NLS orthophotos documentation](https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/product-descriptions/orthophotos)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[18,59,29.2,69.4]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Finland/MAVI/VV/50cm","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Finland/Finland_MAVI_VV_50cm","rel":"related","title":"Run the example for Finland/MAVI/VV/50cm in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Finland/Finland_MAVI_VV_50cm_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Finland_MAVI_VV_50cm#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Finland/MAVI/VV/50cm/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Finland/MAVI/VV/50cm/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"COPERNICUS/MARINE/OC_GLO_BGC/TRANSPARENCY_OLCI_4KM","title":"Global Ocean Colour: Bio-Geo-Chemical, L4, from Satellite Observations, Transparency, OLCI, 4KM","description":"The Global Ocean Colour (Copernicus-GlobColour) dataset is a Bio-Geo-Chemical\n(BGC) product developed by ACRI-ST. Derived from multiple satellite sources\nlike SeaWiFS, MODIS, and OLCI. It provides a comprehensive range of\noceanographic variables, including: Chlorophyll (CHL),\nPhytoplankton Functional types and sizes (PFT), Primary Production (PP),\nSuspended Matter (SPM), Secchi Transparency Depth (ZSD),\nDiffuse Attenuation (KD490), Particulate Backscattering (BBP),\nAbsorption Coefficient (CDM), Reflectance (RRS) and more.\n\nThis dataset provides global-scale data on ocean transparency.\nIts primary layer is the Diffuse Attenuation Coefficient at 490nm (KD490)),\na key indicator of water clarity. Higher KD490values signify lower\ntransparency due to the rapid absorption of blue-green light by constituents\nlike phytoplankton or sediments. This L4 dataset, derived from the Ocean and\nLand Colour Instrument (OLCI), is a valuable resource for monitoring marine\nbio-geochemical processes and assessing water quality.\n\nDocumentation:\n\n * [User's Guide](https://documentation.marine.copernicus.eu/PUM/CMEMS-OC-PUM.pdf)\n\n * [Quality Information Document](https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-101to104-111-113-116-118.pdf)\n\n * [Copernicus Marine Datastore](https://data.marine.copernicus.eu/products)\n\nNOTE: The provider may remove Copernicus Marine data (especially Near\n Real-Time, NRT, observations) from their repository after some time,\n possibly aggregating that data into different (e.g., monthly) products.\n However, the corresponding assets remain available in the Earth Engine\n collection.\n\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-03-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/TRANSPARENCY_OLCI_4KM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_TRANSPARENCY_OLCI_4KM","rel":"related","title":"Run the example for COPERNICUS/MARINE/OC_GLO_BGC/TRANSPARENCY_OLCI_4KM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/COPERNICUS/COPERNICUS_MARINE_OC_GLO_BGC_TRANSPARENCY_OLCI_4KM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_MARINE_OC_GLO_BGC_TRANSPARENCY_OLCI_4KM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/TRANSPARENCY_OLCI_4KM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/COPERNICUS/MARINE/OC_GLO_BGC/TRANSPARENCY_OLCI_4KM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LE07/C02/T2_TOA","title":"USGS Landsat 7 Collection 2 Tier 2 TOA Reflectance","description":"Landsat 7 Collection 2 Tier 2\n  calibrated top-of-atmosphere (TOA) reflectance.\n  Calibration coefficients are extracted from the image metadata.\n  See [Chander et al. (2009)](https://www.sciencedirect.com/science/article/pii/S0034425709000169)\n  for details on the TOA computation.\n\nNote that [Landsat 7's orbit has been drifting to an earlier acquisition time since\n2017](https://www.sciencedirect.com/science/article/pii/S2666017221000134?via%3Dihub).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-12-01T01:15:15Z","2024-01-19T00:08:51.725000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T2_TOA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LE07_C02_T2_TOA","rel":"related","title":"Run the example for LANDSAT/LE07/C02/T2_TOA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LE07_C02_T2_TOA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T2_TOA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T2_TOA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T2_TOA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LM01/C02/T1","title":"USGS Landsat 1 MSS Collection 2 Tier 1 Raw Scenes","description":"Landsat 1 MSS Collection 2 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1972-07-26T14:46:24Z","1978-01-06T16:11:54Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM01/C02/T1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LM01_C02_T1","rel":"related","title":"Run the example for LANDSAT/LM01/C02/T1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LM01_C02_T1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM01_C02_T1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM01/C02/T1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM01/C02/T1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LM01/C02/T2","title":"USGS Landsat 1 MSS Collection 2 Tier 2 Raw Scenes","description":"Landsat 1 MSS Collection 2 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.\nScenes not meeting Tier 1 criteria during\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\nRMSE and other properties to determine the suitability for use in individual\napplications and studies. See more information [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1972-07-25T04:00:01Z","1978-01-07T16:19:36Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM01/C02/T2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LM01_C02_T2","rel":"related","title":"Run the example for LANDSAT/LM01/C02/T2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LM01_C02_T2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM01_C02_T2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM01/C02/T2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM01/C02/T2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LM02/C02/T1","title":"USGS Landsat 2 MSS Collection 2 Tier 1 Raw Scenes","description":"Landsat 2 MSS Collection 2 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-31T10:19:55Z","1982-02-03T00:49:25Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM02/C02/T1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LM02_C02_T1","rel":"related","title":"Run the example for LANDSAT/LM02/C02/T1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LM02_C02_T1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM02_C02_T1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM02/C02/T1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM02/C02/T1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LM02/C02/T2","title":"USGS Landsat 2 MSS Collection 2 Tier 2 Raw Scenes","description":"Landsat 2 MSS Collection 2 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.\nScenes not meeting Tier 1 criteria during\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\nRMSE and other properties to determine the suitability for use in individual\napplications and studies. See more information [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1975-01-24T02:12:31Z","1982-03-12T10:52:37Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM02/C02/T2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LM02_C02_T2","rel":"related","title":"Run the example for LANDSAT/LM02/C02/T2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LM02_C02_T2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM02_C02_T2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM02/C02/T2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM02/C02/T2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LM03/C02/T1","title":"USGS Landsat 3 MSS Collection 2 Tier 1 Raw Scenes","description":"Landsat 3 MSS Collection 2 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1978-06-03T15:56:41Z","1983-02-23T00:21:35Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM03/C02/T1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LM03_C02_T1","rel":"related","title":"Run the example for LANDSAT/LM03/C02/T1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LM03_C02_T1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM03_C02_T1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM03/C02/T1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM03/C02/T1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LM03/C02/T2","title":"USGS Landsat 3 MSS Collection 2 Tier 2 Raw Scenes","description":"Landsat 3 MSS Collection 2 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.\nScenes not meeting Tier 1 criteria during\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\nRMSE and other properties to determine the suitability for use in individual\napplications and studies. See more information [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1978-03-07T02:02:05Z","1983-03-31T20:55:16Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM03/C02/T2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LM03_C02_T2","rel":"related","title":"Run the example for LANDSAT/LM03/C02/T2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LM03_C02_T2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM03_C02_T2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM03/C02/T2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM03/C02/T2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LM04/C02/T1","title":"USGS Landsat 4 MSS Collection 2 Tier 1 Raw Scenes","description":"Landsat 4 MSS Collection 2 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-14T18:22:17Z","1992-08-28T07:14:35Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM04/C02/T1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LM04_C02_T1","rel":"related","title":"Run the example for LANDSAT/LM04/C02/T1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LM04_C02_T1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM04_C02_T1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM04/C02/T1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM04/C02/T1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LM04/C02/T2","title":"USGS Landsat 4 MSS Collection 2 Tier 2 Raw Scenes","description":"Landsat 4 MSS Collection 2 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.\nScenes not meeting Tier 1 criteria during\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\nRMSE and other properties to determine the suitability for use in individual\napplications and studies. See more information [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-06T20:50:11Z","1992-10-15T05:31:24Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM04/C02/T2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LM04_C02_T2","rel":"related","title":"Run the example for LANDSAT/LM04/C02/T2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LM04_C02_T2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM04_C02_T2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM04/C02/T2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM04/C02/T2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LM05/C02/T1","title":"USGS Landsat 5 MSS Collection 2 Tier 1 Raw Scenes","description":"Landsat 5 MSS Collection 2 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-04-07T10:04:11Z","2013-01-01T16:51:58Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM05/C02/T1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LM05_C02_T1","rel":"related","title":"Run the example for LANDSAT/LM05/C02/T1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LM05_C02_T1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM05_C02_T1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM05/C02/T1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM05/C02/T1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LM05/C02/T2","title":"USGS Landsat 5 MSS Collection 2 Tier 2 Raw Scenes","description":"Landsat 5 MSS Collection 2 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.\nScenes not meeting Tier 1 criteria during\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\nRMSE and other properties to determine the suitability for use in individual\napplications and studies. See more information [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-04-07T10:04:11Z","2013-01-01T16:51:58Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM05/C02/T2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LM05_C02_T2","rel":"related","title":"Run the example for LANDSAT/LM05/C02/T2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LM05_C02_T2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LM05_C02_T2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM05/C02/T2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LM05/C02/T2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LT04/C02/T1","title":"USGS Landsat 4 TM Collection 2 Tier 1 Raw Scenes","description":"Landsat 4 TM Collection 2 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T14:19:55Z","1993-12-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LT04_C02_T1","rel":"related","title":"Run the example for LANDSAT/LT04/C02/T1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LT04_C02_T1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C02_T1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LT04/C02/T1_L2","title":"USGS Landsat 4 Level 2, Collection 2, Tier 1","description":"This dataset contains atmospherically corrected\nsurface reflectance and land surface temperature derived from the data\nproduced by the Landsat TM sensor.\nThese images contain 4 visible and near-infrared (VNIR) bands and\n2 short-wave infrared (SWIR) bands processed to orthorectified surface\nreflectance, and one thermal infrared (TIR) band processed to orthorectified\nsurface temperature. They also contain intermediate bands used in\ncalculation of the ST products, as well as QA bands.\n\nLandsat 4 and 5 SR products are created with the Landsat Ecosystem Disturbance\nAdaptive Processing System (LEDAPS) algorithm (version 3.4.0).\nAll Collection 2 ST products are created with a single-channel algorithm\njointly created by the Rochester Institute of Technology (RIT) and\nNational Aeronautics and Space Administration (NASA)\nJet Propulsion Laboratory (JPL).\n\nStrips of collected data are packaged into overlapping \\\"scenes\\\" covering approximately\n170km x 183km using a [standardized reference grid](https://landsat.gsfc.nasa.gov/about/worldwide-reference-system).\n\nSome assets have only SR data, in which case ST bands are present but empty.\nFor assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'.\nFor assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'.\n\n[Additional documentation and usage examples.](/earth-engine/guides/landsat)\n\nLandsat Collection 2 files are publicly available in a [Google Cloud Storage\nbucket](https://console.cloud.google.com/storage/browser/gee-public-data-landsat)\non requester-pays basis. The files are indexed in a regularly updated\nBigQuery table for ease of analysis:\n[earth-engine-public-data.geo_index.landsat_c2_index](https://console.cloud.google.com/bigquery?project=earth-engine-public-data&earth-engine-public-data!2sgeo_index=&inv=1&invt=Ab4V5A&ws=!1m5!1m4!4m3!1searth-engine-public-data!2sgeo_index!3slandsat_c2_index).\n\nData provider notes:\n\n* Data products must contain both optical and thermal data to be\n  successfully processed to surface temperature, as ASTER NDVI is\n  required to temporally adjust the ASTER GED product to the target Landsat\n  scene. Therefore, night time acquisitions cannot be processed to\n  surface temperature.\n\n* A known error exists in the surface temperature retrievals relative\n  to clouds and possibly cloud shadows. The characterization of these\n  issues has been documented by\n  [Cook et al., (2014)](https://doi.org/10.3390/rs61111244).\n\n* ASTER GED contains areas of missing mean emissivity data required for\n  successful ST product generation. If there is missing ASTER GED\n  information, there will be missing ST data in those areas.\n\n* The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes\n  acquired from 2000 through 2008. While this dataset has a global spatial\n  extent, there are areas missing mean emissivity information due to\n  persistent cloud contamination in the ASTER measurements.\n\n* The USGS further screens unphysical values (emissivity < 0.6) in ASTER\n  GED to remove any emissivity underestimation due to undetected clouds. For\n  any given pixel with no ASTER GED input or unphysical emissivity value,\n  the resulting Landsat ST products have missing pixels. The missing Landsat\n  ST pixels will be consistent through time (1982-present) given the static\n  nature of ASTER GED mean climatology data. For more information refer to\n  [landsat-collection-2-surface-temperature-data-gaps-due-missing](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature-data-gaps-due-missing-aster-ged)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T14:19:55Z","1993-06-24T14:26:23Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T1_L2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LT04_C02_T1_L2","rel":"related","title":"Run the example for LANDSAT/LT04/C02/T1_L2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LT04_C02_T1_L2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C02_T1_L2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.usgs.gov/centers/eros/data-citation","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T1_L2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T1_L2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LT04/C02/T1_TOA","title":"USGS Landsat 4 TM Collection 2 Tier 1 TOA Reflectance","description":"Landsat 4 TM Collection 2 Tier 1\n  calibrated top-of-atmosphere (TOA) reflectance.\n  Calibration coefficients are extracted from the image metadata.\n  See [Chander et al. (2009)](https://www.sciencedirect.com/science/article/pii/S0034425709000169)\n  for details on the TOA computation.\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T14:19:55Z","1993-12-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T1_TOA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LT04_C02_T1_TOA","rel":"related","title":"Run the example for LANDSAT/LT04/C02/T1_TOA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LT04_C02_T1_TOA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C02_T1_TOA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T1_TOA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T1_TOA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LT04/C02/T2","title":"USGS Landsat 4 TM Collection 2 Tier 2 Raw Scenes","description":"Landsat 4 TM Collection 2 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.\nScenes not meeting Tier 1 criteria during\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\nRMSE and other properties to determine the suitability for use in individual\napplications and studies. See more information [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T14:18:20Z","1993-11-18T14:54:14Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LT04_C02_T2","rel":"related","title":"Run the example for LANDSAT/LT04/C02/T2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LT04_C02_T2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C02_T2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LT04/C02/T2_L2","title":"USGS Landsat 4 Level 2, Collection 2, Tier 2","description":"This dataset contains atmospherically corrected\nsurface reflectance and land surface temperature derived from the data\nproduced by the Landsat TM sensor.\nThese images contain 4 visible and near-infrared (VNIR) bands and\n2 short-wave infrared (SWIR) bands processed to orthorectified surface\nreflectance, and one thermal infrared (TIR) band processed to orthorectified\nsurface temperature. They also contain intermediate bands used in\ncalculation of the ST products, as well as QA bands.\n\nLandsat 4 and 5 SR products are created with the Landsat Ecosystem Disturbance\nAdaptive Processing System (LEDAPS) algorithm (version 3.4.0).\nAll Collection 2 ST products are created with a single-channel algorithm\njointly created by the Rochester Institute of Technology (RIT) and\nNational Aeronautics and Space Administration (NASA)\nJet Propulsion Laboratory (JPL).\n\nStrips of collected data are packaged into overlapping \\\"scenes\\\" covering approximately\n170km x 183km using a [standardized reference grid](https://landsat.gsfc.nasa.gov/about/worldwide-reference-system).\n\nSome assets have only SR data, in which case ST bands are present but empty.\nFor assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'.\nFor assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'.\n\n[Additional documentation and usage examples.](/earth-engine/guides/landsat)\n\nLandsat Collection 2 files are publicly available in a [Google Cloud Storage\nbucket](https://console.cloud.google.com/storage/browser/gee-public-data-landsat)\non requester-pays basis. The files are indexed in a regularly updated\nBigQuery table for ease of analysis:\n[earth-engine-public-data.geo_index.landsat_c2_index](https://console.cloud.google.com/bigquery?project=earth-engine-public-data&earth-engine-public-data!2sgeo_index=&inv=1&invt=Ab4V5A&ws=!1m5!1m4!4m3!1searth-engine-public-data!2sgeo_index!3slandsat_c2_index).\n\nData provider notes:\n\n* Data products must contain both optical and thermal data to be\n  successfully processed to surface temperature, as ASTER NDVI is\n  required to temporally adjust the ASTER GED product to the target Landsat\n  scene. Therefore, night time acquisitions cannot be processed to\n  surface temperature.\n\n* A known error exists in the surface temperature retrievals relative\n  to clouds and possibly cloud shadows. The characterization of these\n  issues has been documented by\n  [Cook et al., (2014)](https://doi.org/10.3390/rs61111244).\n\n* ASTER GED contains areas of missing mean emissivity data required for\n  successful ST product generation. If there is missing ASTER GED\n  information, there will be missing ST data in those areas.\n\n* The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes\n  acquired from 2000 through 2008. While this dataset has a global spatial\n  extent, there are areas missing mean emissivity information due to\n  persistent cloud contamination in the ASTER measurements.\n\n* The USGS further screens unphysical values (emissivity < 0.6) in ASTER\n  GED to remove any emissivity underestimation due to undetected clouds. For\n  any given pixel with no ASTER GED input or unphysical emissivity value,\n  the resulting Landsat ST products have missing pixels. The missing Landsat\n  ST pixels will be consistent through time (1982-present) given the static\n  nature of ASTER GED mean climatology data. For more information refer to\n  [landsat-collection-2-surface-temperature-data-gaps-due-missing](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature-data-gaps-due-missing-aster-ged)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T14:18:20Z","1993-11-18T14:54:14Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T2_L2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LT04_C02_T2_L2","rel":"related","title":"Run the example for LANDSAT/LT04/C02/T2_L2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LT04_C02_T2_L2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C02_T2_L2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.usgs.gov/centers/eros/data-citation","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T2_L2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T2_L2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LT04/C02/T2_TOA","title":"USGS Landsat 4 TM Collection 2 Tier 2 TOA Reflectance","description":"Landsat 4 TM Collection 2 Tier 2\n  calibrated top-of-atmosphere (TOA) reflectance.\n  Calibration coefficients are extracted from the image metadata.\n  See [Chander et al. (2009)](https://www.sciencedirect.com/science/article/pii/S0034425709000169)\n  for details on the TOA computation.\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1982-08-22T14:18:20Z","1993-11-18T14:54:14Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T2_TOA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LT04_C02_T2_TOA","rel":"related","title":"Run the example for LANDSAT/LT04/C02/T2_TOA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LT04_C02_T2_TOA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C02_T2_TOA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T2_TOA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT04/C02/T2_TOA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LT05/C02/T1","title":"USGS Landsat 5 TM Collection 2 Tier 1 Raw Scenes","description":"Landsat 5 TM Collection 2 Tier 1 DN values, representing scaled, calibrated at-sensor radiance.\n\nLandsat scenes with the highest available data quality\nare placed into Tier 1 and are considered suitable for time-series processing\nanalysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data\nthat have well-characterized radiometry and are inter-calibrated across the\ndifferent Landsat sensors. The georegistration of Tier 1 scenes will be\nconsistent and within prescribed tolerances [<=12 m root mean square error\n(RMSE)]. All Tier 1 Landsat data can be considered consistent and\ninter-calibrated (regardless of sensor) across the full collection. See more\ninformation [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-03-16T16:20:00Z","2012-05-05T17:54:06Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LT05_C02_T1","rel":"related","title":"Run the example for LANDSAT/LT05/C02/T1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LT05_C02_T1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C02_T1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LT05/C02/T1_L2","title":"USGS Landsat 5 Level 2, Collection 2, Tier 1","description":"This dataset contains atmospherically corrected\nsurface reflectance and land surface temperature derived from the data\nproduced by the Landsat TM sensor.\nThese images contain 4 visible and near-infrared (VNIR) bands and\n2 short-wave infrared (SWIR) bands processed to orthorectified surface\nreflectance, and one thermal infrared (TIR) band processed to orthorectified\nsurface temperature. They also contain intermediate bands used in\ncalculation of the ST products, as well as QA bands.\n\nLandsat 4 and 5 SR products are created with the Landsat Ecosystem Disturbance\nAdaptive Processing System (LEDAPS) algorithm (version 3.4.0).\nAll Collection 2 ST products are created with a single-channel algorithm\njointly created by the Rochester Institute of Technology (RIT) and\nNational Aeronautics and Space Administration (NASA)\nJet Propulsion Laboratory (JPL).\n\nStrips of collected data are packaged into overlapping \\\"scenes\\\" covering approximately\n170km x 183km using a [standardized reference grid](https://landsat.gsfc.nasa.gov/about/worldwide-reference-system).\n\nSome assets have only SR data, in which case ST bands are present but empty.\nFor assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'.\nFor assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'.\n\n[Additional documentation and usage examples.](/earth-engine/guides/landsat)\n\nLandsat Collection 2 files are publicly available in a [Google Cloud Storage\nbucket](https://console.cloud.google.com/storage/browser/gee-public-data-landsat)\non requester-pays basis. The files are indexed in a regularly updated\nBigQuery table for ease of analysis:\n[earth-engine-public-data.geo_index.landsat_c2_index](https://console.cloud.google.com/bigquery?project=earth-engine-public-data&earth-engine-public-data!2sgeo_index=&inv=1&invt=Ab4V5A&ws=!1m5!1m4!4m3!1searth-engine-public-data!2sgeo_index!3slandsat_c2_index).\n\nData provider notes:\n\n* Data products must contain both optical and thermal data to be\n  successfully processed to surface temperature, as ASTER NDVI is\n  required to temporally adjust the ASTER GED product to the target Landsat\n  scene. Therefore, night time acquisitions cannot be processed to\n  surface temperature.\n\n* A known error exists in the surface temperature retrievals relative\n  to clouds and possibly cloud shadows. The characterization of these\n  issues has been documented by\n  [Cook et al., (2014)](https://doi.org/10.3390/rs61111244).\n\n* ASTER GED contains areas of missing mean emissivity data required for\n  successful ST product generation. If there is missing ASTER GED\n  information, there will be missing ST data in those areas.\n\n* The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes\n  acquired from 2000 through 2008. While this dataset has a global spatial\n  extent, there are areas missing mean emissivity information due to\n  persistent cloud contamination in the ASTER measurements.\n\n* The USGS further screens unphysical values (emissivity < 0.6) in ASTER\n  GED to remove any emissivity underestimation due to undetected clouds. For\n  any given pixel with no ASTER GED input or unphysical emissivity value,\n  the resulting Landsat ST products have missing pixels. The missing Landsat\n  ST pixels will be consistent through time (1982-present) given the static\n  nature of ASTER GED mean climatology data. For more information refer to\n  [landsat-collection-2-surface-temperature-data-gaps-due-missing](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature-data-gaps-due-missing-aster-ged)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-03-16T16:18:01Z","2012-05-05T17:54:06Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T1_L2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LT05_C02_T1_L2","rel":"related","title":"Run the example for LANDSAT/LT05/C02/T1_L2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LT05_C02_T1_L2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C02_T1_L2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.usgs.gov/centers/eros/data-citation","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T1_L2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T1_L2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LT05/C02/T1_TOA","title":"USGS Landsat 5 TM Collection 2 Tier 1 TOA Reflectance","description":"Landsat 5 TM Collection 2 Tier 1\n  calibrated top-of-atmosphere (TOA) reflectance.\n  Calibration coefficients are extracted from the image metadata.\n  See [Chander et al. (2009)](https://www.sciencedirect.com/science/article/pii/S0034425709000169)\n  for details on the TOA computation.\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-04-19T13:56:39Z","2011-11-08T14:18:44Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T1_TOA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LT05_C02_T1_TOA","rel":"related","title":"Run the example for LANDSAT/LT05/C02/T1_TOA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LT05_C02_T1_TOA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C02_T1_TOA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T1_TOA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T1_TOA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LT05/C02/T2","title":"USGS Landsat 5 TM Collection 2 Tier 2 Raw Scenes","description":"Landsat 5 TM Collection 2 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.\nScenes not meeting Tier 1 criteria during\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\nRMSE and other properties to determine the suitability for use in individual\napplications and studies. See more information [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-03-06T16:20:00Z","2001-02-06T16:13:31Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LT05_C02_T2","rel":"related","title":"Run the example for LANDSAT/LT05/C02/T2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LT05_C02_T2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C02_T2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LT05/C02/T2_L2","title":"USGS Landsat 5 Level 2, Collection 2, Tier 2","description":"This dataset contains atmospherically corrected\nsurface reflectance and land surface temperature derived from the data\nproduced by the Landsat TM sensor.\nThese images contain 4 visible and near-infrared (VNIR) bands and\n2 short-wave infrared (SWIR) bands processed to orthorectified surface\nreflectance, and one thermal infrared (TIR) band processed to orthorectified\nsurface temperature. They also contain intermediate bands used in\ncalculation of the ST products, as well as QA bands.\n\nLandsat 4 and 5 SR products are created with the Landsat Ecosystem Disturbance\nAdaptive Processing System (LEDAPS) algorithm (version 3.4.0).\nAll Collection 2 ST products are created with a single-channel algorithm\njointly created by the Rochester Institute of Technology (RIT) and\nNational Aeronautics and Space Administration (NASA)\nJet Propulsion Laboratory (JPL).\n\nStrips of collected data are packaged into overlapping \\\"scenes\\\" covering approximately\n170km x 183km using a [standardized reference grid](https://landsat.gsfc.nasa.gov/about/worldwide-reference-system).\n\nSome assets have only SR data, in which case ST bands are present but empty.\nFor assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'.\nFor assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'.\n\n[Additional documentation and usage examples.](/earth-engine/guides/landsat)\n\nLandsat Collection 2 files are publicly available in a [Google Cloud Storage\nbucket](https://console.cloud.google.com/storage/browser/gee-public-data-landsat)\non requester-pays basis. The files are indexed in a regularly updated\nBigQuery table for ease of analysis:\n[earth-engine-public-data.geo_index.landsat_c2_index](https://console.cloud.google.com/bigquery?project=earth-engine-public-data&earth-engine-public-data!2sgeo_index=&inv=1&invt=Ab4V5A&ws=!1m5!1m4!4m3!1searth-engine-public-data!2sgeo_index!3slandsat_c2_index).\n\nData provider notes:\n\n* Data products must contain both optical and thermal data to be\n  successfully processed to surface temperature, as ASTER NDVI is\n  required to temporally adjust the ASTER GED product to the target Landsat\n  scene. Therefore, night time acquisitions cannot be processed to\n  surface temperature.\n\n* A known error exists in the surface temperature retrievals relative\n  to clouds and possibly cloud shadows. The characterization of these\n  issues has been documented by\n  [Cook et al., (2014)](https://doi.org/10.3390/rs61111244).\n\n* ASTER GED contains areas of missing mean emissivity data required for\n  successful ST product generation. If there is missing ASTER GED\n  information, there will be missing ST data in those areas.\n\n* The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes\n  acquired from 2000 through 2008. While this dataset has a global spatial\n  extent, there are areas missing mean emissivity information due to\n  persistent cloud contamination in the ASTER measurements.\n\n* The USGS further screens unphysical values (emissivity < 0.6) in ASTER\n  GED to remove any emissivity underestimation due to undetected clouds. For\n  any given pixel with no ASTER GED input or unphysical emissivity value,\n  the resulting Landsat ST products have missing pixels. The missing Landsat\n  ST pixels will be consistent through time (1982-present) given the static\n  nature of ASTER GED mean climatology data. For more information refer to\n  [landsat-collection-2-surface-temperature-data-gaps-due-missing](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature-data-gaps-due-missing-aster-ged)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-03-05T15:38:45Z","2011-11-16T23:32:21Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T2_L2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LT05_C02_T2_L2","rel":"related","title":"Run the example for LANDSAT/LT05/C02/T2_L2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LT05_C02_T2_L2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C02_T2_L2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.usgs.gov/centers/eros/data-citation","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T2_L2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T2_L2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LT05/C02/T2_TOA","title":"USGS Landsat 5 TM Collection 2 Tier 2 TOA Reflectance","description":"Landsat 5 TM Collection 2 Tier 2\n  calibrated top-of-atmosphere (TOA) reflectance.\n  Calibration coefficients are extracted from the image metadata.\n  See [Chander et al. (2009)](https://www.sciencedirect.com/science/article/pii/S0034425709000169)\n  for details on the TOA computation.\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-03-06T16:20:00Z","2001-02-06T16:13:31Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T2_TOA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LT05_C02_T2_TOA","rel":"related","title":"Run the example for LANDSAT/LT05/C02/T2_TOA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LT05_C02_T2_TOA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C02_T2_TOA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T2_TOA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LT05/C02/T2_TOA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/MANGROVE_FORESTS","title":"Global Mangrove Forests Distribution, v1 (2000)","description":"The database was prepared using Landsat satellite data from the year 2000.\nMore than 1,000 Landsat scenes obtained from the USGS Earth Resources\nObservation and Science Center (EROS) were classified using hybrid\nsupervised and unsupervised digital image classification techniques. This\ndatabase is the first, most comprehensive mangrove assessment of the world\n([Giri et al., 2011](https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1466-8238.2010.00584.x)).\nPartial funding of this research was provided by NASA.\n\nThe mangrove database is being used for identifying priority areas\nfor mangrove conservation, studying the role of mangrove forests in\nsaving lives and properties from natural disasters (e.g. tsunami),\ncarbon accounting, and biodiversity conservation. The USGS EROS has\nbeen using the data to study the impact of sea level rise on mangrove\necosystems. The database serves as a baseline for mangrove\nmonitoring.\n\n[General Documentation](https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lulc-mangroves-2000-1.0#documents-and-resources)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2001-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/MANGROVE_FORESTS","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_MANGROVE_FORESTS","rel":"related","title":"Run the example for LANDSAT/MANGROVE_FORESTS in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_MANGROVE_FORESTS_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_MANGROVE_FORESTS#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7927/H4J67DW8","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/MANGROVE_FORESTS/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/MANGROVE_FORESTS/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LARSE/GEDI/GEDI02_B_002_MONTHLY","title":"GEDI L2B Raster Canopy Cover Vertical Profile Metrics (Version 2)","description":"GEDI Level 2B Canopy Cover and Vertical Profile Metrics product (GEDI02_B)\nextracts biophysical metrics from each GEDI waveform. These metrics\nare based on the directional gap probability profile derived from the L1B\nwaveform.\n\nThe vertical step between foliage profile measurements\n(known as dZ in GEDI documentation) is always 5 meters.\n\nThe dataset LARSE/GEDI/GEDI02_B_002_MONTHLY is a raster version\nof the original GEDI02_B product. The raster images are organized as monthly\ncomposites of individual orbits in the corresponding month. Only root-level\ncover, pai and pavd values and their associated quality flags and metadata\nare preserved as raster bands. Each GEDI02_B_002 raster has 109 bands.\n\nSee [User Guide](https://lpdaac.usgs.gov/documents/986/GEDI02_UserGuide_V2.pdf)\nfor more information.\n\nThe Global Ecosystem Dynamics Investigation [GEDI](https://gedi.umd.edu/)\nmission aims to characterize ecosystem structure and dynamics to enable\nradically improved quantification and understanding of the Earth's carbon cycle\nand biodiversity. The GEDI instrument, attached to the International Space\nStation (ISS), collects data globally between 51.6&deg; N and 51.6&deg; S\nlatitudes at the highest resolution and densest sampling of the\n3-dimensional structure of the Earth. The GEDI instrument consists of three\nlasers producing a total of eight beam ground transects, which instantaneously\nsample eight ~25 m footprints spaced approximately every 60 m along-track.\n\nProduct                 | Description\n:---------------------  | :----------------------------------------------\nL2A Vector         | [LARSE/GEDI/GEDI02_A_002](LARSE_GEDI_GEDI02_A_002)\nL2A Monthly raster | [LARSE/GEDI/GEDI02_A_002_MONTHLY](LARSE_GEDI_GEDI02_A_002_MONTHLY)\nL2A table index    | [LARSE/GEDI/GEDI02_A_002_INDEX](LARSE_GEDI_GEDI02_A_002_INDEX)\nL2B Vector         | [LARSE/GEDI/GEDI02_B_002](LARSE_GEDI_GEDI02_B_002)\nL2B Monthly raster | [LARSE/GEDI/GEDI02_B_002_MONTHLY](LARSE_GEDI_GEDI02_B_002_MONTHLY)\nL2B table index    | [LARSE/GEDI/GEDI02_B_002_INDEX](LARSE_GEDI_GEDI02_B_002_INDEX)\nL4A Biomass Vector | [LARSE/GEDI/GEDI04_A_002](LARSE_GEDI_GEDI04_A_002)\nL4A Monthly raster | [LARSE/GEDI/GEDI04_A_002_MONTHLY](LARSE_GEDI_GEDI04_A_002_MONTHLY)\nL4A table index    | [LARSE/GEDI/GEDI04_A_002_INDEX](LARSE_GEDI_GEDI04_A_002_INDEX)\nL4B Biomass        | [LARSE/GEDI/GEDI04_B_002](LARSE_GEDI_GEDI04_B_002)\nL4D Imputed Waveforms | [LARSE/GEDI/GEDI04_D_002](LARSE_GEDI_GEDI04_D_002)","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-51.6,180,51.6]]},"temporal":{"interval":[["2019-03-25T00:00:00Z","2025-07-01T08:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI02_B_002_MONTHLY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LARSE/LARSE_GEDI_GEDI02_B_002_MONTHLY","rel":"related","title":"Run the example for LARSE/GEDI/GEDI02_B_002_MONTHLY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LARSE/LARSE_GEDI_GEDI02_B_002_MONTHLY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LARSE_GEDI_GEDI02_B_002_MONTHLY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI02_B_002_MONTHLY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI02_B_002_MONTHLY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LARSE/GEDI/GEDI04_A_002_MONTHLY","title":"GEDI L4A Raster Aboveground Biomass Density, Version 2.1","description":"This dataset contains Global Ecosystem Dynamics Investigation (GEDI)\nLevel 4A (L4A) Version 2 predictions of the aboveground biomass\ndensity (AGBD; in Mg/ha) and estimates of the prediction standard\nerror within each sampled geolocated laser footprint. In this version,\nthe granules are in sub-orbits.\nHeight metrics from simulated waveforms associated with field estimates\nof AGBD from multiple regions and plant functional types (PFTs) were\ncompiled to generate a calibration dataset for models representing\nthe combinations of world regions and PFTs (i.e., deciduous broadleaf\ntrees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous\nneedleleaf trees, and the combination of grasslands, shrubs, and\nwoodlands).The algorithm setting group selection\nused for GEDI02_A Version 2 has been modified for evergreen broadleaf\ntrees in South America to reduce false positive errors resulting from\nthe selection of waveform modes above ground elevation as the lowest mode.\nThe dataset LARSE/GEDI/GEDI04_A_002_MONTHLY is a raster version\nof the original GEDI04_A product. The raster images are organized as monthly\ncomposites of individual orbits in the corresponding month.\n\nSee [User Guide](https://daac.ornl.gov/GEDI/guides/GEDI_L4A_AGB_Density_V2_1.html)\nfor more information.\n\nThe Global Ecosystem Dynamics Investigation [GEDI](https://gedi.umd.edu/)\nmission aims to characterize ecosystem structure and dynamics to enable\nradically improved quantification and understanding of the Earth's carbon cycle\nand biodiversity. The GEDI instrument, attached to the International Space\nStation (ISS), collects data globally between 51.6&deg; N and 51.6&deg; S\nlatitudes at the highest resolution and densest sampling of the\n3-dimensional structure of the Earth. The GEDI instrument consists of three\nlasers producing a total of eight beam ground transects, which instantaneously\nsample eight ~25 m footprints spaced approximately every 60 m along-track.\n\nProduct                 | Description\n:---------------------  | :----------------------------------------------\nL2A Vector         | [LARSE/GEDI/GEDI02_A_002](LARSE_GEDI_GEDI02_A_002)\nL2A Monthly raster | [LARSE/GEDI/GEDI02_A_002_MONTHLY](LARSE_GEDI_GEDI02_A_002_MONTHLY)\nL2A table index    | [LARSE/GEDI/GEDI02_A_002_INDEX](LARSE_GEDI_GEDI02_A_002_INDEX)\nL2B Vector         | [LARSE/GEDI/GEDI02_B_002](LARSE_GEDI_GEDI02_B_002)\nL2B Monthly raster | [LARSE/GEDI/GEDI02_B_002_MONTHLY](LARSE_GEDI_GEDI02_B_002_MONTHLY)\nL2B table index    | [LARSE/GEDI/GEDI02_B_002_INDEX](LARSE_GEDI_GEDI02_B_002_INDEX)\nL4A Biomass Vector | [LARSE/GEDI/GEDI04_A_002](LARSE_GEDI_GEDI04_A_002)\nL4A Monthly raster | [LARSE/GEDI/GEDI04_A_002_MONTHLY](LARSE_GEDI_GEDI04_A_002_MONTHLY)\nL4A table index    | [LARSE/GEDI/GEDI04_A_002_INDEX](LARSE_GEDI_GEDI04_A_002_INDEX)\nL4B Biomass        | [LARSE/GEDI/GEDI04_B_002](LARSE_GEDI_GEDI04_B_002)\nL4D Imputed Waveforms | [LARSE/GEDI/GEDI04_D_002](LARSE_GEDI_GEDI04_D_002)","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-51.6,180,51.6]]},"temporal":{"interval":[["2019-03-25T00:00:00Z","2025-07-01T08:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI04_A_002_MONTHLY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LARSE/LARSE_GEDI_GEDI04_A_002_MONTHLY","rel":"related","title":"Run the example for LARSE/GEDI/GEDI04_A_002_MONTHLY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LARSE/LARSE_GEDI_GEDI04_A_002_MONTHLY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LARSE_GEDI_GEDI04_A_002_MONTHLY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI04_A_002_MONTHLY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI04_A_002_MONTHLY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LARSE/GEDI/GEDI04_B_002","title":"GEDI L4B Gridded Aboveground Biomass Density (Version 2)","description":"This Global Ecosystem Dynamics Investigation (GEDI) L4B product provides\n1 km x 1 km estimates of mean aboveground biomass density (AGBD) based on\nobservations from mission week 19 starting on 2019-04-18 to mission\nweek 138 ending on 2021-08-04. The GEDI L4A Footprint Biomass product\nconverts each high-quality waveform to an AGBD prediction, and the\nL4B product uses the sample present within the borders of each 1 km cell to\nstatistically infer mean AGBD.\n\nPlease see [User Guide](https://daac.ornl.gov/GEDI/guides/GEDI_L4B_Gridded_Biomass.html)\nfor more information.\n\nThe Global Ecosystem Dynamics Investigation [GEDI](https://gedi.umd.edu/)\nmission aims to characterize ecosystem structure and dynamics to enable\nradically improved quantification and understanding of the Earth's carbon cycle\nand biodiversity. The GEDI instrument, attached to the International Space\nStation (ISS), collects data globally between 51.6&deg; N and 51.6&deg; S\nlatitudes at the highest resolution and densest sampling of the\n3-dimensional structure of the Earth. The GEDI instrument consists of three\nlasers producing a total of eight beam ground transects, which instantaneously\nsample eight ~25 m footprints spaced approximately every 60 m along-track.\n\nProduct                 | Description\n:---------------------  | :----------------------------------------------\nL2A Vector         | [LARSE/GEDI/GEDI02_A_002](LARSE_GEDI_GEDI02_A_002)\nL2A Monthly raster | [LARSE/GEDI/GEDI02_A_002_MONTHLY](LARSE_GEDI_GEDI02_A_002_MONTHLY)\nL2A table index    | [LARSE/GEDI/GEDI02_A_002_INDEX](LARSE_GEDI_GEDI02_A_002_INDEX)\nL2B Vector         | [LARSE/GEDI/GEDI02_B_002](LARSE_GEDI_GEDI02_B_002)\nL2B Monthly raster | [LARSE/GEDI/GEDI02_B_002_MONTHLY](LARSE_GEDI_GEDI02_B_002_MONTHLY)\nL2B table index    | [LARSE/GEDI/GEDI02_B_002_INDEX](LARSE_GEDI_GEDI02_B_002_INDEX)\nL4A Biomass Vector | [LARSE/GEDI/GEDI04_A_002](LARSE_GEDI_GEDI04_A_002)\nL4A Monthly raster | [LARSE/GEDI/GEDI04_A_002_MONTHLY](LARSE_GEDI_GEDI04_A_002_MONTHLY)\nL4A table index    | [LARSE/GEDI/GEDI04_A_002_INDEX](LARSE_GEDI_GEDI04_A_002_INDEX)\nL4B Biomass        | [LARSE/GEDI/GEDI04_B_002](LARSE_GEDI_GEDI04_B_002)\nL4D Imputed Waveforms | [LARSE/GEDI/GEDI04_D_002](LARSE_GEDI_GEDI04_D_002)","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-52,180,52]]},"temporal":{"interval":[["2019-04-18T00:00:00Z","2021-08-04T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI04_B_002","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LARSE/LARSE_GEDI_GEDI04_B_002","rel":"related","title":"Run the example for LARSE/GEDI/GEDI04_B_002 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LARSE/LARSE_GEDI_GEDI04_B_002_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LARSE_GEDI_GEDI04_B_002#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI04_B_002/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LARSE/GEDI/GEDI04_D_002","title":"GEDI L4D Imputed Waveforms, Version 2","description":"The GEDI Level 4D (L4D) product provides a 30-m spatial elaboration of the mission's sample of footprint-level L2A V002, L2B V002, and L4A Version 2.1 products globally between latitudes -51.6 and 51.6 degrees.\n\nA k-nearest neighbors (k-NN) algorithm was used with Landsat time series data to impute high-quality GEDI shots to every 30-m pixel. A different nearest neighbor model was developed for every 10 x 10-km tile, with potential imputed shots drawn from a support area of at least 30 x 30 km centered on the tile. The imputation assigns the single nearest neighbor using Landsat time series imagery centered on the target year 2023.\n\nThe most basic 30-m output is the shot number of the imputed pixel (relayed in fragments across five fields), from which any shot-level quantity may be retrieved. Predictions for commonly used waveform attributes are also included, such as relative height (RH) metrics from the L2A product, canopy cover from L2B, and aboveground biomass density (AGBD) from L4A.\n\nSee [User Guide](https://daac.ornl.gov/GEDI/guides/GEDI_L4D_Imputed_Waveforms.html) for more information.\n\nThe Global Ecosystem Dynamics Investigation [GEDI](https://gedi.umd.edu/)\nmission aims to characterize ecosystem structure and dynamics to enable\nradically improved quantification and understanding of the Earth's carbon cycle\nand biodiversity. The GEDI instrument, attached to the International Space\nStation (ISS), collects data globally between 51.6&deg; N and 51.6&deg; S\nlatitudes at the highest resolution and densest sampling of the\n3-dimensional structure of the Earth. The GEDI instrument consists of three\nlasers producing a total of eight beam ground transects, which instantaneously\nsample eight ~25 m footprints spaced approximately every 60 m along-track.\n\nProduct                 | Description\n:---------------------  | :----------------------------------------------\nL2A Vector         | [LARSE/GEDI/GEDI02_A_002](LARSE_GEDI_GEDI02_A_002)\nL2A Monthly raster | [LARSE/GEDI/GEDI02_A_002_MONTHLY](LARSE_GEDI_GEDI02_A_002_MONTHLY)\nL2A table index    | [LARSE/GEDI/GEDI02_A_002_INDEX](LARSE_GEDI_GEDI02_A_002_INDEX)\nL2B Vector         | [LARSE/GEDI/GEDI02_B_002](LARSE_GEDI_GEDI02_B_002)\nL2B Monthly raster | [LARSE/GEDI/GEDI02_B_002_MONTHLY](LARSE_GEDI_GEDI02_B_002_MONTHLY)\nL2B table index    | [LARSE/GEDI/GEDI02_B_002_INDEX](LARSE_GEDI_GEDI02_B_002_INDEX)\nL4A Biomass Vector | [LARSE/GEDI/GEDI04_A_002](LARSE_GEDI_GEDI04_A_002)\nL4A Monthly raster | [LARSE/GEDI/GEDI04_A_002_MONTHLY](LARSE_GEDI_GEDI04_A_002_MONTHLY)\nL4A table index    | [LARSE/GEDI/GEDI04_A_002_INDEX](LARSE_GEDI_GEDI04_A_002_INDEX)\nL4B Biomass        | [LARSE/GEDI/GEDI04_B_002](LARSE_GEDI_GEDI04_B_002)\nL4D Imputed Waveforms | [LARSE/GEDI/GEDI04_D_002](LARSE_GEDI_GEDI04_D_002)","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2019-04-18T00:00:00Z","2023-03-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI04_D_002","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LARSE/LARSE_GEDI_GEDI04_D_002","rel":"related","title":"Run the example for LARSE/GEDI/GEDI04_D_002 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LARSE/LARSE_GEDI_GEDI04_D_002_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LARSE_GEDI_GEDI04_D_002#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.3334/ORNLDAAC/2455","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI04_D_002/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GEDI04_D_002/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/12KM","title":"Gridded GEDI Vegetation Structure Metrics and Biomass Density with COUNTS metrics, 12KM pixel size","description":"This dataset consists of near-global, analysis-ready, multi-resolution\ngridded vegetation structure metrics derived from NASA Global Ecosystem\nDynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m\ndiameter lidar footprints. This dataset provides a comprehensive\nrepresentation of near-global vegetation structure that is inclusive of\nthe entire vertical profile, based solely on GEDI lidar, and validated with\nindependent data.\n\nThe GEDI sensor, mounted on the International Space\nStation (ISS), uses eight laser beams spaced by 60 m along-track and 600 m\nacross-track on the Earth surface to measure ground elevation and vegetation\nstructure between approximately 52 degrees North and South latitude.\nBetween April 17th 2019 and March 16th 2023, GEDI acquired 11 and 7.7\nbillion quality waveforms suitable for measuring ground elevation and\nvegetation structure, respectively.\n\nIn addition to many of the standard L2 and L4A shot metrics, several\nadditional metrics have been derived which may be particularly useful for\napplications in carbon and water cycling processes in earth system models,\nas well as forest management, biodiversity modeling, and habitat assessment.\nVariables include canopy height, canopy cover, plant area index, foliage\nheight diversity, and plant area volume density at 5 m strata. Refer to the\n[Gridded GEDI Vegetation Structure Metrics and Biomass Density]\n(https://daac.ornl.gov/VEGETATION/guides/GEDI_HighQuality_Shots_Rasters.html)\nfor more information.\n\nEight statistics are included for each GEDI shot metric: mean, bootstrapped\nstandard error of the mean, median, standard deviation, interquartile range,\n95th percentile, Shannon's diversity index, and shot count.\nQuality shot filtering methodology that aligns with the GEDI L4B Gridded\nAboveground Biomass Density, Version 2.1 was used. In comparison to the\ncorresponding GEDI L3 dataset, this dataset provides additional gridded metrics at\nmultiple spatial resolutions and over several temporal periods (annual and\nthe full mission duration).\n\nThis dataset provides GEDI shot metrics aggregated into raster grids at\nthree spatial resolutions: 1 km, 6 km, and 12 km.\nThis dataset uses the pixel size of 12KM with counts metrics.","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-52,180,52]]},"temporal":{"interval":[["2019-04-17T00:00:00Z","2023-03-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/12KM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LARSE/LARSE_GEDI_GRIDDEDVEG_002_COUNTS_V1_12KM","rel":"related","title":"Run the example for LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/12KM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LARSE/LARSE_GEDI_GRIDDEDVEG_002_COUNTS_V1_12KM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LARSE_GEDI_GRIDDEDVEG_002_COUNTS_V1_12KM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/12KM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/12KM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/1KM","title":"Gridded GEDI Vegetation Structure Metrics and Biomass Density with COUNTS metrics, 1KM pixel size","description":"This dataset consists of near-global, analysis-ready, multi-resolution\ngridded vegetation structure metrics derived from NASA Global Ecosystem\nDynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m\ndiameter lidar footprints. This dataset provides a comprehensive\nrepresentation of near-global vegetation structure that is inclusive of\nthe entire vertical profile, based solely on GEDI lidar, and validated with\nindependent data.\n\nThe GEDI sensor, mounted on the International Space\nStation (ISS), uses eight laser beams spaced by 60 m along-track and 600 m\nacross-track on the Earth surface to measure ground elevation and vegetation\nstructure between approximately 52 degrees North and South latitude.\nBetween April 17th 2019 and March 16th 2023, GEDI acquired 11 and 7.7\nbillion quality waveforms suitable for measuring ground elevation and\nvegetation structure, respectively.\n\nIn addition to many of the standard L2 and L4A shot metrics, several\nadditional metrics have been derived which may be particularly useful for\napplications in carbon and water cycling processes in earth system models,\nas well as forest management, biodiversity modeling, and habitat assessment.\nVariables include canopy height, canopy cover, plant area index, foliage\nheight diversity, and plant area volume density at 5 m strata. Refer to the\n[Gridded GEDI Vegetation Structure Metrics and Biomass Density]\n(https://daac.ornl.gov/VEGETATION/guides/GEDI_HighQuality_Shots_Rasters.html)\nfor more information.\n\nEight statistics are included for each GEDI shot metric: mean, bootstrapped\nstandard error of the mean, median, standard deviation, interquartile range,\n95th percentile, Shannon's diversity index, and shot count.\nQuality shot filtering methodology that aligns with the GEDI L4B Gridded\nAboveground Biomass Density, Version 2.1 was used. In comparison to the\ncorresponding GEDI L3 dataset, this dataset provides additional gridded metrics at\nmultiple spatial resolutions and over several temporal periods (annual and\nthe full mission duration).\n\nThis dataset provides GEDI shot metrics aggregated into raster grids at\nthree spatial resolutions: 1 km, 6 km, and 12 km.\nThis dataset uses the pixel size of 1KM with counts metrics.","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-52,180,52]]},"temporal":{"interval":[["2019-04-17T00:00:00Z","2023-03-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/1KM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LARSE/LARSE_GEDI_GRIDDEDVEG_002_COUNTS_V1_1KM","rel":"related","title":"Run the example for LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/1KM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LARSE/LARSE_GEDI_GRIDDEDVEG_002_COUNTS_V1_1KM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LARSE_GEDI_GRIDDEDVEG_002_COUNTS_V1_1KM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/1KM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/1KM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/6KM","title":"Gridded GEDI Vegetation Structure Metrics and Biomass Density with COUNTS metrics, 6KM pixel size","description":"This dataset consists of near-global, analysis-ready, multi-resolution\ngridded vegetation structure metrics derived from NASA Global Ecosystem\nDynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m\ndiameter lidar footprints. This dataset provides a comprehensive\nrepresentation of near-global vegetation structure that is inclusive of\nthe entire vertical profile, based solely on GEDI lidar, and validated with\nindependent data.\n\nThe GEDI sensor, mounted on the International Space\nStation (ISS), uses eight laser beams spaced by 60 m along-track and 600 m\nacross-track on the Earth surface to measure ground elevation and vegetation\nstructure between approximately 52 degrees North and South latitude.\nBetween April 17th 2019 and March 16th 2023, GEDI acquired 11 and 7.7\nbillion quality waveforms suitable for measuring ground elevation and\nvegetation structure, respectively.\n\nIn addition to many of the standard L2 and L4A shot metrics, several\nadditional metrics have been derived which may be particularly useful for\napplications in carbon and water cycling processes in earth system models,\nas well as forest management, biodiversity modeling, and habitat assessment.\nVariables include canopy height, canopy cover, plant area index, foliage\nheight diversity, and plant area volume density at 5 m strata. Refer to the\n[Gridded GEDI Vegetation Structure Metrics and Biomass Density]\n(https://daac.ornl.gov/VEGETATION/guides/GEDI_HighQuality_Shots_Rasters.html)\nfor more information.\n\nEight statistics are included for each GEDI shot metric: mean, bootstrapped\nstandard error of the mean, median, standard deviation, interquartile range,\n95th percentile, Shannon's diversity index, and shot count.\nQuality shot filtering methodology that aligns with the GEDI L4B Gridded\nAboveground Biomass Density, Version 2.1 was used. In comparison to the\ncorresponding GEDI L3 dataset, this dataset provides additional gridded metrics at\nmultiple spatial resolutions and over several temporal periods (annual and\nthe full mission duration).\n\nThis dataset provides GEDI shot metrics aggregated into raster grids at\nthree spatial resolutions: 1 km, 6 km, and 12 km.\nThis dataset uses the pixel size of 6KM with counts metrics.","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-52,180,52]]},"temporal":{"interval":[["2019-04-17T00:00:00Z","2023-03-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/6KM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LARSE/LARSE_GEDI_GRIDDEDVEG_002_COUNTS_V1_6KM","rel":"related","title":"Run the example for LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/6KM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LARSE/LARSE_GEDI_GRIDDEDVEG_002_COUNTS_V1_6KM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LARSE_GEDI_GRIDDEDVEG_002_COUNTS_V1_6KM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/6KM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/COUNTS/V1/6KM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LARSE/GEDI/GRIDDEDVEG_002/V1/12KM","title":"Gridded GEDI Vegetation Structure Metrics and Biomass Density, 12KM pixel size","description":"This dataset consists of near-global, analysis-ready, multi-resolution\ngridded vegetation structure metrics derived from NASA Global Ecosystem\nDynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m\ndiameter lidar footprints. This dataset provides a comprehensive\nrepresentation of near-global vegetation structure that is inclusive of\nthe entire vertical profile, based solely on GEDI lidar, and validated with\nindependent data.\n\nThe GEDI sensor, mounted on the International Space\nStation (ISS), uses eight laser beams spaced by 60 m along-track and 600 m\nacross-track on the Earth surface to measure ground elevation and vegetation\nstructure between approximately 52 degrees North and South latitude.\nBetween April 17th 2019 and March 16th 2023, GEDI acquired 11 and 7.7\nbillion quality waveforms suitable for measuring ground elevation and\nvegetation structure, respectively.\n\nIn addition to many of the standard L2 and L4A shot metrics, several\nadditional metrics have been derived which may be particularly useful for\napplications in carbon and water cycling processes in earth system models,\nas well as forest management, biodiversity modeling, and habitat assessment.\nVariables include canopy height, canopy cover, plant area index, foliage\nheight diversity, and plant area volume density at 5 m strata. Refer to the\n[Gridded GEDI Vegetation Structure Metrics and Biomass Density]\n(https://daac.ornl.gov/VEGETATION/guides/GEDI_HighQuality_Shots_Rasters.html)\nfor more information.\n\nEight statistics are included for each GEDI shot metric: mean, bootstrapped\nstandard error of the mean, median, standard deviation, interquartile range,\n95th percentile, Shannon's diversity index, and shot count.\nQuality shot filtering methodology that aligns with the GEDI L4B Gridded\nAboveground Biomass Density, Version 2.1 was used. In comparison to the\ncorresponding GEDI L3 dataset, this dataset provides additional gridded metrics at\nmultiple spatial resolutions and over several temporal periods (annual and\nthe full mission duration).\n\nThis dataset provides GEDI shot metrics aggregated into raster grids at\nthree spatial resolutions: 1 km, 6 km, and 12 km.\nThis dataset uses the pixel size of 12KM.","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-52,180,52]]},"temporal":{"interval":[["2019-04-17T00:00:00Z","2023-03-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/V1/12KM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LARSE/LARSE_GEDI_GRIDDEDVEG_002_V1_12KM","rel":"related","title":"Run the example for LARSE/GEDI/GRIDDEDVEG_002/V1/12KM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LARSE/LARSE_GEDI_GRIDDEDVEG_002_V1_12KM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LARSE_GEDI_GRIDDEDVEG_002_V1_12KM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/V1/12KM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/V1/12KM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LARSE/GEDI/GRIDDEDVEG_002/V1/1KM","title":"Gridded GEDI Vegetation Structure Metrics and Biomass Density, 1KM pixel size","description":"This dataset consists of near-global, analysis-ready, multi-resolution\ngridded vegetation structure metrics derived from NASA Global Ecosystem\nDynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m\ndiameter lidar footprints. This dataset provides a comprehensive\nrepresentation of near-global vegetation structure that is inclusive of\nthe entire vertical profile, based solely on GEDI lidar, and validated with\nindependent data.\n\nThe GEDI sensor, mounted on the International Space\nStation (ISS), uses eight laser beams spaced by 60 m along-track and 600 m\nacross-track on the Earth surface to measure ground elevation and vegetation\nstructure between approximately 52 degrees North and South latitude.\nBetween April 17th 2019 and March 16th 2023, GEDI acquired 11 and 7.7\nbillion quality waveforms suitable for measuring ground elevation and\nvegetation structure, respectively.\n\nIn addition to many of the standard L2 and L4A shot metrics, several\nadditional metrics have been derived which may be particularly useful for\napplications in carbon and water cycling processes in earth system models,\nas well as forest management, biodiversity modeling, and habitat assessment.\nVariables include canopy height, canopy cover, plant area index, foliage\nheight diversity, and plant area volume density at 5 m strata. Refer to the\n[Gridded GEDI Vegetation Structure Metrics and Biomass Density]\n(https://daac.ornl.gov/VEGETATION/guides/GEDI_HighQuality_Shots_Rasters.html)\nfor more information.\n\nEight statistics are included for each GEDI shot metric: mean, bootstrapped\nstandard error of the mean, median, standard deviation, interquartile range,\n95th percentile, Shannon's diversity index, and shot count.\nQuality shot filtering methodology that aligns with the GEDI L4B Gridded\nAboveground Biomass Density, Version 2.1 was used. In comparison to the\ncorresponding GEDI L3 dataset, this dataset provides additional gridded metrics at\nmultiple spatial resolutions and over several temporal periods (annual and\nthe full mission duration).\n\nThis dataset provides GEDI shot metrics aggregated into raster grids at\nthree spatial resolutions: 1 km, 6 km, and 12 km.\nThis dataset uses the pixel size of 1KM.","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-52,180,52]]},"temporal":{"interval":[["2019-04-17T00:00:00Z","2023-03-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/V1/1KM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LARSE/LARSE_GEDI_GRIDDEDVEG_002_V1_1KM","rel":"related","title":"Run the example for LARSE/GEDI/GRIDDEDVEG_002/V1/1KM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LARSE/LARSE_GEDI_GRIDDEDVEG_002_V1_1KM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LARSE_GEDI_GRIDDEDVEG_002_V1_1KM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/V1/1KM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/V1/1KM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LARSE/GEDI/GRIDDEDVEG_002/V1/6KM","title":"Gridded GEDI Vegetation Structure Metrics and Biomass Density, 6KM pixel size","description":"This dataset consists of near-global, analysis-ready, multi-resolution\ngridded vegetation structure metrics derived from NASA Global Ecosystem\nDynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m\ndiameter lidar footprints. This dataset provides a comprehensive\nrepresentation of near-global vegetation structure that is inclusive of\nthe entire vertical profile, based solely on GEDI lidar, and validated with\nindependent data.\n\nThe GEDI sensor, mounted on the International Space\nStation (ISS), uses eight laser beams spaced by 60 m along-track and 600 m\nacross-track on the Earth surface to measure ground elevation and vegetation\nstructure between approximately 52 degrees North and South latitude.\nBetween April 17th 2019 and March 16th 2023, GEDI acquired 11 and 7.7\nbillion quality waveforms suitable for measuring ground elevation and\nvegetation structure, respectively.\n\nIn addition to many of the standard L2 and L4A shot metrics, several\nadditional metrics have been derived which may be particularly useful for\napplications in carbon and water cycling processes in earth system models,\nas well as forest management, biodiversity modeling, and habitat assessment.\nVariables include canopy height, canopy cover, plant area index, foliage\nheight diversity, and plant area volume density at 5 m strata. Refer to the\n[Gridded GEDI Vegetation Structure Metrics and Biomass Density]\n(https://daac.ornl.gov/VEGETATION/guides/GEDI_HighQuality_Shots_Rasters.html)\nfor more information.\n\nEight statistics are included for each GEDI shot metric: mean, bootstrapped\nstandard error of the mean, median, standard deviation, interquartile range,\n95th percentile, Shannon's diversity index, and shot count.\nQuality shot filtering methodology that aligns with the GEDI L4B Gridded\nAboveground Biomass Density, Version 2.1 was used. In comparison to the\ncorresponding GEDI L3 dataset, this dataset provides additional gridded metrics at\nmultiple spatial resolutions and over several temporal periods (annual and\nthe full mission duration).\n\nThis dataset provides GEDI shot metrics aggregated into raster grids at\nthree spatial resolutions: 1 km, 6 km, and 12 km.\nThis dataset uses the pixel size of 6KM.","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-52,180,52]]},"temporal":{"interval":[["2019-04-17T00:00:00Z","2023-03-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/V1/6KM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LARSE/LARSE_GEDI_GRIDDEDVEG_002_V1_6KM","rel":"related","title":"Run the example for LARSE/GEDI/GRIDDEDVEG_002/V1/6KM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LARSE/LARSE_GEDI_GRIDDEDVEG_002_V1_6KM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LARSE_GEDI_GRIDDEDVEG_002_V1_6KM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/V1/6KM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LARSE/GEDI/GRIDDEDVEG_002/V1/6KM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/LIMA/MOSAIC","title":"Landsat Image Mosaic of Antarctica (LIMA) 16-Bit Pan-Sharpened Mosaic","description":"The Landsat Image Mosaic of Antarctica (LIMA) is a seamless\nand virtually cloudless mosaic created from processed\nLandsat 7 ETM+ scenes.\n\nThis LIMA dataset is the 16-bit Intermediate LIMA.\nThe mosaic consists of pan-sharpened normalized surface reflectance\nscenes (Landsat ETM+ bands 1, 2, 3, and 4). The mosaic was\nconstructed by ordering cloud free images on top and trimming\nimage boundaries when tile discontinuities occurred.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,-55]]},"temporal":{"interval":[["1999-06-30T00:00:00Z","2002-09-04T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/LIMA/MOSAIC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_LIMA_MOSAIC","rel":"related","title":"Run the example for USGS/LIMA/MOSAIC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_LIMA_MOSAIC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_LIMA_MOSAIC#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/LIMA/MOSAIC/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/LIMA/SR","title":"Landsat Image Mosaic of Antarctica (LIMA) - Processed Landsat Scenes (16 bit)","description":"The Landsat Image Mosaic of Antarctica (LIMA) is a seamless\nand virtually cloudless mosaic created from processed\nLandsat 7 ETM+ scenes.\n\nProcessed Landsat Scenes (16 bit) are Level 1Gt NLAPS\nscenes converted to 16 bit, processed with sun-angle\ncorrection, and converted to reflectance values\n([Bindschadler 2008](https://lima.usgs.gov/LIMA_paper.pdf)).\n\nEach Landsat scene is processed with elevation\ndata and sun-angle correction to ensure surface features\nwere accurately represented. The sun's angle in Antarctica\ngives the appearance of a setting sun. Because of the low\nsun angle, as Landsat passes over Antarctica, the outer\nedges of the continent appear brighter than areas closer\nto the South Pole, so scenes have bright and dark areas.\nInconsistent sun angles and shadows where corrected for\nthese scenes. Without this process, mosaicking would produce\na patchwork of scenes since each scene would have a brighter\nand a darker side.\n\nUsers can find individual image metadata available as a table at:\n[USGS/LIMA/SR_METADATA](USGS_LIMA_SR_METADATA)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,-55]]},"temporal":{"interval":[["1999-06-30T00:00:00Z","2002-09-04T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/LIMA/SR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_LIMA_SR","rel":"related","title":"Run the example for USGS/LIMA/SR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_LIMA_SR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_LIMA_SR#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/LIMA/SR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/LIMA/SR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Latvia/Maamet/orthos/cir","title":"Latvia Color InfraRed (CIR) orthophotos","description":"In Latvia, orthophoto maps are prepared in the Latvian coordinate system\nLKS-92 TM according to the TKS-93 map sheet division (scale 1:10000 map\nsheet corresponds to 5 x 5 kilometers in nature). Orthophoto maps are\nprepared for the whole territory of Latvia at the scale of 1:10000, but for\nseparate territories - for cities and densely populated areas - at the scale\nof 1:2000 or 1:1000.\n\nThe CIR dataset has three bands: Near-Infrared, Red, and Green.\n\nFor more information, please see the\n[Latvia orthophotos documentation](https://www.lgia.gov.lv/lv/ortofotokartes-1)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[20.5,55.5,28.6,58.5]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Latvia/Maamet/orthos/cir","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Latvia/Latvia_Maamet_orthos_cir","rel":"related","title":"Run the example for Latvia/Maamet/orthos/cir in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Latvia/Latvia_Maamet_orthos_cir_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Latvia_Maamet_orthos_cir#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Latvia/Maamet/orthos/cir/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Latvia/Maamet/orthos/cir/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Latvia/Maamet/orthos/rgb","title":"Latvia RGB orthophotos","description":"In Latvia, orthophoto maps are prepared in the Latvian coordinate system\nLKS-92 TM according to the TKS-93 map sheet division (scale 1:10000 map\nsheet corresponds to 5 x 5 kilometers in nature). Orthophoto maps are\nprepared for the whole territory of Latvia at the scale of\n1:10000, but for separate territories - for cities and densely populated\nareas - at the scale of 1:2000 or 1:1000.\n\nThe RGB dataset has three bands: red, green, and blue.\n\nFor more information, please see the\n[Latvia orthophotos documentation](https://www.lgia.gov.lv/lv/ortofotokartes-1)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[20.5,55.5,28.6,58.5]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Latvia/Maamet/orthos/rgb","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Latvia/Latvia_Maamet_orthos_rgb","rel":"related","title":"Run the example for Latvia/Maamet/orthos/rgb in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Latvia/Latvia_Maamet_orthos_rgb_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Latvia_Maamet_orthos_rgb#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Latvia/Maamet/orthos/rgb/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Latvia/Maamet/orthos/rgb/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/MEASURES/GFCC/TC/v3","title":"Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m","description":"The Landsat Vegetation Continuous Fields (VCF) tree cover layers contain\nestimates of the percentage of horizontal ground in each 30-m pixel covered\nby woody vegetation greater than 5 meters in height. The dataset is available\nfor four epochs centered on the years 2000, 2005, 2010 and 2015. The dataset\nis derived from the GFCC Surface Reflectance product (GFCC30SR), which is\nbased on enhanced Global Land Survey (GLS) datasets. The GLS datasets are\ncomposed of high-resolution Landsat 5 Thematic Mapper (TM) and Landsat 7\nEnhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution.\n\nTree cover, the proportional, vertically projected area of vegetation\n(including leaves, stems, branches, etc.) of woody plants above a given\nheight, affects terrestrial energy and water exchanges, photosynthesis and\ntranspiration, net primary production, and carbon and nutrient fluxes. Tree\ncover also affects habitat quality and movements of wildlife, residential\nproperty value for humans, and other ecosystem services. The continuous\nclassification scheme of the VCF product enables better depiction of land\ncover gradients than traditional discrete classification schemes.\nImportantly for detection and monitoring of forest changes (e.g.,\ndeforestation and degradation), tree cover provides a measurable attribute\nupon which to define forest cover and its changes. Changes in tree cover\nover time can be used to monitor and retrieve site-specific histories of\nforest change.\n\nThe dataset has been produced for four year epochs: 2000, 2005, 2010, and\n2015 with an image in the collection for each available WRS2 path/row.\n\nDocumentation:\n\n* [User's guide](https://lpdaac.usgs.gov/documents/1371/GFCC_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1370/GFCC_ATBD.pdf)\n\nProvider's Note: Due to the end of NASA MEaSUREs funding, free versions of this dataset are no\nlonger being produced. Interested parties can obtain updated and expanded versions at\nwww.terraPulse.com.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2015-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/MEASURES/GFCC/TC/v3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_MEASURES_GFCC_TC_v3","rel":"related","title":"Run the example for NASA/MEASURES/GFCC/TC/v3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_MEASURES_GFCC_TC_v3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_MEASURES_GFCC_TC_v3.json","rel":"latest-version","title":"NASA/MEASURES/GFCC/TC/v3","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/GLCF/GLCF_GLS_TCC.json","rel":"predecessor-version","title":"GLCF/GLS_TCC","type":"application/json"},{"href":"https://doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/MEASURES/GFCC/TC/v3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/MEASURES/GFCC/TC/v3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MERIT/DEM/v1_0_3","title":"MERIT DEM: Multi-Error-Removed Improved-Terrain DEM","description":"MERIT DEM a high accuracy global DEM at 3 arc second resolution (~90 m at\nthe equator) produced by eliminating major error components from existing DEMs\n(NASA SRTM3 DEM, JAXA AW3D DEM, Viewfinder Panoramas DEM).\n\nMERIT DEM separates absolute bias, stripe noise, speckle\nnoise and tree height bias using multiple satellite datasets and filtering techniques.\nAfter the error removal, land areas mapped with 2 m or better vertical accuracy\nwere increased from 39% to 58%. Significant improvements were found in flat regions\nwhere height errors larger than topography variability, and landscapes such as\nriver networks and hill-valley structures became clearly represented.\n\n'MERIT DEM was developed by processing the following products as baseline data:\n\n* [NASA SRTM3 DEM v2.1](https://dds.cr.usgs.gov/srtm/version2_1/SRTM3)\n* [JAXA AW3D - 30 m DEM v1](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/index.htm)\n* [Viewfinder Panoramas DEM](https://www.viewfinderpanoramas.org/dem3.html)\n\nIn addition to the above baseline dems, these products were used as supplementary data:\n\n* [NASA-NSIDC ICESat/GLAS GLA14 data](https://nsidc.org/data/gla14)\n* [U-Maryland Landsat forest cover data](https://glad.earthengine.app/view/global-forest-change)\n* [NASA Global Forest height data](https://www.nasa.gov/topics/earth/features/forest-height-map.html)\n* [JAMSTEC/U-Tokyo G3WBM water body data](https://hydro.iis.u-tokyo.ac.jp/~yamadai/G3WBM/index.html)\n","license":"ODbL-1.0","extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["1987-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MERIT/DEM/v1_0_3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MERIT/MERIT_DEM_v1_0_3","rel":"related","title":"Run the example for MERIT/DEM/v1_0_3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MERIT/MERIT_DEM_v1_0_3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MERIT_DEM_v1_0_3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/index.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MERIT/DEM/v1_0_3/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MERIT/Hydro_reduced/v1_0_1","title":"MERIT Hydro: Supplementary Visualization Layers","description":"Supplementary visualization layers for [MERIT Hydro](MERIT_Hydro_v1_0_1)","license":"ODbL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1987-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MERIT/Hydro_reduced/v1_0_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MERIT/MERIT_Hydro_reduced_v1_0_1","rel":"related","title":"Run the example for MERIT/Hydro_reduced/v1_0_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MERIT/MERIT_Hydro_reduced_v1_0_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MERIT_Hydro_reduced_v1_0_1#terms-of-use","rel":"license","type":"text/html"},{"href":"http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/index.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MERIT/Hydro_reduced/v1_0_1/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MERIT/Hydro/v1_0_1","title":"MERIT Hydro: Global Hydrography Datasets","description":"MERIT Hydro is a new global flow direction map at 3 arc-second resolution (~90\nm at the equator) derived from the version 1.0.3 of the MERIT DEM elevation data\nand water body datasets (G1WBM, GSWO and OpenStreetMap).\n\nMERIT Hydro contains the output of a new algorithm that\nextracts river networks near-automatically by separating actual inland basins from\nnoise caused by the errors in input elevation data. After a minimum\namount of hand-editing, the constructed hydrography map shows good agreement with\nexisting quality-controlled river network datasets in terms of flow accumulation\narea and river basin shape (see Figure 9a in the paper). The location of river\nstreamlines was realistically aligned with existing satellite-based global\nriver channel data (see Figure 10a in the paper). Relative error\nin the drainage area was smaller than 0.05 for 90% of GRDC gauges, confirming\nthe accuracy of the delineated global river networks. Discrepancies in flow accumulation\narea were found mostly in arid river basins containing depressions that are occasionally\nconnected at high water levels and thus resulting in uncertain watershed boundaries.\n\nMERIT Hydro improves on existing global hydrography datasets in terms of spatial\ncoverage (between 90N and 60S) and representation of small streams, mainly due\nto increased availability of high-quality baseline geospatial datasets.\n\n[You can use this web app to visualize MERIT Hydro data.](https://meritdataset.users.earthengine.app/view/merit-hydro-visualization-and-interactive-map)\n[The app's source code is available.](https://github.com/google/earthengine-community/blob/master/datasets/scripts/Hydro_Visualization.js)\n\nKnown Problems:\n\n* River width (Update from GWD-LR v1): Width algorithm was updated to consider\n  sub-pixel water fraction. Now, 30 m water map is used for width calculation at\n  90 m resolution. Currently river width is calculated for each channel separately\n  in braided/anabranching sections. Merged river width should be calculated.\n* Water body map: There are some inconsistencies between the DEM land sea mask and\n  wate body data (such as new islands along the coast). The quality of OpenStreeetMap\n  water body layer is not uniform in all areas.\n* Channel bifurcations: Channel bifurcation is not well represented in the current\n  version. Each pixel is assumed to have only one downstream direction. Secondary\n  (or multiple) downstream direction should be considered, to represent complex\n  river networks in the delta regions, floodplains, and braided rivers.\n* Underground rivers/tunnels: Major underground rivers/tunnels should be implemented\n  to improve large-scale water balance.\n* River/lake separation: Rivers and lakes need to be separated better for some applications.\n* Below-sea-level areas: The areas below sea level in coastal regions are not well\n  represented in adjusted elevation data.\n* Flow direction over glaciers: Flow direction over glaciers is not well represented,\n  because the elevation of glacier centerline is higher than glacier edge.\n* Supplementary data: It would be better to add location of GRDC gauging stations, water\n  falls, reservoirs, etc.\n\nData Sources:\n\n* [U-Tokyo MERIT DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/index.html)\n* [U-Tokyo G1WBM water body data](http://hydro.iis.u-tokyo.ac.jp/~yamadai/G3WBM/index.html)\n* [OpenStreetMap water body layer](http://hydro.iis.u-tokyo.ac.jp/~yamadai/OSM_water/index.html)\n* [EC-JRC Global Surface Water Occurrence](http://hydro.iis.u-tokyo.ac.jp/~yamadai/G3WBM/index.html)\n* [U-Maryland Landsat forest cover data](https://glad.earthengine.app/view/global-forest-change)\n","license":"ODbL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1987-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MERIT/Hydro/v1_0_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MERIT/MERIT_Hydro_v1_0_1","rel":"related","title":"Run the example for MERIT/Hydro/v1_0_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MERIT/MERIT_Hydro_v1_0_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MERIT_Hydro_v1_0_1#terms-of-use","rel":"license","type":"text/html"},{"href":"http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/index.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MERIT/Hydro/v1_0_1/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MCD12Q1","title":"MCD12Q1.006 MODIS Land Cover Type Yearly Global 500m [deprecated]","description":"The MCD12Q1 V6 product provides global land cover types at yearly intervals\n(2001-2016) derived from six different classification schemes. It is derived\nusing supervised classifications of MODIS Terra and Aqua reflectance data.\nThe supervised classifications then undergo additional post-processing that\nincorporate prior knowledge and ancillary information to further refine\nspecific classes.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/86/MCD12_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD12Q1)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD12Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MCD12Q1","rel":"related","title":"Run the example for MODIS/006/MCD12Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MCD12Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD12Q1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD12Q1.json","rel":"latest-version","title":"MODIS/061/MCD12Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD12Q1.json","rel":"successor-version","title":"MODIS/061/MCD12Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD12Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD12Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MCD12Q2","title":"MCD12Q2.006 Land Cover Dynamics Yearly Global 500m [deprecated]","description":"The MCD12Q2 V6 Land Cover Dynamics product (informally\ncalled the MODIS Global Vegetation Phenology product) provides\nestimates of the timing of vegetation phenology at global scales.\nAdditionally, it provides information related to the range and\nsummation of the enhanced vegetation index (EVI) computed from\nMODIS surface reflectance data at each pixel. It identifies the\nonset of greenness, greenup midpoint, maturity, peak greenness,\nsenescence, greendown midpoint, dormancy, EVI2 minimum, EVI2 amplitude,\nintegrated EVI2 over a vegetation cycle, as well as overall and phenology\nmetric-specific quality information.\n\nThe MCD12Q2 Version 6 data product is derived from time series of the\n2-band Enhanced Vegetation Index (EVI2) calculated from MODIS Nadir\nBidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance\n(NBAR). Vegetation phenology metrics are identified for up to two detected\ngrowing cycles per year. For pixels with more than two valid vegetation\ncycles, the data represent the two cycles with the largest NBAR-EVI2\namplitudes.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD12Q2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MCD12Q2","rel":"related","title":"Run the example for MODIS/006/MCD12Q2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MCD12Q2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD12Q2.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD12Q2.json","rel":"latest-version","title":"MODIS/061/MCD12Q2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD12Q2.json","rel":"successor-version","title":"MODIS/061/MCD12Q2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD12Q2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD12Q2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MCD15A3H","title":"MCD15A3H.006 MODIS Leaf Area Index/FPAR 4-Day Global 500m [deprecated]","description":"The MCD15A3H V6 level 4, Combined Fraction of Photosynthetically\nActive Radiation (FPAR), and Leaf Area Index (LAI) product is a\n4-day composite data set with 500 meter pixel size. The algorithm\nchooses the \\\"best\\\" pixel available from all the acquisitions of\nboth MODIS sensors located on NASA's Terra and Aqua satellites\nfrom within the 4-day period.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/624/MOD15_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/90/MOD15_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD15A3H)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD15A3H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MCD15A3H","rel":"related","title":"Run the example for MODIS/006/MCD15A3H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MCD15A3H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD15A3H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD15A3H.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD15A3H.json","rel":"latest-version","title":"MODIS/061/MCD15A3H","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD15A3H.json","rel":"successor-version","title":"MODIS/061/MCD15A3H","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD15A3H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD15A3H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MCD19A2_GRANULES","title":"MCD19A2.006: Terra & Aqua MAIAC Land Aerosol Optical Depth Daily 1km [deprecated]","description":"The MCD19A2 V6 data product is a MODIS Terra and Aqua combined Multi-angle\nImplementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded\nLevel 2 product produced daily at 1 km resolution. For more information\nsee the [MAIAC user guide](https://lpdaac.usgs.gov/documents/110/MCD19_User_Guide_V6.pdf).\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/110/MCD19_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/111/MCD19_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD19A2)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-26T00:00:00Z","2023-02-17T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD19A2_GRANULES","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MCD19A2_GRANULES","rel":"related","title":"Run the example for MODIS/006/MCD19A2_GRANULES in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MCD19A2_GRANULES_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD19A2_GRANULES#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD19A2.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A2_GRANULES.json","rel":"latest-version","title":"MODIS/061/MCD19A2_GRANULES","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A2_GRANULES.json","rel":"successor-version","title":"MODIS/061/MCD19A2_GRANULES","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD19A2_GRANULES/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD19A2_GRANULES/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MCD43A1","title":"MCD43A1.006 MODIS BRDF-Albedo Model Parameters Daily 500m [deprecated]","description":"The MCD43A1 V6 Bidirectional Reflectance Distribution\nFunction and Albedo (BRDF/Albedo) Model Parameters dataset is\na 500 meter daily 16-day product. The Julian date represents\nthe 9th day of the 16-day retrieval period, and consequently\nthe observations are weighted to estimate the BRDF/Albedo for\nthat day. The MCD43A1 algorithm, as is with all combined products,\nchooses the best representative pixel from a pool that includes\nall the acquisitions from both the Terra and Aqua sensors from\nthe retrieval period.\n\nThe MCD43A1 provides the three model\nweighting parameters (isotropic, volumetric, and geometric) for\neach of the MODIS bands 1 through 7 and the visible (vis), near\ninfrared (nir), and shortwave bands used to derive the Albedo\nand BRDF products (MCD43A3 and MCD43A4). The Mandatory Quality\nlayers for each of the 10 bands are supplied as well.\n\nSee\n[dataset user guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006/mcd43a1_brdif_albedo_model_parameters_product)\nfor more information.\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/97/MCD43_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD43A1)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MCD43A1","rel":"related","title":"Run the example for MODIS/006/MCD43A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MCD43A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD43A1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A1.json","rel":"latest-version","title":"MODIS/061/MCD43A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A1.json","rel":"predecessor-version","title":"MODIS/MCD43A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A1.json","rel":"successor-version","title":"MODIS/061/MCD43A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MCD43A2","title":"MCD43A2.006 MODIS BRDF-Albedo Quality Daily 500m [deprecated]","description":"The MCD43A2 V6 Bidirectional Reflectance Distribution\nFunction and Albedo (BRDF/Albedo) Quality dataset is a 500 meter\ndaily 16-day product. It contains all the quality information\nfor the corresponding 16-day MCD43A3 Albedo and the MCD43A4 Nadir-BRDF\n(NBAR) products.\n\nThe MCD43A2 contains individual band quality\nand observation information for the MODIS land bands\n1-7, along with the overall BRDF/Albedo quality information.\n\nSee\n[dataset user guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006/mcd43a2_albedo_product)\nfor more information.\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/97/MCD43_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD43A2)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MCD43A2","rel":"related","title":"Run the example for MODIS/006/MCD43A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MCD43A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD43A2.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A2.json","rel":"latest-version","title":"MODIS/061/MCD43A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A2.json","rel":"predecessor-version","title":"MODIS/MCD43A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A2.json","rel":"successor-version","title":"MODIS/061/MCD43A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MCD43A3","title":"MCD43A3.006 MODIS Albedo Daily 500m [deprecated]","description":"The MCD43A3 V6 Albedo Model dataset is a daily 16-day\nproduct. It provides both directional hemispherical reflectance\n(black sky albedo) and bihemispherical reflectance (white sky albedo)\nfor each of the MODIS surface reflectance bands (band 1 through\nband 7) as well as 3 broad spectrum bands (visible, near infrared,\nand shortwave).  Each 500m/pixel daily image is generated using\n16 days of data, centered on the given day.  A quality band is\nalso provided for each of the 10 albedo bands.\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/97/MCD43_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD43A3)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MCD43A3","rel":"related","title":"Run the example for MODIS/006/MCD43A3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MCD43A3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43A3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD43A3.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A3.json","rel":"latest-version","title":"MODIS/061/MCD43A3","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A3.json","rel":"successor-version","title":"MODIS/061/MCD43A3","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MCD43A4","title":"MCD43A4.006 MODIS Nadir BRDF-Adjusted Reflectance Daily 500m [deprecated]","description":"The MCD43A4 V6 Nadir Bidirectional Reflectance Distribution\nFunction Adjusted Reflectance (NBAR) product provides 500 meter\nreflectance data of the MODIS \\\"land\\\" bands 1-7. These are adjusted\nusing a bidirectional reflectance distribution function to model\nthe values as if they were collected from a nadir view.  The data\nare produced daily based on a 16-day retrieval period, with the\nimage's date occurring on the 9th day. This product combines data\nfrom both the Terra and Aqua spacecrafts, choosing the best representative\npixel from the 16-day period.\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/97/MCD43_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD43A4)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MCD43A4","rel":"related","title":"Run the example for MODIS/006/MCD43A4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MCD43A4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43A4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD43A4.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A4.json","rel":"latest-version","title":"MODIS/061/MCD43A4","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A4.json","rel":"predecessor-version","title":"MODIS/MCD43A4","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A4.json","rel":"successor-version","title":"MODIS/061/MCD43A4","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43A4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MCD43C3","title":"MCD43C3.006 BRDF/Albedo Daily L3 0.05 Deg CMG [deprecated]","description":"The MCD43C3 Version 6\nBidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Albedo\ndataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05\ndegree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are\ntemporally weighted to the ninth day of the retrieval period which is reflected\nin the Julian date in the file name. This CMG product covers the entire globe\nfor use in climate simulation models.\n\nMCD43C3 provides black-sky albedo (directional hemispherical reflectance)\nand white-sky albedo (bihemispherical reflectance) at local solar noon.\nBlack-sky albedo and white-sky albedo values are available as a separate layer\nfor MODIS spectral bands 1 through 7 as well as the visible, near infrared\n(NIR), and shortwave bands. Along with the 20 albedo layers are ancillary\nlayers for quality, local solar noon, percent finer resolution inputs, snow\ncover, and uncertainty.\n\nSee\n[dataset user guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006/mcd43c3)\nfor more information.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43C3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MCD43C3","rel":"related","title":"Run the example for MODIS/006/MCD43C3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MCD43C3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD43C3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD43C3.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43C3.json","rel":"latest-version","title":"MODIS/061/MCD43C3","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43C3.json","rel":"successor-version","title":"MODIS/061/MCD43C3","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43C3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD43C3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MCD64A1","title":"MCD64A1.006 MODIS Burned Area Monthly Global 500m [deprecated]","description":"The Terra and Aqua combined MCD64A1 Version 6 Burned Area data product is\na monthly, global gridded 500m product containing per-pixel burned-area\nand quality information. The MCD64A1 burned-area mapping approach employs\n500m MODIS Surface Reflectance imagery coupled with 1km MODIS active\nfire observations. The algorithm uses a burn sensitive vegetation index (VI)\nto create dynamic thresholds that are applied to the composite data.\nThe VI is derived from MODIS shortwave infrared atmospherically corrected\nsurface reflectance bands 5 and 7 with a measure of temporal texture.\nThe algorithm identifies the date of burn for the 500m grid cells within\neach individual MODIS tile. The date is encoded in a single data layer as\nthe ordinal day of the calendar year on which the burn occurred, with\nvalues assigned to unburned land pixels and additional special values\nreserved for missing data and water grid cells.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/751/MCD64_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/115/MCD64_ATBD_V6.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MCD64A1)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-11-01T00:00:00Z","2022-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD64A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MCD64A1","rel":"related","title":"Run the example for MODIS/006/MCD64A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MCD64A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD64A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD64A1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD64A1.json","rel":"latest-version","title":"MODIS/061/MCD64A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD64A1.json","rel":"successor-version","title":"MODIS/061/MCD64A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD64A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MCD64A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD08_M3","title":"MOD08_M3.006 Terra Aerosol Cloud Water Vapor Ozone Monthly Global Product 1Deg CMG [deprecated]","description":"MOD08_M3 V6 is an atmosphere global product that contains\nmonthly 1 x 1 degree grid average values of atmospheric parameters.\nThese parameters are related to atmospheric aerosol particle properties,\ntotal ozone burden, atmospheric water vapor, cloud optical and\nphysical properties, and atmospheric stability indices. The product\nalso provides means, standard deviations, QA weighted statistics,\nlog-normal distributions, uncertainty estimates, and statistics\nfor fractions of pixels that satisfy some condition. Below is a\nsubset of the bands, for a complete list see the [MOD08 Band List](https://developers.google.com/earth-engine/MOD08_bands.html).\n\nDocumentation:\n\n* [User's Guide](https://modis-atmos.gsfc.nasa.gov/sites/default/files/ModAtmo/L3_ATBD_C6_C61_2019_02_20.pdf)\n\n* [Science Data Product Software Documentation](https://modis-atmos.gsfc.nasa.gov/sites/default/files/ModAtmo/L3_C61_Changes_v2.pdf)\n\n* [MYD08_M3 product description](https://modis-atmos.gsfc.nasa.gov/products/monthly)\n\n* [File specification document](https://modis-atmos.gsfc.nasa.gov/sites/default/files/ModAtmo/MOD08_M3_fs_3045.txt)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-01T00:00:00Z","2018-04-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD08_M3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD08_M3","rel":"related","title":"Run the example for MODIS/006/MOD08_M3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD08_M3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD08_M3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD08_M3.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD08_M3.json","rel":"latest-version","title":"MODIS/061/MOD08_M3","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD08_M3.json","rel":"successor-version","title":"MODIS/061/MOD08_M3","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD08_M3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD08_M3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD09A1","title":"MOD09A1.006 Terra Surface Reflectance 8-Day Global 500m [deprecated]","description":"The MOD09A1 V6 product provides an estimate of the surface\nspectral reflectance of Terra MODIS bands 1-7 at 500m resolution\nand corrected for atmospheric conditions such as gasses, aerosols,\nand Rayleigh scattering. Along with the seven reflectance bands\nis a quality layer and four observation bands. For each pixel,\na value is selected from all the acquisitions within the 8-day\ncomposite on the basis of high observation coverage, low view angle,\nthe absence of clouds or cloud shadow, and aerosol loading.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD09A1)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD09A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD09A1","rel":"related","title":"Run the example for MODIS/006/MOD09A1 in the Earth Engine Code 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Terra Surface Reflectance Daily Global 1km and 500m [deprecated]","description":"The MODIS Surface Reflectance products provide an estimate\nof the surface spectral reflectance as it would be measured at\nground level in the absence of atmospheric scattering or absorption.\nLow-level data are corrected for atmospheric gases and aerosols.\nMOD09GA version 6 provides bands 1-7 in a daily gridded L2G product\nin the sinusoidal projection, including 500m reflectance values\nand 1km observation and geolocation statistics.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD09GA)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2023-02-17T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD09GA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD09GA","rel":"related","title":"Run the example for MODIS/006/MOD09GA in the Earth Engine Code 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Terra Surface Reflectance Daily Global 250m [deprecated]","description":"The MODIS Surface Reflectance products provide an estimate\nof the surface spectral reflectance as it would be measured at\nground level in the absence of atmospheric scattering or absorption.\nLow-level data are corrected for atmospheric gases and aerosols.\nMOD09GQ version 6 provides bands 1 and 2 at a 250m resolution in\na daily gridded L2G product in the Sinusoidal projection, including\na QC and five observation layers. This product is meant to be used\nin conjunction with the MOD09GA where important quality and viewing\ngeometry information is stored.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD09GQ)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2023-02-17T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD09GQ","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD09GQ","rel":"related","title":"Run the example for MODIS/006/MOD09GQ in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD09GQ_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09GQ#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD09GQ.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GQ.json","rel":"latest-version","title":"MODIS/061/MOD09GQ","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MOD09GQ.json","rel":"predecessor-version","title":"MODIS/MOD09GQ","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GQ.json","rel":"successor-version","title":"MODIS/061/MOD09GQ","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD09GQ/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD09GQ/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD09Q1","title":"MOD09Q1.006 Terra Surface Reflectance 8-Day Global 250m [deprecated]","description":"The MOD09Q1 product provides an estimate of the surface\nspectral reflectance of bands 1 and 2 at 250m resolution and corrected\nfor atmospheric conditions such as gasses, aerosols, and Rayleigh\nscattering. Along with the two reflectance bands, a quality layer\nis also included. For each pixel, a value is selected from all\nthe acquisitions within the 8-day composite on the basis of high\nobservation coverage, low view angle, the absence of clouds or\ncloud shadow, and aerosol loading.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD09Q1)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD09Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD09Q1","rel":"related","title":"Run the example for MODIS/006/MOD09Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD09Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD09Q1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09Q1.json","rel":"latest-version","title":"MODIS/061/MOD09Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MOD09Q1.json","rel":"predecessor-version","title":"MODIS/MOD09Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09Q1.json","rel":"successor-version","title":"MODIS/061/MOD09Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD09Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD09Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD10A1","title":"MOD10A1.006 Terra Snow Cover Daily Global 500m [deprecated]","description":"The MOD10A1 V6 Snow Cover Daily Global 500m product\ncontains snow cover, snow albedo, fractional snow cover, and quality\nassessment (QA) data. Snow cover data are based on a snow mapping\nalgorithm that employs a Normalized Difference Snow Index (NDSI)\nand other criteria tests.\n\n[General documentation](https://doi.org/10.5067/MODIS/MOD10A1.006)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2023-02-17T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD10A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD10A1","rel":"related","title":"Run the example for MODIS/006/MOD10A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD10A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD10A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD10A1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD10A1.json","rel":"latest-version","title":"MODIS/061/MOD10A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MOD10A1.json","rel":"predecessor-version","title":"MODIS/MOD10A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD10A1.json","rel":"successor-version","title":"MODIS/061/MOD10A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD10A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD10A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD11A1","title":"MOD11A1.006 Terra Land Surface Temperature and Emissivity Daily Global 1km [deprecated]","description":"The MOD11A1 V6 product provides daily land surface temperature\n(LST) and emissivity values in a 1200 x 1200 kilometer grid. The\ntemperature value is derived from the MOD11_L2 swath product. Above\n30 degrees latitude, some pixels may have multiple observations\nwhere the criteria for clear-sky are met. When this occurs, the\npixel value is the average of all qualifying observations. Provided\nalong with both the day-time and night-time surface temperature\nbands and their quality indicator layers are MODIS bands 31 and\n32 and six observation layers.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD11A1)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2022-11-15T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD11A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD11A1","rel":"related","title":"Run the example for MODIS/006/MOD11A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD11A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD11A1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A1.json","rel":"latest-version","title":"MODIS/061/MOD11A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MOD11A1.json","rel":"predecessor-version","title":"MODIS/MOD11A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A1.json","rel":"successor-version","title":"MODIS/061/MOD11A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD11A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD11A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD11A2","title":"MOD11A2.006 Terra Land Surface Temperature and Emissivity 8-Day Global 1km [deprecated]","description":"The MOD11A2 V6 product provides an average 8-day land\nsurface temperature (LST)  in a 1200 x 1200 kilometer grid.  Each\npixel value in MOD11A2 is a simple average of all the corresponding\nMOD11A1 LST pixels collected within that 8 day period.  The 8 day\ncompositing period was chosen because twice that period is the\nexact ground track repeat period of the Terra and Aqua platforms.\nIn this product, along with both the day- and night-time surface\ntemperature bands and their quality indicator (QC) layers, are\nalso MODIS bands 31 and 32 and eight observation layers.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD11A2)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2022-11-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD11A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD11A2","rel":"related","title":"Run the example for MODIS/006/MOD11A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD11A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD11A2.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A2.json","rel":"latest-version","title":"MODIS/061/MOD11A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MOD11A2.json","rel":"predecessor-version","title":"MODIS/MOD11A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A2.json","rel":"successor-version","title":"MODIS/061/MOD11A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD11A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD11A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD13A1","title":"MOD13A1.006 Terra Vegetation Indices 16-Day Global 500m [deprecated]","description":"The MOD13A1 V6 product provides a Vegetation Index (VI)\nvalue at a per pixel basis. There are two primary vegetation layers.\nThe first is the Normalized Difference Vegetation Index (NDVI)\nwhich is referred to as the continuity index to the existing National\nOceanic and Atmospheric Administration-Advanced Very High Resolution\nRadiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer\nis the Enhanced Vegetation Index (EVI) that minimizes canopy background\nvariations and maintains sensitivity over dense vegetation conditions.\nThe EVI also uses the blue band to remove residual atmosphere contamination\ncaused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\nand EVI products are computed from atmospherically corrected bi-directional\nsurface reflectances that have been masked for water, clouds, heavy\naerosols, and cloud shadows.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD13A1)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2023-02-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD13A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD13A1","rel":"related","title":"Run the example for MODIS/006/MOD13A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD13A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD13A1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A1.json","rel":"latest-version","title":"MODIS/061/MOD13A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MOD13A1.json","rel":"predecessor-version","title":"MODIS/MOD13A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A1.json","rel":"successor-version","title":"MODIS/061/MOD13A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD13A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD13A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD13A2","title":"MOD13A2.006 Terra Vegetation Indices 16-Day Global 1km [deprecated]","description":"The MOD13A2 V6 product provides two Vegetation Indices (VI): the Normalized\nDifference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).\nThe NDVI is referred to as the continuity index to the existing National\nOceanic and Atmospheric Administration-Advanced Very High Resolution\nRadiometer (NOAA-AVHRR) derived NDVI. The EVI has improved sensitivity over\nhigh biomass regions.\n\nThe algorithm for this product chooses the best available\npixel value from all the acquisitions from the 16-day period. The criteria\nused are low clouds, low view angle, and the highest NDVI/EVI value.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD13A2)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2023-02-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD13A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD13A2","rel":"related","title":"Run the example for MODIS/006/MOD13A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD13A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD13A2.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A2.json","rel":"latest-version","title":"MODIS/061/MOD13A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A2.json","rel":"successor-version","title":"MODIS/061/MOD13A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD13A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD13A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD13Q1","title":"MOD13Q1.006 Terra Vegetation Indices 16-Day Global 250m [deprecated]","description":"The MOD13Q1 V6 product provides a Vegetation Index (VI)\nvalue at a per pixel basis. There are two primary vegetation layers.\nThe first is the Normalized Difference Vegetation Index (NDVI)\nwhich is referred to as the continuity index to the existing National\nOceanic and Atmospheric Administration-Advanced Very High Resolution\nRadiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer\nis the Enhanced Vegetation Index (EVI) that minimizes canopy background\nvariations and maintains sensitivity over dense vegetation conditions.\nThe EVI also uses the blue band to remove residual atmosphere contamination\ncaused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\nand EVI products are computed from atmospherically corrected bi-directional\nsurface reflectances that have been masked for water, clouds, heavy\naerosols, and cloud shadows.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD13A2)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2023-02-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD13Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD13Q1","rel":"related","title":"Run the example for MODIS/006/MOD13Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD13Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD13Q1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13Q1.json","rel":"latest-version","title":"MODIS/061/MOD13Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MOD13Q1.json","rel":"predecessor-version","title":"MODIS/MOD13Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13Q1.json","rel":"successor-version","title":"MODIS/061/MOD13Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD13Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD13Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD14A1","title":"MOD14A1.006: Terra Thermal Anomalies & Fire Daily Global 1km [deprecated]","description":"The MOD14A1 V6 dataset provides daily fire mask composites\nat 1km resolution derived from the MODIS 4- and 11-micrometer radiances.\nThe fire detection strategy is based on absolute detection of a\nfire (when the fire strength is sufficient to detect), and on detection\nrelative to its background (to account for variability of the surface\ntemperature and reflection by sunlight). The product distinguishes\nbetween fire, no fire and no observation. This information is used\nfor monitoring the spatial and temporal distribution of fires in\ndifferent ecosystems, detecting changes in fire distribution and\nidentifying new fire frontiers, wild fires, and changes in the\nfrequency of the fires  or their relative strength.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/88/MOD14_User_Guide_v6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/87/MOD14_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD14A1)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2023-02-17T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD14A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD14A1","rel":"related","title":"Run the example for MODIS/006/MOD14A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD14A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD14A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD14A1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD14A1.json","rel":"latest-version","title":"MODIS/061/MOD14A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD14A1.json","rel":"successor-version","title":"MODIS/061/MOD14A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD14A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD14A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD14A2","title":"MOD14A2.006: Terra Thermal Anomalies & Fire 8-Day Global 1km [deprecated]","description":"The MOD14A2 V6 dataset provides 8-day fire mask composites\nat 1km resolution. It contains the maximum value of the individual\npixel classes over the compositing period. Along with the fire\nmask, an associated quality information layer is also provided.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/88/MOD14_User_Guide_v6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/87/MOD14_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD14A2)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD14A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD14A2","rel":"related","title":"Run the example for MODIS/006/MOD14A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD14A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD14A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD14A2.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD14A2.json","rel":"latest-version","title":"MODIS/061/MOD14A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD14A2.json","rel":"successor-version","title":"MODIS/061/MOD14A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD14A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD14A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD15A2H","title":"MOD15A2H.006: Terra Leaf Area Index/FPAR 8-Day Global 500m [deprecated]","description":"The MOD15A2H V6 MODIS combined Leaf Area Index (LAI) and\nFraction of Photosynthetically Active Radiation (FPAR) product is an 8-day\ncomposite dataset at 500m resolution. The algorithm chooses the \\\"best\\\" pixel\navailable from all the acquisitions of the Terra sensor from within the 8-day period.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/624/MOD15_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/90/MOD15_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD15A2H)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD15A2H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD15A2H","rel":"related","title":"Run the example for MODIS/006/MOD15A2H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD15A2H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD15A2H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD15A2H.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD15A2H.json","rel":"latest-version","title":"MODIS/061/MOD15A2H","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD15A2H.json","rel":"successor-version","title":"MODIS/061/MOD15A2H","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD15A2H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD15A2H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD16A2","title":"MOD16A2.006: Terra Net Evapotranspiration 8-Day Global 500m [deprecated]","description":"The MOD16A2 Version 6 Evapotranspiration/Latent Heat Flux product is an\n8-day composite product produced at 500 meter pixel resolution. The\nalgorithm used for the MOD16 data product collection is based on the logic\nof the Penman-Monteith equation, which includes inputs of daily\nmeteorological reanalysis data along with MODIS remotely sensed data\nproducts such as vegetation property dynamics, albedo, and land cover.\n\nThe pixel values for the two Evapotranspiration layers (ET & PET) are the\nsum of all eight days within the composite period. The pixel values for\nthe two Latent Heat layers (LE & PLE) are the average of all eight days\nwithin the composite period. Note that the last 8-day period of each year\nis a 5 or 6-day composite period, depending on the year.\n\nAccording to\n[the \\\"Layers\\\" section of the dataset docs](https://lpdaac.usgs.gov/products/mod16a2v006),\nthe class assignment in fill values 32761 through 32767 might be inaccurate.\nThey are not included in the EE assets.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/494/MOD16_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/93/MOD16_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD16A2)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2023-02-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD16A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD16A2","rel":"related","title":"Run the example for MODIS/006/MOD16A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD16A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD16A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD16A2.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD16A2.json","rel":"latest-version","title":"MODIS/061/MOD16A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD16A2.json","rel":"successor-version","title":"MODIS/061/MOD16A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD16A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD16A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD17A2H","title":"MOD17A2H.006: Terra Gross Primary Productivity 8-Day Global 500m [deprecated]","description":"The MOD17A2H V6 Gross Primary Productivity (GPP) product\nis a cumulative 8-day composite with a 500m pixel size. The product\nis based on the radiation-use efficiency concept and can be potentially\nused as inputs to data models to calculate terrestrial energy,\ncarbon, water cycle processes, and biogeochemistry of vegetation.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/495/MOD17_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/95/MOD17_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD17A2H)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD17A2H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD17A2H","rel":"related","title":"Run the example for MODIS/006/MOD17A2H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD17A2H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A2H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD17A2H.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A2H.json","rel":"latest-version","title":"MODIS/061/MOD17A2H","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A2H.json","rel":"successor-version","title":"MODIS/061/MOD17A2H","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD17A2H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD17A2H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD17A3H","title":"MOD17A3H.006: Terra Net Primary Production Yearly Global 500m [deprecated]","description":"The MOD17A3H V6 product provides information about\nannual Net Primary Productivity (NPP) at 500m pixel resolution.\n Annual NPP is derived from the sum of the 45 8-day Net Photosynthesis\n(PSN) products (MOD17A2H) from the given year.  The PSN value\nis the difference of the GPP and the Maintenance Respiration\n(MR) (GPP-MR).\n\nThis is a NASA version of this product. Another\nversion is produced by the Numerical Terradynamic Simulation\nGroup ([NTSG](https://www.ntsg.umt.edu)), University of Montana\n(UMT).  The NTSG version corrects the problem with cloud-contaminated\nMODIS LAI-FPAR inputs to the MOD17 algorithm, but its resolution\nis 1km. It is ingested into Earth Engine as MODIS/055/MOD17A3.\n\nFor further details regarding the differences between the NTSG\nand NASA versions of this product, please consult [this document\n](https://lpdaac.usgs.gov/documents/188/MOD17_NTSG_Note.pdf).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2014-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD17A3H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD17A3H","rel":"related","title":"Run the example for MODIS/006/MOD17A3H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD17A3H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A3H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD17A3H.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD17A3HGF.json","rel":"latest-version","title":"MODIS/006/MOD17A3HGF","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD17A3HGF.json","rel":"successor-version","title":"MODIS/006/MOD17A3HGF","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD17A3H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD17A3H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD17A3HGF","title":"MOD17A3HGF.006: Terra Net Primary Production  Gap-Filled Yearly Global 500m [deprecated]","description":"The MOD17A3HGF V6 product provides information about\nannual Net Primary Productivity (NPP) at 500m pixel resolution.\n Annual NPP is derived from the sum of all 8-day Net Photosynthesis\n(PSN) products (MOD17A2H) from the given year.  The PSN value\nis the difference of the Gross Primary Productivity (GPP) and the\nMaintenance Respiration (MR) (GPP-MR).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD17A3HGF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD17A3HGF","rel":"related","title":"Run the example for MODIS/006/MOD17A3HGF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD17A3HGF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A3HGF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD17A3HGF.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A3HGF.json","rel":"latest-version","title":"MODIS/061/MOD17A3HGF","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_055_MOD17A3.json","rel":"predecessor-version","title":"MODIS/055/MOD17A3","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A3HGF.json","rel":"successor-version","title":"MODIS/061/MOD17A3HGF","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD17A3HGF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD17A3HGF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD44B","title":"MOD44B.006 Terra Vegetation Continuous Fields Yearly Global 250m [deprecated]","description":"The Terra MODIS Vegetation Continuous Fields (VCF)\nproduct is a sub-pixel-level representation of surface vegetation\ncover estimates globally. Designed to continuously represent\nEarth's terrestrial surface as a proportion of basic vegetation\ntraits, it provides a gradation of three surface cover components:\npercent tree cover, percent non-tree cover, and percent bare.\nVCF products provide a continuous, quantitative portrayal of\nland surface cover with improved spatial detail, and hence, are\nwidely used in environmental modeling and monitoring applications.\n\nGenerated yearly, the VCF product is produced using monthly\ncomposites of Terra MODIS 250 and 500 meters Land Surface Reflectance\ndata, including all seven bands, and Land Surface Temperature.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1322/MOD44B_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/113/MOD44B_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD44B)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-05T00:00:00Z","2023-07-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD44B","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MOD44B","rel":"related","title":"Run the example for MODIS/006/MOD44B in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MOD44B_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD44B#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD44B.json","rel":"latest-version","title":"MODIS/061/MOD44B","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD44B.json","rel":"successor-version","title":"MODIS/061/MOD44B","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD44B/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MOD44B/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MOD44W","title":"MOD44W.006 Terra Land Water Mask Derived From MODIS and SRTM Yearly Global 250m","description":"The MOD44W V6 land/water mask 250m product is derived using a\ndecision tree classifier trained with MODIS data and validated with the\nMOD44W V5 product. 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The product\nalso provides means, standard deviations, QA weighted statistics,\nlog-normal distributions, uncertainty estimates, and statistics\nfor fractions of pixels that satisfy some condition. Below is a\nsubset of the bands, for a complete list see the [MOD08 Band List](https://developers.google.com/earth-engine/MOD08_bands.html).\n\nDocumentation:\n\n* [User's Guide](https://modis-atmos.gsfc.nasa.gov/sites/default/files/ModAtmo/L3_ATBD_C6_C61_2019_02_20.pdf)\n\n* [Science Data Product Software Documentation](https://modis-atmos.gsfc.nasa.gov/sites/default/files/ModAtmo/L3_C61_Changes_v2.pdf)\n\n* [MYD08_M3 product description](https://modis-atmos.gsfc.nasa.gov/products/monthly)\n\n* [File specification document](https://modis-atmos.gsfc.nasa.gov/sites/default/files/ModAtmo/MOD08_M3_fs_3045.txt)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-01T00:00:00Z","2018-04-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD08_M3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD08_M3","rel":"related","title":"Run the example for MODIS/006/MYD08_M3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD08_M3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD08_M3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD08_M3.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD08_M3.json","rel":"latest-version","title":"MODIS/061/MYD08_M3","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD08_M3.json","rel":"successor-version","title":"MODIS/061/MYD08_M3","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD08_M3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD08_M3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD09A1","title":"MYD09A1.006 Aqua Surface Reflectance 8-Day Global 500m [deprecated]","description":"The MYD09A1 V6 product provides an estimate of the surface\nspectral reflectance of Aqua MODIS bands 1-7 at 500m resolution\nand corrected for atmospheric conditions such as gasses, aerosols,\nand Rayleigh scattering. 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For each pixel,\na value is selected from all the acquisitions within the 8-day\ncomposite on the basis of high observation coverage, low view angle,\nthe absence of clouds or cloud shadow, and aerosol loading.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General 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This product is meant to be used\nin conjunction with the MYD09GA where important quality and viewing\ngeometry information is stored.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD09GQ)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-25T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD09GQ","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD09GQ","rel":"related","title":"Run the example for MODIS/006/MYD09GQ in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD09GQ_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD09GQ#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD09GQ.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GQ.json","rel":"latest-version","title":"MODIS/061/MYD09GQ","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MYD09GQ.json","rel":"predecessor-version","title":"MODIS/MYD09GQ","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GQ.json","rel":"successor-version","title":"MODIS/061/MYD09GQ","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD09GQ/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD09GQ/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD09Q1","title":"MYD09Q1.006 Aqua Surface Reflectance 8-Day Global 250m [deprecated]","description":"The MYD09Q1 product provides an estimate of the surface\nspectral reflectance of bands 1 and 2 at 250m resolution and corrected\nfor atmospheric conditions such as gasses, aerosols, and Rayleigh\nscattering. Along with the two reflectance bands, a quality layer\nis also included. For each pixel, a value is selected from all\nthe acquisitions within the 8-day composite on the basis of high\nobservation coverage, low view angle, the absence of clouds or\ncloud shadow, and aerosol loading.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD09Q1)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-18T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD09Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD09Q1","rel":"related","title":"Run the example for MODIS/006/MYD09Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD09Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD09Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD09Q1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09Q1.json","rel":"latest-version","title":"MODIS/061/MYD09Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MYD09Q1.json","rel":"predecessor-version","title":"MODIS/MYD09Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09Q1.json","rel":"successor-version","title":"MODIS/061/MYD09Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD09Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD09Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD10A1","title":"MYD10A1.006 Aqua Snow Cover Daily Global 500m [deprecated]","description":"The MYD10A1 V6 Snow Cover Daily Global 500m product\ncontains snow cover, snow albedo, fractional snow cover, and quality\nassessment (QA) data. Snow cover data are based on a snow mapping\nalgorithm that employs a Normalized Difference Snow Index (NDSI)\nand other criteria tests.\n\n[General documentation](https://doi.org/10.5067/MODIS/MYD10A1.006)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-25T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD10A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD10A1","rel":"related","title":"Run the example for MODIS/006/MYD10A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD10A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD10A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD10A1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD10A1.json","rel":"latest-version","title":"MODIS/061/MYD10A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MYD10A1.json","rel":"predecessor-version","title":"MODIS/MYD10A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD10A1.json","rel":"successor-version","title":"MODIS/061/MYD10A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD10A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD10A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD11A1","title":"MYD11A1.006 Aqua Land Surface Temperature and Emissivity Daily Global 1km [deprecated]","description":"The MYD11A1 V6 product provides daily land surface temperature\n(LST) and emissivity values in a 1200 x 1200 kilometer grid. The\ntemperature value is derived from the MYD11_L2 swath product. Above\n30 degrees latitude, some pixels may have multiple observations\nwhere the criteria for clear-sky are met. When this occurs, the\npixel value is the average of all qualifying observations. Provided\nalong with both the day-time and night-time surface temperature\nbands and their quality indicator layers are MODIS bands 31 and\n32 and six observation layers.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD11A1)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-25T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD11A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD11A1","rel":"related","title":"Run the example for MODIS/006/MYD11A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD11A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD11A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD11A1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A1.json","rel":"latest-version","title":"MODIS/061/MYD11A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MYD11A1.json","rel":"predecessor-version","title":"MODIS/MYD11A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A1.json","rel":"successor-version","title":"MODIS/061/MYD11A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD11A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD11A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD11A2","title":"MYD11A2.006 Aqua Land Surface Temperature and Emissivity 8-Day Global 1km [deprecated]","description":"The MYD11A2 V6 product provides an average 8-day land\nsurface temperature (LST)  in a 1200 x 1200 kilometer grid.  Each\npixel value in MYD11A2 is a simple average of all the corresponding\nMYD11A1 LST pixels collected within that 8 day period.  The 8 day\ncompositing period was chosen because twice that period is the\nexact ground track repeat period of the Terra and Aqua platforms.\nIn this product, along with both the day- and night-time surface\ntemperature bands and their quality indicator (QC) layers, are\nalso MODIS bands 31 and 32 and eight observation layers.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD11A2)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-18T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD11A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD11A2","rel":"related","title":"Run the example for MODIS/006/MYD11A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD11A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD11A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD11A2.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A2.json","rel":"latest-version","title":"MODIS/061/MYD11A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MYD11A2.json","rel":"predecessor-version","title":"MODIS/MYD11A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A2.json","rel":"successor-version","title":"MODIS/061/MYD11A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD11A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD11A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD13A1","title":"MYD13A1.006 Aqua Vegetation Indices 16-Day Global 500m [deprecated]","description":"The MYD13A1 V6 product provides a Vegetation Index (VI)\nvalue at a per pixel basis. There are two primary vegetation layers.\nThe first is the Normalized Difference Vegetation Index (NDVI)\nwhich is referred to as the continuity index to the existing National\nOceanic and Atmospheric Administration-Advanced Very High Resolution\nRadiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer\nis the Enhanced Vegetation Index (EVI) that minimizes canopy background\nvariations and maintains sensitivity over dense vegetation conditions.\nThe EVI also uses the blue band to remove residual atmosphere contamination\ncaused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\nand EVI products are computed from atmospherically corrected bi-directional\nsurface reflectances that have been masked for water, clouds, heavy\naerosols, and cloud shadows.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD13A1)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD13A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD13A1","rel":"related","title":"Run the example for MODIS/006/MYD13A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD13A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD13A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD13A1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13A1.json","rel":"latest-version","title":"MODIS/061/MYD13A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MYD13A1.json","rel":"predecessor-version","title":"MODIS/MYD13A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13A1.json","rel":"successor-version","title":"MODIS/061/MYD13A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD13A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD13A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD13A2","title":"MYD13A2.006 Aqua Vegetation Indices 16-Day Global 1km [deprecated]","description":"The MYD13A2 V6 product provides two Vegetation Indices (VI): the Normalized\nDifference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).\nThe NDVI is referred to as the continuity index to the existing National\nOceanic and Atmospheric Administration-Advanced Very High Resolution\nRadiometer (NOAA-AVHRR) derived NDVI. The EVI has improved sensitivity over\nhigh biomass regions.\n\nThe algorithm for this product chooses the best available\npixel value from all the acquisitions from the 16-day period. The criteria\nused are low clouds, low view angle, and the highest NDVI/EVI value.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD13A2)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD13A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD13A2","rel":"related","title":"Run the example for MODIS/006/MYD13A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD13A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD13A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD13A2.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13A2.json","rel":"latest-version","title":"MODIS/061/MYD13A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13A2.json","rel":"successor-version","title":"MODIS/061/MYD13A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD13A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD13A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD13Q1","title":"MYD13Q1.006 Aqua Vegetation Indices 16-Day Global 250m [deprecated]","description":"The MYD13Q1 V6 product provides a Vegetation Index (VI)\nvalue at a per pixel basis. There are two primary vegetation layers.\nThe first is the Normalized Difference Vegetation Index (NDVI)\nwhich is referred to as the continuity index to the existing National\nOceanic and Atmospheric Administration-Advanced Very High Resolution\nRadiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer\nis the Enhanced Vegetation Index (EVI) that minimizes canopy background\nvariations and maintains sensitivity over dense vegetation conditions.\nThe EVI also uses the blue band to remove residual atmosphere contamination\ncaused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\nand EVI products are computed from atmospherically corrected bi-directional\nsurface reflectances that have been masked for water, clouds, heavy\naerosols, and cloud shadows.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD13A2)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD13Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD13Q1","rel":"related","title":"Run the example for MODIS/006/MYD13Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD13Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD13Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD13Q1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13Q1.json","rel":"latest-version","title":"MODIS/061/MYD13Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MYD13Q1.json","rel":"predecessor-version","title":"MODIS/MYD13Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13Q1.json","rel":"successor-version","title":"MODIS/061/MYD13Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD13Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD13Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD14A1","title":"MYD14A1.006: Aqua Thermal Anomalies & Fire Daily Global 1km [deprecated]","description":"The MYD14A1 V6 dataset provides daily fire mask composites\nat 1km resolution derived from the MODIS 4- and 11-micrometer radiances.\nThe fire detection strategy is based on absolute detection of a\nfire (when the fire strength is sufficient to detect), and on detection\nrelative to its background (to account for variability of the surface\ntemperature and reflection by sunlight). The product distinguishes\nbetween fire, no fire and no observation. This information is used\nfor monitoring the spatial and temporal distribution of fires in\ndifferent ecosystems, detecting changes in fire distribution and\nidentifying new fire frontiers, wild fires, and changes in the\nfrequency of the fires  or their relative strength.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/88/MOD14_User_Guide_v6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/87/MOD14_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD14A1)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-25T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD14A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD14A1","rel":"related","title":"Run the example for MODIS/006/MYD14A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD14A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD14A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD14A1.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD14A1.json","rel":"latest-version","title":"MODIS/061/MYD14A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD14A1.json","rel":"successor-version","title":"MODIS/061/MYD14A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD14A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD14A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD14A2","title":"MYD14A2.006: Aqua Thermal Anomalies & Fire 8-Day Global 1km [deprecated]","description":"The MYD14A2 V6 dataset provides 8-day fire mask composites\nat 1km resolution. It contains the maximum value of the individual\npixel classes over the compositing period. Along with the fire\nmask, an associated quality information layer is also provided.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/88/MOD14_User_Guide_v6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/87/MOD14_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD14A2)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-18T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD14A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD14A2","rel":"related","title":"Run the example for MODIS/006/MYD14A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD14A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD14A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD14A2.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD14A2.json","rel":"latest-version","title":"MODIS/061/MYD14A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD14A2.json","rel":"successor-version","title":"MODIS/061/MYD14A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD14A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD14A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD15A2H","title":"MYD15A2H.006: Aqua Leaf Area Index/FPAR 8-Day Global 500m [deprecated]","description":"The MYD15A2H V6 MODIS combined Leaf Area Index (LAI) and\nFraction of Photosynthetically Active Radiation (FPAR) product is an 8-day\ncomposite dataset at 500m resolution. The algorithm chooses the \\\"best\\\" pixel\navailable from all the acquisitions of the Aqua sensor from within the 8-day period.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/624/MOD15_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/90/MOD15_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD15A2H)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-18T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD15A2H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD15A2H","rel":"related","title":"Run the example for MODIS/006/MYD15A2H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD15A2H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD15A2H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD15A2H.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD15A2H.json","rel":"latest-version","title":"MODIS/061/MYD15A2H","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD15A2H.json","rel":"successor-version","title":"MODIS/061/MYD15A2H","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD15A2H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD15A2H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD17A2H","title":"MYD17A2H.006: Aqua Gross Primary Productivity 8-Day Global 500m [deprecated]","description":"The MYD17A2H V6 Gross Primary Productivity (GPP) product\nis a cumulative 8-day composite with a 500m resolution. The product\nis based on the radiation-use efficiency concept and can be potentially\nused as inputs to data models to calculate terrestrial energy,\ncarbon, water cycle processes, and biogeochemistry of vegetation.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/495/MOD17_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/95/MOD17_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD17A2H)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-18T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD17A2H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD17A2H","rel":"related","title":"Run the example for MODIS/006/MYD17A2H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD17A2H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD17A2H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD17A2H.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD17A2H.json","rel":"latest-version","title":"MODIS/061/MYD17A2H","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD17A2H.json","rel":"successor-version","title":"MODIS/061/MYD17A2H","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD17A2H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD17A2H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD17A3H","title":"MYD17A3H.006: Aqua Net Primary Production Yearly Global 500m [deprecated]","description":"The MYD17A3H V6 product provides information about\nannual Net Primary Productivity (NPP) at 500m pixel resolution.\n Annual NPP is derived from the sum of the 45 8-day Net Photosynthesis\n(PSN) products (MYD17A2H) from the given year.  The PSN value\nis the difference of the GPP and the Maintenance Respiration\n(MR) (GPP-MR).\n\nThis is a NASA version of this product. Another\nversion is produced by the Numerical Terradynamic Simulation\nGroup ([NTSG](https://www.ntsg.umt.edu)), University of Montana\n(UMT).  The NTSG version corrects the problem with cloud-contaminated\nMODIS LAI-FPAR inputs to the MOD17 algorithm, but its resolution\nis 1km. It is ingested into Earth Engine as MODIS/055/MOD17A3.\n\nFor further details regarding the differences between the NTSG\nand NASA versions of this product, please consult [this document\n](https://lpdaac.usgs.gov/documents/188/MOD17_NTSG_Note.pdf).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-01-01T00:00:00Z","2014-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD17A3H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD17A3H","rel":"related","title":"Run the example for MODIS/006/MYD17A3H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD17A3H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD17A3H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD17A3H.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD17A3HGF.json","rel":"latest-version","title":"MODIS/006/MYD17A3HGF","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD17A3HGF.json","rel":"successor-version","title":"MODIS/006/MYD17A3HGF","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD17A3H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD17A3H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYD17A3HGF","title":"MYD17A3HGF.006: Aqua Net Primary Production  Gap-Filled Yearly Global 500m [deprecated]","description":"Version 6 MODIS data products. Users are advised to transition to the\nimproved Version 6.1.\n\nThe MYD17A3HGF V6 product provides information about\nannual Net Primary Productivity (NPP) at 500m pixel resolution.\n Annual NPP is derived from the sum of all 8-day Net Photosynthesis\n(PSN) products (MYD17A2H) from the given year.  The PSN value\nis the difference of the Gross Primary Productivity (GPP) and the\nMaintenance Respiration (MR) (GPP-MR).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD17A3HGF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYD17A3HGF","rel":"related","title":"Run the example for MODIS/006/MYD17A3HGF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYD17A3HGF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD17A3HGF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD17A3HGF.006","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD17A3HGF.json","rel":"latest-version","title":"MODIS/061/MYD17A3HGF","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD17A3HGF.json","rel":"successor-version","title":"MODIS/061/MYD17A3HGF","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD17A3HGF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYD17A3HGF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/006/MYDOCGA","title":"MYDOCGA.006 Aqua Ocean Reflectance Daily Global 1km","description":"The MYDOCGA V6 ocean reflectance product consists of\n1 kilometer reflectance data from Aqua MODIS bands 8-16. The product\nis referred to as ocean reflectance, because bands 8-16 are used\nprimarily to produce ocean products, but this is not an ocean product\nas the tiles produced are land tiles.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYDOCGA)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-25T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYDOCGA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_006_MYDOCGA","rel":"related","title":"Run the example for MODIS/006/MYDOCGA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_006_MYDOCGA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYDOCGA#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYDOCGA.006","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYDOCGA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/006/MYDOCGA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/055/MOD17A3","title":"MOD17A3.055: Terra Net Primary Production Yearly Global 1km [deprecated]","description":"The MOD17A3 V055 product provides information about\nannual Gross and Net Primary Productivity (NPP) at 1km pixel resolution.\nNet Primary Productivity (NPP) defines the rate at which all plants\nin an ecosystem produce net useful chemical energy.  In other words,\nNPP is equal to the difference between the rate at which plants\nin an ecosystem produce useful chemical energy (or GPP), and the\nrate at which they expend some of that energy for respiration.\n\nThe version 055 of the NPP product is produced by the Numerical\nTerradynamic Simulation Group ([NTSG](https://www.ntsg.umt.edu)),\nUniversity of Montana (UMT).  It corrects the problem with cloud-contaminated\nMODIS LAI-FPAR inputs to the MOD17 algorithm. The original, uncorrected\ndatasets constitute the version 4 NPP products. For further details\nregarding the differences between the NTSG and NASA versions of\nthis product, please consult [this document](https://lpdaac.usgs.gov/documents/188/MOD17_NTSG_Note.pdf)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2014-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/055/MOD17A3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_055_MOD17A3","rel":"related","title":"Run the example for MODIS/055/MOD17A3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_055_MOD17A3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_055_MOD17A3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A3HGF.json","rel":"latest-version","title":"MODIS/061/MOD17A3HGF","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD17A3HGF.json","rel":"successor-version","title":"MODIS/006/MOD17A3HGF","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/055/MOD17A3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/055/MOD17A3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD12C1","title":"MCD12C1.061 MODIS Land Cover Type Yearly Global 0.05 Deg CMG","description":"The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer\n(MODIS) Land Cover Climate Modeling Grid (CMG) (MCD12C1) Version 6.1 data\nproduct provides a spatially aggregated and reprojected version of the tiled\nMCD12Q1 Version 6.1 data product. Maps of the International\nGeosphere-Biosphere Programme (IGBP), University of Maryland (UMD), and Leaf\nArea Index (LAI) classification schemes are provided at yearly intervals at\n0.05 degree (5,600 meter) spatial resolution for the entire globe from 2001\nto 2022. Additionally, sub-pixel proportions of each land cover class in\neach 0.05 degree pixel is provided along with the aggregated quality\nassessment information for each of the three land classification schemes.\n\nProvided in each MCD12C1 Version 6.1 Hierarchical Data Format 4 (HDF4) file\nare layers for Majority Land Cover Type 1-3, Majority Land Cover Type 1-3\nAssessment, and Land Cover Type 1-3 Percent for each class.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1409/MCD12_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/86/MCD12_ATBD.pdf)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD12C1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD12C1","rel":"related","title":"Run the example for MODIS/061/MCD12C1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD12C1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD12C1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD12C1.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD12C1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD12C1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD12Q1","title":"MCD12Q1.061 MODIS Land Cover Type Yearly Global 500m","description":"The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer\n(MODIS) Land Cover Type (MCD12Q1) Version 6.1 data product provides global\nland cover types at yearly intervals. The MCD12Q1 Version 6.1\ndata product is derived using supervised classifications of MODIS Terra and\nAqua reflectance data. Land cover types are derived from the International\nGeosphere-Biosphere Programme (IGBP), University of Maryland (UMD), Leaf\nArea Index (LAI), BIOME-Biogeochemical Cycles (BGC), and Plant Functional\nTypes (PFT) classification schemes. The supervised classifications then\nunderwent additional post-processing that incorporate prior knowledge and\nancillary information to further refine specific classes. Additional land\ncover property assessment layers are provided by the Food and Agriculture\nOrganization (FAO) Land Cover Classification System (LCCS) for land cover,\nland use, and surface hydrology.\n\nLayers for Land Cover Type 1-5, Land Cover Property 1-3, Land Cover Property\nAssessment 1-3, Land Cover Quality Control (QC), and a Land Water Mask are\nalso provided.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1409/MCD12_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/86/MCD12_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MCD12Q1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD12Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD12Q1","rel":"related","title":"Run the example for MODIS/061/MCD12Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD12Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD12Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD12Q1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD12Q1.json","rel":"latest-version","title":"MODIS/061/MCD12Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MCD12Q1.json","rel":"predecessor-version","title":"MODIS/006/MCD12Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD12Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD12Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD12Q2","title":"MCD12Q2.006 Land Cover Dynamics Yearly Global 500m","description":"The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer\n(MODIS) Land Cover Dynamics (MCD12Q2) Version 6.1 data product provides\nglobal land surface phenology metrics at yearly intervals. The MCD12Q2\nVersion 6.1 data product is derived from time series of the 2-band Enhanced\nVegetation Index (EVI2) calculated from MODIS Nadir Bidirectional\nReflectance Distribution Function (BRDF)-Adjusted Reflectance(NBAR).\nVegetation phenology metrics at 500 meter spatial resolution are identified\nfor up to two detected growing cycles per year. For pixels with more than\ntwo valid vegetation cycles, the data represent the two cycles with the\nlargest NBAR-EVI2 amplitudes.\n\nEach asset contains bands are layers for the total number of vegetation\ncycles detected for the product year, the onset of greenness, greenup\nmidpoint, maturity, peak greenness, senescence, greendown midpoint,\ndormancy, EVI2 minimum, EVI2 amplitude, integrated EVI2 over a vegetation\ncycle, as well as overall and phenology metric-specific quality information.\n\nFor areas where the NBAR-EVI2 values are missing due to cloud cover or other\nreasons, the data gaps are filled with good quality NBAR-EVI2 values from\nthe year directly preceding or following the product year.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD12Q2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD12Q2","rel":"related","title":"Run the example for MODIS/061/MCD12Q2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD12Q2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD12Q2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD12Q2.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD12Q2.json","rel":"latest-version","title":"MODIS/061/MCD12Q2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MCD12Q2.json","rel":"predecessor-version","title":"MODIS/006/MCD12Q2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD12Q2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD12Q2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD15A3H","title":"MCD15A3H.061 MODIS Leaf Area Index/FPAR 4-Day Global 500m","description":"The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 4-day period.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/624/MOD15_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/90/MOD15_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MCD15A3H)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-11-05T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD15A3H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD15A3H","rel":"related","title":"Run the example for MODIS/061/MCD15A3H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD15A3H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD15A3H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD15A3H.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD15A3H.json","rel":"latest-version","title":"MODIS/061/MCD15A3H","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MCD15A3H.json","rel":"predecessor-version","title":"MODIS/006/MCD15A3H","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD15A3H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD15A3H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD18A1","title":"MCD18A1.061 Surface Radiation Daily/3-Hour [deprecated]","description":"The MCD18A1 Version 6.1\nis a Moderate Resolution Imaging Spectroradiometer\n(MODIS) Terra and Aqua combined Downward Shortwave Radiation (DSR) gridded\nLevel 3 product produced daily at 1 kilometer pixel resolution with\nestimates of DSR every 3 hours. DSR is incident solar radiation over land\nsurfaces in the shortwave spectrum (300-4,000 nanometers) and is an\nimportant variable in land-surface models that address a variety of\nscientific and application issues. The MCD18 products are based on a\nprototyping algorithm that uses multi-temporal signatures of MODIS data to\nderive surface reflectance and then calculate incident DSR using the look-up\ntable (LUT) approach. The LUTs consider different types of loadings of\naerosols and clouds at a variety of illumination/viewing geometry. Global\nDSR products are generated from MODIS and geostationary satellite data.\nAdditional details regarding the methodology used to create the data are\navailable in the\n[Algorithm Theoretical Basis Document](https://lpdaac.usgs.gov/documents/106/MCD18_ATBD.pdf).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2024-06-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD18A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD18A1","rel":"related","title":"Run the example for MODIS/061/MCD18A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD18A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD18A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD18A1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_062_MCD18A1.json","rel":"latest-version","title":"MODIS/062/MCD18A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_062_MCD18A1.json","rel":"successor-version","title":"MODIS/062/MCD18A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD18A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD18A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD18C2","title":"MCD18C2.061 Photosynthetically Active Radiation Daily 3-Hour [deprecated]","description":"The MCD18C2 Version 6.1\nis a Moderate Resolution Imaging Spectroradiometer\n(MODIS) Terra and Aqua combined Photosynthetically Active Radiation (PAR)\ngridded Level 3 product produced daily at 0.05 degree (5,600 meters at the\nequator) resolution with estimates of PAR every 3 hours. PAR is incident\nsolar radiation in the visible spectrum (400-700 nanometers) and is an\nimportant variable in land-surface models that address a variety of\nscientific and application issues. The MCD18 products are based on a\nprototyping algorithm that uses multi-temporal signatures of MODIS data to\nderive surface reflectance and then calculate incident PAR using the look-up\ntable (LUT) approach. The LUTs consider different types of loadings of\naerosols and clouds at a variety of illumination/viewing geometry. Global\nPAR products are generated from MODIS and geostationary satellite data.\nAdditional details regarding the methodology used to create the data are\navailable in the\n[Algorithm Theoretical Basis Document](https://lpdaac.usgs.gov/documents/106/MCD18_ATBD.pdf)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-02-24T00:00:00Z","2024-06-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD18C2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD18C2","rel":"related","title":"Run the example for MODIS/061/MCD18C2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD18C2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD18C2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD18C2.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_062_MCD18C2.json","rel":"latest-version","title":"MODIS/062/MCD18C2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_062_MCD18C2.json","rel":"successor-version","title":"MODIS/062/MCD18C2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD18C2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD18C2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD19A1_GRANULES","title":"MCD19A1.061: Land Surface BRF Daily L2G Global 500m and 1km","description":"The MCD19A1 Version 6.1 data product is a Moderate Resolution Imaging\nSpectroradiometer (MODIS) Terra and Aqua combined Land Surface Bidirectional\nReflectance Factor (BRF) gridded Level 2 product produced daily at 500 meter\nand 1 kilometer resolution. For more information see the\n[MAIAC user guide](https://lpdaac.usgs.gov/documents/1500/MCD19_User_Guide_V61.pdf).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-12-21T00:00:00Z","2026-11-05T23:55:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD19A1_GRANULES","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD19A1_GRANULES","rel":"related","title":"Run the example for MODIS/061/MCD19A1_GRANULES in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD19A1_GRANULES_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A1_GRANULES#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD19A1.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD19A1_GRANULES/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD19A1_GRANULES/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD19A2_GRANULES","title":"MCD19A2.061: Terra & Aqua MAIAC Land Aerosol Optical Depth Daily 1km","description":"The MCD19A2 V6.1 data product is a MODIS Terra and Aqua combined Multi-angle\nImplementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth\n(AOD) gridded Level 2 product produced daily at 1 km resolution. For more\ninformation see the [MAIAC user guide](https://lpdaac.usgs.gov/documents/1500/MCD19_User_Guide_V61.pdf).\n\nNOTE: This product has been released with the caveat that the reprocessing\nfor the full mission is expected to continue through summer 2023.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-05T23:55:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD19A2_GRANULES","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD19A2_GRANULES","rel":"related","title":"Run the example for MODIS/061/MCD19A2_GRANULES in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD19A2_GRANULES_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A2_GRANULES#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD19A2.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A2_GRANULES.json","rel":"latest-version","title":"MODIS/061/MCD19A2_GRANULES","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MCD19A2_GRANULES.json","rel":"predecessor-version","title":"MODIS/006/MCD19A2_GRANULES","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD19A2_GRANULES/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD19A2_GRANULES/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD43A1","title":"MCD43A1.061 MODIS BRDF-Albedo Model Parameters Daily 500m","description":"The MCD43A1 V6.1 Bidirectional Reflectance Distribution\nFunction and Albedo (BRDF/Albedo) Model Parameters dataset is\na 500 meter daily 16-day product. The Julian date represents\nthe 9th day of the 16-day retrieval period, and consequently\nthe observations are weighted to estimate the BRDF/Albedo for\nthat day. The MCD43A1 algorithm, as is with all combined products,\nchooses the best representative pixel from a pool that includes\nall the acquisitions from both the Terra and Aqua sensors from\nthe retrieval period.\n\nThe MCD43A1 provides the three model\nweighting parameters (isotropic, volumetric, and geometric) for\neach of the MODIS bands 1 through 7 and the visible (vis), near\ninfrared (nir), and shortwave bands used to derive the Albedo\nand BRDF products (MCD43A3 and MCD43A4). The Mandatory Quality\nlayers for each of the 10 bands are supplied as well.\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43a1-brdfalbedo-model-parameters-product/)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/97/MCD43_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MCD43A1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD43A1","rel":"related","title":"Run the example for MODIS/061/MCD43A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD43A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD43A1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A1.json","rel":"latest-version","title":"MODIS/061/MCD43A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MCD43A1.json","rel":"predecessor-version","title":"MODIS/006/MCD43A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD43A2","title":"MCD43A2.061 MODIS BRDF-Albedo Quality Daily 500m","description":"The MCD43A2 V6.1 Bidirectional Reflectance Distribution\nFunction and Albedo (BRDF/Albedo) Quality dataset is a 500 meter\ndaily 16-day product. It contains all the quality information\nfor the corresponding 16-day MCD43A3 Albedo and the MCD43A4 Nadir-BRDF\n(NBAR) products.\n\nThe MCD43A2 contains individual band quality\nand observation information for the MODIS land bands\n1-7, along with the overall BRDF/Albedo quality information.\n\nSee\n[dataset user guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006/mcd43a2_albedo_product)\nfor more information.\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/97/MCD43_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MCD43A2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD43A2","rel":"related","title":"Run the example for MODIS/061/MCD43A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD43A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD43A2.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A2.json","rel":"latest-version","title":"MODIS/061/MCD43A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MCD43A2.json","rel":"predecessor-version","title":"MODIS/006/MCD43A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD43A3","title":"MCD43A3.061 MODIS Albedo Daily 500m","description":"The MCD43A3 V6.1 Albedo Model dataset is a daily 16-day\nproduct. It provides both directional hemispherical reflectance\n(black sky albedo) and bihemispherical reflectance (white sky albedo)\nfor each of the MODIS surface reflectance bands (band 1 through\nband 7) as well as 3 broad spectrum bands (visible, near infrared,\nand shortwave).  Each 500m/pixel daily image is generated using\n16 days of data, centered on the given day.  A quality band is\nalso provided for each of the 10 albedo bands.\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/97/MCD43_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MCD43A3)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43A3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD43A3","rel":"related","title":"Run the example for MODIS/061/MCD43A3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD43A3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD43A3.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A3.json","rel":"latest-version","title":"MODIS/061/MCD43A3","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MCD43A3.json","rel":"predecessor-version","title":"MODIS/006/MCD43A3","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43A3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43A3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD43A4","title":"MCD43A4.061 MODIS Nadir BRDF-Adjusted Reflectance Daily 500m","description":"The MCD43A4 V6.1 Nadir Bidirectional Reflectance Distribution\nFunction Adjusted Reflectance (NBAR) product provides 500 meter\nreflectance data of the MODIS \\\"land\\\" bands 1-7. These are adjusted\nusing a bidirectional reflectance distribution function to model\nthe values as if they were collected from a nadir view.  The data\nare produced daily based on a 16-day retrieval period, with the\nimage's date occurring on the 9th day. This product combines data\nfrom both the Terra and Aqua spacecrafts, choosing the best representative\npixel from the 16-day period.\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/97/MCD43_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MCD43A4)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43A4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD43A4","rel":"related","title":"Run the example for MODIS/061/MCD43A4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD43A4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD43A4.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A4.json","rel":"latest-version","title":"MODIS/061/MCD43A4","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MCD43A4.json","rel":"predecessor-version","title":"MODIS/006/MCD43A4","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43A4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43A4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD43C3","title":"MCD43C3.061 BRDF/Albedo Daily L3 0.05 Deg CMG","description":"The MCD43C3 Version 6.1\nBidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Albedo\ndataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05\ndegree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are\ntemporally weighted to the ninth day of the retrieval period which is reflected\nin the Julian date in the file name. This CMG product covers the entire globe\nfor use in climate simulation models.\n\nMCD43C3 provides black-sky albedo (directional hemispherical reflectance)\nand white-sky albedo (bihemispherical reflectance) at local solar noon.\nBlack-sky albedo and white-sky albedo values are available as a separate layer\nfor MODIS spectral bands 1 through 7 as well as the visible, near infrared\n(NIR), and shortwave bands. Along with the 20 albedo layers are ancillary\nlayers for quality, local solar noon, percent finer resolution inputs, snow\ncover, and uncertainty.\n\nSee\n[dataset user guide](https://www.umb.edu/spectralmass/terra_aqua_modis/v006/mcd43c3)\nfor more information.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43C3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD43C3","rel":"related","title":"Run the example for MODIS/061/MCD43C3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD43C3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43C3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD43C3.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43C3.json","rel":"latest-version","title":"MODIS/061/MCD43C3","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MCD43C3.json","rel":"predecessor-version","title":"MODIS/006/MCD43C3","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43C3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD43C3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MCD64A1","title":"MCD64A1.061 MODIS Burned Area Monthly Global 500m","description":"The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is\na monthly, global gridded 500m product containing per-pixel burned-area\nand quality information. The MCD64A1 burned-area mapping approach employs\n500m MODIS Surface Reflectance imagery coupled with 1km MODIS active\nfire observations. The algorithm uses a burn sensitive vegetation index (VI)\nto create dynamic thresholds that are applied to the composite data.\nThe VI is derived from MODIS shortwave infrared atmospherically corrected\nsurface reflectance bands 5 and 7 with a measure of temporal texture.\nThe algorithm identifies the date of burn for the 500m grid cells within\neach individual MODIS tile. The date is encoded in a single data layer as\nthe ordinal day of the calendar year on which the burn occurred, with\nvalues assigned to unburned land pixels and additional special values\nreserved for missing data and water grid cells.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1006/MCD64_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/115/MCD64_ATBD_V6.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MCD64A1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-11-01T00:00:00Z","2026-08-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD64A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MCD64A1","rel":"related","title":"Run the example for MODIS/061/MCD64A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MCD64A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD64A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD64A1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD64A1.json","rel":"latest-version","title":"MODIS/061/MCD64A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MCD64A1.json","rel":"predecessor-version","title":"MODIS/006/MCD64A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD64A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MCD64A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD08_M3","title":"MOD08_M3.061 Terra Atmosphere Monthly Global Product","description":"MOD08_M3 V6.1 is an atmosphere global product that contains\nmonthly 1 x 1 degree grid average values of atmospheric parameters.\nThese parameters are related to atmospheric aerosol particle properties,\ntotal ozone burden, atmospheric water vapor, cloud optical and\nphysical properties, and atmospheric stability indices. The product\nalso provides means, standard deviations, QA weighted statistics,\nlog-normal distributions, uncertainty estimates, and statistics\nfor fractions of pixels that satisfy some condition. Below is a\nsubset of the bands, for a complete list see the [MOD08 Band List](https://developers.google.com/earth-engine/MOD08_bands.html).\n\nDocumentation:\n\n* [User's Guide](https://modis-atmos.gsfc.nasa.gov/sites/default/files/ModAtmo/L3_ATBD_C6_C61_2019_02_20.pdf)\n\n* [Science Data Product Software Documentation](https://modis-atmos.gsfc.nasa.gov/sites/default/files/ModAtmo/L3_C61_Changes_v2.pdf)\n\n* [MYD08_M3 product description](https://modis-atmos.gsfc.nasa.gov/products/monthly)\n\n* [File specification document](https://modis-atmos.gsfc.nasa.gov/sites/default/files/ModAtmo/MOD08_M3_fs_3045.txt)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD08_M3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD08_M3","rel":"related","title":"Run the example for MODIS/061/MOD08_M3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD08_M3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD08_M3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD08_M3.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD08_M3.json","rel":"latest-version","title":"MODIS/061/MOD08_M3","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD08_M3.json","rel":"predecessor-version","title":"MODIS/006/MOD08_M3","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD08_M3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD08_M3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD09A1","title":"MOD09A1.061 Terra Surface Reflectance 8-Day Global 500m","description":"The MOD09A1 V6.1 product provides an estimate of the surface\nspectral reflectance of Terra MODIS bands 1-7 at 500m resolution\nand corrected for atmospheric conditions such as gasses, aerosols,\nand Rayleigh scattering. Along with the seven reflectance bands\nis a quality layer and four observation bands. For each pixel,\na value is selected from all the acquisitions within the 8-day\ncomposite on the basis of high observation coverage, low view angle,\nthe absence of clouds or cloud shadow, and aerosol loading.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD09A1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD09A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD09A1","rel":"related","title":"Run the example for MODIS/061/MOD09A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD09A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD09A1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09A1.json","rel":"latest-version","title":"MODIS/061/MOD09A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD09A1.json","rel":"predecessor-version","title":"MODIS/006/MOD09A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD09A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD09A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD09CMG","title":"MOD09CMG.061 Terra Surface Reflectance Daily L3 Global 0.05 Deg CMG","description":"The MOD09CMG Version 6.1 product provides an estimate of the surface\nspectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer\n(MODIS) Bands 1 through 7, resampled to 5600 meter pixel resolution and\ncorrected for atmospheric conditions such as gasses, aerosols, and Rayleigh\nscattering. The MOD09CMG data product provides 25 layers including MODIS\nbands 1 through 7; Brightness Temperature data from thermal bands 20, 21,\n31, and 32; along with Quality Assurance (QA) and observation bands.\nThis product is based on a Climate Modeling Grid (CMG) for use in climate\nsimulation models.\n\nNote that scale factors and offsets described in the MODIS documentation\nfor MOD09CMG bands are already applied to assets ingested into Earth\nEngine.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/925/MOD09_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD09CMG)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD09CMG","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD09CMG","rel":"related","title":"Run the example for MODIS/061/MOD09CMG in the Earth Engine Code 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measured at\nground level in the absence of atmospheric scattering or absorption.\nLow-level data are corrected for atmospheric gases and aerosols.\nMOD09GA version 6.1 provides bands 1-7 in a daily gridded L2G product\nin the sinusoidal projection, including 500m reflectance values\nand 1km observation and geolocation statistics.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD09GA)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD09GA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD09GA","rel":"related","title":"Run the example for MODIS/061/MOD09GA in the Earth Engine Code 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This product is meant to be used\nin conjunction with the MOD09GA where important quality and viewing\ngeometry information is stored.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD09GQ)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD09GQ","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD09GQ","rel":"related","title":"Run the example for MODIS/061/MOD09GQ in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD09GQ_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GQ#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD09GQ.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GQ.json","rel":"latest-version","title":"MODIS/061/MOD09GQ","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD09GQ.json","rel":"predecessor-version","title":"MODIS/006/MOD09GQ","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD09GQ/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD09GQ/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD09Q1","title":"MOD09Q1.061 Terra Surface Reflectance 8-Day Global 250m","description":"The MOD09Q1 product provides an estimate of the surface\nspectral reflectance of bands 1 and 2 at 250m resolution and corrected\nfor atmospheric conditions such as gasses, aerosols, and Rayleigh\nscattering. Along with the two reflectance bands, a quality layer\nis also included. For each pixel, a value is selected from all\nthe acquisitions within the 8-day composite on the basis of high\nobservation coverage, low view angle, the absence of clouds or\ncloud shadow, and aerosol loading.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD09Q1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2026-10-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD09Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD09Q1","rel":"related","title":"Run the example for MODIS/061/MOD09Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD09Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD09Q1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09Q1.json","rel":"latest-version","title":"MODIS/061/MOD09Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD09Q1.json","rel":"predecessor-version","title":"MODIS/006/MOD09Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD09Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD09Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD10A1","title":"MOD10A1.061 Terra Snow Cover Daily Global 500m","description":"The MOD10A1 V6.1 Snow Cover Daily Global 500m product\ncontains snow cover, snow albedo, fractional snow cover, and quality\nassessment (QA) data. Snow cover data are based on a snow mapping\nalgorithm that employs a Normalized Difference Snow Index (NDSI)\nand other criteria tests.\n\n[General documentation](https://doi.org/10.5067/MODIS/MOD10A1.061)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD10A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD10A1","rel":"related","title":"Run the example for MODIS/061/MOD10A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD10A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD10A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD10A1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD10A1.json","rel":"latest-version","title":"MODIS/061/MOD10A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD10A1.json","rel":"predecessor-version","title":"MODIS/006/MOD10A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD10A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD10A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD10A2","title":"MOD10A2.061 Terra Snow Cover 8-Day L3 Global 500m","description":"MOD10A2 is a snow cover dataset from the MODIS on the Terra satellite.\nThe dataset reports the maximum snow cover extent during an eight-day\nperiod at the resolution of 500 m.\n\nAn eight-day compositing period was chosen because that is the exact ground\ntrack repeat period of the Terra and Aqua platforms. Snow cover over eight\ndays is mapped as maximum snow extent in one SDS and as a chronology of\nobservations in the other SDS. Eight-day periods begin on the first day of\nthe year and extend into the next year. The product can be produced with\ntwo to eight days of input. There may not always be eight days of input,\nbecause of various reasons, so the user should check the attributes to\ndetermine on what days observations were obtained.\n\n* [General documentation](https://doi.org/10.5067/MODIS/MOD10A2.061)\n* [User's Guide]( https://modis-snow-ice.gsfc.nasa.gov/uploads/snow_user_guide_C6.1_final_revised_april.pdf),\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD10A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD10A2","rel":"related","title":"Run the example for MODIS/061/MOD10A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD10A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD10A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD10A1.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD10A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD10A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD11A1","title":"MOD11A1.061 Terra Land Surface Temperature and Emissivity Daily Global 1km","description":"The MOD11A1 V6.1 product provides daily land surface temperature\n(LST) and emissivity values in a 1200 x 1200 kilometer grid. The\ntemperature value is derived from the MOD11_L2 swath product. Above\n30 degrees latitude, some pixels may have multiple observations\nwhere the criteria for clear-sky are met. When this occurs, the\npixel value is the average of all qualifying observations. Provided\nalong with both the day-time and night-time surface temperature\nbands and their quality indicator layers are MODIS bands 31 and\n32 and six observation layers.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD11A1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD11A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD11A1","rel":"related","title":"Run the example for MODIS/061/MOD11A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD11A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD11A1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A1.json","rel":"latest-version","title":"MODIS/061/MOD11A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD11A1.json","rel":"predecessor-version","title":"MODIS/006/MOD11A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD11A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD11A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD11A2","title":"MOD11A2.061 Terra Land Surface Temperature and Emissivity 8-Day Global 1km","description":"The MOD11A2 V6.1 product provides an average 8-day land surface temperature\n(LST)  in a 1200 x 1200 kilometer grid. Each pixel value in MOD11A2 is a\nsimple average of all the corresponding MOD11A1 LST pixels collected within\nthat 8 day period. The MOD11A2 does a simple averaging of all daily LST\nvalues, without any filtering for specific QA bits. Each of the MOD11A2 QA\nvalues are set based on what majority of input daily QA values are for any\ngiven pixel.\n\nThe 8 day compositing period was chosen because twice that period is the\nexact ground track repeat period of the Terra and Aqua platforms. In this\nproduct, along with both the day- and night-time surface temperature bands\nand their quality indicator (QC) layers, are also MODIS bands 31 and 32 and\neight observation layers.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD11A2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD11A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD11A2","rel":"related","title":"Run the example for MODIS/061/MOD11A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD11A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD11A2.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A2.json","rel":"latest-version","title":"MODIS/061/MOD11A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD11A2.json","rel":"predecessor-version","title":"MODIS/006/MOD11A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD11A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD11A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD13A1","title":"MOD13A1.061 Terra Vegetation Indices 16-Day Global 500m","description":"The MOD13A1 V6.1 product provides a Vegetation Index (VI)\nvalue at a per pixel basis. There are two primary vegetation layers.\nThe first is the Normalized Difference Vegetation Index (NDVI)\nwhich is referred to as the continuity index to the existing National\nOceanic and Atmospheric Administration-Advanced Very High Resolution\nRadiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer\nis the Enhanced Vegetation Index (EVI) that minimizes canopy background\nvariations and maintains sensitivity over dense vegetation conditions.\nThe EVI also uses the blue band to remove residual atmosphere contamination\ncaused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\nand EVI products are computed from atmospherically corrected bi-directional\nsurface reflectances that have been masked for water, clouds, heavy\naerosols, and cloud shadows.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD13A1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2026-10-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD13A1","rel":"related","title":"Run the example for MODIS/061/MOD13A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD13A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD13A1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A1.json","rel":"latest-version","title":"MODIS/061/MOD13A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD13A1.json","rel":"predecessor-version","title":"MODIS/006/MOD13A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD13A2","title":"MOD13A2.061 Terra Vegetation Indices 16-Day Global 1km","description":"The MOD13A2 V6.1 product provides two Vegetation Indices (VI): the Normalized\nDifference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).\nThe NDVI is referred to as the continuity index to the existing National\nOceanic and Atmospheric Administration-Advanced Very High Resolution\nRadiometer (NOAA-AVHRR) derived NDVI. The EVI has improved sensitivity over\nhigh biomass regions.\n\nThe algorithm for this product chooses the best available\npixel value from all the acquisitions from the 16-day period. The criteria\nused are low clouds, low view angle, and the highest NDVI/EVI value.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD13A2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2026-10-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD13A2","rel":"related","title":"Run the example for MODIS/061/MOD13A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD13A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD13A2.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A2.json","rel":"latest-version","title":"MODIS/061/MOD13A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD13A2.json","rel":"predecessor-version","title":"MODIS/006/MOD13A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD13A3","title":"MOD13A3.061 Vegetation Indices Monthly L3 Global 1 km SIN Grid","description":"The MOD13A3 V6.1 product data is provided monthly at 1 kilometer (km)\nspatial resolution. In generating this monthly product, the algorithm\ningests all the MOD13A2 products that overlap the month and employs a\nweighted temporal average.\n\nVegetation indices are used for global monitoring of vegetation conditions\nand are used in products displaying land cover and land cover changes. These\ndata may be used as input for modeling global biogeochemical and hydrologic\nprocesses as well as global and regional climate. Additional applications\ninclude characterizing land surface biophysical properties and processes,\nsuch as primary production and land cover conversion.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/621/MOD13_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD13A3)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13A3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD13A3","rel":"related","title":"Run the example for MODIS/061/MOD13A3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD13A3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13A3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD13A3.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13A3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13A3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD13C1","title":"MOD13C1.061: Terra Vegetation Indices 16-Day L3 Global 0.05 Deg Climate Modeling Grid","description":"The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13C1) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which maintains continuity with the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MOD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer MOD13A2 data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MOD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution.\n\nNote that scale factors and offsets described in the MODIS documentation\nfor MOD13C1 bands are already applied to assets ingested into Earth\nEngine.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2026-10-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13C1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD13C1","rel":"related","title":"Run the example for MODIS/061/MOD13C1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD13C1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13C1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD13C1.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13C1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13C1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD13Q1","title":"MOD13Q1.061 Terra Vegetation Indices 16-Day Global 250m","description":"The MOD13Q1 V6.1 product provides a Vegetation Index (VI)\nvalue at a per pixel basis. There are two primary vegetation layers.\nThe first is the Normalized Difference Vegetation Index (NDVI)\nwhich is referred to as the continuity index to the existing National\nOceanic and Atmospheric Administration-Advanced Very High Resolution\nRadiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer\nis the Enhanced Vegetation Index (EVI) that minimizes canopy background\nvariations and maintains sensitivity over dense vegetation conditions.\nThe EVI also uses the blue band to remove residual atmosphere contamination\ncaused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\nand EVI products are computed from atmospherically corrected bi-directional\nsurface reflectances that have been masked for water, clouds, heavy\naerosols, and cloud shadows.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD13A2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2026-10-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD13Q1","rel":"related","title":"Run the example for MODIS/061/MOD13Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD13Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD13Q1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13Q1.json","rel":"latest-version","title":"MODIS/061/MOD13Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD13Q1.json","rel":"predecessor-version","title":"MODIS/006/MOD13Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD13Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD14A1","title":"MOD14A1.061: Terra Thermal Anomalies & Fire Daily Global 1km","description":"The MOD14A1 V6.1 dataset provides daily fire mask composites\nat 1km resolution derived from the MODIS 4- and 11-micrometer radiances.\nThe fire detection strategy is based on absolute detection of a\nfire (when the fire strength is sufficient to detect), and on detection\nrelative to its background (to account for variability of the surface\ntemperature and reflection by sunlight). The product distinguishes\nbetween fire, no fire and no observation. This information is used\nfor monitoring the spatial and temporal distribution of fires in\ndifferent ecosystems, detecting changes in fire distribution and\nidentifying new fire frontiers, wild fires, and changes in the\nfrequency of the fires  or their relative strength.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1005/MOD14_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/87/MOD14_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD14A1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-10-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD14A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD14A1","rel":"related","title":"Run the example for MODIS/061/MOD14A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD14A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD14A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD14A1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD14A1.json","rel":"latest-version","title":"MODIS/061/MOD14A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD14A1.json","rel":"predecessor-version","title":"MODIS/006/MOD14A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD14A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD14A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD14A2","title":"MOD14A2.061: Terra Thermal Anomalies & Fire 8-Day Global 1km","description":"The MOD14A2 V6.1 dataset provides 8-day fire mask composites\nat 1km resolution. It contains the maximum value of the individual\npixel classes over the compositing period. Along with the fire\nmask, an associated quality information layer is also provided.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/876/MOD14_User_Guide_v6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/87/MOD14_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD14A2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD14A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD14A2","rel":"related","title":"Run the example for MODIS/061/MOD14A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD14A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD14A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD14A2.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD14A2.json","rel":"latest-version","title":"MODIS/061/MOD14A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD14A2.json","rel":"predecessor-version","title":"MODIS/006/MOD14A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD14A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD14A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD15A2H","title":"MOD15A2H.061: Terra Leaf Area Index/FPAR 8-Day Global 500m","description":"The MOD15A2H V6.1 MODIS combined Leaf Area Index (LAI) and\nFraction of Photosynthetically Active Radiation (FPAR) product is an 8-day\ncomposite dataset at 500m resolution. The algorithm chooses the \\\"best\\\" pixel\navailable from all the acquisitions of the Terra sensor from within the 8-day period.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/624/MOD15_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/90/MOD15_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD15A2H)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD15A2H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD15A2H","rel":"related","title":"Run the example for MODIS/061/MOD15A2H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD15A2H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD15A2H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD15A2H.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD15A2H.json","rel":"latest-version","title":"MODIS/061/MOD15A2H","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD15A2H.json","rel":"predecessor-version","title":"MODIS/006/MOD15A2H","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD15A2H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD15A2H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD16A2","title":"MOD16A2.061: Terra Net Evapotranspiration 8-Day Global 500m","description":"The MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an\n8-day composite product produced at 500 meter pixel resolution. The\nalgorithm used for the MOD16 data product collection is based on the logic\nof the Penman-Monteith equation, which includes inputs of daily\nmeteorological reanalysis data along with MODIS remotely sensed data\nproducts such as vegetation property dynamics, albedo, and land cover.\n\nThe pixel values for the two Evapotranspiration layers (ET & PET) are the\nsum of all eight days within the composite period. The pixel values for\nthe two Latent Heat layers (LE & PLE) are the average of all eight days\nwithin the composite period. Note that the last 8-day period of each year\nis a 5 or 6-day composite period, depending on the year.\n\nClass assignment in fill values 32761 through 32767 might be inaccurate. \nThey are not included in the EE assets.\n\nThe Version 6.1 Level-1B (L1B) products have been improved by undergoing\nvarious calibration changes that include: changes to the\nresponse-versus-scan angle (RVS) approach that affects reflectance bands\nfor Aqua and Terra MODIS, corrections to adjust for the optical crosstalk\nin Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS\nforward look-up table (LUT) update for the period 2012 - 2017.\nA polarization correction has been applied to the L1B Reflective Solar\nBands (RSB). The product uses Climatology LAI/FPAR as back up to the\noperational LAI/FPAR.\n\nAccording to [the MODIS Science Team]\n(https://forum.earthdata.nasa.gov/viewtopic.php?p=15070#p15070),\nThe MOD16A2 6.1 product will not have data prior to 2021. The MODIS Science \nteam recommends using the gap-filled MOD16A2GF 6.1 product for data from\n2000 to 2021 -- and then also 2022 when the data are available.\nThey recommend this as the gap-filled product is expected to be superior in\nproduct quality, especially over areas where otherwise the routine product\nperformance would have been negatively impacted by cloudy or poor quality\ninput observations. For the current year, there will not be a gap-filled\nproduct until early the following year (i.e. 2023 data should be available\nin early 2024). So, if a user needs MOD16A2GF 6.1 data for the year 2023\n(or the \"current\" year in the future), they recommend using the MOD16A2 6.1\nproduct, which is generated using the daily observation with climatology\nobservation replacing for the cloudy or poor quality observations.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/494/MOD16_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/93/MOD16_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD16A2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-01-01T00:00:00Z","2026-10-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD16A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD16A2","rel":"related","title":"Run the example for MODIS/061/MOD16A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD16A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD16A2.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD16A2.json","rel":"latest-version","title":"MODIS/061/MOD16A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD16A2.json","rel":"predecessor-version","title":"MODIS/006/MOD16A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD16A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD16A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD16A2GF","title":"MOD16A2GF.061: Terra Net Evapotranspiration Gap-Filled 8-Day Global 500m","description":"The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A2GF\nVersion 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a\nyear-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel\nresolution. The algorithm used for the MOD16 data product collection is\nbased on the logic of the Penman-Monteith equation, which includes inputs of\ndaily meteorological reanalysis data along with MODIS remotely sensed data\nproducts such as vegetation property dynamics, albedo, and land cover.\n\nThe pixel values for the two Evapotranspiration layers (ET and PET) are the\nsum of all eight days within the composite period, and the pixel values for\nthe two Latent Heat layers (LE and PLE) are the average of all eight days\nwithin the composite period. The last acquisition period of each year is a\n5 or 6-day composite period, depending on the year.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/931/MOD16_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/93/MOD16_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD16A2GF)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2025-12-27T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD16A2GF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD16A2GF","rel":"related","title":"Run the example for MODIS/061/MOD16A2GF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD16A2GF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2GF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD16A2GF.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD16A2GF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD16A2GF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD17A2H","title":"MOD17A2H.061: Terra Gross Primary Productivity 8-Day Global 500m","description":"The MOD17A2H V6.1 Gross Primary Productivity (GPP) product\nis a cumulative 8-day composite with a 500m pixel size. The product\nis based on the radiation-use efficiency concept and can be potentially\nused as inputs to data models to calculate terrestrial energy,\ncarbon, water cycle processes, and biogeochemistry of vegetation.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/972/MOD17_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/95/MOD17_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD17A2H)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-01-01T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD17A2H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD17A2H","rel":"related","title":"Run the example for MODIS/061/MOD17A2H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD17A2H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD17A2H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD17A2H.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A2H.json","rel":"latest-version","title":"MODIS/061/MOD17A2H","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD17A2H.json","rel":"predecessor-version","title":"MODIS/006/MOD17A2H","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD17A2H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD17A2H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD17A2HGF","title":"MOD17A2HGF.061: Terra Gross Primary Productivity 8-Day Global 500m","description":"The MOD17A2HGF Version 6.1 Gross Primary Productivity (GPP) product\nis a cumulative 8-day composite of values with 500 meter (m) pixel size\nbased on the radiation use efficiency concept that can be potentially used\nas inputs to data models to calculate terrestrial energy, carbon,\nwater cycle processes, and biogeochemistry of vegetation.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/972/MOD17_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/95/MOD17_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MOD17A2HGF)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-01-01T00:00:00Z","2025-12-27T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD17A2HGF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD17A2HGF","rel":"related","title":"Run the example for MODIS/061/MOD17A2HGF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD17A2HGF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD17A2HGF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD17A2HGF.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD17A2HGF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD17A2HGF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD17A3HGF","title":"MOD17A3HGF.061: Terra Net Primary Production Gap-Filled Yearly Global 500m","description":"The MOD17A3HGF V6.1 product provides information about annual Gross and Net\nPrimary Productivity (GPP and NPP) at 500m pixel resolution. Annual NPP is\nderived from the sum of all 8-day Net Photosynthesis(PSN) products\n(MOD17A2H) from the given year.  The PSN value is the difference of the\nGross Primary Productivity (GPP) and the Maintenance Respiration (MR)\n(GPP-MR).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD17A3HGF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD17A3HGF","rel":"related","title":"Run the example for MODIS/061/MOD17A3HGF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD17A3HGF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD17A3HGF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD17A3HGF.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A3HGF.json","rel":"latest-version","title":"MODIS/061/MOD17A3HGF","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD17A3HGF.json","rel":"predecessor-version","title":"MODIS/006/MOD17A3HGF","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD17A3HGF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD17A3HGF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD21A1D","title":"MOD21A1D.061 Terra Land Surface Temperature and 3-Band Emissivity Daily Global 1km","description":"The MOD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G)\nintermediate LST products at a spatial resolution of 1,000 meters. The L2G\nprocess maps the daily MOD21 swath granules onto a sinusoidal MODIS grid and\nstores all observations falling over a gridded cell for a given day. The\nMOD21A1 algorithm sorts through these observations for each cell and\nestimates the final LST value as an average from all observations that are\ncloud free and have good LST&E accuracies. The daytime average is weighted\nby the observation coverage for that cell. Only observations having an\nobservation coverage greater than a 15% threshold are considered. The\nMOD21A1D product contains the calculated LST as well as quality control, the\nthree emissivity bands, view zenith angle, and time of observation.\n\nNote that scale factors and offsets described in the MODIS documentation\nfor MOD21A1D bands are already applied to assets ingested into Earth\nEngine.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1398/MOD21_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD21A1D)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21A1D","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD21A1D","rel":"related","title":"Run the example for MODIS/061/MOD21A1D in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD21A1D_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21A1D#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD21A1D.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21A1D/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21A1D/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD21A1N","title":"MOD21A1N.061 Terra Land Surface Temperature and 3-Band Emissivity Daily Global 1km","description":"The MOD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G)\nintermediate LST products at a spatial resolution of 1,000 meters. The L2G\nprocess maps the daily MOD21 swath granules onto a sinusoidal MODIS grid and\nstores all observations falling over a gridded cell for a given day. The\nMOD21A1 algorithm sorts through these observations for each cell and\nestimates the final LST value as an average from all observations that are\ncloud free and have good LST&E accuracies. The nighttime average is weighted\nby the observation coverage for that cell. Only observations having an\nobservation coverage greater than a 15% threshold are considered. The\nMOD21A1N product contains the calculated LST as well as quality control, the\nthree emissivity bands, view zenith angle, and time of observation.\n\nNote that scale factors and offsets described in the MODIS documentation\nfor MOD21A1N bands are already applied to assets ingested into Earth\nEngine.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1398/MOD21_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD21A1N)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21A1N","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD21A1N","rel":"related","title":"Run the example for MODIS/061/MOD21A1N in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD21A1N_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21A1N#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD21A1N.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21A1N/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21A1N/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD21C1","title":"MOD21C1.061 Terra Land Surface Temperature and 3-Band Emissivity Daily L3 Global 0.05 Deg CMG","description":"The MOD21C1 dataset is produced daily in a 0.05 degree (5,600 meters at the\nequator) Climate Modeling Grid (CMG) from daytime Level 2 Gridded (L2G)\nintermediate LST products. The L2G process maps the daily MOD21 swath\ngranules onto a sinusoidal MODIS grid and stores all observations falling\nover a gridded cell for a given day. The MOD21C1 algorithm sorts through\nthese observations for each cell and estimates the final LST value as an\naverage from all observations that are cloud free and have good LST&E\naccuracies. The daytime average is weighted by the observation coverage for\nthat cell. Only observations having an observation coverage greater than a\n15% threshold are considered. The MOD21C1 product contains the calculated\nLST as well as quality control, the three emissivity bands, view zenith\nangle, and time of observation.\n\nNote that scale factors and offsets described in the MODIS documentation\nfor MOD21C1 bands are already applied to assets ingested into Earth\nEngine.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1398/MOD21_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD21C1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21C1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD21C1","rel":"related","title":"Run the example for MODIS/061/MOD21C1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD21C1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD21C1.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21C1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21C1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD21C2","title":"MOD21C2.061 Terra Land Surface Temperature and 3-Band Emissivity 8-Day L3 Global 0.05 Deg CMG","description":"The MOD21C2 dataset is an 8-day composite LST product that uses an algorithm\nbased on a simple averaging method. The algorithm calculates the average\nfrom all the cloud free MOD21A1D and MOD21A1N daily acquisitions from the\n8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime\nacquisitions are separate products, the MOD21A2 contains both daytime and\nnighttime acquisitions. The LST, Quality Control (QC), view zenith angle,\nand viewing time have separate day and night bands, while the values for the\nMODIS emissivity bands 29, 31, and 32 are the average of both the nighttime\nand daytime acquisitions.\n\nNote that scale factors and offsets described in the MODIS documentation\nfor MOD21C2 bands are already applied to assets ingested into Earth\nEngine.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1398/MOD21_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD21C2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21C2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD21C2","rel":"related","title":"Run the example for MODIS/061/MOD21C2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD21C2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD21C2.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21C2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21C2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD21C3","title":"MOD21C3.061 Terra Land Surface Temperature and 3-Band Emissivity Monthly L3 Global 0.05 Deg CMG","description":"The MOD21C3 dataset is a monthly composite LST product that uses an\nalgorithm based on a simple averaging method. The algorithm calculates the\naverage from all the cloud free MOD21A1D and MOD21A1N daily acquisitions\nfrom the 8-day period. Unlike the MOD21A1 data sets where the daytime and\nnighttime acquisitions are separate products, the MOD21A2 contains both\ndaytime and nighttime acquisitions. The LST, Quality Control (QC), view\nzenith angle, and viewing time have separate day and night bands, while the\nvalues for the MODIS emissivity bands 29, 31, and 32 are the average of both\nthe nighttime and daytime acquisitions.\n\nNote that scale factors and offsets described in the MODIS documentation\nfor MOD21C3 bands are already applied to assets ingested into Earth\nEngine.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1398/MOD21_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD21C3)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21C3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD21C3","rel":"related","title":"Run the example for MODIS/061/MOD21C3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD21C3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MOD21C3.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21C3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD21C3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MOD44B","title":"MOD44B.061 Terra Vegetation Continuous Fields Yearly Global 250m","description":"The Terra MODIS Vegetation Continuous Fields (VCF)\nproduct is a sub-pixel-level representation of surface vegetation\ncover estimates globally. Designed to continuously represent\nEarth's terrestrial surface as a proportion of basic vegetation\ntraits, it provides a gradation of three surface cover components:\npercent tree cover, percent non-tree cover, and percent bare.\nVCF products provide a continuous, quantitative portrayal of\nland surface cover with improved spatial detail, and hence, are\nwidely used in environmental modeling and monitoring applications.\n\nGenerated yearly, the VCF product is produced using monthly\ncomposites of Terra MODIS 250 and 500 meters Land Surface Reflectance\ndata, including all seven bands, and Land Surface Temperature.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1494/MOD44B_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/113/MOD44B_ATBD.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/mod44bv061/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-05T00:00:00Z","2025-03-06T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD44B","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MOD44B","rel":"related","title":"Run the example for MODIS/061/MOD44B in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MOD44B_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD44B#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD44B.json","rel":"latest-version","title":"MODIS/061/MOD44B","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD44B.json","rel":"predecessor-version","title":"MODIS/006/MOD44B","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD44B/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MOD44B/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD08_M3","title":"MYD08_M3.061 Aqua Atmosphere Monthly Global Product","description":"MYD08_M3 V6.1 is an atmosphere global product that contains\nmonthly 1 x 1 degree grid average values of atmospheric parameters.\nThese parameters are related to atmospheric aerosol particle properties,\ntotal ozone burden, atmospheric water vapor, cloud optical and\nphysical properties, and atmospheric stability indices. The product\nalso provides means, standard deviations, QA weighted statistics,\nlog-normal distributions, uncertainty estimates, and statistics\nfor fractions of pixels that satisfy some condition. Below is a\nsubset of the bands, for a complete list see the\n[MOD08 Band List](https://developers.google.com/earth-engine/MOD08_bands.html).\n\nDocumentation:\n\n* [User's Guide](https://modis-atmos.gsfc.nasa.gov/sites/default/files/ModAtmo/L3_ATBD_C6_C61_2019_02_20.pdf)\n\n* [Science Data Product Software Documentation](https://modis-atmos.gsfc.nasa.gov/sites/default/files/ModAtmo/L3_C61_Changes_v2.pdf)\n\n* [MYD08_M3 product description](https://modis-atmos.gsfc.nasa.gov/products/monthly)\n\n* [File specification document](https://modis-atmos.gsfc.nasa.gov/sites/default/files/ModAtmo/MOD08_M3_fs_3045.txt)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD08_M3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD08_M3","rel":"related","title":"Run the example for MODIS/061/MYD08_M3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD08_M3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD08_M3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD08_M3.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD08_M3.json","rel":"latest-version","title":"MODIS/061/MYD08_M3","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD08_M3.json","rel":"predecessor-version","title":"MODIS/006/MYD08_M3","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD08_M3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD08_M3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD09A1","title":"MYD09A1.061 Aqua Surface Reflectance 8-Day Global 500m","description":"The MYD09A1 V6.1 product provides an estimate of the surface\nspectral reflectance of Aqua MODIS bands 1-7 at 500m resolution\nand corrected for atmospheric conditions such as gasses, aerosols,\nand Rayleigh scattering. Along with the seven reflectance bands\nis a quality layer and four observation bands. For each pixel,\na value is selected from all the acquisitions within the 8-day\ncomposite on the basis of high observation coverage, low view angle,\nthe absence of clouds or cloud shadow, and aerosol loading.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD09A1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-10-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD09A1","rel":"related","title":"Run the example for MODIS/061/MYD09A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD09A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD09A1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09A1.json","rel":"latest-version","title":"MODIS/061/MYD09A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD09A1.json","rel":"predecessor-version","title":"MODIS/006/MYD09A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD09CMG","title":"MYD09CMG.061 Aqua Surface Reflectance Daily L3 Global 0.05 Deg CMG","description":"The MYD09CMG Version 6.1 product provides an estimate of the surface\nspectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer\n(MODIS) Bands 1 through 7, resampled to 5600 meter pixel resolution and\ncorrected for atmospheric conditions such as gasses, aerosols, and Rayleigh\nscattering. The MYD09CMG data product provides 25 layers including MODIS\nbands 1 through 7; Brightness Temperature data from thermal bands 20, 21,\n31, and 32; along with Quality Assurance (QA) and observation bands.\nThis product is based on a Climate Modeling Grid (CMG) for use in climate\nsimulation models.\n\nThis dataset has a striping issue originating from Band 5’s dead\ndetector 20. The MYD09/Band 5 show the dead detector striping in band 5, but\nit has been properly flagged in the QA layer. The science team has been\nnotified and the issue will be addressed in the Collection 7.\nMore details can be found on\n[the known issue page](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=939).\n\nNote that scale factors and offsets described in the MODIS documentation\nfor MYD09CMG bands are already applied to assets ingested into Earth\nEngine.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/925/MOD09_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD09CMG)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09CMG","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD09CMG","rel":"related","title":"Run the example for MODIS/061/MYD09CMG in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD09CMG_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09CMG#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD09CMG.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09CMG/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09CMG/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD09GQ","title":"MYD09GQ.061 Aqua Surface Reflectance Daily Global 250m","description":"The MODIS Surface Reflectance products provide an estimate\nof the surface spectral reflectance as it would be measured at\nground level in the absence of atmospheric scattering or absorption.\nLow-level data are corrected for atmospheric gases and aerosols.\nMYD09GQ version 6.1 provides bands 1 and 2 at a 250m resolution in\na daily gridded L2G product in the Sinusoidal projection, including\na QC and five observation layers. This product is meant to be used\nin conjunction with the MYD09GA where important quality and viewing\ngeometry information is stored.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD09GQ)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09GQ","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD09GQ","rel":"related","title":"Run the example for MODIS/061/MYD09GQ in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD09GQ_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GQ#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD09GQ.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GQ.json","rel":"latest-version","title":"MODIS/061/MYD09GQ","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD09GQ.json","rel":"predecessor-version","title":"MODIS/006/MYD09GQ","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09GQ/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09GQ/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD09Q1","title":"MYD09Q1.061 Aqua Surface Reflectance 8-Day Global 250m","description":"The MYD09Q1 product provides an estimate of the surface\nspectral reflectance of bands 1 and 2 at 250m resolution and corrected\nfor atmospheric conditions such as gasses, aerosols, and Rayleigh\nscattering. Along with the two reflectance bands, a quality layer\nis also included. For each pixel, a value is selected from all\nthe acquisitions within the 8-day composite on the basis of high\nobservation coverage, low view angle, the absence of clouds or\ncloud shadow, and aerosol loading.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD09Q1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-10-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD09Q1","rel":"related","title":"Run the example for MODIS/061/MYD09Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD09Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD09Q1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09Q1.json","rel":"latest-version","title":"MODIS/061/MYD09Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD09Q1.json","rel":"predecessor-version","title":"MODIS/006/MYD09Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD10A1","title":"MYD10A1.061 Aqua Snow Cover Daily Global 500m","description":"The MYD10A1 V6 Snow Cover Daily Global 500m product\ncontains snow cover, snow albedo, fractional snow cover, and quality\nassessment (QA) data. Snow cover data are based on a snow mapping\nalgorithm that employs a Normalized Difference Snow Index (NDSI)\nand other criteria tests.\n\n[General documentation](https://doi.org/10.5067/MODIS/MYD10A1.061)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD10A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD10A1","rel":"related","title":"Run the example for MODIS/061/MYD10A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD10A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD10A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD10A1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD10A1.json","rel":"latest-version","title":"MODIS/061/MYD10A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD10A1.json","rel":"predecessor-version","title":"MODIS/006/MYD10A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD10A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD10A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD11A1","title":"MYD11A1.061 Aqua Land Surface Temperature and Emissivity Daily Global 1km","description":"The MYD11A1 V6.1 product provides daily land surface temperature\n(LST) and emissivity values in a 1200 x 1200 kilometer grid. The\ntemperature value is derived from the MYD11_L2 swath product. Above\n30 degrees latitude, some pixels may have multiple observations\nwhere the criteria for clear-sky are met. When this occurs, the\npixel value is the average of all qualifying observations. Provided\nalong with both the day-time and night-time surface temperature\nbands and their quality indicator layers are MODIS bands 31 and\n32 and six observation layers.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD11A1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD11A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD11A1","rel":"related","title":"Run the example for MODIS/061/MYD11A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD11A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD11A1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A1.json","rel":"latest-version","title":"MODIS/061/MYD11A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD11A1.json","rel":"predecessor-version","title":"MODIS/006/MYD11A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD11A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD11A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD11A2","title":"MYD11A2.061 Aqua Land Surface Temperature and Emissivity 8-Day Global 1km","description":"The MYD11A2 V6.1 product provides an average 8-day land\nsurface temperature (LST)  in a 1200 x 1200 kilometer grid.  Each\npixel value in MYD11A2 is a simple average of all the corresponding\nMYD11A1 LST pixels collected within that 8 day period. The MYD11A2 does a\nsimple averaging of all daily LST values, without any filtering for specific\nQA bits. Each of the MYD11A2 QA values are set based on what majority of\ninput daily QA values are for any given pixel.\n\nThe 8 day compositing period was chosen because twice that period is\nthe exact ground track repeat period of the Terra and Aqua platforms.\nIn this product, along with both the day- and night-time surface\ntemperature bands and their quality indicator (QC) layers, are\nalso MODIS bands 31 and 32 and eight observation layers.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/119/MOD11_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD11A2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-10-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD11A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD11A2","rel":"related","title":"Run the example for MODIS/061/MYD11A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD11A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD11A2.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A2.json","rel":"latest-version","title":"MODIS/061/MYD11A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD11A2.json","rel":"predecessor-version","title":"MODIS/006/MYD11A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD11A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD11A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD13A1","title":"MYD13A1.061 Aqua Vegetation Indices 16-Day Global 500m","description":"The MYD13A1 V6.1 product provides a Vegetation Index (VI)\nvalue at a per pixel basis. There are two primary vegetation layers.\nThe first is the Normalized Difference Vegetation Index (NDVI)\nwhich is referred to as the continuity index to the existing National\nOceanic and Atmospheric Administration-Advanced Very High Resolution\nRadiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer\nis the Enhanced Vegetation Index (EVI) that minimizes canopy background\nvariations and maintains sensitivity over dense vegetation conditions.\nThe EVI also uses the blue band to remove residual atmosphere contamination\ncaused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\nand EVI products are computed from atmospherically corrected bi-directional\nsurface reflectances that have been masked for water, clouds, heavy\naerosols, and cloud shadows.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD13A1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-10-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD13A1","rel":"related","title":"Run the example for MODIS/061/MYD13A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD13A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD13A1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13A1.json","rel":"latest-version","title":"MODIS/061/MYD13A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD13A1.json","rel":"predecessor-version","title":"MODIS/006/MYD13A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD13A2","title":"MYD13A2.061 Aqua Vegetation Indices 16-Day Global 1km","description":"The MYD13A2 V6.1 product provides two Vegetation Indices (VI): the Normalized\nDifference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).\nThe NDVI is referred to as the continuity index to the existing National\nOceanic and Atmospheric Administration-Advanced Very High Resolution\nRadiometer (NOAA-AVHRR) derived NDVI. The EVI has improved sensitivity over\nhigh biomass regions.\n\nThe algorithm for this product chooses the best available\npixel value from all the acquisitions from the 16-day period. The criteria\nused are low clouds, low view angle, and the highest NDVI/EVI value.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD13A2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-10-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD13A2","rel":"related","title":"Run the example for MODIS/061/MYD13A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD13A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD13A2.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13A2.json","rel":"latest-version","title":"MODIS/061/MYD13A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD13A2.json","rel":"predecessor-version","title":"MODIS/006/MYD13A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD13A3","title":"MYD13A3.061 Aqua Vegetation Indices Monthly L3 Global 1 km SIN Grid","description":"The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation\nIndices (MYD13A3) Version 6.1 data are provided monthly at 1 kilometer (km)\nspatial resolution as a gridded Level 3 product in the sinusoidal\nprojection. In generating this monthly product, the algorithm ingests all\nthe MYD13A2 products that overlap the month and employs a weighted temporal\naverage.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/621/MOD13_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD13A3)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-01T00:00:00Z","2026-09-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13A3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD13A3","rel":"related","title":"Run the example for MODIS/061/MYD13A3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD13A3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13A3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD13A3.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13A3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13A3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD13C1","title":"MYD13C1.061: Aqua Vegetation Indices 16-Day L3 Global 0.05 Deg CMG","description":"The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MYD13C1) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which maintains continuity with the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.\n\nThe Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MYD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer MYD13A2 data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution.\n\nNote that scale factors and offsets described in the MODIS documentation\nfor MYD13C1 bands are already applied to assets ingested into Earth\nEngine.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-10-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13C1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD13C1","rel":"related","title":"Run the example for MODIS/061/MYD13C1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD13C1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13C1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD13C1.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13C1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13C1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD13Q1","title":"MYD13Q1.061 Aqua Vegetation Indices 16-Day Global 250m","description":"The MYD13Q1 V6.1 product provides a Vegetation Index (VI)\nvalue at a per pixel basis. There are two primary vegetation layers.\nThe first is the Normalized Difference Vegetation Index (NDVI)\nwhich is referred to as the continuity index to the existing National\nOceanic and Atmospheric Administration-Advanced Very High Resolution\nRadiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer\nis the Enhanced Vegetation Index (EVI) that minimizes canopy background\nvariations and maintains sensitivity over dense vegetation conditions.\nThe EVI also uses the blue band to remove residual atmosphere contamination\ncaused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\nand EVI products are computed from atmospherically corrected bi-directional\nsurface reflectances that have been masked for water, clouds, heavy\naerosols, and cloud shadows.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/103/MOD13_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/104/MOD13_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD13A2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-10-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD13Q1","rel":"related","title":"Run the example for MODIS/061/MYD13Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD13Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD13Q1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13Q1.json","rel":"latest-version","title":"MODIS/061/MYD13Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD13Q1.json","rel":"predecessor-version","title":"MODIS/006/MYD13Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD13Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD14A1","title":"MYD14A1.061: Aqua Thermal Anomalies & Fire Daily Global 1km","description":"The MYD14A1 V6.1 dataset provides daily fire mask composites\nat 1km resolution derived from the MODIS 4- and 11-micrometer radiances.\nThe fire detection strategy is based on absolute detection of a\nfire (when the fire strength is sufficient to detect), and on detection\nrelative to its background (to account for variability of the surface\ntemperature and reflection by sunlight). The product distinguishes\nbetween fire, no fire and no observation. This information is used\nfor monitoring the spatial and temporal distribution of fires in\ndifferent ecosystems, detecting changes in fire distribution and\nidentifying new fire frontiers, wild fires, and changes in the\nfrequency of the fires  or their relative strength.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1005/MOD14_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/87/MOD14_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD14A1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-10-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD14A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD14A1","rel":"related","title":"Run the example for MODIS/061/MYD14A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD14A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD14A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD14A1.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD14A1.json","rel":"latest-version","title":"MODIS/061/MYD14A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD14A1.json","rel":"predecessor-version","title":"MODIS/006/MYD14A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD14A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD14A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD14A2","title":"MYD14A2.061: Aqua Thermal Anomalies & Fire 8-Day Global 1km","description":"The MYD14A2 V6.1 dataset provides 8-day fire mask composites\nat 1km resolution. It contains the maximum value of the individual\npixel classes over the compositing period. Along with the fire\nmask, an associated quality information layer is also provided.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/88/MOD14_User_Guide_v6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/87/MOD14_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD14A2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-10-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD14A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD14A2","rel":"related","title":"Run the example for MODIS/061/MYD14A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD14A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD14A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD14A2.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD14A2.json","rel":"latest-version","title":"MODIS/061/MYD14A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD14A2.json","rel":"predecessor-version","title":"MODIS/006/MYD14A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD14A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD14A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD15A2H","title":"MYD15A2H.061: Aqua Leaf Area Index/FPAR 8-Day Global 500m","description":"The MYD15A2H V6.1 MODIS combined Leaf Area Index (LAI) and\nFraction of Photosynthetically Active Radiation (FPAR) product is an 8-day\ncomposite dataset at 500m resolution. The algorithm chooses the \\\"best\\\" pixel\navailable from all the acquisitions of the Aqua sensor from within the 8-day period.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/624/MOD15_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/90/MOD15_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD15A2H)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-10-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD15A2H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD15A2H","rel":"related","title":"Run the example for MODIS/061/MYD15A2H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD15A2H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD15A2H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD15A2H.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD15A2H.json","rel":"latest-version","title":"MODIS/061/MYD15A2H","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD15A2H.json","rel":"predecessor-version","title":"MODIS/006/MYD15A2H","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD15A2H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD15A2H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD16A2","title":"MYD16A2.061: Aqua Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061","description":"The MYD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an\n8-day composite product at 500 meter pixel resolution. The algorithm used\nfor the MOD16 data product collection is based on the logic of the\nPenman-Monteith equation, which includes inputs of daily meteorological\nreanalysis data along with MODIS remotely sensed data products such as\nvegetation property dynamics, albedo, and land cover.\n\nThe pixel values for the ET, LE, PET and PLE layers are the sum for all days\nwithin the 8-day composite period.Note that the last 8-day period of each\nyear is a 5 or 6-day composite period, depending on the year.\n\nThe Version 6.1 Level-1B (L1B) products have been improved by undergoing\nvarious calibration changes that include: changes to the\nresponse-versus-scan angle (RVS) approach that affects reflectance bands\nfor Aqua and Terra MODIS, corrections to adjust for the optical crosstalk\nin Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS\nforward look-up table (LUT) update for the period 2012 - 2017.\nA polarization correction has been applied to the L1B Reflective Solar\nBands (RSB). The product uses Climatology LAI/FPAR as back up to the\noperational LAI/FPAR.\n\nAccording to [the MODIS Science Team]\n(https://forum.earthdata.nasa.gov/viewtopic.php?p=15070#p15070),\nThe MYD16A2 6.1 product will not have data prior to 2021. The MODIS Science\nteam recommends using the gap-filled MYD16A2GF 6.1 product for data from\n2000 to 2021 -- and then also 2022 when the data are available.\nThey recommend this as the gap-filled product is expected to be superior in\nproduct quality, especially over areas where otherwise the routine product\nperformance would have been negatively impacted by cloudy or poor quality\ninput observations. For the current year, there will not be a gap-filled\nproduct until early the following year (i.e. 2023 data should be available\nin early 2024). So, if a user needs MYD16A2GF 6.1 data for the year 2023\n(or the \"current\" year in the future), they recommend using the MYD16A2 6.1\nproduct, which is generated using the daily observation with climatology\nobservation replacing for the cloudy or poor quality observations.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/494/MOD16_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/93/MOD16_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD16A2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-01-01T00:00:00Z","2026-10-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD16A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD16A2","rel":"related","title":"Run the example for MODIS/061/MYD16A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD16A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD16A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD16A2.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD16A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD16A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD17A2H","title":"MYD17A2H.061: Aqua Gross Primary Productivity 8-Day Global 500m","description":"The MYD17A2H V6.1 Gross Primary Productivity (GPP) product\nis a cumulative 8-day composite with a 500m resolution. The product\nis based on the radiation-use efficiency concept and can be potentially\nused as inputs to data models to calculate terrestrial energy,\ncarbon, water cycle processes, and biogeochemistry of vegetation.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/972/MOD17_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/95/MOD17_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD17A2H)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-01-01T00:00:00Z","2026-10-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD17A2H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD17A2H","rel":"related","title":"Run the example for MODIS/061/MYD17A2H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD17A2H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD17A2H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD17A2H.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD17A2H.json","rel":"latest-version","title":"MODIS/061/MYD17A2H","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD17A2H.json","rel":"predecessor-version","title":"MODIS/006/MYD17A2H","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD17A2H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD17A2H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD17A3HGF","title":"MYD17A3HGF.061: Aqua Net Primary Production Gap-Filled Yearly Global 500m","description":"The MYD17A3HGF V6.1 product provides information about annual Gross and Net\nPrimary Productivity (GPP and NPP) at 500m pixel resolution. Annual NPP is\nderived from the sum of all 8-day Net Photosynthesis(PSN) products\n(MYD17A2H) from the given year.  The PSN value is the difference of the\nGross Primary Productivity (GPP) and the Maintenance Respiration (MR)\n(GPP-MR).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD17A3HGF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD17A3HGF","rel":"related","title":"Run the example for MODIS/061/MYD17A3HGF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD17A3HGF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD17A3HGF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD17A3HGF.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD17A3HGF.json","rel":"latest-version","title":"MODIS/061/MYD17A3HGF","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD17A3HGF.json","rel":"predecessor-version","title":"MODIS/006/MYD17A3HGF","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD17A3HGF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD17A3HGF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD21A1D","title":"MYD21A1D.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily Global 1km","description":"The MYD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G)\nintermediate LST products at a spatial resolution of 1,000 meters. The L2G\nprocess maps the daily MOD21 swath granules onto a sinusoidal MODIS grid and\nstores all observations falling over a gridded cell for a given day. The\nMOD21A1 algorithm sorts through these observations for each cell and\nestimates the final LST value as an average from all observations that are\ncloud free and have good LST&E accuracies. The daytime average is weighted\nby the observation coverage for that cell. Only observations having an\nobservation coverage greater than a 15% threshold are considered. The\nMYD21A1D product contains the calculated LST as well as quality control, the\nthree emissivity bands, view zenith angle, and time of observation.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1398/MOD21_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD21A1D)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21A1D","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD21A1D","rel":"related","title":"Run the example for MODIS/061/MYD21A1D in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD21A1D_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21A1D#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD21A1D.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21A1D/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21A1D/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD21A1N","title":"MYD21A1N.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily Global 1km","description":"The MYD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G)\nintermediate LST products at a spatial resolution of 1,000 meters. The L2G\nprocess maps the daily MOD21 swath granules onto a sinusoidal MODIS grid and\nstores all observations falling over a gridded cell for a given day. The\nMOD21A1 algorithm sorts through these observations for each cell and\nestimates the final LST value as an average from all observations that are\ncloud free and have good LST&E accuracies. The nighttime average is weighted\nby the observation coverage for that cell. Only observations having an\nobservation coverage greater than a 15% threshold are considered. The\nMYD21A1N product contains the calculated LST as well as quality control, the\nthree emissivity bands, view zenith angle, and time of observation.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1398/MOD21_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD21A1N)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21A1N","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD21A1N","rel":"related","title":"Run the example for MODIS/061/MYD21A1N in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD21A1N_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21A1N#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD21A1N.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21A1N/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21A1N/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD21C1","title":"MYD21C1.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily L3 Global 0.05 Deg CMG","description":"The MYD21C1 dataset is produced daily in a 0.05 degree (5,600 meters at the\nequator) Climate Modeling Grid (CMG) from daytime Level 2 Gridded (L2G)\nintermediate LST products. The L2G process maps the daily MYD21 swath\ngranules onto a sinusoidal MODIS grid and stores all observations falling\nover a gridded cell for a given day. The MYD21C1 algorithm sorts through\nthese observations for each cell and estimates the final LST value as an\naverage from all observations that are cloud free and have good LST&E\naccuracies. The daytime average is weighted by the observation coverage for\nthat cell. Only observations having an observation coverage greater than a\n15% threshold are considered. The MYD21C1 product contains the calculated\nLST as well as quality control, the three emissivity bands, view zenith\nangle, and time of observation.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1398/MOD21_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD21C1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21C1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD21C1","rel":"related","title":"Run the example for MODIS/061/MYD21C1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD21C1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21C1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD21C1.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21C1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21C1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD21C2","title":"MYD21C2.061 Aqua Land Surface Temperature and 3-Band Emissivity 8-Day L3 Global 0.05 Deg CMG","description":"The MYD21C2 dataset is an 8-day composite LST product that uses an algorithm\nbased on a simple averaging method. The algorithm calculates the average\nfrom all the cloud free MYD21A1D and MYD21A1N daily acquisitions from the\n8-day period. Unlike the MYD21A1 data sets where the daytime and nighttime\nacquisitions are separate products, the MYD21A2 contains both daytime and\nnighttime acquisitions. The LST, Quality Control (QC), view zenith angle,\nand viewing time have separate day and night bands, while the values for the\nMODIS emissivity bands 29, 31, and 32 are the average of both the nighttime\nand daytime acquisitions.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1398/MOD21_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD21C2)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21C2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD21C2","rel":"related","title":"Run the example for MODIS/061/MYD21C2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD21C2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21C2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD21C2.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21C2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21C2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/061/MYD21C3","title":"MYD21C3.061 Aqua Land Surface Temperature and 3-Band Emissivity Monthly L3 Global 0.05 Deg CMG","description":"The MYD21C3 dataset is a monthly composite LST product that uses an\nalgorithm based on a simple averaging method. The algorithm calculates the\naverage from all the cloud free MYD21A1D and MYD21A1N daily acquisitions\nfrom the 8-day period. Unlike the MYD21A1 data sets where the daytime and\nnighttime acquisitions are separate products, the MYD21A2 contains both\ndaytime and nighttime acquisitions. The LST, Quality Control (QC), view\nzenith angle, and viewing time have separate day and night bands, while the\nvalues for the MODIS emissivity bands 29, 31, and 32 are the average of both\nthe nighttime and daytime acquisitions.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1398/MOD21_User_Guide_V61.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD21C3)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21C3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD21C3","rel":"related","title":"Run the example for MODIS/061/MYD21C3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD21C3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21C3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD21C3.061","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21C3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD21C3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/062/MCD18A1","title":"MCD18A1.062 Surface Radiation Daily/3-Hour","description":"The MCD18A1 Version 6.2\nis a Moderate Resolution Imaging Spectroradiometer\n(MODIS) Terra and Aqua combined Downward Shortwave Radiation (DSR) gridded\nLevel 3 product produced daily at 1 kilometer pixel resolution with\nestimates of DSR every 3 hours. DSR is incident solar radiation over land\nsurfaces in the shortwave spectrum (300-4,000 nanometers) and is an\nimportant variable in land-surface models that address a variety of\nscientific and application issues. The MCD18 products are based on a\nprototyping algorithm that uses multi-temporal signatures of MODIS data to\nderive surface reflectance and then calculate incident DSR using the look-up\ntable (LUT) approach. The LUTs consider different types of loadings of\naerosols and clouds at a variety of illumination/viewing geometry. Global\nDSR products are generated from MODIS and geostationary satellite data.\nAdditional details regarding the methodology used to create the data are\navailable in the\n[Algorithm Theoretical Basis Document](https://lpdaac.usgs.gov/documents/106/MCD18_ATBD.pdf).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-03T00:00:00Z","2026-08-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/062/MCD18A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_062_MCD18A1","rel":"related","title":"Run the example for MODIS/062/MCD18A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_062_MCD18A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_062_MCD18A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD18A1.062","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_062_MCD18A1.json","rel":"latest-version","title":"MODIS/062/MCD18A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD18A1.json","rel":"predecessor-version","title":"MODIS/061/MCD18A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/062/MCD18A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/062/MCD18A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/062/MCD18C2","title":"MCD18C2.062 Photosynthetically Active Radiation Daily 3-Hour","description":"The MCD18C2 Version 6.2\nis a Moderate Resolution Imaging Spectroradiometer\n(MODIS) Terra and Aqua combined Photosynthetically Active Radiation (PAR)\ngridded Level 3 product produced daily at 0.05 degree (5,600 meters at the\nequator) resolution with estimates of PAR every 3 hours. PAR is incident\nsolar radiation in the visible spectrum (400-700 nanometers) and is an\nimportant variable in land-surface models that address a variety of\nscientific and application issues. The MCD18 products are based on a\nprototyping algorithm that uses multi-temporal signatures of MODIS data to\nderive surface reflectance and then calculate incident PAR using the look-up\ntable (LUT) approach. The LUTs consider different types of loadings of\naerosols and clouds at a variety of illumination/viewing geometry. Global\nPAR products are generated from MODIS and geostationary satellite data.\nAdditional details regarding the methodology used to create the data are\navailable in the\n[Algorithm Theoretical Basis Document](https://lpdaac.usgs.gov/documents/106/MCD18_ATBD.pdf)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2026-08-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/062/MCD18C2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_062_MCD18C2","rel":"related","title":"Run the example for MODIS/062/MCD18C2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_062_MCD18C2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_062_MCD18C2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MCD18C2.062","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_062_MCD18C2.json","rel":"latest-version","title":"MODIS/062/MCD18C2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD18C2.json","rel":"predecessor-version","title":"MODIS/061/MCD18C2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/062/MCD18C2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/062/MCD18C2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MCD43A1","title":"MCD43A1.005 BRDF-Albedo Model Parameters 16-Day L3 Global 500m [deprecated]","description":"The MODerate-resolution Imaging Spectroradiometer\n(MODIS) BRDF/Albedo Model Parameters product (MCD43A1) contains datasets\nproviding users with weighting parameters for the models used to derive the\nAlbedo and BRDF products (MCD43A3 and MCD43A4). The models support the spatial\nrelationship and parameter characterization best describing the differences in\nradiation due to the scattering (anisotropy) of each pixel, relying on\nmulti-date, atmospherically corrected, cloud-cleared input data measured over\n16-day periods. Both Terra and Aqua data are used in the generation of this\nproduct, providing the highest probability for quality input data and\ndesignating it as an MCD, meaning Combined, product.\n\nVersion-5 MODIS/Terra+Aqua BRDF/Albedo\nproducts are Validated Stage 1, meaning that accuracy has been\nestimated using a small number of independent measurements obtained\nfrom selected locations and time periods and ground-truth/field\nprogram efforts. Although there may be later improved versions, these\ndata are ready for use in scientific publications.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2017-03-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MCD43A1","rel":"related","title":"Run the example for MODIS/MCD43A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MCD43A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MCD43A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A1.json","rel":"latest-version","title":"MODIS/061/MCD43A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MCD43A1.json","rel":"successor-version","title":"MODIS/006/MCD43A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MCD43A2","title":"MCD43A2.005 BRDF-Albedo Quality 16-Day Global 500m [deprecated]","description":"The MODerate-resolution Imaging\nSpectroradiometer (MODIS) BRDF/Albedo Quality product (MCD43A2)\ndescribes the overall condition of the other BRDF and Albedo\nproducts. The MCD43A2 product contains 16 days of data at 500 meter\nspatial resolution provided in a level-3 gridded data set in\nSinusoidal projection, and includes albedo quality, snow conditions,\nancillary information, and inversion information.  Both Terra and Aqua\ndata are used in the generation of this product, providing the highest\nprobability for quality input data and designating it as an MCD,\nmeaning Combined, product.\n\nVersion-5 MODIS/Terra+Aqua BRDF/Albedo\nproducts are Validated Stage 1, meaning that accuracy has been\nestimated using a small number of independent measurements obtained\nfrom selected locations and time periods and ground-truth/field\nprogram efforts. Although there may be later improved versions, these\ndata are ready for use in scientific publications.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2017-03-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MCD43A2","rel":"related","title":"Run the example for MODIS/MCD43A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MCD43A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MCD43A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A2.json","rel":"latest-version","title":"MODIS/061/MCD43A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MCD43A2.json","rel":"successor-version","title":"MODIS/006/MCD43A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MCD43A4","title":"MCD43A4.005 BRDF-Adjusted Reflectance 16-Day Global 500m [deprecated]","description":"The MODerate-resolution Imaging\nSpectroradiometer (MODIS) Reflectance product MCD43A4 provides\n500-meter reflectance data adjusted using a bidirectional reflectance\ndistribution function (BRDF) to model the values as if they were taken\nfrom nadir view. The MCD43A4 product contains 16 days of data provided\nin a level-3 gridded data set in Sinusoidal projection.\n\nBoth Terra and Aqua data are used in the generation of this product, providing\nthe highest probability for quality input data and designating it as\nan MCD, meaning Combined, product.\n\nVersion-5 MODIS/Terra+Aqua BRDF/Albedo\nproducts are Validated Stage 1, meaning that accuracy has been\nestimated using a small number of independent measurements obtained\nfrom selected locations and time periods and ground-truth/field\nprogram efforts. 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This\nindex is intended to emphasize the charcoal signal in post-fire\nimages. See\n[Chuvieco et al. (2002)](https://www.tandfonline.com/doi/abs/10.1080/01431160210153129)\nfor details. 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See\n[Huete et al. (2002)](https://www.sciencedirect.com/science/article/pii/S0034425702000962)\nfor details. 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NDSI is\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\n1.0. See\n[Riggs et al. (1994)](https://doi.org/10.1109/IGARSS.1994.399618)\nfor details. 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This product is generated from the MODIS/006/MCD43A4 surface reflectance composites.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_006_NDVI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MCD43A4_006_NDVI","rel":"related","title":"Run the example for MODIS/MCD43A4_006_NDVI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MCD43A4_006_NDVI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MCD43A4_006_NDVI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A4_006_NDVI.json","rel":"latest-version","title":"MODIS/MCD43A4_006_NDVI","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A4_NDVI.json","rel":"predecessor-version","title":"MODIS/MCD43A4_NDVI","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_006_NDVI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_006_NDVI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MCD43A4_006_NDWI","title":"MODIS Combined 16-Day NDWI","description":"The Normalized Difference Water Index (NDWI) is sensitive\nto changes in liquid water content of vegetation canopies.  It is\nderived from the Near-IR band and a second IR band, &approx;1.24&mu;m when\navailable and the nearest available IR band otherwise.  It ranges in\nvalue from -1.0 to 1.0.  See\n[Gao (1996)](https://www.sciencedirect.com/science/article/pii/S0034425796000673)\nfor details. This product is generated from the MODIS/006/MCD43A4 surface reflectance composites.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2023-02-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_006_NDWI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MCD43A4_006_NDWI","rel":"related","title":"Run the example for MODIS/MCD43A4_006_NDWI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MCD43A4_006_NDWI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MCD43A4_006_NDWI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A4_006_NDWI.json","rel":"latest-version","title":"MODIS/MCD43A4_006_NDWI","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A4_NDWI.json","rel":"predecessor-version","title":"MODIS/MCD43A4_NDWI","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_006_NDWI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_006_NDWI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MCD43A4_BAI","title":"MODIS Combined 16-Day BAI [deprecated]","description":"The Burn Area Index (BAI) is generated from the Red and\nNear-IR bands, and measures the spectral distance of each pixel from a\nreference spectral point (the measured reflectance of charcoal).  This\nindex is intended to emphasize the charcoal signal in post-fire\nimages. See\n[Chuvieco et al. (2002)](https://www.tandfonline.com/doi/abs/10.1080/01431160210153129)\nfor details. 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See\n[Huete et al. (2002)](https://www.sciencedirect.com/science/article/pii/S0034425702000962)\nfor details. This product is generated from the MODIS/MCD43A4 surface reflectance composites.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2017-03-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_EVI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MCD43A4_EVI","rel":"related","title":"Run the example for MODIS/MCD43A4_EVI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MCD43A4_EVI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MCD43A4_EVI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A4_006_EVI.json","rel":"latest-version","title":"MODIS/MCD43A4_006_EVI","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A4_006_EVI.json","rel":"successor-version","title":"MODIS/MCD43A4_006_EVI","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_EVI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_EVI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MCD43A4_NDSI","title":"MODIS Combined 16-Day NDSI [deprecated]","description":"The Normalized Difference Snow Index is used to\nidentify snow, based on its characteristically higher reflectance in\nthe visible portion of the spectrum compared to the mid-IR.  NDSI is\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\n1.0. See\n[Riggs et al. (1994)](https://doi.org/10.1109/IGARSS.1994.399618)\nfor details. This product is generated from the MODIS/MCD43A4 surface reflectance composites.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2017-03-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_NDSI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MCD43A4_NDSI","rel":"related","title":"Run the example for MODIS/MCD43A4_NDSI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MCD43A4_NDSI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MCD43A4_NDSI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A4_006_NDSI.json","rel":"latest-version","title":"MODIS/MCD43A4_006_NDSI","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A4_006_NDSI.json","rel":"successor-version","title":"MODIS/MCD43A4_006_NDSI","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_NDSI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_NDSI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MCD43A4_NDVI","title":"MODIS Combined 16-Day NDVI [deprecated]","description":"The Normalized Difference Vegetation Index is\ngenerated from the Near-IR and Red bands of each scene as (NIR - Red) /\n(NIR + Red), and ranges in value from -1.0 to 1.0. This product is generated from the MODIS/MCD43A4 surface reflectance composites.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2017-03-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_NDVI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MCD43A4_NDVI","rel":"related","title":"Run the example for MODIS/MCD43A4_NDVI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MCD43A4_NDVI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MCD43A4_NDVI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A4_006_NDVI.json","rel":"latest-version","title":"MODIS/MCD43A4_006_NDVI","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A4_006_NDVI.json","rel":"successor-version","title":"MODIS/MCD43A4_006_NDVI","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_NDVI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_NDVI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MCD43A4_NDWI","title":"MODIS Combined 16-Day NDWI [deprecated]","description":"The Normalized Difference Water Index (NDWI) is sensitive\nto changes in liquid water content of vegetation canopies.  It is\nderived from the Near-IR band and a second IR band, &approx;1.24&mu;m when\navailable and the nearest available IR band otherwise.  It ranges in\nvalue from -1.0 to 1.0.  See\n[Gao (1996)](https://www.sciencedirect.com/science/article/pii/S0034425796000673)\nfor details. This product is generated from the MODIS/MCD43A4 surface reflectance composites.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2017-03-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_NDWI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MCD43A4_NDWI","rel":"related","title":"Run the example for MODIS/MCD43A4_NDWI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MCD43A4_NDWI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MCD43A4_NDWI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A4_006_NDWI.json","rel":"latest-version","title":"MODIS/MCD43A4_006_NDWI","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MCD43A4_006_NDWI.json","rel":"successor-version","title":"MODIS/MCD43A4_006_NDWI","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_NDWI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MCD43A4_NDWI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MOD09A1","title":"MOD09A1.005 Surface Reflectance 8-Day Global 500m [deprecated]","description":"The MODIS Surface Reflectance products\nprovide an estimate of the surface spectral reflectance as it would be\nmeasured at ground level in the absence of atmospheric scattering or\nabsorption. Low-level data are corrected for atmospheric gases and\naerosols, yielding a level-2 basis for several higher-order gridded\nlevel-2 (L2G) and level-3 products.\n\nMOD09A1 provides\nBands 1-7 at 500-meter resolution in an 8-day gridded level-3 product in the\nSinusoidal projection. Each MOD09A1 pixel contains the best possible L2G\nobservation during an 8-day period as selected on the basis of high observation\ncoverage, low view angle, the absence of clouds or cloud shadow, and aerosol\nloading. Science Data Sets provided for this product include reflectance values\nfor Bands 1-7, quality assessment, and the day of the year for the pixel along\nwith solar, view, and zenith angles.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2017-03-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MOD09A1","rel":"related","title":"Run the example for MODIS/MOD09A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MOD09A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD09A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09A1.json","rel":"latest-version","title":"MODIS/061/MOD09A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD09A1.json","rel":"successor-version","title":"MODIS/006/MOD09A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MOD09GA","title":"MOD09GA.005 Terra Surface Reflectance Daily L2G Global 1km and 500m [deprecated]","description":"The MODIS Surface Reflectance products\nprovide an estimate of the surface spectral reflectance as it would be\nmeasured at ground level in the absence of atmospheric scattering or\nabsorption. Low-level data are corrected for atmospheric gases and\naerosols, yielding a level-2 basis for several higher-order gridded\nlevel-2 (L2G) and level-3 products.\n\nMOD09GA provides Bands\n1-7 in a daily gridded L2G product in the Sinusoidal projection,\nincluding 500-meter reflectance values and 1-kilometer observation and\ngeolocation statistics. 500-m Science Data Sets provided for this\nproduct include reflectance for Bands 1-7, a quality rating,\nobservation coverage, observation number, and 250-m scan\ninformation. 1-kilometer Science Data Sets provided include number of\nobservations, quality state, sensor angles, solar angles, geolocation\nflags, and orbit pointers.\n\nBands:\n\n- num_observations_1km: Number of observations, 1km\n- state_1km: Reflectance Data State QA\n   - Bit 0-1: cloud state bits\n      - 00 (0):  clear\n      - 01 (1):  cloudy\n      - 10 (2):  mixed\n      - 11 (3):  not set, assumed clear\n   - Bit 2: cloud shadow data (1 = yes)\n   - Bit 3-5: land/water flag\n      - 000 (0):  shallow ocean\n      - 001 (1):  land\n      - 010 (2):  ocean coastlines and lake shorelines\n      - 011 (3):  shallow inland water\n      - 100 (4):  ephemeral water\n      - 101 (5):  deep inland water\n      - 110 (6):  continental/moderate ocean\n      - 111 (7):  deep ocean\n   - Bit 6-7: aerosol quantity\n      - 00 (0):  climatology\n      - 01 (1):  low\n      - 10 (2):  average\n      - 11 (3):  high\n   - Bit 8-9: cirrus detected\n      - 00 (0):  none\n      - 01 (1):  small\n      - 10 (2):  average\n      - 11 (3):  high\n   - Bit 10: internal cloud algorithm flag data (1 = Cloud)\n   - Bit 11: internal fire algorithm flag data (1 = Fire)\n   - Bit 12: MOD35 snow/ice flag data (1 = yes)\n   - Bit 13: Pixel is adjacent to cloud data (1 = yes)\n   - Bit 14: BRDF correction performed data (1 = yes)\n   - Bit 15: internal snow mask data (1 = Snow)\n- SensorZenith (degrees * 100): sensor zenith angle\n- SensorAzimuth (degrees * 100): sensor azimuth angle\n- Range (scale 25): pixel to sensor\n- SolarZenith (degrees * 100): solar zenith angle\n- SolarAzimuth (degrees * 100): solar azimuth angle\n- gflags: Geolocation flags\n   - Bit 0-2: Fill\n   - Bit 3: Sensor range validity flag (0 = Valid)\n   - Bit 4: Digital elevation model quality flag (0 = Valid, 1 = Missing/inferior)\n   - Bit 5: Terrain data validity (0 = Valid)\n   - Bit 6: Ellipsoid intersection flag (0 = Valid intersection, 1 = No intersection)\n   - Bit 7: Input data flag (0 = Valid)\n- orbit_pnt: Orbit pointer\n- granule_pnt: Granule pointer (v6 only)\n- num_observations_500m: Number of observations, 500m\n- sur_refl_b01: 500m Surface Reflectance Band 1 (620-670 nm)\n- sur_refl_b02: 500m Surface Reflectance Band 2 (841-876 nm)\n- sur_refl_b03: 500m Surface Reflectance Band 3 (459-479 nm)\n- sur_refl_b04: 500m Surface Reflectance Band 4 (545-565 nm)\n- sur_refl_b05: 500m Surface Reflectance Band 5 (1230-1250 nm)\n- sur_refl_b06: 500m Surface Reflectance Band 6 (1628-1652 nm)\n- sur_refl_b07: 500m Surface Reflectance Band 7 (2105-2155 nm)\n- QC_500m: 500m Reflectance Band Quality\n   - Bit 0-1: MODLAND QA bits\n      - 00 (0):  corrected product produced at ideal quality all bands\n      - 01 (1):  corrected product produced at less than ideal quality some or all bands\n      - 10 (2):  corrected product not produced due to cloud effects all bands\n      - 11 (3):  corrected product not produced due to other reasons some or all bands may be fill value [Note that a value of (11) overrides a value of (01)].\n   - Bit 2-5: band 1 data quality four bit range\n      - 0000 (0): highest quality\n      - 1000 (8): dead detector; data interpolated in L1B\n      - 1001 (9): solar zenith &ge; 86 degrees\n      - 1010 (10): solar zenith &ge; 85 and &lt; 86 degrees\n      - 1011 (11): missing input\n      - 1100 (12): internal constant used in place of climatological data for at least one atmospheric constant\n      - 1101 (13): correction out of bounds pixel constrained to extreme allowable value\n      - 1110 (14): L1B data faulty\n      - 1111 (15): not processed due to deep ocean or clouds\n   - Bit 6-9: band 2 data quality four bit range\n   - Bit 10-13: band 3 data quality four bit range\n   - Bit 14-17: band 4 data quality four bit range\n   - Bit 18-21: band 5 data quality four bit range\n   - Bit 22-25: band 6 data quality four bit range\n   - Bit 26-29: band 7 data quality four bit range\n   - Bit 30: atmospheric correction performed data (1 = yes)\n   - Bit 31: adjacency correction performed data (1 = yes)\n- obscov_500m (%): Observation coverage, 500m\n- iobs_res: Observation number in coarser grid\n- q_scan: 250m scan value information\n   - Bit 0: missing observation in quadrant 4 [+0.5 row, +0.5 column] data (1 = yes)\n   - Bit 1: missing observation in quadrant 3 [+0.5 row, -0.5 column] data (1 = yes)\n   - Bit 2: missing observation in quadrant 2 [-0.5 row, +0.5 column]data (1 = yes)\n   - Bit 3: missing observation in quadrant 1 [-0.5 row, -0.5 column] data (1 = yes)\n   - Bit 4: scan of observation in quadrant 4 [+0.5 row, +0.5 column] data (0 = different, 1 = same)\n   - Bit 5: scan of observation in quadrant 3 [+0.5 row, -0.5 column] data (0 = different, 1 = same)\n   - Bit 6: scan of observation in quadrant 2 [-0.5 row, +0.5 column] data (0 = different, 1 = same)\n   - Bit 7: scan of observation in quadrant 1 [-0.5 row, -0.5 column] data (0 = different, 1 = same)\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2017-03-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09GA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MOD09GA","rel":"related","title":"Run the example for MODIS/MOD09GA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MOD09GA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD09GA#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GA.json","rel":"latest-version","title":"MODIS/061/MOD09GA","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD09GA.json","rel":"successor-version","title":"MODIS/006/MOD09GA","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09GA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09GA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MOD09GA_006_BAI","title":"MODIS Terra Daily BAI","description":"The Burn Area Index (BAI) is generated from the Red and\nNear-IR bands, and measures the spectral distance of each pixel from a\nreference spectral point (the measured reflectance of charcoal).  This\nindex is intended to emphasize the charcoal signal in post-fire\nimages. See\n[Chuvieco et al. (2002)](https://www.tandfonline.com/doi/abs/10.1080/01431160210153129)\nfor details. 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See\n[Huete et al. (2002)](https://www.sciencedirect.com/science/article/pii/S0034425702000962)\nfor details. 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This product is generated from the MODIS/MOD09GA surface reflectance composites.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2017-03-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09GA_NDWI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MOD09GA_NDWI","rel":"related","title":"Run the example for MODIS/MOD09GA_NDWI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MOD09GA_NDWI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD09GA_NDWI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MOD09GA_006_NDWI.json","rel":"latest-version","title":"MODIS/MOD09GA_006_NDWI","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MOD09GA_006_NDWI.json","rel":"successor-version","title":"MODIS/MOD09GA_006_NDWI","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09GA_NDWI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09GA_NDWI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MOD09GQ","title":"MOD09GQ.005 Terra Surface Reflectance Daily L2G Global 250m [deprecated]","description":"The MODIS Surface Reflectance products\nprovide an estimate of the surface spectral reflectance as it would be\nmeasured at ground level in the absence of atmospheric scattering or\nabsorption. Low-level data are corrected for atmospheric gases and\naerosols, yielding a level-2 basis for several higher-order gridded\nlevel-2 (L2G) and level-3 products.\n\nMOD09GQ provides Bands\n1 and 2 at a 250-meter resolution in a daily gridded L2G product in\nthe Sinusoidal projection. Science Data Sets provided for this product\ninclude reflectance for Bands 1 and 2, a quality rating, observation\ncoverage, and observation number. This product is meant to be used in\nconjunction with the MOD09GA where important quality and viewing\ngeometry information is stored.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2017-03-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09GQ","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MOD09GQ","rel":"related","title":"Run the example for MODIS/MOD09GQ in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MOD09GQ_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD09GQ#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GQ.json","rel":"latest-version","title":"MODIS/061/MOD09GQ","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD09GQ.json","rel":"successor-version","title":"MODIS/006/MOD09GQ","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09GQ/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09GQ/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MOD09Q1","title":"MOD09Q1.005 Surface Reflectance 8-Day Global 250m [deprecated]","description":"The MODIS Surface Reflectance products\nprovide an estimate of the surface spectral reflectance as it would be\nmeasured at ground level in the absence of atmospheric scattering or\nabsorption. Low-level data are corrected for atmospheric gases and\naerosols, yielding a level-2 basis for several higher-order gridded\nlevel-2 (L2G) and level-3 products.\n\nMOD09Q1 provides Bands 1\nand 2 at 250-meter resolution in an 8-day gridded level-3 product in the\nSinusoidal projection. Each MOD09Q1 pixel contains the best possible L2G\nobservation during an 8-day period as selected on the basis of\nhigh observation coverage, low view angle, the absence of clouds or cloud\nshadow, and aerosol loading. Science Data Sets provided for this product\ninclude reflectance values for Bands 1 and 2, and a quality rating.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2017-03-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MOD09Q1","rel":"related","title":"Run the example for MODIS/MOD09Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MOD09Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD09Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09Q1.json","rel":"latest-version","title":"MODIS/061/MOD09Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD09Q1.json","rel":"successor-version","title":"MODIS/006/MOD09Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD09Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MOD10A1","title":"MOD10A1.005 Terra Snow Cover Daily Global 500m [deprecated]","description":"The MOD10A1 V5 Snow Cover Daily Global 500m product\ncontains snow cover, snow albedo, fractional snow cover, and quality\nassessment (QA) data. Snow cover data are based on a snow mapping\nalgorithm that employs a Normalized Difference Snow Index (NDSI)\nand other criteria tests.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2016-12-26T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD10A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MOD10A1","rel":"related","title":"Run the example for MODIS/MOD10A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MOD10A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD10A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/63NQASRDPDB0","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD10A1.json","rel":"latest-version","title":"MODIS/061/MOD10A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD10A1.json","rel":"successor-version","title":"MODIS/006/MOD10A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD10A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD10A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MOD11A1","title":"MOD11A1.005 Terra Land Surface Temperature and Emissivity Daily Global 1 km Grid SIN [deprecated]","description":"The MODIS/Terra Land Surface Temperature and\nEmissivity (LST/E) product, MOD11A1, provides per-pixel temperature and\nemissivity values, which are produced daily using the generalized split-window\nLST algorithm.  This algorithm is optimally used to separate ranges of\natmospheric column water vapor and lower boundary air surface temperatures into\ntractable sub-ranges. The surface emissivities in bands 31 and 32 are estimated\nfrom land cover types.\n\nThe V5 MOD11A1 products are projected in a Sinusoidal grid by mapping the\nlevel-2 LST product (MOD11_L2) on 1-kilometer (precisely 0.928-km) grids. The\nMOD11A1 product comprises the following Science Data Set (SDS) layers for\ndaytime and nighttime observations:\n\n- LST_Day_1km: Daytime Land Surface Temperatures (K),  Scale 0.02\n- QC_Day: Daytime Surface Temperature quality control assessments\n- Day_view_time: Daytime LST Observation Times (Hours), Scale 0.1\n- Day_view_angle: Daytime View Zenith Angles (Degrees), Offset -65.0\n- LST_Night_1km: Nighttime Land Surface Temperatures (K),  Scale 0.02\n- QC_Night: Nighttime Surface Temperature quality control assessments\n- Night_view_time: Nighttime LST Observation Times (Hours), Scale 0.1\n- Night_view_angle: Nighttime View Zenith Angles (Degrees), Offset -65.0\n- Emis_31: Bands 31 Emissivity, Scale 0.002, Offset 0.49\n- Emis_32: Bands 32 Emissivity, Scale 0.002, Offset 0.49\n- Clear_night_cov: Nighttime Clear Sky Coverage, Scale 0.0005\n- Clear_day_cov: Daytime Clear Sky Coverage, Scale 0.0005\n\nQC bit flags:\n\n- Bits 0-1: Mandatory QC Flag\n   - 00: LST produced, good quality, not necessary to examine detailed QC.\n   - 01: LST produced, unreliable or unquantifiable quality, recommend examination of more detailed QC.\n   - 10: LST not produced due to cloud effects.\n   - 11: LST not produced primarily due to reasons other than clouds.\n- Bits 2-3: Data Quality Flag\n   - 00: Good quality L1B in bands 31 and 32.\n   - 01: Other quality data.\n   - 10: TBD\n   - 11: TBD\n- Bits 4-5: Emissivity Error Flag\n   - 00: Average emissivity error &le; 0.01\n   - 01: Average emissivity error &le; 0.02\n   - 10: Average emissivity error &le; 0.04\n   - 11: Average emissivity error &gt; 0.04\n- Bits 6-7: LST Error Flag\n   - 00: Average LST error &le; 1 K\n   - 01: Average LST error &le; 2 K\n   - 10: Average LST error &le; 3 K\n   - 11: Average LST error &gt; 3 K\n\nThe V5 Terra/MODIS LST/E products, including MOD11A1, are validated to Stage-2\nthrough field campaigns and radiance-based validation studies.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-05T00:00:00Z","2017-03-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD11A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MOD11A1","rel":"related","title":"Run the example for MODIS/MOD11A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MOD11A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD11A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A1.json","rel":"latest-version","title":"MODIS/061/MOD11A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD11A1.json","rel":"successor-version","title":"MODIS/006/MOD11A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD11A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD11A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MOD11A2","title":"MOD11A2.005 Land Surface Temperature and Emissivity 8-Day Global 1km [deprecated]","description":"The level-3 MODIS global Land Surface\nTemperature (LST) and Emissivity 8-day data are composed from the\ndaily 1-kilometer LST product (MOD11A1) and stored on a 1-km\nSinusoidal grid as the average values of clear-sky LSTs during an\n8-day period.\n\nMOD11A2 is comprised of the following layers for daytime and nighttime\nobservations:\n\n- LST_Day_1km: Daytime Land Surface Temperatures (K),  Scale 0.02\n- QC_Day: Daytime Surface Temperature quality control assessments,\n      see QC bit flags\n- Day_view_time: Daytime LST Observation Times (Hours), Scale 0.1\n- Day_view_angl: Daytime View Zenith Angles (Degrees), Offset -65.0\n- LST_Night_1km: Nighttime Land Surface Temperatures (K),  Scale 0.02\n- QC_Night: Nighttime Surface Temperature quality control assessments,\n      see QC bit flags\n- Night_view_time: Nighttime LST Observation Times (Hours), Scale 0.1\n- Night_view_angl: Nighttime View Zenith Angles (Degrees), Offset -65.0\n- Emis_31: Bands 31 Emissivity, Scale 0.002, Offset 0.49\n- Emis_32: Bands 32 Emissivity, Scale 0.002, Offset 0.49\n- Clear_sky_days: Clear Sky Day Coverage, see Clear Sky Flags.\n- Clear_sky_nights: Clear Sky Nighttime Coverage, see Clear Sky Flags.\n\nQC bit flags:\n\n- Bits 0-1: Mandatory QC Flag\n   - 00: LST produced, good quality, not necessary to examine detailed QC.\n   - 01: LST produced, unreliable or unquantifiable quality, recommend examination of more detailed QC.\n   - 10: LST not produced due to cloud effects.\n   - 11: LST not produced primarily due to reasons other than clouds.\n- Bits 2-3: Data Quality Flag\n   - 00: Good quality L1B in bands 31 and 32.\n   - 01: Other quality data.\n   - 10: TBD\n   - 11: TBD\n- Bits 4-5: Emissivity Error Flag\n   - 00: Average emissivity error &le; 0.01\n   - 01: Average emissivity error &le; 0.02\n   - 10: Average emissivity error &le; 0.04\n   - 11: Average emissivity error &gt; 0.04\n- Bits 6-7: LST Error Flag\n   - 00: Average LST error &le; 1 K\n   - 01: Average LST error &le; 2 K\n   - 10: Average LST error &le; 3 K\n   - 11: Average LST error &gt; 3 K\n\nClear Sky Flags:\n\n- Clear Sky Flag for each day (bit 0 is day 1, bit 1 is day 2, etc):\n   - 0: Day/Night is not clear-sky.\n   - 1: Day/Night is clear-sky.\n\nVersion-5 MODIS/Terra Land Surface Temperature/Emissivity products\nare Validated Stage 2, meaning that accuracy has been assessed over a\nwidely distributed set of locations and time periods via several\nground-truth and validation efforts. Although there may be later\nimproved versions, these data are ready for use in scientific\npublications.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-03-05T00:00:00Z","2017-03-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD11A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MOD11A2","rel":"related","title":"Run the example for MODIS/MOD11A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MOD11A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD11A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A2.json","rel":"latest-version","title":"MODIS/061/MOD11A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD11A2.json","rel":"successor-version","title":"MODIS/006/MOD11A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD11A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD11A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MOD13A1","title":"MOD13A1.005 Vegetation Indices 16-Day L3 Global 500m [deprecated]","description":"Global MODIS vegetation indices are designed to\nprovide consistent spatial and temporal comparisons of vegetation conditions.\nBlue, red, and near-infrared reflectances, centered at 469-nanometers,\n645-nanometers, and 858-nanometers, respectively, are used to determine the\nMODIS daily vegetation indices.\n\nThe MODIS Normalized Difference Vegetation Index (NDVI) complements\nNOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products\nprovide continuity for time series historical applications. MODIS also\nincludes a new Enhanced Vegetation Index (EVI) that minimizes canopy\nbackground variations and maintains sensitivity over dense vegetation\nconditions. The EVI also uses the blue band to remove residual atmosphere\ncontamination caused by smoke and sub-pixel thin cloud clouds. The MODIS\nNDVI and EVI products are computed from atmospherically corrected\nbi-directional surface reflectances that have been masked for water, clouds,\nheavy aerosols, and cloud shadows.\n\nGlobal MOD13A1 data are provided every 16 days at 500-meter spatial\nresolution as a gridded level-3 product in the Sinusoidal projection.\nVegetation indices are used for global monitoring of vegetation conditions\nand are used in products displaying land cover and land cover changes. These\ndata may be used as input for modeling global biogeochemical and hydrologic\nprocesses and global and regional climate. These data also may be used for\ncharacterizing land surface biophysical properties and processes, including\nprimary production and land cover conversion.\n\nVersion-5 MODIS Vegetation Indices products have attained Validation Stage\n3.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2017-03-06T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD13A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MOD13A1","rel":"related","title":"Run the example for MODIS/MOD13A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MOD13A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD13A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A1.json","rel":"latest-version","title":"MODIS/061/MOD13A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD13A1.json","rel":"successor-version","title":"MODIS/006/MOD13A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD13A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD13A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MOD13Q1","title":"MOD13Q1.005 Vegetation Indices 16-Day Global 250m [deprecated]","description":"The MODIS Normalized Difference Vegetation Index\n(NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI\nproducts and provides continuity for time series historical applications. MODIS\nalso includes a new Enhanced Vegetation Index (EVI) that minimizes canopy\nbackground variations and maintains sensitivity over dense vegetation\nconditions. The EVI also uses the blue band to remove residual atmosphere\ncontamination caused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\nand EVI products are computed from atmospherically corrected bi-directional\nsurface reflectances that have been masked for water, clouds, heavy aerosols,\nand cloud shadows.\n\nMOD13AQ1 is comprised of the following layers:\n\n- NDVI: Normalized Difference Vegetation Index, Scale 0.0001\n- EVI: Enhanced Vegetation Index, Scale 0.0001\n- DetailedQA: VI Quality, see MOD13Q1 VI Quality\n- sur_refl_b01: Red surface reflectance, scale 0.0001\n- sur_refl_b02: NIR surface reflectance, scale 0.0001\n- sur_refl_b03: Blue surface reflectance, scale 0.0001\n- sur_refl_b07: MIR surface reflectance, scale 0.0001\n- ViewZenith: View zenith angle, scale 0.01\n- SolarZenith: Solar zenith angle, scale 0.01\n- RelativeAzimuth: Relative azimuth angle, scale 0.1\n- DayOfYear: Julian day of year\n- SummaryQA: Pixel reliability summary QA\n   - -1 Fill/No data: Not processed\n   - 0 Good data: Use with confidence\n   - 1 Marginal data: Useful but look at detailed QA for more information\n   - 2 Snow/ice: Pixel covered with snow/ice\n   - 3: Cloudy: Pixel is cloudy\n\nMOD13Q1 IV Quality\n\n- Bits 0-1: MODLAND_QA\n   - 00 (0): VI produced, good quality\n   - 01 (1): VI produced, check other QA\n   - 10 (2): Pixel produced, probably cloudy\n   - 11 (3): Pixel not produced due to other reason (not clouds)\n- Bits 2-5: VI Usefulness\n   - 0000 (0): Highest quality\n   - 0001 (1): Lower quality\n   - 0010 (2): Decreasing quality\n   - 0100 (4): Decreasing quality\n   - 1000 (8): Decreasing quality\n   - 1001 (9): Decreasing quality\n   - 1010 (10): Decreasing quality\n   - 1100 (12): Lowest quality\n   - 1101 (13): Quality so low that it is not useful\n   - 1110 (14): L1B data faulty\n   - 1111 (15): Not useful for any other reason/not processed\n- Bits 6-7: Aerosol quantity\n   - 00 (0): Climatology\n   - 01 (1): Low\n   - 10 (2): Average\n   - 11 (3): High\n- Bit 8: Adjacent cloud detected\n   - 0: No\n   - 1: Yes\n- Bit 9: Atmosphere BRDF correction performed\n   - 0: No\n   - 1: Yes\n- Bit 10: Mixed Clouds\n    - 0: No\n    - 1: Yes\n- Bits 11-13: Land/Water flag\n   - 000 (0): Shallow ocean\n   - 001 (1): Land\n   - 010 (2): Ocean coastlines and lake shorelines\n   - 011 (3): Shallow inland water\n   - 100 (4): Ephemeral water\n   - 101 (5): Deep inland water\n   - 110 (6): Moderate or continental ocean\n   - 111 (7): Deep ocean\n- Bit 14: Possible snow/ice\n   - 0: No\n   - 1: Yes\n- Bit 15: Possible shadow\n   - 0: No\n   - 1: Yes\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2017-03-06T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD13Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MOD13Q1","rel":"related","title":"Run the example for MODIS/MOD13Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MOD13Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD13Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13Q1.json","rel":"latest-version","title":"MODIS/061/MOD13Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MOD13Q1.json","rel":"successor-version","title":"MODIS/006/MOD13Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD13Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD13Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MOD44W/MOD44W_005_2000_02_24","title":"MOD44W.005 Land Water Mask Derived From MODIS and SRTM","description":"The Global Water Mask uses the SWBD (SRTM Water Body\nData) in combination with MODIS 250m data to create a complete\nglobal map of surface water at 250m spatial resolution, circa 2000-2002.  This\ndataset is intended for use in processing of raster data and for\nmasking out water in final raster data products.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:00:00Z","2000-02-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD44W/MOD44W_005_2000_02_24","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MOD44W_MOD44W_005_2000_02_24","rel":"related","title":"Run the example for MODIS/MOD44W/MOD44W_005_2000_02_24 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MOD44W_MOD44W_005_2000_02_24_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MOD44W_MOD44W_005_2000_02_24#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MOD44W/MOD44W_005_2000_02_24/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MYD09A1","title":"MYD09A1.005 Surface Reflectance 8-Day L3 Global 500m [deprecated]","description":"The MODIS Surface Reflectance products\nprovide an estimate of the surface spectral reflectance as it would be\nmeasured at ground level in the absence of atmospheric scattering or\nabsorption. Low-level data are corrected for atmospheric gases and\naerosols, yielding a level-2 basis for several higher-order gridded\nlevel-2 (L2G) and level-3 products.\n\nMYD09A1 provides\nBands 1-7 at 500-meter resolution in an 8-day gridded level-3 product in the\nSinusoidal projection. Each MYD09A1 pixel contains the best possible L2G\nobservation during an 8-day period as selected on the basis of high observation\ncoverage, low view angle, the absence of clouds or cloud shadow, and aerosol\nloading. Science Data Sets provided for this product include reflectance values\nfor Bands 1-7, quality assessment, and the day of the year for the pixel along\nwith solar, view, and zenith angles.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2017-03-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MYD09A1","rel":"related","title":"Run the example for MODIS/MYD09A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MYD09A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09A1.json","rel":"latest-version","title":"MODIS/061/MYD09A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD09A1.json","rel":"successor-version","title":"MODIS/006/MYD09A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MYD09GA","title":"MYD09GA.005 Aqua Surface Reflectance Daily L2G Global 1km and 500m [deprecated]","description":"The MODIS Surface Reflectance products\nprovide an estimate of the surface spectral reflectance as it would be\nmeasured at ground level in the absence of atmospheric scattering or\nabsorption. Low-level data are corrected for atmospheric gases and\naerosols, yielding a level-2 basis for several higher-order gridded\nlevel-2 (L2G) and level-3 products.\n\nMYD09GA provides Bands\n1-7 in a daily gridded L2G product in the Sinusoidal projection,\nincluding 500-meter reflectance values and 1-kilometer observation and\ngeolocation statistics. 500-m Science Data Sets provided for this\nproduct include reflectance for Bands 1-7, a quality rating,\nobservation coverage, observation number, and 250-m scan\ninformation. 1-kilometer Science Data Sets provided include number of\nobservations, quality state, sensor angles, solar angles, geolocation\nflags, and orbit pointers.\n\nBands:\n\n- num_observations_1km: Number of observations, 1km\n- state_1km: Reflectance Data State QA\n   - Bit 0-1: cloud state bits\n      - 00 (0):  clear\n      - 01 (1):  cloudy\n      - 10 (2):  mixed\n      - 11 (3):  not set, assumed clear\n   - Bit 2: cloud shadow data (1 = yes)\n   - Bit 3-5: land/water flag\n      - 000 (0):  shallow ocean\n      - 001 (1):  land\n      - 010 (2):  ocean coastlines and lake shorelines\n      - 011 (3):  shallow inland water\n      - 100 (4):  ephemeral water\n      - 101 (5):  deep inland water\n      - 110 (6):  continental/moderate ocean\n      - 111 (7):  deep ocean\n   - Bit 6-7: aerosol quantity\n      - 00 (0):  climatology\n      - 01 (1):  low\n      - 10 (2):  average\n      - 11 (3):  high\n   - Bit 8-9: cirrus detected\n      - 00 (0):  none\n      - 01 (1):  small\n      - 10 (2):  average\n      - 11 (3):  high\n   - Bit 10: internal cloud algorithm flag data (1 = Cloud)\n   - Bit 11: internal fire algorithm flag data (1 = Fire)\n   - Bit 12: MOD35 snow/ice flag data (1 = yes)\n   - Bit 13: Pixel is adjacent to cloud data (1 = yes)\n   - Bit 14: BRDF correction performed data (1 = yes)\n   - Bit 15: internal snow mask data (1 = Snow)\n- SensorZenith (degrees * 100): sensor zenith angle\n- SensorAzimuth (degrees * 100): sensor azimuth angle\n- Range (scale 25): pixel to sensor\n- SolarZenith (degrees * 100): solar zenith angle\n- SolarAzimuth (degrees * 100): solar azimuth angle\n- gflags: Geolocation flags\n   - Bit 0-2: Fill\n   - Bit 3: Sensor range validity flag (0 = Valid)\n   - Bit 4: Digital elevation model quality flag (0 = Valid, 1 = Missing/inferior)\n   - Bit 5: Terrain data validity (0 = Valid)\n   - Bit 6: Ellipsoid intersection flag (0 = Valid intersection, 1 = No intersection)\n   - Bit 7: Input data flag (0 = Valid)\n- orbit_pnt: Orbit pointer\n- granule_pnt: Granule pointer (v6 only)\n- num_observations_500m: Number of observations, 500m\n- sur_refl_b01: 500m Surface Reflectance Band 1 (620-670 nm)\n- sur_refl_b02: 500m Surface Reflectance Band 2 (841-876 nm)\n- sur_refl_b03: 500m Surface Reflectance Band 3 (459-479 nm)\n- sur_refl_b04: 500m Surface Reflectance Band 4 (545-565 nm)\n- sur_refl_b05: 500m Surface Reflectance Band 5 (1230-1250 nm)\n- sur_refl_b06: 500m Surface Reflectance Band 6 (1628-1652 nm)\n- sur_refl_b07: 500m Surface Reflectance Band 7 (2105-2155 nm)\n- QC_500m: 500m Reflectance Band Quality\n   - Bit 0-1: MODLAND QA bits\n      - 00 (0):  corrected product produced at ideal quality all bands\n      - 01 (1):  corrected product produced at less than ideal quality some or all bands\n      - 10 (2):  corrected product not produced due to cloud effects all bands\n      - 11 (3):  corrected product not produced due to other reasons some or all bands may be fill value [Note that a value of (11) overrides a value of (01)].\n   - Bit 2-5: band 1 data quality four bit range\n      - 0000 (0): highest quality\n      - 1000 (8): dead detector; data interpolated in L1B\n      - 1001 (9): solar zenith &ge; 86 degrees\n      - 1010 (10): solar zenith &ge; 85 and &lt; 86 degrees\n      - 1011 (11): missing input\n      - 1100 (12): internal constant used in place of climatological data for at least one atmospheric constant\n      - 1101 (13): correction out of bounds pixel constrained to extreme allowable value\n      - 1110 (14): L1B data faulty\n      - 1111 (15): not processed due to deep ocean or clouds\n   - Bit 6-9: band 2 data quality four bit range\n   - Bit 10-13: band 3 data quality four bit range\n   - Bit 14-17: band 4 data quality four bit range\n   - Bit 18-21: band 5 data quality four bit range\n   - Bit 22-25: band 6 data quality four bit range\n   - Bit 26-29: band 7 data quality four bit range\n   - Bit 30: atmospheric correction performed data (1 = yes)\n   - Bit 31: adjacency correction performed data (1 = yes)\n- obscov_500m (%): Observation coverage, 500m\n- iobs_res: Observation number in coarser grid\n- q_scan: 250m scan value information\n   - Bit 0: missing observation in quadrant 4 [+0.5 row, +0.5 column] data (1 = yes)\n   - Bit 1: missing observation in quadrant 3 [+0.5 row, -0.5 column] data (1 = yes)\n   - Bit 2: missing observation in quadrant 2 [-0.5 row, +0.5 column]data (1 = yes)\n   - Bit 3: missing observation in quadrant 1 [-0.5 row, -0.5 column] data (1 = yes)\n   - Bit 4: scan of observation in quadrant 4 [+0.5 row, +0.5 column] data (0 = different, 1 = same)\n   - Bit 5: scan of observation in quadrant 3 [+0.5 row, -0.5 column] data (0 = different, 1 = same)\n   - Bit 6: scan of observation in quadrant 2 [-0.5 row, +0.5 column] data (0 = different, 1 = same)\n   - Bit 7: scan of observation in quadrant 1 [-0.5 row, -0.5 column] data (0 = different, 1 = same)\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2017-03-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MYD09GA","rel":"related","title":"Run the example for MODIS/MYD09GA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MYD09GA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09GA#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GA.json","rel":"latest-version","title":"MODIS/061/MYD09GA","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD09GA.json","rel":"successor-version","title":"MODIS/006/MYD09GA","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MYD09GA_006_BAI","title":"MODIS Aqua Daily BAI","description":"The Burn Area Index (BAI) is generated from the Red and\nNear-IR bands, and measures the spectral distance of each pixel from a\nreference spectral point (the measured reflectance of charcoal).  This\nindex is intended to emphasize the charcoal signal in post-fire\nimages. See\n[Chuvieco et al. (2002)](https://www.tandfonline.com/doi/abs/10.1080/01431160210153129)\nfor details. This product is generated from the MODIS/006/MYD09GA surface reflectance composites.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2023-02-25T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GA_006_BAI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MYD09GA_006_BAI","rel":"related","title":"Run the example for MODIS/MYD09GA_006_BAI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MYD09GA_006_BAI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09GA_006_BAI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MYD09GA_006_BAI.json","rel":"latest-version","title":"MODIS/MYD09GA_006_BAI","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_MYD09GA_BAI.json","rel":"predecessor-version","title":"MODIS/MYD09GA_BAI","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GA_006_BAI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GA_006_BAI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MYD09GA_006_EVI","title":"MODIS Aqua Daily EVI","description":"The Enhanced Vegetation Index (EVI) is generated from the\nNear-IR, Red and Blue bands of each scene, and ranges in value from\n-1.0 to 1.0. See\n[Huete et al. (2002)](https://www.sciencedirect.com/science/article/pii/S0034425702000962)\nfor details. 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NDSI is\ncomputed using the Green and Mid-IR bands, and has a range of -1.0 to\n1.0. See\n[Riggs et al. (1994)](https://doi.org/10.1109/IGARSS.1994.399618)\nfor details. 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It is\nderived from the Near-IR band and a second IR band, &approx;1.24&mu;m when\navailable and the nearest available IR band otherwise.  It ranges in\nvalue from -1.0 to 1.0.  See\n[Gao (1996)](https://www.sciencedirect.com/science/article/pii/S0034425796000673)\nfor details. 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This\nindex is intended to emphasize the charcoal signal in post-fire\nimages. See\n[Chuvieco et al. (2002)](https://www.tandfonline.com/doi/abs/10.1080/01431160210153129)\nfor details. 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See\n[Huete et al. (2002)](https://www.sciencedirect.com/science/article/pii/S0034425702000962)\nfor details. 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Low-level data are corrected for atmospheric gases and\naerosols, yielding a level-2 basis for several higher-order gridded\nlevel-2 (L2G) and level-3 products.\n\nMYD09GQ provides Bands\n1 and 2 at a 250-meter resolution in a daily gridded L2G product in\nthe Sinusoidal projection. Science Data Sets provided for this product\ninclude reflectance for Bands 1 and 2, a quality rating, observation\ncoverage, and observation number. This product is meant to be used in\nconjunction with the MOD09GA where important quality and viewing\ngeometry information is stored.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2017-03-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GQ","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MYD09GQ","rel":"related","title":"Run the example for MODIS/MYD09GQ in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MYD09GQ_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09GQ#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GQ.json","rel":"latest-version","title":"MODIS/061/MYD09GQ","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD09GQ.json","rel":"successor-version","title":"MODIS/006/MYD09GQ","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GQ/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09GQ/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MYD09Q1","title":"MYD09Q1.005 Surface Reflectance 8-Day L3 Global 250m [deprecated]","description":"The MODIS Surface Reflectance products\nprovide an estimate of the surface spectral reflectance as it would be\nmeasured at ground level in the absence of atmospheric scattering or\nabsorption. Low-level data are corrected for atmospheric gases and\naerosols, yielding a level-2 basis for several higher-order gridded\nlevel-2 (L2G) and level-3 products.\n\nMYD09Q1 provides Bands 1\nand 2 at 250-meter resolution in an 8-day gridded level-3 product in the\nSinusoidal projection. Each MYD09Q1 pixel contains the best possible L2G\nobservation during an 8-day period as selected on the basis of\nhigh observation coverage, low view angle, the absence of clouds or cloud\nshadow, and aerosol loading. Science Data Sets provided for this product\ninclude reflectance values for Bands 1 and 2, and a quality rating.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2017-03-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MYD09Q1","rel":"related","title":"Run the example for MODIS/MYD09Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MYD09Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD09Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09Q1.json","rel":"latest-version","title":"MODIS/061/MYD09Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD09Q1.json","rel":"successor-version","title":"MODIS/006/MYD09Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD09Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MYD10A1","title":"MYD10A1.005 Aqua Snow Cover Daily Global 500m [deprecated]","description":"The MYD10A1 V5 Snow Cover Daily Global 500m product\ncontains snow cover, snow albedo, fractional snow cover, and quality\nassessment (QA) data. Snow cover data are based on a snow mapping\nalgorithm that employs a Normalized Difference Snow Index (NDSI)\nand other criteria tests.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2016-12-25T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD10A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MYD10A1","rel":"related","title":"Run the example for MODIS/MYD10A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MYD10A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD10A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/ZFAEMQGSR4XD","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD10A1.json","rel":"latest-version","title":"MODIS/061/MYD10A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD10A1.json","rel":"successor-version","title":"MODIS/006/MYD10A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD10A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD10A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MYD11A1","title":"MYD11A1.005 Land Surface Temperature and Emissivity Daily Global 1 km Grid SIN [deprecated]","description":"The MODIS/Aqua Land Surface Temperature and\nEmissivity (LST/E) product, MYD11A1, provides per-pixel temperature and\nemissivity values, which are produced daily using the generalized split-window\nLST algorithm.  This algorithm is optimally used to separate ranges of\natmospheric column water vapor and lower boundary air surface temperatures into\ntractable sub-ranges. The surface emissivities in bands 31 and 32 are estimated\nfrom land cover types.\n\nThe V5 MYD11A1 products are projected in a Sinusoidal grid by mapping the\nlevel-2 LST product (MYD11_L2) on 1-kilometer (precisely 0.928-km) grids. The\nMYD11A1 product comprises the following Science Data Set (SDS) layers for\ndaytime and nighttime observations: LSTs, quality control assessments,\nobservation times, view zenith angles, clear sky coverages, and bands 31 and 32\nemissivities from land cover types.\n\nThe V5 Aqua/MODIS LST/E products, including MYD11A1, are validated to\nStage-2 through field campaigns and radiance-based validation studies.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-08T00:00:00Z","2017-03-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD11A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MYD11A1","rel":"related","title":"Run the example for MODIS/MYD11A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MYD11A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD11A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A1.json","rel":"latest-version","title":"MODIS/061/MYD11A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD11A1.json","rel":"successor-version","title":"MODIS/006/MYD11A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD11A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD11A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MYD11A2","title":"MYD11A2.005 Land Surface Temperature and Emissivity 8-Day L3 Global 1km [deprecated]","description":"The level-3 MODIS global Land Surface\nTemperature (LST) and Emissivity 8-day data are composed from the\ndaily 1-kilometer LST product (MYD11A1) and stored on a 1-km\nSinusoidal grid as the average values of clear-sky LSTs during an\n8-day period.\n\nMYD11A2 is comprised of the following layers for daytime and nighttime\nobservations:\n\n- LST_Day_1km: Daytime Land Surface Temperatures (K),  Scale 0.02\n- QC_Day: Daytime Surface Temperature quality control assessments,\n      see QC bit flags\n- Day_view_time: Daytime LST Observation Times (Hours), Scale 0.1\n- Day_view_angl: Daytime View Zenith Angles (Degrees), Offset -65.0\n- LST_Night_1km: Nighttime Land Surface Temperatures (K),  Scale 0.02\n- QC_Night: Nighttime Surface Temperature quality control assessments,\n      see QC bit flags\n- Night_view_time: Nighttime LST Observation Times (Hours), Scale 0.1\n- Night_view_angl: Nighttime View Zenith Angles (Degrees), Offset -65.0\n- Emis_31: Bands 31 Emissivity, Scale 0.002, Offset 0.49\n- Emis_32: Bands 32 Emissivity, Scale 0.002, Offset 0.49\n- Clear_sky_days: Clear Sky Day Coverage, see Clear Sky Flags.\n- Clear_sky_nights: Clear Sky Nighttime Coverage, see Clear Sky Flags.\n\nQC bit flags:\n\n- Bits 0-1: Mandatory QC Flag\n   - 00: LST produced, good quality, not necessary to examine detailed QC.\n   - 01: LST produced, unreliable or unquantifiable quality, recommend examination of more detailed QC.\n   - 10: LST not produced due to cloud effects.\n   - 11: LST not produced primarily due to reasons other than clouds.\n- Bits 2-3: Data Quality Flag\n   - 00: Good quality L1B in bands 31 and 32.\n   - 01: Other quality data.\n   - 10: TBD\n   - 11: TBD\n- Bits 4-5: Emissivity Error Flag\n   - 00: Average emissivity error &le; 0.01\n   - 01: Average emissivity error &le; 0.02\n   - 10: Average emissivity error &le; 0.04\n   - 11: Average emissivity error &gt; 0.04\n- Bits 6-7: LST Error Flag\n   - 00: Average LST error &le; 1 K\n   - 01: Average LST error &le; 2 K\n   - 10: Average LST error &le; 3 K\n   - 11: Average LST error &gt; 3 K\n\nClear Sky Flags:\n\n- Clear Sky Flag for each day (bit 0 is day 1, bit 1 is day 2, etc):\n   - 0: Day/Night is not clear-sky.\n   - 1: Day/Night is clear-sky.\n\nVersion-5 MODIS/Aqua Land Surface Temperature/Emissivity products\nare Validated Stage 2, meaning that accuracy has been assessed over a\nwidely distributed set of locations and time periods via several\nground-truth and validation efforts. Although there may be later\nimproved versions, these data are ready for use in scientific\npublications.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2017-03-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD11A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MYD11A2","rel":"related","title":"Run the example for MODIS/MYD11A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MYD11A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD11A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A2.json","rel":"latest-version","title":"MODIS/061/MYD11A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD11A2.json","rel":"successor-version","title":"MODIS/006/MYD11A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD11A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD11A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MYD13A1","title":"MYD13A1.005 Vegetation Indices 16-Day L3 Global 500m [deprecated]","description":"Global MODIS vegetation indices are designed to\nprovide consistent spatial and temporal comparisons of vegetation conditions.\nBlue, red, and near-infrared reflectances, centered at 469-nanometers,\n645-nanometers, and 858-nanometers, respectively, are used to determine the\nMODIS daily vegetation indices.\n\nThe MODIS Normalized Difference Vegetation Index (NDVI) complements\nNOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products\nprovide continuity for time series historical applications. MODIS also\nincludes a new Enhanced Vegetation Index (EVI) that minimizes canopy\nbackground variations and maintains sensitivity over dense vegetation\nconditions. The EVI also uses the blue band to remove residual atmosphere\ncontamination caused by smoke and sub-pixel thin cloud clouds. The MODIS\nNDVI and EVI products are computed from atmospherically corrected\nbi-directional surface reflectances that have been masked for water, clouds,\nheavy aerosols, and cloud shadows.\n\nGlobal MYD13A1 data are provided every 16 days at 500-meter spatial\nresolution as a gridded level-3 product in the Sinusoidal projection.\nVegetation indices are used for global monitoring of vegetation conditions\nand are used in products displaying land cover and land cover changes. These\ndata may be used as input for modeling global biogeochemical and hydrologic\nprocesses and global and regional climate. These data also may be used for\ncharacterizing land surface biophysical properties and processes, including\nprimary production and land cover conversion.\n\nVersion-5 MODIS Vegetation Indices products have attained Validation Stage\n3.\n\nVegetation Indices production is phased between Terra and Aqua\nacquisitions, with Terra beginning on Day 001 and Aqua beginning on Day\n008.\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2017-03-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD13A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MYD13A1","rel":"related","title":"Run the example for MODIS/MYD13A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MYD13A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD13A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13A1.json","rel":"latest-version","title":"MODIS/061/MYD13A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD13A1.json","rel":"successor-version","title":"MODIS/006/MYD13A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD13A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD13A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/MYD13Q1","title":"MYD13Q1.005 Vegetation Indices 16-Day Global 250m [deprecated]","description":"The MODIS Normalized Difference Vegetation Index\n(NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI\nproducts and provides continuity for time series historical applications. MODIS\nalso includes a new Enhanced Vegetation Index (EVI) that minimizes canopy\nbackground variations and maintains sensitivity over dense vegetation\nconditions. The EVI also uses the blue band to remove residual atmosphere\ncontamination caused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI\nand EVI products are computed from atmospherically corrected bi-directional\nsurface reflectances that have been masked for water, clouds, heavy aerosols,\nand cloud shadows.\n\nMOD13AQ1 is comprised of the following layers:\n\n- NDVI: Normalized Difference Vegetation Index, Scale 0.0001\n- EVI: Enhanced Vegetation Index, Scale 0.0001\n- DetailedQA: VI Quality, see MOD13Q1 VI Quality\n- sur_refl_b01: Red surface reflectance, scale 0.0001\n- sur_refl_b02: NIR surface reflectance, scale 0.0001\n- sur_refl_b03: Blue surface reflectance, scale 0.0001\n- sur_refl_b07: MIR surface reflectance, scale 0.0001\n- ViewZenith: View zenith angle, scale 0.01\n- SolarZenith: Solar zenith angle, scale 0.01\n- RelativeAzimuth: Relative azimuth angle, scale 0.1\n- DayOfYear: Julian day of year\n- SummaryQA: Pixel reliability summary QA\n   - -1 Fill/No data: Not processed\n   - 0 Good data: Use with confidence\n   - 1 Marginal data: Useful but look at detailed QA for more information\n   - 2 Snow/ice: Pixel covered with snow/ice\n   - 3: Cloudy: Pixel is cloudy\n\nMOD13Q1 IV Quality\n\n- Bits 0-1: MODLAND_QA\n   - 00 (0): VI produced, good quality\n   - 01 (1): VI produced, check other QA\n   - 10 (2): Pixel produced, probably cloudy\n   - 11 (3): Pixel not produced due to other reason (not clouds)\n- Bits 2-5: VI Usefulness\n   - 0000 (0): Highest quality\n   - 0001 (1): Lower quality\n   - 0010 (2): Decreasing quality\n   - 0100 (4): Decreasing quality\n   - 1000 (8): Decreasing quality\n   - 1001 (9): Decreasing quality\n   - 1010 (10): Decreasing quality\n   - 1100 (12): Lowest quality\n   - 1101 (13): Quality so low that it is not useful\n   - 1110 (14): L1B data faulty\n   - 1111 (15): Not useful for any other reason/not processed\n- Bits 6-7: Aerosol quantity\n   - 00 (0): Climatology\n   - 01 (1): Low\n   - 10 (2): Average\n   - 11 (3): High\n- Bit 8: Adjacent cloud detected\n   - 0: No\n   - 1: Yes\n- Bit 9: Atmosphere BRDF correction performed\n   - 0: No\n   - 1: Yes\n- Bit 10: Mixed Clouds\n    - 0: No\n    - 1: Yes\n- Bits 11-13: Land/Water flag\n   - 000 (0): Shallow ocean\n   - 001 (1): Land\n   - 010 (2): Ocean coastlines and lake shorelines\n   - 011 (3): Shallow inland water\n   - 100 (4): Ephemeral water\n   - 101 (5): Deep inland water\n   - 110 (6): Moderate or continental ocean\n   - 111 (7): Deep ocean\n- Bit 14: Possible snow/ice\n   - 0: No\n   - 1: Yes\n- Bit 15: Possible shadow\n   - 0: No\n   - 1: Yes\n\nPlease visit [LP DAAC 'Citing Our Data' page](https://lpdaac.usgs.gov/citing_our_data) for information on citing LP DAAC datasets.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2017-03-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD13Q1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_MYD13Q1","rel":"related","title":"Run the example for MODIS/MYD13Q1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_MYD13Q1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_MYD13Q1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13Q1.json","rel":"latest-version","title":"MODIS/061/MYD13Q1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD13Q1.json","rel":"successor-version","title":"MODIS/006/MYD13Q1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD13Q1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/MYD13Q1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"MODIS/NTSG/MOD16A2/105","title":"MOD16A2: MODIS Global Terrestrial Evapotranspiration 8-Day Global 1km","description":"The MOD16A2 V105 product provides information about\n8-day global terrestrial evapotranspiration at 1km pixel resolution.\nEvapotranspiration (ET) is the sum of evaporation and plant transpiration\nfrom the Earth's surface to the atmosphere. With long-term ET\ndata, the effects of changes in climate, land use, and ecosystems\ndisturbances can be quantified.\n\nThe MOD16A2 product is produced by the Numerical Terradynamic Simulation\nGroup [NTSG](https://www.ntsg.umt.edu/),\nUniversity of Montana (UMT) in conjunction with NASA Earth Observing\nSystem. For more details about the algorithm used see the [algorithm\ntheoretical basis document](https://scholarworks.umt.edu/cgi/viewcontent.cgi?article=1267&context=ntsg_pubs).\n\n* The period of coverage is 8 days with the exception of\nthe last period at the end of the year which is either 5 or 6 days.\nET/PET units are 0.1mm/5-day for December 27-31 of 2001, 2002,\n2003, 2005, 2006, 2007, 2009, 2010, and 0.1mm/6-day for December\n26-31 of 2000, 2004, 2008 (leap years).\n\n** For some pixels in African\nrainforest, the MODIS albedo data from MCD43B2/MCD43B3 have no\ncloud-free data throughout an entire year. As a result, pixels\nfor that year in all data bands are masked out.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2014-12-27T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/NTSG/MOD16A2/105","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_NTSG_MOD16A2_105","rel":"related","title":"Run the example for MODIS/NTSG/MOD16A2/105 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_NTSG_MOD16A2_105_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_NTSG_MOD16A2_105#terms-of-use","rel":"license","type":"text/html"},{"href":"http://files.ntsg.umt.edu/data/MOD16_TP/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/NTSG/MOD16A2/105/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/NTSG/MOD16A2/105/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/NASADEM_HGT/001","title":"NASADEM: NASA 30m Digital Elevation Model","description":"NASADEM is a reprocessing of SRTM data, with improved accuracy by incorporating auxiliary data\nfrom ASTER GDEM, ICESat GLAS, and PRISM datasets.\n\nThe most significant processing improvements involve void reduction through improved\nphase unwrapping and using ICESat GLAS data for control.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/592/NASADEM_User_Guide_V1.pdf)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-56,180,60]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/NASADEM_HGT/001","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_NASADEM_HGT_001","rel":"related","title":"Run the example for NASA/NASADEM_HGT/001 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_NASADEM_HGT_001_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NASADEM_HGT_001#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/NASADEM_HGT/001/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/ECOSTRESS/L2T_LSTE/V2","title":"ECOSTRESS Land Surface Temperature and Emissivity L2 Global 70 m V002","description":"The ECOSTRESS Land Surface Temperature and Emissivity (ECO_L2T_LSTE) V002\ndataset provides Land Surface Temperature (LST), LST error, Quality Control\nflags, Broadband Emissivity, cloud mask, surface height, water mask, and\nview zenith angle data at a 70m spatial resolution. This product is derived\nfrom ECOSTRESS observations and is essential for understanding surface\nenergy balance and water stress.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1655/ECO_L1C-4_Grid_Tile_User_Guide_V2.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1869/ECOSTRESSL3JET_ATBD_V1.pdf)\n\n* [General Documentation](https://github.com/nasa/ECOSTRESS-Data-Resources)\n\n* [Land Processes Distributed Active Archive Center](https://www.earthdata.nasa.gov/centers/lp-daac)\n\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-09T00:00:00Z","2026-11-09T20:25:24Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/ECOSTRESS/L2T_LSTE/V2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_ECOSTRESS_L2T_LSTE_V2","rel":"related","title":"Run the example for NASA/ECOSTRESS/L2T_LSTE/V2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_ECOSTRESS_L2T_LSTE_V2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_ECOSTRESS_L2T_LSTE_V2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/ECOSTRESS/ECO_L2T_LSTE.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ECOSTRESS/L2T_LSTE/V2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ECOSTRESS/L2T_LSTE/V2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/ECOSTRESS/L2T_STARS/V2","title":"ECOSTRESS Tiled Ancillary NDVI and Albedo L2 Global 70 m V002","description":"The ECOSTRESS Tiled Ancillary NDVI and Albedo (ECO_L2T_STARS) V002 dataset\nprovides Normalized Difference Vegetation Index (NDVI) and albedo data at a\n70m spatial resolution. This ancillary product, essential for understanding\nplant water needs and stress, is created through a data fusion process that\ncombines Visible Infrared Imaging Radiometer Suite (VIIRS) and Harmonized\nLandsat Sentinel (HLS) data. The fusion is performed using the STARS\nalgorithm, a Bayesian timeseries methodology, to align the data with\ndaytime ECOSTRESS overpasses. This product is only generated for\ncorresponding ECOSTRESS Land Surface Temperature and Emissivity tiles.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1655/ECO_L1C-4_Grid_Tile_User_Guide_V2.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1869/ECOSTRESSL3JET_ATBD_V1.pdf)\n\n* [General Documentation](https://github.com/nasa/ECOSTRESS-Data-Resources)\n\n* [Land Processes Distributed Active Archive Center](https://www.earthdata.nasa.gov/centers/lp-daac)\n\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-07-09T00:00:00Z","2026-10-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/ECOSTRESS/L2T_STARS/V2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_ECOSTRESS_L2T_STARS_V2","rel":"related","title":"Run the example for NASA/ECOSTRESS/L2T_STARS/V2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_ECOSTRESS_L2T_STARS_V2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_ECOSTRESS_L2T_STARS_V2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/ECOSTRESS/ECO_L2T_STARS.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ECOSTRESS/L2T_STARS/V2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ECOSTRESS/L2T_STARS/V2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/EMIT/L1B/RAD","title":"EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data 60 m","description":"The EMIT Project is part of the Earth Venture-Instrument (EV-I) Program\ndirected by the Program Director of the NASA Earth Science Division (ESD).\nEMIT is comprised of a VSWIR Infrared Dyson imaging spectrometer adapted for\ninstallation on the International Space Station (ISS). EMIT measures\nradiance between 380 and 2500 nanometers, with an approximate 7 nm bandpass.\nData are collected in a swath that is approximately 75 km wide at the\nequator, with an approximate ground sampling distance of 60 m. See the\nprovider's [NASA EMIT Overview](https://lpdaac.usgs.gov/documents/1695/EMIT_L2B_GHG_User_Guide_V1.pdf)\nfor more details.\n\nEMIT was a particularly useful tool for mapping out greenhouse gases,\nincluding methane, carbon dioxide, and water vapor. This is consistent with\nprevious findings from airborne data, but global nature, revisit frequency\nand wide swath of EMIT provided an unprecedented opportunity to investigate\ngreenhouse gas retrievals.\n\nThe EMIT Level 1B At-Sensor Calibrated Radiance and Geolocation\n(EMITL1BRAD) Version 1 data product provides at-sensor calibrated radiance\nvalues along with observation data in a spatially raw, non-orthocorrected\nformat.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-08-09T00:00:00Z","2026-08-31T01:25:20Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/EMIT/L1B/RAD","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_EMIT_L1B_RAD","rel":"related","title":"Run the example for NASA/EMIT/L1B/RAD in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_EMIT_L1B_RAD_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L1B_RAD#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/EMIT/L1B/RAD/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/EMIT/L1B/RAD/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/EMIT/L2A/RFL","title":"EMIT L2A Estimated Surface Reflectance and Uncertainty and Masks 60 m","description":"The EMIT Project is part of the Earth Venture-Instrument (EV-I) Program\ndirected by the Program Director of the NASA Earth Science Division (ESD).\nEMIT is comprised of a VSWIR Infrared Dyson imaging spectrometer adapted for\ninstallation on the International Space Station (ISS). EMIT measures\nradiance between 380 and 2500 nanometers, with an approximate 7 nm bandpass.\nData are collected in a swath that is approximately 75 km wide at the\nequator, with an approximate ground sampling distance of 60 m. See the\nprovider's [NASA EMIT Overview](https://lpdaac.usgs.gov/documents/1695/EMIT_L2B_GHG_User_Guide_V1.pdf)\nfor more details.\n\nEMIT was a particularly useful tool for mapping out greenhouse gases,\nincluding methane, carbon dioxide, and water vapor. This is consistent with\nprevious findings from airborne data, but global nature, revisit frequency\nand wide swath of EMIT provided an unprecedented opportunity to investigate\ngreenhouse gas retrievals.\n\nThe Level 2A data product contains estimated surface reflectance,\nuncertainty, and mask data. In addition, the geolocation of all pixel\ncenters is included as well as the calculation of observation geometry and\nillumination angles on a pixel-by-pixel basis.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-08-09T00:00:00Z","2026-08-31T01:25:20Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/EMIT/L2A/RFL","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_EMIT_L2A_RFL","rel":"related","title":"Run the example for NASA/EMIT/L2A/RFL in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_EMIT_L2A_RFL_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L2A_RFL#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/EMIT/L2A/RFL/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/EMIT/L2A/RFL/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/EMIT/L2B/CH4ENH","title":"Earth Surface Mineral Dust Source Investigation- Methane Enhancement","description":"The EMIT Project is part of the Earth Venture-Instrument (EV-I) Program\ndirected by the Program Director of the NASA Earth Science Division (ESD).\nEMIT is comprised of a VSWIR Infrared Dyson imaging spectrometer adapted for\ninstallation on the International Space Station (ISS). EMIT measures\nradiance between 380 and 2500 nanometers, with an approximate 7 nm bandpass.\nData are collected in a swath that is approximately 75 km wide at the\nequator, with an approximate ground sampling distance of 60 m. See the\nprovider's [NASA EMIT Overview](https://lpdaac.usgs.gov/documents/1695/EMIT_L2B_GHG_User_Guide_V1.pdf)\nfor more details.\n\nEMIT was a particularly useful tool for mapping out greenhouse gases,\nincluding methane, carbon dioxide, and water vapor. This is consistent with\nprevious findings from airborne data, but global nature, revisit frequency\nand wide swath of EMIT provided an unprecedented opportunity to investigate\ngreenhouse gas retrievals.\n\nThe EMIT Level 2B Methane Enhancement Data (EMITL2BCH4ENH) Version 1 data\nproduct is a total vertical column enhancement estimate of methane in parts\nper million meter (ppm m) based on an adaptive matched filter approach.\nEMITL2BCH4ENH provides per-pixel methane enhancement data used to identify\nmethane plume complexes. The initial release of the EMITL2BCH4ENH data\nproduct will only include granules where methane plume complexes have been\nidentified.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-08-10T00:00:00Z","2024-11-30T19:38:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/EMIT/L2B/CH4ENH","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_EMIT_L2B_CH4ENH","rel":"related","title":"Run the example for NASA/EMIT/L2B/CH4ENH in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_EMIT_L2B_CH4ENH_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L2B_CH4ENH#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/EMIT/L2B/CH4ENH/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/EMIT/L2B/CH4ENH/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/EMIT/L2B/CH4PLM","title":"Earth Surface Mineral Dust Source Investigation- Methane Plume Complexes","description":"The EMIT Project is part of the Earth Venture-Instrument (EV-I) Program\ndirected by the Program Director of the NASA Earth Science Division (ESD).\nEMIT is comprised of a VSWIR Infrared Dyson imaging spectrometer adapted for\ninstallation on the International Space Station (ISS). EMIT measures\nradiance between 380 and 2500 nanometers, with an approximate 7 nm bandpass.\nData are collected in a swath that is approximately 75 km wide at the\nequator, with an approximate ground sampling distance of 60 m. See the\nprovider's [NASA EMIT Overview](https://lpdaac.usgs.gov/documents/1695/EMIT_L2B_GHG_User_Guide_V1.pdf)\nfor more details.\n\nEMIT was a particularly useful tool for mapping out greenhouse gases,\nincluding methane, carbon dioxide, and water vapor. This is consistent with\nprevious findings from airborne data, but global nature, revisit frequency\nand wide swath of EMIT provided an unprecedented opportunity to investigate\ngreenhouse gas retrievals.\n\nThe EMIT Level 2B Estimated Methane Plume Complexes (EMITL2BCH4PLM) Version\n1 data product provides estimated methane plume complexes in parts per\nmillion meter (ppm m) along with uncertainty data. The EMITL2BCH4PLM data\nproduct will only be generated where methane plume complexes have been\nidentified.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-08-10T00:00:00Z","2024-10-26T17:21:33Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/EMIT/L2B/CH4PLM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_EMIT_L2B_CH4PLM","rel":"related","title":"Run the example for NASA/EMIT/L2B/CH4PLM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_EMIT_L2B_CH4PLM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L2B_CH4PLM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/EMIT/L2B/CH4PLM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/EMIT/L2B/CH4PLM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GDDP-CMIP6","title":"NEX-GDDP-CMIP6: NASA Earth Exchange Global Daily Downscaled Climate Projections","description":"The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate\nscenarios derived from the General Circulation Model (GCM) runs conducted\nunder the Coupled Model Intercomparison Project Phase 6 (CMIP6, see\n[Thrasher et al. 2022](https://doi.org/10.7917/OFSG3345)) and across two of\nthe four \"Tier 1\" greenhouse gas emissions scenarios known as Shared\nSocioeconomic Pathways (SSPs).\n\nThe CMIP6 GCM runs were developed in support of the Sixth Assessment Report\nof the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset\nincludes downscaled projections from ScenarioMIP model runs for which daily\nscenarios were produced and distributed through the Earth System Grid\nFederation.\n\nThis collection contains 34 different models. One model, \"GFDL-CM4,\" has\ndata for two different configurations that can be differentiated by further\nfiltering on the *grid_label* property.\n\nBands are replaced as new versions become available and the *version*\nproperty is updated with them.\n\nSee also\n[the provider tech note](https://www.nccs.nasa.gov/wp-content/uploads/2024/03/NEX-GDDP-CMIP6-Tech_Note_4.pdf).\n\n[You can submit data questions about CMIP6 to the provider](https://airtable.com/shr01weJfA7DYq6jf)\nand\n[see their answers](https://airtable.com/shrX4mj20TLSH0r2y/tblUMAYpCfCCwucSV).\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2100-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GDDP-CMIP6","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GDDP-CMIP6","rel":"related","title":"Run the example for NASA/GDDP-CMIP6 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GDDP-CMIP6_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GDDP-CMIP6#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC0-1.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GDDP-CMIP6/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GDDP-CMIP6/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GEOS-CF/v1/fcst/tavg1hr","title":"GEOS-CF fcst tavg1hr v1: Goddard Earth Observing System Composition Forecast","description":"This dataset contains meteorological forecast (fcst) of time-averaged\nfrequency data (tavg1hr). Use the 'creation_time' and 'forecast_time' properties\nto select data of interest.\nThe Goddard Earth Observing System Composition Forecast (GEOS-CF) system is\na global constituent prediction system from NASA's [Global Modeling and Assimilation Office (GMAO)](https://gmao.gsfc.nasa.gov/).\n\nGEOS-CF offers a new tool for atmospheric chemistry research, with the goal\nto supplement NASA's broad range of space-based and in-situ observations.\nGEOS-CF expands on the GEOS weather and aerosol modeling system by\nintroducing the [GEOS-Chem](http://wiki.seas.harvard.edu/geos-chem/)\nchemistry module to provide hindcasts and 5-days forecasts of atmospheric\nconstituents including ozone (O<sub>3</sub>), carbon monoxide (CO), nitrogen dioxide\n(NO<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), and fine particulate matter (PM<sub>2.5</sub>). The\nchemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem\nmodel and readily benefits from the innovations provided by the GEOS-Chem\ncommunity. Model outputs are provided on a 0.25&deg; latitude-longitude grid.\nPlease refer to the [GEOS-CF Product Page](https://gmao.gsfc.nasa.gov/gmao-products/geos-cf/) for more details.\n\n[Evaluation of GEOS-CF](https://doi.org/10.1029/2020MS002413) against satellite, ozonesonde, and surface\nobservations for years 2018&ndash;2019 shows realistic simulated\nconcentrations of O<sub>3</sub>, NO<sub>2</sub>, and CO, with normalized mean biases of &minus;0.1\nto 0.3, normalized root mean square errors between 0.1&ndash;0.4, and\ncorrelations between 0.3&ndash;0.8. Comparisons against surface observations\nhighlight the successful representation of air pollutants in many regions of\nthe world and during all seasons, yet also highlight current limitations,\nsuch as a global high bias in SO<sub>2</sub> and an overprediction of summertime O<sub>3</sub>\nover the Southeast United States.\nGEOS-CF v1 generally overestimates aerosols by 20%&ndash;50% due to known\nissues in GEOS-Chem v12.0.1 that have been addressed in later versions. The\n5-days forecasts have skill scores comparable to the 1-day hindcast. Model\nskills can be improved significantly by applying a bias-correction to the\nsurface model output using a machine-learning approach.\n\n**Please Note:** A new version of GEOS-CF, v2, was released in January 2026. The updates \nto the GEOS-CF system include updates to the GEOS atmospheric general \ncirculation model and the GEOS-Chem chemical model, replaying meteorology \nto [GEOS IT](https://gmao.gsfc.nasa.gov/gmao-products/geos-it/), direct assimilation of stratospheric profiles and column ozone, \nand updates to the anthropogenic emission source. Many of the same products \nare available, and some new products have been added, but other products have \nbeen discontinued. Furthermore, GEOS-CF v2 data will not be back-processed to\ncover the same period as GEOS-CF v1. For this reason, we consider GEOS-CF v2 \nto be a continuation and supplement to the GEOS-CF v data record, but not a \nreplacement for GEOS-CF v1 data. Please see the [GEOS-CF system description](https://gmao.gsfc.nasa.gov/gmao-products/geos-cf/system-description_geos-cf/) \nfor a summary of the updates, and consider the \n[preliminary GEOS-CF v2 evaluation results](https://gmao.gsfc.nasa.gov/science-snapshots/improved-atmospheric-composition-forecasting-in-geos-cf-version-2/) \nwhen deciding which version to use in your analysis.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-10-01T00:00:00Z","2026-01-02T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v1/fcst/tavg1hr","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GEOS-CF_v1_fcst_tavg1hr","rel":"related","title":"Run the example for NASA/GEOS-CF/v1/fcst/tavg1hr in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GEOS-CF_v1_fcst_tavg1hr_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_tavg1hr#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v1/fcst/tavg1hr/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v1/fcst/tavg1hr/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GEOS-CF/v2/ana/htf","title":"GEOS-CF Ana htf v2: Goddard Earth Observing System Composition Forecast","description":"This dataset contains analysis (ana) of high-temporal frequency data (htf) at 15 minute intervals. \nIt is created from the original GEOS-CF collection htf_inst_15mn_glo_L1440x721_slv.\nThe Goddard Earth Observing System Composition Forecast (GEOS-CF) system is\na global constituent prediction system from\nNASA's [Global Modeling and Assimilation Office(GMAO)](\nhttps://gmao.gsfc.nasa.gov/). \n\nGEOS-CF offers a tool for atmospheric chemistry research, with the goal\nto supplement NASA's broad range of space-based and in-situ observations.\nGEOS-CF expands on the GEOS weather and aerosol modeling system by\nintroducing the [GEOS-Chem](http://wiki.seas.harvard.edu/geos-chem/)\nchemistry module to provide hindcasts and 5-days forecasts of atmospheric\nconstituents including ozone (O<sub>3</sub>), carbon monoxide (CO), nitrogen dioxide\n(NO<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), coarse particulate matter \n(PM<sub>10</sub>), and fine particulate matter (PM<sub>2.5</sub>). The\nchemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem\nmodel and readily benefits from the innovations provided by the GEOS-Chem\ncommunity. Outputs are provided globally at 0.25&deg; horizontal resolution. \nPlease refer to the [GEOS-CF Product Page](https://gmao.gsfc.nasa.gov/gmao-products/geos-cf/) for more details.\n\nIn October 2025, a major update to the GEOS-CF system was introduced as \nGEOS-CF v2. This change included updates to the GEOS atmospheric general \ncirculation model and the GEOS-Chem chemical model, replaying meteorology to \n[GEOS IT](https://gmao.gsfc.nasa.gov/gmao-products/geos-it/), direct assimilation \nof stratospheric profiles and column ozone from NASA Aura OMI and MLS instruments, \nand updates to the anthropogenic emission source. Evaluation of these updates is \nongoing; results will be added into this description as they become available. \n[Preliminary results](https://gmao.gsfc.nasa.gov/science-snapshots/improved-atmospheric-composition-forecasting-in-geos-cf-version-2/) \nsuggest that, in relation to GEOS-CF v1, GEOS-CF v2 dramatically \nimproves the representation of PM<sub>2.5</sub> and SO<sub>2</sub> near the surface. \nRepresentations of O<sub>3</sub> and NO<sub>2</sub> are generally improved throughout\nmost of the atmospheric column, with and NO<sub>2</sub> being substantially lower in GEOS-CF v2 compared with v1. \nWhile representation of near-surface O<sub>3</sub> is improved compared to GEOS-CF v1, \nthere remain high biases compared with ozone monitor data from the US Environmental Protection Agency.\n\nUsers of GEOS-CF are encouraged to treat outputs from the original system (v1) \nand the new system (v2) separately in their analysis, due to the extensive changes \nbetween the systems.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-08-04T00:00:00Z","2026-11-08T09:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v2/ana/htf","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GEOS-CF_v2_ana_htf","rel":"related","title":"Run the example for NASA/GEOS-CF/v2/ana/htf in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GEOS-CF_v2_ana_htf_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v2_ana_htf#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v2/ana/htf/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v2/ana/htf/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GEOS-CF/v2/ana/tavg1hr","title":"GEOS-CF ana tavg1hr v2: Goddard Earth Observing System Composition Forecast","description":"This dataset contains meteorological analysis (ana) of time-average one hour\ndata (tavg1hr). It is built by merging the original GEOS-CF collections\nchm_tavg_1hr_glo_L1440x721_slv, met_tavg_1hr_glo_L1440x721_slv, and\nxgc_tavg_1hr_glo_L1440x721_slv.\nThe Goddard Earth Observing System Composition Forecast (GEOS-CF) system is\na global constituent prediction system from\nNASA's [Global Modeling and Assimilation Office(GMAO)](\nhttps://gmao.gsfc.nasa.gov/). \n\nGEOS-CF offers a tool for atmospheric chemistry research, with the goal\nto supplement NASA's broad range of space-based and in-situ observations.\nGEOS-CF expands on the GEOS weather and aerosol modeling system by\nintroducing the [GEOS-Chem](http://wiki.seas.harvard.edu/geos-chem/)\nchemistry module to provide hindcasts and 5-days forecasts of atmospheric\nconstituents including ozone (O<sub>3</sub>), carbon monoxide (CO), nitrogen dioxide\n(NO<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), coarse particulate matter \n(PM<sub>10</sub>), and fine particulate matter (PM<sub>2.5</sub>). The\nchemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem\nmodel and readily benefits from the innovations provided by the GEOS-Chem\ncommunity. Outputs are provided globally at 0.25&deg; horizontal resolution. \nPlease refer to the [GEOS-CF Product Page](https://gmao.gsfc.nasa.gov/gmao-products/geos-cf/) for more details.\n\nIn October 2025, a major update to the GEOS-CF system was introduced as \nGEOS-CF v2. This change included updates to the GEOS atmospheric general \ncirculation model and the GEOS-Chem chemical model, replaying meteorology to \n[GEOS IT](https://gmao.gsfc.nasa.gov/gmao-products/geos-it/), direct assimilation \nof stratospheric profiles and column ozone from NASA Aura OMI and MLS instruments, \nand updates to the anthropogenic emission source. Evaluation of these updates is \nongoing; results will be added into this description as they become available. \n[Preliminary results](https://gmao.gsfc.nasa.gov/science-snapshots/improved-atmospheric-composition-forecasting-in-geos-cf-version-2/) \nsuggest that, in relation to GEOS-CF v1, GEOS-CF v2 dramatically \nimproves the representation of PM<sub>2.5</sub> and SO<sub>2</sub> near the surface. \nRepresentations of O<sub>3</sub> and NO<sub>2</sub> are generally improved throughout\nmost of the atmospheric column, with and NO<sub>2</sub> being substantially lower in GEOS-CF v2 compared with v1. \nWhile representation of near-surface O<sub>3</sub> is improved compared to GEOS-CF v1, \nthere remain high biases compared with ozone monitor data from the US Environmental Protection Agency.\n\nUsers of GEOS-CF are encouraged to treat outputs from the original system (v1) \nand the new system (v2) separately in their analysis, due to the extensive changes \nbetween the systems.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-08-04T00:00:00Z","2026-11-08T23:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v2/ana/tavg1hr","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GEOS-CF_v2_ana_tavg1hr","rel":"related","title":"Run the example for NASA/GEOS-CF/v2/ana/tavg1hr in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GEOS-CF_v2_ana_tavg1hr_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v2_ana_tavg1hr#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v2/ana/tavg1hr/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v2/ana/tavg1hr/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GEOS-CF/v2/fcst/htf","title":"GEOS-CF fcst htf v2: Goddard Earth Observing System Composition Forecast","description":"This dataset contains forecast (fcst) of high-temporal frequency data (htf) at 15 minute intervals. \nIt is created from the original GEOS-CF collection htf_inst_15mn_glo_L1440x721_slv. \nUse the 'creation_time' and 'forecast_time' properties to select data of interest.\nThe Goddard Earth Observing System Composition Forecast (GEOS-CF) system is\na global constituent prediction system from\nNASA's [Global Modeling and Assimilation Office(GMAO)](\nhttps://gmao.gsfc.nasa.gov/). \n\nGEOS-CF offers a tool for atmospheric chemistry research, with the goal\nto supplement NASA's broad range of space-based and in-situ observations.\nGEOS-CF expands on the GEOS weather and aerosol modeling system by\nintroducing the [GEOS-Chem](http://wiki.seas.harvard.edu/geos-chem/)\nchemistry module to provide hindcasts and 5-days forecasts of atmospheric\nconstituents including ozone (O<sub>3</sub>), carbon monoxide (CO), nitrogen dioxide\n(NO<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), coarse particulate matter \n(PM<sub>10</sub>), and fine particulate matter (PM<sub>2.5</sub>). The\nchemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem\nmodel and readily benefits from the innovations provided by the GEOS-Chem\ncommunity. Outputs are provided globally at 0.25&deg; horizontal resolution. \nPlease refer to the [GEOS-CF Product Page](https://gmao.gsfc.nasa.gov/gmao-products/geos-cf/) for more details.\n\nIn October 2025, a major update to the GEOS-CF system was introduced as \nGEOS-CF v2. This change included updates to the GEOS atmospheric general \ncirculation model and the GEOS-Chem chemical model, replaying meteorology to \n[GEOS IT](https://gmao.gsfc.nasa.gov/gmao-products/geos-it/), direct assimilation \nof stratospheric profiles and column ozone from NASA Aura OMI and MLS instruments, \nand updates to the anthropogenic emission source. Evaluation of these updates is \nongoing; results will be added into this description as they become available. \n[Preliminary results](https://gmao.gsfc.nasa.gov/science-snapshots/improved-atmospheric-composition-forecasting-in-geos-cf-version-2/) \nsuggest that, in relation to GEOS-CF v1, GEOS-CF v2 dramatically \nimproves the representation of PM<sub>2.5</sub> and SO<sub>2</sub> near the surface. \nRepresentations of O<sub>3</sub> and NO<sub>2</sub> are generally improved throughout\nmost of the atmospheric column, with and NO<sub>2</sub> being substantially lower in GEOS-CF v2 compared with v1. \nWhile representation of near-surface O<sub>3</sub> is improved compared to GEOS-CF v1, \nthere remain high biases compared with ozone monitor data from the US Environmental Protection Agency.\n\nUsers of GEOS-CF are encouraged to treat outputs from the original system (v1) \nand the new system (v2) separately in their analysis, due to the extensive changes \nbetween the systems.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-08-04T00:00:00Z","2026-11-08T09:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v2/fcst/htf","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GEOS-CF_v2_fcst_htf","rel":"related","title":"Run the example for NASA/GEOS-CF/v2/fcst/htf in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GEOS-CF_v2_fcst_htf_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v2_fcst_htf#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v2/fcst/htf/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v2/fcst/htf/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GEOS-CF/v2/fcst/tavg1hr","title":"GEOS-CF fcst tavg1hr v2: Goddard Earth Observing System Composition Forecast","description":"This dataset contains forecast (fcst) of time-averaged\nfrequency data (tavg1hr). It is built by merging the original GEOS-CF collections\nchm_tavg_1hr_glo_L1440x721_slv, met_tavg_1hr_glo_L1440x721_slv, and\nxgc_tavg_1hr_glo_L1440x721_slv. Use the 'creation_time' and 'forecast_time' properties\nto select data of interest.\nThe Goddard Earth Observing System Composition Forecast (GEOS-CF) system is\na global constituent prediction system from\nNASA's [Global Modeling and Assimilation Office(GMAO)](\nhttps://gmao.gsfc.nasa.gov/). \n\nGEOS-CF offers a tool for atmospheric chemistry research, with the goal\nto supplement NASA's broad range of space-based and in-situ observations.\nGEOS-CF expands on the GEOS weather and aerosol modeling system by\nintroducing the [GEOS-Chem](http://wiki.seas.harvard.edu/geos-chem/)\nchemistry module to provide hindcasts and 5-days forecasts of atmospheric\nconstituents including ozone (O<sub>3</sub>), carbon monoxide (CO), nitrogen dioxide\n(NO<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), coarse particulate matter \n(PM<sub>10</sub>), and fine particulate matter (PM<sub>2.5</sub>). The\nchemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem\nmodel and readily benefits from the innovations provided by the GEOS-Chem\ncommunity. Outputs are provided globally at 0.25&deg; horizontal resolution. \nPlease refer to the [GEOS-CF Product Page](https://gmao.gsfc.nasa.gov/gmao-products/geos-cf/) for more details.\n\nIn October 2025, a major update to the GEOS-CF system was introduced as \nGEOS-CF v2. This change included updates to the GEOS atmospheric general \ncirculation model and the GEOS-Chem chemical model, replaying meteorology to \n[GEOS IT](https://gmao.gsfc.nasa.gov/gmao-products/geos-it/), direct assimilation \nof stratospheric profiles and column ozone from NASA Aura OMI and MLS instruments, \nand updates to the anthropogenic emission source. Evaluation of these updates is \nongoing; results will be added into this description as they become available. \n[Preliminary results](https://gmao.gsfc.nasa.gov/science-snapshots/improved-atmospheric-composition-forecasting-in-geos-cf-version-2/) \nsuggest that, in relation to GEOS-CF v1, GEOS-CF v2 dramatically \nimproves the representation of PM<sub>2.5</sub> and SO<sub>2</sub> near the surface. \nRepresentations of O<sub>3</sub> and NO<sub>2</sub> are generally improved throughout\nmost of the atmospheric column, with and NO<sub>2</sub> being substantially lower in GEOS-CF v2 compared with v1. \nWhile representation of near-surface O<sub>3</sub> is improved compared to GEOS-CF v1, \nthere remain high biases compared with ozone monitor data from the US Environmental Protection Agency.\n\nUsers of GEOS-CF are encouraged to treat outputs from the original system (v1) \nand the new system (v2) separately in their analysis, due to the extensive changes \nbetween the systems.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-08-04T00:00:00Z","2026-11-08T09:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v2/fcst/tavg1hr","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GEOS-CF_v2_fcst_tavg1hr","rel":"related","title":"Run the example for NASA/GEOS-CF/v2/fcst/tavg1hr in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GEOS-CF_v2_fcst_tavg1hr_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v2_fcst_tavg1hr#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v2/fcst/tavg1hr/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GEOS-CF/v2/fcst/tavg1hr/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GPM_L3/IMERG_MONTHLY_V07","title":"GPM: Monthly Global Precipitation Measurement (GPM) vRelease 07","description":"Global Precipitation Measurement (GPM) is an international\nsatellite mission to provide next-generation observations of\nrain and snow worldwide every three hours. The Integrated Multi-satellitE\nRetrievals for GPM (IMERG) is the unified algorithm that provides\nrainfall estimates combining data from all passive-microwave\ninstruments in the GPM Constellation.\n\nThis algorithm is intended to intercalibrate, merge, and interpolate\nall satellite microwave precipitation estimates, together with\nmicrowave-calibrated infrared (IR) satellite estimates, precipitation\ngauge analyses, and potentially other precipitation estimators at fine\ntime and space scales for the TRMM and GPM eras over the entire globe.\nThe system is run several times for each observation time, first\ngiving a quick estimate and successively providing better estimates\nas more data arrive. The final step uses monthly gauge data to\ncreate research-level products. See [IMERG Technical Documentation](https://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf)\nfor more details on the algorithm.\n\nDocumentation:\n\n* [Algorithm Theoretical Basis Document](https://arthurhou.pps.eosdis.nasa.gov/Documents/IMERG_V07_ATBD_final.pdf)\n\n* [IMERG Quality Index](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_QI.pdf)\n\n* [Caveats for IMERG extension into TRMM era](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_TRMMera-caveats.pdf)\n\n* [IMERG Technical Documentation](https://arthurhou.pps.eosdis.nasa.gov/Documents/IMERG_TechnicalDocumentation_final.pdf)\n\n* [Release notes; New Morphing algorithm](https://gpm.nasa.gov/sites/default/files/2024-02/IMERG_V07_ReleaseNotes_240221.pdf)\n\n* [Remote-Sensing Reflectance](https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/doc/README.GPM.pdf)\n\n* [Anomalies](https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html)\n\nThis collection contains data from\n[GPM_3IMERGM_07](https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGM_07/summary)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1998-01-01T00:00:00Z","2025-09-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_MONTHLY_V07","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GPM_L3_IMERG_MONTHLY_V07","rel":"related","title":"Run the example for NASA/GPM_L3/IMERG_MONTHLY_V07 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GPM_L3_IMERG_MONTHLY_V07_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_MONTHLY_V07#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/GPM/IMERG/3B-MONTH/07","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_MONTHLY_V07/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_MONTHLY_V07/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GPM_L3/IMERG_V07","title":"GPM: Global Precipitation Measurement (GPM) Release 07","description":"Global Precipitation Measurement (GPM) is an international\nsatellite mission to provide next-generation observations of\nrain and snow worldwide every three hours. The Integrated Multi-satellitE\nRetrievals for GPM (IMERG) is the unified algorithm that provides\nrainfall estimates combining data from all passive-microwave\ninstruments in the GPM Constellation.\n\nThis algorithm is intended to intercalibrate, merge, and interpolate\nall satellite microwave precipitation estimates, together with\nmicrowave-calibrated infrared (IR) satellite estimates, precipitation\ngauge analyses, and potentially other precipitation estimators at fine\ntime and space scales for the TRMM and GPM eras over the entire globe.\nThe system is run several times for each observation time, first\ngiving a quick estimate and successively providing better estimates\nas more data arrive. The final step uses monthly gauge data to\ncreate research-level products. See [IMERG Technical Documentation](https://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf)\nfor more details on the algorithm.\n\nDocumentation:\n\n* [Algorithm Theoretical Basis Document](https://arthurhou.pps.eosdis.nasa.gov/Documents/IMERG_V07_ATBD_final.pdf)\n\n* [IMERG Quality Index](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_QI.pdf)\n\n* [Caveats for IMERG extension into TRMM era](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_TRMMera-caveats.pdf)\n\n* [IMERG Technical Documentation](https://arthurhou.pps.eosdis.nasa.gov/Documents/IMERG_TechnicalDocumentation_final.pdf)\n\n* [Release notes; New Morphing algorithm](https://gpm.nasa.gov/resources/documents/imerg-v07-release-notes)\n\n* [Remote-Sensing Reflectance](https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/doc/README.GPM.pdf)\n\n* [Anomalies](https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html)\n\nThis collection contains provisional products that are regularly replaced\nwith updated versions when the data become available. The products are\nmarked with a metadata property called 'status'. When a product is initially\nmade available, the property value is 'provisional'. Once a provisional\nproduct has been updated with the final version, this value is updated to\n'permanent'. (Note: No 'permanent' products are available beyond 2025/09/30\ndue to the provider's planned transition to version 8; see\n[here](https://gpm.nasa.gov/data/news/imerg-v08-transition-schedule) for\ndetails.)\n\nThis collection contains data from:\n\n* GPM_3IMERGHH_V07 [doi:10.5067/GPM/IMERG/3B-HH-L/07](https://doi.org/10.5067/GPM/IMERG/3B-HH-L/07)\n* GPM_3IMERGHH_07 [doi:10.5067/GPM/IMERG/3B-HH/07](https://doi.org/10.5067/GPM/IMERG/3B-HH/07)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1998-01-01T00:00:00Z","2026-11-09T03:30:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_V07","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GPM_L3_IMERG_V07","rel":"related","title":"Run the example for NASA/GPM_L3/IMERG_V07 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GPM_L3_IMERG_V07_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_V07#terms-of-use","rel":"license","type":"text/html"},{"href":"ftp://198.118.195.100/gpmallversions/V07","rel":"source"},{"href":"ftp://198.118.195.61/data/imerg/late/","rel":"source"},{"href":"https://doi.org/10.5067/GPM/IMERG/3B-HH-L/07","rel":"cite-as"},{"href":"https://doi.org/10.5067/GPM/IMERG/3B-HH/07","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_V07.json","rel":"latest-version","title":"NASA/GPM_L3/IMERG_V07","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_V06.json","rel":"predecessor-version","title":"NASA/GPM_L3/IMERG_V06","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_V07/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GPM_L3/IMERG_V07/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GRACE/MASS_GRIDS_V03/MASCON","title":"GRACE Monthly Mass Grids Release 6.1 Version 3 - Global Mascons [deprecated]","description":"This dataset contains gridded monthly global water storage/height anomalies\nrelative to a time-mean, derived from GRACE and GRACE-FO and processed at\nJPL using the Mascon approach (RL06.1Mv03). These data are provided in a\nsingle data file in netCDF format, and can be used for analysis for ocean,\nice, and hydrology phenomena. The water storage/height anomalies are given\nin equivalent water thickness units (cm). The solution provided here is\nderived from solving for monthly gravity field variations in terms of\ngeolocated spherical cap mass concentration functions, rather than global\nspherical harmonic coefficients. Additionally, realistic geophysical\ninformation is introduced during the solution inversion to intrinsically\nremove correlated error. Thus, these Mascon grids do not need to be\ndestriped or smoothed, like traditional spherical harmonic gravity\nsolutions. The complete Mascon solution consists of 4,551 relatively\nindependent estimates of surface mass change that have been derived using an\nequal-area 3-degree grid of individual mascons. It should be noted that this\ndataset does not correct for leakage errors across coastlines; it is\ntherefore recommended only for users who want to apply their own algorithm\nto separate between land and ocean mass very near coastlines.\nThis RL06.1Mv03 is an updated version of the previous [Tellus JPL Mascon\nRL06Mv02](https://doi.org/10.5067/TEMSC-3JC62). RL06.1Mv03 differs from RL06Mv02 only\nin the Level-1B accelerometer transplant data that is used for the GF2\n(GRACE-FO 2) satellite; RL06.1Mv03 uses the ACH data product.\nFor more information,\nplease visit [GRACE page](https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/).\nFor a detailed description on the Mascon solution, including\nthe mathematical derivation, implementation of geophysical constraints, and\nsolution validation, please see [Watkins et al., 2015](https://doi.org/10.1002/2014JB011547).\nThis product is intended for expert use only; other users are encouraged to\nuse the [CRI-filtered Mascon dataset](https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.1_V3).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-03-31T00:00:00Z","2024-03-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V03/MASCON","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GRACE_MASS_GRIDS_V03_MASCON","rel":"related","title":"Run the example for NASA/GRACE/MASS_GRIDS_V03/MASCON in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GRACE_MASS_GRIDS_V03_MASCON_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_V03_MASCON#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_MASCON.json","rel":"latest-version","title":"NASA/GRACE/MASS_GRIDS_V04/MASCON","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_MASCON.json","rel":"predecessor-version","title":"NASA/GRACE/MASS_GRIDS/MASCON","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_MASCON.json","rel":"successor-version","title":"NASA/GRACE/MASS_GRIDS_V04/MASCON","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V03/MASCON/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V03/MASCON/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GRACE/MASS_GRIDS_V03/MASCON_CRI","title":"GRACE Monthly Mass Grids Version 03 - Global Mascon (CRI Filtered) [deprecated]","description":"This dataset contains gridded monthly global water storage/height anomalies\nrelative to a time-mean, derived from GRACE and GRACE-FO and processed at\nJPL using the Mascon approach (RL06.1Mv03). This version of the data employs\na Coastal Resolution Improvement (CRI) filter that reduces signal leakage\nerrors across coastlines. These data are provided in a single data file in\nnetCDF format, and can be used for analysis for ocean, ice, and hydrology\nphenomena. The water storage/height anomalies are given in equivalent water\nthickness units (cm). The solution provided here is derived from solving for\nmonthly gravity field variations in terms of geolocated spherical cap mass\nconcentration functions, rather than global spherical harmonic coefficients.\nAdditionally, realistic geophysical information is introduced during the\nsolution inversion to intrinsically remove correlated error. Thus, these\nMascon grids do not need to be destriped or smoothed, like traditional\nspherical harmonic gravity solutions. The complete Mascon solution consists\nof 4,551 relatively independent estimates of surface mass change that have\nbeen derived using an equal-area 3-degree grid of individual mascons.\nA subset of these individual mascons span coastlines, and contain mixed land\nand ocean mass change signals. In a post-processing step, the CRI filter is\napplied to those mixed land/ocean Mascons to separate land and ocean mass.\nThe land mask used to perform this separation is provided in the same\ndirectory as this dataset. Since the individual mascons act as an inherent\nsmoother on the gravity field, a set of optional gain factors\n(for continental hydrology applications) that can be applied to the solution\nto study mass change signals at sub-mascon resolution is also provided\nwithin the same data directory as the Mascon data. This RL06.1Mv03 is an\nupdated version of the previous [Tellus JPL Mascon RL06Mv02](https://doi.org/10.5067/TEMSC-3JC62).\nRL06.1Mv03 differs from RL06Mv02 only in the Level-1B accelerometer\ntransplant data that is used for the GF2 (GRACE-FO 2)satellite;\nRL06.1Mv03 uses the ACH data product. For more information,\nplease visit [GRACE page](https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/).\nFor a detailed description on the Mascon solution, including the\nmathematical derivation, implementation of geophysical constraints, and\nsolution validation, please see [Watkins et al., 2015](https://doi.org/10.1002/2014JB011547).\nFor a detailed description of the CRI filter implementation,\nplease see [Wiese et al., 2016](https://doi.org/10.1002/2016WR019344).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-03-31T00:00:00Z","2024-03-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V03/MASCON_CRI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GRACE_MASS_GRIDS_V03_MASCON_CRI","rel":"related","title":"Run the example for NASA/GRACE/MASS_GRIDS_V03/MASCON_CRI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GRACE_MASS_GRIDS_V03_MASCON_CRI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_V03_MASCON_CRI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_MASCON_CRI.json","rel":"latest-version","title":"NASA/GRACE/MASS_GRIDS_V04/MASCON_CRI","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_MASCON_CRI.json","rel":"predecessor-version","title":"NASA/GRACE/MASS_GRIDS/MASCON_CRI","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_MASCON_CRI.json","rel":"successor-version","title":"NASA/GRACE/MASS_GRIDS_V04/MASCON_CRI","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V03/MASCON_CRI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V03/MASCON_CRI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GRACE/MASS_GRIDS_V04/LAND","title":"GRACE Monthly Mass Grids Release 06 Version 04 - Land","description":"The monthly land mass grids contain water mass anomalies given as equivalent\nwater thickness derived from GRACE & GRACE-FO time-variable gravity\nobservations during the specified timespan, and relative to the specified\ntime-mean reference period. The equivalent water thickness represents the\ntotal terrestrial water storage anomalies from soil moisture, snow, surface\nwater (incl. rivers, lakes, reservoirs etc.), as well as groundwater and\naquifers. A glacial isostatic adjustment (GIA) correction has been applied,\nand standard corrections for geocenter (degree-1), C20 (degree-20) and\nC30 (degree-30) are incorporated. Post-processing filters have been applied\nto reduce correlated errors. Version 04 (v04) of the terrestrial water\nstorage data uses updated and consistent C20 and Geocenter corrections\n(i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal\ncorrection to account for the non-spherical shape of the Earth when mapping\ngravity anomalies to surface mass change.\n\nThe GRACE Tellus (GRCTellus) Monthly Mass Grids dataset is produced\nby three centers: CSR (U. Texas / Center for Space Research),\nGFZ (GeoForschungsZentrum Potsdam), and JPL\n(NASA Jet Propulsion Laboratory). Each center is a part\nof the GRACE Ground System and generates Level-2 data\n(spherical harmonic fields) used in this dataset.\nThe output includes spherical harmonic coefficients\nof the gravity field and of the dealiasing fields used to compute them.\nSince each center independently produces the coefficients, the results\nmay be slightly different. It is recommended for most users to\nuse the mean of all three datasets. See the provider's [choosing a\nsolution](https://grace.jpl.nasa.gov/data/choosing-a-solution/) page\nfor more details.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-04-01T00:00:00Z","2017-01-07T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V04/LAND","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GRACE_MASS_GRIDS_V04_LAND","rel":"related","title":"Run the example for NASA/GRACE/MASS_GRIDS_V04/LAND in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GRACE_MASS_GRIDS_V04_LAND_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_V04_LAND#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_LAND.json","rel":"latest-version","title":"NASA/GRACE/MASS_GRIDS_V04/LAND","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_LAND.json","rel":"predecessor-version","title":"NASA/GRACE/MASS_GRIDS/LAND","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V04/LAND/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V04/LAND/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GRACE/MASS_GRIDS_V04/MASCON","title":"GRACE Monthly Mass Grids Release 6.3 Version 4 - Global Mascons","description":"This dataset contains gridded monthly global water storage/height anomalies\nrelative to a time-mean, derived from GRACE and GRACE-FO and processed at\nJPL using the Mascon approach (RL06.3Mv04). These data are provided in a\nsingle data file in netCDF format, and can be used for analysis for ocean,\nice, and hydrology phenomena. The water storage/height anomalies are given\nin equivalent water thickness units (cm). The solution provided here is\nderived from solving for monthly gravity field variations in terms of\ngeolocated spherical cap mass concentration functions, rather than global\nspherical harmonic coefficients. Additionally, realistic geophysical\ninformation is introduced during the solution inversion to intrinsically\nremove correlated error. Thus, these Mascon grids do not need to be\nde-correlated or smoothed, like traditional spherical harmonic gravity\nsolutions.\n\nThe complete Mascon solution consists of 4,551 relatively\nindependent estimates of surface mass change that have been derived using an\nequal-area 3-degree grid of individual mascons. It should be noted that this\ndataset does not correct for leakage errors across coastlines; it is\ntherefore recommended only for users who want to apply their own algorithm\nto separate between land and ocean mass very near coastlines.\n\nFor more information, please visit [GRACE page](https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/).\nFor a detailed description on the Mascon solution, including\nthe mathematical derivation, implementation of geophysical constraints, and\nsolution validation, please see [Watkins et al., 2015](https://doi.org/10.1002/2014JB011547).\nThis product is intended for expert use only; other users are encouraged to\nuse the [CRI-filtered Mascon dataset](https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4).\n\nThis RL06.3Mv04 is an updated version of the previous [Tellus JPL Mascon\nRL06.1Mv03](https://doi.org/10.5067/TEMSC-3MJ63).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-03-31T00:00:00Z","2024-09-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V04/MASCON","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GRACE_MASS_GRIDS_V04_MASCON","rel":"related","title":"Run the example for NASA/GRACE/MASS_GRIDS_V04/MASCON in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GRACE_MASS_GRIDS_V04_MASCON_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_V04_MASCON#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_MASCON.json","rel":"latest-version","title":"NASA/GRACE/MASS_GRIDS_V04/MASCON","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V03_MASCON.json","rel":"predecessor-version","title":"NASA/GRACE/MASS_GRIDS_V03/MASCON","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V04/MASCON/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V04/MASCON/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GRACE/MASS_GRIDS_V04/MASCON_CRI","title":"GRACE Monthly Mass Grids Version 04 - Global Mascon (CRI Filtered)","description":"This dataset contains gridded monthly global water storage/height anomalies\nrelative to a time-mean, derived from GRACE and GRACE-FO and processed at\nJPL using the Mascon approach (RL06.3Mv04). These data are provided in a\nsingle data file in netCDF format, and can be used for analysis for ocean,\nice, and hydrology phenomena. The water storage/height anomalies are given\nin equivalent water thickness units (cm). The solution provided here is\nderived from solving for monthly gravity field variations in terms of\ngeolocated spherical cap mass concentration functions, rather than global\nspherical harmonic coefficients. Additionally, realistic geophysical\ninformation is introduced during the solution inversion to intrinsically\nremove correlated error. Thus, these Mascon grids do not need to be\nde-correlated or smoothed, like traditional spherical harmonic gravity\nsolutions.\n\nThe complete Mascon solution consists of 4,551 relatively\nindependent estimates of surface mass change that have been derived using an\nequal-area 3-degree grid of individual mascons. It should be noted that this\ndataset does not correct for leakage errors across coastlines; it is\ntherefore recommended only for users who want to apply their own algorithm\nto separate between land and ocean mass very near coastlines.\n\nFor more information, please visit [GRACE page](https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/).\nFor a detailed description on the Mascon solution, including\nthe mathematical derivation, implementation of geophysical constraints, and\nsolution validation, please see [Watkins et al., 2015](https://doi.org/10.1002/2014JB011547).\nThis product is intended for expert use only; other users are encouraged to\nuse the [CRI-filtered Mascon dataset](https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.3_V4).\n\nThis RL06.3Mv04 is an updated version of the previous [Tellus JPL Mascon\nRL06.1Mv03](https://doi.org/10.5067/TEMSC-3MJ63).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-03-31T00:00:00Z","2024-09-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V04/MASCON_CRI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GRACE_MASS_GRIDS_V04_MASCON_CRI","rel":"related","title":"Run the example for NASA/GRACE/MASS_GRIDS_V04/MASCON_CRI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GRACE_MASS_GRIDS_V04_MASCON_CRI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_V04_MASCON_CRI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_MASCON_CRI.json","rel":"latest-version","title":"NASA/GRACE/MASS_GRIDS_V04/MASCON_CRI","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V03_MASCON_CRI.json","rel":"predecessor-version","title":"NASA/GRACE/MASS_GRIDS_V03/MASCON_CRI","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V04/MASCON_CRI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V04/MASCON_CRI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GRACE/MASS_GRIDS_V04/OCEAN","title":"GRACE Monthly Mass Grids Release 06 Version 04 - Ocean","description":"GRACE Tellus Monthly Mass Grids provides monthly\ngravitational anomalies relative to a 2004-2010 time-mean baseline.\nThe data contained in this dataset are units of \"Equivalent Water Thickness\"\nwhich represent the deviations of mass in terms of vertical extent\nof water in centimeters. See the provider's [Monthly Mass\nGrids Overview](https://grace.jpl.nasa.gov/data/monthly-mass-grids/)\nfor more details.\n\nThe GRACE Tellus (GRCTellus) Monthly Mass Grids Ocean dataset is produced\nby three centers: CSR (U. Texas / Center for Space Research),\nGFZ (GeoForschungsZentrum Potsdam), and JPL\n(NASA Jet Propulsion Laboratory). Each center is a part\nof the GRACE Ground System and generates Level-2 data\n(spherical harmonic fields) used in this dataset.\nThe output includes spherical harmonic coefficients\nof the gravity field and of the dealiasing fields used to compute them.\nSince each center independently produces the coefficients, the results\nmay be slightly different. It is recommended for most users to\nuse the mean of all three datasets. See the provider's [choosing a\nsolution](https://grace.jpl.nasa.gov/data/choosing-a-solution/) page\nfor more details.\n\n**Note**\n\n* Land leakage correction: Ocean signals are typically weaker\n  than land signals, by factors of 2 and more, on seasonal and\n  interannual time scales. To minimize leakage from high land\n  signals onto ocean signals, a destriping filter has been applied\n  which may cause cause correlations over much larger distances.\n\n* The GRCTellus Ocean datasets are optimized to examine regional\n  ocean bottom pressure, but NOT intended to be spatially\n  averaged to determine global mean ocean mass.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-04-04T00:00:00Z","2017-10-25T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V04/OCEAN","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GRACE_MASS_GRIDS_V04_OCEAN","rel":"related","title":"Run the example for NASA/GRACE/MASS_GRIDS_V04/OCEAN in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GRACE_MASS_GRIDS_V04_OCEAN_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_V04_OCEAN#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_V04_OCEAN.json","rel":"latest-version","title":"NASA/GRACE/MASS_GRIDS_V04/OCEAN","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_OCEAN.json","rel":"predecessor-version","title":"NASA/GRACE/MASS_GRIDS/OCEAN","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V04/OCEAN/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GRACE/MASS_GRIDS_V04/OCEAN/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/GSFC/MERRA/aer_nv/2","title":"MERRA-2 M2I3NVAER: Aerosol Mixing Ratio V5.12.4","description":"M2I3NVAER (or inst3_3d_aer_Nv) is an instantaneous 3-dimensional 3-hourly\ndata collection in Modern-Era Retrospective analysis for Research and\nApplications version 2 (MERRA-2). This collection consists of assimilations\nof aerosol mixing ratio parameters at 72 model layers, such as dust, sulphur\ndioxide, sea salt, black carbon, and organic carbon. The data field is\navailable every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00,...\n,21:00 UTC. Section 4.2 of the\n[MERRA-2 File Specification document](https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich785.pdf)\nprovides\npressure values nominal for a 1000 hPa surface pressure and refers to the\ntop edge of the layer. The lev=1 is for the top layer, and lev=72 is for\nthe bottom (or surface) model layer.\n\nMERRA-2 is the latest version of global atmospheric reanalysis for the\nsatellite era produced by NASA Global Modeling and Assimilation Office\n(GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4.\nThe dataset covers the period of 1980-present with the latency of ~3 weeks\nafter the end of a month.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1980-01-01T00:00:00Z","2026-10-01T21:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/GSFC/MERRA/aer_nv/2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_GSFC_MERRA_aer_nv_2","rel":"related","title":"Run the example for NASA/GSFC/MERRA/aer_nv/2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GSFC_MERRA_aer_nv_2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_aer_nv_2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://earthdata.nasa.gov/collaborate/open-data-services-and-software/data-information-policy","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GSFC/MERRA/aer_nv/2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/GSFC/MERRA/aer_nv/2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/HLS/HLSL30/v002","title":"HLSL30: HLS-2 Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m","description":"The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface\nreflectance (SR) and top of atmosphere (TOA) brightness data from a virtual\nconstellation of satellite sensors. The Operational Land Imager (OLI) is\nhoused aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites, while\nthe Multi-Spectral Instrument (MSI) is mounted aboard Europe's Copernicus\nSentinel-2A and Sentinel-2B satellites. The combined measurement enables\nglobal observations of the land every 2 to 3 days at 30m spatial\nresolution.\nThe HLS project uses a set of algorithms\nto obtain seamless products from OLI and MSI that include atmospheric\ncorrection, cloud and cloud-shadow masking, spatial co-registration and\ncommon gridding, illumination and view angle normalization, and spectral\nbandpass adjustment.\n\nThe HLS project distributes data as two separate products: HLSL30\n(Landsat 8/9) and HLSS30 (Sentinel-2 A/B). They both provide 30m Nadir\nBidirectional Reflectance Distribution Function (BRDF), Adjusted Reflectance\n(NBAR).\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/769/HLS_ATBD_V15_provisional.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/hlsl30v002/)\n* S30 catalog link: [NASA/HLS/HLSS30/v002](NASA_HLS_HLSS30_v002)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-11T00:00:00Z","2026-11-04T23:50:25Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/HLS/HLSL30/v002","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_HLS_HLSL30_v002","rel":"related","title":"Run the example for NASA/HLS/HLSL30/v002 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_HLS_HLSL30_v002_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSL30_v002#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/HLS/HLSL30.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/HLS/HLSL30/v002/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/HLS/HLSL30/v002/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/HLS/HLSS30/v002","title":"HLSS30: HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m","description":"The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface\nreflectance data from the Operational Land Imager (OLI) aboard the joint\nNASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard\nEurope's Copernicus Sentinel-2A satellites. The combined\nmeasurement enables global observations of the land every 2-3 days at\n30-meter (m) spatial resolution.\nThe HLS project uses a set of algorithms\nto obtain seamless products from OLI and MSI that include atmospheric\ncorrection, cloud and cloud-shadow masking, spatial co-registration and\ncommon gridding, illumination and view angle normalization, and spectral\nbandpass adjustment.\n\nThe HLS project distributes data as two separate products: HLSL30\n(Landsat 8/9) and HLSS30 (Sentinel-2 A/B). They both provide 30m Nadir\nBidirectional Reflectance Distribution Function (BRDF), Adjusted Reflectance\n(NBAR).\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/769/HLS_ATBD_V15_provisional.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/hlss30v002/)\n* L30 catalog link: [NASA/HLS/HLSL30/v002](NASA_HLS_HLSL30_v002)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-11-28T00:00:00Z","2026-11-04T23:57:49Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/HLS/HLSS30/v002","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_HLS_HLSS30_v002","rel":"related","title":"Run the example for NASA/HLS/HLSS30/v002 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_HLS_HLSS30_v002_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSS30_v002#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/HLS/HLSS30.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/HLS/HLSS30/v002/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/HLS/HLSS30/v002/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/LANCE/NOAA20_VIIRS/C2","title":"VJ114IMGTDL_NRT Daily Raster: VIIRS (NOAA-20) Band 375m Active Fire","description":"NOAA-20 (JPSS-1) Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fire detection\nproduct is based on the instrument's 375m nominal resolution data. Compared\nto other coarser resolution (&ge; 1km) satellite fire detection products, \nthe improved 375 m data provide greater response over fires of relatively\nsmall areas, as well as improved mapping of large fire perimeters.\nConsequently, the data are well suited for use in support of fire management\n(e.g., near real-time alert systems), as well as other science applications\nrequiring improved fire mapping fidelity.\n\nThe data in the near-real-time dataset are not considered to be of science\nquality.\n\nAdditional information can be found [here](https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-10-08T00:00:00Z","2026-11-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/LANCE/NOAA20_VIIRS/C2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_LANCE_NOAA20_VIIRS_C2","rel":"related","title":"Run the example for NASA/LANCE/NOAA20_VIIRS/C2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_LANCE_NOAA20_VIIRS_C2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_NOAA20_VIIRS_C2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/LANCE/NOAA20_VIIRS/C2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/LANCE/NOAA20_VIIRS/C2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/LANCE/SNPP_VIIRS/C2","title":"VNP14IMGTDL_NRT Daily Raster: VIIRS (S-NPP) Band 375m Active Fire","description":"Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fire detection\nproduct is based on the instrument's 375m nominal resolution data. Compared\nto other coarser resolution (&ge; 1km) satellite fire detection products, \nthe improved 375 m data provide greater response over fires of relatively\nsmall areas, as well as improved mapping of large fire perimeters.\nConsequently, the data are well suited for use in support of fire management\n(e.g., near real-time alert systems), as well as other science applications\nrequiring improved fire mapping fidelity.\n\nThe data in the near-real-time dataset are not considered to be of science\nquality.\n\nAdditional information can be found [here](https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-09-03T00:00:00Z","2026-11-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/LANCE/SNPP_VIIRS/C2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_LANCE_SNPP_VIIRS_C2","rel":"related","title":"Run the example for NASA/LANCE/SNPP_VIIRS/C2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_LANCE_SNPP_VIIRS_C2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_SNPP_VIIRS_C2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/LANCE/SNPP_VIIRS/C2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/LANCE/SNPP_VIIRS/C2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/NEX-DCP30","title":"NEX-DCP30: NASA Earth Exchange Downscaled Climate Projections","description":"The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios\nfor the conterminous United States that are derived from the General\nCirculation Model (GCM) runs conducted under the Coupled Model\nIntercomparison Project Phase 5 (CMIP5, see [Taylor et al. 2012](https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1))\nand across the four greenhouse gas emissions scenarios known as\nRepresentative Concentration Pathways (RCPs, see [Meinshausen et al. 2011](https://rd.springer.com/article/10.1007%2Fs10584-011-0156-z#page-1))\ndeveloped for the Fifth Assessment Report of the Intergovernmental Panel\non Climate Change (IPCC AR5). The purpose of these datasets is to provide\na set of high resolution, bias-corrected climate change projections that\ncan be used to evaluate climate change impacts on processes that are\nsensitive to finer-scale climate gradients and the effects of local\ntopography on climate conditions.\n\nThe dataset contains monthly projections covering the periods from 1950\nthrough 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run).\nIt includes downscaled projections from 33 models. Not every\nscenario contains projections from every model.\n\nNEX-DCP30 was prepared by the Climate Analytics Group and NASA Ames\nResearch Center using the NASA Earth Exchange, and distributed by the NASA\nCenter for Climate Simulation (NCCS).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-125.03,24.07,-66.47,53.74]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2099-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/NEX-DCP30","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_NEX-DCP30","rel":"related","title":"Run the example for NASA/NEX-DCP30 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_NEX-DCP30_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-DCP30#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/NEX-DCP30/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/NEX-DCP30/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/NEX-DCP30_ENSEMBLE_STATS","title":"NEX-DCP30: Ensemble Stats for NASA Earth Exchange Downscaled Climate Projections","description":"The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios\nfor the conterminous United States that are derived from the General\nCirculation Model (GCM) runs conducted under the Coupled Model\nIntercomparison Project Phase 5 (CMIP5, see [Taylor et al. 2012](https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1))\nand across the four greenhouse gas emissions scenarios known as\nRepresentative Concentration Pathways (RCPs, see [Meinshausen et al. 2011](https://rd.springer.com/article/10.1007%2Fs10584-011-0156-z#page-1))\ndeveloped for the Fifth Assessment Report of the Intergovernmental Panel\non Climate Change (IPCC AR5). The purpose of these datasets is to provide\na set of high resolution, bias-corrected climate change projections that\ncan be used to evaluate climate change impacts on processes that are\nsensitive to finer-scale climate gradients and the effects of local\ntopography on climate conditions.\n\nThe dataset contains monthly projections covering the periods from 1950\nthrough 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run).\nIt includes ensemble statistics calculated for each RCP from all model\nruns available for the pr, tasmin, and tasmax bands.\n\nNEX-DCP30 was prepared by the Climate Analytics Group and NASA Ames\nResearch Center using the NASA Earth Exchange, and distributed by the NASA\nCenter for Climate Simulation (NCCS).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-125.03,24.07,-66.47,49.93]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2099-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/NEX-DCP30_ENSEMBLE_STATS","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_NEX-DCP30_ENSEMBLE_STATS","rel":"related","title":"Run the example for NASA/NEX-DCP30_ENSEMBLE_STATS in the Earth Engine Code 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Model (GCM)\nruns conducted under the Coupled Model Intercomparison Project Phase 5\n(CMIP5, see [Taylor et al. 2012](https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1))\nand across two of the four greenhouse gas emissions scenarios known as\nRepresentative Concentration Pathways (RCPs, see [Meinshausen et\nal. 2011](https://rd.springer.com/article/10.1007%2Fs10584-011-0156-z#page-1)).\nThe CMIP5 GCM runs were developed in support of the Fifth Assessment\nReport of the Intergovernmental Panel on Climate Change (IPCC AR5).\n\nThis dataset was prepared by the Climate Analytics Group and NASA Ames\nResearch Center using the NASA Earth Exchange, and distributed by the NASA\nCenter for Climate Simulation (NCCS).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2100-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/NEX-GDDP","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_NEX-GDDP","rel":"related","title":"Run the example for NASA/NEX-GDDP in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_NEX-GDDP_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-GDDP#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/NEX-GDDP/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/NEX-GDDP/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/NLDAS/FORA0125_H002","title":"NLDAS-2: North American Land Data Assimilation System Forcing Fields","description":"Land Data Assimilation System (LDAS) combines multiple\nsources of observations (such as precipitation gauge data, satellite\ndata, and radar precipitation measurements) to produce estimates\nof climatological properties at or near the Earth''s surface.\n\nThis dataset is the primary (default) forcing file (File A) for\nPhase 2 of the North American Land Data Assimilation System (NLDAS-2).\nThe data are in 1/8th-degree grid spacing; the temporal resolution\nis hourly.\n\nNLDAS is a collaboration project among several groups: NOAA/NCEP''s\nEnvironmental Modeling Center (EMC), NASA''s Goddard Space Flight\nCenter (GSFC), Princeton University, the University of Washington,\nthe NOAA/NWS Office of Hydrological Development (OHD), and the\nNOAA/NCEP Climate Prediction Center (CPC). NLDAS is a core project\nwith support from NOAA''s Climate Prediction Program for the Americas\n(CPPA).\n\nDocumentation:\n\n* [Readme](https://hydro1.gesdisc.eosdis.nasa.gov/data/NLDAS/README.NLDAS2.pdf)\n\n* [How-To](https://disc.gsfc.nasa.gov/information/howto?tags=hydrology)\n\n* [GES DISC Hydrology Documentation](https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%20Documentation)\n\n* [GES DISC Data Rods Documentation](https://disc.gsfc.nasa.gov/information/tools?title=Hydrology%20Data%20Rods)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-125.15,24.85,-66.85,53.28]]},"temporal":{"interval":[["1979-01-01T13:00:00Z","2023-03-11T12:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/NLDAS/FORA0125_H002","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA_NLDAS_FORA0125_H002","rel":"related","title":"Run the example for NASA/NLDAS/FORA0125_H002 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NLDAS/NASA_NLDAS_FORA0125_H002_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_NLDAS_FORA0125_H002#terms-of-use","rel":"license","type":"text/html"},{"href":"https://disc.sci.gsfc.nasa.gov/datasets?keywords=NLDAS","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/NLDAS/FORA0125_H002/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/NLDAS/FORA0125_H002/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/OCEANDATA/MODIS-Aqua/L3SMI","title":"Ocean Color SMI: Standard Mapped Image MODIS Aqua Data","description":"This level 3 product includes ocean color and satellite ocean biology data\nproduced or collected under [EOSDIS](https://earthdata.nasa.gov/about).\n\nThis dataset may be used for studying the biology and hydrology of\ncoastal zones, changes in the diversity and geographical distribution of\ncoastal marine habitats, biogeochemical fluxes and their influence in\nEarth's oceans and climate over time, and finally the impact of\nclimate and environmental variability and change on ocean ecosystems\nand the biodiversity they support.\n\nScale factor and offset are already applied.\n\nDocumentation:\n\n* [Ocean Color Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/forum_show.pl)\n\n* [Chlorophyll Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/forum_show.pl)\n\n* [Algorithm Theoretical Basis Document (Chlorophyll)](https://oceancolor.gsfc.nasa.gov/atbd/chlor_a)\n\n* [Algorithm Theoretical Basis Document (Fluorescence Line Height)](https://oceancolor.gsfc.nasa.gov/atbd/nflh)\n\n* [Algorithm Theoretical Basis Document (Particulate Organic Carbon)](https://oceancolor.gsfc.nasa.gov/atbd/poc)\n\n* [Algorithm Theoretical Basis Document (Remote-Sensing Reflectance)](https://oceancolor.gsfc.nasa.gov/atbd/rrs)\n\n* [Processing History](https://oceancolor.gsfc.nasa.gov/reprocessing)\n\nNewer datasets have scaled POC values while older ones don't. This\nscaling gets rid of negative values but older datasets still retain them\nhence the -2147.48 estimated minimum value. For more information, visit the\n[Ocean Color Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/topic_show.pl?pid=33202;hl=poc%20values).\n\n[MODIS-Aqua OceanData](https://oceancolor.gsfc.nasa.gov/data/aqua)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-03T23:40:05Z","2022-02-28T00:20:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/OCEANDATA/MODIS-Aqua/L3SMI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA_OCEANDATA_MODIS-Aqua_L3SMI","rel":"related","title":"Run the example for NASA/OCEANDATA/MODIS-Aqua/L3SMI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/OCEANDATA/NASA_OCEANDATA_MODIS-Aqua_L3SMI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/AQUA/MODIS/L3M/CHL/2018","rel":"cite-as"},{"href":"https://doi.org/10.5067/AQUA/MODIS/L3M/FLH/2018","rel":"cite-as"},{"href":"https://doi.org/10.5067/AQUA/MODIS/L3M/POC/2018","rel":"cite-as"},{"href":"https://doi.org/10.5067/AQUA/MODIS/L3M/RRS/2018","rel":"cite-as"},{"href":"https://doi.org/10.5067/GHMDA-2PJ19","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/OCEANDATA/MODIS-Aqua/L3SMI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/OCEANDATA/MODIS-Aqua/L3SMI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/OCEANDATA/MODIS-Terra/L3SMI","title":"Ocean Color SMI: Standard Mapped Image MODIS Terra Data","description":"This level 3 product includes ocean color and satellite ocean biology data\nproduced or collected under [EOSDIS](https://earthdata.nasa.gov/about).\n\nThis dataset may be used for studying the biology and hydrology of\ncoastal zones, changes in the diversity and geographical distribution of\ncoastal marine habitats, biogeochemical fluxes and their influence in\nEarth's oceans and climate over time, and finally the impact of\nclimate and environmental variability and change on ocean ecosystems\nand the biodiversity they support.\n\nScale factor and offset are already applied.\n\nDocumentation:\n\n* [Ocean Color Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/forum_show.pl)\n\n* [Chlorophyll Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/forum_show.pl)\n\n* [Algorithm Theoretical Basis Document (Chlorophyll)](https://oceancolor.gsfc.nasa.gov/atbd/chlor_a)\n\n* [Algorithm Theoretical Basis Document (Fluorescence Line Height)](https://oceancolor.gsfc.nasa.gov/atbd/nflh)\n\n* [Algorithm Theoretical Basis Document (Particulate Organic Carbon)](https://oceancolor.gsfc.nasa.gov/atbd/poc)\n\n* [Algorithm Theoretical Basis Document (Remote-Sensing Reflectance)](https://oceancolor.gsfc.nasa.gov/atbd/rrs)\n\n* [Processing History](https://oceancolor.gsfc.nasa.gov/reprocessing)\n\n* [MODIS-Terra OceanData](https://oceancolor.gsfc.nasa.gov/data/terra)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-02-24T00:05:01Z","2022-02-28T21:00:01"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/OCEANDATA/MODIS-Terra/L3SMI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA_OCEANDATA_MODIS-Terra_L3SMI","rel":"related","title":"Run the example for NASA/OCEANDATA/MODIS-Terra/L3SMI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/OCEANDATA/NASA_OCEANDATA_MODIS-Terra_L3SMI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Terra_L3SMI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/GHMDT-2PJ19","rel":"cite-as"},{"href":"https://doi.org/10.5067/TERRA/MODIS/L3M/CHL/2018","rel":"cite-as"},{"href":"https://doi.org/10.5067/TERRA/MODIS/L3M/FLH/2018","rel":"cite-as"},{"href":"https://doi.org/10.5067/TERRA/MODIS/L3M/POC/2018","rel":"cite-as"},{"href":"https://doi.org/10.5067/TERRA/MODIS/L3M/RRS/2018","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/OCEANDATA/MODIS-Terra/L3SMI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/OCEANDATA/MODIS-Terra/L3SMI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/OCEANDATA/SeaWiFS/L3SMI","title":"Ocean Color SMI: Standard Mapped Image SeaWiFS Data","description":"This level 3 product includes ocean color and satellite ocean biology data\nproduced or collected under [EOSDIS](https://earthdata.nasa.gov/about).\n\nThis dataset may be used for studying the biology and hydrology of\ncoastal zones, changes in the diversity and geographical distribution of\ncoastal marine habitats, biogeochemical fluxes and their influence in\nEarth's oceans and climate over time, and finally the impact of\nclimate and environmental variability and change on ocean ecosystems\nand the biodiversity they support.\n\nScale factor and offset are already applied.\n\nDocumentation:\n\n* [Ocean Color Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/forum_show.pl)\n\n* [Chlorophyll Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/forum_show.pl)\n\n* [Algorithm Theoretical Basis Document (Chlorophyll)](https://oceancolor.gsfc.nasa.gov/atbd/chlor_a)\n\n* [Algorithm Theoretical Basis Document (Fluorescence Line Height)](https://oceancolor.gsfc.nasa.gov/atbd/nflh)\n\n* [Algorithm Theoretical Basis Document (Particulate Organic Carbon)](https://oceancolor.gsfc.nasa.gov/atbd/poc)\n\n* [Algorithm Theoretical Basis Document (Remote-Sensing Reflectance)](https://oceancolor.gsfc.nasa.gov/atbd/rrs)\n\n* [Processing History](https://oceancolor.gsfc.nasa.gov/reprocessing)\n\n- There are number of missing data dates in this dataset. For example,\n  most dates are missing between 2009-04-29 and 2009-12-01.\n\n- The estimated values for POC might be a result of the data being generated\n  without a scale. For more information, visit the\n  [Ocean Color Forum](https://oceancolor.gsfc.nasa.gov/forum/oceancolor/topic_show.pl?pid=33202;hl=poc%20values).\n\n  [SeaWiFS OceanData](https://oceancolor.gsfc.nasa.gov/data/seawifs)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1997-09-04T15:26:33Z","2010-12-10T19:42:17Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/OCEANDATA/SeaWiFS/L3SMI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA_OCEANDATA_SeaWiFS_L3SMI","rel":"related","title":"Run the example for NASA/OCEANDATA/SeaWiFS/L3SMI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/OCEANDATA/NASA_OCEANDATA_SeaWiFS_L3SMI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_SeaWiFS_L3SMI#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/ORBVIEW-2/SEAWIFS/L3M/CHL/2018","rel":"cite-as"},{"href":"https://doi.org/10.5067/ORBVIEW-2/SEAWIFS/L3M/POC/2018","rel":"cite-as"},{"href":"https://doi.org/10.5067/ORBVIEW-2/SEAWIFS/L3M/RRS/2018","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/OCEANDATA/SeaWiFS/L3SMI/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/OCEANDATA/SeaWiFS/L3SMI/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/ORNL/DAYMET_V3","title":"Daymet V3: Daily Surface Weather and Climatological Summaries [deprecated]","description":"Daymet V3 provides gridded estimates of daily weather parameters for\nUnited States, Mexico, Canada, Hawaii, and Puerto Rico. It is derived\nfrom selected meteorological station data and various supporting data\nsources.\n\nCompared to the previous version, Daymet V3 uses an entirely new suite\nof inputs including:\n\n- NASA SRTM DEM version 2.1.\n\n- Land/Water Mask: MODIS 250 `MOD44W_v2.NASA_ORNL_`\n\n- Horizon files derived from the SRTM DEM.\n\n- Ground station weather inputs from several sources with QA/QC.\n\nDocumentation:\n\n* [ORNL DAAC Dataset Documentation](https://daac.ornl.gov/DAYMET/guides/Daymet_V3_CFMosaics.html)\n\n* [Dataset Documentation](https://daac.ornl.gov/daacdata/daymet/Daymet_V3_CFMosaics/comp/Daymet_V3_CFMosaics.pdf)\n\n* [The THREDDS location for this Collection](https://thredds.daac.ornl.gov/thredds/catalog/ornldaac/1328/catalog.html)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-150.8,1.6,-1.1,84]]},"temporal":{"interval":[["1980-01-01T00:00:00Z","2019-12-31T00:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/DAYMET_V3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA_ORNL_DAYMET_V3","rel":"related","title":"Run the example for NASA/ORNL/DAYMET_V3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/ORNL/NASA_ORNL_DAYMET_V3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_DAYMET_V3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/ORNL/NASA_ORNL_DAYMET_V4.json","rel":"successor-version","title":"NASA/ORNL/DAYMET_V4","type":"application/json"},{"href":"https://doi.org/10.3334/ORNLDAAC/1328","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/DAYMET_V3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/DAYMET_V3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/ORNL/DAYMET_V4","title":"Daymet V4: Daily Surface Weather and Climatological Summaries","description":"Daymet V4 provides gridded estimates of daily weather parameters for\nContinental North America, Hawaii, and Puerto Rico (Data for Puerto Rico is available starting\nin 1950). It is derived from selected meteorological station data and various supporting data\nsources.\n\nCompared to the previous version, Daymet V4 provides effective solutions to known issues and\nfurther considers improvements to what were believed to be input weather station biases.\nImprovements include:\n\n- Reductions in the timing bias of input reporting weather station measurements.\n\n- Improvement to the three-dimensional regression model techniques in the core algorithm.\n\n- A novel approach to handling high elevation temperature measurement biases.\n\nDocumentation:\n\n* [ORNL DAAC Dataset Documentation](https://daac.ornl.gov/DAYMET/guides/Daymet_Daily_V4.html)\n\n* [Dataset Documentation](https://daac.ornl.gov/daacdata/daymet/Daymet_Daily_V4/comp/Daymet_Daily_V4.pdf)\n\n* [The THREDDS location for this Collection](https://thredds.daac.ornl.gov/thredds/catalog/ornldaac/1840/catalog.html)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-150.8,1.6,-1.1,84]]},"temporal":{"interval":[["1980-01-01T00:00:00Z","2021-12-31T00:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/DAYMET_V4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA_ORNL_DAYMET_V4","rel":"related","title":"Run the example for NASA/ORNL/DAYMET_V4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/ORNL/NASA_ORNL_DAYMET_V4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_DAYMET_V4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.3334/ORNLDAAC/1840","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/DAYMET_V4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/DAYMET_V4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/ORNL/biomass_carbon_density/v1","title":"Global Aboveground and Belowground Biomass Carbon Density Maps","description":"This dataset provides temporally consistent and harmonized global maps of aboveground and\nbelowground biomass carbon density for the year 2010 at a 300-m spatial resolution. The\naboveground biomass map integrates land-cover specific, remotely sensed maps of woody,\ngrassland, cropland, and tundra biomass. Input maps were amassed from the published literature\nand, where necessary, updated to cover the focal extent or time period. The belowground biomass\nmap similarly integrates matching maps derived from each aboveground biomass map and land-cover\nspecific empirical models. Aboveground and belowground maps were then integrated separately\nusing ancillary maps of percent tree cover and landcover and a rule-based decision tree. Maps\nreporting the accumulated uncertainty of pixel-level estimates are also provided.\n\nProvider's note: The UN Environment Programme World Conservation Monitoring\nCentre (UNEP-WCMC) carbon biomass dataset\nrepresents conditions between 1982 and 2010 depending on land cover type. The relative patterns\nof carbon stocks are well represented with this dataset. The [NASA/ORNL carbon biomass dataset](https://daac.ornl.gov/VEGETATION/guides/Global_Maps_C_Density_2010.html)\nrepresents biomass conditions for 2010, with uncertainty estimates at the pixel-level.\nAdditional biomass of non-dominant land cover types are represented within each pixel. For more\ndetailed information, please refer to the papers describing each dataset: [WCMC](WCMC_biomass_carbon_density_v1_0)\n[(Soto-Navarro et al. 2020)](https://royalsocietypublishing.org/doi/full/10.1098/rstb.2019.0128)\nand NASA/ORNL [(Spawn et al. 2020)](https://www.nature.com/articles/s41597-020-0444-4).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-61.1,180,84]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/biomass_carbon_density/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA_ORNL_biomass_carbon_density_v1","rel":"related","title":"Run the example for NASA/ORNL/biomass_carbon_density/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/ORNL/NASA_ORNL_biomass_carbon_density_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_biomass_carbon_density_v1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.3334/ORNLDAAC/1763","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/biomass_carbon_density/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/biomass_carbon_density/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/ORNL/global_forest_classification_2020/V1","title":"Global 2020 Forest Classification for IPCC Aboveground Biomass Tier 1 Estimates, V1","description":"This dataset provides classes of global forests delineated by\nstatus/condition in 2020 at approximately 30m resolution. The data support\ngenerating Tier 1 estimates for Aboveground dry woody Biomass Density (AGBD)\nin natural forests in the 2019 Refinement to the 2006 IPCC Guidelines for\nNational Greenhouse Gas Inventories. Forest classes include primary, young\nsecondary (<=20 years), and old secondary forests (>20 years). Classification\nwas based on a Boolean combination of a suite of existing Earth Observation\n(EO) products of forest tree cover, height, age, and land use classification\nlayers representing years 2000 to 2020. This forest status/condition\nclassification prioritizes the reduction of potential errors of commission in\nthe delineations by minimizing the inclusion of ambiguous pixels. Hence, it\nprovides a conservative estimate of global forest area, identifying\napproximately 3.26 billion ha of forests worldwide.\n\n### Quality Assessment\n\nThese data provide a comprehensive compilation of the latest published\ndatasets on forest conditions, but the nonexistence of any independent sample\nof global data that would enable the validation of these delineations is a\nconstraint. Hence, the global forest status/condition classification has not\nbeen validated.\n\n### Data Acquisition, Materials, and Methods\n\nThe forest status/condition classification is created by conducting a\nBoolean analysis of a suite of existing datasets (see Table 1,\n[Hunka et al., 2024](https://doi.org/10.1038/s41597-024-03930-9)),\nincluding satellite-derived forest tree cover, height, age,\nand land use classification layers. In this approach, layers that\nidentify a potential forest status/condition class (e.g. primary forests) are\nmerged, and layers that identify sources of disagreement (e.g. presence of\nplantations or deforestation detected in the delineated primary forests) are\nused to remove areas of potential commission errors.\n\nThe primary forest class is established using datasets identifying\nintact/primary forests, with a high forest integrity index, the presence of\ntree cover and forest heights ≥5 m and no known forest loss events, planted\nforests or plantations.\n\nThe young secondary forest class captures pixels that had changes in\nforest height or cover between 2000 and 2020, excluding planted forests and\nplantations. These forests were identified by heights ≥5 m in 2020 and either\n(a) heights <5 m in 2000 or (b) heights ≥5 m in 2000 but having experienced\ntree cover loss after 2000.\n\nThe old secondary forest class captures the remainder of pixels with\nforests after excluding the primary and young secondary forest classes. These\npixels had forest heights ≥5 m in both 2000 and 2020 with no tree cover loss\nnor forest disturbances detected after 2000, nor any planted forests or\nplantations.\n\n[Schematic of analysis workflow](https://daac.ornl.gov/CMS/guides/CMS_Global_Forest_Age_Fig2.jpg)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2020-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/global_forest_classification_2020/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_ORNL_global_forest_classification_2020_V1","rel":"related","title":"Run the example for NASA/ORNL/global_forest_classification_2020/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_ORNL_global_forest_classification_2020_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_global_forest_classification_2020_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.3334/ORNLDAAC/2345","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/global_forest_classification_2020/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/ORNL/global_forest_classification_2020/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/SMAP/SPL3SMP_E/005","title":"SPL3SMP_E.005 SMAP L3 Radiometer Global Daily 9 km Soil Moisture","description":"This Level-3 (L3) soil moisture product provides a daily composite of global\nland surface conditions retrieved by the Soil Moisture Active Passive (SMAP)\nL-Band radiometer. The daily data here were collected from the descending\n(local solar time of 6 am) and ascending (local solar time of 6 pm) passes.\n\nThe SMAP mission is an orbiting observatory that measures the amount of\nwater in the surface soil everywhere on Earth. A detailed description can be\nfound in the [SMAP Handbook]\n(https://smap.jpl.nasa.gov/system/internal_resources/details/original/178_SMAP_Handbook_FINAL_1_JULY_2014_Web.pdf).\nIt was launched in January 2015 and started operation in April 2015. The\nradar instrument, ceasing operation in early 2015 due to failure of radar\npower supply, collected close to 3 months of science data. The prime mission\nphase of three years was completed in 2018, and since then SMAP has been in\nextended operation phase.\n\nSMAP measures soil moisture every 2-3 days. This permits changes around\nthe world to be observed over time scales ranging from major storms to\nrepeated measurements of changes over the seasons.\n\nEverywhere on Earth not covered with water or not frozen, SMAP measures how\nmuch water is in the top layer of soil. It also distinguishes between ground\nthat is frozen or thawed. Where the ground is not frozen, SMAP measures the\namount of water found between the minerals, rocky material, and organic\nparticles found in soil everywhere in the world (SMAP measures liquid water\nin the top layer of ground but is not able to measure the ice.)\n\nSee the\n[SMAP L3 Soil Moisture User Guide](https://nsidc.org/sites/default/files/spl3smp_e-v005-userguide.pdf)\nand references therein for additional documentation and algorithm details.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-84,180,84]]},"temporal":{"interval":[["2015-03-31T12:00:00Z","2023-03-12T00:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/SMAP/SPL3SMP_E/005","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA_SMAP_SPL3SMP_E_005","rel":"related","title":"Run the example for NASA/SMAP/SPL3SMP_E/005 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/SMAP/NASA_SMAP_SPL3SMP_E_005_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_005#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/SMAP/SPL3SMP_E/005/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/SMAP/SPL3SMP_E/005/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/SMAP/SPL3SMP_E/006","title":"SPL3SMP_E.006 SMAP L3 Radiometer Global Daily 9 km Soil Moisture","description":"Data prior from 2023-12-04 are available in the older\n[NASA/SMAP/SPL3SMP_E/005](NASA_SMAP_SPL3SMP_E_005) collection.\nThey will eventually be reprocessed and added to this collection.\n\nThis Level-3 (L3) soil moisture product provides a daily composite of global\nland surface conditions retrieved by the Soil Moisture Active Passive (SMAP)\nL-Band radiometer. The daily data here were collected from the descending\n(local solar time of 6 am) and ascending (local solar time of 6 pm) passes.\n\nThe SMAP mission is an orbiting observatory that measures the amount of\nwater in the surface soil everywhere on Earth. A detailed description can be\nfound in the\n[SMAP Handbook](https://smap.jpl.nasa.gov/system/internal_resources/details/original/178_SMAP_Handbook_FINAL_1_JULY_2014_Web.pdf).\nIt was launched in January 2015 and started operation in April 2015. The\nradar instrument, ceasing operation in early 2015 due to failure of radar\npower supply, collected close to 3 months of science data. The prime mission\nphase of three years was completed in 2018, and since then SMAP has been in\nextended operation phase.\n\nSMAP measures soil moisture every 2-3 days. This permits changes around\nthe world to be observed over time scales ranging from major storms to\nrepeated measurements of changes over the seasons.\n\nEverywhere on Earth not covered with water or not frozen, SMAP measures how\nmuch water is in the top layer of soil. It also distinguishes between ground\nthat is frozen or thawed. Where the ground is not frozen, SMAP measures the\namount of water found between the minerals, rocky material, and organic\nparticles found in soil everywhere in the world (SMAP measures liquid water\nin the top layer of ground but is not able to measure the ice.)\n\nSPL3SMP_E data are transformed to\n[geographic coordinates using GDAL libraries](https://github.com/google/earthengine-catalog/blob/main/pipelines/smap_convert_l3.py)\nbefore the data are ingested into Google Earth Engine.\n\nSee the\n[SMAP L3 Soil Moisture User Guide](https://nsidc.org/sites/default/files/spl3smp_e-v005-userguide.pdf)\nand references therein for additional documentation and algorithm details.\n\nSee\n[basic](https://developers.google.com/earth-engine/tutorials/community/smap-soil-moisture)\nand\n[advanced](https://developers.google.com/earth-engine/tutorials/community/anomalies-analysis-smo-and-pre)\ntutorials to learn how to use SMAP data in Earth Engine.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-84,180,84]]},"temporal":{"interval":[["2023-12-04T12:00:00Z","2026-11-07T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/SMAP/SPL3SMP_E/006","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_SMAP_SPL3SMP_E_006","rel":"related","title":"Run the example for NASA/SMAP/SPL3SMP_E/006 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_SMAP_SPL3SMP_E_006_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_006#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/SMAP/SPL3SMP_E/006/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/SMAP/SPL3SMP_E/006/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/SMAP/SPL4SMGP/007","title":"SPL4SMGP.007 SMAP L4 Global 3-hourly 9-km Surface and Root Zone Soil Moisture","description":"The SMAP Level-4 (L4) Soil Moisture product includes surface soil moisture\n(0-5 cm vertical average), root-zone soil moisture (0-100 cm vertical\naverage), and additional research products (not validated), including\nsurface meteorological forcing variables, soil temperature,\nevapotranspiration, and net radiation.\n\nSMAP L4 provides uninterrupted soil moisture data.  During outages of the\nSMAP instrument, SMAP L4 soil moisture is based on land model simulations\nalone, without the concomitant assimilation of SMAP brightness temperature\nobservations.  Significant SMAP instrument outages occurred between 19 June\nand 23 July 2019 and between 6 August and 20 September 2022.\n\nSMAP L-band brightness temperature data from descending and ascending\nhalf-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local\nsolar time, respectively) are assimilated into a land surface model that is\ngridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable\nEarth Grid, Version 2.0 (EASE-Grid 2.0) projection.\n\nThe SPL4SMGP product includes a series of 3-hourly time-averaged geophysical\ndata fields from the assimilation system. SPL4SMGP data are transformed to\n[geographic coordinates using GDAL libraries](https://github.com/google/earthengine-community/blob/master/datasets/smap_convert_l4.py)\nbefore the data are ingested into Google Earth Engine.\n\nSee the [SMAP L4 Soil Moisture User Guide](https://nsidc.org/sites/default/files/documents/user-guide/multi_spl4smau-v007-userguide.pdf)\nand references therein for additional documentation and algorithm details.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-84,180,84]]},"temporal":{"interval":[["2015-03-31T12:00:00Z","2023-03-10T22:30:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/SMAP/SPL4SMGP/007","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA_SMAP_SPL4SMGP_007","rel":"related","title":"Run the example for NASA/SMAP/SPL4SMGP/007 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/SMAP/NASA_SMAP_SPL4SMGP_007_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL4SMGP_007#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/SMAP/SPL4SMGP/007/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/SMAP/SPL4SMGP/007/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/SMAP/SPL4SMGP/008","title":"SPL4SMGP.008 SMAP L4 Global 3-hourly 9-km Surface and Root Zone Soil Moisture","description":"The SMAP Level-4 (L4) Soil Moisture product includes surface soil moisture\n(0-5 cm vertical average), root-zone soil moisture (0-100 cm vertical\naverage), and additional research products (not validated), including\nsurface meteorological forcing variables, soil temperature,\nevapotranspiration, and net radiation.\nThis dataset, formally known as the\nSMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture\nGeophysical Data (SPL4SMGP), is considered the primary product of interest\nfor most users of the SMAP Level-4 (L4) soil moisture data.\n\nSMAP L4 provides uninterrupted soil moisture data.  During outages of the\nSMAP instrument, SMAP L4 soil moisture is based on land model simulations\nalone, without the concomitant assimilation of SMAP brightness temperature\nobservations.  Significant SMAP instrument outages occurred between 19 June\nand 23 July 2019 and between 6 August and 20 September 2022.\n\nSMAP L-band brightness temperature data from descending and ascending\nhalf-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local\nsolar time, respectively) are assimilated into a land surface model that is\ngridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable\nEarth Grid, Version 2.0 (EASE-Grid 2.0) projection.\n\nThe SPL4SMGP product includes a series of 3-hourly time-averaged geophysical\ndata fields from the assimilation system. SPL4SMGP data are transformed to\n[geographic coordinates using GDAL libraries](https://github.com/google/earthengine-catalog/blob/main/pipelines/smap_convert_l4.py)\nbefore the data are ingested into Google Earth Engine.\n\nSee the [SMAP L4 Soil Moisture User Guide](https://nsidc.org/sites/default/files/documents/user-guide/multi_spl4smau-v007-userguide.pdf)\nand references therein for additional documentation and algorithm details.\n\nSee\n[basic](https://developers.google.com/earth-engine/tutorials/community/smap-soil-moisture)\nand\n[advanced](https://developers.google.com/earth-engine/tutorials/community/anomalies-analysis-smo-and-pre)\ntutorials to learn how to use SMAP data in Earth Engine.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-85.044,180,85.044]]},"temporal":{"interval":[["2015-03-31T00:00:00Z","2026-11-08T22:30:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/SMAP/SPL4SMGP/008","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_SMAP_SPL4SMGP_008","rel":"related","title":"Run the example for NASA/SMAP/SPL4SMGP/008 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_SMAP_SPL4SMGP_008_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL4SMGP_008#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/T5RUATAQREF8","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_008.json","rel":"latest-version","title":"NASA/SMAP/SPL4SMGP/008","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_007.json","rel":"predecessor-version","title":"NASA/SMAP/SPL4SMGP/007","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/SMAP/SPL4SMGP/008/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/SMAP/SPL4SMGP/008/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/TEMPO/CLDO4_L3_V4","title":"TEMPO gridded cloud fraction and pressure (O2-O2 dimer) V04 (PROVISIONAL)","description":"The TEMPO Level 3 Cloud Product (O2-O2 dimer) provides gridded cloud fraction\nand cloud pressure data. These parameters are derived from the O2-O2 absorption\nband near 477 nm and are essential for air quality retrievals and atmospheric\nmonitoring. The data are created by combining and re-gridding Level 2 files\nfrom single East-West scan cycles using an area-weighted methodology.\n\nDocumentation:\n\n* [User's Guide](https://asdc.larc.nasa.gov/documents/tempo/guide/TEMPO_Level-2-3_trace_gas_clouds_user_guide_V1.2.pdf)\n\n* [General Documentation](https://asdc.larc.nasa.gov/project/TEMPO)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-170,10,-10,80]]},"temporal":{"interval":[["2023-08-02T00:00:00Z","2026-11-09T14:52:12Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/CLDO4_L3_V4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_TEMPO_CLDO4_L3_V4","rel":"related","title":"Run the example for NASA/TEMPO/CLDO4_L3_V4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_TEMPO_CLDO4_L3_V4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_TEMPO_CLDO4_L3_V4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/IS-40e/TEMPO/CLDO4_L3.004","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/CLDO4_L3_V4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/CLDO4_L3_V4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/TEMPO/HCHO_L3","title":"TEMPO gridded HCHO vertical columns V03 [deprecated]","description":"Formaldehyde Level 3 collection provides trace gas information on a\nregular grid covering the TEMPO field of regard for nominal TEMPO\nobservations. Level 3 files are derived by combining information from\nall Level 2 files constituting a TEMPO East-West scan cycle. 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It is generated by removing data points with high cloud\ncover (>50%), extreme solar zenith angle (>80 degrees), and\nmain data quality (flag > 0).\n\n* A version of this dataset without QA filters applied is available as [NASA/TEMPO/NO2_L3_V4](NASA_TEMPO_NO2_L3_V4)\n* [General Documentation](https://asdc.larc.nasa.gov/documents/tempo/guide/TEMPO_Level-2-3_trace_gas_clouds_user_guide_V1.2.pdf)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-08-01T00:00:00Z","2026-11-09T14:52:12Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/NO2_L3_V4_QA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_TEMPO_NO2_L3_V4_QA","rel":"related","title":"Run the example for NASA/TEMPO/NO2_L3_V4_QA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_TEMPO_NO2_L3_V4_QA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_TEMPO_NO2_L3_V4_QA#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/IS-40e/TEMPO/NO2_L3.004","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_TEMPO_NO2_L3_V4_QA.json","rel":"latest-version","title":"NASA/TEMPO/NO2_L3_V4_QA","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_TEMPO_NO2_L3_QA.json","rel":"predecessor-version","title":"NASA/TEMPO/NO2_L3_QA","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/NO2_L3_V4_QA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/NO2_L3_V4_QA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/TEMPO/O3PROF_L3_V4","title":"TEMPO gridded ozone profile V04 (PROVISIONAL)","description":"The TEMPO gridded ozone profile V04 (PROVISIONAL) is a Level 3 dataset\nproviding vertical ozone profile information on a regular grid across\nNorth America. Generated from the TEMPO instrument on the IS-40e platform,\nthe data is created by combining and re-gridding Level 2 files from single\nEast-West scan cycles using an area-weighted methodology. These measurements\nare critical for monitoring atmospheric ozone structure, stratospheric-tropospheric\nexchange, and continental air quality.\n\n### Multi-dimensional Band Flattening\nTo conform to Earth Engine's 2D image band model, multi-dimensional variables\nin the source NetCDF files are flattened during ingestion:\n\n*   **Flat Layer Bands:** Variables containing a vertical layer dimension (24 layers,\n    from layer `0` at the top of the atmosphere to layer `23` near the surface)\n    are split into 24 individual bands suffixed with `_0` to `_23`. These include\n    `ozone_profile`, `ozone_profile_error`, `ozone_profile_precision`,\n    `ozone_apriori_profile`, `ozone_apriori_profile_error`, `ozone_profile_altitude`,\n    and `ozone_profile_pressure`.\n\n*   **Layer Bounds (Top/Bottom) Bands:** Variables defining the layer boundaries are\n    split by both layer index (`0` to `23`) and boundary index (top and bottom bounds),\n    resulting in 48 bands suffixed with `_top` and `_bottom` (e.g., `_0_top`, `_0_bottom`\n    ... `_23_top`, `_23_bottom`). These include `ozone_profile_altitude_bounds` and\n    `ozone_profile_pressure_bounds`.\n\n### Spatial Reference for \"Top\" and \"Bottom\" Bounds\n`_top` and `_bottom` refer to spatial elevation relative to the Earth's\nsurface:\n*   **Altitude**: Value increases with height (`_top` > `_bottom`).\n*   **Pressure**: Value decreases with height (`_top` < `_bottom`).\n\nDocumentation:\n\n* [User's Guide](https://asdc.larc.nasa.gov/documents/tempo/guide/TEMPO_Level-2-3_O3PROF_user_guide_V1.1.pdf)\n\n* [General Documentation](https://asdc.larc.nasa.gov/project/TEMPO)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-170,10,-10,80]]},"temporal":{"interval":[["2023-08-02T00:00:00Z","2026-11-09T13:52:12Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/O3PROF_L3_V4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_TEMPO_O3PROF_L3_V4","rel":"related","title":"Run the example for NASA/TEMPO/O3PROF_L3_V4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_TEMPO_O3PROF_L3_V4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_TEMPO_O3PROF_L3_V4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/IS-40E/TEMPO/O3PROF_L3.004","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/O3PROF_L3_V4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/O3PROF_L3_V4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/TEMPO/O3_L3_QA","title":"TEMPO gridded ozone total column V03 (PROVISIONAL) [deprecated]","description":"The TEMPO gridded ozone total column V03 (PROVISIONAL) is a Level 3 dataset\nproviding essential total column ozone information on a regular grid across\nNorth America. Generated from the TEMPO instrument on the IS-40e platform,\nthe data is created by combining and re-gridding Level 2 files from single\nEast-West scan cycles using an area-weighted methodology. These measurements\nare critical for monitoring continental air quality and atmospheric\nchemistry and are distributed by the NASA Langley Research Center (LaRC)\nASDC.\n\nThis Level 3 product serves as a quality-controlled version of the Level 2\ndata, pre-filtered to include only the highest-quality retrievals.\nSpecifically, it includes pixels with a quality flag of 0, solar and\nviewing zenith angles (SZA/VZA) below 80°, and an effective cloud fraction\nunder 0.5.\n\nThis dataset includes auxiliary measurements such as effective and radiative\ncloud properties, sulfur dioxide index, and terrain pressure. These\nparameters support a wide range of research applications while ensuring\nthat users have access to research-ready data that has already been\nscreened for common retrieval anomalies and extreme geometries.\n\nDocumentation:\n\n* [User's Guide](https://asdc.larc.nasa.gov/documents/tempo/guide/TEMPO_Level-2-3_O3TOT_user_guide_V2.0.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://asdc.larc.nasa.gov/documents/tempo/TEMPO_O3TOT_Algorithm_description_V2.0.pdf)\n\n* [General Documentation](https://asdc.larc.nasa.gov/project/TEMPO)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-170,10,-10,80]]},"temporal":{"interval":[["2023-08-02T00:00:00Z","2025-09-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/O3_L3_QA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_TEMPO_O3_L3_QA","rel":"related","title":"Run the example for NASA/TEMPO/O3_L3_QA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_TEMPO_O3_L3_QA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_TEMPO_O3_L3_QA#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/IS-40e/TEMPO/O3TOT_L3.003","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_TEMPO_O3_L3_V4_QA.json","rel":"latest-version","title":"NASA/TEMPO/O3_L3_V4_QA","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_TEMPO_O3_L3_V4_QA.json","rel":"successor-version","title":"NASA/TEMPO/O3_L3_V4_QA","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/O3_L3_QA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/O3_L3_QA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/TEMPO/O3_L3_V4_QA","title":"TEMPO gridded ozone total column V04 (PROVISIONAL)","description":"The TEMPO gridded ozone total column V04 (PROVISIONAL) is a Level 3 dataset\nproviding essential total column ozone information on a regular grid across\nNorth America. Generated from the TEMPO instrument on the IS-40e platform,\nthe data is created by combining and re-gridding Level 2 files from single\nEast-West scan cycles using an area-weighted methodology. These measurements\nare critical for monitoring continental air quality and atmospheric\nchemistry and are distributed by the NASA Langley Research Center (LaRC)\nASDC.\n\nThis Level 3 product serves as a quality-controlled version of the Level 2\ndata, pre-filtered to include only the highest-quality retrievals.\nSpecifically, it includes pixels with a quality flag of 0, solar and\nviewing zenith angles (SZA/VZA) below 80°, and an effective cloud fraction\nunder 0.5.\n\nThis dataset includes auxiliary measurements such as effective and radiative\ncloud properties, sulfur dioxide index, and terrain pressure. These\nparameters support a wide range of research applications while ensuring\nthat users have access to research-ready data that has already been\nscreened for common retrieval anomalies and extreme geometries.\n\nDocumentation:\n\n* [User's Guide](https://asdc.larc.nasa.gov/documents/tempo/guide/TEMPO_Level-2-3_O3TOT_user_guide_V2.0.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://asdc.larc.nasa.gov/documents/tempo/TEMPO_O3TOT_Algorithm_description_V2.0.pdf)\n\n* [General Documentation](https://asdc.larc.nasa.gov/project/TEMPO)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-170,10,-10,80]]},"temporal":{"interval":[["2023-08-02T00:00:00Z","2026-11-09T14:52:12Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/O3_L3_V4_QA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_TEMPO_O3_L3_V4_QA","rel":"related","title":"Run the example for NASA/TEMPO/O3_L3_V4_QA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_TEMPO_O3_L3_V4_QA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_TEMPO_O3_L3_V4_QA#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/IS-40e/TEMPO/O3TOT_L3.004","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_TEMPO_O3_L3_V4_QA.json","rel":"latest-version","title":"NASA/TEMPO/O3_L3_V4_QA","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_TEMPO_O3_L3_QA.json","rel":"predecessor-version","title":"NASA/TEMPO/O3_L3_QA","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/O3_L3_V4_QA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/TEMPO/O3_L3_V4_QA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/AERDB_D3_VIIRS_NOAA20","title":"AERDB_D3_VIIRS_NOAA20.002: VIIRS/NOAA-20 Deep Blue Aerosol Optical Depth Daily L3 Global 1.0 Deg Grid","description":"The Visible Infrared Imaging Radiometer Suite (VIIRS) Deep Blue Level-3\nDaily Global 1-Degree Gridded product (AERDB_D3_VIIRS_NOAA20) provides daily\nglobal gridded aerosol parameters derived from the NOAA-20 (JPSS-1) VIIRS\nsensor. Data are generated using the Deep Blue (DB) algorithm over land and\nthe Satellite Aerosol for Land and Ocean (SOAR) algorithm over ocean.\n\nThe product includes statistical summaries (mean, minimum, maximum, count,\nand standard deviation) of aerosol optical thickness (AOT) at 550 nm across\nland, ocean, and combined land/ocean scenes, along with Angstrom exponent\nretrievals, fine-mode fraction estimates over ocean, and aerosol type\nclassifications. Data are projected on a regular 1.0 degree equal-angle\nglobal grid (EPSG:4326).\n\nDocumentation:\n\n* [Product User Guide](https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/VIIRS_Deep_Blue_Aerosol_UG_v2_March_2025.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/Product%20Generation%20Algorithms/VIIRS_Deep_Blue_Aerosol_ATBD_March_2025.pdf)\n\n* [Deep Blue Project](https://deepblue.gsfc.nasa.gov/)\n\n* [LAADS DAAC Product Page](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_VIIRS_NOAA20#documentation)\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-02-17T00:00:00Z","2026-11-05T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/AERDB_D3_VIIRS_NOAA20","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_AERDB_D3_VIIRS_NOAA20","rel":"related","title":"Run the example for NASA/VIIRS/002/AERDB_D3_VIIRS_NOAA20 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_AERDB_D3_VIIRS_NOAA20_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_AERDB_D3_VIIRS_NOAA20#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/AERDB_D3_VIIRS_NOAA20.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/AERDB_D3_VIIRS_NOAA20/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/AERDB_D3_VIIRS_NOAA20/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/AERDB_D3_VIIRS_SNPP","title":"AERDB_D3_VIIRS_SNPP.002: VIIRS/SNPP Deep Blue Aerosol Optical Depth Daily L3 Global 1.0 Deg Grid","description":"The Visible Infrared Imaging Radiometer Suite (VIIRS) Deep Blue Level-3\nDaily Global 1-Degree Gridded product (AERDB_D3_VIIRS_SNPP) provides daily\nglobal gridded aerosol parameters derived from the Suomi National\nPolar-orbiting Partnership (SNPP) VIIRS sensor. Data are generated using\nthe Deep Blue (DB) algorithm over land and the Satellite Aerosol for Land\nand Ocean (SOAR) algorithm over ocean.\n\nThe product includes statistical summaries (mean, minimum, maximum, count,\nand standard deviation) of aerosol optical thickness (AOT) at 550 nm across\nland, ocean, and combined land/ocean scenes, along with Angstrom exponent\nretrievals, fine-mode fraction estimates over ocean, and aerosol type\nclassifications. Data are projected on a regular 1.0 degree equal-angle\nglobal grid (EPSG:4326).\n\nDocumentation:\n\n* [Product User Guide](https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/VIIRS_Deep_Blue_Aerosol_UG_v2_March_2025.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/Product%20Generation%20Algorithms/VIIRS_Deep_Blue_Aerosol_ATBD_March_2025.pdf)\n\n* [Deep Blue Project](https://deepblue.gsfc.nasa.gov/)\n\n* [LAADS DAAC Product Page](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_VIIRS_SNPP#documentation)\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-03-01T00:00:00Z","2026-11-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/AERDB_D3_VIIRS_SNPP","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_AERDB_D3_VIIRS_SNPP","rel":"related","title":"Run the example for NASA/VIIRS/002/AERDB_D3_VIIRS_SNPP in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_AERDB_D3_VIIRS_SNPP_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_AERDB_D3_VIIRS_SNPP#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/AERDB_D3_VIIRS_SNPP.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/AERDB_D3_VIIRS_SNPP/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/AERDB_D3_VIIRS_SNPP/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VJ146A2","title":"VJ146A2: VIIRS Lunar Gap-Filled BRDF Nighttime Lights Daily L3 Global 500m","description":"The NOAA-20 (JPSS-1) Visible Infrared Imaging Radiometer Suite (VIIRS)\nsupports a Day-Night Band (DNB) sensor that provides global daily\nmeasurements of nocturnal visible and near-infrared (NIR) light that are\nsuitable for Earth system science and applications. The VIIRS DNB's\nultra-sensitivity in lowlight conditions generates science-quality nighttime\nproducts that manifest substantial improvements in sensor resolution and\ncalibration when compared to previous eras (such as DMSP/OLS). Such\nimprovements allow the VIIRS DNB products to better monitor both the\nmagnitude and signature of nighttime phenomena, and anthropogenic sources\nof light emissions.\n\nVJ146A2 is the short-name for the daily moonlight- and atmosphere-corrected\nNighttime Lights (NTL) product called VIIRS/NOAA-20 Gap-Filled Lunar\nBRDF-Adjusted Nighttime Lights Daily L3 Global 500m Linear Lat Lon Grid.\n\nDocumentation:\n\n* [User's Guide](https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/VIIRS_Black_Marble_UG_v1.3_Sep_2022.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/Product%20Generation%20Algorithms/VIIRS_Black_Marble_ATBD_v1.1_July_2020.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ146A2/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-19T00:00:00Z","2026-11-04T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ146A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VJ146A2","rel":"related","title":"Run the example for NASA/VIIRS/002/VJ146A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VJ146A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VJ146A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VJ146A2.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ146A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ146A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VJ209GA","title":"VJ209GA: VIIRS Surface Reflectance Daily 500m and 1km","description":"The Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface\nreflectance (VJ209GA) Version 2 product provides an estimate of land\nsurface reflectance from the Joint Polar Satellite System (JPSS-2) /\nNOAA-21 VIIRS sensor. Data are provided for three imagery bands\n(I1, I2, I3) at nominal 500 meter resolution (~463 meter) and nine\nmoderate-resolution bands (M1, M2, M3, M4, M5, M7, M8, M10, M11) at nominal\n1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets\nare derived through resampling the native 375 meter and 750 meter VIIRS\nresolutions, respectively, in the L2 input product. These bands are\ncorrected for atmospheric conditions to provide an estimate\nof the surface spectral reflectance as it would be measured at ground level.\n\nThe data is temporally aggregated over each of the 16 possible passes per\nday. When multiple observations are present for each day, only the first\nof the highest-quality observations is included.\n\nThe band scale factors are already applied.\n\nFor additional information, visit the VIIRS [Land Product Quality\nAssessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=J2).\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1657/VNP09_User_Guide_V2.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/122/VNP09_ATBD.pdf)\n\n* [General Documentation](https://www.earthdata.nasa.gov/data/catalog/lpcloud-vj209ga-002)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-02-10T00:00:00Z","2026-11-05T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ209GA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VJ209GA","rel":"related","title":"Run the example for NASA/VIIRS/002/VJ209GA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VJ209GA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VJ209GA#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VJ209GA.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ209GA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ209GA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VJ209H1","title":"VJ209H1: VIIRS Surface Reflectance 8-Day L3 Global 500m","description":"The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) Surface\nReflectance (VJ209H1) Version 2 composite product provides an estimate of\nland surface reflectance from the Joint Polar Satellite System (JPSS-2) /\nNOAA-21 VIIRS sensor for three imagery bands (I1, I2, I3) at nominal\n500 meter resolution (approximately 463 meter). The 500 meter dataset is\nderived through resampling the native 375 meter VIIRS resolution in the L2\ninput product. The data are corrected for atmospheric conditions such as the\neffects of molecular gases, including ozone and water vapor, and for the\neffects of atmospheric aerosols. Each pixel represents the best possible\nLevel 2G observation during an 8-day period, which is selected on the basis\nof high observation coverage, low sensor angle, the absence of clouds or\ncloud shadow, and aerosol loading. Along with the three reflectance bands,\nthis product includes a state quality assurance (QA) layer and a reflectance\nband quality layer.\n\nThe band scale factors are already applied.\n\nFor additional information, visit the VIIRS [Land Product Quality\nAssessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=J2).\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1657/VNP09_User_Guide_V2.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/122/VNP09_ATBD.pdf)\n\n* [General Documentation](https://doi.org/10.5067/VIIRS/VJ209H1.002)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-02-10T00:00:00Z","2026-10-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ209H1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VJ209H1","rel":"related","title":"Run the example for NASA/VIIRS/002/VJ209H1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VJ209H1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VJ209H1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VJ209H1.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ209H1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ209H1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VJ213A1","title":"VJ213A1: VIIRS Vegetation Indices 16-Day 500m","description":"The Joint Polar Satellite System (JPSS-2) / NOAA-21 NASA Visible\nInfrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ213A1)\nVersion 2 data product provides vegetation indices by a process of\nselecting the best available pixel over a 16-day acquisition period\nat 500 meter resolution. The VJ213 data products are designed\nafter the Moderate Resolution Imaging Spectroradiometer (MODIS)\nTerra and Aqua Vegetation Indices product suite to promote the\ncontinuity of the Earth Observation System (EOS) mission.\n\nThe VJ213 algorithm process produces three vegetation indices:\n\n1. Normalized Difference Vegetation Index (NDVI): One of the longest\n   continual remotely-sensed time series observations, using both the red\n   and near-infrared (NIR) bands.\n2. Enhanced Vegetation Index (EVI): A slightly different vegetation index\n   that is more sensitive to canopy cover, while NDVI is more sensitive to\n   chlorophyll.\n3. Enhanced Vegetation Index-2 (EVI2): A reformation of the standard 3-band\n   EVI, using the red band and NIR band. This reformation addresses arising\n   issues when comparing VIIRS EVI to other EVI models that do not include a\n   blue band. EVI2 will eventually become the standard EVI.\n\nAlong with the three Vegetation Indices layers, this product also\nincludes layers for near-infrared (NIR) reflectance; three shortwave\ninfrared (SWIR) reflectance; red, blue, and green reflectance; composite\nday of year; pixel reliability; relative azimuth, view, and sun angles;\nand a quality layer.\n\nThe band scale factors are already applied.\n\nFor additional information, visit the VIIRS [Land Product Quality\nAssessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=J2).\n\nDocumentation:\n\n* [User's Guide and ATBD](https://lpdaac.usgs.gov/documents/1372/VNP13_User_Guide_ATBD_V2.1.2.pdf)\n\n* [General Documentation](https://doi.org/10.5067/VIIRS/VJ213A1.002)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-02-10T00:00:00Z","2026-10-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ213A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VJ213A1","rel":"related","title":"Run the example for NASA/VIIRS/002/VJ213A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VJ213A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VJ213A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VJ213A1.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ213A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ213A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VJ214A1","title":"VJ214A1.002: Thermal Anomalies/Fire Daily L3 Global 1km SIN Grid","description":"The daily NOAA-21 NASA Visible Infrared Imaging Radiometer Suite (VIIRS)\nThermal Anomalies/Fire (VJ214A1) Version 2 data product provides daily\ninformation about active fires and other thermal anomalies. The VJ214A1\ndata product is a global, 1km gridded composite of fire pixels detected\nfrom VIIRS 750m bands over a daily (24-hour) period. The VJ214 data\nproducts are designed after the Moderate Resolution Imaging\nSpectroradiometer (MODIS) Thermal Anomalies/Fire product suite.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/427/VNP14_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/133/VNP14_ATBD.pdf)\n\n* [General Documentation](https://www.earthdata.nasa.gov/data/catalog/lpcloud-vj214a1-002)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=J1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-02-10T00:00:00Z","2026-11-05T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ214A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VJ214A1","rel":"related","title":"Run the example for NASA/VIIRS/002/VJ214A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VJ214A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VJ214A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VJ214A1.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ214A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ214A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VJ215A2H","title":"VJ215A2H.002: LAI/FPAR 8-Day L4 Global 500m SIN Grid","description":"The Visible Infrared Imaging Radiometer Suite (VIIRS) Leaf Area Index (LAI)\nand Fraction of Photosynthetically Active Radiation (FPAR) Version 2 data\nproduct provides information about the vegetative canopy layer at 500 meter\nresolution (VJ215A2H). The VIIRS sensor is located onboard the NOAA-21\nsatellite. LAI is an index that quantifies the one-sided leaf area of a\ncanopy, while FPAR is the fraction of incoming solar energy absorbed through\nphotosynthesis at 400 to 700 nanometers. This product is intentionally\ndesigned after the Terra and Aqua Moderate Resolution Imaging\nSpectroradiometer (MODIS) LAI/FPAR operational algorithm to promote the\ncontinuity of the Earth Observation System (EOS) mission.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/126/VNP15_User_Guide.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/125/VNP15_ATBD.pdf)\n\n* [General Documentation](https://www.earthdata.nasa.gov/data/catalog/lpcloud-vj215a2h-002)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=J1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-02-10T00:00:00Z","2026-10-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ215A2H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VJ215A2H","rel":"related","title":"Run the example for NASA/VIIRS/002/VJ215A2H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VJ215A2H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VJ215A2H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VJ215A2H.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ215A2H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ215A2H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VJ221A1D","title":"VJ221A1D.002: Day Land Surface Temperature and Emissivity Daily 1km","description":"The NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface\nTemperature and Emissivity (LST&E) Day Version 2 product (VJ221A1D) is\ncompiled daily from daytime Level 2 Gridded (L2G) intermediate products.\n\nThe L2G process maps the daily VJ221 swath granules onto a sinusoidal MODIS\ngrid and stores all observations overlapping a gridded cell for a given\nday. The VJ221A1 algorithm sorts through all these observations for each\ncell and estimates the final LST value as an average from all cloud-free\nobservations that have good LST accuracies. Only observations having\nobservation coverage more than a certain threshold (15%) are considered for\nthis averaging.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/429/VNP21_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf)\n\n* [General Documentation](https://www.earthdata.nasa.gov/data/catalog/lpcloud-vj221a1d-002)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=J1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-02-10T00:00:00Z","2026-11-04T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ221A1D","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VJ221A1D","rel":"related","title":"Run the example for NASA/VIIRS/002/VJ221A1D in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VJ221A1D_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VJ221A1D#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VJ221A1D.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ221A1D/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ221A1D/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VJ221A1N","title":"VJ221A1N.002: Night Land Surface Temperature and Emissivity Daily 1km","description":"The NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface\nTemperature and Emissivity (LST&E) Night Version 2 product (VJ221A1N) is\ncompiled daily from nighttime Level 2 Gridded (L2G) intermediate products.\n\nThe L2G process maps the daily VJ221 swath granules onto a sinusoidal MODIS\ngrid and stores all observations overlapping a gridded cell for a given\nnight. The VJ221A1 algorithm sorts through all these observations for each\ncell and estimates the final LST value as an average from all cloud-free\nobservations that have good LST accuracies. Only observations having\nobservation coverage more than a certain threshold (15%) are considered for\nthis averaging.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/429/VNP21_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf)\n\n* [General Documentation](https://www.earthdata.nasa.gov/data/catalog/lpcloud-vj221a1n-002)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=J1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-02-10T00:00:00Z","2026-11-06T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ221A1N","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VJ221A1N","rel":"related","title":"Run the example for NASA/VIIRS/002/VJ221A1N in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VJ221A1N_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VJ221A1N#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VJ221A1N.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ221A1N/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ221A1N/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VJ243IA3","title":"VJ243IA3: BRDF/Albedo Daily L3 Global 500m SIN Grid","description":"The NASA/NOAA NOAA-21 (JPSS-2) Visible Infrared Imaging Radiometer Suite\n(VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo\n(BRDF/Albedo) Version 2 product provides albedo estimates at 500 meter\nresolution. The VJ243IA3 product is produced daily using 16 days of VIIRS\ndata and is weighted temporally to the ninth day. It provides both\ndirectional hemispherical reflectance (black-sky albedo) at local solar noon and bihemispherical reflectance (white-sky\nalbedo) for the three VIIRS imagery bands (I1, I2, and I3). The VJ243 data\nproducts are designed to promote the continuity of NASA's Moderate\nResolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\n\nThe VJ243 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR)\nsemi-empirical kernel-driven BRDF model, with the three kernel weights from\nVJ243IA1 to reconstruct surface anisotropic effects, correcting the\ndirectional reflectance to a common view geometry (VJ243IA4), while\nalso computing integrated black-sky albedo (BSA) at local solar noon and\nwhite-sky albedo (WSA) (VJ243IA3). Researchers can use the BRDF model\nparameters with a simple polynomial, to obtain black-sky albedo at any solar\nillumination angle. Likewise, both the BSA and WSA Science Dataset (SDS)\nlayers can be used with a simple polynomial, to manually estimate\ninstantaneous actual albedo (blue-sky albedo). Additional details regarding\nthe methodology are available in the Algorithm Theoretical Basis Document\n(ATBD).\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/viirs-user-guides-c1-and-c2/vnp43ia3-and-vnp43ma3-albedo-products/)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ243IA3/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-02-07T00:00:00Z","2026-10-29T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ243IA3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VJ243IA3","rel":"related","title":"Run the example for NASA/VIIRS/002/VJ243IA3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VJ243IA3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VJ243IA3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VJ243IA3.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ243IA3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ243IA3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VJ243IA4","title":"VJ243IA4: Nadir BRDF-Adjusted Reflectance Daily L3 Global 500m SIN Grid","description":"The NASA/NOAA NOAA-21 (JPSS-2) Visible Infrared Imaging Radiometer Suite\n(VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF)\nAdjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at\n500 meter resolution. The VJ243IA4 product is produced daily using 16 days\nof VIIRS data and is weighted temporally to the ninth day. The view angle\neffects are removed from the directional reflectances, resulting in a stable and consistent NBAR\nproduct. The VJ243 data products are designed to promote the continuity of\nNASA's Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo\ndata product suite.\n\nThe VJ243 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR)\nsemi-empirical kernel-driven BRDF model, with the three kernel weights from\nVJ243IA1 to reconstruct surface anisotropic effects, correcting the\ndirectional reflectance to a common view geometry (VJ243IA4), while also\ncomputing integrated black-sky albedo (BSA) at local solar noon and\nwhite-sky albedo (WSA) (VJ243IA3). Researchers can use the BRDF model\nparameters with a simple polynomial, to obtain black-sky albedo at any solar\nillumination angle. Likewise, both the BSA and WSA Science Dataset (SDS)\nlayers can be used with a simple polynomial, to manually estimate\ninstantaneous actual albedo (blue-sky albedo). Additional details regarding\nthe methodology are available in the Algorithm Theoretical Basis Document\n(ATBD).\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/viirs-user-guides-c1-and-c2/vnp43ia4-and-vnpma4-nbar-products/)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ243IA4/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-02-07T00:00:00Z","2026-10-29T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ243IA4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VJ243IA4","rel":"related","title":"Run the example for NASA/VIIRS/002/VJ243IA4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VJ243IA4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VJ243IA4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VJ243IA4.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ243IA4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ243IA4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VJ243MA3","title":"VJ243MA3: BRDF/Albedo Daily L3 Global 1km SIN Grid","description":"The NASA/NOAA NOAA-21 (JPSS-2) Visible Infrared Imaging Radiometer Suite\n(VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo\n(BRDF/Albedo) Version 2 product provides albedo estimates at 1000 meter (1 km)\nresolution. The VJ243MA3 product is produced daily using 16 days of VIIRS\ndata and is weighted temporally to the ninth day. It provides both\ndirectional hemispherical reflectance (black-sky albedo) at local solar noon\nand bihemispherical reflectance (white-sky albedo) for the nine VIIRS\nmoderate resolution bands (M1-M5, M7-M8, M10-M11) and three broadbands\n(vis, nir, and shortwave). The VJ243 data products are designed to promote\nthe continuity of NASA's Moderate Resolution Imaging Spectroradiometer\n(MODIS) BRDF/Albedo data product suite.\n\nThe VJ243 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR)\nsemi-empirical kernel-driven BRDF model to reconstruct surface anisotropic\neffects, computing integrated black-sky albedo (BSA) at local solar noon and\nwhite-sky albedo (WSA) (VJ243MA3). Researchers can use the BRDF model\nparameters with a simple polynomial, to obtain black-sky albedo at any solar\nillumination angle. Likewise, both the BSA and WSA Science Dataset (SDS)\nlayers can be used with a simple polynomial, to manually estimate\ninstantaneous actual albedo (blue-sky albedo). Additional details regarding\nthe methodology are available in the Algorithm Theoretical Basis Document\n(ATBD).\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/viirs-user-guides-c1-and-c2/vnp43ia3-and-vnp43ma3-albedo-products/)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VJ243MA3/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2023-02-07T00:00:00Z","2026-10-29T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ243MA3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VJ243MA3","rel":"related","title":"Run the example for NASA/VIIRS/002/VJ243MA3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VJ243MA3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VJ243MA3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VJ243MA3.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ243MA3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VJ243MA3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VNP09GA","title":"VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km","description":"The Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface\nreflectance (VNP09GA) product provides an estimate of land\nsurface reflectance from the Suomi National Polar-Orbiting Partnership\n(S-NPP) VIIRS sensor. Data are provided for three imagery bands\n(I1, I2, I3) at nominal 500 meter resolution (~463 meter) and nine\nmoderate-resolution bands (M1, M2, M3, M4, M5, M7, M8, M10, M11) at nominal\n1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets\nare derived through resampling the native 375 meter and 750 meter VIIRS\nresolutions, respectively, in the L2 input product. These bands are\ncorrected for atmospheric conditions to provide an estimate\nof the surface spectral reflectance as it would be measured at ground level.\n\nThe data is temporally aggregated over each of the 16 possible passes per\nday. When multiple observations are present for each day, only the first\nof the highest-quality observations is included.\n\nThe band scale factors are already applied.\n\nFor additional information, visit the VIIRS [Land Product Quality\nAssessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=SNPP)\n\nDocumentation:\n\n* [User's Guide] (https://lpdaac.usgs.gov/documents/1657/VNP09_User_Guide_V2.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)] (https://lpdaac.usgs.gov/documents/122/VNP09_ATBD.pdf)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2026-11-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP09GA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VNP09GA","rel":"related","title":"Run the example for NASA/VIIRS/002/VNP09GA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VNP09GA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP09GA#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP09GA.002","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP09GA.json","rel":"latest-version","title":"NASA/VIIRS/002/VNP09GA","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP09GA.json","rel":"predecessor-version","title":"NOAA/VIIRS/001/VNP09GA","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP09GA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP09GA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VNP09H1","title":"VNP09H1: VIIRS Surface Reflectance 8-Day L3 Global 500m","description":"The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) Surface\nReflectance (VNP09H1) Version 1 composite product provides an estimate of\nland surface reflectance from the Suomi National Polar-orbiting Partnership\n(Suomi NPP) VIIRS sensor for three imagery bands (I1, I2, I3) at nominal\n500m resolution (~463m). The 500m dataset is derived through resampling the\nnative 375m VIIRS resolution in the L2 input product. The data are corrected\nfor atmospheric conditions such as the effects of molecular gases, including\nozone and water vapor, and for the effects of atmospheric aerosols. Each\npixel represents the best possible Level 2G observation during an 8-day\nperiod, which is selected on the basis of high observation coverage, low\nsensor angle, the absence of clouds or cloud shadow, and aerosol loading.\nThe three reflectance bands, this product includes a state quality assurance\n(QA) layer and a reflectance band quality layer.\n\nDocumentation:\n\n* [User's Guide]( https://lpdaac.usgs.gov/documents/1657/VNP09_User_Guide_V2.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/122/VNP09_ATBD.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp09h1v002/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=SNPP)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP09H1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VNP09H1","rel":"related","title":"Run the example for NASA/VIIRS/002/VNP09H1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VNP09H1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP09H1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP09H1.002","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP09H1.json","rel":"latest-version","title":"NASA/VIIRS/002/VNP09H1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP09H1.json","rel":"predecessor-version","title":"NOAA/VIIRS/001/VNP09H1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP09H1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP09H1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VNP13A1","title":"VNP13A1.002: VIIRS Vegetation Indices 16-Day 500m","description":"The Suomi National Polar-Orbiting Partnership (S-NPP) NASA Visible\nInfrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A1)\ndata product provides vegetation indices by a process of\nselecting the best available pixel over a 16-day acquisition period\nat 500 meter resolution. The VNP13 data products are designed\nafter the Moderate Resolution Imaging Spectroradiometer (MODIS)\nTerra and Aqua Vegetation Indices product suite to promote the\ncontinuity of the Earth Observation System (EOS) mission.\n\nThe VNP13 algorithm process produces three vegetation indices: (1)\nNormalized Difference Vegetation Index (NDVI), (2) the Enhanced\nVegetation Index (EVI), and (3) Enhanced Vegetation Index-2 (EVI2).\n(1) NDVI is one of the longest continual remotely sensed time series\nobservations, using both the red and near-infrared (NIR) bands.\n(2) EVI is a slightly different vegetation index that is more sensitive\nto canopy cover, while NDVI is more sensitive to chlorophyll. (3) EVI2\nis a reformation of the standard 3-band EVI, using the red band and\nNIR band. This reformation addresses arising issues when comparing\nVIIRS EVI to other EVI models that do not include a blue band. EVI2\nwill eventually become the standard EVI.\n\nAlong with the three Vegetation Indices layers, this product also\nincludes layers for near-infrared (NIR) reflectance; three shortwave\ninfrared (SWIR) reflectance-red, blue, and green reflectance; composite\nday of year; pixel reliability; view and sun angles, and a quality layer.\n\nFor additional information, visit the VIIRS [Land Product Quality\nAssessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=SNPP) and see\nthe [User Guide](https://lpdaac.usgs.gov/documents/1372/VNP13_User_Guide_ATBD_V2.1.2.pdf).\n\nDocumentation:\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/VIIRS/1/VNP13A1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-17T00:00:00Z","2026-10-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP13A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VNP13A1","rel":"related","title":"Run the example for NASA/VIIRS/002/VNP13A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VNP13A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP13A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP13A1.002","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP13A1.json","rel":"latest-version","title":"NASA/VIIRS/002/VNP13A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP13A1.json","rel":"predecessor-version","title":"NOAA/VIIRS/001/VNP13A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP13A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP13A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VNP14A1","title":"VNP14A1.002: Thermal Anomalies/Fire Daily L3 Global 1km SIN Grid","description":"The daily Suomi National Polar-Orbiting Partnership NASA Visible Infrared\nImaging Radiometer Suite (VIIRS) Thermal Anomalies/Fire (VNP14A1) Version 1\ndata product provides daily information about active fires and other thermal\nanomalies. The VNP14A1 data product is a global, 1km gridded composite of\nfire pixels detected from VIIRS 750m bands over a daily (24-hour) period.\nThe VNP14 data products are designed after the Moderate Resolution Imaging\nSpectroradiometer (MODIS) Thermal Anomalies/Fire product suite.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/427/VNP14_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/427/VNP14_User_Guide_V1.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp14a1v002/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=SNPP)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2026-11-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP14A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VNP14A1","rel":"related","title":"Run the example for NASA/VIIRS/002/VNP14A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VNP14A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP14A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP14A1.002","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP14A1.json","rel":"latest-version","title":"NASA/VIIRS/002/VNP14A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP14A1.json","rel":"predecessor-version","title":"NOAA/VIIRS/001/VNP14A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP14A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP14A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VNP15A2H","title":"VNP15A2H: LAI/FPAR 8-Day L4 Global 500m SIN Grid","description":"The Visible Infrared Imaging Radiometer Suite (VIIRS) Leaf Area Index (LAI)\nand Fraction of Photosynthetically Active Radiation (FPAR) Version 1 data\nproduct provides information about the vegetative canopy layer at 500 meter\nresolution (VNP15A2H). The VIIRS sensor is located onboard the NOAA/NASA\njoint Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite. LAI\nis an index that quantifies the one-sided leaf area of a canopy, while FPAR\nis the fraction of incoming solar energy absorbed through photosynthesis at\n400 to 700 nanometers. This product is intentionally designed after the\nTerra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS)\nLAI/FPAR operational algorithm to promote the continuity of the Earth\nObservation System (EOS) mission.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/126/VNP15_User_Guide.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/125/VNP15_ATBD.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp15a2hv002/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=SNPP)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-17T00:00:00Z","2026-11-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP15A2H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VNP15A2H","rel":"related","title":"Run the example for NASA/VIIRS/002/VNP15A2H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VNP15A2H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP15A2H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP15A2H.002","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP15A2H.json","rel":"latest-version","title":"NASA/VIIRS/002/VNP15A2H","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP15A2H.json","rel":"predecessor-version","title":"NOAA/VIIRS/001/VNP15A2H","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP15A2H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP15A2H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VNP21A1D","title":"VNP21A1D.002: Day Land Surface Temperature and Emissivity Daily 1km","description":"The NASA Suomi National Polar-Orbiting Partnership (Suomi NPP) Visible\nInfrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and\nEmissivity (LST&E) Day Version 1 product (VNP21A1D) is compiled daily\nfrom daytime Level 2 Gridded (L2G) intermediate products.\n\nThe L2G process maps the daily VNP21 swath granules onto a sinusoidal MODIS\ngrid and stores all observations overlapping a gridded cell for a given\nday. The VNP21A1 algorithm sorts through all these observations for each\ncell and estimates the final LST value as an average from all cloud-free\nobservations that have good LST accuracies. Only observations having\nobservation coverage more than a certain threshold (15%) are considered for\nthis averaging.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/429/VNP21_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp21a1dv002/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=SNPP)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2026-11-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP21A1D","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VNP21A1D","rel":"related","title":"Run the example for NASA/VIIRS/002/VNP21A1D in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VNP21A1D_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP21A1D#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP21A1D.002","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP21A1D.json","rel":"latest-version","title":"NASA/VIIRS/002/VNP21A1D","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP21A1D.json","rel":"predecessor-version","title":"NOAA/VIIRS/001/VNP21A1D","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP21A1D/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP21A1D/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VNP21A1N","title":"VNP21A1N.002: Night Land Surface Temperature and Emissivity Daily 1km","description":"The NASA Suomi National Polar-Orbiting Partnership (Suomi NPP) Visible\nInfrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and\nEmissivity (LST&E) Night Version 1 product (VNP21A1N) is compiled daily\nfrom nighttime Level 2 Gridded (L2G) intermediate products.\n\nThe L2G process maps the daily VNP21 swath granules onto a sinusoidal MODIS\ngrid and stores all observations overlapping a gridded cell for a given\nnight. The VNP21A1 algorithm sorts through all these observations for each\ncell and estimates the final LST value as an average from all cloud-free\nobservations that have good LST accuracies. Only observations having\nobservation coverage more than a certain threshold (15%) are considered for\nthis averaging.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/429/VNP21_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp21a1nv002/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=SNPP)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2026-11-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP21A1N","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VNP21A1N","rel":"related","title":"Run the example for NASA/VIIRS/002/VNP21A1N in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VNP21A1N_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP21A1N#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP21A1N.002","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP21A1N.json","rel":"latest-version","title":"NASA/VIIRS/002/VNP21A1N","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP21A1N.json","rel":"predecessor-version","title":"NOAA/VIIRS/001/VNP21A1N","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP21A1N/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP21A1N/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VNP22Q2","title":"VNP22Q2.002: Land Surface Phenology Yearly L3 Global 500m SIN Grid","description":"The Suomi National Polar-Orbiting Partnership (Suomi NPP) NASA Visible\nInfrared Imaging Radiometer Suite (VIIRS) Land Surface Phenology Yearly\n(VNP22Q2) Version 2 data product provides global land surface phenology (GLSP)\nmetrics at yearly intervals. The VNP22Q2 data product is derived from time\nseries of the two-band Enhanced Vegetation Index (EVI2) calculated from\nVIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted\nReflectance (NBAR). Vegetation phenology metrics at 500 meter spatial\nresolution are identified for up to two detected growing cycles per year.\n\nThe product contains six phenological transition dates: onset of greenness\nincrease, onset of greenness maximum, onset of greenness decrease, onset of\ngreenness minimum, dates of mid-greenup, and senescence phases. The product\nalso includes the growing season length. The greenness-related metrics\nconsist of EVI2 onset of greenness increase, EVI2 onset of greenness\nmaximum, EVI2 growing season, rate of greenness increase, and rate of\ngreenness decrease. The confidence of phenology detection is provided as\ngreenness agreement growing season, proportion of good quality (PGQ)\ngrowing season, PGQ onset greenness increase, PGQ onset greenness maximum,\nPGQ onset greenness decrease, and PGQ onset greenness minimum.\nThe final layer is quality control specifying the overall quality of the\nproduct.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/2263/VNP22_UserGuide_v2.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/2228/VNP22_ATBD_V2.pdf)\n\n* [General Documentation](https://doi.org/10.5067/VIIRS/VNP22Q2.002)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=SNPP)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP22Q2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VNP22Q2","rel":"related","title":"Run the example for NASA/VIIRS/002/VNP22Q2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VNP22Q2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP22Q2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP22Q2.002","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP22Q2.json","rel":"latest-version","title":"NASA/VIIRS/002/VNP22Q2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP22Q2.json","rel":"predecessor-version","title":"NOAA/VIIRS/001/VNP22Q2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP22Q2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP22Q2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VNP43IA3","title":"VNP43IA3: BRDF/Albedo Daily L3 Global 500m SIN Grid","description":"The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible\nInfrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance\nDistribution Function (BRDF) and Albedo (BRDF/Albedo) Version 2 product\nprovides albedo estimates at 500 meter resolution. The VNP43IA3 product is\nproduced daily using 16 days of VIIRS data and is weighted temporally to the\nninth day, which is reflected in the file name. It provides both directional\nhemispherical reflectance (black-sky albedo) at local solar noon and\nbihemispherical reflectance (white-sky albedo) for the three VIIRS imagery\nbands (I1, I2, and I3). The VNP43 data products are designed to promote the\ncontinuity of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS)\nBRDF/Albedo data product suite.\n\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR)\nsemi-empirical kernel-driven BRDF model, with the three kernel weights from\nVNP43IA1 to reconstruct surface anisotropic effects, correcting the\ndirectional reflectance to a common view geometry (VNP43IA4), while\nalso computing integrated black-sky albedo (BSA) at local solar noon and\nwhite-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model\nparameters with a simple polynomial, to obtain black-sky albedo at any solar\nillumination angle. Likewise, both the BSA and WSA Science Dataset (SDS)\nlayers can be used with a simple polynomial, to manually estimate\ninstantaneous actual albedo (blue-sky albedo). Additional details regarding\nthe methodology are available in the Algorithm Theoretical Basis Document\n(ATBD).\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/viirs-user-guides-c1-and-c2/vnp43ia3-and-vnp43ma3-albedo-products/)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/VIIRS/1/VNP43IA3)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-17T00:00:00Z","2026-10-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP43IA3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VNP43IA3","rel":"related","title":"Run the example for NASA/VIIRS/002/VNP43IA3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VNP43IA3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP43IA3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP43IA3.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP43IA3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP43IA3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VNP43IA4","title":"VNP43IA4: BRDF/Albedo Quality Daily L3 Global 500m SIN Grid","description":"The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible\nInfrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance\nDistribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product\nprovides NBAR estimates at 500 meter resolution. The VNP43IA4 product is\nproduced daily using 16 days of VIIRS data and is weighted temporally to the\nninth day, which is reflected in the file name. The view angle effects are\nremoved from the directional reflectances, resulting in a stable and\nconsistent NBAR product. The VNP43 data products are designed to promote the\ncontinuity of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS)\nBRDF/Albedo data product suite.\n\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR)\nsemi-empirical kernel-driven BRDF model, with the three kernel weights from\nVNP43IA1 to reconstruct surface anisotropic effects, correcting the\ndirectional reflectance to a common view geometry (VNP43IA4), while\nalso computing integrated black-sky albedo (BSA) at local solar noon and\nwhite-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model\nparameters with a simple polynomial, to obtain black-sky albedo at any solar\nillumination angle. Likewise, both the BSA and WSA Science Dataset (SDS)\nlayers can be used with a simple polynomial, to manually estimate\ninstantaneous actual albedo (blue-sky albedo). Additional details regarding\nthe methodology are available in the Algorithm Theoretical Basis Document\n(ATBD).\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/viirs-user-guides-c1-and-c2/vnp43ia4-and-vnpma4-nbar-products/)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/VIIRS/2/VNP43IA4)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-17T00:00:00Z","2026-10-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP43IA4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VNP43IA4","rel":"related","title":"Run the example for NASA/VIIRS/002/VNP43IA4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VNP43IA4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP43IA4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP43IA4.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP43IA4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP43IA4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VNP43MA3","title":"VNP43MA3: BRDF/Albedo Daily L3 Global 1km SIN Grid","description":"The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible\nInfrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance\nDistribution Function (BRDF) and Albedo (BRDF/Albedo) Version 2 product\nprovides albedo estimates at 1000 meter (1 km) resolution. The VNP43MA3 product\nis produced daily using 16 days of VIIRS data and is weighted temporally to the\nninth day. It provides both directional\nhemispherical reflectance (black-sky albedo) at local solar noon and\nbihemispherical reflectance (white-sky albedo) for the nine VIIRS moderate\nresolution bands (M1-M5, M7-M8, M10-M11) and three broadbands (vis, nir, and shortwave).\nThe VNP43 data products are designed to promote the continuity of NASA's\nModerate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.\n\nThe VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR)\nsemi-empirical kernel-driven BRDF model to reconstruct surface anisotropic effects,\ncomputing integrated black-sky albedo (BSA) at local solar noon and\nwhite-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model\nparameters with a simple polynomial, to obtain black-sky albedo at any solar\nillumination angle. Likewise, both the BSA and WSA Science Dataset (SDS)\nlayers can be used with a simple polynomial, to manually estimate\ninstantaneous actual albedo (blue-sky albedo). Additional details regarding\nthe methodology are available in the Algorithm Theoretical Basis Document\n(ATBD).\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/viirs-user-guides-c1-and-c2/vnp43ia3-and-vnp43ma3-albedo-products/)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/VIIRS/1/VNP43MA3)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-17T00:00:00Z","2026-10-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP43MA3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VNP43MA3","rel":"related","title":"Run the example for NASA/VIIRS/002/VNP43MA3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VNP43MA3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP43MA3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP43MA3.002","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP43MA3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP43MA3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VNP46A2","title":"VNP46A2: VIIRS Lunar Gap-Filled BRDF Nighttime Lights Daily L3 Global 500m","description":"The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared\nImaging Radiometer Suite (VIIRS) supports a Day-Night Band (DNB) sensor that\nprovides global daily measurements of nocturnal visible and\nnear-infrared(NIR) light that are suitable for Earth system science and\napplications. The VIIRS DNB's ultra-sensitivity in lowlight conditions\nenable us to generate a new set of science-quality nighttime products that\nmanifest substantial improvements in sensor resolution and calibration when\ncompared to the previous era of Defense Meteorological Satellite\nProgram/Operational Linescan System's (DMSP/OLS) nighttime lights image\nproducts. Such improvements allow the VIIRS DNB products to better monitor\nboth the magnitude and signature of nighttime phenomena, and anthropogenic\nsources of light emissions.\n\nVNP46A2 is the short-name for the daily moonlight- and atmosphere-corrected\nNighttime Lights (NTL) product called VIIRS/NPP Gap-Filled Lunar\nBRDF-Adjusted Nighttime Lights Daily L3 Global 500m Linear Lat Lon Grid.\n\nDocumentation:\n\n* [User's Guide](https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/VIIRS_Black_Marble_UG_v1.3_Sep_2022.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/Product%20Generation%20Algorithms/VIIRS_Black_Marble_ATBD_v1.1_July_2020.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VNP46A2/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2026-11-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP46A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VNP46A2","rel":"related","title":"Run the example for NASA/VIIRS/002/VNP46A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VNP46A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP46A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP46A2.002","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP46A2.json","rel":"latest-version","title":"NASA/VIIRS/002/VNP46A2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP46A2.json","rel":"predecessor-version","title":"NOAA/VIIRS/001/VNP46A2","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP46A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP46A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA/VIIRS/002/VNP64A1","title":"VNP64A1.002: Burned Area Monthly L4 Global 500m SIN Grid","description":"The daily Suomi National Polar-Orbiting Partnership (Suomi NPP) NASA Visible\nInfrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 2\ndata product is a monthly, global gridded 500m product containing per-pixel\nburned area and quality information. The VNP64 burned area mapping approach\nemploys 750m VIIRS imagery coupled with 750m VIIRS active fire\nobservations.\n\nVIIRS bands that are both sensitive and insensitive to biomass burning are\nused to detect changes caused by fire and to differentiate them from other\ntypes of change.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1330/VNP64A1_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/572/VNP64A1_ATBD_V1.pdf)\n\n* [General Documentation](https://doi.org/10.5067/VIIRS/VNP64A1.002)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=SNPP)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2026-09-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP64A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_VIIRS_002_VNP64A1","rel":"related","title":"Run the example for NASA/VIIRS/002/VNP64A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA/NASA_VIIRS_002_VNP64A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP64A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP64A1.002","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP64A1.json","rel":"latest-version","title":"NASA/VIIRS/002/VNP64A1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP64A1.json","rel":"predecessor-version","title":"NOAA/VIIRS/001/VNP64A1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP64A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA/VIIRS/002/VNP64A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA_USDA/HSL/SMAP10KM_soil_moisture","title":"NASA-USDA Enhanced SMAP Global Soil Moisture Data [deprecated]","description":"The NASA-USDA Enhanced SMAP Global soil moisture data provides soil moisture information across\nthe globe at 10-km spatial resolution. This dataset includes: surface,\nsubsurface, soil moisture (mm), soil moisture profile (%),\nsurface and subsurface soil moisture anomalies (-).\n\nThe dataset is generated by integrating satellite-derived Soil Moisture Active Passive (SMAP)\nLevel 3 soil moisture observations into the modified two-layer Palmer model using a 1-D\nEnsemble Kalman Filter (EnKF) data assimilation approach.\nSoil moisture anomalies were computed from the climatology of the day of interest.\nThe climatology was estimated based on the full data record of the SMAP satellite observation\nand the 31-day-centered moving-window approach. The assimilation of the SMAP soil moisture\nobservations help improve the model-based soil moisture predictions particularly over poorly\ninstrumented areas of the world that lack good quality precipitation data.\n\nThis dataset was developed by the Hydrological Science Laboratory at NASA's Goddard Space\nFlight Center in cooperation with USDA Foreign Agricultural Services and USDA Hydrology\nand Remote Sensing Lab.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["2015-04-02T12:00:00Z","2022-08-02T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA_USDA/HSL/SMAP10KM_soil_moisture","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA_USDA/NASA_USDA_HSL_SMAP10KM_soil_moisture","rel":"related","title":"Run the example for NASA_USDA/HSL/SMAP10KM_soil_moisture in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA_USDA/NASA_USDA_HSL_SMAP10KM_soil_moisture_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP10KM_soil_moisture#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_008.json","rel":"latest-version","title":"NASA/SMAP/SPL4SMGP/008","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_007.json","rel":"successor-version","title":"NASA/SMAP/SPL4SMGP/007","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA_USDA/HSL/SMAP10KM_soil_moisture/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA_USDA/HSL/SMAP10KM_soil_moisture/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA_USDA/HSL/SMAP_soil_moisture","title":"NASA-USDA SMAP Global Soil Moisture Data [deprecated]","description":"The NASA-USDA Global soil moisture and the NASA-USDA SMAP Global soil\nmoisture datates provide soil moisture information across the globe at\n0.25°x0.25° spatial resolution. These datasets include\n[surface](https://gimms.gsfc.nasa.gov/SMOS/SMAP/Surface_Soil_Moisture_SMAP.pdf)\nand [subsurface](https://gimms.gsfc.nasa.gov/SMOS/SMAP/Sub_SurfaceSoil_Moisture_SMAP.pdf)\nsoil moisture (mm),\n[soil moisture profile](https://gimms.gsfc.nasa.gov/SMOS/SMAP/SoilMoisture_Profile_SMAP.pdf) (%),\nand surface and subsurface soil moisture anomalies. Soil moisture anomalies\nare unitless and represent standardized\nanomalies computed using a 31-days moving window. Values around 0\nindicate typical moisture conditions, while very positive and very\nnegative values indicate extreme wetting (soil moisture conditions are\nabove average) and drying (soil moisture conditions are below average),\nrespectively.\n\nThis dataset is generated by integrating satellite-derived Soil Moisture\nActive Passive (SMAP) Level 3 soil moisture observations into the modified\ntwo-layer Palmer model using a 1-D Ensemble Kalman Filter (EnKF) data\nassimilation approach. The assimilation of the SMAP soil moisture\nobservations helped improve the model-based soil moisture predictions\nparticularly over poorly instrumented areas of the world that lack good\nquality precipitation data.\n\nThis dataset was developed by the Hydrological Science Laboratory (HSL) at\nNASA's Goddard Space Flight Center in cooperation with USDA Foreign\nAgricultural Services and USDA Hydrology and Remote Sensing Lab.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["2015-04-02T12:00:00Z","2020-12-31T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA_USDA/HSL/SMAP_soil_moisture","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA_USDA/NASA_USDA_HSL_SMAP_soil_moisture","rel":"related","title":"Run the example for NASA_USDA/HSL/SMAP_soil_moisture in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA_USDA/NASA_USDA_HSL_SMAP_soil_moisture_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP_soil_moisture#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_008.json","rel":"latest-version","title":"NASA/SMAP/SPL4SMGP/008","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_007.json","rel":"successor-version","title":"NASA/SMAP/SPL4SMGP/007","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA_USDA/HSL/SMAP_soil_moisture/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA_USDA/HSL/SMAP_soil_moisture/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NASA_USDA/HSL/soil_moisture","title":"NASA-USDA Global Soil Moisture Data [deprecated]","description":"The NASA-USDA Global soil moisture and the NASA-USDA SMAP Global soil\nmoisture dataset provide soil moisture information across the globe at\n0.25°x0.25° spatial resolution. These datasets include\n[surface](https://gimms.gsfc.nasa.gov/SMOS/SMAP/Surface_Soil_Moisture_SMAP.pdf)\nand [subsurface](https://gimms.gsfc.nasa.gov/SMOS/SMAP/Sub_SurfaceSoil_Moisture_SMAP.pdf)\nsoil moisture (mm),\n[soil moisture profile](https://gimms.gsfc.nasa.gov/SMOS/SMAP/SoilMoisture_Profile_SMAP.pdf) (%),\nand surface and subsurface soil moisture anomalies. Soil moisture anomalies\nare unitless and represent standardized\nanomalies computed using a 31-days moving window. Values around 0\nindicate typical moisture conditions, while very positive and very\nnegative values indicate extreme wetting (soil moisture conditions are\nabove average) and drying (soil moisture conditions are below average),\nrespectively.\n\nThis dataset is generated by integrating\nsatellite-derived Soil Moisture Ocean Salinity (SMOS) Level 2 soil moisture\nobservations into the modified two-layer Palmer model using a 1-D Ensemble\nKalman Filter (EnKF) data assimilation approach. The assimilation of the\nSMOS soil moisture observations helped improve the model-based soil\nmoisture predictions particularly over poorly instrumented areas\n(e.g., Southern African, Middle East) of the world that lack good quality\nprecipitation data.\n\nThis dataset was developed by the Hydrological Science Laboratory (HSL) at\nNASA's Goddard Space Flight Center in cooperation with USDA Foreign\nAgricultural Services and USDA Hydrology and Remote Sensing Lab.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["2010-01-13T12:00:00Z","2020-12-31T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NASA_USDA/HSL/soil_moisture","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA_USDA/NASA_USDA_HSL_soil_moisture","rel":"related","title":"Run the example for NASA_USDA/HSL/soil_moisture in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NASA_USDA/NASA_USDA_HSL_soil_moisture_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_soil_moisture#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_008.json","rel":"latest-version","title":"NASA/SMAP/SPL4SMGP/008","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_007.json","rel":"successor-version","title":"NASA/SMAP/SPL4SMGP/007","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NASA_USDA/HSL/soil_moisture/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NASA_USDA/HSL/soil_moisture/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/NCEP_DOE_RE2/total_cloud_coverage","title":"NCEP-DOE Reanalysis 2 (Gaussian Grid), Total Cloud Coverage","description":"**This collection is no longer being updated. 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The goal of this joint\neffort is to produce new atmospheric analyses using historical data as\nwell as to produce analyses of the current atmospheric state (Climate Data\nAssimilation System, CDAS). The NCEP/NCAR Reanalysis 1 project is using a\nstate-of-the-art analysis/forecast system to perform data assimilation using\npast data from 1948 to March 2026. The data have 6-hour temporal\nresolution (0000, 0600, 1200, and 1800 UTC) and 2.5 degree spatial\nresolution.\n\nNCEP discontinued this dataset in March 2026 and it is no longer being\nupdated.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1948-01-01T00:00:00Z","2026-03-18T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NCEP_RE/sea_level_pressure","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NCEP_RE/NCEP_RE_sea_level_pressure","rel":"related","title":"Run the example for NCEP_RE/sea_level_pressure in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NCEP_RE/NCEP_RE_sea_level_pressure_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_sea_level_pressure#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NCEP_RE/sea_level_pressure/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NCEP_RE/sea_level_pressure/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NCEP_RE/surface_temp","title":"NCEP/NCAR Reanalysis Data, Surface Temperature [deprecated]","description":"The NCEP/NCAR Reanalysis Project is a joint project between the National\nCenters for Environmental Prediction (NCEP, formerly \"NMC\") and the\nNational Center for Atmospheric Research (NCAR). The goal of this joint\neffort is to produce new atmospheric analyses using historical data as\nwell as to produce analyses of the current atmospheric state (Climate Data\nAssimilation System, CDAS). The NCEP/NCAR Reanalysis 1 project is using a\nstate-of-the-art analysis/forecast system to perform data assimilation using\npast data from 1948 to March 2026. The data have 6-hour temporal\nresolution (0000, 0600, 1200, and 1800 UTC) and 2.5 degree spatial\nresolution.\n\nNCEP discontinued this dataset in March 2026 and it is no longer being\nupdated.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1948-01-01T00:00:00Z","2026-03-18T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NCEP_RE/surface_temp","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NCEP_RE/NCEP_RE_surface_temp","rel":"related","title":"Run the example for NCEP_RE/surface_temp in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NCEP_RE/NCEP_RE_surface_temp_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_temp#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NCEP_RE/surface_temp/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NCEP_RE/surface_temp/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NCEP_RE/surface_wv","title":"NCEP/NCAR Reanalysis Data, Water Vapor [deprecated]","description":"The NCEP/NCAR Reanalysis Project is a joint project between the National\nCenters for Environmental Prediction (NCEP, formerly \"NMC\") and the\nNational Center for Atmospheric Research (NCAR). The goal of this joint\neffort is to produce new atmospheric analyses using historical data as\nwell as to produce analyses of the current atmospheric state (Climate Data\nAssimilation System, CDAS). The NCEP/NCAR Reanalysis 1 project is using a\nstate-of-the-art analysis/forecast system to perform data assimilation using\npast data from 1948 to March 2026. The data have 6-hour temporal\nresolution (0000, 0600, 1200, and 1800 UTC) and 2.5 degree spatial\nresolution.\n\nNCEP discontinued this dataset in March 2026 and it is no longer being\nupdated.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1948-01-01T00:00:00Z","2026-03-18T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NCEP_RE/surface_wv","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NCEP_RE/NCEP_RE_surface_wv","rel":"related","title":"Run the example for NCEP_RE/surface_wv in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NCEP_RE/NCEP_RE_surface_wv_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_wv#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NCEP_RE/surface_wv/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NCEP_RE/surface_wv/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/NGDC/ETOPO1","title":"ETOPO1: Global 1 Arc-Minute Elevation","description":"ETOPO1 is a 1 arc-minute global relief model of Earth''s\nsurface that integrates land topography and ocean bathymetry. 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The 1992\nimage is not directly comparable to any later editions of NLCD.\n\nThis dataset has one image for the continental US for each of the\neight epochs, plus separate images for Alaska, Hawaii, and Puerto Rico in 2001, 2011, and 2016.\n(They could not be merged in due to having a different projection from the continental US\nimages.)\n\nNote that the impervious layers and tree cover layers for Alaska 2011\nonly have data for parts of Alaska.  For the impervious layer, only the\nparts of Kenai Peninsula Borough and Anchorage Municipality are available.\nTree cover is only available on a strip along the coast from the southern\ntip of the Alaskan Panhandle up through the Kenai Peninsula, plus Kodiak\nIsland.\n\nNLCD 2016 Rangeland Component products characterize the percentage of each\n30-meter pixel in the Western United States covered by shrub, herbaceous,\nbare ground, litter, sagebrush, big sagebrush and annual herbaceous, along\nwith estimating shrub height and sagebrush height. These products have\nbeen produced by USGS in collaboration with the Bureau of Land Management.\n\nNLCD products are created by the Multi-Resolution Land Characteristics\n(MRLC) Consortium, a partnership of federal agencies led by the\nU.S. Geological Survey.\n","deprecated":true,"license":"CC0-1.0","extent":{"spatial":{"bbox":[[-130.24,21.75,-63.66,57.68]]},"temporal":{"interval":[["1992-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2016_REL","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD_RELEASES_2016_REL","rel":"related","title":"Run the example for USGS/NLCD_RELEASES/2016_REL in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_RELEASES_2016_REL_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2016_REL#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2019_REL_NLCD.json","rel":"latest-version","title":"USGS/NLCD_RELEASES/2019_REL/NLCD","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD.json","rel":"predecessor-version","title":"USGS/NLCD","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2019_REL_NLCD.json","rel":"successor-version","title":"USGS/NLCD_RELEASES/2019_REL/NLCD","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2016_REL/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2016_REL/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NLCD_RELEASES/2019_REL/NLCD","title":"NLCD 2019: USGS National Land Cover Database, 2019 release","description":"NLCD (the National Land Cover Database) is a 30-m Landsat-based land cover\ndatabase spanning 8 epochs (2001, 2004, 2006, 2008, 2011, 2013, 2016, and\n2019). A ninth epoch for 2021 is also available\n[here](USGS_NLCD_RELEASES_2021_REL_NLCD). The images rely on the\nimperviousness data layer for the urban classes and on a decision-tree\nclassification for the rest.\n\nThis dataset has one image for the continental US for each epoch.\nAlaska, Hawaii, and Puerto Rico data can be found in the previous\n[2016 NLCD release](USGS_NLCD_RELEASES_2016_REL).\n\nNLCD products are created by the Multi-Resolution Land Characteristics\n(MRLC) Consortium, a partnership of federal agencies led by the\nU.S. Geological Survey.\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-130.24,21.75,-63.66,50]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/NLCD","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD_RELEASES_2019_REL_NLCD","rel":"related","title":"Run the example for USGS/NLCD_RELEASES/2019_REL/NLCD in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_RELEASES_2019_REL_NLCD_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2019_REL_NLCD#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2019_REL_NLCD.json","rel":"latest-version","title":"USGS/NLCD_RELEASES/2019_REL/NLCD","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2016_REL.json","rel":"predecessor-version","title":"USGS/NLCD_RELEASES/2016_REL","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/NLCD/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/NLCD/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NLCD_RELEASES/2019_REL/RCMAP/V4/COVER","title":"RCMAP Rangeland Component Timeseries (1985-2020), v04 [deprecated]","description":"Rangeland ecosystems in the western United States have a dynamic response\nto climate change, fire, and other anthropogenic disturbances. The Rangeland\nCondition, Monitoring, Assessment, and Projection (RCMAP) product aims to\ncapture this response by quantifying the percent cover of rangeland\ncomponents, associated error, and trends across the western U.S. using\nLandsat imagery from 1985-2020.\n\nRCMAP quantifies the percent cover of components across the western U.S.\nrangelands (after Rigge et al. 2020) using Landsat imagery from 1985-2020.\nThe RCMAP timeseries consists of eight fractional components: annual\nherbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial\nherbaceous, sagebrush and shrub, and the temporal trends of each. The four\nprimary components (bare ground, shrub, litter, and herbaceous) are designed\nto sum to 100% in each pixel when added to tree canopy cover. The secondary\ncomponents annual herbaceous and perennial herbaceous are subsets of the\nprimary component herbaceous, while non-sagebrush shrub and sagebrush are\nsubsets of shrub. Secondary components cannot have cover greater than their\nrespective primary component. One year, 2012, was excluded from\nthe timeseries due to a lack of quality imagery.\n\nMRLC developed an automated method to identify change in spectral\nconditions between each year in the Landsat archive and the circa 2016 base\nmap. Regression tree models were trained from the unchanged portions of each\nyear in the time series. Post-processing models corrected post-burn\ntrajectories and eliminated noise and illogical change in the predictions.\nThe current generation of RCMAP has been improved with more training data,\nregional-scale Landsat composites, and more robust change detection. MRLC\nassessed the temporal patterns in each component with a linear model and\nstructural change method which determines break points in the timeseries\nusing an 8-year temporal moving window. The linear and structural change\nmethods generally agreed on gross patterns of change, but the latter found\nbreaks more often with most pixels having at least one break point. Data\nprovide spatiotemporal information on the occurrence of breaks, but even\nmore critically, attribute those change events to specific component(s).\nThe spatially, temporally, and thematically (i.e., multi-component) detailed\nspecific information on rangeland condition can contribute to understanding\nmajor patterns of change at local, regional, and continental levels.\n\nSee also:\n\n* Rigge, M., C. Homer, L. Cleeves, D. K. Meyer, B. Bunde, H. Shi, G. Xian,\n  S. Schell, and M. Bobo. 2020. Quantifying western U.S. rangelands as\n  fractional components with multi-resolution remote sensing and in situ\n  data. Remote Sensing 12.\n  [doi:10.3390/rs12030412](https://doi.org/10.3390/rs12030412)\n\n* Rigge, M., C. Homer, H. Shi, D. Meyer, B.\n  Bunde, B. Granneman, K. Postma, P. Danielson, A. Case, and G. Xian. 2021.\n  Rangeland Fractional Components Across the Western United States\n  from 1985 to 2018. Remote Sensing 13:813.\n  [doi:10.3390/rs13040813](https://doi.org/10.3390/rs13040813)\n","deprecated":true,"license":"CC0-1.0","extent":{"spatial":{"bbox":[[-125.0683594,28.45903302,-101.0742188,49.32512199]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2020-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/RCMAP/V4/COVER","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD_RELEASES_2019_REL_RCMAP_V4_COVER","rel":"related","title":"Run the example for USGS/NLCD_RELEASES/2019_REL/RCMAP/V4/COVER in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_RELEASES_2019_REL_RCMAP_V4_COVER_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2019_REL_RCMAP_V4_COVER#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_COVER.json","rel":"latest-version","title":"USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/COVER","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2019_REL_RCMAP_V5_COVER.json","rel":"successor-version","title":"USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/COVER","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/RCMAP/V4/COVER/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/RCMAP/V4/COVER/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/COVER","title":"RCMAP Rangeland Component Timeseries (1985-2021), v05 [deprecated]","description":"The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset\nquantifies the percent cover of rangeland components across the western\nU.S. using Landsat imagery from 1985 to 2021. The RCMAP product suite\nconsists of nine fractional components: annual herbaceous, bare ground,\nherbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush,\nshrub, and tree, in addition to the temporal trends of each\ncomponent. Several enhancements were made to the RCMAP process relative to\nprior generations. First, they have trained time-series predictions directly\nfrom 331 high-resolution sites collected from 2013 to 2018 from Assessment,\nInventory, and Monitoring (AIM) instead of using the 2016 \"base\" map as an\nintermediary. This removes one level of model error and allows the direct\nassociation of high-resolution derived training data to the corresponding\nyear of Landsat imagery. They have incorporated all available (as of\n10/1/22) Bureau of Land Management (BLM), Assessment, Inventory, and\nMonitoring (AIM), and Landscape Monitoring Framework (LMF)\nobservations. LANDFIRE public reference database training observations\nspanning from 1985 to 2015 have been added. Neural network models with Keras\ntuner optimization have replaced Cubist models as the classifier. They have\nadded a tree canopy cover component.  The study area has expanded to include\nall of California, Oregon, and Washington; in prior generations, landscapes\nto the west of the Cascades were excluded. Additional spectral indices have\nbeen added as predictor variables, tasseled cap wetness, brightness, and\ngreenness. Geographic location and elevation above sea level have been added\nas predictor variables. CCDC-Synthetic Landsat images were obtained for 6\nmonthly periods for each region and were added as predictors. These data\naugment the phenologic detail of the 2 seasonal Landsat composites.\n\nPost-processing has been improved with updated fire recovery equations\nstratified by ecosystem resistance and resilience (R and R) classes (Maestas\nand Campbell, 2016) to stratify recovery rates. Ecosystem R and R maps are\nonly available for the sagebrush biome.  They intersected classes with 1985\nto 2020 average water year precipitation to identify precipitation\nthresholds corresponding to R and R classes. Outside of the sagebrush biome,\nprecipitation was used to produce R and R equivalent (low, medium,\nhigh). Due to the fast recovery following fire in California chaparral\n(e.g., Keeley and Keeley, 1981, Storey et al., 2016), they used EPA level 3\necoregions to define a 4th R and R zone. Recovery rates are based on (Arkle\net al., (in press)) who evaluated the recovery of plant functional groups in\n1,278 post-fire rehab plots by time since disturbance stratified by\necosystem resistance and resilience. They have expanded this analysis by\nevaluated postfire-recovery in all AIM and LMF data across the West to\nestablish maximum sage, shrub, and tree cover by time-since fire. Recovery\nlimits in California follow (Keeley and Keeley, 1981 and Storey et al.,\n2016). Second, post-processing has been enhanced through a revised noise\ndetection model. For each pixel, they fit a third order polynomial model for\neach component cover time-series. Observations with a z-score more than 2\nstandard deviations from the mean are removed, and a new third order\npolynomial model (i.e., cleaned fit) is fit to observations within this\nthreshold. Finally, looking again at all observations, those observations\nwith a z-score more than 2 standard deviations from the mean of the cleaned\nfit are replaced with the mean of the prior and subsequent year component\ncover values.\n\nThe mapping area included eight regions which were subsequently mosaicked\nfor all nine components.  These data can be used to answer critical\nquestions regarding the influence of climate change and the suitability of\nmanagement practices. Component products can be downloaded from the\n[Multi-Resolution Land Characteristics Consortium](https://www.mrlc.gov/data).\n\nSee also:\n\n* Rigge, M., C. Homer, L. Cleeves, D. K. Meyer, B. Bunde, H. Shi, G. Xian,\n  S. Schell, and M. Bobo. 2020. Quantifying western U.S. rangelands as\n  fractional components with multi-resolution remote sensing and in situ\n  data. Remote Sensing 12.\n  [doi:10.3390/rs12030412](https://doi.org/10.3390/rs12030412)\n\n* Rigge, M., C. Homer, H. Shi, D. Meyer, B.\n  Bunde, B. Granneman, K. Postma, P. Danielson, A. Case, and G. Xian. 2021.\n  Rangeland Fractional Components Across the Western United States\n  from 1985 to 2018. Remote Sensing 13:813.\n  [doi:10.3390/rs13040813](https://doi.org/10.3390/rs13040813)\n","deprecated":true,"license":"CC0-1.0","extent":{"spatial":{"bbox":[[-125.0683594,28.45903302,-101.0742188,49.32512199]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2021-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/COVER","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD_RELEASES_2019_REL_RCMAP_V5_COVER","rel":"related","title":"Run the example for USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/COVER in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_RELEASES_2019_REL_RCMAP_V5_COVER_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2019_REL_RCMAP_V5_COVER#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_COVER.json","rel":"latest-version","title":"USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/COVER","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2019_REL_RCMAP_V4_COVER.json","rel":"predecessor-version","title":"USGS/NLCD_RELEASES/2019_REL/RCMAP/V4/COVER","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_COVER.json","rel":"successor-version","title":"USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/COVER","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/COVER/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/COVER/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/AVHRR/AOT/V4","title":"NOAA CDR AVHRR AOT: Daily Aerosol Optical Thickness Over Global Oceans, v04","description":"The NOAA Climate Data Record (CDR) of Aerosol Optical Thickness (AOT) is a\ncollection of global daily 0.1 degree derived data from the PATMOS-x AVHRR\nlevel-2b channel 1 (0.63 micron) orbital clear-sky radiance. The aerosol\nproduct is generated from AVHRR imagery in cloud-free conditions during\ndaytime over oceans.\n\nDue to the relatively large uncertainties associated with surface\nreflectance over water glint area and land surface as well as limited\nAVHRR retrieval channels, this dataset only includes retrieval over\nnon-glint water surface (specifically at the anti-solar side of the orbit\nwith viewing angle more than 40 degree away from the specular ray). For\nmore details, see the [Algorithm Description](https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Aerosol_Optical_Thickness/AlgorithmDescription_01B-04.pdf).\n\nImage and data processing by NOAA's National Climatic Data Center.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-01-01T00:00:00Z","2026-06-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/AOT/V4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_AVHRR_AOT_V4","rel":"related","title":"Run the example for NOAA/CDR/AVHRR/AOT/V4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_AVHRR_AOT_V4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_AOT_V4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.25921/w3zj-4y48","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_AOT_V4.json","rel":"latest-version","title":"NOAA/CDR/AVHRR/AOT/V4","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_AOT_V3.json","rel":"predecessor-version","title":"NOAA/CDR/AVHRR/AOT/V3","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/AOT/V4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/AVHRR/AOT/V4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/VIIRS/LAI_FAPAR/V1","title":"NOAA CDR VIIRS LAI FAPAR: Leaf Area Index and Fraction of Absorbed Photosynthetically Active Radiation, Version 1","description":"This Climate Data Record (CDR) combines datasets for Leaf Area Index (LAI)\nand Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), two\nbiophysical variables that can be used to evaluate vegetation stress,\nforecast agricultural yields, and other modeling and resource management\napplications. LAI tracks the one-sided green leaf area per unit of ground\nsurface area, while FAPAR quantifies the solar radiation absorbed by plants\nwithin the photosynthetically active radiation (PAR) spectral region.\nThe LAI/FAPAR CDR generates a daily product on a .05&deg; by .05&deg; grid.\nThe data is generated from the Visible Infrared Imaging Radiometer Suite\n(VIIRS) sensors from 2014 onward.\n\nKnown issues with this dataset include:\n\n- TIMEOFDAY variable contains values that are too large by 1 day\n\n- Latitude values are not correctly associated with the center of\n  the grid cell, error is < 0.002 degrees\n\n- Longitude values are not correctly associated with the center of\n  the grid cell, error is < 0.02 degrees\n\nSee [technical note from the data provider](https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Leaf_Area_Index_and_FAPAR/VIIRS/AlgorithmDescriptionVIIRS_01B-20c.pdf).\n\nProvider's note: the orbital drift of N-19 (the last NOAA satellite\ncarrying the AVHRR sensor) began to severely degrade the retrieved\nproduct quality. Therefore, VIIRS is now the primary sensor being used for\nthese products from 2014-present.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2014-01-01T00:00:00Z","2026-11-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/VIIRS/LAI_FAPAR/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_VIIRS_LAI_FAPAR_V1","rel":"related","title":"Run the example for NOAA/CDR/VIIRS/LAI_FAPAR/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_VIIRS_LAI_FAPAR_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_VIIRS_LAI_FAPAR_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.25921/9x3m-0e02","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/VIIRS/LAI_FAPAR/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/VIIRS/LAI_FAPAR/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CDR/VIIRS/NDVI/V1","title":"NOAA CDR VIIRS NDVI: Normalized Difference Vegetation Index, Version 1","description":"This dataset contains gridded daily Normalized Difference Vegetation Index\n(NDVI) derived from the NOAA Climate Data Record (CDR) of Visible Infrared\nImaging Radiometer Suite (VIIRS) Surface Reflectance. The data record spans\nfrom 2014 onward using data from NOAA polar orbiting\nsatellites. The data are projected on a 0.05 degree x 0.05 degree global\ngrid. This dataset is one of the Land Surface CDR products produced by the\nNASA Goddard Space Flight Center (GSFC) and the University of Maryland\n(UMD).\n\nKnown issues with this dataset include:\n\n- TIMEOFDAY variable contains values that are too large by 1 day\n\n- Latitude values are not correctly associated with the center of\n  the grid cell, error is < 0.002 degrees\n\n- Longitude values are not correctly associated with the center of\n  the grid cell, error is < 0.02 degrees\n\nSee [technical note from the data provider](https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Normalized_Difference_Vegetation_Index/VIIRS/AlgorithmDescriptionVIIRS_01B-20b.pdf).\n\nProvider's note: the orbital drift of N-19 (the last NOAA satellite\ncarrying the AVHRR sensor) began to severely degrade the retrieved\nproduct quality. Therefore, VIIRS is now the primary sensor being used for\nthese products from 2014-present.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2014-01-01T00:00:00Z","2026-11-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/VIIRS/NDVI/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CDR_VIIRS_NDVI_V1","rel":"related","title":"Run the example for NOAA/CDR/VIIRS/NDVI/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CDR_VIIRS_NDVI_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_VIIRS_NDVI_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.25921/gakh-st76","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/VIIRS/NDVI/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CDR/VIIRS/NDVI/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CFSR","title":"CFSR: Climate Forecast System Reanalysis [deprecated]","description":"Starting October 22, 2025, this dataset switched to using Celsius\ninstead of Kelvin units for temperature bands. Please use\n[NOAA/CFSR_HARMONIZED](https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSR_HARMONIZED)\nfor consistent temperature units (Kelvin).\n\nThe National Centers for Environmental Prediction (NCEP) Climate Forecast\nSystem Reanalysis (CFSR) was designed and executed as a global,\nhigh-resolution, coupled atmosphere-ocean-land surface-sea ice system to\nprovide the best estimate of the state of these coupled domains over the\n32-year period of record from January 1979 to March 2011. It has been\nextended as an operational real-time product. The data in Earth Engine are\nonly present starting from December 13, 2018.\n\nForecasts are initialized four times per day (0000, 0600, 1200, and 1800\nUTC). We ingest only a subset of bands from files matching\n`cdas1.t??z.pgrbh**03|00**.grib2`, i.e. files of only 0-hour and 3-hour\nforecasts as the others are omitted. The forecast length is indicated by the\n'forecast_hour' metadata field.\n\nSome images contain only a subset of bands. Using this dataset with both\n\"00\" and \"03\" forecast types will require you to cast the bands across\nthe collection.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-12-13T00:00:00Z","2026-11-09T06:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CFSR","rel":"related","title":"Run the example for NOAA/CFSR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CFSR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSR#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5065/D6513W89","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSR_HARMONIZED.json","rel":"latest-version","title":"NOAA/CFSR_HARMONIZED","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSR_HARMONIZED.json","rel":"successor-version","title":"NOAA/CFSR_HARMONIZED","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CFSR_HARMONIZED","title":"CFSR: Climate Forecast System Reanalysis Harmonized","description":"The National Centers for Environmental Prediction (NCEP) Climate Forecast\nSystem Reanalysis (CFSR) was designed and executed as a global,\nhigh-resolution, coupled atmosphere-ocean-land surface-sea ice system to\nprovide the best estimate of the state of these coupled domains over the\n32-year period of record from January 1979 to March 2011. It has been\nextended as an operational real-time product. The data in Earth Engine are\nonly present starting from December 13, 2018.\n\nForecasts are initialized four times per day (0000, 0600, 1200, and 1800\nUTC). We ingest only a subset of bands from files matching\n`cdas1.t??z.pgrbh**03|00**.grib2`, i.e. files of only 0-hour and 3-hour\nforecasts as the others are omitted. The forecast length is indicated by the\n'forecast_hour' metadata field.\n\nSome images contain only a subset of bands. Using this dataset with both\n\"00\" and \"03\" forecast types will require you to cast the bands across\nthe collection.\n\nThis dataset harmonizes all temperature bands to Kelvin.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-12-13T00:00:00Z","2026-11-09T06:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSR_HARMONIZED","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CFSR_HARMONIZED","rel":"related","title":"Run the example for NOAA/CFSR_HARMONIZED in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CFSR_HARMONIZED_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSR_HARMONIZED#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5065/D6513W89","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSR_HARMONIZED.json","rel":"latest-version","title":"NOAA/CFSR_HARMONIZED","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSR.json","rel":"predecessor-version","title":"NOAA/CFSR","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSR_HARMONIZED/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSR_HARMONIZED/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CFSV2/FOR6H_HARMONIZED","title":"CFSV2: NCEP Climate Forecast System Version 2, 6-Hourly Products Harmonized","description":"The National Centers for Environmental Prediction (NCEP) Climate Forecast\nSystem (CFS) is a fully coupled model representing the interaction between\nthe Earth's atmosphere, oceans, land, and sea ice. CFS was developed at the\nEnvironmental Modeling Center (EMC) at NCEP. The operational CFS was\nupgraded to version 2 (CFSv2) on March 30, 2011.\n\nForecasts are initialized four times per day (0000, 0600, 1200, and 1800\nUTC). This is the same model that was used to create the NCEP Climate\nForecast System Reanalysis (CFSR), and the purpose of the CFSv2 dataset is\nto extend CFSR. We ingest only a subset of bands from files matching\ncdas1.t??z.sfluxgrbf**06**.grib2.\n\nFor more information about CFS, please see the [CFS NOAA site](https://cfs.ncep.noaa.gov/).\n\nThis dataset harmonizes all temperature bands to Kelvin.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z","2026-11-09T06:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSV2/FOR6H_HARMONIZED","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CFSV2_FOR6H_HARMONIZED","rel":"related","title":"Run the example for NOAA/CFSV2/FOR6H_HARMONIZED in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CFSV2_FOR6H_HARMONIZED_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSV2_FOR6H_HARMONIZED#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5065/D61C1TXF","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSV2_FOR6H_HARMONIZED.json","rel":"latest-version","title":"NOAA/CFSV2/FOR6H_HARMONIZED","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSV2_FOR6H.json","rel":"predecessor-version","title":"NOAA/CFSV2/FOR6H","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSV2/FOR6H_HARMONIZED/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CFSV2/FOR6H_HARMONIZED/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CPC/Precipitation","title":"CPC Global Unified Gauge-Based Analysis of Daily Precipitation","description":"The CPC Unified Gauge-Based Analysis of Global Daily Precipitation dataset\noffers daily precipitation estimates over land from 1979 to the present.\nDeveloped by NOAA's Climate Prediction Center (CPC), it leverages an optimal\ninterpolation technique to combine data from a global network of rain\ngauges, with over 30,000 gauges contributing to the retrospective version\n(1979-2005) and around 17,000 to the real-time version (2006-present).\nData is provided at a 0.5-degree resolution and includes both precipitation\namounts (in 0.1 mm) and the number of gauges used for each grid cell,\nallowing users to assess data quality.\n\nThe dataset's quality is acknowledged to be poor over tropical Africa and\nAntarctica, and generally varies with gauge density. Real-time data is\nsubject to revision as more complete station data becomes available.\n\n[This](https://ftp.cpc.ncep.noaa.gov/precip/CPC_UNI_PRCP/GAUGE_GLB/DOCU/)\nfolder has all the technical documentation.\n\nNOTE: The historical data spanning from 1979 to 2005 is not available in the\ncurrent version of the dataset.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-01T00:00:00Z","2026-11-07T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CPC/Precipitation","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CPC_Precipitation","rel":"related","title":"Run the example for NOAA/CPC/Precipitation in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CPC_Precipitation_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CPC_Precipitation#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CPC/Precipitation/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CPC/Precipitation/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/CPC/Temperature","title":"CPC Global Unified Temperature","description":"This dataset provides a gridded analysis of daily surface air temperature\nover global land areas, including daily maximum (Tmax), minimum (Tmin)\ntemperatures. Spanning from 1979 to the present, the data is presented on\n0.5-degree latitude/longitude grids, aligning with the resolution of CPC's\ngauge-based global daily precipitation analysis. The construction of this\ndataset considers orographic effects. Its primary purpose is to support\nclimate monitoring and forecast verification activities. Input data\noriginates from the CPC archive of GTS (Global Telecommunication System)\ndaily reports, incorporating Tmax and Tmin data from approximately 6,000 to\n7,000 global stations.\n\nRefer [this](https://ftp.cpc.ncep.noaa.gov/precip/PEOPLE/wd52ws/global_temp/CPC-GLOBAL-T.pdf)\nfor technical documentation.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CPC/Temperature","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_CPC_Temperature","rel":"related","title":"Run the example for NOAA/CPC/Temperature in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_CPC_Temperature_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_CPC_Temperature#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CPC/Temperature/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/CPC/Temperature/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GFS0P25","title":"GFS: Global Forecast System 384-Hour Predicted Atmosphere Data","description":"The Global Forecast System (GFS) is a weather forecast model produced by\nthe National Centers for Environmental Prediction (NCEP). The GFS dataset\nconsists of selected model outputs (described below) as gridded forecast\nvariables. The 384-hour forecasts, with 1-hour (up to 120 hours)\nand 3-hour (after 120 hours) forecast intervals, are made at 6-hour temporal\nresolution (i.e. updated four times daily). Use the 'creation_time' and\n'forecast_time' properties to select data of interest.\n\nThe GFS is a coupled model, composed of an atmosphere model, an ocean model,\na land/soil model, and a sea ice model which work together to provide an\naccurate picture of weather conditions. Note that this model may change;\nsee [history of recent modifications to the global forecast/analysis system](https://www.emc.ncep.noaa.gov/gmb/STATS/html/model_changes.html)\nand the [documentation](https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php)\nfor more information. There may be significant hour-to-hour and day-to-day\nfluctuations that require noise-reduction techniques to be applied to bands\nbefore analysis.\n\nNote that the available forecast hours and intervals have changed over time:\n\n* From 2015/04/01-2017/07/09: 36-hour forecasts, excluding hour 0, at\n3-hour intervals.\n* From 2017/07/09-2021/06/11: 384-hour forecasts, at 1-hour intervals from\nhours 0-120, at 3-hour intervals from hours 120-240, and 12-hour intervals\nfrom hours 240-384.\n* From 2021/06/12: 384-hour forecasts, at 1-hour intervals from hours 0-120\nand 3-hour intervals from hours 120-384.\n\nSome bands are only available starting on 2025/01/15 as noted in the band\ndescriptions.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-07-01T00:00:00Z","2026-11-10T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GFS0P25","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GFS0P25","rel":"related","title":"Run the example for NOAA/GFS0P25 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GFS0P25_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GFS0P25/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GFS0P25/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/19/FDCC","title":"GOES-19 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS","description":"\n[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Fire (HSC) product contains four images: one in the form\nof a fire mask and the other three with pixel values identifying fire temperature, fire area,\nand fire radiative power.\n\nThe ABI L2+ FHS metadata mask assigns a flag to every earth-navigated pixel\nthat indicates its disposition with respect to the FHS algorithm. Operational users who have\nthe lowest tolerance for false alarms should focus on the \"processed\" and\n\"saturated\" categories (mask codes 10, 11, 30, and 31), but within these categories there\ncan still be false alarms.\n\nOperational data production for GOES-19 began April 7, 2025. Any data prior\nto this date is provisional only.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r)\n\nNOAA provides the following scripts for suggested categories,\ncolor maps, and visualizations:\n\n - [GOES-16-17_FireDetection.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireDetection.js)\n - [GOES-16-17_FireReclassification.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireReclassification.js)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,14.57,180,53.51]]},"temporal":{"interval":[["2025-04-07T00:00:00Z","2026-11-10T21:36:17.100000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/FDCC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_19_FDCC","rel":"related","title":"Run the example for NOAA/GOES/19/FDCC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_19_FDCC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_19_FDCC#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-19/ABI-L2-FDCC/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/FDCC/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/FDCC/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/19/FDCF","title":"GOES-19 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Fire (HSC) product contains four images: one in the form\nof a fire mask and the other three with pixel values identifying fire temperature, fire area,\nand fire radiative power.\n\nThe ABI L2+ FHS metadata mask assigns a flag to every earth-navigated pixel\nthat indicates its disposition with respect to the FHS algorithm. Operational users who have\nthe lowest tolerance for false alarms should focus on the \"processed\" and\n\"saturated\" categories (mask codes 10, 11, 30, and 31), but within these categories there\ncan still be false alarms.\n\nOperational data production for GOES-19 began April 7, 2025. Any data prior\nto this date is provisional.\n\n[README](https://www.ncei.noaa.gov/products/goes-terrestrial-weather-abi-glm)\n\nNOAA provides the following scripts for suggested categories,\ncolor maps, and visualizations:\n\n - [GOES-16-17_FireDetection.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireDetection.js)\n - [GOES-16-17_FireReclassification.js](https://github.com/google/earthengine-community/blob/master/datasets/scripts/GOES-16-17_FireReclassification.js)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-04-07T00:00:00Z","2026-11-10T21:30:20.400000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/FDCF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_19_FDCF","rel":"related","title":"Run the example for NOAA/GOES/19/FDCF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_19_FDCF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_19_FDCF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-19/ABI-L2-FDCF/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/FDCF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/FDCF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/19/MCMIPC","title":"GOES-19 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Cloud and Moisture Imagery products are all at 2km\nresolution. Bands 1-6 are reflective. The dimensionless \"reflectance factor\" quantity is\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\n\nOperational data production for GOES-19 began April 7, 2025. Any data prior\nto this date is provisional.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r-series)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-152.11,14,-49.18,56.77]]},"temporal":{"interval":[["2025-04-07T00:00:00Z","2026-11-10T21:36:17.100000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/MCMIPC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_19_MCMIPC","rel":"related","title":"Run the example for NOAA/GOES/19/MCMIPC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_19_MCMIPC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_19_MCMIPC#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-19/ABI-L2-MCMIPC/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/MCMIPC/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/MCMIPC/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/19/MCMIPF","title":"GOES-19 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Cloud and Moisture Imagery products are all at 2km\nresolution. Bands 1-6 are reflective. The dimensionless \"reflectance factor\" quantity is\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\n\nOperational data production for GOES-19 began April 7, 2025. Any data prior\nto this date is provisional.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r-series)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-04-07T00:00:00Z","2026-11-10T21:20:20.400000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/MCMIPF","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_19_MCMIPF","rel":"related","title":"Run the example for NOAA/GOES/19/MCMIPF in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_19_MCMIPF_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_19_MCMIPF#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-19/ABI-L2-MCMIPF/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/MCMIPF/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/MCMIPF/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/GOES/19/MCMIPM","title":"GOES-19 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Mesoscale","description":"[GOES](https://www.goes.noaa.gov) satellites are geostationary weather satellites run by NOAA.\n\nThe Cloud and Moisture Imagery products are all at 2km\nresolution. Bands 1-6 are reflective. The dimensionless \"reflectance factor\" quantity is\nnormalized by the solar zenith angle. These bands support the characterization of clouds,\nvegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the\nTop-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the\nsurface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.\n\nThe locations of domains 1 and 2 change over time.\n\nOperational data production for GOES-19 began April 7, 2025. Any data prior\nto this date is provisional.\n\n[README](https://www.ncei.noaa.gov/products/satellite/goes-r-series)\n\nNOAA's Office of Satellite and Product Operations has a\n[General Satellite Messages](https://www.ospo.noaa.gov/Operations/messages.html)\nchannel with status updates.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-04-07T00:00:00Z","2026-11-10T21:42:55Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/MCMIPM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_GOES_19_MCMIPM","rel":"related","title":"Run the example for NOAA/GOES/19/MCMIPM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_GOES_19_MCMIPM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_19_MCMIPM#terms-of-use","rel":"license","type":"text/html"},{"href":"https://console.cloud.google.com/storage/browser/gcp-public-data-goes-19/ABI-L2-MCMIPM/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/MCMIPM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/GOES/19/MCMIPM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/NWS/RTMA","title":"RTMA: Real-Time Mesoscale Analysis","description":"The Real-Time Mesoscale Analysis (RTMA) is a high-spatial and temporal\nresolution analysis for near-surface weather conditions.  This dataset\nincludes hourly analyses at 2.5 km for CONUS.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-130.17,20.15,-60.81,52.91]]},"temporal":{"interval":[["2011-01-01T00:00:00Z","2023-03-14T18:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/NWS/RTMA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA_NWS_RTMA","rel":"related","title":"Run the example for NOAA/NWS/RTMA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NWS/NOAA_NWS_RTMA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_NWS_RTMA#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/NWS/RTMA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/NWS/RTMA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/PERSIANN-CDR","title":"PERSIANN-CDR: Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record","description":"PERSIANN-CDR is a daily quasi-global precipitation product\nthat spans the period from 1983-01-01 to present.  The data is\nproduced quarterly, with a typical lag of three months. The product\nis developed by the Center for Hydrometeorology and Remote Sensing\nat the University of California, Irvine (UC-IRVINE/CHRS) using\nGridded Satellite (GridSat-B1) IR data that are derived from merging\nISCCP B1 IR data, along with GPCP version 2.2.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["1983-01-01T00:00:00Z","2026-07-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/PERSIANN-CDR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_PERSIANN-CDR","rel":"related","title":"Run the example for NOAA/PERSIANN-CDR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_PERSIANN-CDR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_PERSIANN-CDR#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.ncei.noaa.gov/data/precipitation-persiann/access/","rel":"source"},{"href":"https://doi.org/10.7289/V51V5BWQ","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/PERSIANN-CDR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/PERSIANN-CDR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/001/VNP09GA","title":"VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km","description":"The Visible Infrared Imaging Radiometer Suite (VIIRS) daily surface\nreflectance (VNP09GA) product provides an estimate of land\nsurface reflectance from the Suomi National Polar-Orbiting Partnership\n(S-NPP) VIIRS sensor. Data are provided for three imagery bands\n(I1, I2, I3) at nominal 500 meter resolution (~463 meter) and nine\nmoderate-resolution bands (M1, M2, M3, M4, M5, M7, M8, M10, M11) at nominal\n1 kilometer (~926 meter) resolution. The 500 meter and 1 kilometer datasets\nare derived through resampling the native 375 meter and 750 meter VIIRS\nresolutions, respectively, in the L2 input product. These bands are\ncorrected for atmospheric conditions to provide an estimate\nof the surface spectral reflectance as it would be measured at ground level.\n\nThe data is temporally aggregated over each of the 16 possible passes per\nday. When multiple observations are present for each day, only the first\nof the highest-quality observations is included.\n\nFor additional information, visit the VIIRS [Land Product Quality\nAssessment website](https://landweb.modaps.eosdis.nasa.gov/NPP_QA/) and see\nthe [User Guide](https://lpdaac.usgs.gov/documents/124/VNP09_User_Guide_V1.6.pdf).\n\nDocumentation:\n\n* [User's Guide] (https://lpdaac.usgs.gov/documents/124/VNP09_User_Guide_V1.6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)] (https://lpdaac.usgs.gov/documents/122/VNP09_ATBD.pdf)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2023-03-12T00:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP09GA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA_VIIRS_001_VNP09GA","rel":"related","title":"Run the example for NOAA/VIIRS/001/VNP09GA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/VIIRS/NOAA_VIIRS_001_VNP09GA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP09GA#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP09GA.001","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP09GA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP09GA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/001/VNP09H1","title":"VNP09H1: VIIRS Surface Reflectance 8-Day L3 Global 500m","description":"The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) Surface\nReflectance (VNP09H1) Version 1 composite product provides an estimate of\nland surface reflectance from the Suomi National Polar-orbiting Partnership\n(Suomi NPP) VIIRS sensor for three imagery bands (I1, I2, I3) at nominal\n500m resolution (~463m). The 500m dataset is derived through resampling the\nnative 375m VIIRS resolution in the L2 input product. The data are corrected\nfor atmospheric conditions such as the effects of molecular gases, including\nozone and water vapor, and for the effects of atmospheric aerosols. Each\npixel represents the best possible Level 2G observation during an 8-day\nperiod, which is selected on the basis of high observation coverage, low\nsensor angle, the absence of clouds or cloud shadow, and aerosol loading.\nThe three reflectance bands, this product includes a state quality assurance\n(QA) layer and a reflectance band quality layer.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/123/VNP09_User_Guide_V1.1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/122/VNP09_ATBD.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp09h1v001/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/NPP_QA/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2023-02-26T00:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP09H1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA_VIIRS_001_VNP09H1","rel":"related","title":"Run the example for NOAA/VIIRS/001/VNP09H1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/VIIRS/NOAA_VIIRS_001_VNP09H1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP09H1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP09H1.001","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP09H1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP09H1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/001/VNP13A1","title":"VNP13A1: VIIRS Vegetation Indices 16-Day 500m","description":"The Suomi National Polar-Orbiting Partnership (S-NPP) NASA Visible\nInfrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A1)\ndata product provides vegetation indices by a process of\nselecting the best available pixel over a 16-day acquisition period\nat 500 meter resolution. The VNP13 data products are designed\nafter the Moderate Resolution Imaging Spectroradiometer (MODIS)\nTerra and Aqua Vegetation Indices product suite to promote the\ncontinuity of the Earth Observation System (EOS) mission.\n\nThe VNP13 algorithm process produces three vegetation indices: (1)\nNormalized Difference Vegetation Index (NDVI), (2) the Enhanced\nVegetation Index (EVI), and (3) Enhanced Vegetation Index-2 (EVI2).\n(1) NDVI is one of the longest continual remotely sensed time series\nobservations, using both the red and near-infrared (NIR) bands.\n(2) EVI is a slightly different vegetation index that is more sensitive\nto canopy cover, while NDVI is more sensitive to chlorophyll. (3) EVI2\nis a reformation of the standard 3-band EVI, using the red band and\nNIR band. This reformation addresses arising issues when comparing\nVIIRS EVI to other EVI models that do not include a blue band. EVI2\nwill eventually become the standard EVI.\n\nAlong with the three Vegetation Indices layers, this product also\nincludes layers for near-infrared (NIR) reflectance; three shortwave\ninfrared (SWIR) reflectance-red, blue, and green reflectance; composite\nday of year; pixel reliability; view and sun angles, and a quality layer.\n\nFor additional information, visit the VIIRS [Land Product Quality\nAssessment website](https://landweb.modaps.eosdis.nasa.gov/NPP_QA/) and see\nthe [User Guide](https://lpdaac.usgs.gov/documents/184/VNP13_User_Guide_ATBD_V2.1.2.pdf).\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/184/VNP13_User_Guide_ATBD_V2.1.2.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/184/VNP13_User_Guide_ATBD_V2.1.2.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/VIIRS/1/VNP13A1)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-17T00:00:00Z","2023-02-18T00:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP13A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA_VIIRS_001_VNP13A1","rel":"related","title":"Run the example for NOAA/VIIRS/001/VNP13A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/VIIRS/NOAA_VIIRS_001_VNP13A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP13A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP13A1.001","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP13A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP13A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/001/VNP14A1","title":"VNP14A1: Thermal Anomalies/Fire Daily L3 Global 1km SIN Grid","description":"The daily Suomi National Polar-Orbiting Partnership NASA Visible Infrared\nImaging Radiometer Suite (VIIRS) Thermal Anomalies/Fire (VNP14A1) Version 1\ndata product provides daily information about active fires and other thermal\nanomalies. The VNP14A1 data product is a global, 1km gridded composite of\nfire pixels detected from VIIRS 750m bands over a daily (24-hour) period.\nThe VNP14 data products are designed after the Moderate Resolution Imaging\nSpectroradiometer (MODIS) Thermal Anomalies/Fire product suite.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/427/VNP14_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/427/VNP14_User_Guide_V1.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp14a1v001/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/NPP_QA/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2023-03-12T00:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP14A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA_VIIRS_001_VNP14A1","rel":"related","title":"Run the example for NOAA/VIIRS/001/VNP14A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/VIIRS/NOAA_VIIRS_001_VNP14A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP14A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP14A1.001","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP14A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP14A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/001/VNP15A2H","title":"VNP15A2H: LAI/FPAR 8-Day L4 Global 500m SIN Grid [deprecated]","description":"The Visible Infrared Imaging Radiometer Suite (VIIRS) Leaf Area Index (LAI)\nand Fraction of Photosynthetically Active Radiation (FPAR) Version 1 data\nproduct provides information about the vegetative canopy layer at 500 meter\nresolution (VNP15A2H). The VIIRS sensor is located onboard the NOAA/NASA\njoint Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite. LAI\nis an index that quantifies the one-sided leaf area of a canopy, while FPAR\nis the fraction of incoming solar energy absorbed through photosynthesis at\n400 to 700 nanometers. This product is intentionally designed after the\nTerra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS)\nLAI/FPAR operational algorithm to promote the continuity of the Earth\nObservation System (EOS) mission.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/126/VNP15_User_Guide.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/125/VNP15_ATBD.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp15a2hv001/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=SNPP)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-17T00:00:00Z","2024-06-16T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP15A2H","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_VIIRS_001_VNP15A2H","rel":"related","title":"Run the example for NOAA/VIIRS/001/VNP15A2H in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_VIIRS_001_VNP15A2H_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP15A2H#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP15A2H.001","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP15A2H.json","rel":"latest-version","title":"NASA/VIIRS/002/VNP15A2H","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP15A2H.json","rel":"successor-version","title":"NASA/VIIRS/002/VNP15A2H","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP15A2H/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP15A2H/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/001/VNP21A1D","title":"VNP21A1D: Day Land Surface Temperature and Emissivity Daily 1km","description":"The NASA Suomi National Polar-Orbiting Partnership (Suomi NPP) Visible\nInfrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and\nEmissivity (LST&E) Day Version 1 product (VNP21A1D) is compiled daily\nfrom daytime Level 2 Gridded (L2G) intermediate products.\n\nThe L2G process maps the daily VNP21 swath granules onto a sinusoidal MODIS\ngrid and stores all observations overlapping a gridded cell for a given\nday. The VNP21A1 algorithm sorts through all these observations for each\ncell and estimates the final LST value as an average from all cloud-free\nobservations that have good LST accuracies. Only observations having\nobservation coverage more than a certain threshold (15%) are considered for\nthis averaging.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/429/VNP21_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp21a1dv001/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/NPP_QA/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2023-02-01T00:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP21A1D","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA_VIIRS_001_VNP21A1D","rel":"related","title":"Run the example for NOAA/VIIRS/001/VNP21A1D in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/VIIRS/NOAA_VIIRS_001_VNP21A1D_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP21A1D#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP21A1D.001","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP21A1D/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP21A1D/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/001/VNP21A1N","title":"VNP21A1N: Night Land Surface Temperature and Emissivity Daily 1km","description":"The NASA Suomi National Polar-Orbiting Partnership (Suomi NPP) Visible\nInfrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and\nEmissivity (LST&E) Night Version 1 product (VNP21A1N) is compiled daily\nfrom nighttime Level 2 Gridded (L2G) intermediate products.\n\nThe L2G process maps the daily VNP21 swath granules onto a sinusoidal MODIS\ngrid and stores all observations overlapping a gridded cell for a given\nnight. The VNP21A1 algorithm sorts through all these observations for each\ncell and estimates the final LST value as an average from all cloud-free\nobservations that have good LST accuracies. Only observations having\nobservation coverage more than a certain threshold (15%) are considered for\nthis averaging.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/429/VNP21_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp21a1nv001/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/NPP_QA/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2023-02-01T00:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP21A1N","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA_VIIRS_001_VNP21A1N","rel":"related","title":"Run the example for NOAA/VIIRS/001/VNP21A1N in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/VIIRS/NOAA_VIIRS_001_VNP21A1N_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP21A1N#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP21A1N.001","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP21A1N/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP21A1N/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/001/VNP22Q2","title":"VNP22Q2: Land Surface Phenology Yearly L3 Global 500m SIN Grid","description":"The Suomi National Polar-Orbiting Partnership (Suomi NPP) NASA Visible\nInfrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product\nprovides global land surface phenology (GLSP) metrics at yearly intervals.\nThe VNP22Q2 data product is derived from time series of the two-band\nEnhanced Vegetation Index (EVI2) calculated from VIIRS Nadir Bidirectional\nReflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR).\nVegetation phenology metrics at 500 meter spatial resolution are identified\nfor up to two detected growing cycles per year.\n\nThe product contains six phenological transition dates: onset of greenness\nincrease, onset of greenness maximum, onset of greenness decrease, onset of\ngreenness minimum, dates of mid-greenup, and senescence phases. The product\nalso includes the growing season length. The greenness-related metrics\nconsist of EVI2 onset of greenness increase, EVI2 onset of greenness\nmaximum, EVI2 growing season, rate of greenness increase, and rate of\ngreenness decrease. The confidence of phenology detection is provided as\ngreenness agreement growing season, proportion of good quality (PGQ)\ngrowing season, PGQ onset greenness increase, PGQ onset greenness maximum,\nPGQ onset greenness decrease, and PGQ onset greenness minimum.\nThe final layer is quality control specifying the overall quality of the\nproduct.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/637/VNP22_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/637/VNP22_User_Guide_V1.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp22q2v001/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/NPP_QA/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2021-01-01T00:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP22Q2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA_VIIRS_001_VNP22Q2","rel":"related","title":"Run the example for NOAA/VIIRS/001/VNP22Q2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/VIIRS/NOAA_VIIRS_001_VNP22Q2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP22Q2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP22Q2.001","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP22Q2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP22Q2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/001/VNP43IA1","title":"VNP43IA1: BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid","description":"The Suomi National Polar-Orbiting Partnership (Suomi NPP) NASA Visible\nInfrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance\nDistribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version\n1 product provides kernel weights (parameters) at 500 resolution. The\nVNP43IA1 product is produced daily using 16 days of VIIRS data, temporally\nweighted to the ninth day, which is reflected in the file name. The VNP43IA1\nproduct provides three spectrally dependent kernel weights, also known as\nmodel parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo),\nwhich can be used to model anisotropic effects of the Earth's surface.\nAll VNP43 data products are designed to promote the continuity of\nNASA's Moderate Resolution Imaging Spectroradiometer (MODIS)\nBRDF/Albedo data product suite.\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/viirs-user-guide/vnp43ia1-and-vnp43ma1-brdf-albedo-model-parameters)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp43ia1v001/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=SNPP)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-17T00:00:00Z","2024-06-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP43IA1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_VIIRS_001_VNP43IA1","rel":"related","title":"Run the example for NOAA/VIIRS/001/VNP43IA1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_VIIRS_001_VNP43IA1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP43IA1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP43IA1.001","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP43IA1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP43IA1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/001/VNP43IA2","title":"VNP43IA2: BRDF/Albedo Quality Daily L3 Global 500m SIN Grid","description":"The Suomi National Polar-Orbiting Partnership (Suomi NPP) NASA Visible\nInfrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance\nDistribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 1 product\nprovides BRDF and Albedo quality at 500m resolution. The VNP43IA2\nproduct is produced daily using 16 days of VIIRS data and is weighted\ntemporally to the ninth day, which is reflected in the file name.\nThe VNP43IA2 product provides information regarding band quality and days\nof valid observation within a 16-day period for the VIIRS imagery bands.\nThe VNP43 data products are designed to promote the continuity of\nNASA&apos;s Moderate Resolution Imaging Spectroradiometer (MODIS)\nBRDF/Albedo data product suite.\n\nDocumentation:\n\n* [User's Guide](https://www.umb.edu/spectralmass/viirs-user-guides-c1-and-c2/vnp43ia2-and-vnp43ma2-brdfalbedo-quality-product/)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp43ia2v001/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/browse?sensor=VIIRS&sat=SNPP)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-17T00:00:00Z","2024-06-09T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP43IA2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_VIIRS_001_VNP43IA2","rel":"related","title":"Run the example for NOAA/VIIRS/001/VNP43IA2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_VIIRS_001_VNP43IA2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP43IA2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP43IA2.001","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP43IA2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP43IA2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/001/VNP46A1","title":"VNP46A1: VIIRS Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night","description":"The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared\nImaging Radiometer Suite (VIIRS) supports a Day-Night Band (DNB) sensor\nthat provides global daily measurements of nocturnal visible and\nnear-infrared (NIR) light that are suitable for Earth system science and\napplications. The VIIRS DNB's ultra-sensitivity in lowlight conditions\nenables us to generate a new set of science-quality nighttime products that\nmanifest substantial improvements in sensor resolution and calibration when\ncompared to the previous era of Defense Meteorological Satellite\nProgram/Operational Linescan System's (DMSP/OLS) nighttime lights image\nproducts.\n\nVNP46A1 is a daily, top-of-atmosphere, at-sensor nighttime radiance product\ncalled VIIRS/NPP Daily Gridded Day Night Band 15 arc-second Linear Lat Lon\nGrid Night. The product contains 26 Science Data Sets (SDS) that include\nsensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar),\ncloud-mask flags, time, shortwave IR radiance, brightness temperatures,\nVIIRS quality flags, moon phase angle, and moon illumination fraction. It\nalso provides Quality Flag (QF) information specific to the cloud-mask,\nVIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB.\n\nDocumentation:\n\n* [User's Guide](https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/VIIRS_Black_Marble_UG_v1.3_Sep_2022.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/Product%20Generation%20Algorithms/VIIRS_Black_Marble_ATBD_v1.1_July_2020.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VNP46A1/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2025-01-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP46A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_VIIRS_001_VNP46A1","rel":"related","title":"Run the example for NOAA/VIIRS/001/VNP46A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_VIIRS_001_VNP46A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP46A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP46A1.001","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP46A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP46A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/001/VNP46A2","title":"VNP46A2: VIIRS Lunar Gap-Filled BRDF Nighttime Lights Daily L3 Global 500m","description":"The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared\nImaging Radiometer Suite (VIIRS) supports a Day-Night Band (DNB) sensor\nthat provides global daily measurements of nocturnal visible and\nnear-infrared (NIR) light that are suitable for Earth system science and\napplications. The VIIRS DNB's ultra-sensitivity in lowlight conditions\nenables us to generate a new set of science-quality nighttime products that\nmanifest substantial improvements in sensor resolution and calibration when\ncompared to the previous era of Defense Meteorological Satellite\nProgram/Operational Linescan System's (DMSP/OLS) nighttime lights image\nproducts.\n\nVNP46A2 dataset is a daily moonlight- and atmosphere-corrected Nighttime\nLights (NTL) product using the Bidirectional Reflectance Distribution\nFunction (BRDF).\n\nDocumentation:\n\n* [User's Guide](https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/VIIRS_Black_Marble_UG_v1.1_July_2020.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/Product%20Generation%20Algorithms/VIIRS_Black_Marble_ATBD_v1.1_July_2020.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VNP46A2/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-19T00:00:00Z","2023-02-05T00:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP46A2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA_VIIRS_001_VNP46A2","rel":"related","title":"Run the example for NOAA/VIIRS/001/VNP46A2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/VIIRS/NOAA_VIIRS_001_VNP46A2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP46A2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP46A2.001","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP46A2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP46A2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/001/VNP64A1","title":"VNP64A1: Burned Area Monthly L4 Global 500m SIN Grid","description":"The daily Suomi National Polar-Orbiting Partnership (Suomi NPP) NASA Visible\nInfrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 1\ndata product is a monthly, global gridded 500m product containing per-pixel\nburned area and quality information. The VNP64 burned area mapping approach\nemploys 750m VIIRS imagery coupled with 750m VIIRS active fire\nobservations.\n\nVIIRS bands that are both sensitive and insensitive to biomass burning are\nused to detect changes caused by fire and to differentiate them from other\ntypes of change.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/1330/VNP64A1_User_Guide_V1.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/572/VNP64A1_ATBD_V1.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/products/vnp64a1v001/)\n\n* [Land Product Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/NPP_QA/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2014-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP64A1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA_VIIRS_001_VNP64A1","rel":"related","title":"Run the example for NOAA/VIIRS/001/VNP64A1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/VIIRS/NOAA_VIIRS_001_VNP64A1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP64A1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VIIRS/VNP64A1.001","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP64A1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/001/VNP64A1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/AOD_EDR/V3","title":"VIIRS Aerosol Optical Depth and Aerosol Particle Size EDRs V3","description":"The VIIRS Aerosol Optical Depth (AOD) and Aerosol Particle Size (APS)\nEnvironmental Data Records (EDRs) product provides aerosol properties\nretrieved from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor.\nThe primary product is the AOD at 550 nm, with additional retrievals at\nother sensor channels and Angstrom Exponent over oceans. The spatial\nresolution is 750 meters.\n\nWe project the source rasters into a north-up lat-lon (EPSG:4326) grid using\n[GDAL libraries](https://github.com/google/earthengine-catalog/blob/main/pipelines/viirs_aod.py).\nData beyond 85°S and 85°N are excluded to avoid geometric complications\nwhen generating asset footprints for bounds filtering.\n\nFor more details and access to raw data, see:\n\n* [JPSS Aerosols page](https://www.star.nesdis.noaa.gov/jpss/aerosols.php)\n* [Algorithm Theoretical Basis Document](https://www.star.nesdis.noaa.gov/jpss/documents/ATBD/ATBD_EPS_Aerosol_AOD_v3.4.pdf)\n* [Google Cloud JPSS main page](https://console.cloud.google.com/marketplace/product/noaa-public/noaa-jpss)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-85,180,85]]},"temporal":{"interval":[["2022-10-29T00:00:00Z","2026-11-09T18:54:36.700000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/AOD_EDR/V3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_VIIRS_AOD_EDR_V3","rel":"related","title":"Run the example for NOAA/VIIRS/AOD_EDR/V3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_VIIRS_AOD_EDR_V3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_AOD_EDR_V3#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/AOD_EDR/V3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/AOD_EDR/V3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/DNB/ANNUAL_V21","title":"VIIRS Nighttime Day/Night Annual Band Composites V2.1","description":"Annual global VIIRS nighttime lights dataset is a time series produced from\nmonthly cloud-free average radiance grids spanning 2013 to 2021.\nData for 2022 are available in the\n[NOAA/VIIRS/DNB/ANNUAL_V22](NOAA_VIIRS_DNB_ANNUAL_V22) dataset.\n\nAn initial filtering step removed sunlit, moonlit and cloudy pixels,\nleading to rough composites that contains lights, fires, aurora and\nbackground. The rough annual composites are made on monthly increments and\nthen combined to form rough annual composites.\n\nThe subsequent steps uses the twelve-month median radiance to discard high\nand low radiance outliers, filtering out most fires and isolating the\nbackground. Background areas are zeroed out using the data range (DR)\ncalculated from 3x3 grid cells. The DR threshold for background is indexed\nto cloud-cover levels, with higher DR thresholds in areas having low numbers\nof cloud-free coverages.\n\nNote: 2012 data are not yet included because of differences in processing.\n(A) 201204-201212, and (B) 201204-201303. Only set (B) has masked median\nand average bands which doesn't follow the pattern there in other year\ndatasets.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-65,180,75]]},"temporal":{"interval":[["2012-04-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/ANNUAL_V21","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_VIIRS_DNB_ANNUAL_V21","rel":"related","title":"Run the example for NOAA/VIIRS/DNB/ANNUAL_V21 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_VIIRS_DNB_ANNUAL_V21_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_ANNUAL_V21#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/ANNUAL_V21/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/ANNUAL_V21/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/DNB/ANNUAL_V22","title":"VIIRS Nighttime Day/Night Annual Band Composites V2.2","description":"Annual global VIIRS nighttime lights dataset is a time series produced from\nmonthly cloud-free average radiance grids for 2022. Data for earlier\nyears are available in the\n[NOAA/VIIRS/DNB/ANNUAL_V21](NOAA_VIIRS_DNB_ANNUAL_V21) dataset.\n\nAn initial filtering step removed sunlit, moonlit and cloudy pixels,\nleading to rough composites that contains lights, fires, aurora and\nbackground. The rough annual composites are made on monthly increments and\nthen combined to form rough annual composites.\n\nThe subsequent steps uses the twelve-month median radiance to discard high\nand low radiance outliers, filtering out most fires and isolating the\nbackground. Background areas are zeroed out using the data range (DR)\ncalculated from 3x3 grid cells. The DR threshold for background is indexed\nto cloud-cover levels, with higher DR thresholds in areas having low numbers\nof cloud-free coverages.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-65,180,75]]},"temporal":{"interval":[["2012-04-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/ANNUAL_V22","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA/NOAA_VIIRS_DNB_ANNUAL_V22","rel":"related","title":"Run the example for NOAA/VIIRS/DNB/ANNUAL_V22 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/NOAA_VIIRS_DNB_ANNUAL_V22_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_ANNUAL_V22#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/ANNUAL_V22/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/ANNUAL_V22/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG","title":"VIIRS Nighttime Day/Night Band Composites Version 1","description":"Monthly average radiance composite images using nighttime data from the\nVisible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB).\n\nAs these data are composited monthly, there are many areas of the globe\nwhere it is impossible to get good quality data coverage for that month.\nThis can be due to cloud cover, especially in the tropical regions, or due\nto solar illumination, as happens toward the poles in their respective\nsummer months. Therefore it is recommended that users of these data utilize\nthe 'cf_cvg' band and not assume a value of zero in the average radiance\nimage means that no lights were observed.\n\nCloud cover is determined using the VIIRS Cloud\nMask product (VCM). In addition, data near the edges of the swath are not\nincluded in the composites (aggregation zones 29-32). Version 1 has NOT\nbeen filtered to screen out lights from aurora, fires, boats, and other\ntemporal lights. This separation is under development and will be included\nin a later version of this time series. Also in development is a method to\nseparate lights from background (non-light) values.\n\nPrior to averaging, the DNB data is filtered to exclude data impacted by\nstray light, lightning, lunar illumination, and cloud-cover.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-65,180,75]]},"temporal":{"interval":[["2012-04-01T00:00:00Z","2022-10-01T00:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA_VIIRS_DNB_MONTHLY_V1_VCMCFG","rel":"related","title":"Run the example for NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/VIIRS/NOAA_VIIRS_DNB_MONTHLY_V1_VCMCFG_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMCFG#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG","title":"VIIRS Stray Light Corrected Nighttime Day/Night Band Composites Version 1","description":"Monthly average radiance composite images using nighttime data from the\nVisible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB).\n\nAs these data are composited monthly, there are many areas of the globe\nwhere it is impossible to get good quality data coverage for that month.\nThis can be due to cloud cover, especially in the tropical regions, or due\nto solar illumination, as happens toward the poles in their respective\nsummer months. Therefore it is recommended that users of these data utilize\nthe 'cf_cvg' band and not assume a value of zero in the average radiance\nimage means that no lights were observed.\n\nCloud cover is determined using the VIIRS Cloud\nMask product (VCM). In addition, data near the edges of the swath are not\nincluded in the composites (aggregation zones 29-32). Version 1 has NOT\nbeen filtered to screen out lights from aurora, fires, boats, and other\ntemporal lights. This separation is under development and will be included\nin a later version of this time series. Also in development is a method to\nseparate lights from background (non-light) values.\n\nThis product is an alternative configuration of the VIIRS DNB using a\nprocedure to correct for stray light. The correction procedure extends\nvisible areas closer to the poles and improves dynamic range. It should be\nnoted some artifacts are introduced due to the procedure used in twilight\nregions; see\n[reference paper](https://spie.org/Publications/Proceedings/Paper/10.1117/12.2023107)\nfor more details. This product excludes data\nimpacted by cloud cover.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-65,180,75]]},"temporal":{"interval":[["2014-01-01T00:00:00Z","2022-11-01T00:00:00"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG","rel":"related","title":"Run the example for NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NOAA/VIIRS/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"NRCan/CDEM","title":"Canadian Digital Elevation Model","description":"The Canadian Digital Elevation Model (CDEM) is part of Natural Resources\nCanada's (NRCan) altimetry system and stems from the existing Canadian\nDigital Elevation Data (CDED). In these data, elevations can be either\nground or reflective surface elevations.\n\nThe CDEM is comprised of multiple DEMs with varying resolutions. These\nvary according to latitude and have a base resolution of 0.75 arc-seconds.\nFor more information see the [Product Specifications](https://ftp.geogratis.gc.ca/pub/nrcan_rncan/elevation/cdem_mnec/doc/CDEM_product_specs.pdf)\n\nContains information licensed under the\n[Open Government Licence - Canada](https://open.canada.ca/en/open-government-licence-canada).\n","license":"OGL-Canada-2.0","extent":{"spatial":{"bbox":[[-142,41,-52,84]]},"temporal":{"interval":[["1945-01-01T00:00:00Z","2011-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/NRCan/CDEM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NRCan/NRCan_CDEM","rel":"related","title":"Run the example for NRCan/CDEM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/NRCan/NRCan_CDEM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/NRCan_CDEM#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/OGL-Canada-2.0.html","rel":"license","type":"text/html"},{"href":"https://ftp.geogratis.gc.ca/pub/nrcan_rncan/elevation/cdem_mnec/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/NRCan/CDEM/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/NRCan/CDEM/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Netherlands/Beeldmateriaal/LUCHTFOTO_RGB","title":"Netherlands orthophotos","description":"Orthophotos of Netherlands is a set of color orthoimages. Two nationwide\naerial photographs are collected per year: a leafless image at 7.5 cm\nresolution in the spring and one with leaves on trees at 25 cm resolution\nin the summer.\n\nFor more information, please see the\n[Netherlands orthophotos documentation](https://opendata.beeldmateriaal.nl/)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[3.2,50.75,7.22,53.7]]},"temporal":{"interval":[["2021-01-01T00:00:00Z","2022-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Netherlands/Beeldmateriaal/LUCHTFOTO_RGB","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Netherlands/Netherlands_Beeldmateriaal_LUCHTFOTO_RGB","rel":"related","title":"Run the example for Netherlands/Beeldmateriaal/LUCHTFOTO_RGB in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Netherlands/Netherlands_Beeldmateriaal_LUCHTFOTO_RGB_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Netherlands_Beeldmateriaal_LUCHTFOTO_RGB#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Netherlands/Beeldmateriaal/LUCHTFOTO_RGB/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Netherlands/Beeldmateriaal/LUCHTFOTO_RGB/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OPERA/DIST/L3_DIST-ANN-HLS/V1","title":"OPERA Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 (DIST-ANN-HLS) V1","description":"The Observational Products for End-Users from Remote Sensing Analysis (OPERA)\nLand Surface Disturbance Annual from Harmonized Landsat Sentinel-2 (DIST-ANN-HLS)\nproduct Version 1 summarizes the DIST-ALERT data product into an annual vegetation\ndisturbance data product.\n\nThe DIST-ANN-HLS product provides annual metrics and classifications of\nvegetation disturbance detected from Harmonized Landsat-8/9 and Sentinel-2A/B\n(HLS) surface reflectance data at 30-meter resolution. Disturbances include abrupt\nevents such as wildfire, logging, and clearing, as well as ongoing or longer-term\nchanges. The product includes both vegetation-specific disturbance metrics (VEG)\nand generic disturbance metrics (GEN) across 21 raster layers.\n\nDocumentation:\n\n  * [DIST Product Suite Website](https://www.jpl.nasa.gov/go/opera/products/dist-product-suite/)\n  * [OPERA DIST-HLS Product Specification Document](https://lpdaac.usgs.gov/documents/1766/OPERA_DIST_HLS_Product_Specification_V1.pdf)\n  * [OPERA DIST-HLS Algorithm Theoretical Basis Document](https://lpdaac.usgs.gov/documents/1835/OPERA_DIST_ATBD__V1.pdf)\n  * [NASA Earthdata Search](https://search.earthdata.nasa.gov/search/granules?p=C2519119034-LPCLOUD)\n  * [Publication: Rapid monitoring of global land change](https://doi.org/10.1038/s41467-025-64014-9)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-84,180,84]]},"temporal":{"interval":[["2023-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OPERA/DIST/L3_DIST-ANN-HLS/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OPERA/OPERA_DIST_L3_DIST-ANN-HLS_V1","rel":"related","title":"Run the example for OPERA/DIST/L3_DIST-ANN-HLS/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OPERA/OPERA_DIST_L3_DIST-ANN-HLS_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OPERA_DIST_L3_DIST-ANN-HLS_V1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ANN-HLS_V1.001","rel":"cite-as"},{"href":"https://doi.org/10.1038/s41467-025-64014-9","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OPERA/DIST/L3_DIST-ANN-HLS/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OPERA/DIST/L3_DIST-ANN-HLS/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OPERA/DSWX/L3_V1/HLS","title":"Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 (DSWx-HLS) V1","description":"This dataset contains Level-3 Dynamic OPERA surface water extent product\nversion 1. The data are validated surface water extent observations\nbeginning April 2023. Known issues and caveats on usage are described\nin the product documentation. The input dataset for generating each product is\nthe Harmonized Landsat-8 and Sentinel-2A/B/C (HLS) product version 2.0.\nHLS products provide surface reflectance (SR) data from the\nOperational Land Imager (OLI) aboard the Landsat 8 satellite and the\nMultiSpectral Instrument (MSI) aboard the Sentinel-2A/B/C satellite.\n\nDocumentation:\n\n  * [DSWx-HLS Product Specification Document](https://d2pn8kiwq2w21t.cloudfront.net/documents/OPERA_DSWx-HLS_ProductSpec_v1.0.0_D-107395_RevB.pdf)\n  * [DSWx Product Suite Website](https://www.jpl.nasa.gov/go/opera/products/dswx-product-suite/)\n  * [DSWx-HLS Algorithm Theoretical Basis Document (ATBD)](https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/opera/atbd/DSWx-HLS_ATBD_V1_4_DAAC_Distribution.pdf)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-84,180,84]]},"temporal":{"interval":[["2023-04-04T04:37:01Z","2026-11-02T23:53:31Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OPERA/DSWX/L3_V1/HLS","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OPERA/OPERA_DSWX_L3_V1_HLS","rel":"related","title":"Run the example for OPERA/DSWX/L3_V1/HLS in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OPERA/OPERA_DSWX_L3_V1_HLS_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OPERA_DSWX_L3_V1_HLS#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/OPDSW-PL3V1","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OPERA/DSWX/L3_V1/HLS/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OPERA/DSWX/L3_V1/HLS/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OPERA/DSWX/L3_V1/S1","title":"Dynamic Surface Water Extent from Sentinel-1 (DSWx-S1) V1","description":"This dataset contains Level-3 Dynamic OPERA surface water extent from\nSentinel-1 (DSWX-S1). DSWx-S1 provides near-global geographical\nmapping of surface water extent over land at a spatial resolution\nof 30 meters over the Military Grid reference System (MGRS) grid\nsystem, with a temporal revisit frequency between 6-12 days.\nUsing Sentinel-1 radar observations, DSWx-S1 maps open inland water\nbodies greater than 3 hectares and 200 meters in width, irrespective\nof cloud conditions and daylight illumination that often pose\nchallenges to optical sensors. Forward production of the DSWx-S1 data\nrecord began in Sept 2024.\n\nDocumentation:\n\n  * [DSWx-S1 Product Specification Document](https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/opera/D-108761_OPERA_DSWx_S1AB_ProductSpec_Rev_A.pdf)\n  * [DSWx Product Suite Website](https://www.jpl.nasa.gov/go/opera/products/dswx-product-suite/)\n  * [DSWx-S1 Algorithm Theoretical Basis Document](https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/opera/atbd/D-108763_Rev_A_OPERA_DSWx_S1_NI_ATBD_20240530_SIGNED.pdf)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-84,180,84]]},"temporal":{"interval":[["2024-08-01T00:00:00Z","2026-11-05T11:50:42Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OPERA/DSWX/L3_V1/S1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OPERA/OPERA_DSWX_L3_V1_S1","rel":"related","title":"Run the example for OPERA/DSWX/L3_V1/S1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OPERA/OPERA_DSWX_L3_V1_S1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OPERA_DSWX_L3_V1_S1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/OPDSWS1-L3V1","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OPERA/DSWX/L3_V1/S1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OPERA/DSWX/L3_V1/S1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OPERA/RTC/L2_V1/S1","title":"OPERA Radiometric Terrain-Corrected SAR Backscatter from Sentinel-1 V1.0","description":"This product consists of radar backscatter normalized with respect to the\ntopography. It maps signals related to the physical properties of ground\nscattering objects, such as surface roughness and soil moisture and/or\nvegetation. The OPERA RTC-S1 product is derived from Copernicus Sentinel-1\nInterferometric Wide (IW) Single Look Complex (SLC) data with a near global\nscope and temporal sampling coincident with the availability of SLC data.\n\nEach OPERA RTC-S1 product corresponds to a single S1 burst projected onto\na pre-defined UTM/Polar stereographic map projection system map grid with\na 30-meter spacing. The Copernicus global 30 m (GLO-30) Digital Elevation\nModel (DEM) is the reference DEM used to correct for the impacts of\ntopography and to geocode the product. The OPERA RTC-S1 product is\nnormalized to the backscatter coefficient gamma0 obtained from the\noriginal radar brightness beta0 through radiometric terrain correction.\n\nDue to the S1 mission’s narrow orbital tube, radar-geometry layers such as\nincidence angle, local incidence angle, number of looks, and RTC Area\nNormalization Factor (ANF) vary slightly over time for each position on the\nground, and therefore are considered static. These static layers are\nprovided separately from the OPERA RTC-S1 product, as they are produced\nonly once or a limited number of times, to account for changes in the DEM,\nin the S1 orbit, or in the static-layers generation algorithm. The static\nlayers are available in the associated\n[OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers dataset](OPERA_RTC_L2_V1_S1_STATIC).\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["2016-04-14T00:00:00Z","2026-11-10T12:42:24Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OPERA/RTC/L2_V1/S1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OPERA/OPERA_RTC_L2_V1_S1","rel":"related","title":"Run the example for OPERA/RTC/L2_V1/S1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OPERA/OPERA_RTC_L2_V1_S1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OPERA_RTC_L2_V1_S1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OPERA/RTC/L2_V1/S1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OPERA/RTC/L2_V1/S1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OPERA/RTC/L2_V1/S1_STATIC","title":"OPERA Radiometric Terrain-Corrected SAR Backscatter from Sentinel-1 Static Layers V1.0","description":"This product contains static radar geometry layers associated with the\n[OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 dataset](OPERA_RTC_L2_V1_S1).\n\nDue to the S1 mission’s narrow orbital tube, radar-geometry layers such as\nincidence angle, local incidence angle, number of looks, and RTC Area\nNormalization Factor (ANF) vary slightly over time for each position on the\nground, and therefore are considered static. These static layers are\nprovided separately from the OPERA RTC-S1 product, as they are produced\nonly once or a limited number of times, to account for changes in the DEM,\nin the S1 orbit, or in the static-layers generation algorithm.\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2014-04-03T00:00:00Z","2014-04-04T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OPERA/RTC/L2_V1/S1_STATIC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OPERA/OPERA_RTC_L2_V1_S1_STATIC","rel":"related","title":"Run the example for OPERA/RTC/L2_V1/S1_STATIC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OPERA/OPERA_RTC_L2_V1_S1_STATIC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OPERA_RTC_L2_V1_S1_STATIC#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OPERA/RTC/L2_V1/S1_STATIC/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OPERA/RTC/L2_V1/S1_STATIC/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OREGONSTATE/PRISM/AN81d","title":"PRISM Daily Spatial Dataset AN81d [deprecated]","description":"The PRISM daily and monthly datasets are gridded weather\ndatasets for the conterminous United States, produced by the\nPRISM Group at Oregon State University.\n\nGrids are\ndeveloped using PRISM (Parameter-elevation Regressions on Independent\nSlopes Model). PRISM interpolation routines simulate how weather\nvaries with elevation, and account for coastal effects,\ntemperature inversions, and terrain barriers that can cause rain\nshadows. Station data are assimilated from many networks across\nthe country. For more information, see the [Descriptions of PRISM\nSpatial Datasets](https://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf).\n\n**Note**\n\n* **Warning**: This dataset should not be\n  used to calculate century-long trends due to non-weather-related\n  variations from station equipment and location changes, openings\n  and closings, varying observation times, and the use of relatively\n  short-term networks. Please see the [dataset documentation\n  ](https://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf)\n  for more details.\n* The assets have start time of noon UTC, not midnight UTC.\n* It takes time for observation networks to conduct quality control and\n  release station data. Therefore,\n  PRISM datasets are re-modeled several times until six months\n  have elapsed, when they are considered permanent. A [release\n  schedule is available](https://www.prism.oregonstate.edu/calendar/).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-125,24,-66,50]]},"temporal":{"interval":[["1981-01-01T00:00:00Z","2020-12-30T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/AN81d","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OREGONSTATE/OREGONSTATE_PRISM_AN81d","rel":"related","title":"Run the example for OREGONSTATE/PRISM/AN81d in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OREGONSTATE/OREGONSTATE_PRISM_AN81d_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81d#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_ANd.json","rel":"latest-version","title":"OREGONSTATE/PRISM/ANd","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_ANd.json","rel":"successor-version","title":"OREGONSTATE/PRISM/ANd","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/AN81d/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/AN81d/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OREGONSTATE/PRISM/AN81m","title":"PRISM Monthly Spatial Dataset AN81m [deprecated]","description":"The PRISM daily and monthly datasets are gridded weather\ndatasets for the conterminous United States, produced by the\nPRISM Group at Oregon State University.\n\nGrids are\ndeveloped using PRISM (Parameter-elevation Regressions on Independent\nSlopes Model). PRISM interpolation routines simulate how weather\nvaries with elevation, and account for coastal effects,\ntemperature inversions, and terrain barriers that can cause rain\nshadows. Station data are assimilated from many networks across\nthe country. For more information, see the [Descriptions of PRISM\nSpatial Datasets](https://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf).\n\n**Note**\n\n* **Warning**: This dataset should not be\n  used to calculate century-long trends due to non-weather-related\n  variations from station equipment and location changes, openings\n  and closings, varying observation times, and the use of relatively\n  short-term networks. Please see the [dataset documentation\n  ](https://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf)\n  for more details.\n* The assets have start time of noon UTC, not midnight UTC.\n* It takes time for observation networks to conduct quality control and\n  release station data. Therefore,\n  PRISM datasets are re-modeled several times until six months\n  have elapsed, when they are considered permanent. A [release\n  schedule is available](https://www.prism.oregonstate.edu/calendar/).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-125,24,-66,50]]},"temporal":{"interval":[["1895-01-01T00:00:00Z","2020-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/AN81m","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OREGONSTATE/OREGONSTATE_PRISM_AN81m","rel":"related","title":"Run the example for OREGONSTATE/PRISM/AN81m in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OREGONSTATE/OREGONSTATE_PRISM_AN81m_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81m#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_ANm.json","rel":"latest-version","title":"OREGONSTATE/PRISM/ANm","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_ANm.json","rel":"successor-version","title":"OREGONSTATE/PRISM/ANm","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/AN81m/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/AN81m/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OREGONSTATE/PRISM/ANd","title":"PRISM Daily Spatial Dataset ANd","description":"The PRISM daily and monthly datasets are gridded weather\ndatasets for the conterminous United States, produced by the\nPRISM Group at Oregon State University.\nThis collection contains both AN81 and AN91\ndata, indicated by the 'dataset_type' property.\n\nGrids are\ndeveloped using PRISM (Parameter-elevation Regressions on Independent\nSlopes Model). PRISM interpolation routines simulate how weather\nvaries with elevation, and account for coastal effects,\ntemperature inversions, and terrain barriers that can cause rain\nshadows. Station data are assimilated from many networks across\nthe country. For more information, see the [Descriptions of PRISM\nSpatial Datasets](https://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf).\n\n**Note**\n\n* **Warning**: This dataset should not be\n  used to calculate century-long trends due to non-weather-related\n  variations from station equipment and location changes, openings\n  and closings, varying observation times, and the use of relatively\n  short-term networks. Please see the [dataset documentation\n  ](https://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf)\n  for more details.\n* The assets have start time of noon UTC, not midnight UTC.\n* It takes time for observation networks to conduct quality control and\n  release station data. Therefore,\n  PRISM datasets are re-modeled several times until six months\n  have elapsed, when they are considered permanent. A [release\n  schedule is available](https://www.prism.oregonstate.edu/calendar/).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-125,24,-66,50]]},"temporal":{"interval":[["1981-01-01T00:00:00Z","2026-11-06T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/ANd","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OREGONSTATE/OREGONSTATE_PRISM_ANd","rel":"related","title":"Run the example for OREGONSTATE/PRISM/ANd in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OREGONSTATE/OREGONSTATE_PRISM_ANd_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_ANd#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_ANd.json","rel":"latest-version","title":"OREGONSTATE/PRISM/ANd","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81d.json","rel":"predecessor-version","title":"OREGONSTATE/PRISM/AN81d","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/ANd/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/ANd/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OREGONSTATE/PRISM/ANm","title":"PRISM Monthly Spatial Dataset ANm","description":"The PRISM daily and monthly datasets are gridded weather\ndatasets for the conterminous United States, produced by the\nPRISM Group at Oregon State University.\nThis collection contains both AN81 and AN91\ndata, as indicated by the 'dataset_type' property.\n\nGrids are\ndeveloped using PRISM (Parameter-elevation Regressions on Independent\nSlopes Model). PRISM interpolation routines simulate how weather\nvaries with elevation, and account for coastal effects,\ntemperature inversions, and terrain barriers that can cause rain\nshadows. Station data are assimilated from many networks across\nthe country. For more information, see the [Descriptions of PRISM\nSpatial Datasets](https://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf).\n\n**Note**\n\n* **Warning**: This dataset should not be\n  used to calculate century-long trends due to non-weather-related\n  variations from station equipment and location changes, openings\n  and closings, varying observation times, and the use of relatively\n  short-term networks. Please see the [dataset documentation\n  ](https://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf)\n  for more details.\n* The assets have start time of noon UTC, not midnight UTC.\n* It takes time for observation networks to conduct quality control and\n  release station data. Therefore,\n  PRISM datasets are re-modeled several times until six months\n  have elapsed, when they are considered permanent. A [release\n  schedule is available](https://www.prism.oregonstate.edu/calendar/).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-125,24,-66,50]]},"temporal":{"interval":[["1895-01-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/ANm","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OREGONSTATE/OREGONSTATE_PRISM_ANm","rel":"related","title":"Run the example for OREGONSTATE/PRISM/ANm in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OREGONSTATE/OREGONSTATE_PRISM_ANm_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_ANm#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_ANm.json","rel":"latest-version","title":"OREGONSTATE/PRISM/ANm","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81m.json","rel":"predecessor-version","title":"OREGONSTATE/PRISM/AN81m","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/ANm/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/ANm/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OREGONSTATE/PRISM/Norm81m","title":"PRISM Long-Term Normals Dataset Norm81m [deprecated]","description":"The PRISM daily and monthly datasets are gridded weather\ndatasets for the conterminous United States, produced by the\nPRISM Group at Oregon State University.\n\nGrids are\ndeveloped using PRISM (Parameter-elevation Regressions on Independent\nSlopes Model). PRISM interpolation routines simulate how weather\nvaries with elevation, and account for coastal effects,\ntemperature inversions, and terrain barriers that can cause rain\nshadows. Station data are assimilated from many networks across\nthe country. For more information, see the [Descriptions of PRISM\nSpatial Datasets](https://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-125,24,-66,50]]},"temporal":{"interval":[["1981-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/Norm81m","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OREGONSTATE/OREGONSTATE_PRISM_Norm81m","rel":"related","title":"Run the example for OREGONSTATE/PRISM/Norm81m in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OREGONSTATE/OREGONSTATE_PRISM_Norm81m_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_Norm81m#terms-of-use","rel":"license","type":"text/html"},{"href":"https://prism.oregonstate.edu/terms/","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_Norm91m.json","rel":"latest-version","title":"OREGONSTATE/PRISM/Norm91m","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_Norm91m.json","rel":"successor-version","title":"OREGONSTATE/PRISM/Norm91m","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/Norm81m/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/Norm81m/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OREGONSTATE/PRISM/Norm91m","title":"PRISM Long-Term Normals Dataset Norm91m","description":"The PRISM daily and monthly datasets are gridded weather\ndatasets for the conterminous United States, produced by the\nPRISM Group at Oregon State University.\n\nGrids are\ndeveloped using PRISM (Parameter-elevation Regressions on Independent\nSlopes Model). PRISM interpolation routines simulate how weather\nvaries with elevation, and account for coastal effects,\ntemperature inversions, and terrain barriers that can cause rain\nshadows. Station data are assimilated from many networks across\nthe country. For more information, see the [Descriptions of PRISM\nSpatial Datasets](https://www.prism.oregonstate.edu/documents/PRISM_datasets.pdf).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-125,24,-66,50]]},"temporal":{"interval":[["1991-01-01T00:00:00Z","2020-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/Norm91m","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OREGONSTATE/OREGONSTATE_PRISM_Norm91m","rel":"related","title":"Run the example for OREGONSTATE/PRISM/Norm91m in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OREGONSTATE/OREGONSTATE_PRISM_Norm91m_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_Norm91m#terms-of-use","rel":"license","type":"text/html"},{"href":"https://prism.oregonstate.edu/terms/","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_Norm91m.json","rel":"latest-version","title":"OREGONSTATE/PRISM/Norm91m","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_Norm81m.json","rel":"predecessor-version","title":"OREGONSTATE/PRISM/Norm81m","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/Norm91m/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OREGONSTATE/PRISM/Norm91m/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OSU/GIMP/2000_ICE_OCEAN_MASK","title":"Greenland Ice & Ocean Mask - Greenland Mapping Project (GIMP)","description":"This dataset provides complete land ice and ocean\nclassification masks at 15 m for the Greenland ice sheet.\nIce cover was mapped using\na combination of orthorectified panchromatic (band 8)\nimagery from the Landsat 7 Enhanced Thematic Mapper\nPlus (ETM+), distributed by the USGS, and RADARSAT-1\nSynthetic Amplitude Radar (SAR) amplitude images produced\nand distributed by I. Joughin at the Applied Physics\nLaboratory, University of Washington.\n\nThe Landsat imagery was acquired for the months of July\nthrough September in 1999, 2000 and 2001 (mostly 2000)\nand the RADARSAT imagery was acquired in fall of 2000.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-89.3211593425295,58.79601275381146,7.555941634834938,83.95386175580668]]},"temporal":{"interval":[["1999-06-30T00:00:00Z","2002-09-04T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OSU/GIMP/2000_ICE_OCEAN_MASK","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OSU/OSU_GIMP_2000_ICE_OCEAN_MASK","rel":"related","title":"Run the example for OSU/GIMP/2000_ICE_OCEAN_MASK in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OSU/OSU_GIMP_2000_ICE_OCEAN_MASK_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OSU_GIMP_2000_ICE_OCEAN_MASK#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/B8X58MQBFUPA","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OSU/GIMP/2000_ICE_OCEAN_MASK/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OSU/GIMP/2000_IMAGERY_MOSAIC","title":"2000 Greenland Mosaic - Greenland Ice Mapping Project (GIMP)","description":"This dataset provides a complete 15 m resolution image mosaic\nof the Greenland ice sheet derived from Landsat 7 ETM+ and\nRADARSAT-1 SAR imagery from the years 1999 to 2002. The methods\ninclude a combination of image cloud masking, pan sharpening,\nimage sampling and resizing, and image coregistration.\nPlease see Howat, 2014 for more information regarding\nprocessing methods.\n\n**Note**\n\n*  Users of GIMP DEM or GIMP 2000 Image Mosaic images may find\nit helpful to mask out areas outside of the Greenland coastline\nusing the corresponding 15 m ocean mask image\nGreenland Ice Mapping Project (GIMP) Land Ice and Ocean\nClassification Mask,\n*  The SAR data are distributed at 20 m resolution. Data were\nup-sampled through bilinear interpolation to 15 m to match\nthe resolution of Landsat band-8.\n\n[General documentation](https://doi.org/10.5067/4RNTRRE4JCYD)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-89.3211593425295,58.79601275381146,7.555941634834938,83.95386175580668]]},"temporal":{"interval":[["1999-06-30T00:00:00Z","2002-09-04T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OSU/GIMP/2000_IMAGERY_MOSAIC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OSU/OSU_GIMP_2000_IMAGERY_MOSAIC","rel":"related","title":"Run the example for OSU/GIMP/2000_IMAGERY_MOSAIC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OSU/OSU_GIMP_2000_IMAGERY_MOSAIC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OSU_GIMP_2000_IMAGERY_MOSAIC#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/4RNTRRE4JCYD","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OSU/GIMP/2000_IMAGERY_MOSAIC/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OSU/GIMP/DEM","title":"Greenland DEM - Greenland Mapping Project (GIMP)","description":"This Digital Elevation Model (DEM) is constructed from a combination\nof ASTER and SPOT-5 DEM's for the ice sheet periphery and margin\n(i.e. below the equilbrium line elevation) south of\napproximately 82.5&deg;N and AVHRR photoclinometry in the ice sheet\ninterior and far north (Scambos and Haran, 2002).\n\nSPOT-5 DEM's were produced and distributed as part of the Spot5\nstereoscopic survey of Polar Ice: Reference Images & Topographies\n(SPIRIT) project (Korona et al., 2009). Ocean surfaces were masked\nusing the GIMP Land Classification mask and replaced with the\nCNES CLS11 mean sea surface height (Schaeffer et al., 2012).\n\n**Note**\n\n*   All land elevation data is horizontally and vertically registered\nto average ICESat elevations for the 2003-2009 time period, and\ntherefore the DEM has a nominal date of 2007, although care must\nbe taken when using the DEM in areas of rapid change, such as\nmajor outlet glaciers south of 70&deg;0N.\n*   The DEM has a resolution of 30 m, although the \\\"true\\\" resolution of the\nDEM will vary from 40 m in areas of SPOT-5 coverage\n(see Korona et al. 2009) to 500 m in areas of photoclinometry.\n*   The ice-sheet-wide root-mean-squared validation error\nrelative to ICESat is +/-10 m, rangining from close to +/- 1 m\nover most ice surfaces to +/- 30 m in areas of high relief.\n\n[General documentation](https://doi.org/10.5067/NV34YUIXLP9W)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-89.32179714240877,58.795926930232035,7.556480769227746,83.95392768557056]]},"temporal":{"interval":[["1999-06-30T00:00:00Z","2002-09-04T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OSU/GIMP/DEM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OSU/OSU_GIMP_DEM","rel":"related","title":"Run the example for OSU/GIMP/DEM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OSU/OSU_GIMP_DEM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OSU_GIMP_DEM#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/NV34YUIXLP9W","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OSU/GIMP/DEM/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OSU/GIMP/ICE_VELOCITY_OPT","title":"MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from Optical Images Version 2","description":"This dataset, part of the NASA Making Earth System Data\nRecords for Use in Research Environments (MEaSUREs) program,\nconsists of mean monthly velocity maps for selected glacier outlet areas.\nThe maps are generated by tracking visible features between optical\nimage pairs acquired by the Landsat 4 and 5 Thematic Mapper (TM),\nthe Landsat 7 Enhanced Thematic Mapper Plus (ETM+), the Landsat 8\nOperational Land Imager (OLI), and the Advanced Spaceborne Thermal\nEmission and Reflection Radiometer (ASTER).\n\n**Note**\n\nMonthly means are calculated from images, which may have acquisition\ndates from the preceding or succeeding month. For the naming convention,\nthe month is determined from where the midpoint Julian dates fall.\nFor example, September monthly means may have been generated from\nimages that were acquired in August or in October but the midpoint\nJulian date between the images falls within September. The exact\ndates used are included as per-image metadata fields.\n\n[General documentation](https://doi.org/10.5067/VM5DZ20MYF5C)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-89.3211593425295,58.79601275381146,7.555941634834938,83.95386175580668]]},"temporal":{"interval":[["1985-03-01T00:00:00Z","2016-09-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OSU/GIMP/ICE_VELOCITY_OPT","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OSU/OSU_GIMP_ICE_VELOCITY_OPT","rel":"related","title":"Run the example for OSU/GIMP/ICE_VELOCITY_OPT in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OSU/OSU_GIMP_ICE_VELOCITY_OPT_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OSU_GIMP_ICE_VELOCITY_OPT#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/VM5DZ20MYF5C","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OSU/GIMP/ICE_VELOCITY_OPT/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OSU/GIMP/ICE_VELOCITY_OPT/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenET/DISALEXI/CONUS/GRIDMET/MONTHLY/v2_0","title":"OpenET DisALEXI Monthly Evapotranspiration v2.0 [deprecated]","description":"Atmosphere-Land Exchange Inverse / Disaggregation of the Atmosphere-Land\nExchange Inverse (ALEXI/DisALEXI)\n\nDisALEXI was recently ported to Google Earth Engine as part of the OpenET\nframework and the baseline ALEXI/DisALEXI model structure is described by\nAnderson et al. (2012, 2018). The ALEXI evapotranspiration (ET) model\nspecifically uses time differential land surface temperature (LST)\nmeasurements from geostationary or moderate resolution polar orbiting\nplatforms to generate regional ET maps. DisALEXI then disaggregates the\nregional ALEXI ET to finer scales using Landsat data (30 m; biweekly) to\nresolve individual farm fields and other landscape features.\n[Additional information](https://openetdata.org/methodologies/)\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2024-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenET/DISALEXI/CONUS/GRIDMET/MONTHLY/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/OpenET_DISALEXI_CONUS_GRIDMET_MONTHLY_v2_0","rel":"related","title":"Run the example for OpenET/DISALEXI/CONUS/GRIDMET/MONTHLY/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/OpenET_DISALEXI_CONUS_GRIDMET_MONTHLY_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenET_DISALEXI_CONUS_GRIDMET_MONTHLY_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_disalexi_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/disalexi/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_disalexi_conus_gridmet_monthly_v2_0.json","rel":"successor-version","title":"projects/openet/assets/disalexi/conus/gridmet/monthly/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/DISALEXI/CONUS/GRIDMET/MONTHLY/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/DISALEXI/CONUS/GRIDMET/MONTHLY/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenET/EEMETRIC/CONUS/GRIDMET/MONTHLY/v2_0","title":"OpenET eeMETRIC Monthly Evapotranspiration v2.0 [deprecated]","description":"Google Earth Engine implementation of the Mapping Evapotranspiration\nat high Resolution with Internalized Calibration model (eeMETRIC)\n\neeMETRIC applies the advanced METRIC algorithms and process of Allen\net al. (2007; 2015) and Allen et al. (2013b), where a singular\nrelationship between the near surface air temperature difference (dT)\nand delapsed land surface temperature (TsDEM) is used to estimate sensible\nheat flux (H) and is applied to each Landsat scene. Automated selection of\nthe hot and cold pixels for an image generally follows a statistical\nisolation procedure described by Allen et al. (2013a) and ReVelle, Kilic\nand Allen (2019a,b). The calibration of H in eeMETRIC utilizes alfalfa\nreference ET calculated from the NLDAS gridded weather dataset using a\nfixed 15% reduction in computed reference ET to account for known biases\nin the gridded data set. The fixed reduction does not impact the\ncalibration accuracy of eeMETRIC and mostly reduces impacts of boundary\nlayer buoyancy correction.\n\nThe identification of candidates for pools of hot and cold pixels has\nevolved in the eeMETRIC implementation of METRIC. The new automated\ncalibration process incorporates the combination of methodologies and\napproaches that stem from two development branches of EEFlux (Allen\net al., 2015). The first branch focused on improving the automated pixel\nselection process using standard lapse rates for land surface temperature\n(LST) without any further spatial delapsing (ReVelle et al., 2019b). The\nsecond branch incorporated a secondary spatial delapsing of LST as well as\nchanges to the pixel selection process (ReVelle et al., 2019a). The final,\ncombined approach is described by Kilic et al. (2021).\n\neeMETRIC employs the aerodynamic-related functions in complex terrain\n(mountains) developed by Allen et al. (2013b) to improve estimates for\naerodynamic roughness, wind speed and boundary layer stability as related\nto estimated terrain roughness, position on a slope and wind direction.\nThese functions tend to increase estimates for H (and reduce ET) on\nwindward slopes and may reduce H (and increase ET) on leeward slopes.\nOther METRIC functions employed in eeMETRIC that have been added since\nthe descriptions provided in Allen et al. (2007 and 2011) include\nreduction in soil heat flux (G) in the presence of organic mulch on the\nground surface, use of an excess aerodynamic resistance for shrublands,\nuse of the Perrier function for trees identified as forest (Allen et\nal., 2018; Santos et al., 2012) and aerodynamic estimation of evaporation\nfrom open water rather than using energy balance (Jensen and Allen 2016;\nAllen et al., 2018). In 2022, the Perrier function was applied to tree\n(orchard) crops and a 3-source partitioning of bulk surface temperature\ninto canopy temperature, shaded soil temperature and sunlit soil\ntemperature was applied to both orchards and vineyards. These latter\napplications were made where orchards and vineyards are identified by\nCDL or, in California, by a state-sponsored land use system. These\nfunctions and other enhancements to the original METRIC model are described\nin the most current METRIC users manual (Allen et al., 2018). eeMETRIC uses\nthe atmospherically corrected surface reflectance and LST from Landsat\nCollection 2 Level 2, with fallback to Collection 2 Level 1 when needed for\nnear real-time estimates.\n\n[Additional information](https://openetdata.org/methodologies/)\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["1999-10-01T00:00:00Z","2024-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenET/EEMETRIC/CONUS/GRIDMET/MONTHLY/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/OpenET_EEMETRIC_CONUS_GRIDMET_MONTHLY_v2_0","rel":"related","title":"Run the example for OpenET/EEMETRIC/CONUS/GRIDMET/MONTHLY/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/OpenET_EEMETRIC_CONUS_GRIDMET_MONTHLY_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenET_EEMETRIC_CONUS_GRIDMET_MONTHLY_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_eemetric_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/eemetric/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_eemetric_conus_gridmet_monthly_v2_0.json","rel":"successor-version","title":"projects/openet/assets/eemetric/conus/gridmet/monthly/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/EEMETRIC/CONUS/GRIDMET/MONTHLY/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/EEMETRIC/CONUS/GRIDMET/MONTHLY/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenET/ENSEMBLE/CONUS/GRIDMET/MONTHLY/v2_0","title":"OpenET Ensemble Monthly Evapotranspiration v2.0 [deprecated]","description":"The OpenET dataset includes satellite-based data on the total amount of\nwater that is transferred from the land surface to the atmosphere through\nthe process of evapotranspiration (ET). OpenET provides ET data from\nmultiple satellite-driven models, and also calculates a single \"ensemble\nvalue\" from the model ensemble. The models currently included in the OpenET\nmodel ensemble are ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and\nSSEBop. The OpenET ensemble ET value is calculated as the mean of the\nensemble after filtering and removing outliers using the median absolute\ndeviation approach. All models currently use Landsat satellite data to\nproduce ET data at a pixel size of 30 meters by 30 meters (0.22 acres per\npixel). The monthly ET dataset provides data on total ET by month as an\nequivalent depth of water in millimeters.\n\n[Additional information](https://openetdata.org/methodologies/)\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["1999-10-01T00:00:00Z","2024-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenET/ENSEMBLE/CONUS/GRIDMET/MONTHLY/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0","rel":"related","title":"Run the example for OpenET/ENSEMBLE/CONUS/GRIDMET/MONTHLY/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_ensemble_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/ensemble/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_ensemble_conus_gridmet_monthly_v2_0.json","rel":"successor-version","title":"projects/openet/assets/ensemble/conus/gridmet/monthly/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/ENSEMBLE/CONUS/GRIDMET/MONTHLY/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/ENSEMBLE/CONUS/GRIDMET/MONTHLY/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenET/GEESEBAL/CONUS/GRIDMET/MONTHLY/v2_0","title":"OpenET geeSEBAL Monthly Evapotranspiration v2.0 [deprecated]","description":"Implementation of geeSEBAL was recently completed within the OpenET\nframework and an overview of the current geeSEBAL version can be found\nin Laipelt et al. (2021), which is based on the original algorithms\ndeveloped by Bastiaanssen et al. (1998). The OpenET geeSEBAL implementation\nuses land surface temperature (LST) data from Landsat Collection 2, in\naddition to NLDAS and gridMET datasets as instantaneous and daily\nmeteorological inputs, respectively. The automated statistical algorithm\nto select the hot and cold endmembers is based on a simplified version of\nthe Calibration using Inverse Modeling at Extreme Conditions (CIMEC)\nalgorithm proposed by Allen et al. (2013), where quantiles of LST and the\nnormalized difference vegetation index (NDVI) values are used to select\nendmember candidates in the Landsat domain area. The cold and wet endmember\ncandidates are selected in well vegetated areas, while the hot and dry\nendmember candidates are selected in the least vegetated cropland areas.\nBased on the selected endmembers, geeSEBAL assumes that in the cold and\nwet endmember all available energy is converted to latent heat (with high\nrates of transpiration), while in the hot and dry endmember all available\nenergy is converted to sensible heat. Finally, estimates of daily\nevapotranspiration are upscaled from instantaneous estimates based on the\nevaporative fraction, assuming it is constant during the daytime without\nsignificant changes in soil moisture and advection. Based on the results\nfrom the OpenET Accuracy Assessment and Intercomparison study, the OpenET\ngeeSEBAL algorithm was modified as follows: (i) the simplified version of\nCIMEC was improved by using additional filters to select the endmembers,\nincluding the use of the USDA Cropland Data Layer (CDL) and filters for\nNDVI, LST and albedo; (ii) corrections to LST for endmembers based on\nantecedent precipitation; (iii) definition of NLDAS wind speed thresholds\nto reduce model instability during the atmospheric correction; and, (iv)\nimprovements to estimate daily net radiation, using FAO-56 as reference\n(Allen et al., 1998). Overall, geeSEBAL performance is dependent on\ntopographic, climate, and meteorological conditions, with higher\nsensitivity and uncertainty related to hot and cold endmember selections\nfor the CIMEC automated calibration, and lower sensitivity and uncertainty\nrelated to meteorological inputs (Laipelt et al., 2021 and Kayser\net al., 2022). To reduce uncertainties related to complex terrain,\nimprovements were added to correct LST and global (incident) radiation on\nthe surface (including the environmental lapse rate, elevation slope\nand aspect) to represent the effects of topographic features on the\nmodel’s endmember selection algorithm and ET estimates.\n\n[Additional information](https://etdata.org/methods/)\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["1999-10-01T00:00:00Z","2024-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenET/GEESEBAL/CONUS/GRIDMET/MONTHLY/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/OpenET_GEESEBAL_CONUS_GRIDMET_MONTHLY_v2_0","rel":"related","title":"Run the example for OpenET/GEESEBAL/CONUS/GRIDMET/MONTHLY/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/OpenET_GEESEBAL_CONUS_GRIDMET_MONTHLY_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenET_GEESEBAL_CONUS_GRIDMET_MONTHLY_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_geesebal_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/geesebal/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_geesebal_conus_gridmet_monthly_v2_0.json","rel":"successor-version","title":"projects/openet/assets/geesebal/conus/gridmet/monthly/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/GEESEBAL/CONUS/GRIDMET/MONTHLY/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/GEESEBAL/CONUS/GRIDMET/MONTHLY/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenET/PTJPL/CONUS/GRIDMET/MONTHLY/v2_0","title":"OpenET PT-JPL Monthly Evapotranspiration v2.0 [deprecated]","description":"Priestley-Taylor Jet Propulsion Laboratory (PT-JPL)\n\nThe core formulation of the PT-JPL model within the OpenET framework has\nnot changed from the original formulation detailed in Fisher et al. (2008).\nHowever, enhancements and updates to model inputs and time integration for\nPT-JPL were made to take advantage of contemporary gridded weather datasets,\nprovide consistency with other models, improve open water evaporation\nestimates, and account for advection over crop and wetland areas in\nsemiarid and arid environments. These changes include the use of\nLandsat surface reflectance and thermal radiation for calculating net\nradiation, photosynthetically active radiation, plant canopy and moisture\nvariables, and use of NLDAS, Spatial CIMIS, and gridMET weather data for\nestimating insolation and ASCE reference ET. Similar to the implementation\nof other OpenET models, estimation of daily and monthly time integrated\nET is based on the fraction of ASCE reference ET. Open water evaporation\nis estimated following a surface energy balance approach of Abdelrady\net al. (2016) that is specific for water bodies by accounting for water\nheat flux as opposed to soil heat flux.\n\n[Additional information](https://openetdata.org/methodologies/)\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["1999-10-01T00:00:00Z","2024-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenET/PTJPL/CONUS/GRIDMET/MONTHLY/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/OpenET_PTJPL_CONUS_GRIDMET_MONTHLY_v2_0","rel":"related","title":"Run the example for OpenET/PTJPL/CONUS/GRIDMET/MONTHLY/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/OpenET_PTJPL_CONUS_GRIDMET_MONTHLY_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenET_PTJPL_CONUS_GRIDMET_MONTHLY_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_ptjpl_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_ptjpl_conus_gridmet_monthly_v2_0.json","rel":"successor-version","title":"projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/PTJPL/CONUS/GRIDMET/MONTHLY/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/PTJPL/CONUS/GRIDMET/MONTHLY/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenET/SIMS/CONUS/GRIDMET/MONTHLY/v2_0","title":"OpenET SIMS Monthly Evapotranspiration v2.0 [deprecated]","description":"Satellite Irrigation Management Support\n\nThe NASA Satellite Irrigation Management Support (SIMS) model was originally\ndeveloped to support satellite mapping of crop coefficients and\nevapotranspiration (ET) from irrigated lands and to increase access to this\ndata to support use in irrigation scheduling and regional assessment of\nagricultural water needs (Melton et al., 2012). SIMS uses a reflectance\nbased approach and incorporates the density coefficient described by Allen\nand Pereira (2009) and Pereira et al. (2020) to compute basal crop\ncoefficients for each 30 x 30 m pixel. The primary change from the most\nrecent SIMS publication (Pereira et al., 2020) for implementation in OpenET\nis the integration of a gridded soil water balance model to account for soil\nevaporation following precipitation events. Results of the OpenET Phase I\nintercomparison and accuracy assessment (Melton et al., 2022) showed that\nSIMS generally performed well for cropland sites during the growing season,\nbut had a persistent low bias during the winter months or other time periods\nwith frequent precipitation. This result was anticipated, since the\nreflectance-based approach used by SIMS is not sensitive to soil\nevaporation. To correct for this underestimation, a soil water balance model\nbased on FAO-56 (Allen et al., 1998) was implemented on Google Earth Engine\nand driven with gridded precipitation data from gridMET to estimate soil\nevaporation coefficients. These coefficients were then combined with the\nbasal crop coefficients calculated by SIMS to calculate total crop\nevapotranspiration using the dual crop coefficient approach. In addition,\na modest positive bias was observed in the SIMS data for periods with low\nor sparse vegetative cover. To correct for this bias, updates were made to\nthe equations that calculate the minimum basal crop coefficient to allow\nlower minimum basal crop coefficient values to be achieved. Full\ndocumentation of the SIMS model, current algorithms, and details and\nequations used in the soil water balance model are included in the\nSIMS user manual.\n\nThe SIMS model calculates ET under well-watered conditions for the current\ncrop growth stage and condition as measured by the satellite data, and SIMS\nis generally expected to have a positive bias for deficit irrigated crops\nand croplands with short-term or intermittent crop water stress. At\npresent, SIMS is only implemented for croplands, and non-agricultural lands\nare masked out in this data collection. Future research will extend the\nvegetation density-crop coefficient approach used within SIMS to other land\ncover types.\n[Additional information](https://openetdata.org/methodologies/)\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["1999-10-01T00:00:00Z","2024-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenET/SIMS/CONUS/GRIDMET/MONTHLY/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/OpenET_SIMS_CONUS_GRIDMET_MONTHLY_v2_0","rel":"related","title":"Run the example for OpenET/SIMS/CONUS/GRIDMET/MONTHLY/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/OpenET_SIMS_CONUS_GRIDMET_MONTHLY_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenET_SIMS_CONUS_GRIDMET_MONTHLY_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_sims_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/sims/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_sims_conus_gridmet_monthly_v2_0.json","rel":"successor-version","title":"projects/openet/assets/sims/conus/gridmet/monthly/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/SIMS/CONUS/GRIDMET/MONTHLY/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/SIMS/CONUS/GRIDMET/MONTHLY/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenET/SSEBOP/CONUS/GRIDMET/MONTHLY/v2_0","title":"OpenET SSEBop Monthly Evapotranspiration v2.0 [deprecated]","description":"Operational Simplified Surface Energy Balance (SSEBop)\n\nThe Operational Simplified Surface Energy Balance (SSEBop) model by Senay\net al. (2013, 2017) is a thermal-based simplified surface energy model for\nestimating actual ET based on the principles of satellite psychrometry\n(Senay 2018). The OpenET SSEBop implementation uses land surface temperature\n(Ts) from Landsat (Collection 2 Level-2 Science Products) with key model\nparameters (cold/wet-bulb reference, Tc, and surface psychrometric\nconstant, 1/dT) derived from a combination of observed surface temperature,\nnormalized difference vegetation index (NDVI), climatological average\n(1980-2017) daily maximum air temperature (Ta, 1-km) from Daymet, and\nnet radiation data from ERA-5. This model implementation uses the Google\nEarth Engine processing framework for connecting key SSEBop ET functions\nand algorithms together when generating both intermediate and aggregated ET\nresults. A detailed study and evaluation of the SSEBop model across CONUS\n(Senay et al., 2022) informs both cloud implementation and assessment for\nwater balance applications at broad scales. Notable model (v0.2.6)\nenhancements and performance against previous versions include additional\ncompatibility with Landsat 9 (launched Sep 2021), global model\nextensibility, and improved parameterization of SSEBop using\nFANO (Forcing and Normalizing Operation) to better estimate ET\nin all landscapes and all seasons regardless of vegetation cover density,\nthereby improving model accuracy by avoiding extrapolation of Tc to\nnon-calibration regions.\n\n[Additional information](https://openetdata.org/methodologies/)\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["1999-10-01T00:00:00Z","2024-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenET/SSEBOP/CONUS/GRIDMET/MONTHLY/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/OpenET_SSEBOP_CONUS_GRIDMET_MONTHLY_v2_0","rel":"related","title":"Run the example for OpenET/SSEBOP/CONUS/GRIDMET/MONTHLY/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/OpenET_SSEBOP_CONUS_GRIDMET_MONTHLY_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenET_SSEBOP_CONUS_GRIDMET_MONTHLY_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_ssebop_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/ssebop/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_ssebop_conus_gridmet_monthly_v2_0.json","rel":"successor-version","title":"projects/openet/assets/ssebop/conus/gridmet/monthly/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/SSEBOP/CONUS/GRIDMET/MONTHLY/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/OpenET/SSEBOP/CONUS/GRIDMET/MONTHLY/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/disalexi/conus/gridmet/monthly/v2_0","title":"OpenET DisALEXI Monthly Evapotranspiration v2.0","description":"Atmosphere-Land Exchange Inverse / Disaggregation of the Atmosphere-Land\nExchange Inverse (ALEXI/DisALEXI).\n\nDisALEXI was ported to Google Earth Engine as part of the OpenET\nframework. The baseline ALEXI/DisALEXI model structure is described by\nAnderson et al. (2012, 2018). The ALEXI evapotranspiration (ET) model\nspecifically uses time differential land surface temperature (LST)\nmeasurements from geostationary or moderate resolution polar orbiting\nplatforms to generate regional ET maps. DisALEXI then disaggregates the\nregional ALEXI ET to finer scales using Landsat data (30 m; biweekly) to\nresolve individual farm fields and other landscape features.\n\nNote: DisALEXI data does not exist before 2001 since the source ALEXI data\nis only available starting in 2001.\n\n[Additional information](https://etdata.org/methods/)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-86,50]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/disalexi/conus/gridmet/monthly/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_disalexi_conus_gridmet_monthly_v2_0","rel":"related","title":"Run the example for projects/openet/assets/disalexi/conus/gridmet/monthly/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_disalexi_conus_gridmet_monthly_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_disalexi_conus_gridmet_monthly_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_disalexi_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/disalexi/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/OpenET_DISALEXI_CONUS_GRIDMET_MONTHLY_v2_0.json","rel":"predecessor-version","title":"OpenET/DISALEXI/CONUS/GRIDMET/MONTHLY/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/disalexi/conus/gridmet/monthly/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/disalexi/conus/gridmet/monthly/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/disalexi/conus/gridmet/monthly/v2_1","title":"OpenET DisALEXI Monthly Evapotranspiration v2.1","description":"Atmosphere-Land Exchange Inverse / Disaggregation of the Atmosphere-Land\nExchange Inverse (ALEXI/DisALEXI).\n\nDisALEXI was ported to Google Earth Engine as part of the OpenET\nframework. The baseline ALEXI/DisALEXI model structure is described by\nAnderson et al. (2012, 2018). The ALEXI evapotranspiration (ET) model\nspecifically uses time differential land surface temperature (LST)\nmeasurements from geostationary or moderate resolution polar orbiting\nplatforms to generate regional ET maps. DisALEXI then disaggregates the\nregional ALEXI ET to finer scales using Landsat data (30 m; biweekly) to\nresolve individual farm fields and other landscape features.\n\nNote: DisALEXI data does not exist before 2001 since the source ALEXI data\nis only available starting in 2001.\n**OpenET Collection v2.1**\n\nOpenET Collection v2.1 is a reprocessed and updated version of Collection\nv2.0, designed primarily to address known v2.0 issues, while also\nincorporating minor model improvements and input-data updates. It is\nexpected that there will be noticeable differences in ET between the two\ncollection versions for some places and times. Some of the updates and changes \ninclude:\n\n* Additional cloud screening and filtering to skip Landsat images with \nunmasked clouds and/or extensive snow cover.\n* Reprocessing to incorporate updates to the NLDAS-2 and GRIDMET\ninput meteorology datasets.\n* Incorporation of the USGS Annual NLCD product for all models that require \nland cover information.\n* Incorporation of the latest USDA CDL for all models that require crop type \ninformation.\n* Updates to the interpolation so monthly ET is only produced when all\ndays in the month have interpolated values (reducing “count=0” months in\ncloudy/snowy or low-coverage periods).\n* Application of an emissivity correction to address a known issue in\nLandsat LST data.\n\n[Additional information](https://etdata.org/methods/)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["2015-10-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/disalexi/conus/gridmet/monthly/v2_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_disalexi_conus_gridmet_monthly_v2_1","rel":"related","title":"Run the example for projects/openet/assets/disalexi/conus/gridmet/monthly/v2_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_disalexi_conus_gridmet_monthly_v2_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_disalexi_conus_gridmet_monthly_v2_1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/disalexi/conus/gridmet/monthly/v2_1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/disalexi/conus/gridmet/monthly/v2_1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/eemetric/conus/gridmet/monthly/v2_0","title":"OpenET eeMETRIC Monthly Evapotranspiration v2.0","description":"Google Earth Engine implementation of the Mapping Evapotranspiration\nat high Resolution with Internalized Calibration model (eeMETRIC).\n\neeMETRIC applies the advanced METRIC algorithms and process of Allen\net al. (2007; 2015) and Allen et al. (2013b), where a singular\nrelationship between the near surface air temperature difference (dT)\nand delapsed land surface temperature (TsDEM) is used to estimate sensible\nheat flux (H) and is applied to each Landsat scene. Automated selection of\nthe hot and cold pixels for an image generally follows a statistical\nisolation procedure described by Allen et al. (2013a) and ReVelle, Kilic\nand Allen (2019a,b). The calibration of H in eeMETRIC utilizes alfalfa\nreference ET calculated from the NLDAS-2 gridded weather dataset using a\nfixed 15% reduction in computed reference ET to account for known biases\nin the gridded data set. The fixed reduction does not impact the\ncalibration accuracy of eeMETRIC and mostly reduces impacts of boundary\nlayer buoyancy correction.\n\nThe identification of candidates for pools of hot and cold pixels has\nevolved in the eeMETRIC implementation of METRIC. The new automated\ncalibration process incorporates the combination of methodologies and\napproaches that stem from two development branches of EEFlux (Allen\net al., 2015). The first branch focused on improving the automated pixel\nselection process using standard lapse rates for land surface temperature\n(LST) without any further spatial delapsing (ReVelle et al., 2019b). The\nsecond branch incorporated a secondary spatial delapsing of LST as well as\nchanges to the pixel selection process (ReVelle et al., 2019a). The final,\ncombined approach is described by Kilic et al. (2021).\n\neeMETRIC employs the aerodynamic-related functions in complex terrain\n(mountains) developed by Allen et al. (2013b) to improve estimates for\naerodynamic roughness, wind speed and boundary layer stability as related\nto estimated terrain roughness, position on a slope and wind direction.\nThese functions tend to increase estimates for H (and reduce ET) on\nwindward slopes and may reduce H (and increase ET) on leeward slopes.\nOther METRIC functions employed in eeMETRIC that have been added since\nthe descriptions provided in Allen et al. (2007 and 2011) include\nreduction in soil heat flux (G) in the presence of organic mulch on the\nground surface, use of an excess aerodynamic resistance for shrublands,\nuse of the Perrier function for trees identified as forest (Allen et\nal., 2018; Santos et al., 2012) and aerodynamic estimation of evaporation\nfrom open water rather than using energy balance (Jensen and Allen 2016;\nAllen et al., 2018). In 2022, the Perrier function was applied to tree\n(orchard) crops and a 3-source partitioning of bulk surface temperature\ninto canopy temperature, shaded soil temperature and sunlit soil\ntemperature was applied to both orchards and vineyards. These latter\napplications were made where orchards and vineyards are identified by\nCDL or, in California, by a state-sponsored land use system. These\nfunctions and other enhancements to the original METRIC model are described\nin the most current METRIC users manual (Allen et al., 2018). eeMETRIC uses\nthe atmospherically corrected surface reflectance and LST from Landsat\nCollection 2 Level 2, with fallback to Collection 2 Level 1 when needed for\nnear real-time estimates.\n\n[Additional information](https://etdata.org/methods/)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-86,50]]},"temporal":{"interval":[["1999-10-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/eemetric/conus/gridmet/monthly/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_eemetric_conus_gridmet_monthly_v2_0","rel":"related","title":"Run the example for projects/openet/assets/eemetric/conus/gridmet/monthly/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_eemetric_conus_gridmet_monthly_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_eemetric_conus_gridmet_monthly_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_eemetric_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/eemetric/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/OpenET_EEMETRIC_CONUS_GRIDMET_MONTHLY_v2_0.json","rel":"predecessor-version","title":"OpenET/EEMETRIC/CONUS/GRIDMET/MONTHLY/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/eemetric/conus/gridmet/monthly/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/eemetric/conus/gridmet/monthly/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/eemetric/conus/gridmet/monthly/v2_0_pre2000","title":"OpenET eeMETRIC Monthly Evapotranspiration v2.0 (1985-1999)","description":"Google Earth Engine implementation of the Mapping Evapotranspiration\nat high Resolution with Internalized Calibration model (eeMETRIC).\n\neeMETRIC applies the advanced METRIC algorithms and process of Allen\net al. (2007; 2015) and Allen et al. (2013b), where a singular\nrelationship between the near surface air temperature difference (dT)\nand delapsed land surface temperature (TsDEM) is used to estimate sensible\nheat flux (H) and is applied to each Landsat scene. Automated selection of\nthe hot and cold pixels for an image generally follows a statistical\nisolation procedure described by Allen et al. (2013a) and ReVelle, Kilic\nand Allen (2019a,b). The calibration of H in eeMETRIC utilizes alfalfa\nreference ET calculated from the NLDAS-2 gridded weather dataset using a\nfixed 15% reduction in computed reference ET to account for known biases\nin the gridded data set. The fixed reduction does not impact the\ncalibration accuracy of eeMETRIC and mostly reduces impacts of boundary\nlayer buoyancy correction.\n\nThe identification of candidates for pools of hot and cold pixels has\nevolved in the eeMETRIC implementation of METRIC. The new automated\ncalibration process incorporates the combination of methodologies and\napproaches that stem from two development branches of EEFlux (Allen\net al., 2015). The first branch focused on improving the automated pixel\nselection process using standard lapse rates for land surface temperature\n(LST) without any further spatial delapsing (ReVelle et al., 2019b). The\nsecond branch incorporated a secondary spatial delapsing of LST as well as\nchanges to the pixel selection process (ReVelle et al., 2019a). The final,\ncombined approach is described by Kilic et al. (2021).\n\neeMETRIC employs the aerodynamic-related functions in complex terrain\n(mountains) developed by Allen et al. (2013b) to improve estimates for\naerodynamic roughness, wind speed and boundary layer stability as related\nto estimated terrain roughness, position on a slope and wind direction.\nThese functions tend to increase estimates for H (and reduce ET) on\nwindward slopes and may reduce H (and increase ET) on leeward slopes.\nOther METRIC functions employed in eeMETRIC that have been added since\nthe descriptions provided in Allen et al. (2007 and 2011) include\nreduction in soil heat flux (G) in the presence of organic mulch on the\nground surface, use of an excess aerodynamic resistance for shrublands,\nuse of the Perrier function for trees identified as forest (Allen et\nal., 2018; Santos et al., 2012) and aerodynamic estimation of evaporation\nfrom open water rather than using energy balance (Jensen and Allen 2016;\nAllen et al., 2018). In 2022, the Perrier function was applied to tree\n(orchard) crops and a 3-source partitioning of bulk surface temperature\ninto canopy temperature, shaded soil temperature and sunlit soil\ntemperature was applied to both orchards and vineyards. These latter\napplications were made where orchards and vineyards are identified by\nCDL or, in California, by a state-sponsored land use system. These\nfunctions and other enhancements to the original METRIC model are described\nin the most current METRIC users manual (Allen et al., 2018). eeMETRIC uses\nthe atmospherically corrected surface reflectance and LST from Landsat\nCollection 2 Level 2, with fallback to Collection 2 Level 1 when needed for\nnear real-time estimates.\n\n\n**OpenET Collection v2.0, pre-2000 (1985-1999)**\n\nNote: This collection should not be merged with the main v2.0\ncollection to compute long-term trends, annual totals, or difference from\naverage type calculations, unless care is taken to identify and fill gaps\nor missing data. This collection was generated using Landsat 5 data only,\nand will have more data gaps (both spatially and temporally) than the main\nv2.0 collection, which incorporates data from multiple Landsat satellites\n(5 & 7, 7 & 8, etc.). This pre-2000 collection may have long periods\nwithout data, especially in the winter months, and large regions without\ndata whatsoever in the earlier years (1985-1993).\n\n[Additional information](https://etdata.org/methods/)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-86,50]]},"temporal":{"interval":[["1984-10-01T00:00:00Z","1999-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/eemetric/conus/gridmet/monthly/v2_0_pre2000","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_eemetric_conus_gridmet_monthly_v2_0_pre2000","rel":"related","title":"Run the example for projects/openet/assets/eemetric/conus/gridmet/monthly/v2_0_pre2000 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_eemetric_conus_gridmet_monthly_v2_0_pre2000_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_eemetric_conus_gridmet_monthly_v2_0_pre2000#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/eemetric/conus/gridmet/monthly/v2_0_pre2000/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/eemetric/conus/gridmet/monthly/v2_0_pre2000/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/eemetric/conus/gridmet/monthly/v2_1","title":"OpenET eeMETRIC Monthly Evapotranspiration v2.1","description":"Google Earth Engine implementation of the Mapping Evapotranspiration\nat high Resolution with Internalized Calibration model (eeMETRIC).\n\neeMETRIC applies the advanced METRIC algorithms and process of Allen\net al. (2007; 2015) and Allen et al. (2013b), where a singular\nrelationship between the near surface air temperature difference (dT)\nand delapsed land surface temperature (TsDEM) is used to estimate sensible\nheat flux (H) and is applied to each Landsat scene. Automated selection of\nthe hot and cold pixels for an image generally follows a statistical\nisolation procedure described by Allen et al. (2013a) and ReVelle, Kilic\nand Allen (2019a,b). The calibration of H in eeMETRIC utilizes alfalfa\nreference ET calculated from the NLDAS-2 gridded weather dataset using a\nfixed 15% reduction in computed reference ET to account for known biases\nin the gridded data set. The fixed reduction does not impact the\ncalibration accuracy of eeMETRIC and mostly reduces impacts of boundary\nlayer buoyancy correction.\n\nThe identification of candidates for pools of hot and cold pixels has\nevolved in the eeMETRIC implementation of METRIC. The new automated\ncalibration process incorporates the combination of methodologies and\napproaches that stem from two development branches of EEFlux (Allen\net al., 2015). The first branch focused on improving the automated pixel\nselection process using standard lapse rates for land surface temperature\n(LST) without any further spatial delapsing (ReVelle et al., 2019b). The\nsecond branch incorporated a secondary spatial delapsing of LST as well as\nchanges to the pixel selection process (ReVelle et al., 2019a). The final,\ncombined approach is described by Kilic et al. (2021).\n\neeMETRIC employs the aerodynamic-related functions in complex terrain\n(mountains) developed by Allen et al. (2013b) to improve estimates for\naerodynamic roughness, wind speed and boundary layer stability as related\nto estimated terrain roughness, position on a slope and wind direction.\nThese functions tend to increase estimates for H (and reduce ET) on\nwindward slopes and may reduce H (and increase ET) on leeward slopes.\nOther METRIC functions employed in eeMETRIC that have been added since\nthe descriptions provided in Allen et al. (2007 and 2011) include\nreduction in soil heat flux (G) in the presence of organic mulch on the\nground surface, use of an excess aerodynamic resistance for shrublands,\nuse of the Perrier function for trees identified as forest (Allen et\nal., 2018; Santos et al., 2012) and aerodynamic estimation of evaporation\nfrom open water rather than using energy balance (Jensen and Allen 2016;\nAllen et al., 2018). In 2022, the Perrier function was applied to tree\n(orchard) crops and a 3-source partitioning of bulk surface temperature\ninto canopy temperature, shaded soil temperature and sunlit soil\ntemperature was applied to both orchards and vineyards. These latter\napplications were made where orchards and vineyards are identified by\nCDL or, in California, by a state-sponsored land use system. These\nfunctions and other enhancements to the original METRIC model are described\nin the most current METRIC users manual (Allen et al., 2018). eeMETRIC uses\nthe atmospherically corrected surface reflectance and LST from Landsat\nCollection 2 Level 2, with fallback to Collection 2 Level 1 when needed for\nnear real-time estimates.\n**OpenET Collection v2.1**\n\nOpenET Collection v2.1 is a reprocessed and updated version of Collection\nv2.0, designed primarily to address known v2.0 issues, while also\nincorporating minor model improvements and input-data updates. It is\nexpected that there will be noticeable differences in ET between the two\ncollection versions for some places and times. Some of the updates and changes \ninclude:\n\n* Additional cloud screening and filtering to skip Landsat images with \nunmasked clouds and/or extensive snow cover.\n* Reprocessing to incorporate updates to the NLDAS-2 and GRIDMET\ninput meteorology datasets.\n* Incorporation of the USGS Annual NLCD product for all models that require \nland cover information.\n* Incorporation of the latest USDA CDL for all models that require crop type \ninformation.\n* Updates to the interpolation so monthly ET is only produced when all\ndays in the month have interpolated values (reducing “count=0” months in\ncloudy/snowy or low-coverage periods).\n* Application of an emissivity correction to address a known issue in\nLandsat LST data.\n\n[Additional information](https://etdata.org/methods/)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["2015-10-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/eemetric/conus/gridmet/monthly/v2_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_eemetric_conus_gridmet_monthly_v2_1","rel":"related","title":"Run the example for projects/openet/assets/eemetric/conus/gridmet/monthly/v2_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_eemetric_conus_gridmet_monthly_v2_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_eemetric_conus_gridmet_monthly_v2_1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/eemetric/conus/gridmet/monthly/v2_1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/eemetric/conus/gridmet/monthly/v2_1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/ensemble/conus/gridmet/monthly/v2_0","title":"OpenET Ensemble Monthly Evapotranspiration v2.0","description":"The OpenET dataset includes satellite-based data on the total amount of\nwater that is transferred from the land surface to the atmosphere through\nthe process of evapotranspiration (ET). OpenET provides ET data from\nmultiple satellite-driven models, and also calculates a single \"ensemble\nvalue\" from the model ensemble. The models currently included in the OpenET\nmodel ensemble are ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and\nSSEBop. The OpenET ensemble ET value is calculated as the mean of the\nensemble after filtering and removing outliers using the median absolute\ndeviation approach. All models currently use Landsat satellite data to\nproduce ET data at a pixel size of 30 meters by 30 meters (0.22 acres per\npixel). The monthly ET dataset provides data on total ET by month as an\nequivalent depth of water in millimeters.\n\nNote: DisALEXI cannot be generated before 2001 and is not included in the\nensemble calculation in 1999 or 2000.\n\n[Additional information](https://etdata.org/methods/)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-86,50]]},"temporal":{"interval":[["1999-10-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ensemble/conus/gridmet/monthly/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_ensemble_conus_gridmet_monthly_v2_0","rel":"related","title":"Run the example for projects/openet/assets/ensemble/conus/gridmet/monthly/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_ensemble_conus_gridmet_monthly_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_ensemble_conus_gridmet_monthly_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_ensemble_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/ensemble/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0.json","rel":"predecessor-version","title":"OpenET/ENSEMBLE/CONUS/GRIDMET/MONTHLY/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ensemble/conus/gridmet/monthly/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ensemble/conus/gridmet/monthly/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/ensemble/conus/gridmet/monthly/v2_0_pre2000","title":"OpenET Ensemble Monthly Evapotranspiration v2.0 (1985-1999)","description":"The OpenET dataset includes satellite-based data on the total amount of\nwater that is transferred from the land surface to the atmosphere through\nthe process of evapotranspiration (ET). OpenET provides ET data from\nmultiple satellite-driven models, and also calculates a single \"ensemble\nvalue\" from the model ensemble. The models currently included in the OpenET\nmodel ensemble are ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and\nSSEBop. The OpenET ensemble ET value is calculated as the mean of the\nensemble after filtering and removing outliers using the median absolute\ndeviation approach. All models currently use Landsat satellite data to\nproduce ET data at a pixel size of 30 meters by 30 meters (0.22 acres per\npixel). The monthly ET dataset provides data on total ET by month as an\nequivalent depth of water in millimeters.\n\nNote: DisALEXI cannot be generated before 2001 and is not included in the\nensemble calculation for this collection.\n\n\n**OpenET Collection v2.0, pre-2000 (1985-1999)**\n\nNote: This collection should not be merged with the main v2.0\ncollection to compute long-term trends, annual totals, or difference from\naverage type calculations, unless care is taken to identify and fill gaps\nor missing data. This collection was generated using Landsat 5 data only,\nand will have more data gaps (both spatially and temporally) than the main\nv2.0 collection, which incorporates data from multiple Landsat satellites\n(5 & 7, 7 & 8, etc.). This pre-2000 collection may have long periods\nwithout data, especially in the winter months, and large regions without\ndata whatsoever in the earlier years (1985-1993).\n\n[Additional information](https://etdata.org/methods/)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-86,50]]},"temporal":{"interval":[["1984-10-01T00:00:00Z","1999-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ensemble/conus/gridmet/monthly/v2_0_pre2000","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_ensemble_conus_gridmet_monthly_v2_0_pre2000","rel":"related","title":"Run the example for projects/openet/assets/ensemble/conus/gridmet/monthly/v2_0_pre2000 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_ensemble_conus_gridmet_monthly_v2_0_pre2000_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_ensemble_conus_gridmet_monthly_v2_0_pre2000#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ensemble/conus/gridmet/monthly/v2_0_pre2000/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ensemble/conus/gridmet/monthly/v2_0_pre2000/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/ensemble/conus/gridmet/monthly/v2_1","title":"OpenET Ensemble Monthly Evapotranspiration v2.1","description":"The OpenET dataset includes satellite-based data on the total amount of\nwater that is transferred from the land surface to the atmosphere through\nthe process of evapotranspiration (ET). OpenET provides ET data from\nmultiple satellite-driven models, and also calculates a single \"ensemble\nvalue\" from the model ensemble. The models currently included in the OpenET\nmodel ensemble are ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and\nSSEBop. The OpenET ensemble ET value is calculated as the mean of the\nensemble after filtering and removing outliers using the median absolute\ndeviation approach. All models currently use Landsat satellite data to\nproduce ET data at a pixel size of 30 meters by 30 meters (0.22 acres per\npixel). The monthly ET dataset provides data on total ET by month as an\nequivalent depth of water in millimeters.\n**OpenET Collection v2.1**\n\nOpenET Collection v2.1 is a reprocessed and updated version of Collection\nv2.0, designed primarily to address known v2.0 issues, while also\nincorporating minor model improvements and input-data updates. It is\nexpected that there will be noticeable differences in ET between the two\ncollection versions for some places and times. Some of the updates and changes \ninclude:\n\n* Additional cloud screening and filtering to skip Landsat images with \nunmasked clouds and/or extensive snow cover.\n* Reprocessing to incorporate updates to the NLDAS-2 and GRIDMET\ninput meteorology datasets.\n* Incorporation of the USGS Annual NLCD product for all models that require \nland cover information.\n* Incorporation of the latest USDA CDL for all models that require crop type \ninformation.\n* Updates to the interpolation so monthly ET is only produced when all\ndays in the month have interpolated values (reducing “count=0” months in\ncloudy/snowy or low-coverage periods).\n* Application of an emissivity correction to address a known issue in\nLandsat LST data.\n\n[Additional information](https://etdata.org/methods/)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["2015-10-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ensemble/conus/gridmet/monthly/v2_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_ensemble_conus_gridmet_monthly_v2_1","rel":"related","title":"Run the example for projects/openet/assets/ensemble/conus/gridmet/monthly/v2_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_ensemble_conus_gridmet_monthly_v2_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_ensemble_conus_gridmet_monthly_v2_1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ensemble/conus/gridmet/monthly/v2_1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ensemble/conus/gridmet/monthly/v2_1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/geesebal/conus/gridmet/monthly/v2_0","title":"OpenET geeSEBAL Monthly Evapotranspiration v2.0","description":"Google Earth Engine implementation of the Surface Energy Balance Algorithm\nfor Land (SEBAL) model.\n\nAn overview of the current geeSEBAL version can be found in Laipelt et al.\n(2021), which is based on the original algorithms developed by\nBastiaanssen et al. (1998). The OpenET geeSEBAL implementation uses land\nsurface temperature (LST) data from Landsat Collection 2, in addition to\nNLDAS-2 and gridMET datasets as instantaneous and daily meteorological\ninputs, respectively.\n\nThe automated statistical algorithm to select the hot and cold endmembers\nis based on a simplified version of the Calibration using Inverse Modeling\nat Extreme Conditions (CIMEC) algorithm proposed by Allen et al. (2013),\nwhere quantiles of LST and the normalized difference vegetation index\n(NDVI) values are used to select endmember candidates in the Landsat\ndomain area. The cold and wet endmember candidates are selected in well\nvegetated areas, while the hot and dry endmember candidates are selected\nin the least vegetated cropland areas.Based on the selected endmembers,\ngeeSEBAL assumes that in the cold and wet endmember all available energy\nis converted to latent heat (with high rates of transpiration), while in\nthe hot and dry endmember all available energy is converted to sensible\nheat. Finally, estimates of daily evapotranspiration are upscaled from\ninstantaneous estimates based on the evaporative fraction, assuming it is\nconstant during the daytime without significant changes in soil moisture\nand advection.\n\nBased on the results from the OpenET Accuracy Assessment and\nIntercomparison study, the OpenET geeSEBAL algorithm was modified as\nfollows:\n\n1. The simplified version of CIMEC was improved by using additional\nfilters to select the endmembers, including the use of the USDA Cropland\nData Layer (CDL) and filters for NDVI, LST and albedo.\n2. Corrections to LST for endmembers based on antecedent precipitation.\n3. Definition of NLDAS-2 wind speed thresholds to reduce model instability\nduring the atmospheric correction.\n4. Improvements to estimate daily net radiation, using FAO-56 as reference\n(Allen et al., 1998).\n\nOverall, geeSEBAL performance is dependent on topographic, climate, and\nmeteorological conditions, with higher sensitivity and uncertainty related\nto hot and cold endmember selections for the CIMEC automated calibration,\nand lower sensitivity and uncertainty related to meteorological inputs\n(Laipelt et al., 2021 and Kayser et al., 2022). To reduce uncertainties\nrelated to complex terrain, improvements were added to correct LST and\nglobal (incident) radiation on the surface (including the environmental\nlapse rate, elevation slope and aspect) to represent the effects of\ntopographic features on the model’s endmember selection algorithm and ET\nestimates.\n\n[Additional information](https://etdata.org/methods/)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-86,50]]},"temporal":{"interval":[["1999-10-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/geesebal/conus/gridmet/monthly/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_geesebal_conus_gridmet_monthly_v2_0","rel":"related","title":"Run the example for projects/openet/assets/geesebal/conus/gridmet/monthly/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_geesebal_conus_gridmet_monthly_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_geesebal_conus_gridmet_monthly_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_geesebal_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/geesebal/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/OpenET_GEESEBAL_CONUS_GRIDMET_MONTHLY_v2_0.json","rel":"predecessor-version","title":"OpenET/GEESEBAL/CONUS/GRIDMET/MONTHLY/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/geesebal/conus/gridmet/monthly/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/geesebal/conus/gridmet/monthly/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/geesebal/conus/gridmet/monthly/v2_0_pre2000","title":"OpenET geeSEBAL Monthly Evapotranspiration v2.0 (1985-1999)","description":"Google Earth Engine implementation of the Surface Energy Balance Algorithm\nfor Land (SEBAL) model.\n\nAn overview of the current geeSEBAL version can be found in Laipelt et al.\n(2021), which is based on the original algorithms developed by\nBastiaanssen et al. (1998). The OpenET geeSEBAL implementation uses land\nsurface temperature (LST) data from Landsat Collection 2, in addition to\nNLDAS-2 and gridMET datasets as instantaneous and daily meteorological\ninputs, respectively.\n\nThe automated statistical algorithm to select the hot and cold endmembers\nis based on a simplified version of the Calibration using Inverse Modeling\nat Extreme Conditions (CIMEC) algorithm proposed by Allen et al. (2013),\nwhere quantiles of LST and the normalized difference vegetation index\n(NDVI) values are used to select endmember candidates in the Landsat\ndomain area. The cold and wet endmember candidates are selected in well\nvegetated areas, while the hot and dry endmember candidates are selected\nin the least vegetated cropland areas.Based on the selected endmembers,\ngeeSEBAL assumes that in the cold and wet endmember all available energy\nis converted to latent heat (with high rates of transpiration), while in\nthe hot and dry endmember all available energy is converted to sensible\nheat. Finally, estimates of daily evapotranspiration are upscaled from\ninstantaneous estimates based on the evaporative fraction, assuming it is\nconstant during the daytime without significant changes in soil moisture\nand advection.\n\nBased on the results from the OpenET Accuracy Assessment and\nIntercomparison study, the OpenET geeSEBAL algorithm was modified as\nfollows:\n\n1. The simplified version of CIMEC was improved by using additional\nfilters to select the endmembers, including the use of the USDA Cropland\nData Layer (CDL) and filters for NDVI, LST and albedo.\n2. Corrections to LST for endmembers based on antecedent precipitation.\n3. Definition of NLDAS-2 wind speed thresholds to reduce model instability\nduring the atmospheric correction.\n4. Improvements to estimate daily net radiation, using FAO-56 as reference\n(Allen et al., 1998).\n\nOverall, geeSEBAL performance is dependent on topographic, climate, and\nmeteorological conditions, with higher sensitivity and uncertainty related\nto hot and cold endmember selections for the CIMEC automated calibration,\nand lower sensitivity and uncertainty related to meteorological inputs\n(Laipelt et al., 2021 and Kayser et al., 2022). To reduce uncertainties\nrelated to complex terrain, improvements were added to correct LST and\nglobal (incident) radiation on the surface (including the environmental\nlapse rate, elevation slope and aspect) to represent the effects of\ntopographic features on the model’s endmember selection algorithm and ET\nestimates.\n\n\n**OpenET Collection v2.0, pre-2000 (1985-1999)**\n\nNote: This collection should not be merged with the main v2.0\ncollection to compute long-term trends, annual totals, or difference from\naverage type calculations, unless care is taken to identify and fill gaps\nor missing data. This collection was generated using Landsat 5 data only,\nand will have more data gaps (both spatially and temporally) than the main\nv2.0 collection, which incorporates data from multiple Landsat satellites\n(5 & 7, 7 & 8, etc.). This pre-2000 collection may have long periods\nwithout data, especially in the winter months, and large regions without\ndata whatsoever in the earlier years (1985-1993).\n\n[Additional information](https://etdata.org/methods/)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["1984-10-01T00:00:00Z","1999-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/geesebal/conus/gridmet/monthly/v2_0_pre2000","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_geesebal_conus_gridmet_monthly_v2_0_pre2000","rel":"related","title":"Run the example for projects/openet/assets/geesebal/conus/gridmet/monthly/v2_0_pre2000 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_geesebal_conus_gridmet_monthly_v2_0_pre2000_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_geesebal_conus_gridmet_monthly_v2_0_pre2000#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/geesebal/conus/gridmet/monthly/v2_0_pre2000/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/geesebal/conus/gridmet/monthly/v2_0_pre2000/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/geesebal/conus/gridmet/monthly/v2_1","title":"OpenET geeSEBAL Monthly Evapotranspiration v2.1","description":"Google Earth Engine implementation of the Surface Energy Balance Algorithm\nfor Land (SEBAL) model.\n\nAn overview of the current geeSEBAL version can be found in Laipelt et al.\n(2021), which is based on the original algorithms developed by\nBastiaanssen et al. (1998). The OpenET geeSEBAL implementation uses land\nsurface temperature (LST) data from Landsat Collection 2, in addition to\nNLDAS-2 and gridMET datasets as instantaneous and daily meteorological\ninputs, respectively.\n\nThe automated statistical algorithm to select the hot and cold endmembers\nis based on a simplified version of the Calibration using Inverse Modeling\nat Extreme Conditions (CIMEC) algorithm proposed by Allen et al. (2013),\nwhere quantiles of LST and the normalized difference vegetation index\n(NDVI) values are used to select endmember candidates in the Landsat\ndomain area. The cold and wet endmember candidates are selected in well\nvegetated areas, while the hot and dry endmember candidates are selected\nin the least vegetated cropland areas.Based on the selected endmembers,\ngeeSEBAL assumes that in the cold and wet endmember all available energy\nis converted to latent heat (with high rates of transpiration), while in\nthe hot and dry endmember all available energy is converted to sensible\nheat. Finally, estimates of daily evapotranspiration are upscaled from\ninstantaneous estimates based on the evaporative fraction, assuming it is\nconstant during the daytime without significant changes in soil moisture\nand advection.\n\nBased on the results from the OpenET Accuracy Assessment and\nIntercomparison study, the OpenET geeSEBAL algorithm was modified as\nfollows:\n\n1. The simplified version of CIMEC was improved by using additional\nfilters to select the endmembers, including the use of the USDA Cropland\nData Layer (CDL) and filters for NDVI, LST and albedo.\n2. Corrections to LST for endmembers based on antecedent precipitation.\n3. Definition of NLDAS-2 wind speed thresholds to reduce model instability\nduring the atmospheric correction.\n4. Improvements to estimate daily net radiation, using FAO-56 as reference\n(Allen et al., 1998).\n\nOverall, geeSEBAL performance is dependent on topographic, climate, and\nmeteorological conditions, with higher sensitivity and uncertainty related\nto hot and cold endmember selections for the CIMEC automated calibration,\nand lower sensitivity and uncertainty related to meteorological inputs\n(Laipelt et al., 2021 and Kayser et al., 2022). To reduce uncertainties\nrelated to complex terrain, improvements were added to correct LST and\nglobal (incident) radiation on the surface (including the environmental\nlapse rate, elevation slope and aspect) to represent the effects of\ntopographic features on the model’s endmember selection algorithm and ET\nestimates.\n**OpenET Collection v2.1**\n\nOpenET Collection v2.1 is a reprocessed and updated version of Collection\nv2.0, designed primarily to address known v2.0 issues, while also\nincorporating minor model improvements and input-data updates. It is\nexpected that there will be noticeable differences in ET between the two\ncollection versions for some places and times. Some of the updates and changes \ninclude:\n\n* Additional cloud screening and filtering to skip Landsat images with \nunmasked clouds and/or extensive snow cover.\n* Reprocessing to incorporate updates to the NLDAS-2 and GRIDMET\ninput meteorology datasets.\n* Incorporation of the USGS Annual NLCD product for all models that require \nland cover information.\n* Incorporation of the latest USDA CDL for all models that require crop type \ninformation.\n* Updates to the interpolation so monthly ET is only produced when all\ndays in the month have interpolated values (reducing “count=0” months in\ncloudy/snowy or low-coverage periods).\n* Application of an emissivity correction to address a known issue in\nLandsat LST data.\n\n[Additional information](https://etdata.org/methods/)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["2015-10-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/geesebal/conus/gridmet/monthly/v2_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_geesebal_conus_gridmet_monthly_v2_1","rel":"related","title":"Run the example for projects/openet/assets/geesebal/conus/gridmet/monthly/v2_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_geesebal_conus_gridmet_monthly_v2_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_geesebal_conus_gridmet_monthly_v2_1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/geesebal/conus/gridmet/monthly/v2_1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/geesebal/conus/gridmet/monthly/v2_1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_0","title":"OpenET PT-JPL Monthly Evapotranspiration v2.0","description":"Priestley-Taylor Jet Propulsion Laboratory (PT-JPL).\n\nThe core formulation of the PT-JPL model within the OpenET framework has\nnot changed from the original formulation detailed in Fisher et al. (2008).\nHowever, enhancements and updates to model inputs and time integration for\nPT-JPL were made to take advantage of contemporary gridded weather datasets,\nprovide consistency with other models, improve open water evaporation\nestimates, and account for advection over crop and wetland areas in\nsemiarid and arid environments. These changes include the use of\nLandsat surface reflectance and thermal radiation for calculating net\nradiation, photosynthetically active radiation, plant canopy and moisture\nvariables, and use of NLDAS, Spatial CIMIS, and gridMET weather data for\nestimating insolation and ASCE reference ET. Similar to the implementation\nof other OpenET models, estimation of daily and monthly time integrated\nET is based on the fraction of ASCE reference ET. Open water evaporation\nis estimated following a surface energy balance approach of Abdelrady\net al. (2016) that is specific for water bodies by accounting for water\nheat flux as opposed to soil heat flux.\n\n[Additional information](https://etdata.org/methods/)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-86,50]]},"temporal":{"interval":[["1999-10-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_ptjpl_conus_gridmet_monthly_v2_0","rel":"related","title":"Run the example for projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_ptjpl_conus_gridmet_monthly_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_ptjpl_conus_gridmet_monthly_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_ptjpl_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/OpenET_PTJPL_CONUS_GRIDMET_MONTHLY_v2_0.json","rel":"predecessor-version","title":"OpenET/PTJPL/CONUS/GRIDMET/MONTHLY/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_0_pre2000","title":"OpenET PT-JPL Monthly Evapotranspiration v2.0 (1985-1999)","description":"Priestley-Taylor Jet Propulsion Laboratory (PT-JPL).\n\nThe core formulation of the PT-JPL model within the OpenET framework has\nnot changed from the original formulation detailed in Fisher et al. (2008).\nHowever, enhancements and updates to model inputs and time integration for\nPT-JPL were made to take advantage of contemporary gridded weather datasets,\nprovide consistency with other models, improve open water evaporation\nestimates, and account for advection over crop and wetland areas in\nsemiarid and arid environments. These changes include the use of\nLandsat surface reflectance and thermal radiation for calculating net\nradiation, photosynthetically active radiation, plant canopy and moisture\nvariables, and use of NLDAS, Spatial CIMIS, and gridMET weather data for\nestimating insolation and ASCE reference ET. Similar to the implementation\nof other OpenET models, estimation of daily and monthly time integrated\nET is based on the fraction of ASCE reference ET. Open water evaporation\nis estimated following a surface energy balance approach of Abdelrady\net al. (2016) that is specific for water bodies by accounting for water\nheat flux as opposed to soil heat flux.\n\n\n**OpenET Collection v2.0, pre-2000 (1985-1999)**\n\nNote: This collection should not be merged with the main v2.0\ncollection to compute long-term trends, annual totals, or difference from\naverage type calculations, unless care is taken to identify and fill gaps\nor missing data. This collection was generated using Landsat 5 data only,\nand will have more data gaps (both spatially and temporally) than the main\nv2.0 collection, which incorporates data from multiple Landsat satellites\n(5 & 7, 7 & 8, etc.). This pre-2000 collection may have long periods\nwithout data, especially in the winter months, and large regions without\ndata whatsoever in the earlier years (1985-1993).\n\n[Additional information](https://etdata.org/methods/)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-86,50]]},"temporal":{"interval":[["1984-10-01T00:00:00Z","1999-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_0_pre2000","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_ptjpl_conus_gridmet_monthly_v2_0_pre2000","rel":"related","title":"Run the example for projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_0_pre2000 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_ptjpl_conus_gridmet_monthly_v2_0_pre2000_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_ptjpl_conus_gridmet_monthly_v2_0_pre2000#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_0_pre2000/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_0_pre2000/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_1","title":"OpenET PT-JPL Monthly Evapotranspiration v2.1","description":"Priestley-Taylor Jet Propulsion Laboratory (PT-JPL).\n\nThe core formulation of the PT-JPL model within the OpenET framework has\nnot changed from the original formulation detailed in Fisher et al. (2008).\nHowever, enhancements and updates to model inputs and time integration for\nPT-JPL were made to take advantage of contemporary gridded weather datasets,\nprovide consistency with other models, improve open water evaporation\nestimates, and account for advection over crop and wetland areas in\nsemiarid and arid environments. These changes include the use of\nLandsat surface reflectance and thermal radiation for calculating net\nradiation, photosynthetically active radiation, plant canopy and moisture\nvariables, and use of NLDAS, Spatial CIMIS, and gridMET weather data for\nestimating insolation and ASCE reference ET. Similar to the implementation\nof other OpenET models, estimation of daily and monthly time integrated\nET is based on the fraction of ASCE reference ET. Open water evaporation\nis estimated following a surface energy balance approach of Abdelrady\net al. (2016) that is specific for water bodies by accounting for water\nheat flux as opposed to soil heat flux.\n**OpenET Collection v2.1**\n\nOpenET Collection v2.1 is a reprocessed and updated version of Collection\nv2.0, designed primarily to address known v2.0 issues, while also\nincorporating minor model improvements and input-data updates. It is\nexpected that there will be noticeable differences in ET between the two\ncollection versions for some places and times. Some of the updates and changes \ninclude:\n\n* Additional cloud screening and filtering to skip Landsat images with \nunmasked clouds and/or extensive snow cover.\n* Reprocessing to incorporate updates to the NLDAS-2 and GRIDMET\ninput meteorology datasets.\n* Incorporation of the USGS Annual NLCD product for all models that require \nland cover information.\n* Incorporation of the latest USDA CDL for all models that require crop type \ninformation.\n* Updates to the interpolation so monthly ET is only produced when all\ndays in the month have interpolated values (reducing “count=0” months in\ncloudy/snowy or low-coverage periods).\n* Application of an emissivity correction to address a known issue in\nLandsat LST data.\n\n[Additional information](https://etdata.org/methods/)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["2015-10-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_ptjpl_conus_gridmet_monthly_v2_1","rel":"related","title":"Run the example for projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_ptjpl_conus_gridmet_monthly_v2_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_ptjpl_conus_gridmet_monthly_v2_1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ptjpl/conus/gridmet/monthly/v2_1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/sims/conus/gridmet/monthly/v2_0","title":"OpenET SIMS Monthly Evapotranspiration v2.0","description":"Satellite Irrigation Management Support (SIMS).\n\nThe NASA Satellite Irrigation Management Support (SIMS) model was originally\ndeveloped to support satellite mapping of crop coefficients and\nevapotranspiration (ET) from irrigated lands and to increase access to this\ndata to support use in irrigation scheduling and regional assessment of\nagricultural water needs (Melton et al., 2012). SIMS uses a reflectance\nbased approach and incorporates the density coefficient described by Allen\nand Pereira (2009) and Pereira et al. (2020) to compute basal crop\ncoefficients for each 30 x 30 m pixel. The primary change from the SIMS\npublication (Pereira et al., 2020) for implementation in OpenET is the\nintegration of a gridded soil water balance model to account for soil\nevaporation following precipitation events.\n\nResults of the OpenET Phase I Intercomparison and Accuracy Assessment\n(Melton et al., 2022) showed that SIMS generally performed well for\ncropland sites during the growing season, but had a persistent low bias\nduring the winter months or other time periods with frequent precipitation.\nThis result was anticipated, since the reflectance-based approach used by\nSIMS is not sensitive to soil evaporation. To correct for this\nunderestimation, a soil water balance model based on FAO-56\n(Allen et al., 1998) was implemented on Google Earth Engine and driven\nwith gridded precipitation data from gridMET to estimate soil evaporation\ncoefficients. These coefficients were then combined with the basal crop\ncoefficients calculated by SIMS to calculate total crop evapotranspiration\nusing the dual crop coefficient approach. In addition, a modest positive\nbias was observed in the SIMS data for periods with low or sparse\nvegetative cover. To correct for this bias, updates were made to the\nequations that calculate the minimum basal crop coefficient to allow lower\nminimum basal crop coefficient values to be achieved. Full documentation\nof the SIMS model, current algorithms, and details and equations used in\nthe soil water balance model are included in the SIMS user manual.\n\nThe SIMS model calculates ET under well-watered conditions for the current\ncrop growth stage and condition as measured by the satellite data, and SIMS\nis generally expected to have a positive bias for deficit irrigated crops\nand croplands with short-term or intermittent crop water stress. At\npresent, SIMS is only implemented for croplands, and non-agricultural lands\nare masked out in this data collection. Future research will extend the\nvegetation density-crop coefficient approach used within SIMS to other land\ncover types.\n\n[Additional information](https://etdata.org/methods/)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-86,50]]},"temporal":{"interval":[["1999-10-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/sims/conus/gridmet/monthly/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_sims_conus_gridmet_monthly_v2_0","rel":"related","title":"Run the example for projects/openet/assets/sims/conus/gridmet/monthly/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_sims_conus_gridmet_monthly_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_sims_conus_gridmet_monthly_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_sims_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/sims/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/OpenET_SIMS_CONUS_GRIDMET_MONTHLY_v2_0.json","rel":"predecessor-version","title":"OpenET/SIMS/CONUS/GRIDMET/MONTHLY/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/sims/conus/gridmet/monthly/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/sims/conus/gridmet/monthly/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/sims/conus/gridmet/monthly/v2_0_pre2000","title":"OpenET SIMS Monthly Evapotranspiration v2.0 (1985-1999)","description":"Satellite Irrigation Management Support (SIMS).\n\nThe NASA Satellite Irrigation Management Support (SIMS) model was originally\ndeveloped to support satellite mapping of crop coefficients and\nevapotranspiration (ET) from irrigated lands and to increase access to this\ndata to support use in irrigation scheduling and regional assessment of\nagricultural water needs (Melton et al., 2012). SIMS uses a reflectance\nbased approach and incorporates the density coefficient described by Allen\nand Pereira (2009) and Pereira et al. (2020) to compute basal crop\ncoefficients for each 30 x 30 m pixel. The primary change from the SIMS\npublication (Pereira et al., 2020) for implementation in OpenET is the\nintegration of a gridded soil water balance model to account for soil\nevaporation following precipitation events.\n\nResults of the OpenET Phase I Intercomparison and Accuracy Assessment\n(Melton et al., 2022) showed that SIMS generally performed well for\ncropland sites during the growing season, but had a persistent low bias\nduring the winter months or other time periods with frequent precipitation.\nThis result was anticipated, since the reflectance-based approach used by\nSIMS is not sensitive to soil evaporation. To correct for this\nunderestimation, a soil water balance model based on FAO-56\n(Allen et al., 1998) was implemented on Google Earth Engine and driven\nwith gridded precipitation data from gridMET to estimate soil evaporation\ncoefficients. These coefficients were then combined with the basal crop\ncoefficients calculated by SIMS to calculate total crop evapotranspiration\nusing the dual crop coefficient approach. In addition, a modest positive\nbias was observed in the SIMS data for periods with low or sparse\nvegetative cover. To correct for this bias, updates were made to the\nequations that calculate the minimum basal crop coefficient to allow lower\nminimum basal crop coefficient values to be achieved. Full documentation\nof the SIMS model, current algorithms, and details and equations used in\nthe soil water balance model are included in the SIMS user manual.\n\nThe SIMS model calculates ET under well-watered conditions for the current\ncrop growth stage and condition as measured by the satellite data, and SIMS\nis generally expected to have a positive bias for deficit irrigated crops\nand croplands with short-term or intermittent crop water stress. At\npresent, SIMS is only implemented for croplands, and non-agricultural lands\nare masked out in this data collection. Future research will extend the\nvegetation density-crop coefficient approach used within SIMS to other land\ncover types.\n\n\n**OpenET Collection v2.0, pre-2000 (1985-1999)**\n\nNote: This collection should not be merged with the main v2.0\ncollection to compute long-term trends, annual totals, or difference from\naverage type calculations, unless care is taken to identify and fill gaps\nor missing data. This collection was generated using Landsat 5 data only,\nand will have more data gaps (both spatially and temporally) than the main\nv2.0 collection, which incorporates data from multiple Landsat satellites\n(5 & 7, 7 & 8, etc.). This pre-2000 collection may have long periods\nwithout data, especially in the winter months, and large regions without\ndata whatsoever in the earlier years (1985-1993).\n\n[Additional information](https://etdata.org/methods/)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-86,50]]},"temporal":{"interval":[["1984-10-01T00:00:00Z","1999-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/sims/conus/gridmet/monthly/v2_0_pre2000","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_sims_conus_gridmet_monthly_v2_0_pre2000","rel":"related","title":"Run the example for projects/openet/assets/sims/conus/gridmet/monthly/v2_0_pre2000 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_sims_conus_gridmet_monthly_v2_0_pre2000_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_sims_conus_gridmet_monthly_v2_0_pre2000#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/sims/conus/gridmet/monthly/v2_0_pre2000/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/sims/conus/gridmet/monthly/v2_0_pre2000/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/sims/conus/gridmet/monthly/v2_1","title":"OpenET SIMS Monthly Evapotranspiration v2.1","description":"Satellite Irrigation Management Support (SIMS).\n\nThe NASA Satellite Irrigation Management Support (SIMS) model was originally\ndeveloped to support satellite mapping of crop coefficients and\nevapotranspiration (ET) from irrigated lands and to increase access to this\ndata to support use in irrigation scheduling and regional assessment of\nagricultural water needs (Melton et al., 2012). SIMS uses a reflectance\nbased approach and incorporates the density coefficient described by Allen\nand Pereira (2009) and Pereira et al. (2020) to compute basal crop\ncoefficients for each 30 x 30 m pixel. The primary change from the SIMS\npublication (Pereira et al., 2020) for implementation in OpenET is the\nintegration of a gridded soil water balance model to account for soil\nevaporation following precipitation events.\n\nResults of the OpenET Phase I Intercomparison and Accuracy Assessment\n(Melton et al., 2022) showed that SIMS generally performed well for\ncropland sites during the growing season, but had a persistent low bias\nduring the winter months or other time periods with frequent precipitation.\nThis result was anticipated, since the reflectance-based approach used by\nSIMS is not sensitive to soil evaporation. To correct for this\nunderestimation, a soil water balance model based on FAO-56\n(Allen et al., 1998) was implemented on Google Earth Engine and driven\nwith gridded precipitation data from gridMET to estimate soil evaporation\ncoefficients. These coefficients were then combined with the basal crop\ncoefficients calculated by SIMS to calculate total crop evapotranspiration\nusing the dual crop coefficient approach. In addition, a modest positive\nbias was observed in the SIMS data for periods with low or sparse\nvegetative cover. To correct for this bias, updates were made to the\nequations that calculate the minimum basal crop coefficient to allow lower\nminimum basal crop coefficient values to be achieved. Full documentation\nof the SIMS model, current algorithms, and details and equations used in\nthe soil water balance model are included in the SIMS user manual.\n\nThe SIMS model calculates ET under well-watered conditions for the current\ncrop growth stage and condition as measured by the satellite data, and SIMS\nis generally expected to have a positive bias for deficit irrigated crops\nand croplands with short-term or intermittent crop water stress. At\npresent, SIMS is only implemented for croplands, and non-agricultural lands\nare masked out in this data collection. Future research will extend the\nvegetation density-crop coefficient approach used within SIMS to other land\ncover types.\n**OpenET Collection v2.1**\n\nOpenET Collection v2.1 is a reprocessed and updated version of Collection\nv2.0, designed primarily to address known v2.0 issues, while also\nincorporating minor model improvements and input-data updates. It is\nexpected that there will be noticeable differences in ET between the two\ncollection versions for some places and times. Some of the updates and changes \ninclude:\n\n* Additional cloud screening and filtering to skip Landsat images with \nunmasked clouds and/or extensive snow cover.\n* Reprocessing to incorporate updates to the NLDAS-2 and GRIDMET\ninput meteorology datasets.\n* Incorporation of the USGS Annual NLCD product for all models that require \nland cover information.\n* Incorporation of the latest USDA CDL for all models that require crop type \ninformation.\n* Updates to the interpolation so monthly ET is only produced when all\ndays in the month have interpolated values (reducing “count=0” months in\ncloudy/snowy or low-coverage periods).\n* Application of an emissivity correction to address a known issue in\nLandsat LST data.\n\n[Additional information](https://etdata.org/methods/)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["2015-10-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/sims/conus/gridmet/monthly/v2_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_sims_conus_gridmet_monthly_v2_1","rel":"related","title":"Run the example for projects/openet/assets/sims/conus/gridmet/monthly/v2_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_sims_conus_gridmet_monthly_v2_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_sims_conus_gridmet_monthly_v2_1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/sims/conus/gridmet/monthly/v2_1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/sims/conus/gridmet/monthly/v2_1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/ssebop/conus/gridmet/monthly/v2_0","title":"OpenET SSEBop Monthly Evapotranspiration v2.0","description":"Operational Simplified Surface Energy Balance (SSEBop).\n\nThe Operational Simplified Surface Energy Balance (SSEBop) model by Senay\net al. (2013, 2017) is a thermal-based simplified surface energy model for\nestimating actual ET based on the principles of satellite psychrometry\n(Senay 2018). The OpenET SSEBop implementation uses land surface temperature\n(Ts) from Landsat (Collection 2 Level-2 Science Products) with key model\nparameters (cold/wet-bulb reference, Tc, and surface psychrometric\nconstant, 1/dT) derived from a combination of observed surface temperature,\nnormalized difference vegetation index (NDVI), climatological average\n(1980-2017) daily maximum air temperature (Ta, 1-km) from Daymet, and\nnet radiation data from ERA-5. This model implementation uses the Google\nEarth Engine processing framework for connecting key SSEBop ET functions\nand algorithms together when generating both intermediate and aggregated ET\nresults. A detailed study and evaluation of the SSEBop model across CONUS\n(Senay et al., 2022) informs both cloud implementation and assessment for\nwater balance applications at broad scales. Notable model (v0.2.6)\nenhancements and performance against previous versions include additional\ncompatibility with Landsat 9 (launched Sep 2021), global model\nextensibility, and improved parameterization of SSEBop using\nFANO (Forcing and Normalizing Operation) to better estimate ET\nin all landscapes and all seasons regardless of vegetation cover density,\nthereby improving model accuracy by avoiding extrapolation of Tc to\nnon-calibration regions.\n\n[Additional information](https://etdata.org/methods/)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-86,50]]},"temporal":{"interval":[["1999-10-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ssebop/conus/gridmet/monthly/v2_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_ssebop_conus_gridmet_monthly_v2_0","rel":"related","title":"Run the example for projects/openet/assets/ssebop/conus/gridmet/monthly/v2_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_ssebop_conus_gridmet_monthly_v2_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_ssebop_conus_gridmet_monthly_v2_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/projects_openet_assets_ssebop_conus_gridmet_monthly_v2_0.json","rel":"latest-version","title":"projects/openet/assets/ssebop/conus/gridmet/monthly/v2_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/OpenET/OpenET_SSEBOP_CONUS_GRIDMET_MONTHLY_v2_0.json","rel":"predecessor-version","title":"OpenET/SSEBOP/CONUS/GRIDMET/MONTHLY/v2_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ssebop/conus/gridmet/monthly/v2_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ssebop/conus/gridmet/monthly/v2_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/ssebop/conus/gridmet/monthly/v2_0_pre2000","title":"OpenET SSEBop Monthly Evapotranspiration v2.0 (1985-1999)","description":"Operational Simplified Surface Energy Balance (SSEBop).\n\nThe Operational Simplified Surface Energy Balance (SSEBop) model by Senay\net al. (2013, 2017) is a thermal-based simplified surface energy model for\nestimating actual ET based on the principles of satellite psychrometry\n(Senay 2018). The OpenET SSEBop implementation uses land surface temperature\n(Ts) from Landsat (Collection 2 Level-2 Science Products) with key model\nparameters (cold/wet-bulb reference, Tc, and surface psychrometric\nconstant, 1/dT) derived from a combination of observed surface temperature,\nnormalized difference vegetation index (NDVI), climatological average\n(1980-2017) daily maximum air temperature (Ta, 1-km) from Daymet, and\nnet radiation data from ERA-5. This model implementation uses the Google\nEarth Engine processing framework for connecting key SSEBop ET functions\nand algorithms together when generating both intermediate and aggregated ET\nresults. A detailed study and evaluation of the SSEBop model across CONUS\n(Senay et al., 2022) informs both cloud implementation and assessment for\nwater balance applications at broad scales. Notable model (v0.2.6)\nenhancements and performance against previous versions include additional\ncompatibility with Landsat 9 (launched Sep 2021), global model\nextensibility, and improved parameterization of SSEBop using\nFANO (Forcing and Normalizing Operation) to better estimate ET\nin all landscapes and all seasons regardless of vegetation cover density,\nthereby improving model accuracy by avoiding extrapolation of Tc to\nnon-calibration regions.\n\n\n**OpenET Collection v2.0, pre-2000 (1985-1999)**\n\nNote: This collection should not be merged with the main v2.0\ncollection to compute long-term trends, annual totals, or difference from\naverage type calculations, unless care is taken to identify and fill gaps\nor missing data. This collection was generated using Landsat 5 data only,\nand will have more data gaps (both spatially and temporally) than the main\nv2.0 collection, which incorporates data from multiple Landsat satellites\n(5 & 7, 7 & 8, etc.). This pre-2000 collection may have long periods\nwithout data, especially in the winter months, and large regions without\ndata whatsoever in the earlier years (1985-1993).\n\n[Additional information](https://etdata.org/methods/)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-86,50]]},"temporal":{"interval":[["1984-10-01T00:00:00Z","1999-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ssebop/conus/gridmet/monthly/v2_0_pre2000","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_ssebop_conus_gridmet_monthly_v2_0_pre2000","rel":"related","title":"Run the example for projects/openet/assets/ssebop/conus/gridmet/monthly/v2_0_pre2000 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_ssebop_conus_gridmet_monthly_v2_0_pre2000_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_ssebop_conus_gridmet_monthly_v2_0_pre2000#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ssebop/conus/gridmet/monthly/v2_0_pre2000/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ssebop/conus/gridmet/monthly/v2_0_pre2000/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/openet/assets/ssebop/conus/gridmet/monthly/v2_1","title":"OpenET SSEBop Monthly Evapotranspiration v2.1","description":"Operational Simplified Surface Energy Balance (SSEBop).\n\nThe Operational Simplified Surface Energy Balance (SSEBop) model\n(Senay et al., 2013; 2023) is a thermal based approach for estimating\nactual evapotranspiration (ET) using the principles of satellite\npsychrometry (Senay, 2018). As one of the core models in the OpenET\nensemble, SSEBop provides a computationally efficient framework for large-\narea, operational ET mapping.\n\nThe OpenET Collection 2.1 implementation of SSEBop relies on two primary\ninputs:\n\n1. Land surface temperature from Landsat Collection 2 Level 2 Science\nProducts.\n2. Gridded reference ET from gridMET (Abotzoglou, 2013).\n\nKey model parameters, including the cold/wet bulb reference temperature\n(Tc) and the surface psychrometric constant (1/dT), are derived using a\ncombination of observed surface temperature, normalized difference\nvegetation index (NDVI), and ERA5 net radiation data\n(DOI: 10.5066/P9JBW6R9). These components are integrated within the Google\nEarth Engine processing environment, which links the full suite of SSEBop\nalgorithms to generate both intermediate outputs and aggregated ET\nproducts. Extensive evaluation across the conterminous United States\n(Senay et al., 2022; Volk et al., 2024; Ji et al., 2025; Khand et al, 2025)\nhas guided the cloud-based implementation and demonstrated the model’s\nutility for applications such as crop water use assessment and regional\nwater budget analysis.\n\nFor OpenET Collection 2.1, SSEBop model v0.7.1 (Senay et al., 2026)\nincludes a notable enhancement: an improved implementation of the Forcing\nand Normalizing Operation (FANO) equation for determining Tc. This update\nstrengthens the model’s ability to represent wet surfaces with low NDVI,\nimproving performance in areas where vegetation signals are weak or mixed,\nparticularly over farmlands and grasslands during sparse canopy cover.\n**OpenET Collection v2.1**\n\nOpenET Collection v2.1 is a reprocessed and updated version of Collection\nv2.0, designed primarily to address known v2.0 issues, while also\nincorporating minor model improvements and input-data updates. It is\nexpected that there will be noticeable differences in ET between the two\ncollection versions for some places and times. Some of the updates and changes \ninclude:\n\n* Additional cloud screening and filtering to skip Landsat images with \nunmasked clouds and/or extensive snow cover.\n* Reprocessing to incorporate updates to the NLDAS-2 and GRIDMET\ninput meteorology datasets.\n* Incorporation of the USGS Annual NLCD product for all models that require \nland cover information.\n* Incorporation of the latest USDA CDL for all models that require crop type \ninformation.\n* Updates to the interpolation so monthly ET is only produced when all\ndays in the month have interpolated values (reducing “count=0” months in\ncloudy/snowy or low-coverage periods).\n* Application of an emissivity correction to address a known issue in\nLandsat LST data.\n\n[Additional information](https://etdata.org/methods/)","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-126,25,-66,50]]},"temporal":{"interval":[["2015-10-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ssebop/conus/gridmet/monthly/v2_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenET/projects_openet_assets_ssebop_conus_gridmet_monthly_v2_1","rel":"related","title":"Run the example for projects/openet/assets/ssebop/conus/gridmet/monthly/v2_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenET/projects_openet_assets_ssebop_conus_gridmet_monthly_v2_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_openet_assets_ssebop_conus_gridmet_monthly_v2_1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ssebop/conus/gridmet/monthly/v2_1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/openet/assets/ssebop/conus/gridmet/monthly/v2_1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenLandMap/CLM/CLM_LST_MOD11A2-DAYNIGHT_M/v01","title":"OpenLandMap Long-term Land Surface Temperature Monthly Day-Night Difference","description":"Long-term MODIS LST day-time and night-time\ndifferences at 1 km based on the 2000-2017 time series\n\nDerived using the [data.table package and quantile function in R](https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/MOD11A2).\nFor more info about the MODIS LST product see [this page](https://lpdaac.usgs.gov/products/mod11a2v006/).\nAntarctica is not included.\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/CLM/CLM_LST_MOD11A2-DAYNIGHT_M/v01","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_CLM_CLM_LST_MOD11A2-DAYNIGHT_M_v01","rel":"related","title":"Run the example for OpenLandMap/CLM/CLM_LST_MOD11A2-DAYNIGHT_M/v01 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_CLM_CLM_LST_MOD11A2-DAYNIGHT_M_v01_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_CLM_CLM_LST_MOD11A2-DAYNIGHT_M_v01#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.1420114","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/CLM/CLM_LST_MOD11A2-DAYNIGHT_M/v01/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenLandMap/CLM/CLM_LST_MOD11A2-DAY_M/v01","title":"OpenLandMap Long-term Land Surface Temperature Daytime Monthly Median","description":"Land Surface Temperature daytime monthly mean value 2000-2017.\n\nDerived using the [data.table package and quantile function in R](https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/MOD11A2).\nFor more info about the MODIS LST product see [this page](https://lpdaac.usgs.gov/products/mod11a2v006/).\nAntarctica is not included.\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/CLM/CLM_LST_MOD11A2-DAY_M/v01","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_CLM_CLM_LST_MOD11A2-DAY_M_v01","rel":"related","title":"Run the example for OpenLandMap/CLM/CLM_LST_MOD11A2-DAY_M/v01 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_CLM_CLM_LST_MOD11A2-DAY_M_v01_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_CLM_CLM_LST_MOD11A2-DAY_M_v01#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.1420114","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/CLM/CLM_LST_MOD11A2-DAY_M/v01/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenLandMap/CLM/CLM_LST_MOD11A2-DAY_SD/v01","title":"OpenLandMap Long-term Land Surface Temperature Daytime Monthly Standard Deviation","description":"Long-term MODIS LST day-time and night-time temperatures standard deviation\nat 1 km based on the 2000-2017 time series.\n\nDerived using the [data.table package and quantile function in R](https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/MOD11A2).\nFor more info about the MODIS LST product see [this page](https://lpdaac.usgs.gov/products/mod11a2v006/).\nAntarctica is not included.\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/CLM/CLM_LST_MOD11A2-DAY_SD/v01","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_CLM_CLM_LST_MOD11A2-DAY_SD_v01","rel":"related","title":"Run the example for OpenLandMap/CLM/CLM_LST_MOD11A2-DAY_SD/v01 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_CLM_CLM_LST_MOD11A2-DAY_SD_v01_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_CLM_CLM_LST_MOD11A2-DAY_SD_v01#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.1420114","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/CLM/CLM_LST_MOD11A2-DAY_SD/v01/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenLandMap/CLM/CLM_PRECIPITATION_SM2RAIN_M/v01","title":"OpenLandMap Precipitation Monthly","description":"Monthly precipitation in mm at 1 km resolution based on [SM2RAIN-ASCAT\n2007-2018](https://doi.org/10.5281/zenodo.2615278),\nIMERG, CHELSA Climate, and WorldClim.\n\n[Downscaled to 1 km resolution using gdalwarp](https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/clim1km)\n(cubic splines) and an average between [WorldClim](https://www.worldclim.org/data/worldclim21.html),\n[CHELSA Climate](https://chelsa-climate.org/),\nand [IMERG monthly product](https://gpm.nasa.gov/data/imerg) (see, e.g, \"3B-MO-L.GIS.IMERG.20180601.V05B.tif\").\n3x higher weight is given to the SM2RAIN-ASCAT data since it assumed to be more accurate. Processing steps are available\n[here](https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/clim1km).\nAntarctica is not included.\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/CLM/CLM_PRECIPITATION_SM2RAIN_M/v01","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_CLM_CLM_PRECIPITATION_SM2RAIN_M_v01","rel":"related","title":"Run the example for OpenLandMap/CLM/CLM_PRECIPITATION_SM2RAIN_M/v01 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_CLM_CLM_PRECIPITATION_SM2RAIN_M_v01_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_CLM_CLM_PRECIPITATION_SM2RAIN_M_v01#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/ODbL-1.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.1435912","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/CLM/CLM_PRECIPITATION_SM2RAIN_M/v01/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenLandMap/PNV/PNV_BIOME-TYPE_BIOME00K_C/v01","title":"OpenLandMap Potential Distribution of Biomes","description":"Potential Natural Vegetation biomes global predictions of classes\n(based on predictions using the BIOMES 6000 dataset's 'current biomes' category.)\n\nPotential Natural Vegetation (PNV) is the vegetation cover in equilibrium with climate\nthat would exist at a given location non-impacted by human activities. PNV is useful for raising\npublic awareness about land degradation and for estimating land potential.\nThis dataset contains results of predictions of\n- (1) global distribution of biomes based on the BIOME 6000 data set (8057 modern pollen-based site reconstructions),\n- (2) distribution of forest tree species in Europe based on detailed occurrence records (1,546,435 ground observations), and\n- (3) global monthly Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) values (30,301 randomly-sampled points).\n\nTo report an issue or artifact in data, please use [this link](https://github.com/envirometrix/PNVmaps/issues).\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2002-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/PNV/PNV_BIOME-TYPE_BIOME00K_C/v01","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_PNV_PNV_BIOME-TYPE_BIOME00K_C_v01","rel":"related","title":"Run the example for OpenLandMap/PNV/PNV_BIOME-TYPE_BIOME00K_C/v01 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_PNV_PNV_BIOME-TYPE_BIOME00K_C_v01_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_PNV_PNV_BIOME-TYPE_BIOME00K_C_v01#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/ODbL-1.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7910/DVN/QQHCIK","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/PNV/PNV_BIOME-TYPE_BIOME00K_C/v01/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenLandMap/PNV/PNV_FAPAR_PROBA-V_D/v01","title":"OpenLandMap Potential FAPAR Monthly","description":"Potential Natural Vegetation FAPAR predicted monthly median (based on PROB-V FAPAR 2014-2017).\n[Description](https://gitlab.com/openlandmap/global-layers/#potential-natural-vegetation).\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2002-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/PNV/PNV_FAPAR_PROBA-V_D/v01","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_PNV_PNV_FAPAR_PROBA-V_D_v01","rel":"related","title":"Run the example for OpenLandMap/PNV/PNV_FAPAR_PROBA-V_D/v01 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_PNV_PNV_FAPAR_PROBA-V_D_v01_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_PNV_PNV_FAPAR_PROBA-V_D_v01#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/ODbL-1.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.7910/DVN/QQHCIK","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/PNV/PNV_FAPAR_PROBA-V_D/v01/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenLandMap/SOL/SOL_BULKDENS-FINEEARTH_USDA-4A1H_M/v02","title":"OpenLandMap Soil Bulk Density","description":"Soil bulk density (fine earth) 10 x kg / m<sup>3</sup> at 6 standard\ndepths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution.\n\nProcessing steps are described in detail [here](https://gitlab.com/openlandmap/global-layers/LandGISmaps/tree/master/soil).\nAntarctica is not included.\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_BULKDENS-FINEEARTH_USDA-4A1H_M/v02","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_SOL_SOL_BULKDENS-FINEEARTH_USDA-4A1H_M_v02","rel":"related","title":"Run the example for OpenLandMap/SOL/SOL_BULKDENS-FINEEARTH_USDA-4A1H_M/v02 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_BULKDENS-FINEEARTH_USDA-4A1H_M_v02_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_BULKDENS-FINEEARTH_USDA-4A1H_M_v02#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.1475970","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_BULKDENS-FINEEARTH_USDA-4A1H_M/v02/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenLandMap/SOL/SOL_CLAY-WFRACTION_USDA-3A1A1A_M/v02","title":"OpenLandMap Clay Content","description":"Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60,\n100 and 200 cm) at 250 m resolution\n\nBased on machine learning predictions from global compilation of soil profiles and samples. Processing\nsteps are described in detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil).\nAntarctica is not included.\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_CLAY-WFRACTION_USDA-3A1A1A_M/v02","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02","rel":"related","title":"Run the example for OpenLandMap/SOL/SOL_CLAY-WFRACTION_USDA-3A1A1A_M/v02 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.1476854","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_CLAY-WFRACTION_USDA-3A1A1A_M/v02/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenLandMap/SOL/SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P/v01","title":"OpenLandMap Predicted Hapludalfs Probability","description":"Predicted USDA soil great groups at 250 m (probabilities).\n\nDistribution of the USDA soil great groups based on machine learning predictions\nfrom global compilation of soil profiles. To learn more about soil great groups\nplease refer to the [Illustrated Guide to Soil Taxonomy - NRCS - USDA](https://www.nrcs.usda.gov/sites/default/files/2022-06/Illustrated_Guide_to_Soil_Taxonomy.pdf).\n\n* Processing steps are described in detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil)\n* Antarctica is not included.\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P/v01","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P_v01","rel":"related","title":"Run the example for OpenLandMap/SOL/SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P/v01 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P_v01_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P_v01#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.1476844","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P/v01/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenLandMap/SOL/SOL_GRTGROUP_USDA-SOILTAX_C/v01","title":"OpenLandMap USDA Soil Taxonomy Great Groups","description":"Predicted USDA soil great group probabilities at 250m.\n\nDistribution of the USDA soil great groups based on machine learning predictions\nfrom global compilation of soil profiles. To learn more about soil great groups\nplease refer to the [Illustrated Guide to Soil Taxonomy - NRCS - USDA](https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download/?cid=stelprdb1247203.pdf).\n\n* Processing steps are described in detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil)\n* Antarctica is not included.\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_GRTGROUP_USDA-SOILTAX_C/v01","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01","rel":"related","title":"Run the example for OpenLandMap/SOL/SOL_GRTGROUP_USDA-SOILTAX_C/v01 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.1476844","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_GRTGROUP_USDA-SOILTAX_C/v01/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenLandMap/SOL/SOL_ORGANIC-CARBON_USDA-6A1C_M/v02","title":"OpenLandMap Soil Organic Carbon Content","description":"Soil organic carbon content in x 5 g / kg at 6 standard depths\n(0, 10, 30, 60, 100 and 200 cm) at 250 m resolution\n\nPredicted from a global compilation of soil points. Processing steps are described\nin detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil).\nAntarctica is not included.\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_ORGANIC-CARBON_USDA-6A1C_M/v02","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02","rel":"related","title":"Run the example for OpenLandMap/SOL/SOL_ORGANIC-CARBON_USDA-6A1C_M/v02 in the Earth Engine Code 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cm) at 250 m resolution\n\nProcessing steps are described in detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil).\nAntarctica is not included.\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_PH-H2O_USDA-4C1A2A_M/v02","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02","rel":"related","title":"Run the example for OpenLandMap/SOL/SOL_PH-H2O_USDA-4C1A2A_M/v02 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.1475459","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_PH-H2O_USDA-4C1A2A_M/v02/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenLandMap/SOL/SOL_SAND-WFRACTION_USDA-3A1A1A_M/v02","title":"OpenLandMap Sand Content","description":"Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution\n\nBased on machine learning predictions from global compilation of soil profiles and samples.\nProcessing steps are described in detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil).\nAntarctica is not included.\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_SAND-WFRACTION_USDA-3A1A1A_M/v02","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_SOL_SOL_SAND-WFRACTION_USDA-3A1A1A_M_v02","rel":"related","title":"Run the example for OpenLandMap/SOL/SOL_SAND-WFRACTION_USDA-3A1A1A_M/v02 in the Earth Engine Code 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for 6 soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m\n\nDerived from predicted soil texture fractions using the soiltexture package in R.\nProcessing steps are described in detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil).\nAntarctica is not included.\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_TEXTURE-CLASS_USDA-TT_M/v02","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_SOL_SOL_TEXTURE-CLASS_USDA-TT_M_v02","rel":"related","title":"Run the example for OpenLandMap/SOL/SOL_TEXTURE-CLASS_USDA-TT_M/v02 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_TEXTURE-CLASS_USDA-TT_M_v02_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_TEXTURE-CLASS_USDA-TT_M_v02#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.1475451","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_TEXTURE-CLASS_USDA-TT_M/v02/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"OpenLandMap/SOL/SOL_WATERCONTENT-33KPA_USDA-4B1C_M/v01","title":"OpenLandMap Soil Water Content at 33kPa (Field Capacity)","description":"Soil water content (volumetric %) for 33kPa and 1500kPa suctions\npredicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution\n\nTraining points are based on a global compilation of soil profiles:\n\n* [USDA NCSS](https://ncsslabdatamart.sc.egov.usda.gov/)\n* [AfSPDB](https://www.isric.org/projects/africa-soil-profiles-database-afsp)\n* [ISRIC WISE](https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/a351682c-330a-4995-a5a1-57ad160e621c)\n* [EGRPR](http://egrpr.esoil.ru/)\n* [SPADE](https://esdac.jrc.ec.europa.eu/content/soil-profile-analytical-database-2)\n* [CanNPDB](https://open.canada.ca/data/en/dataset/6457fad6-b6f5-47a3-9bd1-ad14aea4b9e0)\n* [UNSODA](https://data.nal.usda.gov/dataset/unsoda-20-unsaturated-soil-hydraulic-database-database-and-program-indirect-methods-estimating-unsaturated-hydraulic-properties)\n* [SWIG](https://doi.pangaea.de/10.1594/PANGAEA.885492)\n* [HYBRAS](https://www.cprm.gov.br/en/Hydrology/Research-and-Innovation/HYBRAS-4208.html)\n* [HydroS](https://doi.org/10.4228/ZALF.2003.273)\n\nData import steps are available [here](https://gitlab.com/openlandmap/compiled-ess-point-data-sets).\nSpatial prediction steps are described in detail [here](https://gitlab.com/openlandmap/global-layers/tree/master/soil/soil_water).\nNote: these are actually measured and mapped soil content values; no Pedo-Transfer-Functions\nhave been used (except to fill in the missing NCSS bulk densities). Available\nwater capacity in mm (derived as a difference between field capacity and wilting\npoint multiplied by layer thickness) per layer is available [here](https://doi.org/10.5281/zenodo.2629148).\nAntarctica is not included.\n\nTo access and visualize maps outside of Earth Engine, use [this page](https://opengeohub.org/about-openlandmap).\n\nIf you discover a bug, artifact or inconsistency in the LandGIS maps\nor if you have a question please use the following channels:\n\n *  [Technical issues and questions about the code](https://gitlab.com/openlandmap/global-layers/issues)\n *  [General questions and comments](https://disqus.com/home/forums/landgis/)\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z","2018-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_WATERCONTENT-33KPA_USDA-4B1C_M/v01","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/OpenLandMap/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01","rel":"related","title":"Run the example for OpenLandMap/SOL/SOL_WATERCONTENT-33KPA_USDA-4B1C_M/v01 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5281/zenodo.2629589","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/OpenLandMap/SOL/SOL_WATERCONTENT-33KPA_USDA-4B1C_M/v01/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Oxford/MAP/EVI_5km_Monthly","title":"Oxford MAP EVI: Malaria Atlas Project Gap-Filled Enhanced Vegetation Index [deprecated]","description":"The underlying dataset for this Enhanced Vegetation Index (EVI)\nproduct is MODIS BRDF-corrected imagery (MCD43B4), which was gap-filled\nusing the approach outlined in Weiss et al. 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(2014) to eliminate missing data caused by factors\nsuch as cloud cover. Gap-free outputs were then aggregated temporally and\nspatially to produce the monthly &asymp;5km product.\n\nThis dataset was produced by Harry Gibson and Daniel Weiss of the\nMalaria Atlas Project (Big Data Institute, University of Oxford,\nUnited Kingdom, [https://malariaatlas.org/](https://malariaatlas.org/)).\n","deprecated":true,"license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-03-01T00:00:00Z","2015-06-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/LST_Day_5km_Monthly","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Oxford/Oxford_MAP_LST_Day_5km_Monthly","rel":"related","title":"Run the example for Oxford/MAP/LST_Day_5km_Monthly in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Oxford/Oxford_MAP_LST_Day_5km_Monthly_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_LST_Day_5km_Monthly#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_LST_Day_v061_1km_Monthly.json","rel":"latest-version","title":"projects/malariaatlasproject/assets/LST_Day_v061/1km/Monthly","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_LST_Day_v061_1km_Monthly.json","rel":"successor-version","title":"projects/malariaatlasproject/assets/LST_Day_v061/1km/Monthly","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/LST_Day_5km_Monthly/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/LST_Day_5km_Monthly/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Oxford/MAP/LST_Night_5km_Monthly","title":"Oxford MAP LST: Malaria Atlas Project Gap-Filled Nighttime Land Surface Temperature [deprecated]","description":"The underlying dataset for this nighttime product is MODIS land surface\ntemperature data (MOD11A2), which was gap-filled using the approach outlined\nin Weiss et al. (2014) to eliminate missing data caused by factors such as\ncloud cover. Gap-free outputs were then aggregated temporally and spatially\nto produce the monthly &asymp;5km product.\n\nThis dataset was produced by Harry Gibson and Daniel Weiss of the\nMalaria Atlas Project (Big Data Institute, University of Oxford,\nUnited Kingdom, [https://malariaatlas.org/](https://malariaatlas.org/)).\n","deprecated":true,"license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-03-01T00:00:00Z","2015-06-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/LST_Night_5km_Monthly","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Oxford/Oxford_MAP_LST_Night_5km_Monthly","rel":"related","title":"Run the example for Oxford/MAP/LST_Night_5km_Monthly in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Oxford/Oxford_MAP_LST_Night_5km_Monthly_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_LST_Night_5km_Monthly#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_LST_Night_v061_1km_Monthly.json","rel":"latest-version","title":"projects/malariaatlasproject/assets/LST_Night_v061/1km/Monthly","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_LST_Night_v061_1km_Monthly.json","rel":"successor-version","title":"projects/malariaatlasproject/assets/LST_Night_v061/1km/Monthly","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/LST_Night_5km_Monthly/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/LST_Night_5km_Monthly/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Oxford/MAP/TCB_5km_Monthly","title":"Oxford MAP TCB: Malaria Atlas Project Gap-Filled Tasseled Cap Brightness [deprecated]","description":"This gap-filled Tasseled Cap Brightness (TCB) dataset was created by\napplying the tasseled-cap equations defined in Lobser and Cohen (2007) to\nMODIS BRDF-corrected imagery (MCD43B4). The resulting data were gap-filled\nusing the approach outlined in Weiss et al. (2014) to eliminate missing\ndata caused by factors such as cloud cover, and then the data were\naggregated temporally and spatially to produce the monthly &asymp;5km product.\n\nThis dataset was produced by Harry Gibson and Daniel Weiss of the\n[Malaria Atlas Project](https://malariaatlas.org/).\n","deprecated":true,"license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-02-01T00:00:00Z","2015-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/TCB_5km_Monthly","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Oxford/Oxford_MAP_TCB_5km_Monthly","rel":"related","title":"Run the example for Oxford/MAP/TCB_5km_Monthly in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Oxford/Oxford_MAP_TCB_5km_Monthly_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_TCB_5km_Monthly#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_TCB_v061_1km_Monthly.json","rel":"latest-version","title":"projects/malariaatlasproject/assets/TCB_v061/1km/Monthly","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_TCB_v061_1km_Monthly.json","rel":"successor-version","title":"projects/malariaatlasproject/assets/TCB_v061/1km/Monthly","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/TCB_5km_Monthly/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/TCB_5km_Monthly/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Oxford/MAP/TCW_5km_Monthly","title":"Oxford MAP TCW: Malaria Atlas Project Gap-Filled Tasseled Cap Wetness [deprecated]","description":"This gap-filled Tasseled Cap Wetness (TCW) dataset was created by applying\nthe tasseled-cap equations defined in Lobser and Cohen (2007) to\nMODIS BRDF-corrected imagery (MCD43B4). The resulting data were\ngap-filled using the approach outlined in Weiss et al. (2014) to eliminate\nmissing data caused by factors such as cloud cover, and then the data were\naggregated temporally and spatially to produce the monthly &asymp;5km product.\n\nThis dataset was produced by Harry Gibson and Daniel Weiss of the\nMalaria Atlas Project (Big Data Institute, University of Oxford,\nUnited Kingdom, [https://malariaatlas.org/](https://malariaatlas.org/)).\n","deprecated":true,"license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-02-01T00:00:00Z","2015-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/TCW_5km_Monthly","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Oxford/Oxford_MAP_TCW_5km_Monthly","rel":"related","title":"Run the example for Oxford/MAP/TCW_5km_Monthly in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Oxford/Oxford_MAP_TCW_5km_Monthly_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_TCW_5km_Monthly#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_TCW_v061_1km_Monthly.json","rel":"latest-version","title":"projects/malariaatlasproject/assets/TCW_v061/1km/Monthly","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_TCW_v061_1km_Monthly.json","rel":"successor-version","title":"projects/malariaatlasproject/assets/TCW_v061/1km/Monthly","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/TCW_5km_Monthly/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/TCW_5km_Monthly/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Oxford/MAP/accessibility_to_cities_2015_v1_0","title":"Accessibility to Cities 2015 [deprecated]","description":"This global accessibility map enumerates land-based\ntravel time to the nearest densely-populated area for all areas\nbetween 85 degrees north and 60 degrees south for a nominal year 2015.\n\nDensely-populated areas are defined as contiguous areas\nwith 1,500 or more inhabitants per square kilometer or a majority\nof built-up land cover types coincident with a population center\nof at least 50,000 inhabitants.\n\nThis map was produced through\na collaboration between the University of Oxford Malaria Atlas\nProject (MAP), Google, the European Union Joint Research Centre\n(JRC), and the University of Twente, Netherlands. The underlying\ndatasets used to produce the map include roads (comprising the\nfirst ever global-scale use of Open Street Map and Google roads\ndatasets), railways, rivers, lakes, oceans, topographic conditions\n(slope and elevation), landcover types, and national borders.\n\nThese datasets were each allocated a speed or speeds of travel in terms\nof time to cross each pixel of that type. The datasets were then\ncombined to produce a &ldquo;friction surface&rdquo;, a map where\nevery pixel is allocated a nominal overall speed of travel based\non the types occurring within that pixel. Least-cost-path algorithms\n(running in Google Earth Engine and, for high-latitude areas, in\nR) were used in conjunction with this friction surface to calculate\nthe time of travel from all locations to the nearest city (by travel\ntime). Cities were determined using the high-density-cover product\ncreated by the Global Human Settlement Project.\n\nEach pixel in the resultant accessibility map thus represents the modeled shortest\ntime from that location to a city.\n\nSource dataset credits are as described in the accompanying paper.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2016-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/accessibility_to_cities_2015_v1_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Oxford/Oxford_MAP_accessibility_to_cities_2015_v1_0","rel":"related","title":"Run the example for Oxford/MAP/accessibility_to_cities_2015_v1_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Oxford/Oxford_MAP_accessibility_to_cities_2015_v1_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_accessibility_to_cities_2015_v1_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_accessibility_to_cities_2015_v1_0.json","rel":"latest-version","title":"projects/malariaatlasproject/assets/accessibility/accessibility_to_cities/2015_v1_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_accessibility_to_cities_2015_v1_0.json","rel":"successor-version","title":"projects/malariaatlasproject/assets/accessibility/accessibility_to_cities/2015_v1_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/accessibility_to_cities_2015_v1_0/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Oxford/MAP/accessibility_to_healthcare_2019","title":"Accessibility to Healthcare 2019 [deprecated]","description":"This global accessibility map enumerates land-based travel time (in\nminutes) to the nearest hospital or clinic for all areas between 85\ndegrees north and 60 degrees south for a nominal year 2019. It also\nincludes \"walking-only\" travel time, using non-motorized means of\ntransportation only.\n\nMajor data collection efforts underway by OpenStreetMap, Google Maps, and\nacademic researchers have been harnessed to compile the most complete\ncollection of healthcare facility locations to date. This map was\nproduced through a collaboration between MAP (University of Oxford),\nTelethon Kids Institute (Perth, Australia), Google, and the University\nof Twente, Netherlands.\n\nThis project builds on previous work published by Weiss et al 2018\n([doi:10.1038/nature25181](https://doi.org/10.1038/nature25181)).\nWeiss et al (2018) utilised datasets for roads\n(comprising the first ever global-scale use of Open Street Map and Google\nroads datasets), railways, rivers, lakes, oceans, topographic conditions\n(slope and elevation), landcover types, and national borders. These\ndatasets were each allocated a speed or speeds of travel in terms of time\nto cross each pixel of that type. The datasets were then combined to\nproduce a \"friction surface\": a map where every pixel is allocated a\nnominal overall speed of travel based on the types occurring within that\npixel. For the current project, an updated friction surface was created to\nincorporate recent improvements within OSM roads data.\n\nLeast-cost-path algorithms (run in Google Earth Engine and, for\nhigh-latitude areas, in R) were used in conjunction with this friction\nsurface to calculate the time of travel from all locations to the nearest\n(in time) healthcare facility. The healthcare facilities dataset utilized\nlocation data from two of the largest global databases: (1) OSM data that\nwas collated and made available for download at\n[www.healthsites.io](https://www.healthsites.io/); and (2) data\nextracted from Google Maps. The global datasets were augmented with\ncontinental-scale facility locations that were recently published for\nAfrica and Australia. To facilitate comparisons between data sources, only\nfacilities defined as hospitals and clinics were used. Multiple points\nfound within the same pixel were merged to match the resolution of the\nanalysis as defined by the selected gridded representation of the Earth's\nsurface. Each pixel in the resultant accessibility map thus represents the\nmodelled shortest time (in minutes) from that location to a hospital or\nclinic.\n\nSource dataset credits are as described in the accompanying paper.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2019-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/accessibility_to_healthcare_2019","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Oxford/Oxford_MAP_accessibility_to_healthcare_2019","rel":"related","title":"Run the example for Oxford/MAP/accessibility_to_healthcare_2019 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Oxford/Oxford_MAP_accessibility_to_healthcare_2019_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_accessibility_to_healthcare_2019#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_accessibility_to_healthcare_2019.json","rel":"latest-version","title":"projects/malariaatlasproject/assets/accessibility/accessibility_to_healthcare/2019","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_accessibility_to_healthcare_2019.json","rel":"successor-version","title":"projects/malariaatlasproject/assets/accessibility/accessibility_to_healthcare/2019","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/accessibility_to_healthcare_2019/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Oxford/MAP/friction_surface_2015_v1_0","title":"Global Friction Surface 2015 [deprecated]","description":"This global friction surface enumerates land-based travel\nspeed for all land pixels between 85 degrees north and 60 degrees\nsouth for a nominal year 2015.\n\nThis map was produced through\na collaboration between the University of Oxford Malaria Atlas\nProject (MAP), Google, the European Union Joint Research Centre\n(JRC), and the University of Twente, Netherlands. The underlying\ndatasets used to produce the map include roads (comprising the\nfirst ever global-scale use of Open Street Map and Google roads\ndatasets), railways, rivers, lakes, oceans, topographic conditions\n(slope and elevation), landcover types, and national borders.\n\nThese datasets were each allocated a speed or speeds of travel in terms\nof time to cross each pixel of that type. The datasets were then\ncombined to produce this &ldquo;friction surface&rdquo;, a map\nwhere every pixel is allocated a nominal overall speed of travel\nbased on the types occurring within that pixel, with the fastest\ntravel mode intersecting the pixel being used to determine the\nspeed of travel in that pixel (with some exceptions such as national\nboundaries, which have the effect of imposing a travel time penalty).\n\nThis map represents the travel speed from this allocation process, expressed\nin units of minutes required to travel one meter. It forms the\nunderlying dataset behind the global accessibility map described\nin the referenced paper.\n\n'Source dataset credits are as described in the accompanying paper.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2016-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/friction_surface_2015_v1_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Oxford/Oxford_MAP_friction_surface_2015_v1_0","rel":"related","title":"Run the example for Oxford/MAP/friction_surface_2015_v1_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Oxford/Oxford_MAP_friction_surface_2015_v1_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_friction_surface_2015_v1_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_friction_surface_2019_v5_1.json","rel":"latest-version","title":"projects/malariaatlasproject/assets/accessibility/friction_surface/2019_v5_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/Oxford/Oxford_MAP_friction_surface_2019.json","rel":"successor-version","title":"Oxford/MAP/friction_surface_2019","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_friction_surface_2015_v1_0.json","rel":"successor-version","title":"projects/malariaatlasproject/assets/accessibility/friction_surface/2015_v1_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/friction_surface_2015_v1_0/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Oxford/MAP/friction_surface_2019","title":"Global Friction Surface 2019 [deprecated]","description":"This global friction surface enumerates land-based travel speed for all land pixels between 85 degrees north and 60 degrees south for a nominal year 2019.  It also includes \"walking-only\" travel speed, using non-motorized means of transportation only.\nThis map was produced through a collaboration between MAP (University of Oxford), Telethon Kids Institute (Perth, Australia), Google, and the University of Twente, Netherlands.\nThis project builds on previous work published by Weiss et al 2018 ([doi:10.1038/nature25181](https://doi.org/10.1038/nature25181)). Weiss et al (2018) utilised datasets for roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a \"friction surface\"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. For the current project, an updated friction surface was created to incorporate recent improvements within OSM roads data.\nDifferences between this friction surface and the 2015 version (Weiss et al. 2018) are not necessarily indicative of changes in infrastructure (e.g., new roads being built). Such discrepancies are far more likely to be associated with improved data quality, in particular updates made to OSM road coverage. As a result, comparisons between the friction surfaces and resulting travel time maps should be done cautiously and generally not interpreted as representing changes in access over time.\nThis map represents the travel speed from this allocation process, expressed in units of minutes required to travel one meter. It forms the underlying dataset behind the global healthcare accessibility map described in the referenced paper.\n\nSource dataset credits are as described in the accompanying paper.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2019-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/friction_surface_2019","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Oxford/Oxford_MAP_friction_surface_2019","rel":"related","title":"Run the example for Oxford/MAP/friction_surface_2019 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Oxford/Oxford_MAP_friction_surface_2019_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_friction_surface_2019#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_friction_surface_2019_v5_1.json","rel":"latest-version","title":"projects/malariaatlasproject/assets/accessibility/friction_surface/2019_v5_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/Oxford/Oxford_MAP_friction_surface_2015_v1_0.json","rel":"predecessor-version","title":"Oxford/MAP/friction_surface_2015_v1_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_friction_surface_2019_v5_1.json","rel":"successor-version","title":"projects/malariaatlasproject/assets/accessibility/friction_surface/2019_v5_1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Oxford/MAP/friction_surface_2019/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"RUB/RUBCLIM/LCZ/global_lcz_map/latest","title":"Global map of Local Climate Zones, latest version","description":"Since their introduction in 2012, Local Climate Zones (LCZs) emerged as\na new standard for characterizing urban landscapes, providing a holistic\nclassification approach that takes into account micro-scale land-cover and\nassociated physical properties.\n\nThis global map of Local Climate Zones, at 100m pixel size and\nrepresentative for the nominal year 2018, is derived from multiple earth\nobservation datasets and expert LCZ class labels. LCZ_Filter is the\nrecommended band for most users. The other classification band, LCZ, is\nonly provided as it is used to calculate the LCZ_Probability band.\n\nThe LCZ scheme complements other land use / land cover schemes by\nits focus on urban and rural landscape types, which can be described by\nany of the 17 classes in the LCZ scheme. Out of the 17 LCZ classes, 10\nreflect the 'built' environment, and each LCZ type is associated with\ngeneric numerical descriptions of key urban canopy parameters critical to\nmodel atmospheric responses to urbanisation.  In addition, since LCZs were\noriginally designed as a new framework for urban heat island studies, they\nalso contain a limited set (7) of 'natural' land-cover classes that can be\nused as 'control' or 'natural reference' areas.\n\nAs these seven natural classes in the LCZ scheme can not capture the\nheterogeneity of the world's existing natural ecosystems, we advise\nusers - if required - to combine the built LCZ classes with any other\nland-cover product that provides a wider range of natural land-cover\nclasses.\n\nSee also:\n\n* [LCZ Typology](https://doi.org/10.1175/BAMS-D-11-00019.1)\n\n* [Global map of LCZs - paper](https://doi.org/10.5194/essd-14-3835-2022)\n\n* [Global map of LCZs - dataset](https://doi.org/10.5281/zenodo.6364593)\n\n* [LCZ Gaussian filtering](https://doi.org/10.1038/s41597-020-00605-z)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/RUB/RUBCLIM/LCZ/global_lcz_map/latest","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/RUB/RUB_RUBCLIM_LCZ_global_lcz_map_latest","rel":"related","title":"Run the example for RUB/RUBCLIM/LCZ/global_lcz_map/latest in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/RUB/RUB_RUBCLIM_LCZ_global_lcz_map_latest_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/RUB_RUBCLIM_LCZ_global_lcz_map_latest#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/RUB/RUB_RUBCLIM_LCZ_global_lcz_map_latest.json","rel":"latest-version","title":"RUB/RUBCLIM/LCZ/global_lcz_map/latest","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/RUB/RUB_RUBCLIM_LCZ_global_lcz_map_v1.json","rel":"predecessor-version","title":"RUB/RUBCLIM/LCZ/global_lcz_map/v1","type":"application/json"},{"href":"https://doi.org/10.5194/essd-14-3835-2022","rel":"source"},{"href":"https://doi.org/10.5281/zenodo.6364593","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/RUB/RUBCLIM/LCZ/global_lcz_map/latest/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/RUB/RUBCLIM/LCZ/global_lcz_map/latest/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"RUB/RUBCLIM/LCZ/global_lcz_map/v1","title":"Global map of Local Climate Zones, v1 [deprecated]","description":"Since their introduction in 2012, Local Climate Zones (LCZs) emerged as\na new standard for characterizing urban landscapes, providing a holistic\nclassification approach that takes into account micro-scale land-cover and\nassociated physical properties.\n\nThis global map of Local Climate Zones, at 100m pixel size and\nrepresentative for the nominal year 2018, is derived from multiple earth\nobservation datasets and expert LCZ class labels. LCZ_Filter is the\nrecommended band for most users. The other classification band, LCZ, is\nonly provided as it is used to calculate the LCZ_Probability band.\n\nThe LCZ scheme complements other land use / land cover schemes by\nits focus on urban and rural landscape types, which can be described by\nany of the 17 classes in the LCZ scheme. Out of the 17 LCZ classes, 10\nreflect the 'built' environment, and each LCZ type is associated with\ngeneric numerical descriptions of key urban canopy parameters critical to\nmodel atmospheric responses to urbanisation.  In addition, since LCZs were\noriginally designed as a new framework for urban heat island studies, they\nalso contain a limited set (7) of 'natural' land-cover classes that can be\nused as 'control' or 'natural reference' areas.\n\nAs these seven natural classes in the LCZ scheme can not capture the\nheterogeneity of the world's existing natural ecosystems, we advise\nusers - if required - to combine the built LCZ classes with any other\nland-cover product that provides a wider range of natural land-cover\nclasses.\n\nSee also:\n\n* [LCZ Typology](https://doi.org/10.1175/BAMS-D-11-00019.1)\n\n* [Global map of LCZs - paper](https://doi.org/10.5194/essd-14-3835-2022)\n\n* [Global map of LCZs - dataset](https://doi.org/10.5281/zenodo.6364593)\n\n* [LCZ Gaussian filtering](https://doi.org/10.1038/s41597-020-00605-z)\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/RUB/RUBCLIM/LCZ/global_lcz_map/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/RUB/RUB_RUBCLIM_LCZ_global_lcz_map_v1","rel":"related","title":"Run the example for RUB/RUBCLIM/LCZ/global_lcz_map/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/RUB/RUB_RUBCLIM_LCZ_global_lcz_map_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/RUB_RUBCLIM_LCZ_global_lcz_map_v1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/RUB/RUB_RUBCLIM_LCZ_global_lcz_map_latest.json","rel":"latest-version","title":"RUB/RUBCLIM/LCZ/global_lcz_map/latest","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/RUB/RUB_RUBCLIM_LCZ_global_lcz_map_latest.json","rel":"successor-version","title":"RUB/RUBCLIM/LCZ/global_lcz_map/latest","type":"application/json"},{"href":"https://doi.org/10.5194/essd-14-3835-2022","rel":"source"},{"href":"https://doi.org/10.5281/zenodo.6364593","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/RUB/RUBCLIM/LCZ/global_lcz_map/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/RUB/RUBCLIM/LCZ/global_lcz_map/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"SKYSAT/GEN-A/PUBLIC/ORTHO/MULTISPECTRAL","title":"Planet SkySat Public Ortho Imagery, Multispectral","description":"This data from Planet labs Inc. SkySat satellites was collected for\nthe experimental \\\"Skybox for Good Beta\\\" program in 2015, as well as for\nvarious crisis response events and a few other projects. The data is\navailable in both a 5-band Multispectral/Pan collection, and a Pansharpened\nRGB collection.\n\nEach image's asset ID contains the acquisition date and time, for\nexample, image s01_20150304T080608Z was acquired on March 4, 2015 at\n08:06 Zulu (UTC). For more information, please see the\n[Planet Imagery Product Specifications](https://www.planet.com/products/#planet-monitoring)\nand visit the\n[Planet Imagery and Archive site](https://www.planet.com/latest-satellite-imagery-gallery/)\n\nThis Multispectral/Pan collection contains images with five 16-bit bands\nshifted up from the original 12-bit data. The B, G, R, and Near-IR bands\nhave a resolution of approximately 2m per pixel, while the Pan band is\napproximately 0.8m resolution (closer to 1m for off-nadir images).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-70,180,53]]},"temporal":{"interval":[["2014-07-03T00:00:00Z","2016-12-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/SKYSAT/GEN-A/PUBLIC/ORTHO/MULTISPECTRAL","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/SKYSAT/SKYSAT_GEN-A_PUBLIC_ORTHO_MULTISPECTRAL","rel":"related","title":"Run the example for SKYSAT/GEN-A/PUBLIC/ORTHO/MULTISPECTRAL in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/SKYSAT/SKYSAT_GEN-A_PUBLIC_ORTHO_MULTISPECTRAL_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/SKYSAT_GEN-A_PUBLIC_ORTHO_MULTISPECTRAL#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/SKYSAT/GEN-A/PUBLIC/ORTHO/MULTISPECTRAL/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/SKYSAT/GEN-A/PUBLIC/ORTHO/MULTISPECTRAL/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"SKYSAT/GEN-A/PUBLIC/ORTHO/RGB","title":"Planet SkySat Public Ortho Imagery, RGB","description":"This data from Planet labs Inc. SkySat satellites was collected for\nthe experimental \\\"Skybox for Good Beta\\\" program in 2015, as well as for\nvarious crisis response events and a few other projects. The data is\navailable in both a 5-band Multispectral/Pan collection, and a Pansharpened\nRGB collection.\n\nEach image's asset ID contains the acquisition date and time, for\nexample, image s01_20150304T080608Z was acquired on March 4, 2015 at\n08:06 Zulu (UTC). For more information, please see the\n[Planet Imagery Product Specifications](https://assets.planet.com/docs/Planet_Combined_Imagery_Product_Specs_letter_screen.pdf)\nand visit the\n[Planet Imagery and Archive](https://www.planet.com/products/planet-imagery/) site.\n\nThis RGB collection contains images with three pansharpened, 8-bit bands.\nThe resolution is approximately 0.8m per pixel (closer\nto 1m for off-nadir images).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-70,180,53]]},"temporal":{"interval":[["2014-07-03T00:00:00Z","2016-12-24T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/SKYSAT/GEN-A/PUBLIC/ORTHO/RGB","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/SKYSAT/SKYSAT_GEN-A_PUBLIC_ORTHO_RGB","rel":"related","title":"Run the example for SKYSAT/GEN-A/PUBLIC/ORTHO/RGB in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/SKYSAT/SKYSAT_GEN-A_PUBLIC_ORTHO_RGB_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/SKYSAT_GEN-A_PUBLIC_ORTHO_RGB#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/SKYSAT/GEN-A/PUBLIC/ORTHO/RGB/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/SKYSAT/GEN-A/PUBLIC/ORTHO/RGB/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"SNU/ESL/BESS/Rad/v1","title":"Breathing Earth System Simulator (BESS) Radiation v1","description":"Breathing Earth System Simulator (BESS) is a simplified process-based model\nthat couples atmosphere and canopy radiative transfers, canopy\nphotosynthesis, transpiration, and energy balance. It couples an atmospheric\nradiative transfer model and artificial neural network with forcings from\nMODIS atmospheric products to generate 5-km daily products.\n\nPublications:\n\n* Ryu Youngryel, Chongya Jiang, Hideki Kobayashi, Matteo Detto,\n  MODIS-derived global land products of shortwave radiation and diffuse and\n  total photosynthetically active radiation at 5km resolution from 2000.\n  Remote Sensing of Environment, Volume 204,\n  2018.\n  [doi:10.1016/j.rse.2017.09.021](https://doi.org/10.1016/j.rse.2017.09.021)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2021-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/SNU/ESL/BESS/Rad/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/SNU/SNU_ESL_BESS_Rad_v1","rel":"related","title":"Run the example for SNU/ESL/BESS/Rad/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/SNU/SNU_ESL_BESS_Rad_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/SNU_ESL_BESS_Rad_v1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://drive.google.com/drive/folders/1B2aSPkgnGsSdQx4XtX4H21O7tP6kq-pG","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/SNU/ESL/BESS/Rad/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/SNU/ESL/BESS/Rad/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Slovakia/orthos/25cm","title":"Slovakia orthophotos","description":"Orthophotomosaic of the Slovak Republic is a set of color orthoimages\nwithout overlaps, gaps and visible brightness and color differences along\nthe connecting lines.\n\nFor more information, please see the\n[Slovakia orthophotos documentation](https://www.geoportal.sk/files/zbgis/orto/technicka_sprava_ortofotomozaika_sr_2017-2019.pdf)\n","license":"proprietary","extent":{"spatial":{"bbox":[[16.5,47.44,22.33,49.36]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Slovakia/orthos/25cm","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Slovakia/Slovakia_orthos_25cm","rel":"related","title":"Run the example for Slovakia/orthos/25cm in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Slovakia/Slovakia_orthos_25cm_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Slovakia_orthos_25cm#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Slovakia/orthos/25cm/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Slovakia/orthos/25cm/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Spain/PNOA/PNOA10","title":"Spain RGB orthophotos 10 cm","description":"Mosaics of orthophotos from flights carried out between 2007 and 2018 by\nvarious public administration bodies at 10cm pixel resolution.\nThis data is provided by National Plan for Aerial Orthophotography Spain\n([PNOA](https://pnoa.ign.es)).\n\nFor more information, please see the\n[Spain orthophotos documentation](https://pnoa.ign.es/web/portal/pnoa-imagen/proceso-fotogrametrico)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-9.32,36.11,4.7,43.48]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Spain/PNOA/PNOA10","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Spain/Spain_PNOA_PNOA10","rel":"related","title":"Run the example for Spain/PNOA/PNOA10 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Spain/Spain_PNOA_PNOA10_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Spain_PNOA_PNOA10#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Spain/PNOA/PNOA10/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Spain/PNOA/PNOA10/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Switzerland/SWISSIMAGE/orthos/10cm","title":"SWISSIMAGE 10 cm RGB imagery","description":"The SWISSIMAGE 10 cm orthophoto mosaic is an assembly of new color digital\naerial images over the whole of Switzerland with a ground resolution of\n10 cm in the plains and the main alpine valleys and 25 cm in the Alps. It\nis updated annually.\n\nFor more information, please see the\n[SWISSIMAGE10cm_FR documentation](https://backend.swisstopo.admin.ch/fileservice/sdweb-docs-prod-swisstopoch-files/files/2023/11/14/a84642dc-5feb-48e5-af6b-55df4ae7a10b.pdf)\n\nThis RGB collection contains digital aerial images with three bands.\nStandard deviation for the precision in position: +/- 0.15 m for the ground\nsample distance of 0.1 m.\n","license":"proprietary","extent":{"spatial":{"bbox":[[5.9,45.8,10.6,47.8]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Switzerland/SWISSIMAGE/orthos/10cm","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Switzerland/Switzerland_SWISSIMAGE_orthos_10cm","rel":"related","title":"Run the example for Switzerland/SWISSIMAGE/orthos/10cm in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Switzerland/Switzerland_SWISSIMAGE_orthos_10cm_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Switzerland_SWISSIMAGE_orthos_10cm#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Switzerland/SWISSIMAGE/orthos/10cm/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Switzerland/SWISSIMAGE/orthos/10cm/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"TEMPLATE/IMAGE_COLLECTION_V2_3","title":"A short title for the dataset goes here V2.3","description":"Write enough here for most people to understand:\n- what the dataset is for\n- what the dataset limitations are\n- how it was created\n- and anything else essential to know when working with the data.\n\nThings to consider when writing:\n- The first paragraph should be a concise description of the dataset and\n  not a general introduction.\n- Write in the third person.\n- The text will be processed as CommonMark (a.k.a. Markdown) with multiple\n  different processors. Note that Google's Markdown can handle tables, but\n  STAC Browser does not support tables.\n- Define all acronyms, e.g. evapotranspiration (ET).\n- Make links to additional resources or manuals when appropriate.\n- Bare links must be put in Markdown links, e.g.\n    - https://earthengine.google.com/ is just text\n    - [https://earthengine.google.com/](https://earthengine.google.com/)\n      is actually a link.\n- The triple pipe operator (`|||`) allows for writing multiline strings.\n- Indent 2 spaces under the |||.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/TEMPLATE/IMAGE_COLLECTION_V2_3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/TEMPLATE/TEMPLATE_IMAGE_COLLECTION_V2_3","rel":"related","title":"Run the example for TEMPLATE/IMAGE_COLLECTION_V2_3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/TEMPLATE/TEMPLATE_IMAGE_COLLECTION_V2_3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/TEMPLATE_IMAGE_COLLECTION_V2_3#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/TEMPLATE/IMAGE_COLLECTION_V2_3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/TEMPLATE/IMAGE_COLLECTION_V2_3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"TEMPLATE/IMAGE_V2_1","title":"A short title for the dataset goes here V2.1","description":"Write enough here for most people to understand:\n- what the dataset is for\n- what the dataset limitations are\n- how it was created\n- and anything else essential to know when working with the data.\n\nThings to consider when writing:\n- The first paragraph should be a concise description of the dataset and\n  not a general introduction.\n- Write in the third person.\n- The text will be processed as CommonMark (a.k.a. Markdown) with multiple\n  different processors. Note that Google's Markdown can handle tables, but\n  STAC Browser does not support tables.\n- Define all acronyms, e.g. evapotranspiration (ET).\n- Make links to additional resources or manuals when appropriate.\n- Bare links must be put in Markdown links, e.g.\n    - https://earthengine.google.com/ is just text\n    - [https://earthengine.google.com/](https://earthengine.google.com/)\n      is actually a link.\n- The triple pipe operator (`|||`) allows for writing multiline strings.\n- Indent 2 spaces under the |||.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2006-01-24T00:00:00Z","2011-05-13T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/TEMPLATE/IMAGE_V2_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/TEMPLATE/TEMPLATE_IMAGE_V2_1","rel":"related","title":"Run the example for TEMPLATE/IMAGE_V2_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/TEMPLATE/TEMPLATE_IMAGE_V2_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/TEMPLATE_IMAGE_V2_1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/TEMPLATE/IMAGE_V2_1/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"TERN/AET/CMRSET_LANDSAT_V2_1","title":"Actual Evapotranspiration for Australia (CMRSET Landsat V2.1) [deprecated]","description":"This dataset provides accurate actual evapotranspiration (AET or ETa) for\nAustralia using the CMRSET algorithm. The AET band (named 'ETa') contains\nthe average daily value from the CMRSET model for all cloud-free Landsat\nobservations in that month (indicated with value 3 in the AET Data Source QA\nbits). After the Landsat 7 ETM+ Scan Line Corrector (SLC) failed on 31 May\n2003, Landsat 7 ETM+ data are only used if there are no cloud-free Landsat 5\nTM or Landsat 8 OLI data for that month. If there is no cloud-free Landsat\navailable, then pixels are infilled with Landsat-VIIRS blended output\n(indicated with value 2 in the AET Data Source QA bits). If there is no\nVIIRS available in a month, then missing monthly AET values are linearly\ninterpolated (indicated with value 1 in the AET Data Source QA bits). This\nmeans monthly 30 m AET data covering all Australia, with no gaps due to\ncloud, are available and ready to use.\n\nAccurate AET information is important for irrigation, food security, and\nenvironmental management. Like many other parts of the world, water\navailability in Australia is limited and AET is the largest consumptive\ncomponent of the water balance. In Australia 70% of available water is used\nfor crop and pasture irrigation. Better monitoring will support improved\nwater use efficiency in this sector, with any water savings available as\nenvironmental flows. Additionally, ground-water dependent ecosystems (GDE)\noccupy a small area yet are \"biodiversity hotspots\". Knowing their water\nneeds enables enhanced management of these critical areas. AET can also be\nused to model the catchment water balance. If used in water balance (mass\nbalance) calculations, then this AET value needs to be multiplied by the\nnumber of days in the month.\n\nTo let the developers know you are using this dataset, to get information\non updates, or if you have any questions please contact:\ntim.mcvicar@csiro.au, tom.vanniel@csiro.au, jamie.vleeshouwer@csiro.au .\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[110,-45,155,-10]]},"temporal":{"interval":[["2012-02-01T00:00:00Z","2021-02-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/TERN/AET/CMRSET_LANDSAT_V2_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/TERN/TERN_AET_CMRSET_LANDSAT_V2_1","rel":"related","title":"Run the example for TERN/AET/CMRSET_LANDSAT_V2_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/TERN/TERN_AET_CMRSET_LANDSAT_V2_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/TERN_AET_CMRSET_LANDSAT_V2_1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/TERN/TERN_AET_CMRSET_LANDSAT_V2_2.json","rel":"latest-version","title":"TERN/AET/CMRSET_LANDSAT_V2_2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/TERN/TERN_AET_CMRSET_LANDSAT_V2_2.json","rel":"successor-version","title":"TERN/AET/CMRSET_LANDSAT_V2_2","type":"application/json"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/TERN/AET/CMRSET_LANDSAT_V2_1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/TERN/AET/CMRSET_LANDSAT_V2_1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"TERN/AET/CMRSET_LANDSAT_V2_2","title":"Actual Evapotranspiration for Australia (CMRSET Landsat V2.2)","description":"This dataset provides accurate actual evapotranspiration (AET or ETa) for\nAustralia using the CMRSET algorithm. The AET band (named 'ETa') contains\nthe average daily value from the CMRSET model for all cloud-free Landsat\nobservations in that month (indicated with value 3 in the AET Data Source QA\nbits). After the Landsat 7 ETM+ Scan Line Corrector (SLC) failed on 31 May\n2003, Landsat 7 ETM+ data are only used if there are no cloud-free Landsat 5\nTM or Landsat 8 OLI data for that month. If there is no cloud-free Landsat\ndata available, pixels are infilled with blended data. The blended data will\nbe blended Landsat-MODIS until Feb 2012, then blended Landsat-VIIRS onwards\n(indicated with value 2 in the AET Data Source QA bits). If there is no\nblended data available in a month, then missing monthly AET values are\nlinearly interpolated (indicated with value 1 in the AET Data Source QA\nbits). This means monthly 30 m AET data covering all Australia, with no gaps\ndue to cloud, are available and ready to use.\n\nAccurate AET information is important for irrigation, food security, and\nenvironmental management. Like many other parts of the world, water\navailability in Australia is limited and AET is the largest consumptive\ncomponent of the water balance. In Australia 70% of available water is used\nfor crop and pasture irrigation. Better monitoring will support improved\nwater use efficiency in this sector, with any water savings available as\nenvironmental flows. Additionally, ground-water dependent ecosystems (GDE)\noccupy a small area yet are \"biodiversity hotspots\". Knowing their water\nneeds enables enhanced management of these critical areas. AET can also be\nused to model the catchment water balance. If used in water balance (mass\nbalance) calculations, then this AET value needs to be multiplied by the\nnumber of days in the month.\n\nTo let the developers know you are using this dataset, to get information on\nupdates, or if you have any questions please contact: tim.mcvicar@csiro.au,\ntom.vanniel@csiro.au, jamie.vleeshouwer@csiro.au.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[110,-45,155,-10]]},"temporal":{"interval":[["2000-02-01T00:00:00Z","2026-08-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/TERN/AET/CMRSET_LANDSAT_V2_2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/TERN/TERN_AET_CMRSET_LANDSAT_V2_2","rel":"related","title":"Run the example for TERN/AET/CMRSET_LANDSAT_V2_2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/TERN/TERN_AET_CMRSET_LANDSAT_V2_2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/TERN_AET_CMRSET_LANDSAT_V2_2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/TERN/TERN_AET_CMRSET_LANDSAT_V2_2.json","rel":"latest-version","title":"TERN/AET/CMRSET_LANDSAT_V2_2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/TERN/TERN_AET_CMRSET_LANDSAT_V2_1.json","rel":"predecessor-version","title":"TERN/AET/CMRSET_LANDSAT_V2_1","type":"application/json"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/TERN/AET/CMRSET_LANDSAT_V2_2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/TERN/AET/CMRSET_LANDSAT_V2_2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LE07/C02/T2","title":"USGS Landsat 7 Collection 2 Tier 2 Raw Scenes","description":"Landsat 7 Collection 2 Tier 2 DN values, representing scaled, calibrated at-sensor radiance.\nScenes not meeting Tier 1 criteria during\nprocessing are assigned to Tier 2. This includes Systematic terrain (L1GT) and\nSystematic (L1GS) processed scenes, as well as any L1TP scenes that do not meet\nthe Tier 1 specifications due to significant cloud cover, insufficient ground\ncontrol, and other factors. Users interested in Tier 2 scenes can analyze the\nRMSE and other properties to determine the suitability for use in individual\napplications and studies. See more information [in the USGS\ndocs](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collections).\n\nNote that [Landsat 7's orbit has been drifting to an earlier acquisition time since\n2017](https://www.sciencedirect.com/science/article/pii/S2666017221000134?via%3Dihub).\n","license":"PDDL-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-05-28T01:05:28Z","2024-01-19T00:08:51.725000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LE07_C02_T2","rel":"related","title":"Run the example for LANDSAT/LE07/C02/T2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LE07_C02_T2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDSAT/LE07/C02/T2_L2","title":"USGS Landsat 7 Level 2, Collection 2, Tier 2","description":"This dataset contains atmospherically corrected\nsurface reflectance and land surface temperature derived from the data\nproduced by the Landsat 7 ETM+ sensor.\nThese images contain 4 visible and near-infrared (VNIR) bands and\n2 short-wave infrared (SWIR) bands processed to orthorectified surface\nreflectance, and one thermal infrared (TIR) band processed to orthorectified\nsurface temperature. They also contain intermediate bands used in\ncalculation of the ST products, as well as QA bands.\n\nLandsat 7 SR products are created with the Landsat Ecosystem Disturbance\nAdaptive Processing System (LEDAPS) algorithm (version 3.4.0).\nAll Collection 2 ST products are created with a single-channel algorithm\njointly created by the Rochester Institute of Technology (RIT) and\nNational Aeronautics and Space Administration (NASA)\nJet Propulsion Laboratory (JPL).\n\nStrips of collected data are packaged into overlapping \\\"scenes\\\" covering approximately\n170km x 183km using a [standardized reference grid](https://landsat.gsfc.nasa.gov/about/worldwide-reference-system).\n\nSome assets have only SR data, in which case ST bands are present but empty.\nFor assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'.\nFor assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'.\n\n[Additional documentation and usage examples.](/earth-engine/guides/landsat)\n\nLandsat Collection 2 files are publicly available in a [Google Cloud Storage\nbucket](https://console.cloud.google.com/storage/browser/gee-public-data-landsat)\non requester-pays basis. The files are indexed in a regularly updated\nBigQuery table for ease of analysis:\n[earth-engine-public-data.geo_index.landsat_c2_index](https://console.cloud.google.com/bigquery?project=earth-engine-public-data&earth-engine-public-data!2sgeo_index=&inv=1&invt=Ab4V5A&ws=!1m5!1m4!4m3!1searth-engine-public-data!2sgeo_index!3slandsat_c2_index).\n\nData provider notes:\n\n* Data products must contain both optical and thermal data to be\n  successfully processed to surface temperature, as ASTER NDVI is\n  required to temporally adjust the ASTER GED product to the target Landsat\n  scene. Therefore, night time acquisitions cannot be processed to\n  surface temperature.\n\n* A known error exists in the surface temperature retrievals relative\n  to clouds and possibly cloud shadows. The characterization of these\n  issues has been documented by\n  [Cook et al., (2014)](https://doi.org/10.3390/rs61111244).\n\n* ASTER GED contains areas of missing mean emissivity data required for\n  successful ST product generation. If there is missing ASTER GED\n  information, there will be missing ST data in those areas.\n\n* The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes\n  acquired from 2000 through 2008. While this dataset has a global spatial\n  extent, there are areas missing mean emissivity information due to\n  persistent cloud contamination in the ASTER measurements.\n\n* The USGS further screens unphysical values (emissivity < 0.6) in ASTER\n  GED to remove any emissivity underestimation due to undetected clouds. For\n  any given pixel with no ASTER GED input or unphysical emissivity value,\n  the resulting Landsat ST products have missing pixels. The missing Landsat\n  ST pixels will be consistent through time (1982-present) given the static\n  nature of ASTER GED mean climatology data. For more information refer to\n  [landsat-collection-2-surface-temperature-data-gaps-due-missing](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature-data-gaps-due-missing-aster-ged)\n\nNote that [Landsat 7's orbit has been drifting to an earlier acquisition time since\n2017](https://www.sciencedirect.com/science/article/pii/S2666017221000134?via%3Dihub).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-05-28T01:06:16Z","2024-01-19T00:08:51.725000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T2_L2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDSAT/LANDSAT_LE07_C02_T2_L2","rel":"related","title":"Run the example for LANDSAT/LE07/C02/T2_L2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDSAT/LANDSAT_LE07_C02_T2_L2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T2_L2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.usgs.gov/centers/eros/data-citation","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T2_L2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/LANDSAT/LE07/C02/T2_L2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"TNC/HM/v3/300m_c","title":"TNC Global Human Modification v3 (Change-Consistent 300m)","description":"The Global Human Modification v3 dataset measures cumulative human pressures on\nglobal terrestrial ecosystems (excluding Antarctica). It tracks data from 1990 to 2022 and is\nintended to help quantify human impact for conservation planning, policy-making, and research.\nThis dataset is the \"Change-Consistent\" Series (\"c\" series) covering the years 1990, 1995, 2000,\n2005, 2010, 2015, and 2020 at a 300-meter resolution. This series uses consistent input data\nto allow for valid temporal change analysis across these years. The modification score per pixel\nis calculated using modification = footprint proportion * stressor intensity. The values are\nFloat32 ranging from 0.0 to 1.0.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","2020-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/TNC/HM/v3/300m_c","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/TNC/TNC_HM_v3_300m_c","rel":"related","title":"Run the example for TNC/HM/v3/300m_c in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/TNC/TNC_HM_v3_300m_c_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/TNC_HM_v3_300m_c#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/TNC/HM/v3/300m_c/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/TNC/HM/v3/300m_c/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"TNC/HM/v3/90m_s","title":"TNC Global Human Modification v3 (Static Snapshot 90m)","description":"The Global Human Modification v3 dataset measures cumulative human pressures on global\nterrestrial ecosystems (excluding Antarctica). It tracks data from 1990 to 2022 and is\nintended to help quantify human impact for conservation planning, policy-making, and research.\n\nThis is the 2022 static snapshot (\"s\" series) available at 90 meter resolution.\nBecause it uses updated satellite and land-cover inputs, it is not recommended for\ndirect quantitative comparison with the 1990–2020 \"c\" series.\n\nThe modification score per pixel is calculated using the formula:\nmodification = footprint proportion multiplied by stressor intensity.\nThe data values are stored as 32-bit floats ranging from 0.0 to 1.0, where\n0.0 indicates no detectable human modification and 1.0 represents the maximum modeled modification.\n\nThis dataset provides data for eight IUCN-aligned threat categories, plus one combined index.\nIt was rigorously validated using a spatially balanced sample from the Global Land\nUse Emergent Dataset (GLUED). It achieved a root-mean-squared error (RMSE) of 0.178 at 90m.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-01-01T00:00:00Z","2022-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/TNC/HM/v3/90m_s","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/TNC/TNC_HM_v3_90m_s","rel":"related","title":"Run the example for TNC/HM/v3/90m_s in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/TNC/TNC_HM_v3_90m_s_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/TNC_HM_v3_90m_s#terms-of-use","rel":"license","type":"text/html"},{"href":"https://gdra-tnc.org/current/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/TNC/HM/v3/90m_s/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/TNC/HM/v3/90m_s/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"TOMS/MERGED","title":"TOMS and OMI Merged Ozone Data v3 [deprecated]","description":"The Total Ozone Mapping Spectrometer (TOMS) data represent the primary\nlong-term, continuous record of satellite-based observations available for\nuse in monitoring global and regional trends in total ozone over the past\n25 years. The data are produced by the Laboratory for Atmospheres at NASA's\nGoddard Space Flight Center. Version 8 TOMS data products include level 3\ngridded data (1.0 x 1.25 deg). The Ozone Monitoring Instrument (OMI), aboard\nthe Aura satellite (July 2004 - current), has a higher resolution\n(1.0 x 1.0 deg).\n\nThese data represent a merged ozone product from TOMS/EarthProbe,\nTOMS/Nimbus-7, TOMS/Meteor-3, OMI/Aura and USGS-interpolated data for dates\nwith no data.\n\n\n[Additional TOMS and OMI information](https://ozoneaq.gsfc.nasa.gov/missions/)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1978-11-01T00:00:00Z","2026-04-11T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/TOMS/MERGED","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/TOMS/TOMS_MERGED","rel":"related","title":"Run the example for TOMS/MERGED in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/TOMS/TOMS_MERGED_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/TOMS_MERGED#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/TOMS/TOMS_MERGED_V4.json","rel":"latest-version","title":"TOMS/MERGED_V4","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/TOMS/TOMS_MERGED_V4.json","rel":"successor-version","title":"TOMS/MERGED_V4","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/TOMS/MERGED/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/TOMS/MERGED/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"TOMS/MERGED_V4","title":"TOMS and OMI Merged Ozone Data v4","description":"The Total Ozone Mapping Spectrometer (TOMS) data represent the primary\nlong-term, continuous record of satellite-based observations available for\nuse in monitoring global and regional trends in total ozone over the past\n25 years. The data are produced by the Laboratory for Atmospheres at NASA's\nGoddard Space Flight Center. Version 8 TOMS data products include level 3\ngridded data (1.0 x 1.25 deg). The Ozone Monitoring Instrument (OMI), aboard\nthe Aura satellite (July 2004 - current), has a higher resolution\n(1.0 x 1.0 deg).\n\nThese data represent a merged ozone product from TOMS/EarthProbe,\nTOMS/Nimbus-7, TOMS/Meteor-3, OMI/Aura and USGS-interpolated data for dates\nwith no data.\n\n\nSee [this document](https://acd-ext.gsfc.nasa.gov/anonftp/toms/omi/Changes_OMI_TO3_col4_col3.pdf) for a summary of changes between v3 and v4.\n\n[Additional TOMS and OMI information](https://ozoneaq.gsfc.nasa.gov/missions/)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1978-11-01T00:00:00Z","2026-11-07T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/TOMS/MERGED_V4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/TOMS/TOMS_MERGED_V4","rel":"related","title":"Run the example for TOMS/MERGED_V4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/TOMS/TOMS_MERGED_V4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/TOMS_MERGED_V4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/TOMS/TOMS_MERGED_V4.json","rel":"latest-version","title":"TOMS/MERGED_V4","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/TOMS/TOMS_MERGED.json","rel":"predecessor-version","title":"TOMS/MERGED","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/TOMS/MERGED_V4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/TOMS/MERGED_V4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"TRMM/3B42","title":"TRMM 3B42: 3-Hourly Precipitation Estimates","description":"The Tropical Rainfall Measuring Mission (TRMM) is a\njoint mission between NASA and the Japan Aerospace Exploration\nAgency (JAXA) designed to monitor and study tropical rainfall.\nThe 34B2 product contains a gridded, TRMM-adjusted, merged infrared\nprecipitation (mm/hr) and RMS precipitation-error estimate, with\na 3-hour temporal resolution and a 0.25 degree spatial resolution.\n\nSee the [algorithm description](https://trmm.gsfc.nasa.gov/3b42.html)\nand the [file specification](https://storm.pps.eosdis.nasa.gov/storm/data/docs/filespec.TRMM.V7.3B42.pdf)\nfor details.\n\nDocumentation:\n\n* [PI Documentation](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/3B42_3B43_doc_V7.pdf)\n\n* [File Specification for TRMM Products](https://pps.gsfc.nasa.gov/Documents/filespec.TRMM.V7.pdf)\n\n* [Comparison between TRMM versions 6 and 7](https://pps.gsfc.nasa.gov/Documents/formatChangesV7.pdf)\n\n* [Readme](https://disc2.gesdisc.eosdis.nasa.gov/data/TRMM_L3/TRMM_3B42/doc/README.TRMM_V7.pdf)\n\n* [Details of the TMPA algorithm used in this product](https://pmm.nasa.gov/sites/default/files/imce/3B42_3B43_TMPA_restart.pdf)\n\n* [TRMM Data Gaps](https://web.archive.org/web/20200701000000*/ftp://gpmweb2.pps.eosdis.nasa.gov/tsdis/AB/docs/anomalous.html)\n\n* [Transition from TMPA to IMERG](https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/TMPA-to-IMERG_transition.pdf)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-50,180,50]]},"temporal":{"interval":[["1998-01-01T00:00:00Z","2019-12-31T21:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/TRMM/3B42","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/TRMM/TRMM_3B42","rel":"related","title":"Run the example for TRMM/3B42 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/TRMM/TRMM_3B42_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B42#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/TRMM/3B42/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/TRMM/3B42/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"TRMM/3B43V7","title":"TRMM 3B43: Monthly Precipitation Estimates","description":"**This collection is no longer being updated. See\n[IMERG monthly](https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_MONTHLY_V06)**\n\nThis dataset algorithmically merges microwave data from multiple satellites,\nincluding SSMI, SSMIS, MHS, AMSU-B and AMSR-E, each inter-calibrated to the\nTRMM Combined Instrument.\n\nAlgorithm 3B43 is executed once per calendar month to produce the single,\nbest-estimate precipitation rate and RMS precipitation-error estimate field\n(3B43) by combining the 3-hourly merged high-quality/IR estimates (3B42)\nwith the monthly accumulated Global Precipitation Climatology Centre (GPCC)\nrain gauge analysis.\n\nAll of the global precipitation datasets have some calibrating data source,\nwhich is necessary to control bias differences between contributing\nsatellites.  The multi-satellite data are averaged to the monthly scale and\ncombined with the Global Precipitation Climatology Centre's (GPCC) monthly\nsurface precipitation gauge analysis. In each case the multi-satellite data\nare adjusted to the large-area mean of the gauge analysis, where available\n(mostly over land), and then combined with the gauge analysis using a\nsimple inverse estimated-random-error variance weighting.  Regions with poor\ngauge coverage, like central Africa and the oceans, have a higher weighting\non the satellite input.\n\nSee the [algorithm description](https://trmm.gsfc.nasa.gov/3b43.html)\nand the [file specification](https://pps.gsfc.nasa.gov/Documents/filespec.TRMM.V7.pdf)\nfor details.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-50,180,50]]},"temporal":{"interval":[["1998-01-01T00:00:00Z","2019-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/TRMM/3B43V7","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/TRMM/TRMM_3B43V7","rel":"related","title":"Run the example for TRMM/3B43V7 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/TRMM/TRMM_3B43V7_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B43V7#terms-of-use","rel":"license","type":"text/html"},{"href":"https://disc2.gesdisc.eosdis.nasa.gov/data/TRMM_L3/TRMM_3B43.7/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/TRMM/3B43V7/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/TRMM/3B43V7/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"TUBerlin/BigEarthNet/v1","title":"TUBerlin/BigEarthNet/v1","description":"BigEarthNet is a new large-scale Sentinel-2 benchmark archive, consisting of\n590,326 Sentinel-2 image patches.  To construct BigEarthNet, 125 Sentinel-2\ntiles were acquired between June 2017 and May 2018 over the 10 countries\n(Austria, Belgium, Finland, Ireland, Kosovo, Lithuania, Luxembourg,\nPortugal, Serbia, Switzerland) of Europe. All the tiles were atmospherically\ncorrected by the Sentinel-2 Level 2A product generation and formatting tool\n(sen2cor).  Then, they were divided into 590,326 non-overlapping image\npatches. Each image patch was annotated by the multiple land-cover classes\n(i.e., multi-labels) that were provided from the CORINE Land Cover database\nof the year 2018 (CLC 2018).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-9,36.9,31.6,68.1]]},"temporal":{"interval":[["2017-06-01T00:00:00Z","2018-05-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/TUBerlin/BigEarthNet/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/TUBerlin/TUBerlin_BigEarthNet_v1","rel":"related","title":"Run the example for TUBerlin/BigEarthNet/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/TUBerlin/TUBerlin_BigEarthNet_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/TUBerlin_BigEarthNet_v1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/TUBerlin/BigEarthNet/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/TUBerlin/BigEarthNet/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Tsinghua/DESS/ChinaTerraceMap/v1","title":"DESS China Terrace Map v1","description":"This dataset is a China terrace map at 30 m resolution in 2018. It was\ndeveloped through supervised pixel-based classification using multisource and\nmulti-temporal data based on the Google Earth Engine platform. The overall\naccuracy and kappa coefficient achieved 94% and 0.72, respectively. This first\n30 m China terrace map can be used for studies on soil erosion, food\nsecurity, biogeochemical cycle, biodiversity, and ecosystem service\nassessments.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[70,0,140,60]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Tsinghua/DESS/ChinaTerraceMap/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Tsinghua/Tsinghua_DESS_ChinaTerraceMap_v1","rel":"related","title":"Run the example for Tsinghua/DESS/ChinaTerraceMap/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Tsinghua/Tsinghua_DESS_ChinaTerraceMap_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Tsinghua_DESS_ChinaTerraceMap_v1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5194/essd-13-2437-2021","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Tsinghua/DESS/ChinaTerraceMap/v1/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Tsinghua/FROM-GLC/GAIA/v10","title":"Tsinghua FROM-GLC Year of Change to Impervious Surface","description":"This dataset contains annual change information of global impervious surface area from 1985 to\n2018 at a 30m resolution. Change from pervious to impervious was determined using a combined\napproach of supervised classification and temporal consistency checking. Impervious pixels are\ndefined as above 50% impervious. The year of the transition (from pervious to impervious) can\nbe identified from the pixel value, ranging from 34 (year: 1985) to 1 (year: 2018). For\nexample, the impervious surface in 1990 can be revealed as the pixel value greater than 29\n(see the lookup table). This dataset is temporally consistent, following the conversion from\npervious (e.g., non-urban) to impervious (e.g., urban) monotonically. For more information\nabout the mapping approach and assessment, see\n[Annual maps of global artificial impervious area (GAIA) between 1985 and 2018\n(Gong et al. 2020)](https://doi.org/10.1016/j.rse.2019.111510).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Tsinghua/FROM-GLC/GAIA/v10","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Tsinghua/Tsinghua_FROM-GLC_GAIA_v10","rel":"related","title":"Run the example for Tsinghua/FROM-GLC/GAIA/v10 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Tsinghua/Tsinghua_FROM-GLC_GAIA_v10_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Tsinghua_FROM-GLC_GAIA_v10#terms-of-use","rel":"license","type":"text/html"},{"href":"http://data.ess.tsinghua.edu.cn/fromglc2017v1.html","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Tsinghua/FROM-GLC/GAIA/v10/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UCSB-CHC/CHIRPS/V3/DAILY_RNL","title":"CHIRPS Precipitation Daily Reanalysis: Climate Hazards Center InfraRed Precipitation With Station Data (Version 3.0, ERA5-based)","description":"The Climate Hazards Center Infrared Precipitation with Stations (CHIRPS v3)\nis a 40+ year, high-resolution quasi-global rainfall dataset. It spans\n60&deg;N to 60&deg;S and covers all longitudes, providing data from 1981 to\nnear-present. CHIRPS v3 combines satellite-based thermal infrared rainfall\nestimates with in-situ station observations to produce a 0.05&deg; gridded\nrainfall time series over land. This is version 3.0 of the dataset, for more\ninformation see [the CHC page](https://www.chc.ucsb.edu/data/chirps3).\n\nCHIRPS is fundamentally a pentad and monthly product from which other\ntime steps are derived. This daily dataset ('rnl') uses daily precipitation\nfrom the ECMWF ERA Reanalysis v5 (ERA5) data product to partition\npentadal CHIRPS-v3 precipitation totals into daily amounts.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["1981-01-01T00:00:00Z","2026-09-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHC/CHIRPS/V3/DAILY_RNL","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UCSB-CHC/UCSB-CHC_CHIRPS_V3_DAILY_RNL","rel":"related","title":"Run the example for UCSB-CHC/CHIRPS/V3/DAILY_RNL in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UCSB-CHC/UCSB-CHC_CHIRPS_V3_DAILY_RNL_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHC_CHIRPS_V3_DAILY_RNL#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHC/CHIRPS/V3/DAILY_RNL/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHC/CHIRPS/V3/DAILY_RNL/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UCSB-CHC/CHIRPS/V3/DAILY_SAT","title":"CHIRPS Precipitation Daily Near-Real-Time: Climate Hazards Center InfraRed Precipitation With Station Data (Version 3.0, IMERG-based)","description":"The Climate Hazards Center Infrared Precipitation with Stations (CHIRPS v3)\nis a 40+ year, high-resolution quasi-global rainfall dataset. It spans\n60&deg;N to 60&deg;S and covers all longitudes, providing data from 1981 to\nnear-present. CHIRPS v3 combines satellite-based thermal infrared rainfall\nestimates with in-situ station observations to produce a 0.05&deg; gridded\nrainfall time series over land. This is version 3.0 of the dataset, for more\ninformation see [the CHC page](https://www.chc.ucsb.edu/data/chirps3).\n\nCHIRPS is fundamentally a pentad and monthly product from which other\ntime steps are derived. This daily dataset ('sat') uses daily precipitation\nfrom the NASA IMERG Late V07 data product (IMERG) to partition pentadal\nCHIRPS-v3 precipitation totals into daily amounts.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["1981-01-01T00:00:00Z","2026-09-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHC/CHIRPS/V3/DAILY_SAT","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UCSB-CHC/UCSB-CHC_CHIRPS_V3_DAILY_SAT","rel":"related","title":"Run the example for UCSB-CHC/CHIRPS/V3/DAILY_SAT in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UCSB-CHC/UCSB-CHC_CHIRPS_V3_DAILY_SAT_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHC_CHIRPS_V3_DAILY_SAT#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHC/CHIRPS/V3/DAILY_SAT/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHC/CHIRPS/V3/DAILY_SAT/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UCSB-CHC/CHIRPS/V3/PENTAD","title":"CHIRPS Precipitation Pentad: Climate Hazards Center InfraRed Precipitation With Station Data (Version 3.0)","description":"The Climate Hazards Center Infrared Precipitation with Stations (CHIRPS v3)\nis a 40+ year, high-resolution quasi-global rainfall dataset. It spans\n60&deg;N to 60&deg;S and covers all longitudes, providing data from 1981 to\nnear-present. CHIRPS v3 combines satellite-based thermal infrared rainfall\nestimates with in-situ station observations to produce a 0.05&deg; gridded\nrainfall time series over land. This is version 3.0 of the dataset, for more\ninformation see [the CHC page](https://www.chc.ucsb.edu/data/chirps3).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["1981-01-01T00:00:00Z","2026-09-26T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHC/CHIRPS/V3/PENTAD","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UCSB-CHC/UCSB-CHC_CHIRPS_V3_PENTAD","rel":"related","title":"Run the example for UCSB-CHC/CHIRPS/V3/PENTAD in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UCSB-CHC/UCSB-CHC_CHIRPS_V3_PENTAD_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHC_CHIRPS_V3_PENTAD#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHC/CHIRPS/V3/PENTAD/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHC/CHIRPS/V3/PENTAD/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UCSB-CHG/CHIRPS/DAILY","title":"CHIRPS Precipitation Daily: Climate Hazards Center InfraRed Precipitation With Station Data (Version 2.0 Final)","description":"Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS)\nis a 30+ year quasi-global rainfall dataset. CHIRPS incorporates\n0.05° resolution satellite imagery with in-situ station data\nto create gridded rainfall time series for trend analysis and seasonal\ndrought monitoring.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-50,180,50]]},"temporal":{"interval":[["1981-01-01T00:00:00Z","2026-09-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHG/CHIRPS/DAILY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UCSB-CHG/UCSB-CHG_CHIRPS_DAILY","rel":"related","title":"Run the example for UCSB-CHG/CHIRPS/DAILY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UCSB-CHG/UCSB-CHG_CHIRPS_DAILY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHG/CHIRPS/DAILY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHG/CHIRPS/DAILY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UCSB-CHG/CHIRPS/PENTAD","title":"CHIRPS Precipitation Pentad: Climate Hazards Center InfraRed Precipitation With Station Data (Version 2.0 Final)","description":"Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS)\nis a 30+ year quasi-global rainfall dataset. CHIRPS incorporates\n0.05° resolution satellite imagery with in-situ station data\nto create gridded rainfall time series for trend analysis and seasonal\ndrought monitoring.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-50,180,50]]},"temporal":{"interval":[["1981-01-01T00:00:00Z","2026-09-26T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHG/CHIRPS/PENTAD","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UCSB-CHG/UCSB-CHG_CHIRPS_PENTAD","rel":"related","title":"Run the example for UCSB-CHG/CHIRPS/PENTAD in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UCSB-CHG/UCSB-CHG_CHIRPS_PENTAD_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHG/CHIRPS/PENTAD/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHG/CHIRPS/PENTAD/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UCSB-CHG/CHIRTS/DAILY","title":"CHIRTS Temperature Daily: Climate Hazards Center InfraRed Temperature with Stations daily data product","description":"The Climate Hazards Center InfraRed Temperature with Stations daily\ntemperature data product (CHIRTS-daily; Verdin et al. 2020) is a quasi\nglobal, high-resolution gridded dataset\n(0.05&deg;&thinsp;&times;&thinsp;0.05&deg; resolution, 60&deg;S - 70&deg;N)\nthat provides daily minimum (Tmin) and maximum 2-meter temperatures (Tmax)\nand four derived variables: saturation vapor pressure (SVP),\nvapor pressure deficit (VPD), relative humidity (RH), and heat index (HI).\nCHIRTS temperature products are designed to support analysis of temperature\nextremes and variability, especially in regions with a low density of\nstation observations.\n\nCHIRTS-daily is created by merging a high-quality, high-resolution monthly\nmaximum temperature dataset, the Climate Hazards Center InfraRed\nTemperature with Stations monthly maximum temperature climate record\n(CHIRTSmax; Funk et al. 2019), with daily temperatures from the European\nCentre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5).\nThe result is a high-resolution, daily temperature dataset that maintains\nspatio-temporal information from monthly CHIRTSmax and the daily and diurnal\ntemperature variability from ERA5. Monthly CHIRTSmax are based on:\n\n   1. A Tmax climatology constructed using geostatistical regressions and\n      long-term averages of FAO station observations, ERA5 temperatures, and\n      several other geographic predictors.\n\n   2. Estimates of Tmax variability using approximately 15,000 in situ\n      observations and high-resolution\n      (0.05&deg;&thinsp;&times;&thinsp;0.05&deg;) satellite observations.\n      These data are from Berkeley Earth and Global Telecommunication\n      System (GTS) station reports and cloud-screened GridSat geostationary\n      satellite thermal infrared brightness temperatures.\n\nDaily Tmax values are produced using downscaled ERA5 Tmax anomalies and\nhigh-resolution CHIRTSmax. Daily Tmin values are created by removing the\ndownscaled ERA5 diurnal temperature range (Tmax - Tmin).\nDaily SVP, VPD, RH, and HI are computed using CHIRTS-daily Tmin and Tmax,\nalongside hourly ERA5 inputs for other meteorological variables\n(see Williams et al., 2024, for details). CHIRTS-daily, version 1, covers\nthe period from 1983 to 2016.\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-180,-60,180,70]]},"temporal":{"interval":[["1983-01-01T00:00:00Z","2016-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHG/CHIRTS/DAILY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UCSB-CHG/UCSB-CHG_CHIRTS_DAILY","rel":"related","title":"Run the example for UCSB-CHG/CHIRTS/DAILY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UCSB-CHG/UCSB-CHG_CHIRTS_DAILY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRTS_DAILY#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHG/CHIRTS/DAILY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UCSB-CHG/CHIRTS/DAILY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UCSB/CHC/CMIP6/v1","title":"The Climate Hazards Center (CHC) Coupled Model Intercomparison Project Phase 6 (CHC-CMIP6)","description":"CHC-CMIP6 was explicitly developed to support the analysis of\nclimate-related hazards over the recent past and in the near-future.\n\nThis climate projection dataset contains global, daily gridded data for the\nobservational (1983-2016) and projection (2030 and 2050) periods to be used\nin the identification and monitoring of hydroclimatic extremes. The dataset\ncontains global daily high resolution (0.05&#176;) grids of the Climate\nHazards InfraRed Temperature with Stations (CHIRTS-daily) temperature\nproduct, the Climate Hazards InfraRed Precipitation with Stations (CHIRPS)\nprecipitation product, and ERA5-derived relative humidity, from which Vapor\nPressure Deficits (VPD) and maximum Wet Bulb Globe Temperatures (WBGTmax)\nwere derived.\n\nLarge CMIP6 ensembles from the Shared Socioeconomic Pathway (SSP) 245 and\nSSP 585 scenarios were used to develop high resolution (0.05&#176;) daily\n2030 and 2050 delta fields.\n\nFor more information, see\n[The Climate Hazards Center (CHC) Coupled Model Intercomparison Project\nPhase 6 (CHC-CMIP6)](https://chc.ucsb.edu/data/chc-cmip6).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-50,180,50]]},"temporal":{"interval":[["1983-01-01T00:00:00Z","2016-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UCSB/CHC/CMIP6/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UCSB/UCSB_CHC_CMIP6_v1","rel":"related","title":"Run the example for UCSB/CHC/CMIP6/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UCSB/UCSB_CHC_CMIP6_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UCSB_CHC_CMIP6_v1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UCSB/CHC/CMIP6/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UCSB/CHC/CMIP6/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UK/EA/ENGLAND_1M_TERRAIN/2022","title":"England 1m Composite DTM/DSM (Environment Agency)","description":"The LIDAR Composite DTM/DSM is a raster terrain model covering ~99% of\nEngland at 1m spatial resolution, produced by the UK Environment Agency in\n2022. The model contains 3 bands of terrain data: a Digital Terrain Model\n(DTM), a first return Digital Surface Model (DSM), and a last return DSM.\n\nThe DTM is produced from the last or only laser pulse returned to the\nsensor. Surface objects are removed from the DSM, using bespoke algorithms\nand manual editing of the data, to produce a terrain model of just the\nsurface. The DTM is derived from a combination of EA Time Stamped archive\nand National LIDAR Program surveys, which have been merged and re-sampled\nto give the best possible coverage. Where repeat surveys have been\nundertaken the newest, best resolution data is used. Where data was\nresampled a bilinear interpolation was used before being merged. The 2022\nLIDAR Composite contains surveys undertaken between 6th June 2000 and 2nd\nApril 2022.\n\nThe first return DSM is produced from the first or only laser pulse returned\nto the sensor and includes heights of objects, such as vehicles, buildings\nand vegetation, as well as the terrain surface where the first or only\nreturn was the ground. The first return DSM is derived from data captured as\npart of the National LIDAR Program between 11 November 2016 and 5th May\n2022. This program divided England into ~300 blocks for survey over\ncontinuous winters from 2016 onwards. These surveys are merged together to\ncreate the first return LIDAR composite using a feathering technique along\nthe overlaps to remove any small differences in elevation between surveys.\nPlease refer to the data provider's metadata index catalogs which show for\nany location which survey was used in the production of the LIDAR composite.\nThe first return DSM will not match in coverage or extent of the last return\nDSM, as the last return DSM composite is produced from both the National\nLIDAR Program and Timeseries surveys.\n\nThe last return DSM is produced from the last or only laser pulse returned\nto the sensor and includes heights of objects, such as vehicles, buildings\nand vegetation, as well as the terrain surface. The last return DTM is\nderived from a combination of EA Time Stamped archive and National LIDAR\nProgram surveys, which have been merged and re-sampled to give the best\npossible coverage. Where repeat surveys have been undertaken the newest,\nbest resolution data is used. Where data was resampled a bilinear\ninterpolation was used before being merged. The 2022 LIDAR Composite\ncontains surveys undertaken between 6th June 2000 and 2nd April 2022. Please\nrefer to the data provider's metadata index catalogs which show for any\nlocation which survey was used in the production of the LIDAR composite.\n\nThe data is aligned to the OS National grid and presented in metres,\nreferenced to Ordinance Survey Newlyn and using the OSTN'15 transformation\nmethod. All individual LIDAR surveys going into the production of the\ncomposites had a vertical accuracy of +/-15cm root-mean-square error.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-06-06T00:00:00Z","2022-04-02T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UK/EA/ENGLAND_1M_TERRAIN/2022","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UK/UK_EA_ENGLAND_1M_TERRAIN_2022","rel":"related","title":"Run the example for UK/EA/ENGLAND_1M_TERRAIN/2022 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UK/UK_EA_ENGLAND_1M_TERRAIN_2022_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UK_EA_ENGLAND_1M_TERRAIN_2022#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.data.gov.uk/dataset/01b3ee39-da3f-47b6-83da-dc98e73a461f/lidar-composite-digital-terrain-model-dtm-1m","rel":"source"},{"href":"https://www.data.gov.uk/dataset/92534f24-0b92-4b28-9986-347cf6678b39/lidar-composite-first-return-digital-surface-model-fz_dsm-1m","rel":"source"},{"href":"https://environment.data.gov.uk/dataset/9ba4d5ac-d596-445a-9056-dae3ddec0178","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UK/EA/ENGLAND_1M_TERRAIN/2022/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UK/NHM/BII/V1","title":"Biodiversity Intactness Index, V1","description":"The Biodiversity Intactness Index (BII) indicates the average abundance\nof a large, taxonomically, and ecologically diverse set of naturally-occurring\nspecies in a terrestrial area, relative to a baseline with minimal human\nimpacts. BII was first proposed by Scholes & Biggs (2005). BII estimates the\nstatus of local terrestrial biodiversity; average BII is meaningful at any\nspatial scale, making it easy to estimate status and trends within any\ndesired region (e.g. UN subregion, country or biome).\n\nBII was first estimated globally by Newbold et al. (2016) as part of the\n[PREDICTS project](www.nhm.ac.uk/our-science/research/projects/predicts.html)\nby combining models of overall abundance with models of abundance-based\ncompositional similarity and global fine-scale (1km) estimates of land use\nand other pressures. The modelling framework has now been refined,\nespecially for compositional similarity, improving the ability to detect human\nimpacts on assemblage composition. This approach to BII estimation is\ndiscussed in detail by Purvis et al. (2018).\n\nBII is estimated based on statistical models of how local biodiversity\nresponds to land use and related pressures, it can be projected for any\npast or future date for which estimates of pressures are available.\nAs a result, BII can report not only on current status and recent\ntrends (using observed data on pressures), but also on the longer history\nof biodiversity change; and it can be used for policy screening of scenarios.\n\nMore information is available in the provider's\n[Dataset Description](https://www.bipindicators.net/indicators/biodiversity-intactness-index).\n\nNote:\n\n*   It is a global dataset, though at present, only data for the year 2005 is\n    available.\n\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2005-01-01T00:00:00Z","2005-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UK/NHM/BII/V1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UK/UK_NHM_BII_V1","rel":"related","title":"Run the example for UK/NHM/BII/V1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UK/UK_NHM_BII_V1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UK_NHM_BII_V1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UK/NHM/BII/V1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UK/NHM/BII/V1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UMD/GLAD/PRIMARY_HUMID_TROPICAL_FORESTS/v1","title":"Primary Humid Tropical Forests","description":"Primary humid tropical forests provide numerous global ecosystem services,\nbut are under continuing threat of clearing from economic drivers. To facilitate\nnational land use planning and balancing the goals of economic development and\nmaintenance of ecosystem services, a primary humid tropical forest map was\ncreated by the UMD GLAD team. The primary forest extent was mapped\nfor the year 2001 at a spatial resolution of 30 meters using globally acquired,\nfree-of-charge, and consistently processed Landsat imagery.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2002-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UMD/GLAD/PRIMARY_HUMID_TROPICAL_FORESTS/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UMD/UMD_GLAD_PRIMARY_HUMID_TROPICAL_FORESTS_v1","rel":"related","title":"Run the example for UMD/GLAD/PRIMARY_HUMID_TROPICAL_FORESTS/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UMD/UMD_GLAD_PRIMARY_HUMID_TROPICAL_FORESTS_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UMD_GLAD_PRIMARY_HUMID_TROPICAL_FORESTS_v1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UMD/GLAD/PRIMARY_HUMID_TROPICAL_FORESTS/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UMD/GLAD/PRIMARY_HUMID_TROPICAL_FORESTS/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UMD/hansen/global_forest_change_2013","title":"Hansen Global Forest Change v1.0 (2000-2012) [deprecated]","description":"Results from time-series analysis of Landsat images in characterizing\nglobal forest extent and change.\n\nThe 'first' and 'last' bands are reference multispectral imagery from the\nfirst and last available years for Landsat spectral bands 3, 4, 5, and 7.\nReference composite imagery represents median observations from a set of\nquality-assessed growing-season observations for each of these bands.\n\nPlease see the [User Notes](https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.0.html)\nfor this Version 1.0, as well as the associated journal article:\nHansen, Potapov, Moore, Hancher et al. \"High-resolution global maps of\n21st-century forest cover change.\" Science 342.6160 (2013): 850-853.\n\nNote that updated versions of this data are available. The newest version,\nVersion 1.9 (produced with data through 2021), is available as\nUMD/hansen/global_forest_change_2021_v1_9.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2013-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UMD/hansen/global_forest_change_2013","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UMD/UMD_hansen_global_forest_change_2013","rel":"related","title":"Run the example for UMD/hansen/global_forest_change_2013 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UMD/UMD_hansen_global_forest_change_2013_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2013#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/UMD/UMD_hansen_global_forest_change_2025_v1_13.json","rel":"latest-version","title":"UMD/hansen/global_forest_change_2025_v1_13","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/UMD/UMD_hansen_global_forest_change_2014.json","rel":"successor-version","title":"UMD/hansen/global_forest_change_2014","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UMD/hansen/global_forest_change_2013/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UMD/hansen/global_forest_change_2014","title":"Hansen Global Forest Change v1.1 (2000-2013) [deprecated]","description":"Results from time-series analysis of Landsat images in characterizing\nglobal forest extent and change.\n\nThe 'first' and 'last' bands are reference multispectral imagery from the\nfirst and last available years for Landsat spectral bands 3, 4, 5, and 7.\nReference composite imagery represents median observations from a set of\nquality-assessed growing-season observations for each of these bands.\n\nPlease see the [User Notes](https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.1.html)\nfor this Version 1.1 update, as well as the associated journal article:\nHansen, Potapov, Moore, Hancher et al. \"High-resolution global maps of\n21st-century forest cover change.\" Science 342.6160 (2013): 850-853.\n\nNote that updated versions of this data are available. The newest version,\nVersion 1.9 (produced with data through 2021), is available as\nUMD/hansen/global_forest_change_2021_v1_9.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2014-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UMD/hansen/global_forest_change_2014","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UMD/UMD_hansen_global_forest_change_2014","rel":"related","title":"Run the example for UMD/hansen/global_forest_change_2014 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UMD/UMD_hansen_global_forest_change_2014_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2014#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/UMD/UMD_hansen_global_forest_change_2025_v1_13.json","rel":"latest-version","title":"UMD/hansen/global_forest_change_2025_v1_13","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/UMD/UMD_hansen_global_forest_change_2013.json","rel":"predecessor-version","title":"UMD/hansen/global_forest_change_2013","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/UMD/UMD_hansen_global_forest_change_2015.json","rel":"successor-version","title":"UMD/hansen/global_forest_change_2015","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UMD/hansen/global_forest_change_2014/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UMD/hansen/global_forest_change_2015","title":"Hansen Global Forest Change v1.2 (2000-2014) [deprecated]","description":"Results from time-series analysis of Landsat images in characterizing\nglobal forest extent and change.\n\nThe 'first' and 'last' bands are reference multispectral imagery from the\nfirst and last available years for Landsat spectral bands 3, 4, 5, and 7.\nReference composite imagery represents median observations from a set of\nquality-assessed growing-season observations for each of these bands.\n\nPlease see the [User Notes](https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.2.html)\nfor this Version 1.2 update, as well as the associated journal article:\nHansen, Potapov, Moore, Hancher et al. \"High-resolution global maps of\n21st-century forest cover change.\" Science 342.6160 (2013): 850-853.\n\nNote that updated versions of this data are available. The newest version,\nVersion 1.9 (produced with data through 2021), is available as\nUMD/hansen/global_forest_change_2021_v1_9.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2015-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UMD/hansen/global_forest_change_2015","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UMD/UMD_hansen_global_forest_change_2015","rel":"related","title":"Run the example for UMD/hansen/global_forest_change_2015 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UMD/UMD_hansen_global_forest_change_2015_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2015#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/UMD/UMD_hansen_global_forest_change_2025_v1_13.json","rel":"latest-version","title":"UMD/hansen/global_forest_change_2025_v1_13","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/UMD/UMD_hansen_global_forest_change_2014.json","rel":"predecessor-version","title":"UMD/hansen/global_forest_change_2014","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/UMD/UMD_hansen_global_forest_change_2015_v1_3.json","rel":"successor-version","title":"UMD/hansen/global_forest_change_2015_v1_3","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UMD/hansen/global_forest_change_2015/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UMD/hansen/global_forest_change_2015_v1_3","title":"Hansen Global Forest Change v1.3 (2000-2015) [deprecated]","description":"Results from time-series analysis of Landsat images in characterizing\nglobal forest extent and change.\n\nThe 'first' and 'last' bands are reference multispectral imagery from the\nfirst and last available years for Landsat spectral bands 3, 4, 5, and 7.\nReference composite imagery represents median observations from a set of\nquality-assessed growing-season observations for each of these bands.\n\nPlease see the [User Notes](https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.3.html)\nfor this Version 1.3 update, as well as the associated journal article:\nHansen, Potapov, Moore, Hancher et al. \"High-resolution global maps of\n21st-century forest cover change.\" Science 342.6160 (2013): 850-853.\n\nNote that updated versions of this data are available. 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See the [supplement](https://static-content.springer.com/esm/art%3A10.1038%2Fs43247-023-01152-2/MediaObjects/43247_2023_1152_MOESM3_ESM.docx)\nto our [paper](https://www.nature.com/articles/s43247-023-01152-2) on the impacts of irrigation on streamflow.\n\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-124.5,31.3,-99,49]]},"temporal":{"interval":[["1986-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UMT/Climate/IrrMapper_RF/v1_2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UMT/UMT_Climate_IrrMapper_RF_v1_2","rel":"related","title":"Run the example for UMT/Climate/IrrMapper_RF/v1_2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UMT/UMT_Climate_IrrMapper_RF_v1_2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UMT_Climate_IrrMapper_RF_v1_2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/UMT/UMT_Climate_IrrMapper_RF_v1_2.json","rel":"latest-version","title":"UMT/Climate/IrrMapper_RF/v1_2","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/UMT/UMT_Climate_IrrMapper_RF_v1_1.json","rel":"predecessor-version","title":"UMT/Climate/IrrMapper_RF/v1_1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UMT/Climate/IrrMapper_RF/v1_2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UMT/Climate/IrrMapper_RF/v1_2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UMT/NTSG/v2/LANDSAT/GPP","title":"Landsat Gross Primary Production CONUS","description":"The Landsat Gross Primary Production (GPP) CONUS dataset estimates GPP using\nLandsat Surface Reflectance for CONUS. GPP is the amount of\ncarbon captured by plants in an ecosystem and is an essential component in\nthe calculations of Net Primary Production (NPP). GPP is calculated using the\nMOD17 algorithm (see [MOD17 User\nGuide](https://www.ntsg.umt.edu/files/modis/MOD17UsersGuide2015_v3.pdf)) with\nLandsat Surface Reflectance, gridMET, and the National Land Cover Database.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-124.84,24.42,-64.82,49.72]]},"temporal":{"interval":[["1986-01-01T00:00:00Z","2021-12-19T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/LANDSAT/GPP","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UMT/UMT_NTSG_v2_LANDSAT_GPP","rel":"related","title":"Run the example for UMT/NTSG/v2/LANDSAT/GPP in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UMT/UMT_NTSG_v2_LANDSAT_GPP_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UMT_NTSG_v2_LANDSAT_GPP#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/LANDSAT/GPP/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/LANDSAT/GPP/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UMT/NTSG/v2/LANDSAT/NPP","title":"Landsat Net Primary Production CONUS","description":"The Landsat Net Primary Production (NPP) CONUS dataset estimates NPP using\nLandsat Surface Reflectance for CONUS. NPP is the amount of\ncarbon captured by plants in an ecosystem, after accounting for losses due to\nrespiration. NPP is calculated using the MOD17 algorithm (see [MOD17 User\nGuide](https://www.ntsg.umt.edu/files/modis/MOD17UsersGuide2015_v3.pdf)) with\nLandsat Surface Reflectance, gridMET, and the National Land Cover Database.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-124.84,24.42,-64.82,49.72]]},"temporal":{"interval":[["1986-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/LANDSAT/NPP","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UMT/UMT_NTSG_v2_LANDSAT_NPP","rel":"related","title":"Run the example for UMT/NTSG/v2/LANDSAT/NPP in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UMT/UMT_NTSG_v2_LANDSAT_NPP_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UMT_NTSG_v2_LANDSAT_NPP#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/LANDSAT/NPP/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/LANDSAT/NPP/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UMT/NTSG/v2/MODIS/GPP","title":"MODIS Gross Primary Production CONUS","description":"The MODIS Gross Primary Production (GPP) CONUS dataset estimates GPP using\nMODIS Surface Reflectance for CONUS. GPP is the amount of\ncarbon captured by plants in an ecosystem and is an essential component in\nthe calculations of Net Primary Production (NPP). GPP is calculated using the\nMOD17 algorithm (see [MOD17 User\nGuide](https://www.ntsg.umt.edu/files/modis/MOD17UsersGuide2015_v3.pdf)) with\nMODIS Surface Reflectance, gridMET, and the National Land Cover Database.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-124.84,24.42,-64.82,49.72]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2021-12-19T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/MODIS/GPP","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UMT/UMT_NTSG_v2_MODIS_GPP","rel":"related","title":"Run the example for UMT/NTSG/v2/MODIS/GPP in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UMT/UMT_NTSG_v2_MODIS_GPP_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UMT_NTSG_v2_MODIS_GPP#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/MODIS/GPP/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/MODIS/GPP/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UMT/NTSG/v2/MODIS/NPP","title":"MODIS Net Primary Production CONUS","description":"The MODIS Net Primary Production (NPP) CONUS dataset estimates NPP using\nMODIS Surface Reflectance for CONUS. NPP is the amount of\ncarbon captured by plants in an ecosystem, after accounting for losses due to\nrespiration. NPP is calculated using the MOD17 algorithm (see [MOD17 User\nGuide](https://www.ntsg.umt.edu/files/modis/MOD17UsersGuide2015_v3.pdf)) with\nMODIS Surface Reflectance, gridMET, and the National Land Cover Database.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-124.84,24.42,-64.82,49.72]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2019-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/MODIS/NPP","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UMT/UMT_NTSG_v2_MODIS_NPP","rel":"related","title":"Run the example for UMT/NTSG/v2/MODIS/NPP in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UMT/UMT_NTSG_v2_MODIS_NPP_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UMT_NTSG_v2_MODIS_NPP#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/MODIS/NPP/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UMT/NTSG/v2/MODIS/NPP/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UQ/murray/Intertidal/v1_1/data_mask","title":"Murray Global Intertidal Change Data Mask","description":"The Murray Global Intertidal Change Dataset contains global maps of tidal flat\necosystems produced via a supervised classification of 707,528 Landsat\nArchive images. Each pixel was classified into tidal flat, permanent water\nor other with reference to a globally distributed set of training data.\n\nThe classification was implemented along the entire global coastline\nbetween 60° North and 60° South from 1 January 1984 to 31 December 2016.\nThe image collection consists consists of a time-series of 11 global maps\nof tidal flats at 30m pixel resolution for set time-periods (1984-1986;\n1987-1989; 1990-1992; 1993-1995; 1996-1998; 1999-2001; 2002-2004;\n2005-2007; 2008-2010; 2011-2013; 2014-2016)\n\nFlag indicating the spatial limits of the implementation of the tidal flat\nclassifier as defined by altitudinal (+100m) and bathymetric (-100m) limits.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UQ/murray/Intertidal/v1_1/data_mask","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UQ/UQ_murray_Intertidal_v1_1_data_mask","rel":"related","title":"Run the example for UQ/murray/Intertidal/v1_1/data_mask in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UQ/UQ_murray_Intertidal_v1_1_data_mask_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UQ_murray_Intertidal_v1_1_data_mask#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.1038/s41586-018-0805-8","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UQ/murray/Intertidal/v1_1/data_mask/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UQ/murray/Intertidal/v1_1/global_intertidal","title":"Murray Global Intertidal Change Classification","description":"The Murray Global Intertidal Change Dataset contains global maps of tidal flat\necosystems produced via a supervised classification of 707,528 Landsat\nArchive images. Each pixel was classified into tidal flat, permanent water\nor other with reference to a globally distributed set of training data.\n\nThe classification was implemented along the entire global coastline\nbetween 60° North and 60° South from 1 January 1984 to 31 December 2016.\nThe image collection consists consists of a time-series of 11 global maps\nof tidal flats at 30m pixel resolution for set time-periods (1984-1986;\n1987-1989; 1990-1992; 1993-1995; 1996-1998; 1999-2001; 2002-2004;\n2005-2007; 2008-2010; 2011-2013; 2014-2016)\n\nThis product depicts tidal flat ecosystems around the global coastline.\n\nPixels classified as tidal flat in the analysis represent several types of\ntidal flat ecosystems, including unconsolidated fine-grain sediments (tidal\nmudflats), unconsolidated coarse-grain sediments (tidal sand flats), and\nconsolidated sediments, organic material or rocks (wide tidal\nrock-platforms), while excluding spectral signatures indicating the\npresence of vegetation dominated intertidal ecosystems such as mangroves\nand vegetated marshes. The analysis aimed to identify pixels that are\nsubject to regular tidal inundation, and therefore may also include other\nintertidal systems where intertidal dynamics are observable.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UQ/murray/Intertidal/v1_1/global_intertidal","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UQ/UQ_murray_Intertidal_v1_1_global_intertidal","rel":"related","title":"Run the example for UQ/murray/Intertidal/v1_1/global_intertidal in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UQ/UQ_murray_Intertidal_v1_1_global_intertidal_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UQ_murray_Intertidal_v1_1_global_intertidal#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.1038/s41586-018-0805-8","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UQ/murray/Intertidal/v1_1/global_intertidal/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UQ/murray/Intertidal/v1_1/global_intertidal/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UQ/murray/Intertidal/v1_1/qa_pixel_count","title":"Murray Global Intertidal Change QA Pixel Count","description":"The Murray Global Intertidal Change Dataset contains global maps of tidal flat\necosystems produced via a supervised classification of 707,528 Landsat\nArchive images. Each pixel was classified into tidal flat, permanent water\nor other with reference to a globally distributed set of training data.\n\nThe classification was implemented along the entire global coastline\nbetween 60° North and 60° South from 1 January 1984 to 31 December 2016.\nThe image collection consists consists of a time-series of 11 global maps\nof tidal flats at 30m pixel resolution for set time-periods (1984-1986;\n1987-1989; 1990-1992; 1993-1995; 1996-1998; 1999-2001; 2002-2004;\n2005-2007; 2008-2010; 2011-2013; 2014-2016)\n\nThe number of Landsat images used to develop the Landsat covariate layers\nin each time step of the tidal flat classification. Each image in the image\ncollection refers to a single time step.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,60]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UQ/murray/Intertidal/v1_1/qa_pixel_count","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UQ/UQ_murray_Intertidal_v1_1_qa_pixel_count","rel":"related","title":"Run the example for UQ/murray/Intertidal/v1_1/qa_pixel_count in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UQ/UQ_murray_Intertidal_v1_1_qa_pixel_count_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UQ_murray_Intertidal_v1_1_qa_pixel_count#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.1038/s41586-018-0805-8","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UQ/murray/Intertidal/v1_1/qa_pixel_count/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UQ/murray/Intertidal/v1_1/qa_pixel_count/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USDA/NAIP/DOQQ","title":"NAIP: National Agriculture Imagery Program","description":"The National Agriculture Imagery Program (NAIP) acquires aerial imagery\nduring the agricultural growing seasons in the continental U.S.\n\nNAIP projects are contracted each year based upon available funding and the\nimagery acquisition cycle. Beginning in 2003, NAIP was acquired on\na 5-year cycle. 2008 was a transition year, and a three-year cycle began\nin 2009.\n\nNAIP imagery is acquired at a one-meter ground sample distance (GSD) with a\nhorizontal accuracy that matches within six meters of photo-identifiable\nground control points, which are used during image inspection.\n\nOlder images were collected using 3 bands (Red, Green, and Blue: RGB), but\nnewer imagery is usually collected with an additional near-infrared band\n(RGBN). RGB asset ids begin with 'n_', NRG asset ids begin with 'c_', RGBN\nasset ids begin with 'm_'.\n\nSome older images have GSD of 2 meters.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-124.84,24.42,-64.82,49.72]]},"temporal":{"interval":[["2002-06-15T00:00:00Z","2023-11-17T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USDA/NAIP/DOQQ","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USDA/USDA_NAIP_DOQQ","rel":"related","title":"Run the example for USDA/NAIP/DOQQ in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USDA/USDA_NAIP_DOQQ_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USDA_NAIP_DOQQ#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USDA/NAIP/DOQQ/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USDA/NAIP/DOQQ/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USDA/NASS/CDL","title":"USDA NASS Cropland Data Layers","description":"The Cropland Data Layer (CDL) is a crop-specific land cover data layer\ncreated annually for the continental United States using moderate\nresolution satellite imagery and extensive agricultural ground truth.\nThe CDL is created by the USDA, National Agricultural Statistics\nService (NASS), Research and Development Division, Geospatial\nInformation Branch, Spatial Analysis Research Section.\n\nFor detailed FAQ please visit [CropScape and Cropland Data\nLayers - FAQs](https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php).\n\nTo explore details about the classification accuracies and utility of\nthe data, see\n[state-level omission and commission errors by crop type and year](https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php).\n\nThe asset date is aligned with the calendar year of harvest. For most crops\nthe planted and harvest year are the same. Some exceptions: winter wheat is\nunique, as it is planted in the prior year. A hay crop like alfalfa could\nhave been planted years prior.\n\nFor winter wheat the data also have a class called\n\"Double Crop Winter Wheat/Soybeans\". Some mid-latitude areas of the US have\nconditions such that a second crop (usually soybeans) can be planted\nimmediately after the harvest of winter wheat and itself still be harvested\nwithin the same year. So for mapping winter wheat areas use both classes\n(use both values 24 and 26).\n\nWhile the CDL date is aligned with year of harvest, the map itself is\nmore representative of what was planted. In other words, a small percentage\nof fields on a given year will not be harvested.\n\nSome non-agricultural categories are duplicate due to\n[two very different epochs in methodology](https://www.google.com/url?sa=D&q=https%3A%2F%2Fwww.nass.usda.gov%2FResearch_and_Science%2FCropland%2F).\n\nThe non-ag codes 63-65 and 81-88 are holdovers from the older\nmethodology and will only appear in CDLs from 2007 and earlier. The non-ag\ncodes from 111-195 are from the current methodology which uses the USGS NLCD\nas non-ag training and will only appear in CDLs 2007 and newer.\n\n2007 was a transition year so there may be both sets of categories in the\n2007 national product but will not appear within the same state.\n\nNote: The 2024 CDL only has the data band. The cultivated and\nconfidence bands are yet to be released by the provider.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-127,24,-65,51]]},"temporal":{"interval":[["1997-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USDA/NASS/CDL","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USDA/USDA_NASS_CDL","rel":"related","title":"Run the example for USDA/NASS/CDL in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USDA/USDA_NASS_CDL_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USDA/NASS/CDL/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USDA/NASS/CDL/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USDA/SOLUS100/V0","title":"SOLUS: Soil properties of the conterminous United States at 100-m resolution","description":"SOLUS (Soil Landscapes of the United States) is a national map product\ndeveloped by the National Cooperative Soil Survey that is focused on\nproviding a consistent set of spatially continuous soil property maps to\nsupport large scope soil investigations and land use decisions. SOLUS maps\nuse a digital soil mapping framework that combines multiple sources of soil\nsurvey data with environmental covariate data and machine learning.\nSOLUS100 is 100 m spatial resolution. Each 100 m raster cell represents\na 100 m by 100 m square on the ground with soil property values\nestimated at seven depths: 0, 5, 15, 30, 60, 100, and 150 cm.\n\nIt provides estimates for soil organic carbon, bulk density, pH,\ntexture fractions (clay, silt, sand), calcium carbonate equivalent,\nand depth to an anylithic contact (e.g. bedrock).\n\nEach soil property is represented by a separate image in this collection.\nFor each soil property except depth to anylithic contact, bands are\nprovided for 7 depths: 0, 5, 15, 30, 60, 100, and 150cm. For each depth,\n4 layers are provided: predicted value, lower 95% confidence limit,\nupper 95% confidence limit, and a relative property index.\nFor depth to anylithic contact, a single image provides the 4 layers\nrepresenting depth in cm.\n\nImage IDs correspond to soil properties:\n\n*   `soc`: Soil organic carbon (%)\n*   `bd`: Bulk density (g/cm^3)\n*   `phh2o`: pH in water\n*   `claytotal`: Clay (%)\n*   `silttotal`: Silt (%)\n*   `sandtotal`: Sand (%)\n*   `caco3`: Calcium Carbonate (%)\n*   `anylithicdpt`: Depth to an Anylithic Contact (cm)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-125,24,-66,50]]},"temporal":{"interval":[["2023-12-15T00:00:00Z","2024-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USDA/SOLUS100/V0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USDA/USDA_SOLUS100_V0","rel":"related","title":"Run the example for USDA/SOLUS100/V0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USDA/USDA_SOLUS100_V0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USDA_SOLUS100_V0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USDA/SOLUS100/V0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USDA/SOLUS100/V0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USDA/WRC/v0","title":"Wildfire Risk to Communities v0","description":"This dataset depicts components of wildfire risk for all lands in the United\nStates that: 1) are landscape-wide (i.e., measurable at every pixel across \nthe landscape); and 2) represent in situ risk - risk at the location where\nthe adverse effects take place on the landscape.\n\nNational wildfire hazard datasets of annual burn probability and fire\nintensity, generated by the USDA Forest Service, Rocky Mountain Researc\nStation and Pyrologix LLC, form the foundation of the Wildfire Risk to\nCommunities data. Vegetation and wildland fuels data from LANDFIRE 2020\n(version 2.2.0) were used as input to two different but related geospatial\nfire simulation systems. Annual burn probability was produced with the USFS\ngeospatial fire simulator (FSim) at a relatively coarse cell size of 270\nmeters (m). The burn probability raster data was upsampled to the native\n30 m resolution of the LANDFIRE fuel and vegetation data, to bring the data\ndown to a finer resolution more useful for assessing hazard and risk to\ncommunities. In this upsampling process, the provider also spread values of\nmodeled burn probability into developed areas represented in LANDFIRE fuels\ndata as non-burnable.\n\nBurn probability rasters represent landscape\nconditions as of the end of 2020. Fire intensity characteristics were\nmodeled at 30 m resolution using a process that performs a comprehensive\nset of FlamMap runs spanning the full range of weather-related\ncharacteristics that occur during a fire season and then integrates those\nruns into a variety of results based on the likelihood of those weather\ntypes occurring. Before the fire intensity modeling, the LANDFIRE 2020 data\nwere updated to reflect fuels disturbances occurring in 2021 and 2022. As\nsuch, the fire intensity datasets represent landscape conditions as of the\nend of 2022.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-67.933,18.854,63.904]]},"temporal":{"interval":[["2020-12-31T00:00:00Z","2022-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USDA/WRC/v0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USDA/USDA_WRC_v0","rel":"related","title":"Run the example for USDA/WRC/v0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USDA/USDA_WRC_v0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USDA_WRC_v0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USDA/WRC/v0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USDA/WRC/v0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USFS/GTAC/LCMS/v2020-5","title":"USFS Landscape Change Monitoring System v2020.5 (Conterminous United States and Southeastern Alaska) [deprecated]","description":"This product is part of the Landscape Change Monitoring System (LCMS) data suite.\nIt shows LCMS-modeled change, land cover, and/or land use classes for each year. This\nLCMS version covers the conterminous United States (CONUS) and Southeastern Alaska (SEAK).\n\nLCMS is a remote sensing-based system for mapping and monitoring landscape change across the\nUnited States. Its objective is to develop a consistent approach using the latest technology\nand advancements in change detection to produce a \"best available\" map of landscape change.\n\nOutputs include three annual products: change, land cover, and land use.\nChange relates specifically to vegetation cover and includes slow loss, fast loss (which also\nincludes hydrologic changes such as inundation or desiccation), and gain. These values are\npredicted for each year of the Landsat time series and serve as the foundational products for\nLCMS. Land cover and land use maps depict life-form level land cover and broad-level land use\nfor each year.\n\nBecause no algorithm performs best in all situations, LCMS uses an ensemble of models as\npredictors, which improves map accuracy across a range of ecosystems and change processes\n(Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer\na holistic depiction of landscape change across the United States over the past four decades.\n\nPredictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs\nfrom the LandTrendr and CCDC change detection algorithms, and terrain information. These\ncomponents are all accessed and processed using Google Earth Engine (Gorelick et al., 2017).\n\nTo produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and\nTDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to\nLandsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual\nmedoid is then computed to summarize each year into a single composite.\n\nThe composite time series is temporally segmented using LandTrendr\n(Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018).\n\nAll cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm\n(Zhu and Woodcock, 2014).\n\nThe raw composite values, LandTrendr fitted values, pair-wise differences, segment duration,\nchange magnitude, and slope, and CCDC September 1 sine and cosine coefficients\n(first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine\nof aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National\nElevation Dataset (NED), are used as independent predictor variables in a Random Forest\n(Breiman, 2001) model.\n\nReference data are collected using TimeSync, a web-based tool that helps\nanalysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).\n\n**Additional Resources**\n\n* [A more detailed code example of using LCMS data.](https://github.com/google/earthengine-community/blob/master/datasets/scripts/LCMS_Visualization.js)\n\n* The [LCMS Data Explorer](https://apps.fs.usda.gov/lcms-viewer) is a web-based application that\n  provides users the ability to view, analyze, summarize and download LCMS data.\n\n* Please see the [LCMS Methods Brief](https://data.fs.usda.gov/geodata/rastergateway/LCMS/LCMS_v2020-5_Methods.pdf)\n  for more detailed information regarding methods and accuracy assessment, or the\n  [LCMS Geodata Clearinghouse](https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.php)\n  for data downloads, metadata, and support documents.\n\nContact [sm.fs.lcms@usda.gov] with any\nquestions or specific data requests.\n\n* **Breiman, L., 2001.**\nMachine Learning. *Springer*,\n45(3): 261-277\n[doi:10.1023/a:1017934522171](https://doi.org/10.1023/a:1017934522171)\n\n* **Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K., 2019.**\nEmpirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM\ntop of atmosphere spectral characteristics over the conterminous United States. In Remote\nSensing of Environment. *Science Direct*,\n221: 274-285\n[doi:10.1016/j.rse.2018.11.012](https://doi.org/10.1016/j.rse.2018.11.012)\n\n* **Cohen, W. B., Yang, Z., and Kennedy, R., 2010.**\nDetecting trends in forest disturbance and recovery using yearly Landsat time series: 2.\nTimeSync - Tools for calibration and validation. In Remote Sensing of Environment.\n*Science Direct*,\n114(12): 2911-2924\n[doi:10.1016/j.rse.2010.07.010](https://doi.org/10.1016/j.rse.2010.07.010)\n\n* **Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N., 2018.**\nA LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of\nEnvironment. *Science Direct*,\n205: 131-140\n[doi:10.1016/j.rse.2017.11.015](https://doi.org/10.1016/j.rse.2017.11.015)\n\n* **Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z., 2018.**\nMapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of\nEnvironment. *Science Direct*,\n204: 717-728\n[doi:10.1016/j.rse.2017.09.029](https://doi.org/10.1016/j.rse.2017.09.029)\n\n* **Kennedy, R. E., Yang, Z., and Cohen, W. B., 2010.**\nDetecting trends in forest disturbance and recovery using yearly Landsat time series: 1.\nLandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment.\n*Science Direct*,\n114(12): 2897-2910\n[doi:10.1016/j.rse.2010.07.008](https://doi.org/10.1016/j.rse.2010.07.008)\n\n* **Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S., 2018.**\nImplementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing. *MDPI*,\n10(5): 691\n[doi:10.3390/rs10050691](https://doi.org/10.3390/rs10050691)\n\n* **Weiss, A.D., 2001.**\nTopographic position and landforms analysis Poster Presentation, ESRI Users Conference,\nSan Diego, CA.\n\n* **Zhu, Z., and Woodcock, C. E. (2012).**\nObject-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of\nEnvironment. *Science Direct*,\n118: 83-94\n[doi:10.1016/j.rse.2011.10.028](https://doi.org/10.1016/j.rse.2011.10.028)\n\n* **Zhu, Z., and Woodcock, C. E., 2014.**\nContinuous change detection and classification of land cover using all available Landsat data.\nIn Remote Sensing of Environment. *Science Direct*,\n144: 152-171\n[doi:10.1016/j.rse.2014.01.011](https://doi.org/10.1016/j.rse.2014.01.011)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-135.286387,20.38379,-56.446306,52.459364]]},"temporal":{"interval":[["1985-06-01T00:00:00Z","2020-09-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2020-5","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USFS/USFS_GTAC_LCMS_v2020-5","rel":"related","title":"Run the example for USFS/GTAC/LCMS/v2020-5 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USFS/USFS_GTAC_LCMS_v2020-5_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_LCMS_v2020-5#terms-of-use","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.php","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_LCMS_v2021-7.json","rel":"successor-version","title":"USFS/GTAC/LCMS/v2021-7","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2020-5/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2020-5/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USFS/GTAC/LCMS/v2020-6","title":"USFS Landscape Change Monitoring System v2020.6 (Puerto Rico - US Virgin Islands only) [deprecated]","description":"This product is part of the Landscape Change Monitoring System (LCMS) data\nsuite. It shows LCMS-modeled change, land cover, and/or land use classes\nfor each year. This LCMS version covers Puerto Rico U.S. Virgin Islands (PRUSVI).\n\nLCMS is a remote sensing-based system for mapping and monitoring landscape\nchange across the United States. Its objective is to develop a consistent\napproach using the latest technology and advancements in change detection to\nproduce a \"best available\" map of landscape change.\n\nOutputs include three annual products: change, land cover, and land use.\nChange relates specifically to vegetation cover and includes slow loss, fast\nloss (which also includes hydrologic changes such as inundation or\ndesiccation), and gain. These values are predicted for each year of the\nLandsat time series and serve as the foundational products for LCMS. Land\ncover and land use maps depict life-form level land cover and broad-level\nland use for each year.\n\nBecause no algorithm performs best in all situations, LCMS uses an ensemble\nof models as predictors, which improves map accuracy across a range of\necosystems and change processes (Healey et al., 2018). The resulting suite\nof LCMS change, land cover, and land use maps offer a holistic depiction of\nlandscape change across the United States over the past four decades.\n\nPredictor layers for the LCMS model include outputs from the LandTrendr and\nCCDC change detection algorithms, and terrain information. These components\nare all accessed and processed using Google Earth Engine (Gorelick et al.,\n2017).\n\nLandsat Tier 1 and Sentinel 2A, 2B Level-1C top of atmosphere reflectance\ndata are used directly in CCDC and to produce annual composites for\nLandTrendr. cFmask (Zhu and Woodcock, 2012) (Landsat-only), cloudScore\n(Chastain et al., 2019) (Landsat-only), and s2cloudless (Sentinel-Hub, 2021)\n(Sentinel 2-only) are used to mask clouds, while TDOM (Chastain et al.,\n2019) is used to mask cloud shadows (Landsat and Sentinel 2). For\nLandTrendr, the annual medoid is then computed to summarize cloud and cloud\nshadow-free values from each year into a single composite.\n\nThe composite time series is temporally segmented using LandTrendr (Kennedy\net al., 2010; Kennedy et al., 2018; Cohen et al., 2018).\n\nAll cloud and cloud shadow free values are also temporally segmented using\nthe CCDC algorithm (Zhu and Woodcock, 2014).\n\nThe raw composite values, LandTrendr fitted values, pair-wise differences,\nsegment duration, change magnitude, and slope, and CCDC September 1 sine and\ncosine coefficients (first 3 harmonics), fitted values, and pairwise\ndifferences, along with elevation, slope, sine of aspect, cosine of aspect,\nand topographic position indices (Weiss, 2001) from the Digital Elevation\nModels of Puerto Rico (Taylor et al 2008) and the National Oceanic and\nAtmospheric Administration Digital Elevation Model for USVI (Love et\nal. 2014), are used as independent predictor variables in a Random Forest\n(Breiman, 2001) model.\n\nReference data are collected using TimeSync, a web-based tool that helps\nanalysts visualize and interpret the Landsat data record from 1984-present\n(Cohen et al., 2010).\n\n**Additional Resources**\n\n* [A more detailed code example of using LCMS data](https://github.com/google/earthengine-community/blob/master/datasets/scripts/LCMS_Visualization.js).\n\n* The [LCMS Data Explorer](https://apps.fs.usda.gov/lcms-viewer) is a web-based application that\n  provides users the ability to view, analyze, summarize and download LCMS data.\n\n* Please see the [LCMS Methods Brief](https://data.fs.usda.gov/geodata/rastergateway/LCMS/LCMS_v2020-6_Methods.pdf)\n  for more detailed information regarding methods and accuracy assessment, or the\n  [LCMS Geodata Clearinghouse](https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.php)\n  for data downloads, metadata, and support documents.\n\nContact [sm.fs.lcms@usda.gov] with any\nquestions or specific data requests.\n* **Breiman, L., 2001.**\nMachine Learning. *Springer*,\n45(3): 261-277\n[doi:10.1023/a:1017934522171](https://doi.org/10.1023/a:1017934522171)\n\n* **Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K., 2019.**\nEmpirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM\ntop of atmosphere spectral characteristics over the conterminous United States. In Remote\nSensing of Environment. *Science Direct*,\n221: 274-285\n[doi:10.1016/j.rse.2018.11.012](https://doi.org/10.1016/j.rse.2018.11.012)\n\n* **Cohen, W. B., Yang, Z., and Kennedy, R., 2010.**\nDetecting trends in forest disturbance and recovery using yearly Landsat time series: 2.\nTimeSync - Tools for calibration and validation. In Remote Sensing of Environment.\n*Science Direct*,\n114(12): 2911-2924\n[doi:10.1016/j.rse.2010.07.010](https://doi.org/10.1016/j.rse.2010.07.010)\n\n* **Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N., 2018.**\nA LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of\nEnvironment. *Science Direct*,\n205: 131-140\n[doi:10.1016/j.rse.2017.11.015](https://doi.org/10.1016/j.rse.2017.11.015)\n\n* **Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B.,\nGorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy,\nR. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V.,\nVogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z., 2018.**\nMapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of\nEnvironment. *Science Direct*,\n204: 717-728\n[doi:10.1016/j.rse.2017.09.029](https://doi.org/10.1016/j.rse.2017.09.029)\n\n* **Kennedy, R. E., Yang, Z., and Cohen, W. B., 2010.**\nDetecting trends in forest disturbance and recovery using yearly Landsat time series: 1.\nLandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment.\n*Science Direct*,\n114(12): 2897-2910\n[doi:10.1016/j.rse.2010.07.008](https://doi.org/10.1016/j.rse.2010.07.008)\n\n* **Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S., 2018.**\nImplementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing. *MDPI*,\n10(5): 691\n[doi:10.3390/rs10050691](https://doi.org/10.3390/rs10050691)\n\n* **Love, M.R., Sutherland, M., Beasley, L., Carignan, K.S., Eakins, B.W.\n(2014).** Digital Elevation Models of the U.S. Virgin Islands.\nIn NOAA National Geophysical Data Center Internal Publication.\n\n* **Sentinel-Hub (2021).** Sentinel 2 Cloud Detector. [Online]. Available\nat:\n[https://github.com/sentinel-hub/sentinel2-cloud-detector](https://github.com/sentinel-hub/sentinel2-cloud-detector)\n(Accessed: 2021)\n\n* **Taylor, L.A., Eakins, B.W., Carignan, K.S., Warnken, R.R., Sazonova, T.,\nSchoolcraft, D.C. (2008).** Digital Elevation Models of Puerto Rico:\nProcedures, Data Sources and Analysis. In NOAA Technical Memorandum NESDIS\nNGDC-13, National Geophysical Data Center, Boulder, CO. (27 pp).\n\n* **Weiss, A.D., 2001.**\nTopographic position and landforms analysis Poster Presentation, ESRI Users Conference,\nSan Diego, CA.\n\n* **Zhu, Z., and Woodcock, C. E. (2012).**\nObject-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of\nEnvironment. *Science Direct*,\n118: 83-94\n[doi:10.1016/j.rse.2011.10.028](https://doi.org/10.1016/j.rse.2011.10.028)\n\n* **Zhu, Z., and Woodcock, C. E., 2014.**\nContinuous change detection and classification of land cover using all available Landsat data.\nIn Remote Sensing of Environment. *Science Direct*,\n144: 152-171\n[doi:10.1016/j.rse.2014.01.011](https://doi.org/10.1016/j.rse.2014.01.011)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-135.286387,20.38379,-56.446306,52.459364]]},"temporal":{"interval":[["1985-06-01T00:00:00Z","2021-05-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2020-6","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USFS/USFS_GTAC_LCMS_v2020-6","rel":"related","title":"Run the example for USFS/GTAC/LCMS/v2020-6 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USFS/USFS_GTAC_LCMS_v2020-6_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_LCMS_v2020-6#terms-of-use","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.php","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_LCMS_v2022-8.json","rel":"latest-version","title":"USFS/GTAC/LCMS/v2022-8","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_LCMS_v2022-8.json","rel":"successor-version","title":"USFS/GTAC/LCMS/v2022-8","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2020-6/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2020-6/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USFS/GTAC/LCMS/v2021-7","title":"USFS Landscape Change Monitoring System v2021.7 (Conterminous United States and Southeastern Alaska) [deprecated]","description":"This product is part of the Landscape Change Monitoring System (LCMS) data\nsuite. It shows LCMS-modeled change, land cover, and/or land use classes\nfor each year. This LCMS version covers the conterminous United States (CONUS)\nand Southeastern Alaska (SEAK).\n\nLCMS is a remote sensing-based system for mapping and monitoring landscape\nchange across the United States. Its objective is to develop a consistent\napproach using the latest technology and advancements in change detection to\nproduce a \"best available\" map of landscape change.\n\nOutputs include three annual products: change, land cover, and land use.\nChange relates specifically to vegetation cover and includes slow loss, fast\nloss (which also includes hydrologic changes such as inundation or\ndesiccation), and gain. These values are predicted for each year of the\nLandsat time series and serve as the foundational products for LCMS. Land\ncover and land use maps depict life-form level land cover and broad-level\nland use for each year.\n\nBecause no algorithm performs best in all situations, LCMS uses an ensemble\nof models as predictors, which improves map accuracy across a range of\necosystems and change processes (Healey et al., 2018). The resulting suite\nof LCMS change, land cover, and land use maps offer a holistic depiction of\nlandscape change across the United States over the past four decades.\n\nPredictor layers for the LCMS model include outputs from the LandTrendr and\nCCDC change detection algorithms, and terrain information. These components\nare all accessed and processed using Google Earth Engine (Gorelick et al.,\n2017).\n\nLandsat Tier 1 and Sentinel 2A, 2B Level-1C top of atmosphere reflectance\ndata are used directly in CCDC and to produce annual composites for\nLandTrendr. The cFmask cloud masking algorithm (Foga et al., 2017), which is\nan implementation of Fmask 2.0 (Zhu and Woodcock, 2012) (Landsat-only),\ncloudScore (Chastain et al., 2019) (Landsat-only), and s2cloudless\n(Sentinel-Hub, 2021) (Sentinel 2-only) are used to mask clouds, while TDOM\n(Chastain et al., 2019) is used to mask cloud shadows (Landsat and Sentinel\n2). For LandTrendr, the annual medoid is then computed to summarize cloud\nand cloud shadow-free values from each year into a single composite.\n\nThe composite time series is temporally segmented using LandTrendr (Kennedy\net al., 2010; Kennedy et al., 2018; Cohen et al., 2018).\n\nAll cloud and cloud shadow free values are also temporally segmented using\nthe CCDC algorithm (Zhu and Woodcock, 2014).\n\nThe raw composite values, LandTrendr fitted values, pair-wise differences,\nsegment duration, change magnitude, and slope, and CCDC September 1 sine and\ncosine coefficients (first 3 harmonics), fitted values, and pairwise\ndifferences, along with elevation, slope, sine of aspect, cosine of aspect,\nand topographic position indices (Weiss, 2001) from the 10 m National\nElevation Dataset (NED) (Gesch et al., 2009) was used, and for SEAK, the 30\nm NED was used, are used as independent predictor variables in a Random\nForest (Breiman, 2001) model.\n\nReference data are collected using TimeSync, a web-based tool that helps\nanalysts visualize and interpret the Landsat data record from 1984-present\n(Cohen et al., 2010).\n\n**Additional Resources**\n\n* [A more detailed code example of using LCMS data](https://github.com/google/earthengine-community/blob/master/datasets/scripts/LCMS_Visualization.js).\n\n* The [LCMS Data Explorer](https://apps.fs.usda.gov/lcms-viewer) is a\n  web-based application that provides users the ability to view, analyze,\n  summarize and download LCMS data.\n\n* Please see the [LCMS Methods Brief](https://data.fs.usda.gov/geodata/rastergateway/LCMS/LCMS_v2021-7_Methods.pdf)\n  for more detailed information regarding methods and accuracy assessment, or the\n  [LCMS Geodata Clearinghouse](https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.php)\n  for data downloads, metadata, and support documents.\n\nContact [sm.fs.lcms@usda.gov] with any questions or specific data requests.\n* **Breiman, L., 2001.**\nRandom Forests. In Machine Learning. *Springer*,\n45: 5-32\n[doi:10.1023/A:1010933404324](https://doi.org/10.1023/A:1010933404324)\n\n* **Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K., 2019.**\nEmpirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM\ntop of atmosphere spectral characteristics over the conterminous United States. In Remote\nSensing of Environment. *Science Direct*,\n221: 274-285\n[doi:10.1016/j.rse.2018.11.012](https://doi.org/10.1016/j.rse.2018.11.012)\n\n* **Cohen, W. B., Yang, Z., and Kennedy, R., 2010.**\nDetecting trends in forest disturbance and recovery using yearly Landsat time series: 2.\nTimeSync - Tools for calibration and validation. In Remote Sensing of Environment.\n*Science Direct*,\n114(12): 2911-2924\n[doi:10.1016/j.rse.2010.07.010](https://doi.org/10.1016/j.rse.2010.07.010)\n\n* **Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N., 2018.**\nA LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of\nEnvironment. *Science Direct*,\n205: 131-140\n[doi:10.1016/j.rse.2017.11.015](https://doi.org/10.1016/j.rse.2017.11.015)\n\n* **Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann,\nT., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B., 2017.** Cloud\ndetection algorithm comparison and validation for operational Landsat data\nproducts. In Remote Sensing of Environment.  *Science Direct*, 194: 379-390\n[doi:10.1016/j.rse.2017.03.026](http://doi.org/10.1016/j.rse.2017.03.026)\n\n* **Gesch, D., Evans, G., Mauck, J., Hutchinson, J., & Carswell, W. J.,\n2009.** The National Map - Elevation.  *In Fact Sheet*,\n[doi:10.3133/fs20093053 ](https://doi.org/10.3133/fs20093053)\n\n* **Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B.,\nGorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy,\nR. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V.,\nVogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z., 2018.**\nMapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of\nEnvironment. *Science Direct*,\n204: 717-728\n[doi:10.1016/j.rse.2017.09.029](https://doi.org/10.1016/j.rse.2017.09.029)\n\n* **Kennedy, R. E., Yang, Z., and Cohen, W. B., 2010.**\nDetecting trends in forest disturbance and recovery using yearly Landsat time series: 1.\nLandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment.\n*Science Direct*,\n114(12): 2897-2910\n[doi:10.1016/j.rse.2010.07.008](https://doi.org/10.1016/j.rse.2010.07.008)\n\n* **Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S., 2018.**\nImplementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing. *MDPI*,\n10(5): 691\n[doi:10.3390/rs10050691](https://doi.org/10.3390/rs10050691)\n\n* **Sentinel-Hub, 2021.**\nSentinel 2 Cloud Detector. [Online].\nAvailable at: [https://github.com/sentinel-hub/sentinel2-cloud-detector](https://github.com/sentinel-hub/sentinel2-cloud-detector)\n\n* **Weiss, A.D., 2001.**\nTopographic position and landforms analysis Poster Presentation, ESRI Users Conference,\nSan Diego, CA.\n\n* **Zhu, Z., and Woodcock, C. E. (2012).**\nObject-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of\nEnvironment. *Science Direct*,\n118: 83-94\n[doi:10.1016/j.rse.2011.10.028](https://doi.org/10.1016/j.rse.2011.10.028)\n\n* **Zhu, Z., and Woodcock, C. E., 2014.**\nContinuous change detection and classification of land cover using all available Landsat data.\nIn Remote Sensing of Environment. *Science Direct*,\n144: 152-171\n[doi:10.1016/j.rse.2014.01.011](https://doi.org/10.1016/j.rse.2014.01.011)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-135.286387,20.38379,-56.446306,52.459364]]},"temporal":{"interval":[["1985-06-01T00:00:00Z","2021-09-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2021-7","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USFS/USFS_GTAC_LCMS_v2021-7","rel":"related","title":"Run the example for USFS/GTAC/LCMS/v2021-7 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USFS/USFS_GTAC_LCMS_v2021-7_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_LCMS_v2021-7#terms-of-use","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.php","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_LCMS_v2020-5.json","rel":"predecessor-version","title":"USFS/GTAC/LCMS/v2020-5","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_LCMS_v2022-8.json","rel":"successor-version","title":"USFS/GTAC/LCMS/v2022-8","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2021-7/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2021-7/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USFS/GTAC/LCMS/v2022-8","title":"USFS Landscape Change Monitoring System v2022.8 (CONUS and OCONUS) [deprecated]","description":"This product is part of the Landscape Change Monitoring System (LCMS) data suite.\nIt shows LCMS-modeled change, land cover, and/or land use classes for each year that\ncovers the CONUS and OCONUS.\n\nLCMS is a remote sensing-based system for mapping and monitoring landscape change across the\nUnited States. Its objective is to develop a consistent approach using the latest technology\nand advancements in change detection to produce a \"best available\" map of landscape change.\n\nOutputs include three annual products: change, land cover, and land use.\nChange relates specifically to vegetation cover and includes slow loss, fast loss (which also\nincludes hydrologic changes such as inundation or desiccation), and gain. These values are\npredicted for each year of the Landsat time series and serve as the foundational products for\nLCMS. Land cover and land use maps depict life-form level land cover and broad-level land use\nfor each year.\n\nBecause no algorithm performs best in all situations, LCMS uses an ensemble of models as\npredictors, which improves map accuracy across a range of ecosystems and change processes\n(Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer\na holistic depiction of landscape change across the United States over the past four decades.\n\nPredictor layers for the LCMS model include outputs\nfrom the LandTrendr and CCDC change detection algorithms, and terrain information. These\ncomponents are all accessed and processed using Google Earth Engine (Gorelick et al., 2017).\n\nFor CCDC, United States Geological Survey (USGS) Collection 2 Landsat Tier 1 surface reflectance\ndata were used for the CONUS, and Landsat Tier 1 top of atmosphere reflectance data for SEAK, PRUSVI\nand HI. To produce annual composites for LandTrendr, USGS Collection 2 Landsat Tier 1 and Sentinel 2A,\n2B Level-1C top of atmosphere reflectance data were used. The cFmask cloud masking algorithm\n(Foga et al., 2017), which is an implementation of Fmask 2.0 (Zhu and Woodcock, 2012)\n(Landsat-only), cloudScore (Chastain et al., 2019) (Landsat-only), and s2cloudless\n(Sentinel-Hub, 2021) (Sentinel 2-only) are used to mask clouds, while TDOM\n(Chastain et al., 2019) is used to mask cloud shadows (Landsat and Sentinel\n2). For LandTrendr, the annual medoid is then computed to summarize cloud\nand cloud shadow-free values from each year into a single composite.\n\nThe composite time series is temporally segmented using LandTrendr\n(Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018).\n\nAll cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm\n(Zhu and Woodcock, 2014).\n\nThe raw composite values, LandTrendr fitted values, pair-wise differences,\nsegment duration, change magnitude, and slope, and CCDC September 1 sine and\ncosine coefficients (first 3 harmonics), fitted values, and pairwise\ndifferences, along with elevation, slope, sine of aspect, cosine of aspect,\nand topographic position indices (Weiss, 2001) from the 10 m USGS 3D Elevation\nProgram (3DEP) data (U.S. Geological Survey, 2019), are used as\nindependent predictor variables in a Random Forest (Breiman, 2001) model.\n\nReference data are collected using TimeSync, a web-based tool that helps\nanalysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).\n\n**Additional Resources**\n\n* [A more detailed code example of using LCMS data](https://github.com/google/earthengine-community/blob/master/datasets/scripts/LCMS_Visualization.js).\n\n* The [LCMS Data Explorer](https://apps.fs.usda.gov/lcms-viewer) is a web-based application that\n  provides users the ability to view, analyze, summarize and download LCMS data.\n\n* Please see the [LCMS Methods Brief](https://data.fs.usda.gov/geodata/rastergateway/LCMS/LCMS_v2022-8_Methods.pdf)\n  for more detailed information regarding methods and accuracy assessment, or the\n  [LCMS Geodata Clearinghouse](https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.php)\n  for data downloads, metadata, and support documents.\n\nContact [sm.fs.lcms@usda.gov] with any\nquestions or specific data requests.\n\n* **Breiman, L., 2001.**\nRandom Forests. In Machine Learning. *Springer*,\n45: 5-32\n[doi:10.1023/A:1010933404324](https://doi.org/10.1023/A:1010933404324)\n\n* **Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K., 2019.**\nEmpirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM\ntop of atmosphere spectral characteristics over the conterminous United States. In Remote\nSensing of Environment. *Science Direct*,\n221: 274-285\n[doi:10.1016/j.rse.2018.11.012](https://doi.org/10.1016/j.rse.2018.11.012)\n\n* **Cohen, W. B., Yang, Z., and Kennedy, R., 2010.**\nDetecting trends in forest disturbance and recovery using yearly Landsat time series: 2.\nTimeSync - Tools for calibration and validation. In Remote Sensing of Environment.\n*Science Direct*,\n114(12): 2911-2924\n[doi:10.1016/j.rse.2010.07.010](https://doi.org/10.1016/j.rse.2010.07.010)\n\n* **Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N., 2018.**\nA LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of\nEnvironment. *Science Direct*,\n205: 131-140\n[doi:10.1016/j.rse.2017.11.015](https://doi.org/10.1016/j.rse.2017.11.015)\n\n* **Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann,\nT., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B., 2017.** Cloud\ndetection algorithm comparison and validation for operational Landsat data\nproducts. In Remote Sensing of Environment.  *Science Direct*, 194: 379-390\n[doi:10.1016/j.rse.2017.03.026](http://doi.org/10.1016/j.rse.2017.03.026)\n\n* **U.S. Geological Survey, 2019.** USGS 3D Elevation Program Digital Elevation\nModel, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m\n\n* **Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B.,\nGorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy,\nR. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V.,\nVogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z., 2018.**\nMapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of\nEnvironment. *Science Direct*,\n204: 717-728\n[doi:10.1016/j.rse.2017.09.029](https://doi.org/10.1016/j.rse.2017.09.029)\n\n* **Kennedy, R. E., Yang, Z., and Cohen, W. B., 2010.**\nDetecting trends in forest disturbance and recovery using yearly Landsat time series: 1.\nLandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment.\n*Science Direct*,\n114(12): 2897-2910\n[doi:10.1016/j.rse.2010.07.008](https://doi.org/10.1016/j.rse.2010.07.008)\n\n* **Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S., 2018.**\nImplementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing. *MDPI*,\n10(5): 691\n[doi:10.3390/rs10050691](https://doi.org/10.3390/rs10050691)\n\n* **Sentinel-Hub, 2021.**\nSentinel 2 Cloud Detector. [Online].\nAvailable at: [https://github.com/sentinel-hub/sentinel2-cloud-detector](https://github.com/sentinel-hub/sentinel2-cloud-detector)\n\n* **Weiss, A.D., 2001.**\nTopographic position and landforms analysis Poster Presentation, ESRI Users Conference,\nSan Diego, CAZhu, Z., and Woodcock, C. E. (2012).\nObject-based cloud and cloud shadow detection in Landsat imagery.\n118: 83-94\n\n* **Zhu, Z., and Woodcock, C. E., 2012.**\nObject-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of\nEnvironment. *Science Direct*,\n118: 83-94\n[doi:10.1016/j.rse.2011.10.028](https://doi.org/10.1016/j.rse.2011.10.028)\n\n* **Zhu, Z., and Woodcock, C. E., 2014.**\nContinuous change detection and classification of land cover using all available Landsat data.\nIn Remote Sensing of Environment. *Science Direct*,\n144: 152-171\n[doi:10.1016/j.rse.2014.01.011](https://doi.org/10.1016/j.rse.2014.01.011)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-135.286387,20.38379,-56.446306,52.459364]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2022-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2022-8","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USFS/USFS_GTAC_LCMS_v2022-8","rel":"related","title":"Run the example for USFS/GTAC/LCMS/v2022-8 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USFS/USFS_GTAC_LCMS_v2022-8_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_LCMS_v2022-8#terms-of-use","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.php","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_LCMS_v2021-7.json","rel":"predecessor-version","title":"USFS/GTAC/LCMS/v2021-7","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_LCMS_v2023-9.json","rel":"successor-version","title":"USFS/GTAC/LCMS/v2023-9","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2022-8/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2022-8/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USFS/GTAC/LCMS/v2023-9","title":"USFS Landscape Change Monitoring System v2023.9 (CONUS and OCONUS) [deprecated]","description":"This product is part of the Landscape Change Monitoring System (LCMS) data suite.\nIt shows LCMS-modeled change, land cover, and/or land use classes for each year that\ncovers the Conterminous United States (CONUS) and areas outside the CONUS (OCONUS) that\ninclude Southeastern Alaska (SEAK), Puerto Rico-US Virgin Islands (PRUSVI), and Hawaii (HI).\n\nLCMS is a remote sensing-based system for mapping and monitoring landscape change across the\nUnited States. Its objective is to develop a consistent approach using the latest technology\nand advancements in change detection to produce a \"best available\" map of landscape change.\n\nOutputs include three annual products: change, land cover, and land use.\nChange relates specifically to vegetation cover and includes slow loss, fast loss (which also\nincludes hydrologic changes such as inundation or desiccation), and gain. These values are\npredicted for each year of the Landsat time series and serve as the foundational products for\nLCMS. Land cover and land use maps depict life-form level land cover and broad-level land use\nfor each year.\n\nBecause no algorithm performs best in all situations, LCMS uses an ensemble of models as\npredictors, which improves map accuracy across a range of ecosystems and change processes\n(Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer\na holistic depiction of landscape change across the United States since 1985.\n\nPredictor layers for the LCMS model include outputs\nfrom the LandTrendr and CCDC change detection algorithms, and terrain information. These\ncomponents are all accessed and processed using Google Earth Engine (Gorelick et al., 2017).\n\nFor CCDC, United States Geological Survey (USGS) Collection 2 Landsat Tier 1 surface reflectance\ndata were used for the CONUS, and Landsat Tier 1 top of atmosphere reflectance data for SEAK, PRUSVI\nand HI. To produce annual composites for LandTrendr, USGS Collection 2 Landsat Tier 1 and Sentinel 2A,\n2B Level-1C top of atmosphere reflectance data were used. The cFmask cloud masking algorithm\n(Foga et al., 2017), which is an implementation of Fmask 2.0 (Zhu and Woodcock, 2012)\n(Landsat-only), cloudScore (Chastain et al., 2019) (Landsat-only), s2cloudless\n(Sentinel-Hub, 2021) and Cloud Score plus (Pasquarella et al., 2023) (Sentinel 2-only) are\nused to mask clouds, while TDOM (Chastain et al., 2019) is used to mask cloud shadows (Landsat and Sentinel\n2). For LandTrendr, the annual medoid is then computed to summarize cloud\nand cloud shadow-free values from each year into a single composite.\n\nThe composite time series is temporally segmented using LandTrendr\n(Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018).\n\nAll cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm\n(Zhu and Woodcock, 2014).\n\nPredictor data include raw composite values, LandTrendr fitted values, pair-wise differences,\nsegment duration, change magnitude, and slope, and CCDC sine and\ncosine coefficients (first 3 harmonics), fitted values, and pairwise\ndifferences, along with elevation, slope, sine of aspect, cosine of aspect,\nand topographic position indices (Weiss, 2001) from the 10 m USGS 3D Elevation\nProgram (3DEP) data (U.S. Geological Survey, 2019).\n\nReference data are collected using TimeSync, a web-based tool that helps\nanalysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).\n\nRandom Forests models (Breiman, 2001) were trained using reference data from TimeSync and predictor data\nfrom LandTrendr, CCDC, and terrain indices to predict annual change, land cover, and land use classes.\nFollowing modeling, we instituted a series of probability thresholds and rulesets using ancillary datasets\nto improve qualitative map outputs and reduce commission and omission. More information can be found in\nthe LCMS Methods Brief included in the Description.\n\n**Additional Resources**\n\n* [A more detailed code example of using LCMS data](https://github.com/google/earthengine-community/blob/master/datasets/scripts/LCMS_Visualization.js).\n\n* The [LCMS Data Explorer](https://apps.fs.usda.gov/lcms-viewer) is a web-based application that\n  provides users the ability to view, analyze, summarize and download LCMS data.\n\n* Please see the [LCMS Methods Brief](https://data.fs.usda.gov/geodata/rastergateway/LCMS/LCMS_v2023-9_Methods.pdf)\n  for more detailed information regarding methods and accuracy assessment, or the\n  [LCMS Geodata Clearinghouse](https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.php)\n  for data downloads, metadata, and support documents.\n\n* The CONUS land use product was updated on July 2nd 2024, to correct an issue with the developed class.\n\n* PRUSVI and HI data were released October 1st 2024.\n\nContact [sm.fs.lcms@usda.gov] with any\nquestions or specific data requests.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-135.286387,20.38379,-56.446306,52.459364]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2023-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2023-9","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USFS/USFS_GTAC_LCMS_v2023-9","rel":"related","title":"Run the example for USFS/GTAC/LCMS/v2023-9 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USFS/USFS_GTAC_LCMS_v2023-9_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_LCMS_v2023-9#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_LCMS_v2022-8.json","rel":"predecessor-version","title":"USFS/GTAC/LCMS/v2022-8","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_LCMS_v2024-10.json","rel":"successor-version","title":"USFS/GTAC/LCMS/v2024-10","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2023-9/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2023-9/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USFS/GTAC/LCMS/v2024-10","title":"USFS Landscape Change Monitoring System  v2024-10 (CONUS and OCONUS) [deprecated]","description":"This product is part of the Landscape Change Monitoring System (LCMS) data suite.\nIt shows LCMS-modeled change, land cover, and/or land use classes for each year and\ncovers the Conterminous United States (CONUS) as well as areas outside the CONUS (OCONUS) \nincluding Alaska (AK), Puerto Rico-US Virgin Islands (PRUSVI), and Hawaii (HAWAII). \n\nLCMS is a remote sensing-based system for mapping and monitoring landscape change across the\nUnited States. Its objective is to develop a consistent approach using the latest technology\nand advancements in change detection to produce a \"best available\" map of landscape change.\n\nOutputs include three annual products: change, land cover, and land use. The change model output relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS. We apply a ruleset based on ancillary datasets to create the final change product, which is a\nrefinement/reclassification of the modeled change to 15 classes that explicitly provide information on the cause of landscape change (e.g., Tree Removal, Wildfire, Wind damage). Land cover and land use maps depict life-form level land cover and broad-level land use for each year.\n\nBecause no algorithm performs best in all situations, LCMS uses an ensemble of models as\npredictors, which improves map accuracy across a range of ecosystems and change processes\n(Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer\na holistic depiction of landscape change across the United States since 1985.\n\nPredictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection \nalgorithms, and terrain information. These components are all accessed and processed using Google \nEarth Engine (Gorelick et al., 2017).\n\nTo produce annual composites for LandTrendr, USGS Collection 2 Landsat Tier 1 and Sentinel 2A,\n2B Level-1C top of atmosphere reflectance data were used. The cFmask cloud masking algorithm\n(Foga et al., 2017), which is an implementation of Fmask 2.0 (Zhu and Woodcock, 2012)\n(Landsat-only), cloudScore (Chastain et al., 2019) (Landsat-only), s2cloudless\n(Sentinel-Hub, 2021) and Cloud Score+ (Pasquarella et al., 2023) (Sentinel 2-only) are \nused to mask clouds, while TDOM (Chastain et al., 2019) is used to mask cloud shadows (Landsat and Sentinel\n2). For LandTrendr, the annual medoid is then computed to summarize cloud and cloud shadow-free values \nfrom each year into a single composite. For CCDC, United States Geological Survey (USGS) Collection 2 \nLandsat Tier 1 surface reflectance data were used for the CONUS, and Landsat Tier 1 top of atmosphere \nreflectance data for AK, PRUSVI, and HAWAII. \n\nThe composite time series is temporally segmented using LandTrendr\n(Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018).\n\nAll cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm\n(Zhu and Woodcock, 2014).\n\nPredictor data include raw composite values, LandTrendr fitted values, pair-wise differences,\nsegment duration, change magnitude, and slope, and CCDC sine and\ncosine coefficients (first 3 harmonics), fitted values, and pairwise\ndifferences, along with elevation, slope, sine of aspect, cosine of aspect,\nand topographic position indices (Weiss, 2001) from the 10 m USGS 3D Elevation\nProgram (3DEP) data (U.S. Geological Survey, 2019).\n\nReference data are collected using TimeSync, a web-based tool that helps\nanalysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).\n\nRandom Forest models (Breiman, 2001) were trained using reference data from TimeSync and predictor data \nfrom LandTrendr, CCDC, and terrain indices to predict annual change, land cover, and land use classes. \nFollowing modeling, we institute a series of probability thresholds and rulesets using ancillary datasets \nto improve qualitative map outputs and reduce commission and omission. More information can be found in \nthe LCMS Methods Brief included in the Description. \n\n**Additional Resources**\n\n* [A more detailed code example of using LCMS data](https://github.com/google/earthengine-community/blob/master/datasets/scripts/LCMS_Visualization.js).\n\n* The [LCMS Data Explorer](https://apps.fs.usda.gov/lcms-viewer) is a web-based application that\n  provides users the ability to view, analyze, summarize and download LCMS data.\n\n* Please see the [LCMS Methods Brief](https://data.fs.usda.gov/geodata/rastergateway/LCMS/LCMS_v2024-10_Methods.pdf)\n  for more detailed information regarding methods and accuracy assessment, or the\n  [LCMS Geodata Clearinghouse](https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.php)\n  for data downloads, metadata, and support documents.\n\n* The string HAWAII will be updated to HI in the upcoming v2025.11 data release.\n\nContact [sm.fs.lcms@usda.gov](mailto:sm.fs.lcms@usda.gov) with any\nquestions or specific data requests.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-135.286387,20.38379,-56.446306,52.459364]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2024-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2024-10","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USFS/USFS_GTAC_LCMS_v2024-10","rel":"related","title":"Run the example for USFS/GTAC/LCMS/v2024-10 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USFS/USFS_GTAC_LCMS_v2024-10_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_LCMS_v2024-10#terms-of-use","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.php","rel":"source"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11.json","rel":"successor-version","title":"projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_LCMS_v2023-9.json","rel":"predecessor-version","title":"USFS/GTAC/LCMS/v2023-9","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11.json","rel":"successor-version","title":"projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2024-10/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/LCMS/v2024-10/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USFS/GTAC/MTBS/annual_burn_severity_mosaics/v1","title":"Monitoring Trends in Burn Severity (MTBS) Burn Severity Images","description":"The burn severity mosaics consist of thematic raster images of MTBS burn\nseverity classes for all currently completed MTBS fires for the continental\nUnited States, Alaska, Hawaii and Puerto Rico. Mosaicked burn severity\nimages are compiled annually for each year by US State and the continental\nUnited States.\nMonitoring Trends in Burn Severity (MTBS) is an interagency program whose goal\nis to consistently map the burn severity and extent of large fires across all\nlands of the United States from 1984 to present. This includes all fires 1000\nacres or greater in the western United States and 500 acres or greater in the\neastern Unites States. The extent of coverage includes the continental U.S.,\nAlaska, Hawaii and Puerto Rico.\n\nThe program is conducted by the U.S. Geological Survey Center for Earth\nResources Observation and Science (EROS) and the USDA Forest Service Geospatial\nTechnology and Applications Center (GTAC). MTBS was first enacted in 2005,\nprimarily to meet the information needs of the Wildland Fire Leadership Council\n(WFLC).  The primary objective at that time was to provide data to the WFLC for\nmonitoring the effectiveness of the ten-year National Fire Plan. The scope of\nthe program has grown since inception and provides data to a wide range of\nusers. These include national policy-makers such as WFLC and others who are\nfocused on implementing and monitoring national fire management strategies;\nfield management units such as national forests, parks and other federal and\ntribal lands that benefit from the availability of GIS-ready maps and data;\nother federal land cover mapping programs such as LANDFIRE which utilizes burn\nseverity data in their own efforts; and academic and agency research entities\ninterested in fire severity data over significant geographic and temporal\nextents.\n\nMTBS data are freely available to the public and are generated by leveraging\nother national programs including the Landsat satellite program, jointly\ndeveloped and managed by the USGS and NASA. Landsat data are analyzed through a\nstandardized and consistent methodology, generating products at a 30 meter\nresolution dating back to 1984. One of the greatest strengths of the program is\nthe consistency of the data products which would be impossible without the\nhistoric Landsat archive, the largest in the world.\n\nYou can visit the [MTBS Project Website](https://www.mtbs.gov) for more\ninformation.\n\nYou can also visit the\n[MTBS Data Explorer](https://apps.fs.usda.gov/lcms-viewer/mtbs.html)\nto learn more and interact with the data.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-135.286387,20.38379,-56.446306,52.459364]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2024-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/MTBS/annual_burn_severity_mosaics/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USFS/USFS_GTAC_MTBS_annual_burn_severity_mosaics_v1","rel":"related","title":"Run the example for USFS/GTAC/MTBS/annual_burn_severity_mosaics/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USFS/USFS_GTAC_MTBS_annual_burn_severity_mosaics_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_MTBS_annual_burn_severity_mosaics_v1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/MTBS/annual_burn_severity_mosaics/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/MTBS/annual_burn_severity_mosaics/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USFS/GTAC/TreeMap/v2016","title":"USFS TreeMap v2016 (Conterminous United States) [deprecated]","description":"This product is part of the TreeMap data suite. It provides detailed spatial\ninformation on forest characteristics including number of live and dead trees,\nbiomass, and carbon across the entire forested extent of the continental United\nStates in 2016.\n\nTreeMap v2016 contains one image, a 22-band 30 x 30m resolution gridded map\nof the forests of the continental United States circa 2016, with each band\nrepresenting an attribute derived from select FIA data (and one band\nrepresenting the TreeMap ID). Examples of attributes include forest type,\ncanopy cover percent, live tree stocking, live/dead tree biomass, and carbon\nin live/dead trees.\n\nTreeMap products are the output of a random forest machine learning algorithm\nthat assigns the most similar Forest Inventory Analysis (FIA) plot to each\npixel of gridded LANDFIRE input data. The objective is to combine the\ncomplimentary strengths of detailed-but-spatially-sparse FIA data with\nless-detailed-but-spatially-comprehensive LANDFIRE data to produce better\nestimations of forest characteristics at a variety of scales. TreeMap is being\nused in both the private and public sectors for projects including fuel\ntreatment planning, snag hazard mapping, and estimation of terrestrial\ncarbon resources.\n\nTreeMap is distinct from other imputed forest vegetation products in that it\nprovides an FIA plot identifier to each pixel whereas other datasets provide\nforest characteristics such as live basal area (e.g., Ohmann and Gregory 2002;\nPierce Jr et al. 2009; Wilson, Lister, and Riemann 2012). The FIA plot\nidentifier can be linked to the hundreds of variables and attributes recorded\nfor each tree and plot in the FIA DataMart, FIA's public repository of plot\ninformation (Forest Inventory Analysis 2022a).\n\nThe 2016 methodology includes disturbance as a response variable, resulting in\nincreased accuracy in mapping disturbed areas. Within-class accuracy was over\n90% for forest cover, height, vegetation group, and disturbance code when\ncompared to LANDFIRE maps. At least one pixel within the radius of validation\nplots matched the class of predicted values in 57.5% of cases for forest cover,\n80.0% for height, 80.0% for tree species with highest basal area, and 87.4%\nfor disturbance.\n\n**Additional Resources**\n\n* Please see the [TreeMap 2016 Publication](https://www.fs.usda.gov/research/treesearch/65597)\n  for more detailed information regarding methods and accuracy assessment.\n\n* The [TreeMap 2016 Data Explorer](https://apps.fs.usda.gov/lcms-viewer/treemap.html)\n  is a web-based application that provides users the ability to view and\n  download TreeMap attribute data.\n\n* The [TreeMap Research Data Archive](https://www.fs.usda.gov/rds/archive/Catalog/RDS-2021-0074)\n  for the full dataset download, metadata, and support documents.\n\n* [TreeMap Raster Data Gateway](https://data.fs.usda.gov/geodata/rastergateway/treemap/)\n  for TreeMap attribute data downloads, metadata, and support documents.\n\n* [FIA Database Manual version 9.4](https://research.fs.usda.gov/understory/forest-inventory-and-analysis-database-user-guide-nfi))\n  for more detailed information on the attributes included in TreeMap 2016.\n\nContact [sm.fs.treemaphelp@usda.gov] with any\nquestions or specific data requests.\n\n* **Forest Inventory Analysis. 2022a.**\nForest Inventory Analysis DataMart.\nForest Inventory Analysis DataMart FIADB_1.9.0. 2022. [https://apps.fs.usda.gov/fia/datamart/datamart.html](https://apps.fs.usda.gov/fia/datamart/datamart.html).\n\n* **Ohmann, Janet L and Matthew J Gregory. 2002.**\nPredictive Mapping of Forest Composition and Structure with Direct Gradient\nAnalysis and Nearest- Neighbor Imputation in Coastal Oregon, USA.\nCan. J. For. Res. 32:725-741. [doi: 10.1139/X02-011](https://doi.org/10.1139/X02-011).\n\n* **Pierce, Kenneth B Jr, Janet L Ohmann, Michael C Wimberly,\nMatthew J Gregory, and Jeremy S Fried. 2009.**\nMapping Wildland Fuels and Forest Structure for Land Management: A Comparison\nof Nearest Neighbor Imputation and Other Methods.\nCan. J. For. Res. 39: 1901-1916. [doi:10.1139/X09-102](https://doi.org/10.1139/X09-102).\n\n* **Wilson, B Tyler, Andrew J Lister, and Rachel I Riemann. 2012.**\nA Nearest-Neighbor Imputation Approach to Mapping Tree Species over Large\nAreas Using Forest Inventory Plots and Moderate Resolution Raster Data.\nForest Ecol. Manag. 271:182-198. [doi: 10.1016/j. foreco.2012.02.002](https://doi.org/10.1016/j.foreco.2012.02.002).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-128.97722,22.76862,-65.25445,51.64968]]},"temporal":{"interval":[["2016-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/TreeMap/v2016","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USFS/USFS_GTAC_TreeMap_v2016","rel":"related","title":"Run the example for USFS/GTAC/TreeMap/v2016 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USFS/USFS_GTAC_TreeMap_v2016_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_TreeMap_v2016#terms-of-use","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/treemap/","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/treemap/","rel":"source"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_TreeMap_Product_Version_2023-1.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/TreeMap/Product_Version/2023-1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_TreeMap_v2020.json","rel":"successor-version","title":"USFS/GTAC/TreeMap/v2020","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/TreeMap/v2016/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/TreeMap/v2016/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USFS/GTAC/TreeMap/v2020","title":"USFS TreeMap v2020 [deprecated]","description":"This product is part of the TreeMap data suite. It provides detailed spatial\ninformation on forest characteristics including number of live and dead trees,\nbiomass, and carbon across the entire forested extent of the United States in\n2020.\n\nTreeMap v2020 contains 22-band 30 x 30m resolution gridded map images per study\narea, of the forests of the United States circa 2020, with each band\nrepresenting an attribute derived from select Forest Inventory Analysis (FIA)\ndata (and one band representing the TreeMap ID). Examples of attributes include\nforest type, canopy cover percent, live tree stocking, live/dead tree biomass,\nand carbon in live/dead trees.\n\nTreeMap products are the output of a random forest machine learning algorithm\nthat assigns the most similar FIA plot to each pixel of gridded LANDFIRE input\ndata. The objective is to combine the complimentary strengths of\ndetailed-but-spatially-sparse FIA data with less-detailed-but-spatially-\ncomprehensive LANDFIRE data to produce better estimations of forest\ncharacteristics at a variety of scales. TreeMap is being used in both the\nprivate and public sectors for projects including fuel treatment planning,\nsnag hazard mapping, and estimation of terrestrial carbon resources.\n\nTreeMap is distinct from other imputed forest vegetation products in that it\nprovides an FIA plot identifier to each pixel whereas other datasets provide\nforest characteristics such as live basal area (e.g., Ohmann and Gregory 2002;\nPierce Jr et al. 2009; Wilson, Lister, and Riemann 2012). The FIA plot\nidentifier can be linked to the hundreds of variables and attributes recorded\nfor each tree and plot in the FIA DataMart, FIA's public repository of plot\ninformation (Forest Inventory Analysis 2022a).\n\nThe TreeMap 2020 CONUS dataset featured here updates the TreeMap 2016 dataset\nto landscape conditions circa 2020 and updates the methods by: 1) using a\ndifferent suite of climate variables in the imputation and 2) improving species\ncomposition assignments to prevent plots being imputed to areas where their\nexisting vegetation type was not present, an issue which affected a small number\nof pixels in previous TreeMap versions.\n\nTreeMap v2020 was produced using the methods described in\n[Riley et al. (2022)](https://research.fs.usda.gov/treesearch/65597)\nbut differ from TreeMap v2016 in that: 1) the climatic variables were obtained\nfrom DayMet and included precipitation, shortwave radiation, soil water\nequivalent, maximum temperature, minimum temperature, vapor pressure, and vapor\npressure deficit; and 2) plots available for imputation in each LANDFIRE zone\nwere limited to those plots with a tree species that were present either in the\nplots found within the LANDFIRE zone, or in the zones immediately bordering it,\naccording to the FIA plots located within the zone. This reduced not only plots\nwith Existing Vegetation Type not present in the zone but also plots with trees\noutside of their observed range.\n\nThe results showed good correspondence between the target LANDFIRE data and the\nimputed plot data, with an overall within-class agreement of 94.3% for forest\ncover, 99.0% for forest height, 95.6% for vegetation group, and 95.5% for\ndisturbance code. Of 69,800 single-condition FIA plots available to Random\nForest, 64,745 of these (92.7%) were utilized in the imputation to 2,687,805,994\nforested pixels.\n\nOther TreeMap vintages can be found at:\n\n**Additional Resources**\n\n* Please see the [TreeMap 2016 Publication](https://www.fs.usda.gov/research/treesearch/65597)\n  for more detailed information regarding methods and accuracy assessment.\n\n* The [TreeMap Data Explorer](https://apps.fs.usda.gov/lcms-viewer/treemap.html)\n  is a web-based application that provides users the ability to view and\n  download TreeMap attribute data.\n\n* Visit the [TreeMap Research Data Archive](https://www.fs.usda.gov/rds/archive/catalog/RDS-2025-0031)\n  for the full dataset download, metadata, and support documents.\n\n* Visit the [TreeMap Raster Data Gateway](https://data.fs.usda.gov/geodata/rastergateway/treemap/)\n  for TreeMap attribute data downloads, metadata, and support documents.\n\n* See the [FIA Database Manual version 9.3](https://research.fs.usda.gov/understory/forest-inventory-and-analysis-database-user-guide-nfi)\n  for more detailed information on the attributes included in TreeMap 2020.\n\n* The [Treemap 2016 vintage](https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_TreeMap_v2016)\n  contains landscape conditions of the forests of the United States circa 2016.\n\n* The [Treemap 2022 vintage](https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_TreeMap_v2022)\n  contains landscape conditions of the forests of the United States circa 2022.\n\nContact [sm.fs.treemaphelp@usda.gov] with any\nquestions or specific data requests.\n\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-128.97722,22.76862,-65.25445,51.64968]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/TreeMap/v2020","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USFS/USFS_GTAC_TreeMap_v2020","rel":"related","title":"Run the example for USFS/GTAC/TreeMap/v2020 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USFS/USFS_GTAC_TreeMap_v2020_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_TreeMap_v2020#terms-of-use","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/treemap/","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/treemap/","rel":"source"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_TreeMap_Product_Version_2023-1.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/TreeMap/Product_Version/2023-1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_TreeMap_v2016.json","rel":"predecessor-version","title":"USFS/GTAC/TreeMap/v2016","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_TreeMap_v2022.json","rel":"successor-version","title":"USFS/GTAC/TreeMap/v2022","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/TreeMap/v2020/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/TreeMap/v2020/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USFS/GTAC/TreeMap/v2022","title":"USFS TreeMap v2022 [deprecated]","description":"This product is part of the TreeMap data suite. It provides detailed spatial\ninformation on forest characteristics including number of live and dead trees,\nbiomass, and carbon across the entire forested extent of the United States in\n2022.\n\nTreeMap v2022 contains 22-band 30 x 30m resolution gridded map images per study\narea, of the forests of the United States circa 2022, with each band\nrepresenting an attribute derived from select Forest Inventory Analysis (FIA)\ndata (and one band representing the TreeMap ID). Examples of attributes include\nforest type, canopy cover percent, live tree stocking, live/dead tree biomass,\nand carbon in live/dead trees.\n\nTreeMap products are the output of a random forest machine learning algorithm\nthat assigns the most similar FIA plot to each pixel of gridded LANDFIRE input\ndata. The objective is to combine the complimentary strengths of\ndetailed-but-spatially-sparse FIA data with less-detailed-but-spatially-\ncomprehensive LANDFIRE data to produce better estimations of forest\ncharacteristics at a variety of scales. TreeMap is being used in both the\nprivate and public sectors for projects including fuel treatment planning,\nsnag hazard mapping, and estimation of terrestrial carbon resources.\n\nTreeMap is distinct from other imputed forest vegetation products in that it\nprovides an FIA plot identifier to each pixel whereas other datasets provide\nforest characteristics such as live basal area (e.g., Ohmann and Gregory 2002;\nPierce Jr et al. 2009; Wilson, Lister, and Riemann 2012). The FIA plot\nidentifier can be linked to the hundreds of variables and attributes recorded\nfor each tree and plot in the FIA DataMart, FIA's public repository of plot\ninformation (Forest Inventory Analysis 2022a).\n\nThe TreeMap 2022 CONUS dataset featured here updates the TreeMap 2016 dataset\nto landscape conditions circa 2022 and updates the methods by: 1) using a\ndifferent suite of climate variables in the imputation and 2) improving species\ncomposition assignments to prevent plots being imputed to areas where their\nexisting vegetation type was not present, an issue which affected a small number\nof pixels in previous TreeMap versions.\n\nTreeMap v2022 was produced using the methods described in\n[Riley et al. (2022)](https://research.fs.usda.gov/treesearch/65597)\nbut differ from TreeMap v2016 in that: 1) the climatic variables were obtained\nfrom DayMet and included precipitation, shortwave radiation, soil water\nequivalent, maximum temperature, minimum temperature, vapor pressure, and vapor\npressure deficit; and 2) plots available for imputation in each LANDFIRE zone\nwere limited to those plots with a tree species that were present either in the\nplots found within the LANDFIRE zone, or in the zones immediately bordering it,\naccording to the FIA plots located within the zone. This reduced not only plots\nwith Existing Vegetation Type not present in the zone but also plots with trees\noutside of their observed range.\n\nThe results showed good correspondence between the target LANDFIRE data and the\nimputed plot data, with an overall within-class agreement of 94.3% for forest\ncover, 99.0% for forest height, 95.6% for vegetation group, and 95.5% for\ndisturbance code. Of 69,800 single-condition FIA plots available to Random\nForest, 64,745 of these (92.7%) were utilized in the imputation to 2,687,805,994\nforested pixels.\n\n\n\n**Additional Resources**\n\n* Please see the [TreeMap 2016 Publication](https://www.fs.usda.gov/research/treesearch/65597)\n  for more detailed information regarding methods and accuracy assessment.\n\n* The [TreeMap Data Explorer](https://apps.fs.usda.gov/lcms-viewer/treemap.html)\n  is a web-based application that provides users the ability to view and\n  download TreeMap attribute data.\n\n* Visit the [TreeMap Research Data Archive](https://www.fs.usda.gov/rds/archive/catalog/RDS-2025-0031)\n  for the full dataset download, metadata, and support documents.\n\n* Visit the [TreeMap Raster Data Gateway](https://data.fs.usda.gov/geodata/rastergateway/treemap/)\n  for TreeMap attribute data downloads, metadata, and support documents.\n\n* See the [FIA Database Manual version 9.3](https://research.fs.usda.gov/understory/forest-inventory-and-analysis-database-user-guide-nfi)\n  for more detailed information on the attributes included in TreeMap 2020.\n\n* The [Treemap 2016 vintage](https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_TreeMap_v2016)\n  contains landscape conditions of the forests of the United States circa 2016.\n\n* The [Treemap 2020 vintage](https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_TreeMap_v2020)\n  contains landscape conditions of the forests of the United States circa 2020.\n\nContact [sm.fs.treemaphelp@usda.gov] with any\nquestions or specific data requests.\n\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-128.97722,22.76862,-65.25445,51.64968]]},"temporal":{"interval":[["2022-01-01T00:00:00Z","2023-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/TreeMap/v2022","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USFS/USFS_GTAC_TreeMap_v2022","rel":"related","title":"Run the example for USFS/GTAC/TreeMap/v2022 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USFS/USFS_GTAC_TreeMap_v2022_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USFS_GTAC_TreeMap_v2022#terms-of-use","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/treemap/","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/treemap/","rel":"source"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_TreeMap_Product_Version_2023-1.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/TreeMap/Product_Version/2023-1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_TreeMap_v2020.json","rel":"predecessor-version","title":"USFS/GTAC/TreeMap/v2020","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_TreeMap_Product_Version_2023-1.json","rel":"successor-version","title":"projects/gtac-data-publish/assets/TreeMap/Product_Version/2023-1","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/TreeMap/v2022/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USFS/GTAC/TreeMap/v2022/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/3DEP/10m_collection","title":"USGS 3DEP 10m National Map Seamless (1/3 Arc-Second)","description":"This is the seamless 3DEP DEM dataset for the U.S.\nwith full coverage of the 48 conterminous states, Hawaii, and\nU.S. territories. Alaska coverage is partially available now and is being\nexpanded to statewide coverage as part of the Alaska Mapping Initiative.\nGround spacing is approximately 10 meters north/south, but variable\neast/west due to convergence of meridians with latitude.\n\nThe 1m dataset is ingested\nas [USGS_3DEP_1m](USGS_3DEP_1m).\n\nDataset uploaded by [Farmers Business Network](https://fbn.com).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-171,-16.6,164,76.9]]},"temporal":{"interval":[["1924-01-01T00:00:00Z","2022-05-04T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/3DEP/10m_collection","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_3DEP_10m_collection","rel":"related","title":"Run the example for USGS/3DEP/10m_collection in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_3DEP_10m_collection_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m_collection#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.usgs.gov/information-policies-and-instructions/crediting-usgs","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_3DEP_10m_collection.json","rel":"latest-version","title":"USGS/3DEP/10m_collection","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_3DEP_10m.json","rel":"predecessor-version","title":"USGS/3DEP/10m","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/3DEP/10m_collection/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/3DEP/10m_collection/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/GMTED2010","title":"GMTED2010: Global Multi-resolution Terrain Elevation Data 2010, Breakline Emphasis [deprecated]","description":"The Global Multi-resolution Terrain Elevation Data\n2010 (GMTED2010) dataset contains elevation data for the globe\ncollected from various sources. The version of the dataset available\nhere is Breakline Emphasis, 7.5 arc-seconds resolution. Breakline\nemphasis maintains the critical topographic features (streams or\nridges) within the landscape by maintaining any minimum elevation\nor maximum elevation value on a breakline that passes within the\nspecified analysis window. More details are available in the dataset\n[report](https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf).\n\nThe primary source dataset for GMTED2010 is NGA''s SRTM Digital\nTerrain Elevation Data (DTED&reg;, [https://www2.jpl.nasa.gov/srtm/](https://www2.jpl.nasa.gov/srtm/))\n(void-filled) 1-arc-second data. For the geographic areas outside\nthe SRTM coverage area and to fill in remaining holes in the SRTM\ndata, the following sources were used: non-SRTM DTED&reg;, Canadian\nDigital Elevation Data (CDED) at two resolutions, Satellite Pour\nl''Observation de la Terre (SPOT 5) Reference3D, National Elevation\nDataset (NED) for the continental United States and Alaska, GEODATA\n9 second digital elevation model (DEM) for Australia, an Antarctica\nsatellite radar and laser altimeter DEM, and a Greenland satellite\nradar altimeter DEM.\n\nThis dataset replaces the GTOPO30 Elevation Model.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-56,180,84]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/GMTED2010","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_GMTED2010","rel":"related","title":"Run the example for USGS/GMTED2010 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_GMTED2010_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GMTED2010#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_GMTED2010_FULL.json","rel":"latest-version","title":"USGS/GMTED2010_FULL","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_GMTED2010_FULL.json","rel":"successor-version","title":"USGS/GMTED2010_FULL","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/GMTED2010/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/GMTED2010_FULL","title":"GMTED2010: Global Multi-resolution Terrain Elevation Data 2010","description":"The Global Multi-resolution Terrain Elevation Data\n2010 (GMTED2010) dataset contains elevation data for the globe\ncollected from various sources at 7.5 arc-seconds resolution. More details\nare available in the dataset [report](https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf).\n\nThe primary source dataset for GMTED2010 is NGA''s SRTM Digital\nTerrain Elevation Data (DTED&reg;, [https://www2.jpl.nasa.gov/srtm/](https://science.jpl.nasa.gov/projects/srtm/))\n(void-filled) 1-arc-second data. For the geographic areas outside\nthe SRTM coverage area and to fill in remaining holes in the SRTM\ndata, the following sources were used: non-SRTM DTED&reg;, Canadian\nDigital Elevation Data (CDED) at two resolutions, Satellite Pour\nl''Observation de la Terre (SPOT 5) Reference3D, National Elevation\nDataset (NED) for the continental United States and Alaska, GEODATA\n9 second digital elevation model (DEM) for Australia, an Antarctica\nsatellite radar and laser altimeter DEM, and a Greenland satellite\nradar altimeter DEM.\n\nThis dataset replaces the GTOPO30 Elevation Model.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-56,180,84]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/GMTED2010_FULL","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_GMTED2010_FULL","rel":"related","title":"Run the example for USGS/GMTED2010_FULL in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_GMTED2010_FULL_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GMTED2010_FULL#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_GMTED2010_FULL.json","rel":"latest-version","title":"USGS/GMTED2010_FULL","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_GMTED2010.json","rel":"predecessor-version","title":"USGS/GMTED2010","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/GMTED2010_FULL/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/GTOPO30","title":"GTOPO30: Global 30 Arc-Second Elevation","description":"GTOPO30 is a global digital elevation model (DEM) with\na horizontal grid spacing of 30 arc seconds (approximately 1 kilometer).\nThe DEM was derived from several raster and vector sources of topographic\ninformation.  Completed in late 1996, GTOPO30 was developed over\na three-year period through a collaborative effort led by the U.S.\nGeological Survey''s Center for Earth Resources Observation and\nScience (EROS). The following organizations  participated by contributing\nfunding or source data:  the National Aeronautics  and Space Administration\n(NASA), the United Nations Environment Programme/Global Resource\nInformation Database (UNEP/GRID), the U.S. Agency for International\nDevelopment (USAID), the Instituto Nacional de Estadistica Geografica\ne Informatica (INEGI) of Mexico, the Geographical Survey Institute  (GSI)\nof Japan, Manaaki Whenua Landcare Research of New Zealand, and\nthe  Scientific Committee on Antarctic Research (SCAR).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1996-01-01T00:00:00Z","1996-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/GTOPO30","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_GTOPO30","rel":"related","title":"Run the example for USGS/GTOPO30 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_GTOPO30_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_GTOPO30#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/GTOPO30/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NED","title":"USGS National Elevation Dataset 1/3 Arc-Second [deprecated]","description":"The National Elevation Dataset (NED) used to be* the\nprimary elevation data product of the USGS. The NED is a seamless\ndataset with the best available raster elevation data of the\nconterminous United States, parts of Alaska, Hawaii, and some\nterritorial islands. The NED is derived from diverse source data\nthat are processed to a common coordinate system and unit of\nvertical measure. NED data are distributed in conformance with\nthe North American Datum of 1983 (NAD 83). All elevation values\nare in meters and, over the conterminous United States, are referenced\nto the North American Vertical Datum of 1988 (NAVD 88). The vertical\nreference will vary in other areas.\n\n*For over 15 years, the NED was the primary elevation data product\nof the USGS' National Map. However, it has been renamed and now\nconsidered just one component of elevation in The National\nMap after the 3D Elevation Program became operational.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-171,-15,164,70]]},"temporal":{"interval":[["2012-02-07T00:00:00Z","2012-02-07T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NED","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NED","rel":"related","title":"Run the example for USGS/NED in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NED_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NED#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_3DEP_10m_collection.json","rel":"latest-version","title":"USGS/3DEP/10m_collection","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_3DEP_10m.json","rel":"successor-version","title":"USGS/3DEP/10m","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NED/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NLCD","title":"NLCD: USGS National Land Cover Database [deprecated]","description":"This dataset is superseded by newer datasets:\n\n* 2016 landcover and imperviousness data in\n  [USGS/NLCD_RELEASES/2019_REL/NLCD](USGS_NLCD_RELEASES_2019_REL_NLCD).\n* 2019 landcover and imperviousness data in\n  [USGS/NLCD_RELEASES/2019_REL/NLCD](USGS_NLCD_RELEASES_2019_REL_NLCD).\n\nNLCD (the National Land Cover Database) is a 30-m Landsat-based land cover\ndatabase spanning 8 epochs (1992, 2001, 2004, 2006, 2008, 2011, 2013 and 2016).\n1992 data are primarily based on unsupervised classification of Landsat data,\nwhile the rest of the images rely on the imperviousness data layer for the\nurban classes and on a decision-tree classification for the rest. The 1992\nimage is not directly comparable to any later editions of NLCD.\n\nThis dataset has one image for the continental US for each of the\neight epochs, plus separate images for Alaska, Hawaii, and Puerto Rico in 2001 and 2011.\n(They could not be merged in due to having a different projection from the continental US\nimages.)\n\nNote that the impervious layers and tree cover layers for Alaska 2011\nonly have data for parts of Alaska.  For the impervious layer, only the\nparts of Kenai Peninsula Borough and Anchorage Municipality are available.\nTree cover is only available on a strip along the coast from the southern\ntip of the Alaskan Panhandle up through the Kenai Peninsula, plus Kodiak\nIsland.\n\nNLCD 2016 Shrub Component products characterize the percentage of each\n30-meter pixel in the Western United States covered by shrub, herbaceous,\nbare ground, litter, sagebrush, big sagebrush and annual herbaceous, along\nwith estimating shrub height and sagebrush height. These products have\nbeen produced by USGS in collaboration with the Bureau of Land Management.\n\nNLCD products are created by the Multi-Resolution Land Characteristics\n(MRLC) Consortium, a partnership of federal agencies led by the\nU.S. Geological Survey.\n","deprecated":true,"license":"CC0-1.0","extent":{"spatial":{"bbox":[[-130.24,21.75,-63.66,57.68]]},"temporal":{"interval":[["1992-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD","rel":"related","title":"Run the example for USGS/NLCD in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2019_REL_NLCD.json","rel":"latest-version","title":"USGS/NLCD_RELEASES/2019_REL/NLCD","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2016_REL.json","rel":"successor-version","title":"USGS/NLCD_RELEASES/2016_REL","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/TRENDS","title":"RCMAP Rangeland Component Timeseries Trends (1985-2021), v05 [deprecated]","description":"The RCMAP product suite includes nine fractional components: annual\nherbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial\nherbaceous, sagebrush, shrub, and tree. It also includes rule-based error\nmaps and the temporal trends of each component. Data characterize the\npercentage of each 30-meter pixel in the Western United States covered by\neach component for each year from 1985-2021, providing change information\nfor 36 years.\n\nThe temporal patterns in each RCMAP component are assessed with two\napproaches:\n1) linear trends and 2) a breaks and stable states method with an 8-year\ntemporal moving window based on structural change at the pixel level.\nLinear trend products include slope and p-value calculated from least\nsquares linear regression. The slope represents the average percent cover\nchange per year over the time series and the p-value reflects the\nconfidence of change in each pixel. The structural change method partitions\nthe time series into segments of similar slope values, with statistically\nsignificant break-points indicating perturbations to the prior trajectory.\nThe break point trends analysis suite relies on structural break methods,\nresulting in the identification of the number and timing of breaks in the\nfollowing statistics are produced: 1) for each component, each year, the\npresence/absence of breaks, 2) the slope, p-value, and standard error of\nthe segment occurring in each year, 3) the overall model R2 (quality of\nmodel fit to the temporal profile), and 4) an index, Total Change Intensity.\nThis index reflects the total amount of change occurring across components\nin that pixel. The linear and structural change methods generally agreed on\npatterns of change, but the latter found breaks more often, with at least\none break point in most pixels. The structural change model provides more\nrobust statistics on the significant minority of pixels with non-monotonic\ntrends, while detrending some interannual signal potentially superfluous\nfrom a long-term perspective.\n","deprecated":true,"license":"CC0-1.0","extent":{"spatial":{"bbox":[[-125.07,28.46,-101.07,49.33]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/TRENDS","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD_RELEASES_2019_REL_RCMAP_V5_TRENDS","rel":"related","title":"Run the example for USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/TRENDS in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_RELEASES_2019_REL_RCMAP_V5_TRENDS_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2019_REL_RCMAP_V5_TRENDS#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_TRENDS.json","rel":"latest-version","title":"USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_TRENDS.json","rel":"successor-version","title":"USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/TRENDS/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/TRENDS_YEAR","title":"RCMAP Rangeland Trends Year for Component Timeseries (1985-2021), v05 [deprecated]","description":"This collection includes RCMAP yearly products from 1985 through 2021. The RCMAP product suite includes nine fractional components: annual\nherbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial\nherbaceous, sagebrush, shrub, and tree. It also includes rule-based error\nmaps and the temporal trends of each component. Data characterize the\npercentage of each 30-meter pixel in the Western United States covered by\neach component for each year from 1985-2021, providing change information\nfor 36 years.\n\nThe temporal patterns in each RCMAP component are assessed with two\napproaches:\n1) linear trends and 2) a breaks and stable states method with an 8-year\ntemporal moving window based on structural change at the pixel level.\nLinear trend products include slope and p-value calculated from least\nsquares linear regression. The slope represents the average percent cover\nchange per year over the time series and the p-value reflects the\nconfidence of change in each pixel. The structural change method partitions\nthe time series into segments of similar slope values, with statistically\nsignificant break-points indicating perturbations to the prior trajectory.\nThe break point trends analysis suite relies on structural break methods,\nresulting in the identification of the number and timing of breaks in the\nfollowing statistics are produced: 1) for each component, each year, the\npresence/absence of breaks, 2) the slope, p-value, and standard error of\nthe segment occurring in each year, 3) the overall model R2 (quality of\nmodel fit to the temporal profile), and 4) an index, Total Change Intensity.\nThis index reflects the total amount of change occurring across components\nin that pixel. The linear and structural change methods generally agreed on\npatterns of change, but the latter found breaks more often, with at least\none break point in most pixels. The structural change model provides more\nrobust statistics on the significant minority of pixels with non-monotonic\ntrends, while detrending some interannual signal potentially superfluous\nfrom a long-term perspective.\n","deprecated":true,"license":"CC0-1.0","extent":{"spatial":{"bbox":[[-125.068,28.459,-101.074,49.325]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/TRENDS_YEAR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD_RELEASES_2019_REL_RCMAP_V5_TRENDS_YEAR","rel":"related","title":"Run the example for USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/TRENDS_YEAR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_RELEASES_2019_REL_RCMAP_V5_TRENDS_YEAR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2019_REL_RCMAP_V5_TRENDS_YEAR#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_TRENDS_YEAR.json","rel":"latest-version","title":"USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS_YEAR","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_TRENDS_YEAR.json","rel":"successor-version","title":"USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS_YEAR","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/TRENDS_YEAR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2019_REL/RCMAP/V5/TRENDS_YEAR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NLCD_RELEASES/2020_REL/NALCMS","title":"Land Cover of North America at 30 meters, 2020","description":"The 2020 North American Land Cover 30-meter dataset was produced as part of\nthe North American Land Change Monitoring System (NALCMS), a trilateral\neffort between Natural Resources Canada, the United States Geological\nSurvey, and three Mexican organizations including the National Institute of\nStatistics and Geography (Instituto Nacional de Estad&iacute;stica y\nGeograf&iacute;a), National Commission for the Knowledge and Use of the\nBiodiversity (Comisi&oacute;n Nacional Para el Conocimiento y Uso de la\nBiodiversidad), and the National Forestry Commission of Mexico\n(Comisi&oacute;n Nacional Forestal).  The collaboration is facilitated by\nthe Commission for Environmental Cooperation, an international organization\ncreated by the Canada, Mexico, and United States governments under the North\nAmerican Agreement on Environmental Cooperation to promote environmental\ncollaboration between the three countries.\n\nThe general objective of NALCMS is to devise, through collective effort, a\nharmonized multi-scale land cover monitoring approach which ensures high\naccuracy and consistency in monitoring land cover changes at the North\nAmerican scale and which meets each country's specific requirements.\n\nThis 30-meter dataset of North American Land Cover reflects land cover\ninformation for 2020 from Mexico and Canada, 2019 over the conterminous\nUnited States and 2021 over Alaska.  Each country developed its own\nclassification method to identify Land Cover classes and then provided an\ninput layer to produce a continental Land Cover map across North America.\nCanada, Mexico, and the United States developed their own 30-meter land\ncover products; see specific sections on data generation below.\n\nThe main inputs for image classification were 30-meter Landsat 8 Collection\n2 Level 1 data in the three countries (Canada, the United States and\nMexico). Image selection processes and reduction to specific spectral bands\nvaried among the countries due to study-site-specific requirements.  While\nCanada selected most images from the year 2020 with a few from 2019 and\n2021, the Conterminous United States employed mainly images from 2019, while\nAlaska land cover maps are mainly based on the use of images from 2021. The\nland cover map for Mexico was based on land cover change detection between\n2015 and 2020 Mexico Landsat 8 mosaics.\n\nIn order to generate a seamless and consistent land cover map of North\nAmerica, national maps were generated for Canada by the CCRS; for Mexico by\nCONABIO, INEGI, and CONAFOR; and for the United States by the USGS. Each\ncountry chose their own approaches, ancillary data, and land cover mapping\nmethodologies to create national datasets. This North America dataset was\nproduced by combining the national land cover datasets.  The integration of\nthe three national products merged four Land Cover map sections, Alaska,\nCanada, the conterminous United States and Mexico.\n\nSee also:\n\n* Natural Resources Canada has North American Land Cover information\n  available online at\n  [https://open.canada.ca/data/en/dataset/ee1580ab-a23d-4f86-a09b-79763677eb47](\n  https://open.canada.ca/data/en/dataset/ee1580ab-a23d-4f86-a09b-79763677eb47)\n\n* The National Commission for the Knowledge and Use of Biodiversity has\n  North American Land Cover information available online at\n  [https://www.biodiversidad.gob.mx/monitoreo/cobertura-suelo](\n    https://www.biodiversidad.gob.mx/monitoreo/cobertura-suelo)\n\n* The U.S. Geological Survey has North American Land Cover information\n  available online at [www.mrlc.gov](https://www.mrlc.gov)\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-175,14,-50,84]]},"temporal":{"interval":[["2019-01-01T00:00:00Z","2021-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2020_REL/NALCMS","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD_RELEASES_2020_REL_NALCMS","rel":"related","title":"Run the example for USGS/NLCD_RELEASES/2020_REL/NALCMS in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_RELEASES_2020_REL_NALCMS_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2020_REL_NALCMS#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2020_REL/NALCMS/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NLCD_RELEASES/2021_REL/NLCD","title":"NLCD 2021: USGS National Land Cover Database, 2021 release","description":"The U.S. Geological Survey (USGS), in partnership with several federal\nagencies, has now developed and released seven National Land Cover\nDatabase (NLCD) products: NLCD 1992, 2001, 2006, 2011, 2016, 2019,\nand 2021. Beginning with the 2016 release, land cover products were\ncreated for two-to-three-year intervals between 2001 and the most\nrecent year. These products provide spatially explicit and reliable\ninformation on the Nation’s land cover and land cover change.\nNLCD continues to provide innovative, consistent, and robust\nmethodologies for production of a multi-temporal land cover and\nland cover change database.\n\nThe NLCD 2021 release is update based, so the Land Cover and Impervious\nSurface products released in [2019](USGS_NLCD_RELEASES_2019_REL_NLCD)\nare unchanged and used directly with NLCD 2021 for change analysis\nthough the NLCD timespan. Science products and the change index are\nupdated and will need to be reacquired to contain the additional 2021\nchange. These new products use a streamlined compositing process for\nassembling and preprocessing Landsat imagery and geospatial ancillary\ndatasets; a temporally, spectrally, and spatially integrated land cover\nchange analysis strategy; a theme-based post-classification protocol for\ngenerating land cover and change products; a continuous fields\nbiophysical parameters modeling method; and a scripted operational system.\nThe overall accuracy of the 2019 Level I land cover was 91%. Results from\nthis study confirm the robustness of this comprehensive and highly\nautomated procedure for NLCD 2021 operational mapping\n(see [doi:10.1080/15481603](https://doi.org/10.1080/15481603.2023.2181143)\nfor the latest accuracy assessment publication). Questions about the NLCD\n2021 land cover product can be directed to the NLCD 2021 land cover\nmapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or\nmrlc@usgs.gov. See included spatial metadata for more details.\n\nPlease see National Land Cover Database (NLCD) [2019 NLCD release](https://doi.org/10.5066/P9KZCM54)\nfor the 2019 release of NLCD which is used with the 2021 release for\ncomparisons through the years. Also refer to the larger NLCD Community\npage for all things NLCD related National Land Cover Database (NLCD)\n[NLCD Community Page](https://www.sciencebase.gov/catalog/item/6345b637d34e342aee0863aa).\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-130.24,21.75,-63.66,50]]},"temporal":{"interval":[["2021-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2021_REL/NLCD","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD_RELEASES_2021_REL_NLCD","rel":"related","title":"Run the example for USGS/NLCD_RELEASES/2021_REL/NLCD in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_RELEASES_2021_REL_NLCD_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2021_REL_NLCD#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2021_REL/NLCD/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2021_REL/NLCD/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NLCD_RELEASES/2021_REL/TCC/v2021-4","title":"USFS Tree Canopy Cover v2021-4 (CONUS and OCONUS) [deprecated]","description":"This product is part of the Tree Canopy Cover (TCC) data suite. It includes modeled TCC, standard error (SE), and \nNational Land Cover Database's (NLCD) TCC data for each year. TCC data produced by the the United States Department \nof Agriculture, Forest Service (USFS) are included in the Multi-Resolution Land Characteristics (MRLC) consortium \nthat is part of the National Land Cover Database (NLCD) project managed by the United States (US) Geological Survey (USGS).\n\nThe Science TCC product and NLCD TCC are remote sensing-based map output produced by the USFS. The objecive of TCC Science \nand NLCD TCC are to develop a consistent approach using the latest technology and advancements in TCC mapping to produce a \n\"best available\" map of TCC across the Conterminous United States (CONUS) and southeast Alaska, Hawaii and \nPuerto Rico-US Virgin Islands (OCONUS).\n\nModel outputs include Science TCC, Science SE and NLCD TCC. Science TCC and SE include data from 2008 through 2021. \nNLCD TCC include data from 2011 through 2021, with data fully masked in 2008, 2009 and 2010. \n\n*Science TCC is the raw direct model outputs.\n\n*Science SE is the model standard deviation of the predicted values from all regression trees. \n*The NLCD TCC product undergoes further post processing applied to the annual Science TCC images, \nwhich includes several masking (water and non-tree agriculture), filtering, and minimum-mapping unit (MMU) routines, \nas well as processes that reduce interannual noise and return longer duration trends.  \n\nEach image includes a data mask band that has three values representing areas of no data (0), mapped tree canopy cover(1), \nand non-processing area (2). The non-processing areas are pixels in the study area with no cloud or cloud shadow-free data. No data\nand non-processing area pixels are masked in TCC and SE images.\n\nDue to CONUS size and wide variety of ecotones, CONUS modeling was broken up into 54 480x480 km tiles. For each tile, \na unique random forest model was built using 2011 fitted LandTrendr, 2011 CDL, and terrain data. All reference data \nthat were part of the 70% available for model calibration that intersected tiles within a 5x5 window around the center \ntile were used to train the random forest model. That model was then applied to the center tile. For OCONUS, one model \nwas applied to each study area, and no tiles were used. \n\nPredictor layers for the TCC model include outputs from the LandTrendr and terrain information. These\ncomponents are all accessed and processed using Google Earth Engine (Gorelick et al., 2017).\n\nTo produce annual composites for LandTrendr, USGS Collection 2 Landsat Tier 1 and Sentinel 2A, \n2B Level-1C top of atmosphere reflectance data were used. The cFmask cloud masking algorithm \n(Foga et al., 2017), which is an implementation of Fmask 2.0 (Zhu and Woodcock, 2012) \n(Landsat-only), cloudScore (Chastain et al., 2019) (Landsat-only), and s2cloudless\n(Sentinel-Hub, 2021) (Sentinel 2-only) are used to mask clouds, while TDOM\n(Chastain et al., 2019) is used to mask cloud shadows (Landsat and Sentinel\n2). For LandTrendr, the annual medoid is then computed to summarize cloud\nand cloud shadow-free values from each year into a single composite.\n\nThe composite time series is temporally segmented using LandTrendr\n(Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018).\n\nThe raw composite values, LandTrendr fitted values, pair-wise differences,\nsegment duration, change magnitude, and slope, along with elevation, slope, sine of \naspect, and cosine of aspect from the 10 m USGS 3D. Elevation Program (3DEP) data \n(U.S. Geological Survey, 2019), are used as independent predictor variables in a Random \nForest (Breiman, 2001) model.\n\nReference data are collected from USFS Forest Inventory and Analysis (FIA) photo-interpreted \nTCC data, and used to make wall-to wall TCC predictions on a pixel-wise basis. \n\n**Additional Resources**\n\nPlease see the [TCC Methods Brief](https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/docs/TCC_v2021-4_Methods.pdf)\nfor more detailed information regarding methods and accuracy assessment, or the\n[TCC Geodata Clearinghouse](https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/)\nfor data downloads, metadata, and support documents.\n\nContact [sm.fs.tcc@usda.gov](mail to:sm.fs.tcc@usda.gov) with any\nquestions or specific data requests.\n\n* **Breiman, L., 2001.**\nRandom Forests. In Machine Learning. *Springer*,\n45: 5-32\n[doi:10.1023/A:1010933404324](https://doi.org/10.1023/A:1010933404324)\n\n* **Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K., 2019.**\nEmpirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM\ntop of atmosphere spectral characteristics over the conterminous United States. In Remote\nSensing of Environment. *Science Direct*,\n221: 274-285\n[doi:10.1016/j.rse.2018.11.012](https://doi.org/10.1016/j.rse.2018.11.012)\n\n* **Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N., 2018.**\nA LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of\nEnvironment. *Science Direct*,\n205: 131-140\n[doi:10.1016/j.rse.2017.11.015](https://doi.org/10.1016/j.rse.2017.11.015)\n\n* **Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann,\nT., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B., 2017.** Cloud\ndetection algorithm comparison and validation for operational Landsat data\nproducts. In Remote Sensing of Environment.  *Science Direct*, 194: 379-390\n[doi:10.1016/j.rse.2017.03.026](http://doi.org/10.1016/j.rse.2017.03.026)\n\n* **U.S. Geological Survey, 2019.** USGS 3D Elevation Program Digital Elevation \nModel, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m\n\n* **Kennedy, R. E., Yang, Z., and Cohen, W. B., 2010.**\nDetecting trends in forest disturbance and recovery using yearly Landsat time series: 1.\nLandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment.\n*Science Direct*,\n114(12): 2897-2910\n[doi:10.1016/j.rse.2010.07.008](https://doi.org/10.1016/j.rse.2010.07.008)\n\n* **Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S., 2018.**\nImplementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing. *MDPI*,\n10(5): 691\n[doi:10.3390/rs10050691](https://doi.org/10.3390/rs10050691)\n\n* **Sentinel-Hub, 2021.**\nSentinel 2 Cloud Detector. [Online].\nAvailable at: [https://github.com/sentinel-hub/sentinel2-cloud-detector](https://github.com/sentinel-hub/sentinel2-cloud-detector)\n\n* **Zhu, Z., and Woodcock, C. E., 2012.**. \nObject-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of\nEnvironment. *Science Direct*, \n118: 83-94\n[doi:10.1016/j.rse.2011.10.028](https://doi.org/10.1016/j.rse.2011.10.028)\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-135.286387,20.38379,-56.446306,52.459364]]},"temporal":{"interval":[["2008-06-01T00:00:00Z","2021-09-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2021_REL/TCC/v2021-4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD_RELEASES_2021_REL_TCC_v2021-4","rel":"related","title":"Run the example for USGS/NLCD_RELEASES/2021_REL/TCC/v2021-4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_RELEASES_2021_REL_TCC_v2021-4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2021_REL_TCC_v2021-4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_TCC_Product_Version_2025-6.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/TCC/Product_Version/2025-6","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2023_REL_TCC_v2023-5.json","rel":"successor-version","title":"USGS/NLCD_RELEASES/2023_REL/TCC/v2023-5","type":"application/json"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2021_REL/TCC/v2021-4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2021_REL/TCC/v2021-4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/COVER","title":"RCMAP Rangeland Component Timeseries (1985-2023), v06","description":"'The RCMAP (Rangeland Condition Monitoring Assessment and Projection)\ndataset quantifies the percent cover of rangeland components across\nwestern North America using Landsat imagery from 1985-2023. The RCMAP\nproduct suite consists of ten fractional components: annual herbaceous,\nbare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous,\nsagebrush, shrub, tree, and shrub height in addition to the temporal\ntrends of each component. Several enhancements were made to the RCMAP\nprocess relative to prior generations. First, high-resolution training\nwas revised using an improved neural-net classifier and modelling approach.\nThese data serve as foundation to the RCMAP approach. The training\ndatabase was further improved by incorporating additional datasets. Next,\nthe Landsat compositing approach was improved to better capture the range\nof conditions from across each year and through time. These composites are\nbased on Collection 2 Landsat data with improved geolocation accuracy and\ndynamic range. Finally, the Canadian portion of the sagebrush biome was\nincluded, which expanded the study area by 29,199 km2.\n\nProcessing efficiency has been increased using open-source software and\nUSGS High-Performance Computing (HPC) resources. The mapping area included\neight regions which were subsequently mosaicked. These data can be used to\nanswer critical questions regarding the influence of climate change and\nthe suitability of management practices. Component products can be\ndownloaded at\n[Multi-Resolution Land Characteristics Consortium](https://www.mrlc.gov/data).\n\nSee also:\n\n* Rigge, M., H. Shi, C. Homer, P. Danielson, and B. Granneman. 2019.\n  Long-term trajectories of fractional component change in the Northern\n  Great Basin, USA. Ecosphere 10(6):e02762.\n  [doi:10.1002/ecs2.2762](https://doi.org/10.1002/ecs2.2762)\n\n* Rigge, M., C. Homer, L. Cleeves, D. K. Meyer, B. Bunde, H. Shi, G. Xian,\n  S. Schell, and M. Bobo. 2020. Quantifying western U.S. rangelands as\n  fractional components with multi-resolution remote sensing and in situ\n  data. Remote Sensing 12.\n  [doi:10.3390/rs12030412](https://doi.org/10.3390/rs12030412)\n\n* Rigge, M., C. Homer, H. Shi, D. Meyer, B.\n  Bunde, B. Granneman, K. Postma, P. Danielson, A. Case, and G. Xian. 2021.\n  Rangeland Fractional Components Across the Western United States\n  from 1985 to 2018. Remote Sensing 13:813.\n  [doi:10.3390/rs13040813](https://doi.org/10.3390/rs13040813).',\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-128.0026,26.5157,-99.6758,51.5761]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2023-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/COVER","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_COVER","rel":"related","title":"Run the example for USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/COVER in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_COVER_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_COVER#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/COVER/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/COVER/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS","title":"RCMAP Rangeland Trends for Component Timeseries (1985-2023), v06","description":"The RCMAP (Rangeland Condition Monitoring Assessment and Projection)\ndataset quantifies the percent cover of rangeland components across\nwestern North America using Landsat imagery from 1985-2023. The RCMAP\nproduct suite consists of ten fractional components: annual herbaceous,\nbare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous,\nsagebrush, shrub, tree, and shrub height in addition to the temporal\ntrends of each component. Several enhancements were made to the RCMAP\nprocess relative to prior generations. First, high-resolution training\nwas revised using an improved neural-net classifier and modelling approach.\nThese data serve as foundation to the RCMAP approach. The training\ndatabase was further improved by incorporating additional datasets. Next,\nthe Landsat compositing approach was improved to better capture the range\nof conditions from across each year and through time. These composites are\nbased on Collection 2 Landsat data with improved geolocation accuracy and\ndynamic range. Finally, the Canadian portion of the sagebrush biome was\nincluded, which expanded the study area by 29,199 km2.\n\nProcessing efficiency has been increased using open-source software and\nUSGS High-Performance Computing (HPC) resources. The mapping area included\neight regions which were subsequently mosaicked. These data can be used to\nanswer critical questions regarding the influence of climate change and\nthe suitability of management practices. Component products can be\ndownloaded at\n[Multi-Resolution Land Characteristics Consortium](https://www.mrlc.gov/data).\n\nThe temporal patterns were assessed in each RCMAP component with two\napproaches, 1) linear trends and 2) a breaks and stable states method\nwith an 8-year temporal moving window based on structural change at the\npixel level. Linear trend products include slope and p-value calculated\nfrom least squares linear regression. The slope represents the average\npercent cover change per year over the times-series and the p-value\nreflects the confidence of change in each pixel. The structural change\nmethod partitions the time-series into segments of similar slope values,\nwith statistically significant breakpoints indicating perturbations to the\nprior trajectory. The break point trends analysis suite relies on\nstructural break methods, resulting in the identification of the number\nand timing of breaks in the time-series, and the significance of each\nsegment. The following statistics were produced: 1) for each component,\neach year, the presence/absence of breaks, 2) the slope, p-value, and\nstandard error of the segment occurring in each year, 3) the overall\nmodel R2 (quality of model fit to the temporal profile), and 4) an index,\nTotal Change Intensity. This index reflects the total amount of change\noccurring across components in that pixel. The linear and structural\nchange methods generally agreed on patterns of change, but the latter\nfound breaks more often, with at least one break point in most pixels.\nThe structural change model provides more robust statistics on the\nsignificant minority of pixels with non-monotonic trends, while\ndetrending some interannual signal potentially superfluous from a\nlong-term perspective.\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-128.0026,26.5157,-99.6758,51.5761]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2023-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_TRENDS","rel":"related","title":"Run the example for USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_TRENDS_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_TRENDS#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS_YEAR","title":"RCMAP Rangeland Trends Year for Component Timeseries  (1985-2023), v06","description":"This collection includes RCMAP yearly products from 1985 through 2023. The RCMAP (Rangeland Condition Monitoring Assessment and Projection)\ndataset quantifies the percent cover of rangeland components across\nwestern North America using Landsat imagery from 1985-2023. The RCMAP\nproduct suite consists of ten fractional components: annual herbaceous,\nbare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous,\nsagebrush, shrub, tree, and shrub height in addition to the temporal\ntrends of each component. Several enhancements were made to the RCMAP\nprocess relative to prior generations. First, high-resolution training\nwas revised using an improved neural-net classifier and modelling approach.\nThese data serve as foundation to the RCMAP approach. The training\ndatabase was further improved by incorporating additional datasets. Next,\nthe Landsat compositing approach was improved to better capture the range\nof conditions from across each year and through time. These composites are\nbased on Collection 2 Landsat data with improved geolocation accuracy and\ndynamic range. Finally, the Canadian portion of the sagebrush biome was\nincluded, which expanded the study area by 29,199 km2.\n\nProcessing efficiency has been increased using open-source software and\nUSGS High-Performance Computing (HPC) resources. The mapping area included\neight regions which were subsequently mosaicked. These data can be used to\nanswer critical questions regarding the influence of climate change and\nthe suitability of management practices. Component products can be\ndownloaded at\n[Multi-Resolution Land Characteristics Consortium](https://www.mrlc.gov/data).\n\nThe temporal patterns were assessed in each RCMAP component with two\napproaches, 1) linear trends and 2) a breaks and stable states method\nwith an 8-year temporal moving window based on structural change at the\npixel level. Linear trend products include slope and p-value calculated\nfrom least squares linear regression. The slope represents the average\npercent cover change per year over the times-series and the p-value\nreflects the confidence of change in each pixel. The structural change\nmethod partitions the time-series into segments of similar slope values,\nwith statistically significant breakpoints indicating perturbations to the\nprior trajectory. The break point trends analysis suite relies on\nstructural break methods, resulting in the identification of the number\nand timing of breaks in the time-series, and the significance of each\nsegment. The following statistics were produced: 1) for each component,\neach year, the presence/absence of breaks, 2) the slope, p-value, and\nstandard error of the segment occurring in each year, 3) the overall\nmodel R2 (quality of model fit to the temporal profile), and 4) an index,\nTotal Change Intensity. This index reflects the total amount of change\noccurring across components in that pixel. The linear and structural\nchange methods generally agreed on patterns of change, but the latter\nfound breaks more often, with at least one break point in most pixels.\nThe structural change model provides more robust statistics on the\nsignificant minority of pixels with non-monotonic trends, while\ndetrending some interannual signal potentially superfluous from a\nlong-term perspective.\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-128.0026,26.5157,-99.6758,51.5761]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2023-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS_YEAR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_TRENDS_YEAR","rel":"related","title":"Run the example for USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS_YEAR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_TRENDS_YEAR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2023_REL_RCMAP_V6_TRENDS_YEAR#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS_YEAR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS_YEAR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/NLCD_RELEASES/2023_REL/TCC/v2023-5","title":"USFS Tree Canopy Cover v2023-5 (CONUS and OCONUS) [deprecated]","description":"**Overview**\n\nThe Tree Canopy Cover (TCC) data suite, produced by the United States Department of Agriculture, Forest Service (USFS), \nare annual remote sensing-based map outputs spanning from 1985-2023.\nThese data support the National Land Cover Database (NLCD) project, \nwhich is managed by the US Geological Survey (USGS) as part of the Multi-Resolution Land Characteristics (MRLC) consortium. \nThe project aims to use the latest technology to create a consistent, \"best available\" map of tree canopy cover. \nThe geographic scope includes the Conterminous United States (CONUS) and OCONUS regions (Southeast Alaska (SEAK), Hawaii, \nPuerto Rico, and the US Virgin Islands (PRUSVI)). \n\n**Products**\n\nThe TCC data suite includes three products:\n\n* **Science TCC:** The raw, direct outputs from the model.\n\n* **Science standard error (SE):** The model standard deviation of the predicted values from all regression trees. \n\n* **NLCD TCC:** A refined product derived from the annual Science TCC images. It undergoes post-processing to \nreduce interannual noise, highlight long-term trends, and mask specific features (such as water and non-tree agriculture).  \n\nEach image includes a data mask band that has three values representing areas of no data (0), mapped tree canopy cover(1), \nand non-processing area (2). The non-processing areas are pixels in the study area with no cloud or cloud shadow-free data. No data\nand non-processing area pixels are masked in TCC and SE images.\n\n**Data and Methods**\n\nWe developed training data and random forest models for CONUS, SEAK, PRUSVI and HAWAII using the \nUSFS Forest Inventory and Analysis (FIA) photo-interpreted TCC as reference data. We leveraged Google Earth Engine (GEE) \n(Gorelick et al., 2017) to process fitted LandTrendr and terrain predictors. Terrain data from the 3D Elevation Program (3DEP) \n(U.S. Geological Survey, 2019) include elevation, slope, sine of aspect, and cosine of aspect. \nFor CONUS, we also included the Crop Data Layer (CDL) as a predictor (Lin et al., 2022). \n\nWe utilized USGS Collection 2 Landsat Tier 1 and Sentinel 2A/2B Level-1C top of atmosphere reflectance imagery to \nproduce annual medoid composites. To ensure data quality, we applied various algorithms to mask clouds and shadows, \nincluding cFmask (Foga et al., 2017; Zhu and Woodcock, 2012), cloudScore (Chastain et al., 2019), s2cloudless (Sentinel-Hub, 2021), \nCloud Score+ (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019). Once masked, we computed annual medoids to create \na single cloud-free composite for each year. Finally, the composite time series was temporally segmented using LandTrendr \n(Kennedy et al., 2010, 2018; Cohen et al., 2018). \n\nFor CONUS, we used 70% of the reference data for calibration and 30% for independent error assessment. \nGiven the ecological diversity of CONUS, we divided the modeling area into 54 tiles (480 km × 480 km). \nOn local computers we built a unique random forest model for each tile (Breiman, 2001), training it on reference data intersecting a \n5×5 window around the center tile. The models were then deployed in GEE to predict wall-to-wall TCC. For OCONUS regions, \nwe used an 80/20 split and developed a single random forest model for each region.\n\n**Additional Resources**\n\n* Please see the [TCC Methods Brief](https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/docs/TCC_v2023-5_Methods.pdf) or \n[Science of Remote Sensing journal article](https://www.sciencedirect.com/science/article/pii/S2666017225001075)\nfor detailed information regarding methods and accuracy assessment. \n\n* Please see the [TCC Geodata Clearinghouse](https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/)\nfor data downloads, metadata, and support documents.\n\n* The string HAWAII will be updated to HI in the upcoming v2025.6 data release.\n\nContact [sm.fs.tcc@usda.gov] with any\nquestions or specific data requests.\n\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-135.286387,20.38379,-56.446306,52.459364]]},"temporal":{"interval":[["1985-06-01T00:00:00Z","2023-09-30T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2023_REL/TCC/v2023-5","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_NLCD_RELEASES_2023_REL_TCC_v2023-5","rel":"related","title":"Run the example for USGS/NLCD_RELEASES/2023_REL/TCC/v2023-5 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_NLCD_RELEASES_2023_REL_TCC_v2023-5_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2023_REL_TCC_v2023-5#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_TCC_Product_Version_2025-6.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/TCC/Product_Version/2025-6","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2021_REL_TCC_v2021-4.json","rel":"predecessor-version","title":"USGS/NLCD_RELEASES/2021_REL/TCC/v2021-4","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_TCC_Product_Version_2025-6.json","rel":"successor-version","title":"projects/gtac-data-publish/assets/TCC/Product_Version/2025-6","type":"application/json"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2023_REL/TCC/v2023-5/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/USGS/NLCD_RELEASES/2023_REL/TCC/v2023-5/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"USGS/SRTMGL1_003","title":"NASA SRTM Digital Elevation 30m","description":"The Shuttle Radar Topography Mission (SRTM, see [Farr\net al. 2007](https://onlinelibrary.wiley.com/doi/10.1029/2005RG000183/full))\ndigital elevation data is an international research effort that\nobtained digital elevation models on a near-global scale. This\nSRTM V3 product (SRTM Plus) is provided by NASA JPL\nat a resolution of 1 arc-second (approximately 30m).\n\nThis dataset has undergone a void-filling process using open-source data\n(ASTER GDEM2, GMTED2010, and NED), as opposed to other versions that\ncontain voids or have been void-filled with commercial sources.\nFor more information on the different versions see the\n[SRTM Quick Guide](https://lpdaac.usgs.gov/documents/13/SRTM_Quick_Guide.pdf).\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf)\n\n* [General Documentation](https://lpdaac.usgs.gov/documents/13/SRTM_Quick_Guide.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://doi.org/10.1029/2005RG000183)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-56,180,60]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/USGS/SRTMGL1_003","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/USGS/USGS_SRTMGL1_003","rel":"related","title":"Run the example for USGS/SRTMGL1_003 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/USGS/USGS_SRTMGL1_003_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/USGS/SRTMGL1_003/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"UTOKYO/WTLAB/KBDI/v1","title":"KBDI: Keetch-Byram Drought Index","description":"Keetch-Byram Drought Index (KBDI) is a continuous reference scale for estimating the dryness of\nthe soil and duff layers. The index increases for each day without rain (the amount of increase\ndepends on the daily high temperature) and decreases when it rains. This system is based\nprimarily on recent rainfall patterns. It is a measure of meteorological drought; it reflects\nwater gain or loss within the soil.\n\nThe scale ranges from 0 (no moisture deficit) to 800 (extreme drought). The range of the index\nis determined by assuming that there is 20 cm of moisture in a saturated soil that is readily\navailable to the vegetation (Keetch and Byram, 1968). KBDI is world widely used for drought\nmonitoring for national weather forecast, wildfire prevention and usefully especially in regions\nwith rain-fed crops.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[60,-60,180,60]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/UTOKYO/WTLAB/KBDI/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UTOKYO/UTOKYO_WTLAB_KBDI_v1","rel":"related","title":"Run the example for UTOKYO/WTLAB/KBDI/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/UTOKYO/UTOKYO_WTLAB_KBDI_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/UTOKYO_WTLAB_KBDI_v1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/UTOKYO/WTLAB/KBDI/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/UTOKYO/WTLAB/KBDI/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"VITO/PROBAV/C1/S1_TOC_100M","title":"PROBA-V C1 Top Of Canopy Daily Synthesis 100m","description":"Proba-V is a satellite mission tasked to map land cover and vegetation\ngrowth. It was designed to provide continuity for the VGT optical instrument\nfrom the SPOT-4 and SPOT-5 missions.\n\nThe sensor collects data in three VNIR (visible and near-infrared) bands\nand one SWIR (short-wave infrared) spectral band with a 2250km field of\nview. Global images are produced every 2 days at 300m resolution, and a 100m\nimage from nadir observations every 5 days. These images are later\ncomposited to produce this daily synthesis dataset. The description of\nthe compositing and atmospheric correction procedures can be found in\nthe [user manual](https://publications.vito.be/2017-1333-probav-products-user-manual.pdf).\n\nThe reflectances provided in this dataset are presented as Digital Count\nNumbers (DN) and must be converted according to the guidelines in\nSection 4.6.1 of the [user manual](https://publications.vito.be/2017-1333-probav-products-user-manual.pdf).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-10-17T00:00:00Z","2021-10-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/C1/S1_TOC_100M","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/VITO/VITO_PROBAV_C1_S1_TOC_100M","rel":"related","title":"Run the example for VITO/PROBAV/C1/S1_TOC_100M in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/VITO/VITO_PROBAV_C1_S1_TOC_100M_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_100M#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_100M.json","rel":"latest-version","title":"VITO/PROBAV/C1/S1_TOC_100M","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_S1_TOC_100M.json","rel":"predecessor-version","title":"VITO/PROBAV/S1_TOC_100M","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/C1/S1_TOC_100M/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/C1/S1_TOC_100M/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"VITO/PROBAV/C1/S1_TOC_333M","title":"PROBA-V C1 Top Of Canopy Daily Synthesis 333m","description":"Proba-V is a satellite mission tasked to map land cover and vegetation\ngrowth. It was designed to provide continuity for the VGT optical instrument\nfrom the SPOT-4 and SPOT-5 missions.\n\nThe sensor collects data in three VNIR (visible and near-infrared) bands\nand one SWIR (short-wave infrared) spectral band with a 2250km field of\nview. Global images are produced every 2 days at 300m resolution, and a 100m\nimage from nadir observations every 5 days. These images are later\ncomposited to produce this daily synthesis dataset. The description of\nthe compositing and atmospheric correction procedures can be found in\nthe [user manual](https://publications.vito.be/2017-1333-probav-products-user-manual.pdf).\n\nThe reflectances provided in this dataset are presented as Digital Count\nNumbers (DN) and must be converted according to the guidelines in\nSection 4.6.1 of the [user manual](https://publications.vito.be/2017-1333-probav-products-user-manual.pdf).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-10-17T00:00:00Z","2021-10-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/C1/S1_TOC_333M","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/VITO/VITO_PROBAV_C1_S1_TOC_333M","rel":"related","title":"Run the example for VITO/PROBAV/C1/S1_TOC_333M in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/VITO/VITO_PROBAV_C1_S1_TOC_333M_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_333M#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_333M.json","rel":"latest-version","title":"VITO/PROBAV/C1/S1_TOC_333M","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_S1_TOC_333M.json","rel":"predecessor-version","title":"VITO/PROBAV/S1_TOC_333M","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/C1/S1_TOC_333M/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/C1/S1_TOC_333M/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"VITO/PROBAV/S1_TOC_100M","title":"PROBA-V C0 Top Of Canopy Daily Synthesis 100m [deprecated]","description":"Proba-V is a satellite mission tasked to map land cover and vegetation\ngrowth. It was designed to provide continuity for the VGT optical instrument\nfrom the SPOT-4 and SPOT-5 missions.\n\nThe sensor collects data in three VNIR (visible and near-infrared) bands\nand one SWIR (short-wave infrared) spectral band with a 2250km field of\nview. Global images are produced every 2 days at 300m resolution, and a 100m\nimage from nadir observations every 5 days. These images are later\ncomposited to produce this daily synthesis dataset. The description of\nthe compositing and atmospheric correction procedures can be found in\nthe [user manual](https://publications.vito.be/2017-1333-probav-products-user-manual.pdf).\n\nThe reflectances provided in this dataset are presented as Digital Count\nNumbers (DN) and must be converted according to the guidelines in\nSection 4.6.1 of the [user manual](https://publications.vito.be/2017-1333-probav-products-user-manual.pdf).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-10-17T00:00:00Z","2016-12-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/S1_TOC_100M","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/VITO/VITO_PROBAV_S1_TOC_100M","rel":"related","title":"Run the example for VITO/PROBAV/S1_TOC_100M in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/VITO/VITO_PROBAV_S1_TOC_100M_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_S1_TOC_100M#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_100M.json","rel":"latest-version","title":"VITO/PROBAV/C1/S1_TOC_100M","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_100M.json","rel":"successor-version","title":"VITO/PROBAV/C1/S1_TOC_100M","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/S1_TOC_100M/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/S1_TOC_100M/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"VITO/PROBAV/S1_TOC_333M","title":"PROBA-V C0 Top Of Canopy Daily Synthesis 333m [deprecated]","description":"Proba-V is a satellite mission tasked to map land cover and vegetation\ngrowth. It was designed to provide continuity for the VGT optical instrument\nfrom the SPOT-4 and SPOT-5 missions.\n\nThe sensor collects data in three VNIR (visible and near-infrared) bands\nand one SWIR (short-wave infrared) spectral band with a 2250km field of\nview. Global images are produced every 2 days at 300m resolution, and a 100m\nimage from nadir observations every 5 days. These images are later\ncomposited to produce this daily synthesis dataset. The description of\nthe compositing and atmospheric correction procedures can be found in\nthe [user manual](https://publications.vito.be/2017-1333-probav-products-user-manual.pdf).\n\nThe reflectances provided in this dataset are presented as Digital Count\nNumbers (DN) and must be converted according to the guidelines in\nSection 4.6.1 of the [user manual](https://publications.vito.be/2017-1333-probav-products-user-manual.pdf).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-10-17T00:00:00Z","2016-12-14T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/S1_TOC_333M","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/VITO/VITO_PROBAV_S1_TOC_333M","rel":"related","title":"Run the example for VITO/PROBAV/S1_TOC_333M in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/VITO/VITO_PROBAV_S1_TOC_333M_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_S1_TOC_333M#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_333M.json","rel":"latest-version","title":"VITO/PROBAV/C1/S1_TOC_333M","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_333M.json","rel":"successor-version","title":"VITO/PROBAV/C1/S1_TOC_333M","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/S1_TOC_333M/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/VITO/PROBAV/S1_TOC_333M/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WCMC/biomass_carbon_density/v1_0","title":"WCMC Above and Below Ground Biomass Carbon Density","description":"This dataset represents above- and below-ground terrestrial carbon storage (tonnes (t) of C per hectare (ha)) for circa 2010.\nThe dataset was constructed by combining the most reliable publicly available datasets and overlaying them with the ESA CCI landcover map for the year 2010 (ESA, 2017), assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell's landcover type.\nInput carbon datasets were identified through a literature review of existing datasets on biomass carbon in terrestrial ecosystems published in peer-reviewed literature.\nTo determine which datasets to combine to produce the global carbon density map, identified datasets were evaluated based on resolution, accuracy, biomass definition and reference date (see Table 1 in paper cited for further information on datasets selected).\nAfter aggregating each selected dataset to a nominal scale of 300 m resolution, forest categories in the CCI ESA 2010 landcover dataset were used to extract above-ground biomass from Santoro et al. 2018 for forest areas.\nWoodland and savanna biomass were then incorporated for Africa from Bouvet et al. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside of forest.\nBiomass from croplands, sparse vegetation and grassland landcover classes from CCI ESA, in addition to shrubland areas outside Africa missing from Santoro et al. 2018, were extracted from were extracted from Xia et al. 2014. and Spawn et al. 2017 averaged by ecological zone for each landcover type.\nBelow-ground biomass were added using root-to-shoot ratios from the 2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No below-ground values were assigned to croplands as ratios were unavailable. Above-and-below-ground biomass were then summed together and multiplied by 0.5 to convert to carbon, generating a single above-and-below-ground biomass carbon layer.\nThis dataset has not been validated.\n\nReferences:\n\n* Bouvet, A. et al. 2018. An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR. Remote Sensing of the Environment 206, 156-173.\n* ESA (2017) [Land Cover CCI Product User Guide Version 2. Tech. Rep.](https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf).\n* IPCC (2006) 2006 IPCC guidelines for national greenhouse gas inventories (eds HS Eggleston, L Buendia, K Miwa, T Ngara, K Tanabe.) Kanagawa, Japan: IGES.\n* Santoro, M. et al. (2018). A detailed portrait of the forest aboveground biomass pool for the year 2010 obtained from multiple remote sensing observations. Geophysical Research Abstracts 20, EGU2018-EG18932.\n* Spawn SA et al. (2017) New global biomass map for the year 2010. New Orleans, LA: American Geophysical Union.\n* Xia, J. et al. (2014) Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006. Remote Sensing 6, 1783-1802.\n\nProvider's note: The UN Environment Programme World Conservation Monitoring\nCentre (UNEP-WCMC) carbon biomass dataset\nrepresents conditions between 1982 and 2010 depending on land cover type. The relative patterns\nof carbon stocks are well represented with this dataset. The [NASA/ORNL carbon biomass dataset](https://daac.ornl.gov/VEGETATION/guides/Global_Maps_C_Density_2010.html)\nrepresents biomass conditions for 2010, with uncertainty estimates at the pixel-level.\nAdditional biomass of non-dominant land cover types are represented within each pixel. For more\ndetailed information, please refer to the papers describing each dataset: WCMC\n[(Soto-Navarro et al. 2020)](https://royalsocietypublishing.org/doi/full/10.1098/rstb.2019.0128)\nand [NASA/ORNL](NASA_ORNL_biomass_carbon_density_v1) [(Spawn et al. 2020)](https://www.nature.com/articles/s41597-020-0444-4).\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WCMC/biomass_carbon_density/v1_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WCMC/WCMC_biomass_carbon_density_v1_0","rel":"related","title":"Run the example for WCMC/biomass_carbon_density/v1_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WCMC/WCMC_biomass_carbon_density_v1_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WCMC_biomass_carbon_density_v1_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.34892/rh7v-hg80","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WCMC/biomass_carbon_density/v1_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WCMC/biomass_carbon_density/v1_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WHBU/NBAR_1YEAR","title":"MODIS 1-year Nadir BRDF-Adjusted Reflectance (NBAR) Mosaic","description":"The mosaics are created by using the MODIS 8-Day 500 meter\nBRDF-Albedo Quality product.  Data Quality flags are used to select\nthe best observations from the MODIS 8-day 500 meter Nadir\nBRDF-Adjusted Reflectance imagery product.  This specific mosaic picks\nobservations from the three highest quality categories over\na 1-year period of MODIS data.\n\nThe MODIS NBAR annual mosaics have been used by government\nagencies and NGOs in tropical countries as input to generate\naboveground biomass/carbon maps. The Woods Hole Research Center has\nbeen using the dataset for country capacity building and technical\ntraining workshops in tropical countries.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60.1,180,60.1]]},"temporal":{"interval":[["2000-02-18T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WHBU/NBAR_1YEAR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WHBU/WHBU_NBAR_1YEAR","rel":"related","title":"Run the example for WHBU/NBAR_1YEAR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WHBU/WHBU_NBAR_1YEAR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WHBU_NBAR_1YEAR#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.1088/1748-9326/3/4/045011","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WHBU/NBAR_1YEAR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WHBU/NBAR_1YEAR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WHBU/NBAR_2YEAR","title":"MODIS 2-year Nadir BRDF-Adjusted Reflectance (NBAR) Mosaic","description":"The mosaics are created by using the MODIS 8-Day 500 meter\nBRDF-Albedo Quality product.  Data Quality flags are used to select\nthe best observations from the MODIS 8-day 500 meter Nadir\nBRDF-Adjusted Reflectance imagery product.  This specific mosaic picks\nobservations from the three highest quality categories over\na 2-year period of MODIS data.\n\nThe MODIS NBAR annual mosaics have been used by government\nagencies and NGOs in tropical countries as input to generate\naboveground biomass/carbon maps. The Woods Hole Research Center has\nbeen using the dataset for country capacity building and technical\ntraining workshops in tropical countries.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60.1,180,60.1]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WHBU/NBAR_2YEAR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WHBU/WHBU_NBAR_2YEAR","rel":"related","title":"Run the example for WHBU/NBAR_2YEAR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WHBU/WHBU_NBAR_2YEAR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WHBU_NBAR_2YEAR#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.1088/1748-9326/3/4/045011","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WHBU/NBAR_2YEAR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WHBU/NBAR_2YEAR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WHBU/NBAR_3YEAR","title":"MODIS 3-year Nadir BRDF-Adjusted Reflectance (NBAR) Mosaic","description":"The mosaics are created by using the MODIS 8-Day 500 meter\nBRDF-Albedo Quality product.  Data Quality flags are used to select\nthe best observations from the MODIS 8-day 500 meter Nadir\nBRDF-Adjusted Reflectance imagery product.  This specific mosaic picks\nobservations from the three highest quality categories over\na 3-year period of MODIS data.\n\nThe MODIS NBAR annual mosaics have been used by government\nagencies and NGOs in tropical countries as input to generate\naboveground biomass/carbon maps. The Woods Hole Research Center has\nbeen using the dataset for country capacity building and technical\ntraining workshops in tropical countries.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-60.1,180,60.1]]},"temporal":{"interval":[["2002-01-01T00:00:00Z","2017-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WHBU/NBAR_3YEAR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WHBU/WHBU_NBAR_3YEAR","rel":"related","title":"Run the example for WHBU/NBAR_3YEAR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WHBU/WHBU_NBAR_3YEAR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WHBU_NBAR_3YEAR#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.1088/1748-9326/3/4/045011","rel":"cite-as"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WHBU/NBAR_3YEAR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WHBU/NBAR_3YEAR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WHRC/biomass/tropical","title":"WHRC Pantropical National Level Carbon Stock Dataset","description":"A national-level map of above-ground live woody biomass density for\ntropical countries at 500m. This dataset was assembled from a combination\nof co-located field measurements, LiDAR observations, and imagery recorded\nfrom the Moderate Resolution Imaging Spectroradiometer (MODIS).\n","license":"proprietary_non_commercial","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2012-01-29T00:00:00Z","2012-01-29T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WHRC/biomass/tropical","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WHRC/WHRC_biomass_tropical","rel":"related","title":"Run the example for WHRC/biomass/tropical in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WHRC/WHRC_biomass_tropical_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WHRC_biomass_tropical#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WHRC/biomass/tropical/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WORLDCLIM/V1/BIO","title":"WorldClim BIO Variables V1","description":"WorldClim V1 Bioclim provides bioclimatic variables that are derived from\nthe monthly temperature and rainfall in order to generate more biologically\nmeaningful values.\n\nThe bioclimatic variables represent annual trends (e.g., mean annual\ntemperature, annual precipitation), seasonality (e.g., annual range in\ntemperature and precipitation), and extreme or limiting environmental\nfactors (e.g., temperature of the coldest and warmest month, and\nprecipitation of the wet and dry quarters).\n\nThe bands scheme follows that of ANUCLIM, except that for temperature\nseasonality the standard deviation was used because a coefficient of\nvariation does not make sense with temperatures between -1 and 1.\n\nWorldClim version 1 was developed by Robert J. Hijmans, Susan Cameron,\nand Juan Parra, at the Museum of Vertebrate Zoology, University of\nCalifornia, Berkeley, in collaboration with Peter Jones and Andrew Jarvis\n(CIAT), and with Karen Richardson (Rainforest CRC).\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1960-01-01T00:00:00Z","1991-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WORLDCLIM/V1/BIO","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WORLDCLIM/WORLDCLIM_V1_BIO","rel":"related","title":"Run the example for WORLDCLIM/V1/BIO in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WORLDCLIM/WORLDCLIM_V1_BIO_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WORLDCLIM_V1_BIO#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WORLDCLIM/V1/BIO/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WORLDCLIM/V1/MONTHLY","title":"WorldClim Climatology V1","description":"WorldClim version 1 has average monthly global climate data for minimum,\nmean, and maximum temperature and for precipitation.\n\nWorldClim version 1 was developed by Robert J. Hijmans, Susan Cameron,\nand Juan Parra, at the Museum of Vertebrate Zoology, University\nof California, Berkeley, in collaboration with Peter Jones and\nAndrew Jarvis (CIAT), and with Karen Richardson (Rainforest CRC).\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1960-01-01T00:00:00Z","1991-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WORLDCLIM/V1/MONTHLY","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WORLDCLIM/WORLDCLIM_V1_MONTHLY","rel":"related","title":"Run the example for WORLDCLIM/V1/MONTHLY in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WORLDCLIM/WORLDCLIM_V1_MONTHLY_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WORLDCLIM_V1_MONTHLY#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WORLDCLIM/V1/MONTHLY/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WORLDCLIM/V1/MONTHLY/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WRI/Aqueduct_Flood_Hazard_Maps/V2","title":"WRI Aqueduct Floods Hazard Maps Version 2","description":"Aqueduct Floods data measures riverine and coastal food risks under both\ncurrent baseline conditions and future projections in 2030, 2050, and 2080.\nIn addition to providing hazard maps and assessing risks, Aqueduct Floods\nhelps to conduct comprehensive cost-benefit analysis to evaluate the value\nof dike flood protection strategies.\n\nAqueduct Floods aims to empower disaster risk analysts and managers with\nquantitative information on food risks and adaptation strategy costs, and to\nhelp inform policy and investment decision-making.\n\nGoogle Earth Engine team recommendation:\n\nThis dataset can be used for:\n\n* Larger scale regional analysis: what is the broad risk to the big area of\n  the size of a US county level / large city?\n* Preliminary assessments: when you need a quick, regional-scale \"first\n  look\" at potential changes in river discharge without needing precise\n  floodplain inundation maps.\n* Relative changes: This looks at different climate scenarios so can be\n  used for generalized changes in magnitude of impacts\n\nThis dataset should not be used for:\n\n* Detailed flood inundation mapping: such as looking at properties specific\n  risk\n* Flat, lowland rivers: the simplification of the equations do not take\n  into account backwater effects (water flowing backwards) which happens a\n  lot in flood plains\n* Analyzing hydraulic structures: it is not suitable for assessing the\n  specific impact of structures like bridges, levees, or weirs that cause\n  significant backwater effects\n\nPlease see\n[the technical note](https://files.wri.org/d8/s3fs-public/aqueduct-floods-methodology.pdf)\nfrom data provider for full details on the methods used and to\nunderstand if this data is right for your application.\n\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2080-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WRI/Aqueduct_Flood_Hazard_Maps/V2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WRI/WRI_Aqueduct_Flood_Hazard_Maps_V2","rel":"related","title":"Run the example for WRI/Aqueduct_Flood_Hazard_Maps/V2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WRI/WRI_Aqueduct_Flood_Hazard_Maps_V2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_Aqueduct_Flood_Hazard_Maps_V2#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WRI/Aqueduct_Flood_Hazard_Maps/V2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WRI/Aqueduct_Flood_Hazard_Maps/V2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WRI/GFW/FORMA/alerts","title":"FORMA Alerts","description":"**NOTE from WRI**: WRI decided to stop updating FORMA alerts. The goal was\nto simplify the [Global Forest Watch](https://www.globalforestwatch.org)\nuser experience and reduce redundancy.\nWe found that [Terra-i](http://www.terra-i.org/terra-i.html) and\n[GLAD](https://glad-forest-alert.appspot.com/) were more frequently used.\nMoreover, using GLAD as a standard, found that Terra-i outperformed\nFORMA globally.\n\nFORMA alerts are detected using a combination of two MODIS\nproducts: NDVI (Normalized Difference Vegetation Index) and FIRMS\n(Fires Information for Resource Management System). NDVI updates are\nprocessed every 16 days, while fire updates are processed daily. Models\nare developed individually for each ecogroup to relate the two inputs to\nthe area of clearing, using the Hansen annual tree cover loss data to train\nthe model. The minimum threshold to qualify as an alert is 25% of the pixel\ncleared, though thresholds vary by ecogroup to minimize false positives.\nHere is an [example script](https://code.earthengine.google.com/f29b1e4360f3fc36847bd789ceeb20f6)\nfor a quick introduction to the FORMA datasets.\n\nThe percentage of clearing takes a value of 0, no clearing detected,\nor in the range [ecogroup_bound:100), where ecogroup_bound is given by\nWRI/GFW/FORMA/thresholds .\nThe time periods over which data is collected varies by N-days, where N is the number\nof days between the alert_date and the last MODIS NDVI update.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-120,-50,180,40]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2019-05-18T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/alerts","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WRI/WRI_GFW_FORMA_alerts","rel":"related","title":"Run the example for WRI/GFW/FORMA/alerts in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WRI/WRI_GFW_FORMA_alerts_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_alerts#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/WRI/WRI_GFW_FORMA_alerts.json","rel":"latest-version","title":"WRI/GFW/FORMA/alerts","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/FORMA/FORMA_FORMA_500m.json","rel":"predecessor-version","title":"FORMA/FORMA_500m","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/alerts/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WRI/GFW/FORMA/raw_output_firms","title":"FORMA Raw Output FIRMS","description":"**NOTE from WRI**: WRI decided to stop updating FORMA alerts. The goal was\nto simplify the [Global Forest Watch](https://www.globalforestwatch.org)\nuser experience and reduce redundancy.\nWe found that [Terra-i](http://www.terra-i.org/terra-i.html) and\n[GLAD](https://glad-forest-alert.appspot.com/) were more frequently used.\nMoreover, using GLAD as a standard, found that Terra-i outperformed\nFORMA globally.\n\nFORMA alerts are detected using a combination of two MODIS\nproducts: NDVI (Normalized Difference Vegetation Index) and FIRMS\n(Fires Information for Resource Management System). NDVI updates are\nprocessed every 16 days, while fire updates are processed daily. Models\nare developed individually for each ecogroup to relate the two inputs to\nthe area of clearing, using the Hansen annual tree cover loss data to train\nthe model. The minimum threshold to qualify as an alert is 25% of the pixel\ncleared, though thresholds vary by ecogroup to minimize false positives.\nHere is an [example script](https://code.earthengine.google.com/f29b1e4360f3fc36847bd789ceeb20f6)\nfor a quick introduction to the FORMA datasets.\n\nThe images in this ImageCollection contain the raw FORMA data calculated after new\nMODIS FIRMS data becomes available, approximately every day.\n\nEach band gives a percentage of clearing (from 0 to 100) for\ndifferent accumulation periods. \"N\" is the number of days between the latest FIRMS update\nand the previous NDVI update. N is given by the 'date_delta' property.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-120,-50,180,40]]},"temporal":{"interval":[["2017-04-08T00:00:00Z","2019-05-18T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/raw_output_firms","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WRI/WRI_GFW_FORMA_raw_output_firms","rel":"related","title":"Run the example for WRI/GFW/FORMA/raw_output_firms in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WRI/WRI_GFW_FORMA_raw_output_firms_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_raw_output_firms#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/raw_output_firms/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/raw_output_firms/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WRI/GFW/FORMA/raw_output_ndvi","title":"FORMA Raw Output NDVI","description":"**NOTE from WRI**: WRI decided to stop updating FORMA alerts. The goal was\nto simplify the [Global Forest Watch](https://www.globalforestwatch.org)\nuser experience and reduce redundancy.\nWe found that [Terra-i](http://www.terra-i.org/terra-i.html) and\n[GLAD](https://glad-forest-alert.appspot.com/) were more frequently used.\nMoreover, using GLAD as a standard, found that Terra-i outperformed\nFORMA globally.\n\nFORMA alerts are detected using a combination of two MODIS\nproducts: NDVI (Normalized Difference Vegetation Index) and FIRMS\n(Fires Information for Resource Management System). NDVI updates are\nprocessed every 16 days, while fire updates are processed daily. Models\nare developed individually for each ecogroup to relate the two inputs to\nthe area of clearing, using the Hansen annual tree cover loss data to train\nthe model. The minimum threshold to qualify as an alert is 25% of the pixel\ncleared, though thresholds vary by ecogroup to minimize false positives.\nHere is an [example script](https://code.earthengine.google.com/f29b1e4360f3fc36847bd789ceeb20f6)\nfor a quick introduction to the FORMA datasets.\n\nThe images in this ImageCollection contain the raw FORMA data calculated after new\nMODIS NDVI data becomes available, approximately every 16 days.\n\nThe image contains 4 bands. The 'delta', 'near_term_delta', and 'clearing' bands\nall give a percentage of clearing (from 0 to 100) for different accumulation periods.\nAccuracy gives a measure of confidence in these predictions\n(0 = very little confidence, 100 is high confidence).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-120,-50,180,40]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2019-04-23T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/raw_output_ndvi","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WRI/WRI_GFW_FORMA_raw_output_ndvi","rel":"related","title":"Run the example for WRI/GFW/FORMA/raw_output_ndvi in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WRI/WRI_GFW_FORMA_raw_output_ndvi_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_raw_output_ndvi#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/raw_output_ndvi/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/raw_output_ndvi/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WRI/GFW/FORMA/thresholds","title":"FORMA Alert Thresholds","description":"**NOTE from WRI**: WRI decided to stop updating FORMA alerts. The goal was\nto simplify the [Global Forest Watch](https://www.globalforestwatch.org)\nuser experience and reduce redundancy.\nWe found that [Terra-i](http://www.terra-i.org/terra-i.html) and\n[GLAD](https://glad-forest-alert.appspot.com/) were more frequently used.\nMoreover, using GLAD as a standard, found that Terra-i outperformed\nFORMA globally.\n\nFORMA alerts are detected using a combination of two MODIS\nproducts: NDVI (Normalized Difference Vegetation Index) and FIRMS\n(Fires Information for Resource Management System). NDVI updates are\nprocessed every 16 days, while fire updates are processed daily. Models\nare developed individually for each ecogroup to relate the two inputs to\nthe area of clearing, using the Hansen annual tree cover loss data to train\nthe model. The minimum threshold to qualify as an alert is 25% of the pixel\ncleared, though thresholds vary by ecogroup to minimize false positives.\nHere is an [example script](https://code.earthengine.google.com/f29b1e4360f3fc36847bd789ceeb20f6)\nfor a quick introduction to the FORMA datasets.\n\nThis image contains the thresholds at which, when crossed, a FORMA alert is\nproduced. The values are equal to max(25,40%*RegionalMax), where RegionalMax\nis the historical maximum clearing for a pixel in this ecogroup.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-120,-50,180,40]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2016-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/thresholds","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WRI/WRI_GFW_FORMA_thresholds","rel":"related","title":"Run the example for WRI/GFW/FORMA/thresholds in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WRI/WRI_GFW_FORMA_thresholds_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_thresholds#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/thresholds/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WRI/GFW/FORMA/vegetation_tstats","title":"FORMA Vegetation T-Statistics","description":"**NOTE from WRI**: WRI decided to stop updating FORMA alerts. The goal was\nto simplify the [Global Forest Watch](https://www.globalforestwatch.org)\nuser experience and reduce redundancy.\nWe found that [Terra-i](http://www.terra-i.org/terra-i.html) and\n[GLAD](https://glad-forest-alert.appspot.com/) were more frequently used.\nMoreover, using GLAD as a standard, found that Terra-i outperformed\nFORMA globally.\n\nFORMA alerts are detected using a combination of two MODIS\nproducts: NDVI (Normalized Difference Vegetation Index) and FIRMS\n(Fires Information for Resource Management System). NDVI updates are\nprocessed every 16 days, while fire updates are processed daily. Models\nare developed individually for each ecogroup to relate the two inputs to\nthe area of clearing, using the Hansen annual tree cover loss data to train\nthe model. The minimum threshold to qualify as an alert is 25% of the pixel\ncleared, though thresholds vary by ecogroup to minimize false positives.\nHere is an [example script](https://code.earthengine.google.com/f29b1e4360f3fc36847bd789ceeb20f6)\nfor a quick introduction to the FORMA datasets.\n\nThe images in this ImageCollection contain the \"reversed rectified\nt-statistics\" used in calculating NTT, the vegetation color index derived\nfrom MODIS NDVI that FORMA uses to measure browning. Using a sum reducer on\nover various date ranges in this ImageCollection produces an \"NTT\" image.\n\nThe images are broken by \"ecogroup\".\n","license":"proprietary","extent":{"spatial":{"bbox":[[-120,-50,180,40]]},"temporal":{"interval":[["2012-01-01T00:00:00Z","2019-04-23T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/vegetation_tstats","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WRI/WRI_GFW_FORMA_vegetation_tstats","rel":"related","title":"Run the example for WRI/GFW/FORMA/vegetation_tstats in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WRI/WRI_GFW_FORMA_vegetation_tstats_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_GFW_FORMA_vegetation_tstats#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/vegetation_tstats/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WRI/GFW/FORMA/vegetation_tstats/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WRI/SBTN/naturalLands/v1","title":"SBTN Natural Lands Map v1","description":"The SBTN Natural Lands Map v1 is a 2020 baseline map of natural and\nnon-natural land covers intended for use by companies setting\n[science-based targets for nature](https://sciencebasedtargetsnetwork.org/companies/take-action/),\nspecifically the SBTN Land target #1: no conversion of natural ecosystems.\n\n\"Natural\" and \"non-natural\" definitions were adapted from the\n[Accountability Framework initiative's definition of a natural\necosystem](https://accountability-framework.org/use-the-accountability-framework/definitions/natural-ecosystem/)\nas \"one that substantially resembles - in terms of species composition,\nstructure, and ecological function - what would be found in a given area\nin the absence of major human impacts\" and can include managed ecosystems\nas well as degraded ecosystems that are expected to regenerate either\nnaturally or through management (AFi 2024). The SBTN Natural Lands Map\noperationalizes this definition by using proxies based on available data\nthat align with AFi guidance to the extent possible.\n\nThis map was made by compiling existing global\nand regional data.You can find the full technical note explaining the\nmethodology linked on the\n[Natural Lands GitHub](https://github.com/wri/natural-lands-map).\nThis work was a collaboration between Land & Carbon Lab at the World\nResources Institute, World Wildlife Fund US, Systemiq, and SBTN.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,75]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WRI/SBTN/naturalLands/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WRI/WRI_SBTN_naturalLands_v1","rel":"related","title":"Run the example for WRI/SBTN/naturalLands/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WRI/WRI_SBTN_naturalLands_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_SBTN_naturalLands_v1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-NC-SA-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WRI/SBTN/naturalLands/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WRI/SBTN/naturalLands/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WRI/SBTN/naturalLands/v1_1/2020","title":"SBTN Natural Lands Map v1.1","description":"The SBTN Natural Lands Map v1.1 is a 2020 baseline map of natural and\nnon-natural land covers intended for use by companies setting\n[science-based targets for nature](https://sciencebasedtargetsnetwork.org/companies/take-action/),\nspecifically the SBTN Land target #1: no conversion of natural ecosystems.\n\n\"Natural\" and \"non-natural\" definitions were adapted from the\n[Accountability Framework initiative's definition of a natural\necosystem](https://accountability-framework.org/use-the-accountability-framework/definitions/natural-ecosystem/)\nas \"one that substantially resembles - in terms of species composition,\nstructure, and ecological function - what would be found in a given area\nin the absence of major human impacts\" and can include managed ecosystems\nas well as degraded ecosystems that are expected to regenerate either\nnaturally or through management (AFi 2024). The SBTN Natural Lands Map\noperationalizes this definition by using proxies based on available data\nthat align with AFi guidance to the extent possible.\n\nThis map was made by compiling existing global\nand regional data.You can find the full technical note explaining the\nmethodology linked on the\n[Natural Lands GitHub](https://github.com/wri/natural-lands-map).\nThis work was a collaboration between Land & Carbon Lab at the World\nResources Institute, World Wildlife Fund US, Systemiq, and SBTN.\n","license":"CC-BY-SA-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,75]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WRI/SBTN/naturalLands/v1_1/2020","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WRI/WRI_SBTN_naturalLands_v1_1_2020","rel":"related","title":"Run the example for WRI/SBTN/naturalLands/v1_1/2020 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WRI/WRI_SBTN_naturalLands_v1_1_2020_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_SBTN_naturalLands_v1_1_2020#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-SA-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WRI/SBTN/naturalLands/v1_1/2020/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WRI/SBTN/naturalLands/v1/2020","title":"SBTN Natural Lands Map v1","description":"The SBTN Natural Lands Map v1 is a 2020 baseline map of natural and\nnon-natural land covers intended for use by companies setting\n[science-based targets for nature](https://sciencebasedtargetsnetwork.org/companies/take-action/),\nspecifically the SBTN Land target #1: no conversion of natural ecosystems.\n\n\"Natural\" and \"non-natural\" definitions were adapted from the\n[Accountability Framework initiative's definition of a natural\necosystem](https://accountability-framework.org/use-the-accountability-framework/definitions/natural-ecosystem/)\nas \"one that substantially resembles - in terms of species composition,\nstructure, and ecological function - what would be found in a given area\nin the absence of major human impacts\" and can include managed ecosystems\nas well as degraded ecosystems that are expected to regenerate either\nnaturally or through management (AFi 2024). The SBTN Natural Lands Map\noperationalizes this definition by using proxies based on available data\nthat align with AFi guidance to the extent possible.\n\nThis map was made by compiling existing global\nand regional data.You can find the full technical note explaining the\nmethodology linked on the\n[Natural Lands GitHub](https://github.com/wri/natural-lands-map).\nThis work was a collaboration between Land & Carbon Lab at the World\nResources Institute, World Wildlife Fund US, Systemiq, and SBTN.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,75]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WRI/SBTN/naturalLands/v1/2020","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WRI/WRI_SBTN_naturalLands_v1_2020","rel":"related","title":"Run the example for WRI/SBTN/naturalLands/v1/2020 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WRI/WRI_SBTN_naturalLands_v1_2020_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WRI_SBTN_naturalLands_v1_2020#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-NC-SA-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WRI/SBTN/naturalLands/v1/2020/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WWF/HydroSHEDS/03CONDEM","title":"WWF HydroSHEDS Hydrologically Conditioned DEM, 3 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\ninformation for regional and global-scale applications in a consistent\nformat. It offers a suite of geo-referenced datasets (vector and\nraster) at various scales, including river networks, watershed\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\nis based on elevation data obtained in 2000 by NASA's Shuttle Radar\nTopography Mission (SRTM).\n\nThis hydrologically conditioned elevation dataset is\nthe result of an iterative conditioning and correction process.\nNote that the conditioning process alters the original DEM and\nmay render it incorrect for applications other than deriving drainage\ndirections. Endorheic basins (inland sinks) are ''seeded'' with\na no-data cell at their lowest point in order to terminate the\nflow. Full details of the underlying digital elevation model are\navailable in the HydroSHEDS website and documentation.\n\nThis dataset is at 3 arc-second resolution. The datasets available\nat 3 arc-seconds are the Void-Filled DEM, Hydrologically Conditioned\nDEM, and Drainage (Flow) Direction.\n\nThere are two areas with incorrect negative values of -100\nclose to Vancouver, Canada around (50.16, -123.85) and Australia\n(-14.96, 129.62)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-67.3,180,60]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/03CONDEM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WWF/WWF_HydroSHEDS_03CONDEM","rel":"related","title":"Run the example for WWF/HydroSHEDS/03CONDEM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WWF/WWF_HydroSHEDS_03CONDEM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_03CONDEM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/03CONDEM/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WWF/HydroSHEDS/03DIR","title":"WWF HydroSHEDS Drainage Direction, 3 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\ninformation for regional and global-scale applications in a consistent\nformat. It offers a suite of geo-referenced datasets (vector and\nraster) at various scales, including river networks, watershed\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\nis based on elevation data obtained in 2000 by NASA's Shuttle Radar\nTopography Mission (SRTM).\n\nThis drainage direction dataset defines the direction\nof flow from each cell in the conditioned DEM to its steepest\ndown-slope neighbor. Values of drainage direction vary from 1\nto 128. All final outlet cells to the ocean are flagged with\na value of 0. All cells that mark the lowest point of an endorheic\nbasin (inland sink) are flagged with a value of -1. The drainage\ndirection values follow the convention adopted by ESRI's flow\ndirection implementation: 1=E, 2=SE, 4=S, 8=SW, 16=W, 32=NW,\n64=N, 128=NE. \\\n\\\n This dataset is at 3 arc-second resolution.\nThe datasets available at 3 arc-seconds are the Void-Filled DEM,\nHydrologically Conditioned DEM, and Drainage (Flow) Direction.\n\nNote that the quality of the HydroSHEDS data is significantly lower for regions above\n60 degrees northern latitude as there is no underlying SRTM elevation data available\nand thus a coarser-resolution DEM was (HYDRO1k provided by USGS).\n\nHydroSHEDS was developed by the World Wildlife Fund (WWF)\nConservation Science Program in partnership with the U.S. Geological\nSurvey, the International Centre for Tropical Agriculture, The\nNature Conservancy, and the Center for Environmental Systems Research\nof the University of Kassel, Germany.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-67.3,180,62]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/03DIR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WWF/WWF_HydroSHEDS_03DIR","rel":"related","title":"Run the example for WWF/HydroSHEDS/03DIR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WWF/WWF_HydroSHEDS_03DIR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_03DIR#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/03DIR/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WWF/HydroSHEDS/03VFDEM","title":"WWF HydroSHEDS Void-Filled DEM, 3 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\ninformation for regional and global-scale applications in a consistent\nformat. It offers a suite of geo-referenced datasets (vector and\nraster) at various scales, including river networks, watershed\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\nis based on elevation data obtained in 2000 by NASA's Shuttle Radar\nTopography Mission (SRTM).\n\nThis void-filled elevation dataset is the first step\ntowards producing the conditioned DEM datasets. Spikes and wells\nin the SRTM data were detected and voided out. Small voids were\nfilled by interpolation of surrounding elevations. The ocean\nwas set to an elevation of 0 meters, and lakes, islands, and\nrivers were filled using other techniques. Full details of the\nunderlying digital elevation model are available in the HydroSHEDS\nwebsite and documentation.\n\nThis dataset is at 3 arc-second\nresolution. The datasets available at 3 arc-seconds are the Void-Filled\nDEM, Hydrologically Conditioned DEM, and Drainage (Flow) Direction.\n\nNote that the quality of the HydroSHEDS data is significantly lower for regions above\n60 degrees northern latitude as there is no underlying SRTM elevation data available\nand thus a coarser-resolution DEM was (HYDRO1k provided by USGS).\n\nHydroSHEDS was developed by the World Wildlife Fund (WWF)\nConservation Science Program in partnership with the U.S. Geological\nSurvey, the International Centre for Tropical Agriculture, The\nNature Conservancy, and the Center for Environmental Systems Research\nof the University of Kassel, Germany.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-67.3,180,60]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/03VFDEM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WWF/WWF_HydroSHEDS_03VFDEM","rel":"related","title":"Run the example for WWF/HydroSHEDS/03VFDEM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WWF/WWF_HydroSHEDS_03VFDEM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_03VFDEM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/03VFDEM/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WWF/HydroSHEDS/15ACC","title":"WWF HydroSHEDS Flow Accumulation, 15 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\ninformation for regional and global-scale applications in a consistent\nformat. It offers a suite of geo-referenced datasets (vector and\nraster) at various scales, including river networks, watershed\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\nis based on elevation data obtained in 2000 by NASA's Shuttle Radar\nTopography Mission (SRTM).\n\nThis flow accumulation dataset defines the amount of\nupstream area (in number of cells) draining into each cell. The\ndrainage direction layer is used to define which cells flow into\nthe target cell. The number of accumulated cells is essentially\na measure of the upstream catchment area. However, since the\ncell size of the HydroSHEDS data set depends on latitude, the\ncell accumulation value cannot directly be translated into drainage\nareas in square kilometers. Values range from 1 at topographic\nhighs (river sources) to very large numbers (on the order of\nmillions of cells) at the mouths of large rivers.\n\nThis dataset is at 15 arc-second resolution. The datasets available\n at 15 arc-seconds are the Hydrologically Conditioned DEM, Drainage\n(Flow) Direction, and Flow Accumulation.\n\nNote that the quality of the HydroSHEDS data is significantly lower for regions above\n60 degrees northern latitude as there is no underlying SRTM elevation data available\nand thus a coarser-resolution DEM was (HYDRO1k provided by USGS).\n\nHydroSHEDS was developed by the World Wildlife Fund (WWF)\nConservation Science Program in partnership with the U.S. Geological\nSurvey, the International Centre for Tropical Agriculture, The\nNature Conservancy, and the Center for Environmental Systems Research\nof the University of Kassel, Germany.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-67.3,180,62]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/15ACC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WWF/WWF_HydroSHEDS_15ACC","rel":"related","title":"Run the example for WWF/HydroSHEDS/15ACC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WWF/WWF_HydroSHEDS_15ACC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_15ACC#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/15ACC/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WWF/HydroSHEDS/15CONDEM","title":"WWF HydroSHEDS Hydrologically Conditioned DEM, 15 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\ninformation for regional and global-scale applications in a consistent\nformat. It offers a suite of geo-referenced datasets (vector and\nraster) at various scales, including river networks, watershed\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\nis based on elevation data obtained in 2000 by NASA's Shuttle Radar\nTopography Mission (SRTM).\n\nThis hydrologically conditioned elevation dataset is the result of an iterative conditioning and correction process. Note that the conditioning process alters the original DEM and may render it incorrect for applications other than deriving drainage directions. Endorheic basins (inland sinks) are 'seeded' with a no-data cell at their lowest point in order to terminate the flow. Full details of the underlying digital elevation model are available in the HydroSHEDS website and documentation.\n\nThis dataset is at 15 arc-second resolution. The datasets available at 15 arc-seconds are the Hydrologically Conditioned DEM, Drainage (Flow) Direction, and Flow Accumulation.\n\nNote that the quality of the HydroSHEDS data is significantly lower for regions above\n60 degrees northern latitude as there is no underlying SRTM elevation data available\nand thus a coarser-resolution DEM was (HYDRO1k provided by USGS).\n\nHydroSHEDS was developed by the World Wildlife Fund (WWF)\nConservation Science Program in partnership with the U.S. Geological\nSurvey, the International Centre for Tropical Agriculture, The\nNature Conservancy, and the Center for Environmental Systems Research\nof the University of Kassel, Germany.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-67.3,180,60]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/15CONDEM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WWF/WWF_HydroSHEDS_15CONDEM","rel":"related","title":"Run the example for WWF/HydroSHEDS/15CONDEM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WWF/WWF_HydroSHEDS_15CONDEM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_15CONDEM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/15CONDEM/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WWF/HydroSHEDS/15DIR","title":"WWF HydroSHEDS Drainage Direction, 15 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\ninformation for regional and global-scale applications in a consistent\nformat. It offers a suite of geo-referenced datasets (vector and\nraster) at various scales, including river networks, watershed\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\nis based on elevation data obtained in 2000 by NASA's Shuttle Radar\nTopography Mission (SRTM).\n\nThis drainage direction dataset defines the direction\nof flow from each cell in the conditioned DEM to its steepest\ndown-slope neighbor. Values of drainage direction vary from 1\nto 128. All final outlet cells to the ocean are flagged with\na value of 0. All cells that mark the lowest point of an endorheic\nbasin (inland sink) are flagged with a value of -1. The drainage\ndirection values follow the convention adopted by ESRI's flow\ndirection implementation: 1=E, 2=SE, 4=S, 8=SW, 16=W, 32=NW,\n64=N, 128=NE.\n\nThis dataset is at 15 arc-second resolution.\nThe datasets available at 15 arc-seconds are the Hydrologically\nConditioned DEM, Drainage (Flow) Direction, and Flow Accumulation.\n\nNote that the quality of the HydroSHEDS data is significantly lower for regions above\n60 degrees northern latitude as there is no underlying SRTM elevation data available\nand thus a coarser-resolution DEM was (HYDRO1k provided by USGS).\n\nHydroSHEDS was developed by the World Wildlife Fund (WWF)\nConservation Science Program in partnership with the U.S. Geological\nSurvey, the International Centre for Tropical Agriculture, The\nNature Conservancy, and the Center for Environmental Systems Research\nof the University of Kassel, Germany.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-67.3,180,62]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/15DIR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WWF/WWF_HydroSHEDS_15DIR","rel":"related","title":"Run the example for WWF/HydroSHEDS/15DIR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WWF/WWF_HydroSHEDS_15DIR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_15DIR#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/15DIR/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WWF/HydroSHEDS/30ACC","title":"WWF HydroSHEDS Flow Accumulation, 30 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\ninformation for regional and global-scale applications in a consistent\nformat. It offers a suite of geo-referenced datasets (vector and\nraster) at various scales, including river networks, watershed\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\nis based on elevation data obtained in 2000 by NASA's Shuttle Radar\nTopography Mission (SRTM).\n\nThis flow accumulation dataset defines the amount of\nupstream area (in number of cells) draining into each cell. The\ndrainage direction layer is used to define which cells flow into\nthe target cell. The number of accumulated cells is essentially\na measure of the upstream catchment area. However, since the\ncell size of the HydroSHEDS data set depends on latitude, the\ncell accumulation value cannot directly be translated into drainage\nareas in square kilometers. Values range from 1 at topographic\nhighs (river sources) to very large numbers (on the order of\nmillions of cells) at the mouths of large rivers.\n\nThis dataset\nis at 30 arc-second resolution. The datasets available at 30\narc-seconds are the Hydrologically Conditioned DEM, Drainage\n(Flow) Direction, and Flow Accumulation.\n\nNote that the quality of the HydroSHEDS data is significantly lower for regions above\n60 degrees northern latitude as there is no underlying SRTM elevation data available\nand thus a coarser-resolution DEM was (HYDRO1k provided by USGS).\n\nHydroSHEDS was developed by the World Wildlife Fund (WWF)\nConservation Science Program in partnership with the U.S. Geological\nSurvey, the International Centre for Tropical Agriculture, The\nNature Conservancy, and the Center for Environmental Systems Research\nof the University of Kassel, Germany.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-67.3,180,62]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/30ACC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WWF/WWF_HydroSHEDS_30ACC","rel":"related","title":"Run the example for WWF/HydroSHEDS/30ACC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WWF/WWF_HydroSHEDS_30ACC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_30ACC#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/30ACC/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WWF/HydroSHEDS/30CONDEM","title":"WWF HydroSHEDS Hydrologically Conditioned DEM, 30 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\ninformation for regional and global-scale applications in a consistent\nformat. It offers a suite of geo-referenced datasets (vector and\nraster) at various scales, including river networks, watershed\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\nis based on elevation data obtained in 2000 by NASA's Shuttle Radar\nTopography Mission (SRTM).\n\nThis hydrologically conditioned elevation dataset is\nthe result of an iterative conditioning and correction process.\nNote that the conditioning process alters the original DEM and\nmay render it incorrect for applications other than deriving drainage\ndirections. Endorheic basins (inland sinks) are ''seeded'' with\na no-data cell at their lowest point in order to terminate the\nflow. Full details of the underlying digital elevation model are\navailable in the HydroSHEDS website and documentation.\n\nThis dataset is at 30 arc-second resolution. The datasets available\nat 30 arc-seconds are the Hydrologically Conditioned DEM, Drainage\n(Flow) Direction, and Flow Accumulation.\n\nNote that the quality of the HydroSHEDS data is significantly lower for regions above\n60 degrees northern latitude as there is no underlying SRTM elevation data available\nand thus a coarser-resolution DEM was (HYDRO1k provided by USGS).\n\nHydroSHEDS was developed by the World Wildlife Fund (WWF)\nConservation Science Program in partnership with the U.S. Geological\nSurvey, the International Centre for Tropical Agriculture, The\nNature Conservancy, and the Center for Environmental Systems Research\nof the University of Kassel, Germany.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-67.3,180,60]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/30CONDEM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WWF/WWF_HydroSHEDS_30CONDEM","rel":"related","title":"Run the example for WWF/HydroSHEDS/30CONDEM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WWF/WWF_HydroSHEDS_30CONDEM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_30CONDEM#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/30CONDEM/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WWF/HydroSHEDS/30DIR","title":"WWF HydroSHEDS Drainage Direction, 30 Arc-Seconds","description":"HydroSHEDS is a mapping product that provides hydrographic\ninformation for regional and global-scale applications in a consistent\nformat. It offers a suite of geo-referenced datasets (vector and\nraster) at various scales, including river networks, watershed\nboundaries, drainage directions, and flow accumulations. HydroSHEDS\nis based on elevation data obtained in 2000 by NASA's Shuttle Radar\nTopography Mission (SRTM).\n\nThis drainage direction dataset defines the direction\nof flow from each cell in the conditioned DEM to its steepest\ndown-slope neighbor. Values of drainage direction vary from 1\nto 128. All final outlet cells to the ocean are flagged with\na value of 0. All cells that mark the lowest point of an endorheic\nbasin (inland sink) are flagged with a value of -1. The drainage\ndirection values follow the convention adopted by ESRI's flow\ndirection implementation: 1=E, 2=SE, 4=S, 8=SW, 16=W, 32=NW,\n64=N, 128=NE. \\\n\\\n This dataset is at 30 arc-second resolution.\nThe datasets available at 30 arc-seconds are the Hydrologically\nConditioned DEM, Drainage (Flow) Direction, and Flow Accumulation.\n\nNote that the quality of the HydroSHEDS data is significantly lower for regions above\n60 degrees northern latitude as there is no underlying SRTM elevation data available\nand thus a coarser-resolution DEM was (HYDRO1k provided by USGS).\n\nHydroSHEDS was developed by the World Wildlife Fund (WWF)\nConservation Science Program in partnership with the U.S. Geological\nSurvey, the International Centre for Tropical Agriculture, The\nNature Conservancy, and the Center for Environmental Systems Research\nof the University of Kassel, Germany.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-67.3,180,62]]},"temporal":{"interval":[["2000-02-11T00:00:00Z","2000-02-22T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/30DIR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WWF/WWF_HydroSHEDS_30DIR","rel":"related","title":"Run the example for WWF/HydroSHEDS/30DIR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WWF/WWF_HydroSHEDS_30DIR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_30DIR#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WWF/HydroSHEDS/30DIR/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WorldPop/GP/100m/R2025A/v1/pop","title":"WorldPop Global Project Population Data (R2025A): Estimated Residential Population per 100x100m Grid Square","description":"The WorldPop project aims to provide detailed and open-access population\ndistribution datasets, built using transparent and peer-reviewed approaches,\nto facilitate the accurate measurement of the impacts of population growth,\nfor monitoring changes, and for planning interventions.\n\nFull details on the methods and datasets used in constructing the\ndata, along with open access publications, are provided on the\n[WorldPop website](https://www.worldpop.org/). In brief, recent census-based\npopulation counts matched to their associated administrative units are\ndisaggregated to ~100x100m grid cells through machine learning approaches\nthat exploit the relationships between population densities and a range of\ngeospatial covariate layers. The mapping approach\nis Random Forest-based dasymetric redistribution.\n\nThis dataset depicts estimated number of people\nresiding in each grid cell for annual epochs from 2015 through 2030 (Release R2025A, v1).\nIndividual country rasters are constrained to settlement areas and adjusted to\nmatch national population estimates.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2031-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/GP/100m/R2025A/v1/pop","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WorldPop/WorldPop_GP_100m_R2025A_v1_pop","rel":"related","title":"Run the example for WorldPop/GP/100m/R2025A/v1/pop in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WorldPop/WorldPop_GP_100m_R2025A_v1_pop_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WorldPop_GP_100m_R2025A_v1_pop#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/WorldPop/WorldPop_GP_100m_R2025A_v1_pop.json","rel":"latest-version","title":"WorldPop/GP/100m/R2025A/v1/pop","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/WorldPop/WorldPop_GP_100m_pop.json","rel":"predecessor-version","title":"WorldPop/GP/100m/pop","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/GP/100m/R2025A/v1/pop/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/GP/100m/R2025A/v1/pop/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WorldPop/GP/100m/pop","title":"WorldPop Global Project Population Data: Estimated Residential Population per 100x100m Grid Square [deprecated]","description":"Global high-resolution, contemporary data on human population distributions are\na prerequisite for the accurate measurement of the impacts of population\ngrowth, for monitoring changes, and for planning interventions. The WorldPop\nproject aims to meet these needs through the provision of detailed and open\naccess population distribution datasets built using transparent and\npeer-reviewed approaches.\n\nFull details on the methods and datasets used in constructing the\ndata, along with open access publications, are provided on the\n[WorldPop website](https://www.worldpop.org/). In brief, recent census-based\npopulation counts matched to their associated administrative units are\ndisaggregated to ~100x100m grid cells through machine learning approaches\nthat exploit the relationships between population densities and a range of\ngeospatial covariate layers. The mapping approach\nis Random Forest-based dasymetric redistribution.\n\nThis dataset depict estimated number of people\nresiding in each grid cell in 2010, 2015, and other years.\n\nFor 2020, the breakdown of population by age and sex is available in\nthe [WorldPop/GP/100m/pop_age_sex](WorldPop_GP_100m_pop_age_sex)\nand [WorldPop/GP/100m/pop_age_sex_cons_unadj](WorldPop_GP_100m_pop_age_sex_cons_unadj)\ncollections.\n\nFurther WorldPop gridded datasets on population age structures,\npoverty, urban growth, and population dynamics are freely available on\nthe WorldPop website. WorldPop represents a collaboration between\nresearchers at the University of Southampton, Universite Libre de\nBruxelles, and University of Louisville. The project is principally funded by\nthe Bill and Melinda Gates Foundation.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/GP/100m/pop","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WorldPop/WorldPop_GP_100m_pop","rel":"related","title":"Run the example for WorldPop/GP/100m/pop in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WorldPop/WorldPop_GP_100m_pop_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WorldPop_GP_100m_pop#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/WorldPop/WorldPop_GP_100m_R2025A_v1_pop.json","rel":"latest-version","title":"WorldPop/GP/100m/R2025A/v1/pop","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/WorldPop/WorldPop_POP.json","rel":"predecessor-version","title":"WorldPop/POP","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/WorldPop/WorldPop_GP_100m_R2025A_v1_pop.json","rel":"successor-version","title":"WorldPop/GP/100m/R2025A/v1/pop","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/GP/100m/pop/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/GP/100m/pop/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WorldPop/GP/100m/pop_age_sex","title":"WorldPop Global Project Population Data: Estimated Age and Sex Structures of Residential Population per 100x100m Grid Square","description":"Global high-resolution, contemporary data on human population distributions are\na prerequisite for the accurate measurement of the impacts of population\ngrowth, for monitoring changes, and for planning interventions. The WorldPop\nproject aims to meet these needs through the provision of detailed and open\naccess population distribution datasets built using transparent and\npeer-reviewed approaches.\n\nFull details on the methods and datasets used in constructing the\ndata, along with open access publications, are provided on the\n[WorldPop website](https://www.worldpop.org/). In brief, recent census-based\npopulation counts matched to their associated administrative units are\ndisaggregated to ~100x100m grid cells through machine learning approaches\nthat exploit the relationships between population densities and a range of\ngeospatial covariate layers. The mapping approach\nis Random Forest-based dasymetric redistribution.\n\nThis dataset contains breakdown of estimated population by age\nand gender groups. Currently only 2020 data are present.\n\n[See explanation of constrained vs unconstrained datasets](https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained).\n\nFurther WorldPop gridded datasets on population age structures,\npoverty, urban growth, and population dynamics are freely available on\nthe WorldPop website. WorldPop represents a collaboration between\nresearchers at the University of Southampton, Universite Libre de\nBruxelles, and University of Louisville. The project is principally funded by\nthe Bill and Melinda Gates Foundation.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/GP/100m/pop_age_sex","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WorldPop/WorldPop_GP_100m_pop_age_sex","rel":"related","title":"Run the example for WorldPop/GP/100m/pop_age_sex in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WorldPop/WorldPop_GP_100m_pop_age_sex_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WorldPop_GP_100m_pop_age_sex#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/GP/100m/pop_age_sex/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/GP/100m/pop_age_sex/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WorldPop/GP/100m/pop_age_sex_cons_unadj","title":"WorldPop Global Project Population Data: Constrained Estimated Age and Sex Structures of Residential Population per 100x100m Grid Square","description":"Global high-resolution, contemporary data on human population distributions are\na prerequisite for the accurate measurement of the impacts of population\ngrowth, for monitoring changes, and for planning interventions. The WorldPop\nproject aims to meet these needs through the provision of detailed and open\naccess population distribution datasets built using transparent and\npeer-reviewed approaches.\n\nFull details on the methods and datasets used in constructing the\ndata, along with open access publications, are provided on the\n[WorldPop website](https://www.worldpop.org/). In brief, recent census-based\npopulation counts matched to their associated administrative units are\ndisaggregated to ~100x100m grid cells through machine learning approaches\nthat exploit the relationships between population densities and a range of\ngeospatial covariate layers. The mapping approach\nis Random Forest-based dasymetric redistribution.\n\nThis dataset contains top-down constrained breakdown of\nestimated population by age and gender groups. Currently only 2020 data\nare present.\n\nTop-down constrained age/sex structure estimate datasets for individual\ncountries for 2020 at 100m spatial resolution with country totals adjusted to\nmatch the corresponding official United Nations population estimates that have\nbeen prepared by the Population Division of the Department of Economic and\nSocial Affairs of the United Nations Secretariat\n([2019 Revision of World Population Prospects](https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/EXCEL_FILES/1_Population/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx)).\n\n[See explanation of constrained vs unconstrained datasets](https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained).\n\nFurther WorldPop gridded datasets on population age structures,\npoverty, urban growth, and population dynamics are freely available on\nthe WorldPop website. WorldPop represents a collaboration between\nresearchers at the University of Southampton, Universite Libre de\nBruxelles, and University of Louisville. The project is principally funded by\nthe Bill and Melinda Gates Foundation.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/GP/100m/pop_age_sex_cons_unadj","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WorldPop/WorldPop_GP_100m_pop_age_sex_cons_unadj","rel":"related","title":"Run the example for WorldPop/GP/100m/pop_age_sex_cons_unadj in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WorldPop/WorldPop_GP_100m_pop_age_sex_cons_unadj_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WorldPop_GP_100m_pop_age_sex_cons_unadj#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/GP/100m/pop_age_sex_cons_unadj/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/GP/100m/pop_age_sex_cons_unadj/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"WorldPop/POP","title":"WorldPop Project Population Data: Estimated Residential Population per 100x100m Grid Square [deprecated]","description":"High-resolution, contemporary data on human population distributions are\na prerequisite for the accurate measurement of the impacts of population\ngrowth, for monitoring changes, and for planning interventions. The WorldPop\nproject aims to meet these needs through the provision of detailed and open\naccess population distribution datasets built using transparent and\npeer-reviewed approaches.\n\nFull details on the methods and datasets used in constructing the data,\nalong with open access publications, are provided on the [WorldPop\nwebsite](https://www.worldpop.org/). In brief, recent census-based\npopulation counts matched to their associated administrative units are\ndisaggregated to &asymp;100x100m grid cells through machine learning\napproaches that exploit the relationships between population densities and a\nrange of geospatial covariate layers. The datasets depict estimated number\nof people residing in each grid cell in 2010, 2015, and other years.\n\nFurther WorldPop gridded datasets on population age structures,\npoverty, urban growth, and population dynamics are freely available on\nthe WorldPop website. WorldPop represents a collaboration between\nresearchers at the University of Southampton, Universite Libre de\nBruxelles, and University of Louisville. The project is principally\nfunded by the Bill and Melinda Gates Foundation.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-59.94,180,54.08]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2016-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/POP","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/WorldPop/WorldPop_POP","rel":"related","title":"Run the example for WorldPop/POP in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/WorldPop/WorldPop_POP_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/WorldPop_POP#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/WorldPop/WorldPop_GP_100m_R2025A_v1_pop.json","rel":"latest-version","title":"WorldPop/GP/100m/R2025A/v1/pop","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/WorldPop/WorldPop_GP_100m_pop.json","rel":"successor-version","title":"WorldPop/GP/100m/pop","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/POP/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/WorldPop/POP/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"YALE/YCEO/UHI/Summer_UHI_yearly_pixel/v4","title":"YCEO Surface Urban Heat Islands: Pixel-Level Composites of Yearly Summertime Daytime and Nighttime Intensity","description":"This dataset contains annual, summertime, and wintertime surface urban\nheat island (SUHI) intensities for day and night for over 10,000 urban clusters\nthroughout the world. The dataset was created using the MODIS 8-day TERRA and\nAQUA land surface temperature (LST) products, the Landscan urban extent\ndatabase, the Global Multi-resolution Terrain Elevation Data 2010, and the\nEuropean Space Agency (ESA) Climate Change Initiative (CCI) land cover data\nusing the Simplified Urban-Extent Algorithm. The product is available both at\nthe pixel level (at 300 m resolution after downscaling) and as urban cluster\nmeans from 2003 to 2018. The monthly composites are only available as urban\ncluster means.\n\nA summary of older versions,\nincluding changes from the dataset created and analyzed in the originally\npublished manuscript can be found on the\n[Yale Center for Earth Observation website](https://yceo.yale.edu/research/global-surface-uhi-explorer).\nThe dataset can also be explored using the [Global Surface UHI\nExplorer web application](https://yceo.users.earthengine.app/view/uhimap).\n\nThe dataset is split into the following six components:\n\n1. **UHI_all_averaged:** Image containing cluster-mean\ncomposite daytime and nighttime SUHI intensity for annual, summer,\nand winter.\n\n2. **UHI_monthly_averaged:** Image containing cluster-mean\nmonthly composites of daytime and nighttime SUHI intensity.\n\n3. **UHI_yearly_averaged:** Image collection of cluster-mean\nyearly composites of daytime and nighttime SUHI intensity from 2003.\nto 2018.\n\n4. **UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) annual daytime and nighttime\nSUHI intensity from 2003 to 2018.\n\n5. **Summer_UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) summertime daytime and\nnighttime SUHI intensity from 2003 to 2018.\n\n6. **Winter_UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) wintertime daytime and\nnighttime SUHI intensity from 2003 to 2018.\n\nThis asset is the fifth component.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-49.98,180,69.7]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/Summer_UHI_yearly_pixel/v4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/YALE/YALE_YCEO_UHI_Summer_UHI_yearly_pixel_v4","rel":"related","title":"Run the example for YALE/YCEO/UHI/Summer_UHI_yearly_pixel/v4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/YALE/YALE_YCEO_UHI_Summer_UHI_yearly_pixel_v4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/YALE_YCEO_UHI_Summer_UHI_yearly_pixel_v4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://yceo.yale.edu/research/global-surface-uhi-explorer","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/Summer_UHI_yearly_pixel/v4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/Summer_UHI_yearly_pixel/v4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"YALE/YCEO/UHI/UHI_all_averaged/v4","title":"YCEO Surface Urban Heat Islands: Spatially-Averaged Daytime and Nighttime Intensity for Annual, Summer, and Winter","description":"This dataset contains annual, summertime, and wintertime surface urban\nheat island (SUHI) intensities for day and night for over 10,000 urban clusters\nthroughout the world. The dataset was created using the MODIS 8-day TERRA and\nAQUA land surface temperature (LST) products, the Landscan urban extent\ndatabase, the Global Multi-resolution Terrain Elevation Data 2010, and the\nEuropean Space Agency (ESA) Climate Change Initiative (CCI) land cover data\nusing the Simplified Urban-Extent Algorithm. The product is available both at\nthe pixel level (at 300 m resolution after downscaling) and as urban cluster\nmeans from 2003 to 2018. The monthly composites are only available as urban\ncluster means.\n\nA summary of older versions,\nincluding changes from the dataset created and analyzed in the originally\npublished manuscript can be found on the\n[Yale Center for Earth Observation website](https://yceo.yale.edu/research/global-surface-uhi-explorer).\nThe dataset can also be explored using the [Global Surface UHI\nExplorer web application](https://yceo.users.earthengine.app/view/uhimap).\n\nThe dataset is split into the following six components:\n\n1. **UHI_all_averaged:** Image containing cluster-mean\ncomposite daytime and nighttime SUHI intensity for annual, summer,\nand winter.\n\n2. **UHI_monthly_averaged:** Image containing cluster-mean\nmonthly composites of daytime and nighttime SUHI intensity.\n\n3. **UHI_yearly_averaged:** Image collection of cluster-mean\nyearly composites of daytime and nighttime SUHI intensity from 2003.\nto 2018.\n\n4. **UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) annual daytime and nighttime\nSUHI intensity from 2003 to 2018.\n\n5. **Summer_UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) summertime daytime and\nnighttime SUHI intensity from 2003 to 2018.\n\n6. **Winter_UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) wintertime daytime and\nnighttime SUHI intensity from 2003 to 2018.\n\nThis asset is the first component.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-49.98,180,69.7]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/UHI_all_averaged/v4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/YALE/YALE_YCEO_UHI_UHI_all_averaged_v4","rel":"related","title":"Run the example for YALE/YCEO/UHI/UHI_all_averaged/v4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/YALE/YALE_YCEO_UHI_UHI_all_averaged_v4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/YALE_YCEO_UHI_UHI_all_averaged_v4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://yceo.yale.edu/research/global-surface-uhi-explorer","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/UHI_all_averaged/v4/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"YALE/YCEO/UHI/UHI_monthly_averaged/v4","title":"YCEO Surface Urban Heat Islands: Spatially-Averaged Monthly Composites of Daytime and Nighttime Intensity","description":"This dataset contains annual, summertime, and wintertime surface urban\nheat island (SUHI) intensities for day and night for over 10,000 urban clusters\nthroughout the world. The dataset was created using the MODIS 8-day TERRA and\nAQUA land surface temperature (LST) products, the Landscan urban extent\ndatabase, the Global Multi-resolution Terrain Elevation Data 2010, and the\nEuropean Space Agency (ESA) Climate Change Initiative (CCI) land cover data\nusing the Simplified Urban-Extent Algorithm. The product is available both at\nthe pixel level (at 300 m resolution after downscaling) and as urban cluster\nmeans from 2003 to 2018. The monthly composites are only available as urban\ncluster means.\n\nA summary of older versions,\nincluding changes from the dataset created and analyzed in the originally\npublished manuscript can be found on the\n[Yale Center for Earth Observation website](https://yceo.yale.edu/research/global-surface-uhi-explorer).\nThe dataset can also be explored using the [Global Surface UHI\nExplorer web application](https://yceo.users.earthengine.app/view/uhimap).\n\nThe dataset is split into the following six components:\n\n1. **UHI_all_averaged:** Image containing cluster-mean\ncomposite daytime and nighttime SUHI intensity for annual, summer,\nand winter.\n\n2. **UHI_monthly_averaged:** Image containing cluster-mean\nmonthly composites of daytime and nighttime SUHI intensity.\n\n3. **UHI_yearly_averaged:** Image collection of cluster-mean\nyearly composites of daytime and nighttime SUHI intensity from 2003.\nto 2018.\n\n4. **UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) annual daytime and nighttime\nSUHI intensity from 2003 to 2018.\n\n5. **Summer_UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) summertime daytime and\nnighttime SUHI intensity from 2003 to 2018.\n\n6. **Winter_UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) wintertime daytime and\nnighttime SUHI intensity from 2003 to 2018.\n\nThis asset is the second component.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-49.98,180,69.7]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/UHI_monthly_averaged/v4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/YALE/YALE_YCEO_UHI_UHI_monthly_averaged_v4","rel":"related","title":"Run the example for YALE/YCEO/UHI/UHI_monthly_averaged/v4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/YALE/YALE_YCEO_UHI_UHI_monthly_averaged_v4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/YALE_YCEO_UHI_UHI_monthly_averaged_v4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://yceo.yale.edu/research/global-surface-uhi-explorer","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/UHI_monthly_averaged/v4/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"YALE/YCEO/UHI/UHI_yearly_averaged/v4","title":"YCEO Surface Urban Heat Islands: Spatially-Averaged Yearly Composites of Annual Daytime and Nighttime Intensity","description":"This dataset contains annual, summertime, and wintertime surface urban\nheat island (SUHI) intensities for day and night for over 10,000 urban clusters\nthroughout the world. The dataset was created using the MODIS 8-day TERRA and\nAQUA land surface temperature (LST) products, the Landscan urban extent\ndatabase, the Global Multi-resolution Terrain Elevation Data 2010, and the\nEuropean Space Agency (ESA) Climate Change Initiative (CCI) land cover data\nusing the Simplified Urban-Extent Algorithm. The product is available both at\nthe pixel level (at 300 m resolution after downscaling) and as urban cluster\nmeans from 2003 to 2018. The monthly composites are only available as urban\ncluster means.\n\nA summary of older versions,\nincluding changes from the dataset created and analyzed in the originally\npublished manuscript can be found on the\n[Yale Center for Earth Observation website](https://yceo.yale.edu/research/global-surface-uhi-explorer).\nThe dataset can also be explored using the [Global Surface UHI\nExplorer web application](https://yceo.users.earthengine.app/view/uhimap).\n\nThe dataset is split into the following six components:\n\n1. **UHI_all_averaged:** Image containing cluster-mean\ncomposite daytime and nighttime SUHI intensity for annual, summer,\nand winter.\n\n2. **UHI_monthly_averaged:** Image containing cluster-mean\nmonthly composites of daytime and nighttime SUHI intensity.\n\n3. **UHI_yearly_averaged:** Image collection of cluster-mean\nyearly composites of daytime and nighttime SUHI intensity from 2003.\nto 2018.\n\n4. **UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) annual daytime and nighttime\nSUHI intensity from 2003 to 2018.\n\n5. **Summer_UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) summertime daytime and\nnighttime SUHI intensity from 2003 to 2018.\n\n6. **Winter_UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) wintertime daytime and\nnighttime SUHI intensity from 2003 to 2018.\n\nThis asset is the third component.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-49.98,180,69.7]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/UHI_yearly_averaged/v4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/YALE/YALE_YCEO_UHI_UHI_yearly_averaged_v4","rel":"related","title":"Run the example for YALE/YCEO/UHI/UHI_yearly_averaged/v4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/YALE/YALE_YCEO_UHI_UHI_yearly_averaged_v4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/YALE_YCEO_UHI_UHI_yearly_averaged_v4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://yceo.yale.edu/research/global-surface-uhi-explorer","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/UHI_yearly_averaged/v4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/UHI_yearly_averaged/v4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"YALE/YCEO/UHI/UHI_yearly_pixel/v4","title":"YCEO Surface Urban Heat Islands: Pixel-Level Annual Daytime and Nighttime Intensity","description":"This dataset contains annual, summertime, and wintertime surface urban\nheat island (SUHI) intensities for day and night for over 10,000 urban clusters\nthroughout the world. The dataset was created using the MODIS 8-day TERRA and\nAQUA land surface temperature (LST) products, the Landscan urban extent\ndatabase, the Global Multi-resolution Terrain Elevation Data 2010, and the\nEuropean Space Agency (ESA) Climate Change Initiative (CCI) land cover data\nusing the Simplified Urban-Extent Algorithm. The product is available both at\nthe pixel level (at 300 m resolution after downscaling) and as urban cluster\nmeans from 2003 to 2018. The monthly composites are only available as urban\ncluster means.\n\nA summary of older versions,\nincluding changes from the dataset created and analyzed in the originally\npublished manuscript can be found on the\n[Yale Center for Earth Observation website](https://yceo.yale.edu/research/global-surface-uhi-explorer).\nThe dataset can also be explored using the [Global Surface UHI\nExplorer web application](https://yceo.users.earthengine.app/view/uhimap).\n\nThe dataset is split into the following six components:\n\n1. **UHI_all_averaged:** Image containing cluster-mean\ncomposite daytime and nighttime SUHI intensity for annual, summer,\nand winter.\n\n2. **UHI_monthly_averaged:** Image containing cluster-mean\nmonthly composites of daytime and nighttime SUHI intensity.\n\n3. **UHI_yearly_averaged:** Image collection of cluster-mean\nyearly composites of daytime and nighttime SUHI intensity from 2003.\nto 2018.\n\n4. **UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) annual daytime and nighttime\nSUHI intensity from 2003 to 2018.\n\n5. **Summer_UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) summertime daytime and\nnighttime SUHI intensity from 2003 to 2018.\n\n6. **Winter_UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) wintertime daytime and\nnighttime SUHI intensity from 2003 to 2018.\n\nThis asset is the fourth component.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-49.98,180,69.7]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/UHI_yearly_pixel/v4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/YALE/YALE_YCEO_UHI_UHI_yearly_pixel_v4","rel":"related","title":"Run the example for YALE/YCEO/UHI/UHI_yearly_pixel/v4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/YALE/YALE_YCEO_UHI_UHI_yearly_pixel_v4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/YALE_YCEO_UHI_UHI_yearly_pixel_v4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://yceo.yale.edu/research/global-surface-uhi-explorer","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/UHI_yearly_pixel/v4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/UHI_yearly_pixel/v4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"YALE/YCEO/UHI/Winter_UHI_yearly_pixel/v4","title":"YCEO Surface Urban Heat Islands: Pixel-Level Yearly Composites of Wintertime Daytime and Nighttime Intensity","description":"This dataset contains annual, summertime, and wintertime surface urban\nheat island (SUHI) intensities for day and night for over 10,000 urban clusters\nthroughout the world. The dataset was created using the MODIS 8-day TERRA and\nAQUA land surface temperature (LST) products, the Landscan urban extent\ndatabase, the Global Multi-resolution Terrain Elevation Data 2010, and the\nEuropean Space Agency (ESA) Climate Change Initiative (CCI) land cover data\nusing the Simplified Urban-Extent Algorithm. The product is available both at\nthe pixel level (at 300 m resolution after downscaling) and as urban cluster\nmeans from 2003 to 2018. The monthly composites are only available as urban\ncluster means.\n\nA summary of older versions,\nincluding changes from the dataset created and analyzed in the originally\npublished manuscript can be found on the\n[Yale Center for Earth Observation website](https://yceo.yale.edu/research/global-surface-uhi-explorer).\nThe dataset can also be explored using the [Global Surface UHI\nExplorer web application](https://yceo.users.earthengine.app/view/uhimap).\n\nThe dataset is split into the following six components:\n\n1. **UHI_all_averaged:** Image containing cluster-mean\ncomposite daytime and nighttime SUHI intensity for annual, summer,\nand winter.\n\n2. **UHI_monthly_averaged:** Image containing cluster-mean\nmonthly composites of daytime and nighttime SUHI intensity.\n\n3. **UHI_yearly_averaged:** Image collection of cluster-mean\nyearly composites of daytime and nighttime SUHI intensity from 2003.\nto 2018.\n\n4. **UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) annual daytime and nighttime\nSUHI intensity from 2003 to 2018.\n\n5. **Summer_UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) summertime daytime and\nnighttime SUHI intensity from 2003 to 2018.\n\n6. **Winter_UHI_yearly_pixel:** Image collection of spatially\ndisaggregated (nominal scale of 300 m) wintertime daytime and\nnighttime SUHI intensity from 2003 to 2018.\n\nThis asset is the sixth component.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-49.98,180,69.7]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/Winter_UHI_yearly_pixel/v4","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/YALE/YALE_YCEO_UHI_Winter_UHI_yearly_pixel_v4","rel":"related","title":"Run the example for YALE/YCEO/UHI/Winter_UHI_yearly_pixel/v4 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/YALE/YALE_YCEO_UHI_Winter_UHI_yearly_pixel_v4_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/YALE_YCEO_UHI_Winter_UHI_yearly_pixel_v4#terms-of-use","rel":"license","type":"text/html"},{"href":"https://yceo.yale.edu/research/global-surface-uhi-explorer","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/Winter_UHI_yearly_pixel/v4/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/YALE/YCEO/UHI/Winter_UHI_yearly_pixel/v4/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/climate-and-sustainability/assets/gem-15_v1","title":"GEM-15: Near-Global Surface and Terrain Elevation Models (15m) (Experimental)","description":"Google's Near-Global Elevation Models at 15m (GEM-15) is an experimental\n(under review) near-global elevation dataset at 15m grid spacing derived\nfrom sub-meter optical stereo satellite imagery. The dataset is built from\noptical stereo imagery acquired through 2024 (primarily 2022&ndash;2024\n30cm Pl&eacute;iades Neo imagery, supplemented by WorldView-2/3/4,\nGeoEye-1, Pl&eacute;iades 1A/1B, and 1.5m SPOT stereo imagery), aggregating\nnative 0.5m DSM reconstructions and 1.0m DTM predictions to 15m resolution.\n\nAlongside the primary 15m Digital Surface Model (`dsm`), GEM-15 includes\nwithin-cell elevation percentiles (`dsm_percentile_5`, `dsm_percentile_50`,\n`dsm_percentile_95`), sub-grid elevation standard deviation (`dsm_std`), and\na bare-earth Digital Terrain Model (`dtm`) predicted at 1.0m resolution\nusing a deep learning model prior to aggregation.\n\n* Explore the elevation layers interactively through the\n  [GEM-15 Earth Engine App](https://climate-and-sustainability.projects.earthengine.app/view/gem-15).\n* Access the\n  [Google Cloud Storage (GCS) COGs](https://storage.googleapis.com/climate-and-sustainability/gem-15)\n  (`gs://climate-and-sustainability/gem-15`) and\n  [STAC catalog](https://storage.googleapis.com/climate-and-sustainability/gem-15/stac/catalog.json).\n* Find the evaluation code on\n  [GitHub](https://github.com/google-research/google-research/tree/master/gem_15).\n\nAll elevation layers report orthometric heights in meters referenced to the\nEGM96 geoid vertical datum ([EPSG:5773](https://epsg.io/5773)) and are\nprojected in local UTM zones at 15m pixel resolution.\n\n**Possible Uses:**\n\n* **Flood and inundation modeling:** Using the 15m bare-earth `dtm` and\n  `dsm` layers for watershed, flow and inundation modeling. \n* **Canopy and building height estimation:** Combining `dsm_percentile_95`\n  (or `dsm`) with `dtm` to estimate above-ground building and forest canopy\n  heights (`dsm - dtm`) and local vertical variability (`dsm_std`). Note\n  that super-tall (~>50m) buildings are often not captured well due to\n  stereo limitations.\n* **Topographic analysis:** Using the 15m elevation layers for satellite\n  imagery orthorectification and slope or aspect calculation.\n\n**Limitations:**\n\n* **Multi-temporal imagery:** Tiles are constructed from multi-view imagery\n  across three sensor tiers: 63.2% of mapped tiles use 2022&ndash;2024 30cm\n  Pl&eacute;iades Neo imagery, 24.4% use other sub-meter sensors\n  (WorldView-2/3/4, GeoEye-1, Pl&eacute;iades 1A/1B), and 12.4% use 1.5m\n  SPOT imagery. Temporal transitions can appear along acquisition\n  boundaries.\n* **Coverage exclusions:** Coverage spans latitudes 60&deg;S to 84&deg;N and\n  excludes Russia, China, Mongolia, Greenland, and Antarctica. India data is\n  offset (with the offsets tapering off outside India's border over tens of\n  kilometers) to comply with India Geospatial regulations.\n* **Water masking and voids:** Water bodies (plus a 9m buffer), snow/ice,\n  salt pans, and other low-texture surfaces (~1&ndash;3% of cells), and\n  pixels differing by more than 50m from Copernicus GLO-30 are masked as\n  no-data.\n* **Dense forest canopy:** Because the elevations are derived from optical\n  stereo imagery rather than LiDAR, bare-earth `dtm` accuracy is lower in\n  dense forests where the ground is not visible from above.\n* **Absolute vertical accuracy:** Regional mean offsets of several meters\n  relative to airborne lidar occur (-4.8 m to +1.9 m across the\n  validation regions in the paper). Where absolute precision is critical,\n  debias against local control.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-179,-61,180,84]]},"temporal":{"interval":[["2025-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/climate-and-sustainability/assets/gem-15_v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/climate-and-sustainability/projects_climate-and-sustainability_assets_gem-15_v1","rel":"related","title":"Run the example for projects/climate-and-sustainability/assets/gem-15_v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/climate-and-sustainability/projects_climate-and-sustainability_assets_gem-15_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_climate-and-sustainability_assets_gem-15_v1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/climate-and-sustainability/assets/gem-15_v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/climate-and-sustainability/assets/gem-15_v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_enhancements_v1_0","title":"MAPL-EMIT: Modeled Methane Enhancement and Plume Probability","description":"The Methane Analysis and Plume Localization with EMIT (MAPL-EMIT) enhancements dataset provides high-resolution (60m) methane (CH₄) enhancement maps and plume probabilities derived from radiance data captured by the Earth Surface Mineral Dust Source Investigation (EMIT) instrument which were generated and released as part of the research paper: \"Publication to be updated, preprint: [https://arxiv.org/abs/2604.10094](https://arxiv.org/abs/2604.10094)\"\n\nMethane point-source emissions are a major driver of near-term climate forcing. Traditional space-based identification methods often rely on manual analyst review or matched-filter techniques that can miss smaller plumes. MAPL-EMIT addresses this using a deep learning vision transformer framework that simultaneously analyzes the full radiance spectrum and the spatial context of neighboring pixels. Validated on millions of synthetic plumes and real-world benchmarks, the model achieves significantly lower detection limits, capturing weaker plumes and more plausible emissions than human analysts.\n\nThis dataset provides model-generated granule-level methane enhancements (in ppm-m) along with plume probability scores for each pixel. You can also explore and visualize the enhancement data interactively through the [MAPL-EMIT EE App](https://nature-trace.projects.earthengine.app/view/mapl-emit). If you are interested in plume level data, please see [MAPL-EMIT Plumes](https://developers.google.com/earth-engine/datasets/catalog/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_plumes_v1_0).\n\nThe data is provided on a per-pixel basis:\n\n* **Enhancements:** The estimated column-integrated methane enhancement (ppm-m).\n* **Plume Probability:** A score (0 to 1) representing the model's confidence that the pixel contains a methane plume.\n\n**Example Uses:**\n\n* **Enhancement based flux inversion:** Using the granule level enhancements paired with winds and other relevant sources to produce flux inversion estimates. \n* **Algorithm Development & Cross-Validation:** Utilizing the raw pixel-level enhancements and probabilities to develop custom plume identification algorithms, or comparing against other retrieval techniques.\n* **Detailed Morphological Studies:** Analyzing the continuous spread and diffusion of a plume without relying on pre-computed discrete bounding polygons.\n\n**Limitations:**\n\n* **Enhancement noise:** The enhancements can be noisy, especially when we are looking at very low enhancements which can either be missed out by the model entirely or incorrectly estimated. We observe that the SNR (signal-to-noise ratio) for MAPL-EMIT enhancements is much higher than Matched Filter or other approaches, however, noise still exists and should be handled accordingly.\n* **Instrument coverage:** Data coverage is constrained by EMIT's orbital sampling and field-of-view; observations are only available for locations and times when the instrument collected measurements from the International Space Station.\n* **Environmental Factors:** Model performance degrades in scenarios with heavy cloud cover, shadows, or low-albedo (dark) surfaces, which reduce the quality of the hyperspectral radiance signal.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-08-10T00:00:00Z","2026-06-09T17:16:18Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_enhancements_v1_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/climate-and-sustainability/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_enhancements_v1_0","rel":"related","title":"Run the example for projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_enhancements_v1_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/climate-and-sustainability/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_enhancements_v1_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_enhancements_v1_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/climate-and-sustainability/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_enhancements_v1_0.json","rel":"latest-version","title":"projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_enhancements_v1_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/nature-trace/projects_nature-trace_assets_ghg_emit_mapl_emit_enhancements_v1_0.json","rel":"predecessor-version","title":"projects/nature-trace/assets/ghg/emit/mapl_emit_enhancements_v1_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_enhancements_v1_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_enhancements_v1_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_plumes_v1_0","title":"MAPL-EMIT: Modeled Methane Plumes","description":"The Methane Analysis and Plume Localization with EMIT (MAPL-EMIT) dataset provides high-resolution (60m) methane (CH₄) plume complex maps and plume source locations derived from radiance data captured by the Earth Surface Mineral Dust Source Investigation (EMIT) instrument.\n\nMethane point-source emissions are a major driver of near-term climate forcing. Traditional space-based identification methods often rely on manual analyst review or matched-filter techniques that can miss smaller emissions and are laborious to characterize and delineate. MAPL-EMIT addresses this using a deep learning vision transformer framework that simultaneously analyzes the full radiance spectrum and the spatial context of neighboring pixels. Validated on millions of synthetic plumes and real-world benchmarks, the model provides automated plume detection across full-granule radiances, estimating per-plume spatial extents, column integrated enhancements (ppm-m increase over background) and source locations.\n\nThis dataset provides model-predicted outputs for methane plume complexes. For each identified plume, the dataset provides the corresponding methane enhancements, an instance mask delineating the plume's shape, and the most likely source location. You can also explore and visualize the plumes interactively through the [MAPL-EMIT EE App](https://nature-trace.projects.earthengine.app/view/mapl-emit). If you are interested in granule level enhancements and plume probabilities, please see\n[MAPL-EMIT Enhancements](https://developers.google.com/earth-engine/datasets/catalog/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_enhancements_v1_0)\n\nEach plume has a 'confidence' property containing one of the following confidence scores:\n\n* **High confidence:** Plumes detected three or more times across different EMIT observations over time. Human review of a random subset of these plumes yielded a false positive rate of ~3-5%. This low error rate is comparable to existing human-annotated datasets, making high-confidence plumes suitable for direct downstream use.\n* **Medium confidence:** Plumes without a temporal match across multiple EMIT observations. Human review indicates a false positive rate of ~50-55% for this category. Due to the high likelihood of false positives, users should exercise caution and apply secondary filtering before downstream analysis. Recommended filtering strategies include matching plumes with known infrastructure, analyzing spectral signatures, applying d_norm or d_cor spectral scores, and cross-referencing with local wind direction.\n\n**Example Uses:**\n\n* **Automated Plume Delineation & Source Localization:** Identifying the spatial footprint of methane plumes and attributing them to specific infrastructure features (e.g., wells, landfills, compressor stations), even amid overlapping plumes.\n* **Global Methane Tracking:** Scanning the complete EMIT data catalog to automatically construct a comprehensive database of global methane point sources.\n* **Impact Quantification:** Combining instance masks and enhancement values to estimate leak severity from previously undetected or uncaptured sources.\n\n**Limitations:**\n\n* **False Positives:** Automated detection inherently carries a risk of false positives. When utilizing medium-confidence plumes, it is critical to treat detections as candidates and filter them against known locations of physical infrastructure, employ temporal aggregations to confirm persistent sources, or other verification methods.\n* **False Negatives:** The model may miss some plumes, particularly those with low enhancements or small spatial extents. Approximately 16% of expert-annotated NASA EMIT L2B plumes are missed by the model.\n* **Enhancement vs. emission rate:** Reported pixel values represent atmospheric column enhancements (ppm-m), not emission rates (kg/hr). Estimating emission flux requires using methods like IME along with external wind fields and introduces additional uncertainty.\n* **Instrument coverage:** Data coverage is constrained by EMIT's orbital sampling and field-of-view; observations are only available for locations and times when the instrument collected measurements from the International Space Station.\n* **Environmental Factors:** Model performance degrades in scenarios with heavy cloud cover, shadows, or low-albedo (dark) surfaces, which reduce the quality of the hyperspectral radiance signal.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-08-10T00:00:00Z","2026-06-09T03:18:57Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_plumes_v1_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/climate-and-sustainability/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_plumes_v1_0","rel":"related","title":"Run the example for projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_plumes_v1_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/climate-and-sustainability/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_plumes_v1_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_plumes_v1_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/climate-and-sustainability/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_plumes_v1_0.json","rel":"latest-version","title":"projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_plumes_v1_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/nature-trace/projects_nature-trace_assets_ghg_emit_mapl_emit_plumes_v1_0.json","rel":"predecessor-version","title":"projects/nature-trace/assets/ghg/emit/mapl_emit_plumes_v1_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_plumes_v1_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_plumes_v1_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/edf-methanesat-ee/assets/mair/L3concentration","title":"MethaneAIR L3 Concentration V1.1.0","description":"This dataset provides geospatial data for the total column dry air mole\nfraction of methane in the atmosphere, \"XCH4\", as observed by the MethaneAIR\nimaging spectrometer. XCH4 is defined as the total column amount (number of\nmolecules) of methane (\"CH4\") divided by the total amount of air (number of\nmolecules, water vapor amount removed) along the line of sight from the\nairborne spectrometer to the earth's surface and back up to the sun.\nAdditional data layers are provided for reference: observed albedo at 1622\nnm, surface pressure, and terrain height.\n\nThe aircraft was flown at 11.5 - 13 km altitude (37,000 - 43,000 feet),\nacquiring 10 frames per second with a swath of 4.5 - 4.8 km with a 25 degree\nfield of view and 896 pixels across track, giving a native pixel size at\nnadir of approximately 25m along track and 5 m across track. The\nspectroscopic data are aggregated by 5 across track, giving a mean pixel\nsize of 25m x 25m. Concentrations (XCH4) are retrieved from these high\nresolution (0.1 nm sampling, 0.3nm resolution), spatially distributed\nspectra ([Chan Miller et al. (2024)](https://doi.org/10.5194/egusphere-2023-1962)),\nthen gridded on a 0.0001 x 0.0001 degree grid (approximately 10m x 8m over\nthe US). The precision of the gridded data is approximately 25 parts per\nbillion (1s, about 1.3%), and a spatial correlation length (1/e) of about\n70m. There are some variations from flight-to-flight according to operating\nconditions, look angle and surface albedo.\n\nThe flight pattern was typically a series of oval swaths in a Zamboni\npattern to cover a region, typically about 70 km x 100 km, over a 2 - 3 hour\ninterval. The data presented here are averaged where flight tracks overlap,\ntypically at the edges of each swath and in the urns at the top and bottom\nof the Zamboni pattern.\n\nThis dataset was generated using MethaneAIR measurements taken on flights\nbetween 09 July 2025 and 10 October 2025. MethaneAIR is managed by\n[MethaneSAT LLC](https://www.methanesat.org/), a wholly owned subsidiary of\nEnvironmental Defense Fund. Methane emission fluxes were produced using a\ngeostatistical inverse modeling framework on each of these Zamboni swaths.\n\nFor additional information about the MethaneAIR instrument, instrument\ncalibration and emission detections, please refer to recent publications by\n[Loughner et al. (2021)](https://doi.org/10.1175/JAMC-D-20-0158.1),\n[Staebell et al. (2021)](https://doi.org/10.5194/amt-14-3737-2021),\n[Conway et al. (2023)](https://doi.org/10.5194/amt-2023-111),\n[Chulakadabba et al. (2023)](https://doi.org/10.5194/egusphere-2023-822),\n[Abbadi et al. (2023)](https://doi.org/10.31223/X51D4C),\n[Omara et al. (2023)](https://doi.org/10.5194/essd-15-3761-2023),\nand [Miller et al. (2023)](https://doi.org/10.5194/egusphere-2023-1962).\n\nContact the data provider for more information about the project at this link:\n[https://www.methanesat.org/contact/](https://www.methanesat.org/contact/).\n\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2025-07-09T00:00:00Z","2025-10-10T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/mair/L3concentration","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/edf-methanesat-ee/projects_edf-methanesat-ee_assets_mair_L3concentration","rel":"related","title":"Run the example for projects/edf-methanesat-ee/assets/mair/L3concentration in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/edf-methanesat-ee/projects_edf-methanesat-ee_assets_mair_L3concentration_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_edf-methanesat-ee_assets_mair_L3concentration#terms-of-use","rel":"license","type":"text/html"},{"href":"https://www.methanesat.org/sites/default/files/2025-02/MethaneSAT%20-%20Content%20License%20Terms%20of%20Use%20%28Revised%202-12-2025%29%5B25%5D.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/mair/L3concentration/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/mair/L3concentration/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/edf-methanesat-ee/assets/msat/L3concentration","title":"MethaneSAT L3 Concentration V1.0.0","description":"This dataset provides the most recently processed geospatial data for the\ncolumn-averaged dry-air mole fraction of methane in the atmosphere, \"XCH4\",\nretrieved from measurements by the MethaneSAT imaging spectrometer. XCH4 is\ndefined as the total column amount (number of molecules above a unit surface\narea) of methane (\"CH4\") divided by the total amount of air (number of molecules\nabove a unit surface area, water vapor amount removed) along the line of sight\nfrom the satellite's spectrometer to the Earth's surface and back up to the Sun.\nAdditional data layers are provided for reference: observed albedo at 1622 nm,\nsurface pressure, and terrain height.\n\nThe spatial footprint of individual MethaneSAT soundings is 110 m x 400 m when\nviewing at nadir, with the long side in the satellite's flight direction, and\nwith the short side that can be stretched wider at higher viewing zenith angles\n(which is why some targets appear more rectangular than others). At nadir, the\nswath width is ~220 km. The data on these native soundings is Level 2 (L2) data.\nThe precision of the L2 XCH4 is 22-35 ppb (corresponding to 0.7-0.2 surface albedo).\nL2 concentrations (XCH4) are retrieved from the measured high resolution (0.08 nm\nsampling, 0.24 nm resolution) spectra from the 1.6 micron band using the CO2-Proxy\nmethod ([Chan Miller et al. (2024)](https://doi.org/10.5194/amt-17-5429-2024)).\nThe Level 3 (L3) dataset is created by regridding the L2 data from the native\nsounding locations to a regular 45 m x 45 m grid using the approach detailed in\n[Sun et al. (2018)](https://doi.org/10.5194/amt-11-6679-2018).\n\nNot all data products (L3 concentration, L4 area and L4 points) are available for all\ncollection IDs. Contact the data provider for more information about the project at this link:\n[https://www.methanesat.org/contact/](https://www.methanesat.org/contact/).\n\n---\n\n7/2/2025 Important Update:\n\nAs you may be aware, we lost contact with the satellite in June of 2025. After exploring all\npossible recovery options, we have confirmed that it is no longer functioning, due to an\nundetermined problem with the outer platform carrying our methane detector. While there is\nno question this is a setback, we are undeterred in our efforts to drive down methane pollution.\nPlease see our official statement here:\n[MethaneSAT Loses Contact with Satellite | MethaneSAT](https://www.methanesat.org/project-updates/methanesat-loses-contact-satellite).\n\nWhat this means for the existing datasets: They will remain accessible on Google platforms and\non our web portal for the foreseeable future. Should there be any changes to data availability,\nwe will notify you well in advance.\n\nCurrent state: While we don't have all the answers yet, we plan to leverage our advanced Data\nProcessing Platform (DPP) to quantify other streams of satellite and/or aerial data. We will also\ntake the necessary time to evaluate the best next step in our efforts to enable methane\nreductions. To stay up to date with further updates, feel free to sign up for our\n[newsletter](https://mailchi.mp/methanesat/methanesat-newsletter-sign-up). For\nadditional information about the data products, algorithms, or calibration, view the publications\non our [website](https://www.methanesat.org/scienceandresearch).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2024-05-22T00:00:00Z","2025-06-20T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/msat/L3concentration","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/edf-methanesat-ee/projects_edf-methanesat-ee_assets_msat_L3concentration","rel":"related","title":"Run the example for projects/edf-methanesat-ee/assets/msat/L3concentration in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/edf-methanesat-ee/projects_edf-methanesat-ee_assets_msat_L3concentration_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_edf-methanesat-ee_assets_msat_L3concentration#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/edf-methanesat-ee/projects_edf-methanesat-ee_assets_msat_L3concentration.json","rel":"latest-version","title":"projects/edf-methanesat-ee/assets/msat/L3concentration","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/edf-methanesat-ee/projects_edf-methanesat-ee_assets_public-preview_L3concentration.json","rel":"predecessor-version","title":"projects/edf-methanesat-ee/assets/public-preview/L3concentration","type":"application/json"},{"href":"https://www.methanesat.org/sites/default/files/2025-02/MethaneSAT%20-%20Content%20License%20Terms%20of%20Use%20%28Revised%202-12-2025%29%5B25%5D.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/msat/L3concentration/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/msat/L3concentration/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/edf-methanesat-ee/assets/msat/L4area","title":"MethaneSAT L4 Area Sources V1.0.0","description":"This dataset provides the most recently processed data for methane emissions on an\napproximately 4 km x 4 km grid. These data include total methane emissions and their spatial\ndistribution at high resolution to meet global methane mitigation goals.\n\nThese methane emissions estimates were made using the Column Observations to Regional\nEmissions (CORE) algorithm. The primary input to CORE is methane concentration observations\nin the form of column-averaged dry-air mole fractions of methane (XCH4). An atmospheric\ntransport model; the Stochastic Time-Inverted Lagrangian Transport (STILT) model;\n[Lin et al. (2003)](https://doi.org/10.1029/2002JD003161),\n[Fasoli et al. (2018)](https://doi.org/10.5194/gmd-11-2813-2018);\ndriven by meteorological data from the National Centers for Environmental Prediction\n([NCEP](https://www.weather.gov/ncep/)) Global Forecast System\n([GFS](https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php));\nlinks variations in observed\nXCH4 to potential upwind sources. Emissions and inflow across the upwind boundary are\ninferred with a Bayesian inverse model using Hamiltonian Monte Carlo (HMC) sampling\nimplemented in the Stan probabilistic programming framework\n([Carpenter et al., 2017](https://www.jstatsoft.org/article/view/v076i01)). The\nCORE algorithm will continue to be updated as the team improves the methodology. Any\nsignificant changes in methodology will be documented through updates to the MethaneSAT\nLevel-4 Dispersed Emissions Product\n[Algorithm Theoretical Basis Document](https://zenodo.org/records/20030295) (ATBD).\n\nThese observations made by MethaneSAT are broadly consistent with independent empirical\ndata where available from other sources. Not all data products (L3 concentration, L4 area and\nL4 point) are available for all collection IDs. Individual heat maps can have spatial misallocations\ndue to several factors, including wind field uncertainty, observational uncertainty and variable\nemission rates. Users are advised to interpret individual emission grids with caution. For more\nabout definitions and limitations, see our\n[technical explanation](https://www.methanesat.org/sites/default/files/2026-07/MethaneSAT%20Dispersed%20Area%20Emissions%20Definitions%20and%20Limitations.pdf).\nTo learn more about MethaneSAT algorithm versions, see this [overview](https://www.methanesat.org/sites/default/files/2026-07/Overview%20of%20MethaneSAT%20Algorithm%20Versions.pdf).\n\nContact the data provider for more information about the project at this link:\n[https://www.methanesat.org/contact/](https://www.methanesat.org/contact/).\n\n---\n\n7/2/2025 Important Update:\n\nNote: The satellite lost contact with the ground in June of 2025:\n[MethaneSAT Loses Contact with Satellite | MethaneSAT](https://www.methanesat.org/project-updates/methanesat-loses-contact-satellite).\nExisting datasets will remain accessible on Google platforms and on our\nweb portal for the foreseeable future. They have been reprocessed using greatly improved\nalgorithms and released in August, 2026. The reprocessed data are superior in all respects\ncompared to our interim releases earlier in 2026, and additional scenes have been added that\nwere not available previously. These datasets should be used for future analysis in preference to\nprevious releases, which are deprecated.\n\nTo stay up to date with further updates, please sign up for our\n[newsletter](https://mailchi.mp/methanesat/methanesat-newsletter-sign-up). For additional\ninformation about the data products, algorithms, or calibration, view the publications on our\n[website](https://www.methanesat.org/scienceandresearch).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2024-05-01T00:00:00Z","2025-06-20T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/msat/L4area","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/edf-methanesat-ee/projects_edf-methanesat-ee_assets_msat_L4area","rel":"related","title":"Run the example for projects/edf-methanesat-ee/assets/msat/L4area in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/edf-methanesat-ee/projects_edf-methanesat-ee_assets_msat_L4area_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_edf-methanesat-ee_assets_msat_L4area#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/edf-methanesat-ee/projects_edf-methanesat-ee_assets_msat_L4area.json","rel":"latest-version","title":"projects/edf-methanesat-ee/assets/msat/L4area","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/edf-methanesat-ee/projects_edf-methanesat-ee_assets_public-preview_L4area_v2.json","rel":"predecessor-version","title":"projects/edf-methanesat-ee/assets/public-preview/L4area_v2","type":"application/json"},{"href":"https://www.methanesat.org/sites/default/files/2025-02/MethaneSAT%20-%20Content%20License%20Terms%20of%20Use%20%28Revised%202-12-2025%29%5B25%5D.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/msat/L4area/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/msat/L4area/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/edf-methanesat-ee/assets/public-preview/L3concentration","title":"MethaneSAT L3 Concentration Public Preview V1.0.0 [deprecated]","description":"This early \"Public Preview\" dataset provides geospatial data for the\ncolumn-averaged dry-air mole fraction of methane in the atmosphere, \"XCH4\",\nretrieved from measurements by the MethaneSAT imaging spectrometer. XCH4 is\ndefined as the total column amount (number of molecules above a unit surface\narea) of methane (\"CH4\") divided by the total amount of air (number of molecules\nabove a unit surface area, water vapor amount removed) along the line of sight\nfrom the satellite’s spectrometer to the Earth's surface and back up to the Sun.\nAdditional data layers are provided for reference: observed albedo at 1622 nm,\nsurface pressure, and terrain height.\n\nThe spatial footprint of individual MethaneSAT soundings is 110 m x 400 m when\nviewing at nadir, with the long side in the satellite’s flight direction, and\nwith the short side that can be stretched wider at higher viewing zenith angles\n(which is why some targets appear more rectangular than others). At nadir, the\nswath width is ~220 km. The data on these native soundings is Level 2 (L2) data.\nThe precision of the L2 XCH4 is 22-35 ppb (corresponding to 0.7-0.2 surface albedo).\nL2 concentrations (XCH4) are retrieved from the measured high resolution (0.08 nm\nsampling, 0.24 nm resolution) spectra from the 1.6 micron band using the CO2-Proxy\nmethod ([Chan Miller et al. (2024)](https://doi.org/10.5194/amt-17-5429-2024)).\nThe Level 3 (L3) dataset is created by regridding the L2 data from the native\nsounding locations to a regular 45 m x 45 m grid using the approach detailed in\n[Sun et al. (2018)](https://doi.org/10.5194/amt-11-6679-2018).\n\nNot all data products (L3 concentration, L4 area and L4 points) are available for all\ncollection IDs. Contact the data provider for more information about the project at this link:\n[https://www.methanesat.org/contact/](https://www.methanesat.org/contact/).\n\n---\n\n7/2/2025 Important Update: \n\nAs you may be aware, we recently lost contact with the satellite. After exploring all possible  \nrecovery options, we have now confirmed that it is no longer functioning, due to an\nundetermined problem with the outer platform carrying our methane detector. While there is no\nquestion this is a setback, we are undeterred in our efforts to drive down methane pollution.\nPlease see our official statement here:\n[MethaneSAT Loses Contact with Satellite | MethaneSAT](https://www.methanesat.org/project-updates/methanesat-loses-contact-satellite).\n\nWhat this means for the Public Preview data: The existing datasets will remain accessible on\nGoogle platforms and on our web portal for the foreseeable future. Additionally, over the next\nfew months, we will release substantial new data collected by MethaneSAT prior to the loss of\ncontact. This will include hundreds of scenes (of targets that are roughly 200kmx200km). We\nhope this will be useful for you. Should there be any changes to data availability, we will\nnotify you well in advance.\n\nLooking ahead: While we don’t have all the answers yet, we plan to leverage our advanced Data\nProcessing Platform (DPP) to quantify other streams of satellite and/or aerial data. We will\nalso take the necessary time to evaluate the best next step in our efforts to enable methane\nreductions. To stay up to date with further updates, feel free to sign up for our\n[newsletter](https://mailchi.mp/methanesat/methanesat-newsletter-sign-up).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2024-05-22T00:00:00Z","2025-06-14T11:12:26.422000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/public-preview/L3concentration","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/edf-methanesat-ee/projects_edf-methanesat-ee_assets_public-preview_L3concentration","rel":"related","title":"Run the example for projects/edf-methanesat-ee/assets/public-preview/L3concentration in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/edf-methanesat-ee/projects_edf-methanesat-ee_assets_public-preview_L3concentration_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_edf-methanesat-ee_assets_public-preview_L3concentration#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/edf-methanesat-ee/projects_edf-methanesat-ee_assets_msat_L3concentration.json","rel":"latest-version","title":"projects/edf-methanesat-ee/assets/msat/L3concentration","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/edf-methanesat-ee/projects_edf-methanesat-ee_assets_msat_L3concentration.json","rel":"successor-version","title":"projects/edf-methanesat-ee/assets/msat/L3concentration","type":"application/json"},{"href":"https://www.methanesat.org/sites/default/files/2025-02/MethaneSAT%20-%20Content%20License%20Terms%20of%20Use%20%28Revised%202-12-2025%29%5B25%5D.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/public-preview/L3concentration/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/public-preview/L3concentration/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/edf-methanesat-ee/assets/public-preview/L4area","title":"MethaneSAT L4 Area Sources Public Preview V1.0.0 [deprecated]","description":"*The dispersed area emissions model is still in development and not\nrepresentative of a final product.*\n\nThis early \"Public Preview\" dataset provides high precision data for methane\nemissions from dispersed area sources. These emissions data come from the\nAppalachian, Permian, and Uinta basins in the United States; the Amu Darya and\nSouth Caspian basins in Turkmenistan; and the Maturin basin in Venezuela. These\nnovel measurements demonstrate the importance of quantifying total methane emissions\nwith high resolution to meet global methane mitigation goals.\n\nDispersed area emissions are estimated from methane concentration observations\nin the form of column-averaged dry-air mole fractions of methane (XCH4) using\nan inverse model. An atmospheric transport model - the Stochastic Time-Inverted\nLagrangian Transport (STILT) model; [Lin et al. (2003)](https://doi.org/10.1029/2002JD003161),\n[Fasoli et al. (2018)](https://doi.org/10.5194/gmd-11-2813-2018); driven\nby meteorological data from the National Centers for Environmental\nPrediction ([NCEP](https://www.weather.gov/ncep/)) Global Forecast System\n([GFS](https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php))\n- is used to link variations in observed XCH4 to potential upwind sources.\n\nThe sources of variation in observed XCH4 include 1) dispersed area emissions,\n2) discrete point sources, 3) inflow across the domain boundary, and\n4) the background concentration. Discrete point source emissions are\ndetermined individually using a divergence integral approach described by\n[Chulakadabba et al. (2023)](https://egusphere.copernicus.org/preprints/2023/egusphere-2023-822/)\nand prescribed to the inverse model. XCH4 inflow across the domain boundary\nand dispersed area emissions are then estimated simultaneously using an\ninverse model with an enforced non-negative solution.\n\nTotal emissions for a scene–from both dispersed area and point source emissions–may be\nobtained by summing the area emissions and point source emissions for a given collection ID\n(see L4 Point Sources Public Preview).\n\nThis set of initial observations made by MethaneSAT are consistent with\nindependent empirical data where available from other sources. Not all data products\n(L3 concentration, L4 area and L4 points) are available for all collection IDs.\nContact the data provider for more information about the project at this\nlink: [https://www.methanesat.org/contact/](https://www.methanesat.org/contact/).\n\n---\n\n7/2/2025 Important Update: \n\nAs you may be aware, we recently lost contact with the satellite. After exploring all possible  \nrecovery options, we have now confirmed that it is no longer functioning, due to an\nundetermined problem with the outer platform carrying our methane detector. While there is no\nquestion this is a setback, we are undeterred in our efforts to drive down methane pollution.\nPlease see our official statement here:\n[MethaneSAT Loses Contact with Satellite | MethaneSAT](https://www.methanesat.org/project-updates/methanesat-loses-contact-satellite).\n\nWhat this means for the Public Preview data: The existing datasets will remain accessible on\nGoogle platforms and on our web portal for the foreseeable future. Additionally, over the next\nfew months, we will release substantial new data collected by MethaneSAT prior to the loss of\ncontact. This will include hundreds of scenes (of targets that are roughly 200kmx200km). We\nhope this will be useful for you. Should there be any changes to data availability, we will\nnotify you well in advance.\n\nLooking ahead: While we don’t have all the answers yet, we plan to leverage our advanced Data\nProcessing Platform (DPP) to quantify other streams of satellite and/or aerial data. We will\nalso take the necessary time to evaluate the best next step in our efforts to enable methane\nreductions. To stay up to date with further updates, feel free to sign up for our\n[newsletter](https://mailchi.mp/methanesat/methanesat-newsletter-sign-up).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2024-10-25T00:00:00Z","2025-01-17T21:24:43Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/public-preview/L4area","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/edf-methanesat-ee/projects_edf-methanesat-ee_assets_public-preview_L4area","rel":"related","title":"Run the example for projects/edf-methanesat-ee/assets/public-preview/L4area in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/edf-methanesat-ee/projects_edf-methanesat-ee_assets_public-preview_L4area_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_edf-methanesat-ee_assets_public-preview_L4area#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/edf-methanesat-ee/projects_edf-methanesat-ee_assets_msat_L4area.json","rel":"latest-version","title":"projects/edf-methanesat-ee/assets/msat/L4area","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/edf-methanesat-ee/projects_edf-methanesat-ee_assets_public-preview_L4area_v2.json","rel":"successor-version","title":"projects/edf-methanesat-ee/assets/public-preview/L4area_v2","type":"application/json"},{"href":"https://www.methanesat.org/sites/default/files/2025-02/MethaneSAT%20-%20Content%20License%20Terms%20of%20Use%20%28Revised%202-12-2025%29%5B25%5D.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/public-preview/L4area/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/public-preview/L4area/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/edf-methanesat-ee/assets/public-preview/L4area_v2","title":"MethaneSAT L4 Area Sources Public Preview V2.0.0 [deprecated]","description":"*The dispersed area emissions model is still in development and not\nrepresentative of a final product.*\n\nThis early \"Public Preview\" dataset provides high precision data for methane\nemissions from dispersed area sources. These novel measurements demonstrate\nthe importance of quantifying total methane emissions with high resolution\nto meet global methane mitigation goals.\n\nTotal emissions for a scene may be obtained by summing the area emissions\nfor a given collection ID.\n\nDispersed area emissions are estimated from methane concentration observations\nin the form of column-averaged dry-air mole fractions of methane (XCH4) using\nan inverse model. An atmospheric transport model - the Stochastic Time-Inverted\nLagrangian Transport (STILT) model; [Lin et al. (2003)](https://doi.org/10.1029/2002JD003161),\n[Fasoli et al. (2018)](https://doi.org/10.5194/gmd-11-2813-2018); driven\nby meteorological data from the National Centers for Environmental\nPrediction ([NCEP](https://www.weather.gov/ncep/)) Global Forecast System\n([GFS](https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php))\n- is used to link variations in observed XCH4 to potential upwind sources.\n\nThese predictive linkages are used to invert an estimated set of emissions\nfrom the observations of MethaneSAT using a novel Markov Chain Monte Carlo\ntechnique nicknamed CORE (Conserved and Optimized Retrieval of Emissions).\nInflow across the domain boundary and pre-existing background concentrations\nare estimated, but not reported. \n\nThis set of initial observations made by MethaneSAT are consistent with\nindependent empirical data where available from other sources. Not all data\nproducts (L3 concentration, L4 area and L4 points) are available for all\ncollection IDs. Individual heat maps can have spatial misallocations due to\nseveral factors, including wind field uncertainty, observational\nuncertainty and variable emission rates. Users are advised to interpret\nindividual emission grids with caution. For more about definitions and\nlimitations, see our\n[technical explanation](https://www.methanesat.org/sites/default/files/2025-11/MethaneSAT%20Dispersed%20Area%20Emissions_Definitions%20and%20Limitations.pdf).\nContact the data provider for more information about the project at this\nlink:\n[https://www.methanesat.org/contact/](https://www.methanesat.org/contact/).\n\n---\n\n7/2/2025 Important Update: \n\nAs you may be aware, we recently lost contact with the satellite. After\nexploring all possible recovery options, we have now confirmed that it is\nno longer functioning, due to an undetermined problem with the outer\nplatform carrying our methane detector. While there is no question this is\na setback, we are undeterred in our efforts to drive down methane pollution.\nPlease see our official statement here:\n[MethaneSAT Loses Contact with Satellite | MethaneSAT](https://www.methanesat.org/project-updates/methanesat-loses-contact-satellite).\n\nWhat this means for the Public Preview data: The existing datasets will\nremain accessible on Google platforms and on our web portal for the\nforeseeable future. Additionally, over the next few months, we will release\nsubstantial new data collected by MethaneSAT prior to the loss of contact.\nThis will include hundreds of scenes (of targets that are roughly\n200kmx200km). We hope this will be useful for you. Should there be any\nchanges to data availability, we will notify you well in advance.\n\nLooking ahead: While we don’t have all the answers yet, we plan to leverage\nour advanced Data Processing Platform (DPP) to quantify other streams of\nsatellite and/or aerial data. We will also take the necessary time to\nevaluate the best next step in our efforts to enable methane reductions. To\nstay up to date with further updates, feel free to sign up for our\n[newsletter](https://mailchi.mp/methanesat/methanesat-newsletter-sign-up).\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2024-05-01T00:00:00Z","2025-06-14T11:12:26.422000Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/public-preview/L4area_v2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/edf-methanesat-ee/projects_edf-methanesat-ee_assets_public-preview_L4area_v2","rel":"related","title":"Run the example for projects/edf-methanesat-ee/assets/public-preview/L4area_v2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/edf-methanesat-ee/projects_edf-methanesat-ee_assets_public-preview_L4area_v2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_edf-methanesat-ee_assets_public-preview_L4area_v2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/edf-methanesat-ee/projects_edf-methanesat-ee_assets_msat_L4area.json","rel":"latest-version","title":"projects/edf-methanesat-ee/assets/msat/L4area","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/edf-methanesat-ee/projects_edf-methanesat-ee_assets_public-preview_L4area.json","rel":"predecessor-version","title":"projects/edf-methanesat-ee/assets/public-preview/L4area","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/edf-methanesat-ee/projects_edf-methanesat-ee_assets_msat_L4area.json","rel":"successor-version","title":"projects/edf-methanesat-ee/assets/msat/L4area","type":"application/json"},{"href":"https://www.methanesat.org/sites/default/files/2025-02/MethaneSAT%20-%20Content%20License%20Terms%20of%20Use%20%28Revised%202-12-2025%29%5B25%5D.pdf","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/public-preview/L4area_v2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/edf-methanesat-ee/assets/public-preview/L4area_v2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/ee-earth-timelapse/assets/annual","title":"Earth Timelapse v0","description":"The Earth Timelapse dataset provides a four-decade visual record of\nplanetary change spanning from 1984 to 2022. Synthesized by Google from\npetabytes of spaceborne observations acquired by NASA/USGS Landsat missions\n(Landsat 4, 5, 7, 8, and 9) and ESA's Copernicus Sentinel-2 constellation,\nthe dataset is offered as annual mosaics for visual interpretation.\n\nThe annual mosaics are globally complete, gap-filled, and water-masked\nvisual basemaps that power the public Google Earth Timelapse interactive\nviewer. Pre-1999 observation voids are smoothly reconstructed using\npixel-wise temporal linear regression interpolation across adjacent\nvalid observation years. Open ocean waters are replaced with styled\nNOAA ETOPO1 shaded-relief bathymetry and global water masking (using Hansen\nGFC and MOD44W datasets). These data are suitable for: visual\ninterpretation, educational storytelling, base mapping, or custom video\nexports.\n\nNote: The RGB bands contain visually normalized 8-bit values derived from\nTop-of-Atmosphere (TOA) reflectance (contrast-stretched from [0.02, 0.50]\nwith gamma 1.7 and matched to a MODIS BRDF baseline), not surface\nreflectance. Timelapse is not recommended for quantitative radiometric\nanalysis.\n\nSee [the Timelapse developer documentation](https://developers.google.com/earth-engine/timelapse/about)\nfor more details on data characteristics, processing, and limitations.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2022-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/ee-earth-timelapse/assets/annual","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ee-earth-timelapse/projects_ee-earth-timelapse_assets_annual","rel":"related","title":"Run the example for projects/ee-earth-timelapse/assets/annual in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ee-earth-timelapse/projects_ee-earth-timelapse_assets_annual_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_ee-earth-timelapse_assets_annual#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/ee-earth-timelapse/assets/annual/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/ee-earth-timelapse/assets/annual/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/ee-pkurelab/assets/LHScat","title":"LHScat:A global long-term, high-resolution satellite radar backscatter datasets","description":"The LHScat dataset is the first long-term (since 1992), high-resolution (∼8.9\u2009km) \nmonthly C-band satellite radar backscatter dataset over global land areas. It integrated \nsignals from the European Remote Sensing satellite (ERS; 1992–2001; C-band; 5.3\u2009GHz),\nQuick Scatterometer (QSCAT, 1999–2009; Ku-band; 13.4\u2009GHz), and the Advanced \nScatterometer (ASCAT; since 2007; C-band; 5.255\u2009GHz). The 6-year data gap between \nC-band ERS and ASCAT was filled by modelling a substitute C-band signal during 1999–2009 \nfrom Ku-band QSCAT signals and climatic information. The quality of the merged radar \nsignal was assessed by computing the Pearson correlation coefficient (Pearson\\'s r) and root mean square error (RMSE) \nbetween the C-band and the corrected Ku-band signals in the overlapping years (1999–2001 and 2007–2009). \nTo facilitate storage, the radar signals were scaled by a factor of 100 and converted to unsigned Int16.\nFor more detailed information, please refer to the paper describing LHScat dataset(https://essd.copernicus.org/articles/15/1577/2023/).\n","license":"CC-BY-NC-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1992-04-01T00:00:00Z","2021-08-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/ee-pkurelab/assets/LHScat","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ee-pkurelab/projects_ee-pkurelab_assets_LHScat","rel":"related","title":"Run the example for projects/ee-pkurelab/assets/LHScat in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ee-pkurelab/projects_ee-pkurelab_assets_LHScat_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_ee-pkurelab_assets_LHScat#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/ee-pkurelab/assets/LHScat/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/ee-pkurelab/assets/LHScat/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/ee-pkurelab/assets/LHScatQC","title":"LHScat Quality Control","description":"The LHScat dataset is the first long-term (since 1992), high-resolution (∼8.9\u2009km) \nmonthly C-band satellite radar backscatter dataset over global land areas. It integrated \nsignals from the European Remote Sensing satellite (ERS; 1992–2001; C-band; 5.3\u2009GHz),\nQuick Scatterometer (QSCAT, 1999–2009; Ku-band; 13.4\u2009GHz), and the Advanced \nScatterometer (ASCAT; since 2007; C-band; 5.255\u2009GHz). The 6-year data gap between \nC-band ERS and ASCAT was filled by modelling a substitute C-band signal during 1999–2009 \nfrom Ku-band QSCAT signals and climatic information. The quality of the merged radar \nsignal was assessed by computing the Pearson correlation coefficient (Pearson\\'s r) and Root Mean Square Error (RMSE) \nbetween the C-band and the corrected Ku-band signals in the overlapping years (1999–2001 and 2007–2009). \nSince the signals were merged from several sensors, the Pearson\\'s r and RMSE values can be used to select \npixels with high data merging quality. For more detailed information, please refer to the paper \ndescribing LHScat dataset(https://essd.copernicus.org/articles/15/1577/2023/).\n","license":"CC-BY-NC-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1992-04-01T00:00:00Z","2021-08-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/ee-pkurelab/assets/LHScatQC","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ee-pkurelab/projects_ee-pkurelab_assets_LHScatQC","rel":"related","title":"Run the example for projects/ee-pkurelab/assets/LHScatQC in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ee-pkurelab/projects_ee-pkurelab_assets_LHScatQC_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_ee-pkurelab_assets_LHScatQC#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/ee-pkurelab/assets/LHScatQC/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/forestdatapartnership/assets/cocoa/model_2024a","title":"Cocoa Probability model 2024a [deprecated]","description":"\n**Note: This dataset is not yet peer-reviewed.  Please see the GitHub\nREADME associated with this model for more information.**\n\nThis image collection provides per-pixel probability that the underlying\narea is occupied by cocoa.\n\nThe probability estimates are provided at 10 meter resolution, and have\nbeen generated by a machine learning model. This dataset corresponds to\n2020 and 2023 output from model 2024a in the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main/models/cocoa)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.forestdatapartnership.org/)\nwhich aims to halt and reverse forest loss from commodity production\nby collaboratively improving global monitoring, supply chain tracking,\nand restoration.\n\n**Note that this dataset has separate terms of use for commercial users of\nEarth Engine.  Please see \"Terms of Use\" tab for details.**\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the\nmaps continuously improves. To provide map-based feedback on this\ncollection, please see our\n[Collect Earth Online instance](https://app.collect.earth/collection?projectId=50862)\nand follow\n[these instructions](https://collect-earth-online-doc.readthedocs.io/en/latest/collection/simplified.html).\n\nIf you would like to provide general feedback or additional datasets to\nimprove these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\n**Limitations**: Model output is limited to selected countries as calendar\nyear composites for 2020 and 2023. Not all regions of the output are\nrepresented by training data. Accuracy is reported in aggregate, is based on\na notional threshold, and will vary geographically and with user chosen\nthresholds. Sensor artifacts based on data availability, cross-track\nnonuniformity, or cloudiness may be visually apparent in output\nprobabilities and result in classification errors at some thresholds.\nGeographic scope: Côte d'Ivoire, Ghana.\n\n","deprecated":true,"license":"various","extent":{"spatial":{"bbox":[[-11.35,0.61,18.17,14.51]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2023-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/cocoa/model_2024a","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_cocoa_model_2024a","rel":"related","title":"Run the example for projects/forestdatapartnership/assets/cocoa/model_2024a in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_cocoa_model_2024a_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_cocoa_model_2024a#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_cocoa_model_2026a.json","rel":"latest-version","title":"projects/forestdatapartnership/assets/cocoa/model_2026a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_cocoa_model_2025a.json","rel":"successor-version","title":"projects/forestdatapartnership/assets/cocoa/model_2025a","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/cocoa/model_2024a/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/cocoa/model_2024a/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/forestdatapartnership/assets/cocoa/model_2025a","title":"Cocoa Probability model 2025a [deprecated]","description":"This image collection provides estimated per-pixel probability that the\nunderlying area is occupied by the commodity.  The probability estimates are\nprovided at 10 meter resolution, and have been generated by a machine\nlearning model. For details, see the technical documentation on the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.forestdatapartnership.org/) which\naims to halt and reverse forest loss from commodity production by\ncollaboratively improving global monitoring, supply chain tracking, and\nrestoration.\n\nThis dataset currently covers the following countries: Côte d'Ivoire, Ghana,\nIndonesia, Ecuador, Peru, Colombia.\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the maps\ncontinuously improves. If you would like to provide general feedback or\nadditional datasets to improve these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\nLimitations: Model output is limited to selected countries as calendar year\ncomposites for 2020 and 2023. Not all regions of the output are well\nrepresented by training data. Accuracy is reported in aggregate, and will\nvary geographically and with user chosen thresholds. Sensor artifacts based\non data availability, cross-track nonuniformity, or cloudiness may be\nvisually apparent in output probabilities and result in classification\nerrors at some thresholds.\n\nPlease see this\n[GitHub README](https://github.com/google/forest-data-partnership/tree/main/models/model_2025a)\nfor more information.\n\n**Note that this dataset has separate terms of use for commercial users of\nEarth Engine. Please see \"Terms of Use\" tab for details.**\n\nThe difference between the 2025a and 2025b versions is that the 2025b version is based on AlphaEarth Foundations Satellite Embeddings, which enables expanded geographic and temporal coverage over 2025a. Additionally, 2025b includes additional input data and an evolved input data processing pipeline. 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The probability estimates are\nprovided at 10 meter resolution, and have been generated by a machine\nlearning model. For details, see the technical documentation on the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.fao.org/in-action/forest-data-partnership) which\naims to halt and reverse forest loss from commodity production by\ncollaboratively improving global monitoring, supply chain tracking, and\nrestoration.\n\nThis dataset currently covers the pan-tropical region (-24 to 24 degrees latitude).\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the maps\ncontinuously improves. If you would like to provide general feedback or\nadditional datasets to improve these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\nPlease see this\n[GitHub README](https://github.com/google/forest-data-partnership/tree/main/models/model_2025b)\nfor technical documentation and limitations of this dataset.\n\nThe difference between the 2025a and 2025b versions is that the 2025b version is based on AlphaEarth Foundations Satellite Embeddings, which enables expanded geographic and temporal coverage over 2025a. Additionally, 2025b includes additional input data and an evolved input data processing pipeline. 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The probability estimates are\nprovided at 10 meter resolution, and have been generated by a machine\nlearning model. For details, see the technical documentation on the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.fao.org/in-action/forest-data-partnership) which\naims to halt and reverse forest loss from commodity production by\ncollaboratively improving global monitoring, supply chain tracking, and\nrestoration.\n\nThis dataset currently covers the pan-tropical region (-24 to 24 degrees latitude).\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the maps\ncontinuously improves. If you would like to provide general feedback or\nadditional datasets to improve these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\nPlease see this\n[GitHub README](https://github.com/google/forest-data-partnership/tree/main/models/model_2026a)\nfor technical documentation and limitations of this dataset.\n\nThe difference between the 2025b and 2026a versions is that the 2026a version includes additional input data, bug fixes, and an evolved input data processing pipeline.","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-24,180,24]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2024-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/cocoa/model_2026a","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_cocoa_model_2026a","rel":"related","title":"Run the example for projects/forestdatapartnership/assets/cocoa/model_2026a in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_cocoa_model_2026a_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_cocoa_model_2026a#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_cocoa_model_2026a.json","rel":"latest-version","title":"projects/forestdatapartnership/assets/cocoa/model_2026a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_cocoa_model_2025b.json","rel":"predecessor-version","title":"projects/forestdatapartnership/assets/cocoa/model_2025b","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/cocoa/model_2026a/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/cocoa/model_2026a/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/forestdatapartnership/assets/coffee/model_2025a","title":"Coffee Probability model 2025a [deprecated]","description":"This image collection provides estimated per-pixel probability that the\nunderlying area is occupied by the commodity.  The probability estimates are\nprovided at 10 meter resolution, and have been generated by a machine\nlearning model. For details, see the technical documentation on the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.forestdatapartnership.org/) which\naims to halt and reverse forest loss from commodity production by\ncollaboratively improving global monitoring, supply chain tracking, and\nrestoration.\n\nThis dataset currently covers the following countries: Brazil, Vietnam,\nIndonesia, Colombia, Ethiopia, Uganda, Honduras, Peru, Nicaragua,\nEl Salvador.\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the maps\ncontinuously improves. If you would like to provide general feedback or\nadditional datasets to improve these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\nLimitations: Model output is limited to selected countries as calendar year\ncomposites for 2020 and 2023. Not all regions of the output are well\nrepresented by training data. Accuracy is reported in aggregate, and will\nvary geographically and with user chosen thresholds. Sensor artifacts based\non data availability, cross-track nonuniformity, or cloudiness may be\nvisually apparent in output probabilities and result in classification\nerrors at some thresholds.\n\nPlease see this\n[GitHub README](https://github.com/google/forest-data-partnership/tree/main/models/model_2025a)\nfor more information.\n\n**Note that this dataset has separate terms of use for commercial users of\nEarth Engine. Please see \"Terms of Use\" tab for details.**\n\nThe difference between the 2025a and 2025b versions is that the 2025b version is based on AlphaEarth Foundations Satellite Embeddings, which enables expanded geographic and temporal coverage over 2025a. Additionally, 2025b includes additional input data and an evolved input data processing pipeline. 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The probability estimates are\nprovided at 10 meter resolution, and have been generated by a machine\nlearning model. For details, see the technical documentation on the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.fao.org/in-action/forest-data-partnership) which\naims to halt and reverse forest loss from commodity production by\ncollaboratively improving global monitoring, supply chain tracking, and\nrestoration.\n\nThis dataset currently covers the pan-tropical region (-24 to 24 degrees latitude).\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the maps\ncontinuously improves. If you would like to provide general feedback or\nadditional datasets to improve these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\nPlease see this\n[GitHub README](https://github.com/google/forest-data-partnership/tree/main/models/model_2025b)\nfor technical documentation and limitations of this dataset.\n\nThe difference between the 2025a and 2025b versions is that the 2025b version is based on AlphaEarth Foundations Satellite Embeddings, which enables expanded geographic and temporal coverage over 2025a. Additionally, 2025b includes additional input data and an evolved input data processing pipeline. 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The probability estimates are\nprovided at 10 meter resolution, and have been generated by a machine\nlearning model. For details, see the technical documentation on the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.fao.org/in-action/forest-data-partnership) which\naims to halt and reverse forest loss from commodity production by\ncollaboratively improving global monitoring, supply chain tracking, and\nrestoration.\n\nThis dataset currently covers the pan-tropical region (-24 to 24 degrees latitude).\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the maps\ncontinuously improves. If you would like to provide general feedback or\nadditional datasets to improve these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\nPlease see this\n[GitHub README](https://github.com/google/forest-data-partnership/tree/main/models/model_2026a)\nfor technical documentation and limitations of this dataset.\n\nThe difference between the 2025b and 2026a versions is that the 2026a version includes additional input data, bug fixes, and an evolved input data processing pipeline.","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-24,180,24]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2024-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/coffee/model_2026a","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_coffee_model_2026a","rel":"related","title":"Run the example for projects/forestdatapartnership/assets/coffee/model_2026a in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_coffee_model_2026a_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_coffee_model_2026a#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_coffee_model_2026a.json","rel":"latest-version","title":"projects/forestdatapartnership/assets/coffee/model_2026a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_coffee_model_2025b.json","rel":"predecessor-version","title":"projects/forestdatapartnership/assets/coffee/model_2025b","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/coffee/model_2026a/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/coffee/model_2026a/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/forestdatapartnership/assets/community_forests/ForestPersistence_2020","title":"Forest Persistence v0","description":"\n\nThis image provides a per-pixel score (in [0, 1]) that indicates whether \nthe pixel area is occupied by undisturbed forest in year 2020.\n\nThese scores are provided at 30 meter resolution, and have\nbeen generated by a convergence of evidence approach to combining multiple \nforest cover data sets into a level of continuous support for the presence \nof undisturbed forest, defined as naturally regenerating forest with \n\"no clearly visible indications of human activities and the ecological \nprocesses are not significantly disturbed\" or forests \"predominantly \ncomposed of trees established through natural regeneration.\"  The \nnumerical support, called ForestPersistence, is created from an ensemble of \nexisting datasets selected for their ability to describe forest \ndisturbance and management history, consistent with the definition.  \n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the\nmaps continuously improves. To provide map-based feedback on this\ncollection, please see our\n[Collect Earth Online instance](https://app.collect.earth/collection?projectId=50862)\nand follow\n[these instructions](https://collect-earth-online-doc.readthedocs.io/en/latest/collection/simplified.html).\n\nIf you would like to provide general feedback or additional datasets to\nimprove these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\nFor more information about this dataset, see \n[the README](https://github.com/google/forest-data-partnership/tree/main/models/model_2024a/forests)\non Google's Forest Data Partnership GitHub site. For more information about\nthe Forest Data Partnership, visit the \n[Forest Data Partnership website](https://www.forestdatapartnership.org/).\n\n**Limitations**: Geographic scope is limited to +/- 80 degrees latitude \nand to calendar year 2020. Not all geographic regions are equally \nrepresented. For example, China, Russia, Australia, New Zealand and other \ncountries lack regionally specific datasets in the ensemble. Boreal forest \nmay not be well represented. This dataset is built from existing datasets, \nand any limitations and errors associated with those datasets may be \npresent. Landsat TimeLapse is a Google developed product that has not \nbeen used with LandTrendr previously. A probability sample of these types \nof forest cover coincident with the year 2020 is not available, making \naccuracy difficult to assess.\n\n\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/community_forests/ForestPersistence_2020","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_community_forests_ForestPersistence_2020","rel":"related","title":"Run the example for projects/forestdatapartnership/assets/community_forests/ForestPersistence_2020 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_community_forests_ForestPersistence_2020_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_community_forests_ForestPersistence_2020#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/community_forests/ForestPersistence_2020/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/forestdatapartnership/assets/community_palm/20240312","title":"Palm Probability v20240312 [deprecated]","description":"This image collection provides per-pixel probability that the underlying\narea is occupied by palm.\n\nThese probability estimates are provided at 10 meter resolution, and have\nbeen generated by a machine learning model. Labeled examples of known palm\nand non-palm were supplied by community contributors to the Forest Data\nPartnership. The predictors are annual composites built from publicly\navailable satellite imagery provided by Sentinel-1, Sentinel-2,\nand ALOS DSM. Probability predictions are provided for an individual year,\ndenoted by the start and end time on each image in the collection. For the\ninitial version, predictions based on mosaics of 2020 imagery and 2023\nimagery are included. Methods are described in\n[this preprint](https://goo.gle/a_community_palm_model).\n\nThe community data product is meant to evolve over time, as more data comes\nin from the community and the community model to produce the maps\ncontinuously improves. If you would like to provide feedback on these\nlayers, please visit our\n[Collect Earth Online](https://app.collect.earth/review-institution?institutionId=2494)\nproject to contribute.\n\nIf you are interested in contributing datasets to this effort,\n[please reach out through this form](https://goo.gle/fdap-data).\n\nThis dataset corresponds to the output from the\n[20240312 model on GitHub](https://github.com/google/forest-data-partnership/tree/main/models).\n\nFor more information about the Forest Data Partnership, visit our\n[website](https://www.forestdatapartnership.org/).\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[92.99,-11.94,132.71,11.71]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2023-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/community_palm/20240312","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_community_palm_20240312","rel":"related","title":"Run the example for projects/forestdatapartnership/assets/community_palm/20240312 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_community_palm_20240312_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_community_palm_20240312#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2026a.json","rel":"latest-version","title":"projects/forestdatapartnership/assets/palm/model_2026a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2024a.json","rel":"successor-version","title":"projects/forestdatapartnership/assets/palm/model_2024a","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/community_palm/20240312/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/community_palm/20240312/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/forestdatapartnership/assets/palm/model_2024a","title":"Palm Probability model 2024a [deprecated]","description":"\n**Note: This dataset is not yet peer-reviewed.  Please see the GitHub \nREADME associated with this model for more information.**\n\nThis image collection provides per-pixel probability that the underlying\narea is occupied by palm.\n\nThe probability estimates are provided at 10 meter resolution, and have\nbeen generated by a machine learning model. This dataset corresponds to \n2020 and 2023 output from model 2024a in the \n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main/models/palm) \non Github.\n\nThe primary purpose of this image collection is to support the mission of \nthe [Forest Data Partnership](https://www.forestdatapartnership.org/) \nwhich aims to halt and reverse forest loss from commodity production \nby collaboratively improving global monitoring, supply chain tracking,\nand restoration. \n\n**Note that this dataset has separate terms of use for commercial users of \nEarth Engine.  Please see \"Terms of Use\" tab for details.**\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the\nmaps continuously improves. To provide map-based feedback on this\ncollection, please see our\n[Collect Earth Online instance](https://app.collect.earth/collection?projectId=50862)\nand follow\n[these instructions](https://collect-earth-online-doc.readthedocs.io/en/latest/collection/simplified.html).\n\nIf you would like to provide general feedback or additional datasets to\nimprove these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\n","deprecated":true,"license":"various","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2023-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/palm/model_2024a","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2024a","rel":"related","title":"Run the example for projects/forestdatapartnership/assets/palm/model_2024a in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2024a_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_palm_model_2024a#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2026a.json","rel":"latest-version","title":"projects/forestdatapartnership/assets/palm/model_2026a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_community_palm_20240312.json","rel":"predecessor-version","title":"projects/forestdatapartnership/assets/community_palm/20240312","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2025a.json","rel":"successor-version","title":"projects/forestdatapartnership/assets/palm/model_2025a","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/palm/model_2024a/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/palm/model_2024a/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/forestdatapartnership/assets/palm/model_2025a","title":"Palm Probability model 2025a [deprecated]","description":"This image collection provides estimated per-pixel probability that the\nunderlying area is occupied by the commodity.  The probability estimates are\nprovided at 10 meter resolution, and have been generated by a machine\nlearning model. For details, see the technical documentation on the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.forestdatapartnership.org/) which\naims to halt and reverse forest loss from commodity production by\ncollaboratively improving global monitoring, supply chain tracking, and\nrestoration.\n\nThis dataset currently covers the following countries: Indonesia, Malaysia,\nThailand, Nigeria, Colombia, Brazil, Côte d'Ivoire, Ghana, Ecuador, and\nHonduras.\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the maps\ncontinuously improves. If you would like to provide general feedback or\nadditional datasets to improve these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\nLimitations: Model output is limited to selected countries as calendar year\ncomposites for 2020 and 2023. Not all regions of the output are well\nrepresented by training data. Accuracy is reported in aggregate, and will\nvary geographically and with user chosen thresholds. Sensor artifacts based\non data availability, cross-track nonuniformity, or cloudiness may be\nvisually apparent in output probabilities and result in classification\nerrors at some thresholds.\n\nPlease see this\n[GitHub README](https://github.com/google/forest-data-partnership/tree/main/models/model_2025a)\nfor more information.\n\n**Note that this dataset has separate terms of use for commercial users of\nEarth Engine. Please see \"Terms of Use\" tab for details.**\n\n\nThe difference between the 2025a and 2025b versions is that the 2025b version is based on AlphaEarth Foundations Satellite Embeddings, which enables expanded geographic and temporal coverage over 2025a. Additionally, 2025b includes additional input data and an evolved input data processing pipeline. Note that the 2025a model may perform better in some contexts.","deprecated":true,"license":"various","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2023-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/palm/model_2025a","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2025a","rel":"related","title":"Run the example for projects/forestdatapartnership/assets/palm/model_2025a in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2025a_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_palm_model_2025a#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2026a.json","rel":"latest-version","title":"projects/forestdatapartnership/assets/palm/model_2026a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2024a.json","rel":"predecessor-version","title":"projects/forestdatapartnership/assets/palm/model_2024a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2025b.json","rel":"successor-version","title":"projects/forestdatapartnership/assets/palm/model_2025b","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/palm/model_2025a/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/palm/model_2025a/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/forestdatapartnership/assets/palm/model_2025b","title":"Palm Probability model 2025b [deprecated]","description":"This image collection provides estimated per-pixel probability that the\nunderlying area is occupied by the commodity.  The probability estimates are\nprovided at 10 meter resolution, and have been generated by a machine\nlearning model. For details, see the technical documentation on the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.fao.org/in-action/forest-data-partnership) which\naims to halt and reverse forest loss from commodity production by\ncollaboratively improving global monitoring, supply chain tracking, and\nrestoration.\n\nThis dataset currently covers the pan-tropical region (-24 to 24 degrees latitude).\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the maps\ncontinuously improves. If you would like to provide general feedback or\nadditional datasets to improve these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\nPlease see this\n[GitHub README](https://github.com/google/forest-data-partnership/tree/main/models/model_2025b)\nfor technical documentation and limitations of this dataset.\n\nThe difference between the 2025a and 2025b versions is that the 2025b version is based on AlphaEarth Foundations Satellite Embeddings, which enables expanded geographic and temporal coverage over 2025a. Additionally, 2025b includes additional input data and an evolved input data processing pipeline. Note that the 2025a model may perform better in some contexts.","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-24,180,24]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2024-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/palm/model_2025b","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2025b","rel":"related","title":"Run the example for projects/forestdatapartnership/assets/palm/model_2025b in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2025b_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_palm_model_2025b#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2026a.json","rel":"latest-version","title":"projects/forestdatapartnership/assets/palm/model_2026a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2025a.json","rel":"predecessor-version","title":"projects/forestdatapartnership/assets/palm/model_2025a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2026a.json","rel":"successor-version","title":"projects/forestdatapartnership/assets/palm/model_2026a","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/palm/model_2025b/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/palm/model_2025b/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/forestdatapartnership/assets/palm/model_2026a","title":"Palm Probability model 2026a","description":"This image collection provides estimated per-pixel probability that the\nunderlying area is occupied by the commodity.  The probability estimates are\nprovided at 10 meter resolution, and have been generated by a machine\nlearning model. For details, see the technical documentation on the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.fao.org/in-action/forest-data-partnership) which\naims to halt and reverse forest loss from commodity production by\ncollaboratively improving global monitoring, supply chain tracking, and\nrestoration.\n\nThis dataset currently covers the pan-tropical region (-24 to 24 degrees latitude).\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the maps\ncontinuously improves. If you would like to provide general feedback or\nadditional datasets to improve these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\nPlease see this\n[GitHub README](https://github.com/google/forest-data-partnership/tree/main/models/model_2026a)\nfor technical documentation and limitations of this dataset.\n\nThe difference between the 2025b and 2026a versions is that the 2026a version includes additional input data, bug fixes, and an evolved input data processing pipeline.","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-24,180,24]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2024-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/palm/model_2026a","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2026a","rel":"related","title":"Run the example for projects/forestdatapartnership/assets/palm/model_2026a in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2026a_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_palm_model_2026a#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2026a.json","rel":"latest-version","title":"projects/forestdatapartnership/assets/palm/model_2026a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2025b.json","rel":"predecessor-version","title":"projects/forestdatapartnership/assets/palm/model_2025b","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/palm/model_2026a/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/palm/model_2026a/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/forestdatapartnership/assets/rubber/model_2024a","title":"Rubber Tree Probability model 2024a [deprecated]","description":"\n**Note: This dataset is not yet peer-reviewed.  Please see the GitHub \nREADME associated with this model for more information.**\n\nThis image collection provides per-pixel probability that the underlying\narea is occupied by rubber trees.\n\nThe probability estimates are provided at 10 meter resolution, and have\nbeen generated by a machine learning model. This dataset corresponds to \n2020 and 2023 output from model 2024a in the \n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main/models/rubber) \non Github.\n\nThe primary purpose of this image collection is to support the mission of \nthe [Forest Data Partnership](https://www.forestdatapartnership.org/) \nwhich aims to halt and reverse forest loss from commodity production \nby collaboratively improving global monitoring, supply chain tracking,\nand restoration. \n\n**Note that this dataset has separate terms of use for commercial users of \nEarth Engine.  Please see \"Terms of Use\" tab for details.**\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the\nmaps continuously improves. To provide map-based feedback on this\ncollection, please see our\n[Collect Earth Online instance](https://app.collect.earth/collection?projectId=50862)\nand follow\n[these instructions](https://collect-earth-online-doc.readthedocs.io/en/latest/collection/simplified.html).\n\nIf you would like to provide general feedback or additional datasets to\nimprove these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\n**Limitations**: Model output is limited to selected countries as calendar\nyear composites for 2020 and 2023. Not all regions of the output are\nrepresented by training data. Accuracy is reported in aggregate, is based\non a notional threshold, and will vary geographically and with user chosen\nthresholds. Sensor artifacts based on data availability, cross-track\nnonuniformity, or cloudiness may be visually apparent in output\nprobabilities and result in classification errors at some thresholds.\nGeographic scope:  SE Asia (Thailand, Indonesia, Vietnam, Malaysia,\nPhilippines, Hainan Island), Africa (Côte d'Ivoire, Ghana).\n\n\n","deprecated":true,"license":"various","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2023-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/rubber/model_2024a","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2024a","rel":"related","title":"Run the example for projects/forestdatapartnership/assets/rubber/model_2024a in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2024a_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_rubber_model_2024a#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2026a.json","rel":"latest-version","title":"projects/forestdatapartnership/assets/rubber/model_2026a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2025a.json","rel":"successor-version","title":"projects/forestdatapartnership/assets/rubber/model_2025a","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/rubber/model_2024a/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/rubber/model_2024a/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/forestdatapartnership/assets/rubber/model_2025a","title":"Rubber Tree Probability model 2025a [deprecated]","description":"This image collection provides estimated per-pixel probability that the\nunderlying area is occupied by the commodity.  The probability estimates are\nprovided at 10 meter resolution, and have been generated by a machine\nlearning model. For details, see the technical documentation on the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.forestdatapartnership.org/) which\naims to halt and reverse forest loss from commodity production by\ncollaboratively improving global monitoring, supply chain tracking, and\nrestoration.\n\nThis dataset currently covers the following countries: Thailand, Indonesia,\nViet Nam, Côte d'Ivoire, China, Malaysia, Brazil, Mexico, and Sri Lanka.\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the maps\ncontinuously improves. If you would like to provide general feedback or\nadditional datasets to improve these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\nLimitations: Model output is limited to selected countries as calendar year\ncomposites for 2020 and 2023. Not all regions of the output are well\nrepresented by training data. Accuracy is reported in aggregate, and will\nvary geographically and with user chosen thresholds. Sensor artifacts based\non data availability, cross-track nonuniformity, or cloudiness may be\nvisually apparent in output probabilities and result in classification\nerrors at some thresholds.\n\nPlease see this\n[GitHub README](https://github.com/google/forest-data-partnership/tree/main/models/model_2025a)\nfor more information.\n\n**Note that this dataset has separate terms of use for commercial users of\nEarth Engine. Please see \"Terms of Use\" tab for details.**\n\nPlease see this [GitHub README](https://github.com/google/forest-data-partnership/tree/main/models/model_2025a) for more information.\n\nThe difference between the 2025a and 2025b versions is that the 2025b version is based on AlphaEarth Foundations Satellite Embeddings, which enables expanded geographic and temporal coverage over 2025a. Additionally, 2025b includes additional input data and an evolved input data processing pipeline. Note that the 2025a model may perform better in some contexts.","deprecated":true,"license":"various","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2023-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/rubber/model_2025a","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2025a","rel":"related","title":"Run the example for projects/forestdatapartnership/assets/rubber/model_2025a in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2025a_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_rubber_model_2025a#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2026a.json","rel":"latest-version","title":"projects/forestdatapartnership/assets/rubber/model_2026a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2024a.json","rel":"predecessor-version","title":"projects/forestdatapartnership/assets/rubber/model_2024a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2025b.json","rel":"successor-version","title":"projects/forestdatapartnership/assets/rubber/model_2025b","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/rubber/model_2025a/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/rubber/model_2025a/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/forestdatapartnership/assets/rubber/model_2025b","title":"Rubber Probability model 2025b [deprecated]","description":"This image collection provides estimated per-pixel probability that the\nunderlying area is occupied by the commodity.  The probability estimates are\nprovided at 10 meter resolution, and have been generated by a machine\nlearning model. For details, see the technical documentation on the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.fao.org/in-action/forest-data-partnership) which\naims to halt and reverse forest loss from commodity production by\ncollaboratively improving global monitoring, supply chain tracking, and\nrestoration.\n\nThis dataset currently covers the pan-tropical region (-24 to 24 degrees latitude).\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the maps\ncontinuously improves. If you would like to provide general feedback or\nadditional datasets to improve these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\nPlease see this\n[GitHub README](https://github.com/google/forest-data-partnership/tree/main/models/model_2025b)\nfor technical documentation and limitations of this dataset.\n\nThe difference between the 2025a and 2025b versions is that the 2025b version is based on AlphaEarth Foundations Satellite Embeddings, which enables expanded geographic and temporal coverage over 2025a. Additionally, 2025b includes additional input data and an evolved input data processing pipeline. Note that the 2025a model may perform better in some contexts.","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-24,180,24]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2024-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/rubber/model_2025b","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2025b","rel":"related","title":"Run the example for projects/forestdatapartnership/assets/rubber/model_2025b in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2025b_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_rubber_model_2025b#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2026a.json","rel":"latest-version","title":"projects/forestdatapartnership/assets/rubber/model_2026a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2025a.json","rel":"predecessor-version","title":"projects/forestdatapartnership/assets/rubber/model_2025a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2026a.json","rel":"successor-version","title":"projects/forestdatapartnership/assets/rubber/model_2026a","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/rubber/model_2025b/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/rubber/model_2025b/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/forestdatapartnership/assets/rubber/model_2026a","title":"Rubber Probability model 2026a","description":"This image collection provides estimated per-pixel probability that the\nunderlying area is occupied by the commodity.  The probability estimates are\nprovided at 10 meter resolution, and have been generated by a machine\nlearning model. For details, see the technical documentation on the\n[Forest Data Partnership repo](https://github.com/google/forest-data-partnership/tree/main)\non Github.\n\nThe primary purpose of this image collection is to support the mission of\nthe [Forest Data Partnership](https://www.fao.org/in-action/forest-data-partnership) which\naims to halt and reverse forest loss from commodity production by\ncollaboratively improving global monitoring, supply chain tracking, and\nrestoration.\n\nThis dataset currently covers the pan-tropical region (-24 to 24 degrees latitude).\n\nThis community data product is meant to evolve over time, as more data\nbecomes available from the community and the model used to produce the maps\ncontinuously improves. If you would like to provide general feedback or\nadditional datasets to improve these layers, please reach out through\n[this form](https://goo.gle/fdap-data).\n\nPlease see this\n[GitHub README](https://github.com/google/forest-data-partnership/tree/main/models/model_2026a)\nfor technical documentation and limitations of this dataset.\n\nThe difference between the 2025b and 2026a versions is that the 2026a version includes additional input data, bug fixes, and an evolved input data processing pipeline.","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-24,180,24]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2024-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/rubber/model_2026a","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2026a","rel":"related","title":"Run the example for projects/forestdatapartnership/assets/rubber/model_2026a in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2026a_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_rubber_model_2026a#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2026a.json","rel":"latest-version","title":"projects/forestdatapartnership/assets/rubber/model_2026a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2025b.json","rel":"predecessor-version","title":"projects/forestdatapartnership/assets/rubber/model_2025b","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/rubber/model_2026a/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/forestdatapartnership/assets/rubber/model_2026a/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/gcp-public-data-weathernext/assets/126478713_1_0","title":"WeatherNext Gen Forecasts [deprecated]","description":"> **DEPRECATION NOTICE**: This dataset is scheduled for deprecation on\n> **July 15, 2026**. To ensure service continuity, users must migrate all\n> active workflows to **WeatherNext 2**. For more details, see our\n> [deprecation page](https://developers.google.com/weathernext/guides/deprecation).\n\nWeatherNext Gen is an experimental dataset of global medium-range ensemble\nweather forecasts produced by an operational version of Google DeepMind's\n[diffusion-based ensemble weather model](https://www.nature.com/articles/s41586-024-08252-9).\n\nThe experimental dataset includes real-time and historic data. Real-time\ndata is any data that relates to a time that is no more than 48 hours in\nthe past (\"Real-Time Experimental Data\"), while historic data is any data\nthat relates to a time that is more than 48 hours ago (\"Historic\nExperimental Data\"). This dataset includes major surface fields including\ntemperature, wind, precipitation, humidity, geopotential, sea surface\ntemperature, vertical velocity, and pressure. The spatial resolution is\n0.25 degrees. Forecast init times have 6 hour resolution (00z, 06z, 12z, 18z).\nForecast lead times have 12 hour resolution up to a max lead time of 15 days.\n\nIf you are interested in accessing the experimental dataset, please fill\nout this [WeatherNext Data Request form](https://docs.google.com/forms/d/e/1FAIpQLSeCf1JY8G78UDWzbm0ly9kJxfSjUIJT5WyMR_HiNqCm-IHIBg/viewform).\n\nMore information on the model is described in \"[GenCast: Diffusion-based ensemble forecasting for medium-range weather](https://www.nature.com/articles/s41586-024-08252-9)\".\nThe model used to produce this experimental dataset is an operational\nversion derived from that research model. Please note that the accuracy\nof this operational model may not exactly correspond to the accuracy\nreported for the research model, and that additional variables may be\nincluded in this forecast dataset. The forecast dataset generated by the\nresearch model and which was used to generate the results in the above paper\ncan be found at `gs://weathernext/weathernext_gen_research_2019`.\n\nIf you have any questions on using this experimental dataset, or would\nlike to use it for purposes not currently permitted under the terms of use\nset out below, please contact weathernext@google.com.\n\n**Dissemination Schedule**\n\nAll 50 members of the ensemble forecast are released to BigQuery and Earth\nEngine. All members are released at the same time. All times are in the UTC\ntime zone and are rough estimates (with typical ± 15 minutes variance). On\noccasion, times may vary up to ± 60 minutes or more. If data delivery\nexceeds +60 minutes, please inform us via weathernext@google.com.\n\nForecast runs (init time) | Forecast dissemination schedule\n:------------------------ | :------------------------------\n00:00                     | 08:05\n06:00                     | 14:05\n12:00                     | 20:05\n18:00                     | 02:05\n\n**Accessing Raw Data (.zarr)**\n\nA bucket containing the raw .zarr files for the 2020-present historical dataset\n(\"Historic Experimental Data\") is available at `gs://weathernext/126478713_1_0/zarr`.\nAdditionally, 2019 historical forecasts, an extra year of forecasts evaluated in [“Probabilistic weather forecasting with machine learning”](https://www.nature.com/articles/s41586-024-08252-9), are available at `gs://weathernext/weathernext_gen_research_2019`.\nThe 2019 forecast dataset is an additional year of forecast data, complementing the 2020-2024 data available through this dataset listing. The 2019 forecasts are, as in the paper, ERA5 initialized forecasts for a model trained on ERA5.\nTo gain access to those resources, please request access through the same [WeatherNext Data Request form](https://docs.google.com/forms/d/e/1FAIpQLSeCf1JY8G78UDWzbm0ly9kJxfSjUIJT5WyMR_HiNqCm-IHIBg/viewform?usp=preview).\n\n**Acknowledgements**\n\nThe experimental data was generated by models which communicate with and/or\nreference the following separate libraries and packages:\n\n*   Data and products of the European Centre for Medium-range Weather Forecasts\n    (ECMWF), as modified by Google.\n*   Modified Copernicus Climate Change Service information 2023. Neither the\n    European Commission nor ECMWF is responsible for any use that may be made of\n    the Copernicus information or data it contains.\n*   ECMWF HRES datasets\n    *   Copyright statement: Copyright \"© 2023 European Centre for Medium-Range\n        Weather Forecasts (ECMWF)\".\n    *   Source: [www.ecmwf.int](http://www.ecmwf.int)\n    *   License Statement: ECMWF open data is published under a Creative Commons\n        Attribution 4.0 International (CC BY 4.0).\n        [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n    *   Disclaimer: ECMWF does not accept any liability whatsoever for any error\n        or omission in the data, their availability, or for any loss or damage\n        arising from their use.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2026-07-29T06:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/126478713_1_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_126478713_1_0","rel":"related","title":"Run the example for projects/gcp-public-data-weathernext/assets/126478713_1_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_126478713_1_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_126478713_1_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_2_0_0.json","rel":"latest-version","title":"projects/gcp-public-data-weathernext/assets/weathernext_2_0_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_2_0_0.json","rel":"successor-version","title":"projects/gcp-public-data-weathernext/assets/weathernext_2_0_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/126478713_1_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/126478713_1_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/gcp-public-data-weathernext/assets/59572747_4_0","title":"WeatherNext Graph Forecasts [deprecated]","description":"> **DEPRECATION NOTICE**: This dataset is scheduled for deprecation on\n> **July 15, 2026**. To ensure service continuity, users must migrate all\n> active workflows to **WeatherNext 2**. For more details, see our\n> [deprecation page](https://developers.google.com/weathernext/guides/deprecation).\n\nWeatherNext Graph is an experimental dataset of global medium-range weather\nforecasts produced by an operational version of Google DeepMind's\n[graphical neural network weather model](https://www.science.org/stoken/author-tokens/ST-1550/full).\n\nThe experimental dataset includes real-time and historic data. Real-time\ndata is any data that relates to a time that is no more than 48 hours in\nthe past (\"Real-Time Experimental Data\"), while historic data is any data\nthat relates to a time that is more than 48 hours ago (\"Historic\nExperimental Data\"). This dataset includes major surface fields including\ntemperature, wind, precipitation, humidity, geopotential, vertical velocity,\nand pressure. The spatial resolution is 0.25 degrees. Forecast init times\nhave 6 hour resolution (00z, 06z, 12z, 18z). Forecast lead times have 6\nhour resolution up to a max lead time of 10 days.\n\nIf you are interested in accessing the experimental dataset, please fill\nout this [WeatherNext Data Request form](https://docs.google.com/forms/d/e/1FAIpQLSeCf1JY8G78UDWzbm0ly9kJxfSjUIJT5WyMR_HiNqCm-IHIBg/viewform).\n\nMore information on the model is in \"[Learning skillful\nmedium-range global weather forecasting](https://www.science.org/stoken/author-tokens/ST-1550/full)\".\nThe model used to produce this experimental dataset is an operational\nversion derived from that research model (formerly known as GraphCast).\nPlease note that the accuracy of this operational model may not directly\ncorrespond to the accuracy reported for the research model, and that\nadditional variables may be included in this forecast dataset.\n\nIf you have any questions on using this experimental dataset, or would\nlike to use it for purposes not currently permitted under the terms of use\nset out below, please contact weathernext@google.com.\n\n**Dissemination Schedule**\n\nAll steps are released at once. All times are in the UTC time zone and are\nrough  estimates (with typical ± 15 minutes variance). On occasion, times\nmay vary up to ± 60 minutes or more. If data delivery exceeds +60 minutes,\nplease inform us via weathernext@google.com.\n\nForecast runs (init time) | Forecast dissemination schedule\n:------------------------ | :------------------------------\n00:00                     | 06:45\n06:00                     | 12:45\n12:00                     | 18:45\n18:00                     | 00:45\n\n**Accessing Raw Data (.zarr)**\n\nA bucket containing the raw .zarr files for the historical dataset\n(\"Historic Experimental Data\") is available at `gs://weathernext/59572747_4_0/zarr`. To gain access to\nthe bucket, please request access through the same [WeatherNext Data Request form](https://docs.google.com/forms/d/e/1FAIpQLSeCf1JY8G78UDWzbm0ly9kJxfSjUIJT5WyMR_HiNqCm-IHIBg/viewform?usp=preview),\nand note your interest in receiving access to the raw .zarr files.\n\n**Acknowledgements**\n\nThe experimental data was generated by models which communicate with and/or\nreference the following separate libraries and packages:\n\n*   Data and products of the European Centre for Medium-range Weather Forecasts\n    (ECMWF), as modified by Google.\n*   Modified Copernicus Climate Change Service information 2023. Neither the\n    European Commission nor ECMWF is responsible for any use that may be made of\n    the Copernicus information or data it contains.\n*   ECMWF HRES datasets\n    *   Copyright statement: Copyright \"© 2023 European Centre for Medium-Range\n        Weather Forecasts (ECMWF)\".\n    *   Source: [www.ecmwf.int](http://www.ecmwf.int)\n    *   License Statement: ECMWF open data is published under a Creative Commons\n        Attribution 4.0 International (CC BY 4.0).\n        [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n    *   Disclaimer: ECMWF does not accept any liability whatsoever for any error\n        or omission in the data, their availability, or for any loss or damage\n        arising from their use.\n","deprecated":true,"license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2026-07-29T06:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/59572747_4_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_59572747_4_0","rel":"related","title":"Run the example for projects/gcp-public-data-weathernext/assets/59572747_4_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_59572747_4_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_59572747_4_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_2_0_0_mean.json","rel":"latest-version","title":"projects/gcp-public-data-weathernext/assets/weathernext_2_0_0_mean","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_2_0_0_mean.json","rel":"successor-version","title":"projects/gcp-public-data-weathernext/assets/weathernext_2_0_0_mean","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/59572747_4_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/59572747_4_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/gcp-public-data-weathernext/assets/PARDIG_1_LR","title":"PARDIG LR (Long-Record)","description":"PARDIG (Precipitation AI Reanalysis - Densely Inferred GPM) is an\nexperimental dataset of global AI-driven precipitation estimates by Google\nDeepMind and Google Research, produced by the model described in: *Global\nPrecipitation Estimates Learned from Sparse Satellite Observations*.\n\nPARDIG is produced by an AI model that is trained to predict precipitation\nestimates from the Combined Radar-Radiometer Algorithm (CORRA), part of the\nNASA/JAXA Global Precipitation Measurement (GPM) Core Observatory.\nPARDIG-LR relies on ECMWF's ERA5, NOAA's GridSat-B1, and a global mosaic of\npolar-orbiting (LEO) microwave sounder observations as its inputs. PARDIG\nprecipitation estimates are provided at a 15 minute cadence and 0.1&deg;\nspatial resolution.\n\nThis image collection provides the full historical archive of PARDIG-LR\n(Long-Record) from 1999 through 2023. The dataset is also available in Zarr\nformat in our Google Cloud Storage bucket (`gs://pardig_1/LR/`). The\ncompanion dataset, PARDIG-NRT (Near-Real-Time), is also available on\n[Earth Engine](https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_PARDIG_1_NRT)\nand on Google Cloud Storage (`gs://pardig_1/NRT/`).\n\nBoth datasets are publicly available in the Earth Engine Data Catalog and\non Google Cloud Storage. Users can access them via their Google Cloud\naccount immediately, without needing to explicitly apply for access.\n\nFor more details about PARDIG and related products, visit the\n[Developer Guide](https://developers.google.com/weathernext/guides/pardig).\n\n**Acknowledgements**\n\nThis experimental dataset was generated by a model using data and products\nof the European Centre for Medium-Range Weather Forecasts (ECMWF), as well\nas additional third-party providers. For a full list of attribution and\nacknowledgements, please see our acknowledgements page.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z","2023-12-31T23:45:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/PARDIG_1_LR","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_PARDIG_1_LR","rel":"related","title":"Run the example for projects/gcp-public-data-weathernext/assets/PARDIG_1_LR in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_PARDIG_1_LR_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_PARDIG_1_LR#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/PARDIG_1_LR/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/PARDIG_1_LR/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/gcp-public-data-weathernext/assets/PARDIG_1_NRT","title":"PARDIG NRT (Near-Real-Time)","description":"PARDIG (Precipitation AI Reanalysis - Densely Inferred GPM) is an\nexperimental dataset of global AI-driven precipitation estimates by Google\nDeepMind and Google Research, produced by the model described in: *Global\nPrecipitation Estimates Learned from Sparse Satellite Observations*.\n\nPARDIG is produced by an AI model that is trained to predict precipitation\nestimates from the Combined Radar-Radiometer Algorithm (CORRA), part of the\nNASA/JAXA Global Precipitation Measurement (GPM) Core Observatory.\nPARDIG-NRT relies on ECMWF's HRES and on\n[SatWeave](https://developers.google.com/weathernext/guides/satweave), a\nglobal mosaic of geostationary satellite observations, as its inputs.\nPARDIG precipitation estimates are provided at a 15 minute cadence and\n0.1&deg; spatial resolution.\n\nThis image collection provides a historical archive of PARDIG-NRT\n(Near-Real-Time). Live operational near-real-time updates and the full\nhistorical Zarr datasets are available in our Google Cloud Storage bucket\n(`gs://pardig_1/NRT/`). The companion dataset, PARDIG-LR (Long-Record), is\nalso available on\n[Earth Engine](https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_PARDIG_1_LR)\nand on Google Cloud (`gs://pardig_1/LR/`).\n\nBoth datasets are publicly available in the Earth Engine Data Catalog and\non Google Cloud Storage. Users can access them via their Google Cloud\naccount immediately, without needing to explicitly apply for access.\n\nFor more details about PARDIG and related products, visit the\n[Developer Guide](https://developers.google.com/weathernext/guides/pardig).\n\n**Acknowledgements**\n\nThis experimental dataset was generated by a model using data and products\nof the European Centre for Medium-Range Weather Forecasts (ECMWF), as well\nas additional third-party providers. For a full list of attribution and\nacknowledgements, please see our acknowledgements page.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-01-01T07:45:00Z","2026-10-22T12:30:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/PARDIG_1_NRT","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_PARDIG_1_NRT","rel":"related","title":"Run the example for projects/gcp-public-data-weathernext/assets/PARDIG_1_NRT in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_PARDIG_1_NRT_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_PARDIG_1_NRT#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/PARDIG_1_NRT/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/PARDIG_1_NRT/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/gcp-public-data-weathernext/assets/weathernext_2_0_0","title":"WeatherNext 2","description":"WeatherNext 2 is an experimental dataset of global medium-range ensemble\nweather forecasts produced by an operational version of Google DeepMind's\n[Functional Network Generative weather model](https://arxiv.org/abs/2506.10772).\n\nThe experimental dataset includes real-time and historic data. Real-time\ndata is any data that relates to a time that is no more than 48 hours in\nthe past (\"Real-Time Experimental Data\"), while historic data is any data\nthat relates to a time that is more than 48 hours ago (\"Historic\nExperimental Data\"). This dataset includes major surface fields including\ntemperature, wind, precipitation, humidity, geopotential, sea surface\ntemperature, vertical velocity, and pressure. The spatial resolution is\n0.25 degrees. Forecast init times have 6 hour resolution (00z, 06z, 12z, 18z).\nForecast lead times have 6 hour resolution up to a max lead time of 15 days.\n\nIf you are interested in accessing the experimental dataset, please fill\nout this [WeatherNext Data Request form](https://docs.google.com/forms/d/e/1FAIpQLSeCf1JY8G78UDWzbm0ly9kJxfSjUIJT5WyMR_HiNqCm-IHIBg/viewform).\n\nMore information on the model is described in \"[Skillful joint probabilistic weather forecasting from marginals](https://arxiv.org/abs/2506.10772)\".\nThe model used to produce this experimental dataset is an operational\nversion derived from that research model. Please note that the accuracy\nof this operational model may not exactly correspond to the accuracy\nreported for the research model, and that additional variables may be\nincluded in this forecast dataset.\n\nTo learn more about WeatherNext, including model specifications and\ntechnical documentation, please visit our [WeatherNext developer's guide](http://developers.google.com/weathernext).\n\nIf you have any questions on using this experimental dataset, or would\nlike to use it for purposes not currently permitted under the terms of use\nset out below, please contact weathernext@google.com. Any information collected\nvia email will be used in accordance with [Google's privacy policy](https://policies.google.com/privacy).\n\n\n**Dissemination Schedule**\n\nAll 64 members of the ensemble forecast are released to BigQuery and Earth\nEngine. All members are released at the same time. All times are in the UTC\ntime zone and are rough estimates (with typical ± 15 minutes variance). On\noccasion, times may vary up to ± 60 minutes or more. If data delivery\nexceeds +60 minutes, please inform us via weathernext@google.com.\n\nForecast runs (init time) | Forecast dissemination schedule\n:------------------------ | :------------------------------\n00:00                     | 07:30\n06:00                     | 13:30\n12:00                     | 19:30\n18:00                     | 01:30\n\n**Accessing Raw Data (.zarr)**\n\nA bucket containing the raw .zarr files for the 2022-present historical dataset\n(\"Historic Experimental Data\") is available at `gs://weathernext/weathernext_2_0_0/zarr`.\nTo gain access to those resources, please request access through the same [WeatherNext Data Request form](https://docs.google.com/forms/d/e/1FAIpQLSeCf1JY8G78UDWzbm0ly9kJxfSjUIJT5WyMR_HiNqCm-IHIBg/viewform?usp=preview).\n\n**Acknowledgements**\n\nThe experimental data was generated by models which communicate with and/or\nreference the following separate libraries and packages:\n\n*   Data and products of the European Centre for Medium-range Weather Forecasts\n    (ECMWF), as modified by Google.\n*   Modified Copernicus Climate Change Service information 2023. Neither the\n    European Commission nor ECMWF is responsible for any use that may be made of\n    the Copernicus information or data it contains.\n*   ECMWF HRES datasets\n    *   Copyright statement: Copyright \"© 2023 European Centre for Medium-Range\n        Weather Forecasts (ECMWF)\".\n    *   Source: [www.ecmwf.int](http://www.ecmwf.int)\n    *   License Statement: ECMWF open data is published under a Creative Commons\n        Attribution 4.0 International (CC BY 4.0).\n        [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n    *   Disclaimer: ECMWF does not accept any liability whatsoever for any error\n        or omission in the data, their availability, or for any loss or damage\n        arising from their use.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-01-01T00:00:00Z","2026-11-10T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/weathernext_2_0_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_2_0_0","rel":"related","title":"Run the example for projects/gcp-public-data-weathernext/assets/weathernext_2_0_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_2_0_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_weathernext_2_0_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_2_0_0.json","rel":"latest-version","title":"projects/gcp-public-data-weathernext/assets/weathernext_2_0_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_126478713_1_0.json","rel":"predecessor-version","title":"projects/gcp-public-data-weathernext/assets/126478713_1_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/weathernext_2_0_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/weathernext_2_0_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/gcp-public-data-weathernext/assets/weathernext_2_0_0_mean","title":"WeatherNext 2 Mean","description":"WeatherNext 2 Mean is the **mean of 64 ensembles** of an experimental\ndataset of global medium-range ensemble weather forecasts produced by an\noperational version of Google DeepMind's [Functional Network Generative weather model](https://arxiv.org/abs/2506.10772).\nFor a dataset containing all ensemble members, please see the (non-mean)\n[WeatherNext 2 dataset](projects_gcp-public-data-weathernext_assets_weathernext_2_0_0).\n\nThe experimental dataset includes real-time and historic data. Real-time\ndata is any data that relates to a time that is no more than 48 hours in\nthe past (\"Real-Time Experimental Data\"), while historic data is any data\nthat relates to a time that is more than 48 hours ago (\"Historic\nExperimental Data\"). This dataset includes major surface fields including\ntemperature, wind, precipitation, humidity, geopotential, sea surface\ntemperature, vertical velocity, and pressure. The spatial resolution is\n0.25 degrees. Forecast init times have 6 hour resolution (00z, 06z, 12z, 18z).\nForecast lead times have 6 hour resolution up to a max lead time of 15 days.\n\nIf you are interested in accessing the experimental dataset, please fill\nout this [WeatherNext Data Request form](https://docs.google.com/forms/d/e/1FAIpQLSeCf1JY8G78UDWzbm0ly9kJxfSjUIJT5WyMR_HiNqCm-IHIBg/viewform).\n\nMore information on the model is described in \"[Skillful joint probabilistic weather forecasting from marginals](https://arxiv.org/abs/2506.10772)\".\nThe model used to produce this experimental dataset is an operational\nversion derived from that research model. Please note that the accuracy\nof this operational model may not exactly correspond to the accuracy\nreported for the research model, and that additional variables may be\nincluded in this forecast dataset.\n\nTo learn more about WeatherNext, including model specifications and\ntechnical documentation, please visit our [WeatherNext developer's guide](http://developers.google.com/weathernext).\n\nIf you have any questions on using this experimental dataset, or would\nlike to use it for purposes not currently permitted under the terms of use\nset out below, please contact weathernext@google.com. Any information collected\nvia email will be used in accordance with [Google's privacy policy](https://policies.google.com/privacy).\n\n\n**Dissemination Schedule**\n\nEnsemble mean of all steps are released at once. All times are in the UTC\ntime zone and are rough estimates (with typical ± 15 minutes variance). On\noccasion, times may vary up to ± 60 minutes or more. If data delivery\nexceeds +60 minutes, please inform us via weathernext@google.com.\n\nForecast runs (init time) | Forecast dissemination schedule\n:------------------------ | :------------------------------\n00:00                     | 07:30\n06:00                     | 13:30\n12:00                     | 19:30\n18:00                     | 01:30\n\n**Accessing Raw Data (.zarr)**\n\nA bucket containing the raw .zarr files for the 2022-present historical dataset\n(\"Historic Experimental Data\") is available at `gs://weathernext/weathernext_2_0_0_mean/zarr`.\nTo gain access to those resources, please request access through the same [WeatherNext Data Request form](https://docs.google.com/forms/d/e/1FAIpQLSeCf1JY8G78UDWzbm0ly9kJxfSjUIJT5WyMR_HiNqCm-IHIBg/viewform?usp=preview).\n\n**Acknowledgements**\n\nThe experimental data was generated by models which communicate with and/or\nreference the following separate libraries and packages:\n\n*   Data and products of the European Centre for Medium-range Weather Forecasts\n    (ECMWF), as modified by Google.\n*   Modified Copernicus Climate Change Service information 2023. Neither the\n    European Commission nor ECMWF is responsible for any use that may be made of\n    the Copernicus information or data it contains.\n*   ECMWF HRES datasets\n    *   Copyright statement: Copyright \"© 2023 European Centre for Medium-Range\n        Weather Forecasts (ECMWF)\".\n    *   Source: [www.ecmwf.int](http://www.ecmwf.int)\n    *   License Statement: ECMWF open data is published under a Creative Commons\n        Attribution 4.0 International (CC BY 4.0).\n        [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n    *   Disclaimer: ECMWF does not accept any liability whatsoever for any error\n        or omission in the data, their availability, or for any loss or damage\n        arising from their use.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-01-01T00:00:00Z","2026-11-10T12:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/weathernext_2_0_0_mean","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_2_0_0_mean","rel":"related","title":"Run the example for projects/gcp-public-data-weathernext/assets/weathernext_2_0_0_mean in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_2_0_0_mean_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_weathernext_2_0_0_mean#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_2_0_0_mean.json","rel":"latest-version","title":"projects/gcp-public-data-weathernext/assets/weathernext_2_0_0_mean","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_59572747_4_0.json","rel":"predecessor-version","title":"projects/gcp-public-data-weathernext/assets/59572747_4_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/weathernext_2_0_0_mean/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/weathernext_2_0_0_mean/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/gcp-public-data-weathernext/assets/weathernext_3_0_0_0p05deg","title":"WeatherNext 3 (0.05&deg;)","description":"WeatherNext 3 (0.05&deg;) dataset is an experimental dataset of global\nmedium-range **ensemble weather forecasts** by Google DeepMind and Google\nResearch, produced by an operational version of the model described in:\n[WeatherNext 3: Increasing resolution and performance of global weather\nmodels with raw observations](https://arxiv.org/abs/2609.03582).\n\n**Data Access**\n\nTo request access to the experimental dataset, please submit the\n[WeatherNext Data Request form](https://docs.google.com/forms/d/e/1FAIpQLSeCf1JY8G78UDWzbm0ly9kJxfSjUIJT5WyMR_HiNqCm-IHIBg/viewform).\n\n**Model Highlights**\n\n*   **High resolution & rapid refresh**: 0.05&deg; (~5 km) global spatial\n    resolution for some variables, native 1-hour forecast timesteps,\n    hourly initialization, and lead times extending up to 15 days.\n*   **Real-time observational grounding**: Integrates global geostationary\n    satellite mosaics directly into model inputs, enabling rapid assimilation of\n    current weather patterns.\n*   **Calibrated surface variables**: Calibrated against ground weather\n    station networks for surface temperature/dewpoint and satellite\n    observations (IMERG) for precipitation.\n*   **Solar & energy-focused fields**: Predicts multi-layer cloud cover and\n    downward, direct, and clear-sky solar irradiance to support renewable energy\n    and environmental modeling.\n\nThis Earth Engine collection provides ensemble summary statistics (mean and spread)\nderived from the operational WeatherNext 3 ensemble, optimized for geospatial analysis.\nIt includes station head forecasts for 2m temperature and dew point across six precomputed\nstatistical metrics -- specifically mean, p10, p25, p50, p75, and p90 (total of 12 bands).\n\nForecast Horizon:\n\n*   **6-hourly inits (00, 06, 12, 18 UTC)**: 360-hour (15-day) horizon.\n*   **Interim inits**: 48-hour horizon.\n    *   2026 to present: every hour (01-05, 07-11, 13-17, 19-23 UTC).\n    *   2024-2025: 03, 09, 15, 21 UTC only.\n\nLearn more about WeatherNext [here](https://developers.google.com/weathernext/guides/models).\nFor access to the full ensemble dataset and atmospheric data, go to our\n[Google Cloud Storage bucket](https://developers.google.com/weathernext/guides/gcs).\n\n**Data Availability**\n\nThe experimental dataset includes both real-time and historic data:\n\n*   **Real-Time Experimental Data**: Any data relating to a time more recent than 1 hour ago.\n*   **Historic Experimental Data**: Any data relating to a time 1 hour ago or older.\n\n**More information**\n\n*   **Model guide and specifications**: For more details on WeatherNext,\n    including technical details and specifications, visit the\n    [WeatherNext Models Guide](https://developers.google.com/weathernext/guides/models)\n    and the [WeatherNext Developer Guide](https://developers.google.com/weathernext).\n*   **Research paper**: More information on the underlying research model is\n    available [here](https://arxiv.org/abs/2609.03582). Please note that\n    the accuracy of this operational model may not directly correspond to the\n    accuracy reported for the research model, and that additional variables\n    may be included in this forecast dataset.\n*   **Contact**: For questions or usage outside permitted terms,\n    contact weathernext@google.com. Any information collected via email will\n    be used in accordance with [Google's Privacy Policy](https://policies.google.com/privacy).\n\n**Dissemination Schedule**\n\nFor forecast release timing, expected delivery windows, and latency across platforms,\nsee the [WeatherNext Dissemination Schedule](https://developers.google.com/weathernext/guides/dissemination).\n\n**Acknowledgements**\n\nThis experimental dataset was generated by a model using data and products of\nthe European Centre for Medium-Range Weather Forecasts (ECMWF), as well as\nadditional third-party providers.  For a full list of attribution and acknowledgements,\nplease see our [acknowledgements page](https://storage.googleapis.com/weathernext-public/weathernext-3-attributions-acknowledgements.pdf).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2024-01-01T00:00:00Z","2026-11-10T15:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/weathernext_3_0_0_0p05deg","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_3_0_0_0p05deg","rel":"related","title":"Run the example for projects/gcp-public-data-weathernext/assets/weathernext_3_0_0_0p05deg in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_3_0_0_0p05deg_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_weathernext_3_0_0_0p05deg#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/weathernext_3_0_0_0p05deg/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/weathernext_3_0_0_0p05deg/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/gcp-public-data-weathernext/assets/weathernext_3_0_0_0p1deg","title":"WeatherNext 3 (0.1&deg;)","description":"WeatherNext 3 (0.1&deg;) dataset is an experimental dataset of global\nmedium-range **ensemble weather forecasts** by Google DeepMind and Google\nResearch, produced by an operational version of the model described in:\n[WeatherNext 3: Increasing resolution and performance of global weather\nmodels with raw observations](https://arxiv.org/abs/2609.03582).\n\n**Data Access**\n\nTo request access to the experimental dataset, please submit the\n[WeatherNext Data Request form](https://docs.google.com/forms/d/e/1FAIpQLSeCf1JY8G78UDWzbm0ly9kJxfSjUIJT5WyMR_HiNqCm-IHIBg/viewform).\n\n**Model Highlights**\n\n*   **High resolution & rapid refresh**: 0.1&deg; (~11 km) global spatial\n    resolution, native 1-hour forecast timesteps, hourly initialization,\n    and lead times extending up to 15 days.\n*   **Real-time observational grounding**: Integrates global geostationary\n    satellite mosaics directly into model inputs, enabling rapid assimilation of\n    current weather patterns.\n*   **Calibrated surface variables**: Calibrated against ground weather\n    station networks for surface temperature/dewpoint and satellite\n    observations (IMERG) for precipitation.\n*   **Solar & energy-focused fields**: Predicts multi-layer cloud cover and\n    downward, direct, and clear-sky solar irradiance to support renewable energy\n    and environmental modeling.\n\nThis Earth Engine collection provides ensemble summary statistics (mean and spread)\nderived from the operational WeatherNext 3 ensemble, optimized for geospatial analysis.\nIt includes 19 0.1&deg; gridded surface variables across six precomputed\nstatistical metrics -- specifically mean, p10, p25, p50, p75, and p90 (total of 114 bands).\n\nForecast Horizon:\n\n*   **6-hourly inits (00, 06, 12, 18 UTC)**: 360-hour (15-day) horizon.\n*   **Interim inits**: 48-hour horizon.\n    *   2026 to present: every hour (01-05, 07-11, 13-17, 19-23 UTC).\n    *   2024-2025: 03, 09, 15, 21 UTC only.\n\nLearn more about WeatherNext [here](https://developers.google.com/weathernext/guides/models).\nFor access to the full ensemble dataset and atmospheric data, go to our\n[Google Cloud Storage bucket](https://developers.google.com/weathernext/guides/gcs).\n\n**Data Availability**\n\nThe experimental dataset includes both real-time and historic data:\n\n*   **Real-Time Experimental Data**: Any data relating to a time more recent than 1 hour ago.\n*   **Historic Experimental Data**: Any data relating to a time 1 hour ago or older.\n\n**More information**\n\n*   **Model guide and specifications**: For more details on WeatherNext,\n    including technical details and specifications, visit the\n    [WeatherNext Models Guide](https://developers.google.com/weathernext/guides/models)\n    and the [WeatherNext Developer Guide](https://developers.google.com/weathernext).\n*   **Research paper**: More information on the underlying research model is\n    available [here](https://arxiv.org/abs/2609.03582). Please note that\n    the accuracy of this operational model may not directly correspond to the\n    accuracy reported for the research model, and that additional variables\n    may be included in this forecast dataset.\n*   **Contact**: For questions or usage outside permitted terms,\n    contact weathernext@google.com. Any information collected via email will\n    be used in accordance with [Google's Privacy Policy](https://policies.google.com/privacy).\n\n**Dissemination Schedule**\n\nFor forecast release timing, expected delivery windows, and latency across platforms,\nsee the [WeatherNext Dissemination Schedule](https://developers.google.com/weathernext/guides/dissemination).\n\n**Acknowledgements**\n\nThis experimental dataset was generated by a model using data and products of\nthe European Centre for Medium-Range Weather Forecasts (ECMWF), as well as\nadditional third-party providers.  For a full list of attribution and acknowledgements,\nplease see our [acknowledgements page](https://storage.googleapis.com/weathernext-public/weathernext-3-attributions-acknowledgements.pdf).\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2024-01-01T00:00:00Z","2026-11-10T15:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/weathernext_3_0_0_0p1deg","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_3_0_0_0p1deg","rel":"related","title":"Run the example for projects/gcp-public-data-weathernext/assets/weathernext_3_0_0_0p1deg in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_weathernext_3_0_0_0p1deg_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_weathernext_3_0_0_0p1deg#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/weathernext_3_0_0_0p1deg/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcp-public-data-weathernext/assets/weathernext_3_0_0_0p1deg/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/gcpm041u-lemur/assets/scanfi_v12/SCANFI_v1_2","title":"SCANFI: the Spatialized CAnadian National Forest Inventory data product1.2","description":"This data publication contains a set of 30m resolution raster files representing 2020\nCanadian wall-to-wall maps of broad land cover type, forest canopy height, degree of\ncrown closure and aboveground tree biomass, along with species composition of several\nmajor tree species. The Spatialized CAnadian National Forest Inventory data product (SCANFI)\nwas developed using the newly updated National Forest Inventory photo-plot dataset, which\nconsists of a regular sample grid of photo-interpreted high-resolution imagery covering all\nof Canada’s non-arctic landmass. SCANFI was produced using temporally harmonized summer and\nwinter Landsat spectral imagery along with hundreds of tile-level regional models based on\na novel k-nearest neighbours and random forest imputation method.\n\nA full description of all methods and validation analyses can be found in [Guindon et al. (2024)](https://doi.org/10.1139/cjfr-2023-0118).\nAs the Arctic ecozones are outside NFI’s covered areas, the vegetation attributes in these\nregions were predicted using a single random forest model. The vegetation attributes in these\narctic areas could not be rigorously validated. The \"articExtrapolationArea\" band identifies these zones.\n\nData limitations:\n\n1. The spectral disturbances of some areas disturbed by pests are not comprehensively represented in the\ntraining set, thus making it impossible to predict all defoliation cases. One such area, severely impacted by\nthe recent eastern spruce budworm outbreak, is located on the North Shore of the St-Lawrence River. These\nforests are misrepresented in our training data, there is therefore an imprecision in the estimates.\n\n2. Attributes of open stand classes, namely shrub, herbs, rock and bryoid, are more difficult to estimate\nthrough the photointerpretation of aerial images. Therefore, these estimates could be less reliable than the\nforest attribute estimates.\n\n3. As reported in the [manuscript](https://doi.org/10.1139/cjfr-2023-0118), the uncertainty of tree species cover predictions is relatively high. This\nis particularly true for less abundant tree species, such as ponderosa pine and tamarack. The tree species\nlayers are therefore suitable for regional and coarser scale studies. Also, the broadleaf proportion are\nslightly underestimated in this product version.\n\n4. Our validation indicates that the areas in Yukon exhibit a notably lower R2 value. Consequently, estimates\nwithin these regions are less dependable.\n\n5. Urban areas and roads are classified as rock, according to the 2020 Agriculture and Agri-Food Canada\nland-use classification map. Even though those areas contain mostly buildings and infrastructure, they may\nalso contain trees. Forested urban parks are usually classified as forested areas. Vegetation attributes are\nalso predicted for forested areas in agricultural regions.\n\nThis dataset can also be downloaded from Canada's [\"Open Government\"](https://doi.org/10.23687/18e6a919-53fd-41ce-b4e2-44a9707c52dc) website.\n\nContains information licensed under the\n[Open Government Licence - Canada](https://open.canada.ca/en/open-government-licence-canada)\n","license":"OGL-Canada-2.0","extent":{"spatial":{"bbox":[[-142,41,-52,84]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2021-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/gcpm041u-lemur/assets/scanfi_v12/SCANFI_v1_2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/gcpm041u-lemur/projects_gcpm041u-lemur_assets_scanfi_v12_SCANFI_v1_2","rel":"related","title":"Run the example for projects/gcpm041u-lemur/assets/scanfi_v12/SCANFI_v1_2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/gcpm041u-lemur/projects_gcpm041u-lemur_assets_scanfi_v12_SCANFI_v1_2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_gcpm041u-lemur_assets_scanfi_v12_SCANFI_v1_2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/OGL-Canada-2.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/gcpm041u-lemur/assets/scanfi_v12/SCANFI_v1_2/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/geoscience-aus-cat/assets/NIDEM","title":"National Intertidal Digital Elevation Model 25m 1.0.0 [deprecated]","description":"The National Intertidal Digital Elevation Model (NIDEM; Bishop-Taylor et\nal. 2018, 2019) is a continental-scale elevation dataset for Australia's\nexposed intertidal zone. NIDEM provides the first three-dimensional\nrepresentation of Australia's intertidal sandy beaches and shores, tidal\nflats and rocky shores and reefs at 25 m spatial resolution, addressing a\nkey gap between the availability of sub-tidal bathymetry and terrestrial\nelevation data. NIDEM was generated by combining global tidal modelling with\na 30-year time series archive of spatially and spectrally calibrated Landsat\nsatellite data managed within the Digital Earth Australia (DEA) platform.\nNIDEM complements existing intertidal extent products, and provides data to\nsupport a new suite of use cases that require a more detailed understanding\nof the three-dimensional topography of the intertidal zone, such as\nhydrodynamic modelling, coastal risk management and ecological habitat\nmapping.\n\nFor more information, please see the\n[DEA Intertidal Elevation](https://cmi.ga.gov.au/data-products/dea/325/dea-intertidal-elevation-landsat#basics)\n\nThis product is part of the [Digital Earth Australia Program](https://www.dea.ga.gov.au/)\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[108.81,-44.41,157.82,-9.13]]},"temporal":{"interval":[["1986-08-16T00:00:00Z","2017-07-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/geoscience-aus-cat/assets/NIDEM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/geoscience-aus-cat/projects_geoscience-aus-cat_assets_NIDEM","rel":"related","title":"Run the example for projects/geoscience-aus-cat/assets/NIDEM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/geoscience-aus-cat/projects_geoscience-aus-cat_assets_NIDEM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_geoscience-aus-cat_assets_NIDEM#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.1016/j.ecss.2019.03.006","rel":"cite-as","type":"text/html"},{"href":"https://dapds00.nci.org.au/thredds/catalog/fk4/datacube/002/NIDEM/geotiff/catalog.html","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/geoscience-aus-cat/assets/NIDEM/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/geoscience-aus-cat/assets/ga_ls5t_nbart_gm_cyear_3","title":"DEA Geometric Median and Median Absolute Deviation - Landsat 5 3.1.0 [deprecated]","description":"This product provides statistical tools to exploit the time series of Landsat 5 data available in Digital Earth Australia, providing annual images of general conditions and how much an area changes for a given year.\n\nThe geomedian part of the product provides an \"average\" cloud-free image over the given year. The geomedian image is calculated with a multi-dimensional median, using all the spectral measurements from the satellite imagery at the same time in order to maintain the relationships among the measurements.\n\nThe median absolute deviation part of the product uses three measures of variance, each of which provides a \"second order\" high dimensional statistical composite for the given year. 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Earth observation data recorded over a period of time firstly allows the observation of the state of land cover at a specific time and secondly the way that land cover changes by comparison between times.\n\nFor more information, please see the [DEA Landcover Landsat](https://cmi.ga.gov.au/data-products/dea/607/dea-land-cover-landsat#basics)\n\nThis product is part of the [Digital Earth Australia Program](https://www.dea.ga.gov.au/)\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[108.81,-44.41,157.82,-9.13]]},"temporal":{"interval":[["1988-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/geoscience-aus-cat/assets/ga_ls_landcover_class_cyear_2","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/geoscience-aus-cat/projects_geoscience-aus-cat_assets_ga_ls_landcover_class_cyear_2","rel":"related","title":"Run the example for projects/geoscience-aus-cat/assets/ga_ls_landcover_class_cyear_2 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/geoscience-aus-cat/projects_geoscience-aus-cat_assets_ga_ls_landcover_class_cyear_2_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_geoscience-aus-cat_assets_ga_ls_landcover_class_cyear_2#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.3390/data4040143","rel":"cite-as","type":"text/html"},{"href":"https://doi.org/10.1080/20964471.2021.1948179","rel":"cite-as","type":"text/html"},{"href":"http://dea-public-data.s3-website-ap-southeast-2.amazonaws.com/?prefix=derivative/ga_ls_landcover_class_cyear_2/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/geoscience-aus-cat/assets/ga_ls_landcover_class_cyear_2/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/geoscience-aus-cat/assets/ga_ls_landcover_class_cyear_2/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/geoscience-aus-cat/assets/ga_ls_wo_fq_cyear_3","title":"DEA Water Observations Statistics 3.1.6 [deprecated]","description":"Digital Earth Australia (DEA) Water Observations uses an algorithm to classify each pixel from Landsat satellite imagery as 'wet', 'dry', or 'invalid'. Water Observations Statistics provides information on how many times each year the Landsat satellites were able to clearly see an area, how many times those observations were wet, and what that means for the percentage of time that water was observed in the landscape.\n\nCombining the classified pixels into summaries covering each year gives the information on where water is usually, and where it is rarely. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. \n\nFor more information, please see the [DEA Water Observations Statistics Landsat](https://cmi.ga.gov.au/data-products/dea/686/dea-water-observations-statistics-landsat)\n\nThis product is part of the [Digital Earth Australia Program](https://www.dea.ga.gov.au/)\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[108.81,-44.41,157.82,-9.13]]},"temporal":{"interval":[["1987-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/geoscience-aus-cat/assets/ga_ls_wo_fq_cyear_3","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/geoscience-aus-cat/projects_geoscience-aus-cat_assets_ga_ls_wo_fq_cyear_3","rel":"related","title":"Run the example for projects/geoscience-aus-cat/assets/ga_ls_wo_fq_cyear_3 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/geoscience-aus-cat/projects_geoscience-aus-cat_assets_ga_ls_wo_fq_cyear_3_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_geoscience-aus-cat_assets_ga_ls_wo_fq_cyear_3#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.1016/j.rse.2015.11.003","rel":"cite-as","type":"text/html"},{"href":"https://data.dea.ga.gov.au/?prefix=derivative/ga_ls_wo_fq_cyear_3/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/geoscience-aus-cat/assets/ga_ls_wo_fq_cyear_3/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/geoscience-aus-cat/assets/ga_ls_wo_fq_cyear_3/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/global-pasture-watch/assets/ggc-30m/v1/cultiv-grassland_p","title":"GPW Annual Probabilities of Cultivated Grasslands v1","description":"This dataset provides global annual probability maps of cultivated\ngrassland from 2000 to 2022 at 30-m spatial resolution. \nProduced by the Land &#38;\nCarbon Lab Global Pasture Watch initiative, the mapped grassland extent\nincludes any land cover type, which contains at least 30% of dry or wet\nlow vegetation, dominated by grasses and forbs (less than 3 meters) \nand a:\n\n- maximum of 50% tree canopy cover (greater than 5 meters),\n- maximum of 70% of other woody vegetation (scrubs and open shrubland), and\n- maximum of 50% active cropland cover in mosaic landscapes of cropland\n &#38; other vegetation.\n\nThe grassland extent is classified into two classes:\n- **Cultivated grassland**: Areas where grasses and other forage plants have\nbeen intentionally planted and managed, as well as areas of native \ngrassland-type vegetation where they clearly exhibit active and\nheavy management for specific human-directed uses, such as directed\ngrazing of livestock.\n- **Natural/Semi-natural grassland**: Relatively undisturbed native\ngrasslands/short-height vegetation, such as steppes and tundra, \nas well as areas that have experienced varying degrees of human \nactivity in the past, which may contain a mix of native and \nintroduced species due to historical land use and natural processes. \nIn general, they exhibit natural-looking patterns of varied vegetation \nand clearly ordered hydrological relationships throughout the landscape.\n\nThe implemented methodology considered [GLAD Landsat ARD-2 images \n](https://glad.umd.edu/ard) (processed into cloud-free bi-monthly \naggregates, see [Consoli et al, 2024](https://doi.org/10.7717/peerj.18585)\n), accompanied by climatic, landform and proximity covariates, \nspatiotemporal machine learning (per-class Random Forest) and over \n2.3 million reference samples (visually interpreted in Very High \nResolution imagery). Custom probability thresholds (based on five-fold \nspatial cross-validation and balanced precision and recall values) \nwere used to derive dominant class maps, 0.32 and 0.42 for \ncultivated and natural/semi-natural grassland probability thresholds, respectively.\n\n**Limitations:** Grassland extent is partly under-predicted in southeastern\nAfrica (Zimbabwe and Mozambique) and in eastern Australia (shrublands and\nwoodlands of the Mulga ecoregion). Cropland is misclassified as grassland\nin parts of northern Africa, the Arabian Peninsula, Western Australia, \nNew Zealand, the center of Bolivia, and Mato Grosso state (Brazil). Due \nto the Landsat 7 SLC failure, regular stripes of grassland probabilities \nare visible at parcel-level, particularly in the year 2012. The usage of \ncoarser resolution layers (accessibility maps and MODIS products) \nintroduced curvilinear macroscopic errors (due to the downscaling \nstrategy based on cubicspline) in Uruguay, Southwest Argentina, South \nof Angola and in the Sahel region in Africa. Users need to be aware \nof the limitations and known issues; whilst considering them \ncarefully to ensure appropriate use of maps at this initial prediction \nstage. GPW is working actively to collect systematic feedback via the [Geo-Wiki \nplatform](https://www.geo-wiki.org), validate the current version \nand improve future versions of the dataset.\n\n**For more information see [Parente et. al, 2024](http://doi.org/10.1038/s41597-024-04139-6),\n[Zenodo](https://zenodo.org/records/13890401) and \n[Global Pasture Watch GitHub site](https://github.com/wri/global-pasture-watch)**","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2023-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/ggc-30m/v1/cultiv-grassland_p","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/global-pasture-watch/projects_global-pasture-watch_assets_ggc-30m_v1_cultiv-grassland_p","rel":"related","title":"Run the example for projects/global-pasture-watch/assets/ggc-30m/v1/cultiv-grassland_p in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/global-pasture-watch/projects_global-pasture-watch_assets_ggc-30m_v1_cultiv-grassland_p_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_cultiv-grassland_p#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/ggc-30m/v1/cultiv-grassland_p/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/ggc-30m/v1/cultiv-grassland_p/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/global-pasture-watch/assets/ggc-30m/v1/grassland_c","title":"GPW Annual Dominant Class of Grasslands v1","description":"This dataset provides global annual dominant class maps of grasslands \n(cultivated and natural/semi-natural) from 2000 to 2022 at 30-m spatial \nresolution. \nProduced by the Land &#38;\nCarbon Lab Global Pasture Watch initiative, the mapped grassland extent\nincludes any land cover type, which contains at least 30% of dry or wet\nlow vegetation, dominated by grasses and forbs (less than 3 meters) \nand a:\n\n- maximum of 50% tree canopy cover (greater than 5 meters),\n- maximum of 70% of other woody vegetation (scrubs and open shrubland), and\n- maximum of 50% active cropland cover in mosaic landscapes of cropland\n &#38; other vegetation.\n\nThe grassland extent is classified into two classes:\n- **Cultivated grassland**: Areas where grasses and other forage plants have\nbeen intentionally planted and managed, as well as areas of native \ngrassland-type vegetation where they clearly exhibit active and\nheavy management for specific human-directed uses, such as directed\ngrazing of livestock.\n- **Natural/Semi-natural grassland**: Relatively undisturbed native\ngrasslands/short-height vegetation, such as steppes and tundra, \nas well as areas that have experienced varying degrees of human \nactivity in the past, which may contain a mix of native and \nintroduced species due to historical land use and natural processes. \nIn general, they exhibit natural-looking patterns of varied vegetation \nand clearly ordered hydrological relationships throughout the landscape.\n\nThe implemented methodology considered [GLAD Landsat ARD-2 images \n](https://glad.umd.edu/ard) (processed into cloud-free bi-monthly \naggregates, see [Consoli et al, 2024](https://doi.org/10.7717/peerj.18585)\n), accompanied by climatic, landform and proximity covariates, \nspatiotemporal machine learning (per-class Random Forest) and over \n2.3 million reference samples (visually interpreted in Very High \nResolution imagery). Custom probability thresholds (based on five-fold \nspatial cross-validation and balanced precision and recall values) \nwere used to derive dominant class maps, 0.32 and 0.42 for \ncultivated and natural/semi-natural grassland probability thresholds, respectively.\n\n**Limitations:** Grassland extent is partly under-predicted in southeastern\nAfrica (Zimbabwe and Mozambique) and in eastern Australia (shrublands and\nwoodlands of the Mulga ecoregion). Cropland is misclassified as grassland\nin parts of northern Africa, the Arabian Peninsula, Western Australia, \nNew Zealand, the center of Bolivia, and Mato Grosso state (Brazil). Due \nto the Landsat 7 SLC failure, regular stripes of grassland probabilities \nare visible at parcel-level, particularly in the year 2012. The usage of \ncoarser resolution layers (accessibility maps and MODIS products) \nintroduced curvilinear macroscopic errors (due to the downscaling \nstrategy based on cubicspline) in Uruguay, Southwest Argentina, South \nof Angola and in the Sahel region in Africa. Users need to be aware \nof the limitations and known issues; whilst considering them \ncarefully to ensure appropriate use of maps at this initial prediction \nstage. GPW is working actively to collect systematic feedback via the [Geo-Wiki \nplatform](https://www.geo-wiki.org), validate the current version \nand improve future versions of the dataset.\n\n**For more information see [Parente et. al, 2024](http://doi.org/10.1038/s41597-024-04139-6),\n[Zenodo](https://zenodo.org/records/13890401) and \n[Global Pasture Watch GitHub site](https://github.com/wri/global-pasture-watch)**","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2023-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/ggc-30m/v1/grassland_c","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/global-pasture-watch/projects_global-pasture-watch_assets_ggc-30m_v1_grassland_c","rel":"related","title":"Run the example for projects/global-pasture-watch/assets/ggc-30m/v1/grassland_c in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/global-pasture-watch/projects_global-pasture-watch_assets_ggc-30m_v1_grassland_c_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_grassland_c#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/ggc-30m/v1/grassland_c/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/ggc-30m/v1/grassland_c/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/global-pasture-watch/assets/ggc-30m/v1/nat-semi-grassland_p","title":"GPW Annual Probabilities of Natural/Semi-natural Grasslands v1","description":"This dataset provides global annual probability maps of natural/semi-natural\ngrassland from 2000 to 2022 at 30-m spatial resolution. \nProduced by the Land &#38;\nCarbon Lab Global Pasture Watch initiative, the mapped grassland extent\nincludes any land cover type, which contains at least 30% of dry or wet\nlow vegetation, dominated by grasses and forbs (less than 3 meters) \nand a:\n\n- maximum of 50% tree canopy cover (greater than 5 meters),\n- maximum of 70% of other woody vegetation (scrubs and open shrubland), and\n- maximum of 50% active cropland cover in mosaic landscapes of cropland\n &#38; other vegetation.\n\nThe grassland extent is classified into two classes:\n- **Cultivated grassland**: Areas where grasses and other forage plants have\nbeen intentionally planted and managed, as well as areas of native \ngrassland-type vegetation where they clearly exhibit active and\nheavy management for specific human-directed uses, such as directed\ngrazing of livestock.\n- **Natural/Semi-natural grassland**: Relatively undisturbed native\ngrasslands/short-height vegetation, such as steppes and tundra, \nas well as areas that have experienced varying degrees of human \nactivity in the past, which may contain a mix of native and \nintroduced species due to historical land use and natural processes. \nIn general, they exhibit natural-looking patterns of varied vegetation \nand clearly ordered hydrological relationships throughout the landscape.\n\nThe implemented methodology considered [GLAD Landsat ARD-2 images \n](https://glad.umd.edu/ard) (processed into cloud-free bi-monthly \naggregates, see [Consoli et al, 2024](https://doi.org/10.7717/peerj.18585)\n), accompanied by climatic, landform and proximity covariates, \nspatiotemporal machine learning (per-class Random Forest) and over \n2.3 million reference samples (visually interpreted in Very High \nResolution imagery). Custom probability thresholds (based on five-fold \nspatial cross-validation and balanced precision and recall values) \nwere used to derive dominant class maps, 0.32 and 0.42 for \ncultivated and natural/semi-natural grassland probability thresholds, respectively.\n\n**Limitations:** Grassland extent is partly under-predicted in southeastern\nAfrica (Zimbabwe and Mozambique) and in eastern Australia (shrublands and\nwoodlands of the Mulga ecoregion). Cropland is misclassified as grassland\nin parts of northern Africa, the Arabian Peninsula, Western Australia, \nNew Zealand, the center of Bolivia, and Mato Grosso state (Brazil). Due \nto the Landsat 7 SLC failure, regular stripes of grassland probabilities \nare visible at parcel-level, particularly in the year 2012. The usage of \ncoarser resolution layers (accessibility maps and MODIS products) \nintroduced curvilinear macroscopic errors (due to the downscaling \nstrategy based on cubicspline) in Uruguay, Southwest Argentina, South \nof Angola and in the Sahel region in Africa. Users need to be aware \nof the limitations and known issues; whilst considering them \ncarefully to ensure appropriate use of maps at this initial prediction \nstage. GPW is working actively to collect systematic feedback via the [Geo-Wiki \nplatform](https://www.geo-wiki.org), validate the current version \nand improve future versions of the dataset.\n\n**For more information see [Parente et. al, 2024](http://doi.org/10.1038/s41597-024-04139-6),\n[Zenodo](https://zenodo.org/records/13890401) and \n[Global Pasture Watch GitHub site](https://github.com/wri/global-pasture-watch)**","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2023-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/ggc-30m/v1/nat-semi-grassland_p","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/global-pasture-watch/projects_global-pasture-watch_assets_ggc-30m_v1_nat-semi-grassland_p","rel":"related","title":"Run the example for projects/global-pasture-watch/assets/ggc-30m/v1/nat-semi-grassland_p in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/global-pasture-watch/projects_global-pasture-watch_assets_ggc-30m_v1_nat-semi-grassland_p_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_nat-semi-grassland_p#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/ggc-30m/v1/nat-semi-grassland_p/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/ggc-30m/v1/nat-semi-grassland_p/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/global-pasture-watch/assets/ggpp-30m/v1/ugpp_m","title":"GPW Annual uncalibrated Gross Primary Productivity (uGPP) v1","description":"This dataset provides global uncalibrated EO-based Gross Primary Productivity \nfrom 2000 at 30-m spatial resolution. \nProduced by the Land &#38; Carbon Lab Global Pasture Watch initiative, the current dataset provides \nGross Primary Productivity (GPP) values globally at 30-m spatial resolution from 2000 onwards. \nGPP values are modeled via a **light use efficiency (LUE)** approach, \nwhere [**GLAD Landsat ARD** (collection-2)](https://glad.umd.edu/ard/home) are aggregated every two months \n([Consoli et al., 2024](https://peerj.com/articles/18585/)) and combined with 1-km **MODIS \ntemperature** data and 1° **CERES Photosynthetically Active Radiation** (PAR). \n\nTo keep the dataset flexible, the maximum light use efficiency (LUEmax) is set to 1 gC/m²/day/MJ \nfor **all land cover types**, allowing the users to later calibrate the \nGPP values according to specific land cover maps or regional conditions.\n\n**Bi-monthly uncalibrated Gross Primary Productivity (uGPP)** values (available in [OpenLandMap STAC](https://stac.openlandmap.org/gpw_ugpp.daily-30m/collection.json)) are averaged by each year and accumulated over the full 365-day period to produce\nglobal annual uGPP values, expressed in units of gC/m²/year.\n\n**Grassland GPP** values are computed on-the-fly using [GEE App](https://global-pasture-watch.projects.earthengine.app/view/ggpp-30m).\n\n**Limitations:** \n\n- **Input data resolution mismatch**: The dataset is provided at 30 m resolution, but key input variables for temperature (MOD11A1) \nand photosynthetically active radiation (CERES PAR) were derived from much coarser products (1 km and ~111 km, respectively).\nThe downscaling of this information can introduce uncertainty and may not capture fine-scale microclimatic conditions affecting plant productivity.\n\n- **Data artifacts**: The dataset contains known visual artifacts, including vertical stripes (\"stripe effect\") in some areas, which are a result \nof issues with the Landsat 7 sensor (Scan Line Corrector failure) and the subsequent gap-filling process used to create the underlying \nreflectance archive ([Consoli et al., 2024](https://peerj.com/articles/18585/)). These artifacts can disrupt the spatial continuity \nof GPP estimates during cloudy and snow cover periods\n\n- **Temporal resolution**: The data is produced at a bimonthly temporal resolution. This timeframe may not be sufficient \nto capture key growth periods or a plant's rapid responses (intense rainfall) to environmental changes, making \nit difficult to accurately capture productivity peaks and seasonal variation.\n\n- **Grassland calibration**: Grassland GPP values are calculated using a single maximum light use efficiency (LUEmax) \nparameter (0.86 gC/m²/year/MJ) for all global grasslands, based on the MOD17 algorithm. This value is not optimized \nfor specific grassland types or local conditions. As a result, the model shows a tendency to underestimate GPP when \ncompared to ground-based flux tower measurements.\n\n- **Dependence on grassland maps accuracy**: The accuracy of the grassland GPP values is contingent on the accuracy of the \nunderlying [GPW grassland maps](https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_grassland_c).\nAny misclassification of land cover in the source maps (e.g., shrublands or croplands identified as grassland) will \nlead to corresponding errors in the GPP estimates for those locations.\n\n**For more information see [Isik et. al, 2025](https://doi.org/10.7717/peerj.19774),\n[Zenodo](https://doi.org/10.5281/zenodo.15675358) and \n[Global Pasture Watch GitHub site](https://github.com/wri/global-pasture-watch)**","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2024-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/ggpp-30m/v1/ugpp_m","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/global-pasture-watch/projects_global-pasture-watch_assets_ggpp-30m_v1_ugpp_m","rel":"related","title":"Run the example for projects/global-pasture-watch/assets/ggpp-30m/v1/ugpp_m in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/global-pasture-watch/projects_global-pasture-watch_assets_ggpp-30m_v1_ugpp_m_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggpp-30m_v1_ugpp_m#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/ggpp-30m/v1/ugpp_m/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/ggpp-30m/v1/ugpp_m/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/global-pasture-watch/assets/gld-1km/v1/livestock-headcount-faostat_m","title":"GPW Annual livestock headcount FAOSTAT-adjusted v1","description":"This dataset provides global livestock headcount from 2000 to 2022 at 1-km spatial resolution.\nProduced by the Land &#38; Carbon Lab’s Global Pasture Watch initiative, the current dataset provides global headcount predictions adjusted to FAOSTAT national statistics. The dataset is based on the largest known compilation of subnational livestock census data ([harmonized from 55,336 administrative units across 147 countries](https://doi.org/10.5281/zenodo.17665040)) and is modeled via machine learning (Random Forest), using 128 environmental, socioeconomic, and anthropogenic spatial layers (including terrain elevation, MODIS data, aridity index, accessibility metrics, religious population distribution, etc). Modeling areas were restricted to [annual layer of potential land for livestock production](https://doi.org/10.5281/zenodo.14933679), derived from Landsat-based cropland and grassland extents from 2000 to 2022.\n\nThe dataset is designed to support environmental and agricultural applications (refining GHG emission estimates, modeling nutrient cycles, estimating soil carbon sequestration rates, informing national livestock policies, etc). The final predictions were adjusted using a linear country-by-country scaling approach so that aggregated national totals match the statistics provided by FAOSTAT. This adjustment constrains national totals to FAOSTAT but does not validate where within a country livestock are placed; the subnational allocation is modeled and should be treated as such. For subnational applications requiring high precision, users are encouraged to apply local calibration factors from national agencies rather than relying entirely on the FAOSTAT-adjusted estimates. \n\nEstimates of 95% probability prediction interval values (lower and upper boundaries around mean predictions; 2.5th & 97.5th percentiles) and raw densities and headcount predictions are available in Zenodo:\n\n- [Annual cattle densities](https://doi.org/10.5281/zenodo.17486471)\n- [Annual goat densities](https://doi.org/10.5281/zenodo.17490112)\n- [Annual sheep densities](https://doi.org/10.5281/zenodo.17490692)\n- [Annual horse densities](https://doi.org/10.5281/zenodo.17490457)\n- [Annual buffalo densities](https://doi.org/10.5281/zenodo.17485929)\n\nThe dataset is also available in [OpenLandMap STAC](https://browser.stac.opengeohub.org/cat/landmetric).\n\n**Limitations:** \n\n**Modeled 1 km allocation, not observed livestock locations**: The pixels represent a statistical redistribution of coarse subnational census counts across [annual layer of potential land for livestock production](https://doi.org/10.5281/zenodo.14933679); they do not mark observed herds or farms. Model accuracy was evaluated on held-out census polygons using polygon-mean covariates. As the mean census unit is ~2,900 km² (SD ~21,500), these metrics describe agreement at the administrative-unit level and do not characterize the accuracy of the 1 km detail. Treat the fine-scale pattern as a plausible disaggregation, not an independently validated 1 km estimate.\n\n**FAOSTAT consistency is not local accuracy**: The country-by-country rescaling guarantees that national totals match FAOSTAT by construction; it does not constrain or validate where within a country animals are placed. National totals can therefore be correct while subnational allocation remains uncertain.\n\n**Discontinuities at national borders**: Because headcounts are rescaled to FAOSTAT national totals separately for each country, abrupt changes can appear across international boundaries — for example between Mongolia and its neighbours, where reported national totals differ strongly. These are artifacts of the adjustment, not real density gradients.\n\n**Irregular and incomplete census data**: Livestock census data are often irregular, incomplete, and can be overestimated due to double counting caused by animal mobility across census boundaries or changes in farm ownership. Furthermore, spatial availability varies widely; many developing countries only provide data at coarse administrative scales (state/county level), which may lead to systematic underestimation of undocumented livestock.\n\n**Mismatched input scales and unrealistic densities**: Livestock densities are derived by combining administrative headcount estimates with the [annual layer of potential land for livestock production](https://doi.org/10.5281/zenodo.14933679). In regions dominated by landless livestock systems or feedlots, the high number of livestock may not match the mapped potential land, resulting in unrealistically high density values (locally exceeding 2,500 cattle heads km⁻²).\n\n**Uniform allocation across livestock species**: The intermediate layer representing [potential land for livestock production](https://doi.org/10.5281/zenodo.14933679) is applied uniformly as an input for all modeled livestock species. The current model does not account for the complexities of different livestock management systems (dairy vs. cow-calf vs. finishing systems) or distinct species-specific behaviors, such as goats acting primarily as browsers. \n\n**Underestimation of high-density areas and unreliable prediction intervals**: The selected machine learning models struggle to predict areas with very high livestock density, likely underestimating intense livestock hotspots. The prediction intervals are much narrower than their nominal 95% level implies and understate the true uncertainty. Although labeled as 95% intervals, Prediction Interval Coverage Probability (PICP) was found to be 30-45%, meaning that held-out census-polygon observations fell inside the prediction intervals only about 30-45% of the time. These intervals should not be used as probabilistic bounds for risk thresholds, exceedance probabilities, or downstream error propagation without independent recalibration.\n\n**Temporal coverage vs. validated dynamics**: The layers span 2000–2022 annually, but there is no temporal hold-out validation. Much of the inter-annual signal derives from FAOSTAT national totals and the annually varying land-cover inputs, while the per-pixel density model relies substantially on static and long-term covariates; some socioeconomic inputs are held constant and carried forward (e.g., HDI to 2015, nighttime lights to ~2020). Interpret short-term (year-to-year) change cautiously.\n\n**Interrupted Goode Homolosine projection caveats**: To ensure precise spatial alignment and minimize distortions, all modeling was conducted in an equal-area coordinate system, specifically the Interrupted Goode Homolosine projection (used for all layers in [Zenodo](https://doi.org/10.5281/zenodo.20396303)). Prior to GEE ingestion, the dataset was converted to the EPSG:4326 coordinate system. Because the original headcount data represents absolute values per 1 km², a spatial redistribution approach was used to correct for projection-induced distortions (see [Github](https://github.com/wri/global-pasture-watch/blob/main/gld-1km/workflow/08-reprojection_to_wgs84.py)). This adjustment preserves total national values for consistency with FAOSTAT statistics, though it may slightly shift the local allocation of headcounts within country borders.\n\n**For more information see [Parente et. al, 2026](https://dx.doi.org/10.7717/peerj.21494), [Zenodo](https://doi.org/10.5281/zenodo.20396303) and [Global Pasture Watch GitHub site](https://github.com/wri/global-pasture-watch)**","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2022-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/gld-1km/v1/livestock-headcount-faostat_m","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/global-pasture-watch/projects_global-pasture-watch_assets_gld-1km_v1_livestock-headcount-faostat_m","rel":"related","title":"Run the example for projects/global-pasture-watch/assets/gld-1km/v1/livestock-headcount-faostat_m in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/global-pasture-watch/projects_global-pasture-watch_assets_gld-1km_v1_livestock-headcount-faostat_m_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_gld-1km_v1_livestock-headcount-faostat_m#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/gld-1km/v1/livestock-headcount-faostat_m/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/gld-1km/v1/livestock-headcount-faostat_m/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/global-pasture-watch/assets/gsvh-30m/v1/short-veg-height_m","title":"GPW Annual short vegetation height v1","description":"This dataset provides global median vegetation height\nfrom 2000 at 30-m spatial resolution. \nProduced by the Land &#38; Carbon Lab’s Global Pasture Watch initiative, this dataset provides **median vegetation \nheight** values (50th percentile) globally at 30-m spatial resolution from 2000 onwards. The dataset is based on \n**ICESat-2 ATL08** median height of vegetation returns and is modeled via machine learning (ensemble Gradient Boosted Trees) \nusing the [**GLAD Landsat ARD** (collection-2)](https://glad.umd.edu/ard/home) \naggregated every two months (see [Consoli et al., 2024](https://peerj.com/articles/18585/)) and combined with \nadditional covariates, including **terrain elevation** ([GEDTM30](https://doi.org/10.7717/peerj.19673)),\n**geometric mean temperature**, and **MODIS long-term temperature** and **water vapor**.\n\nOriginally designed to support the monitoring of **open ecosystems** (grasslands, open shrublands, savannas, tundra), the \ndataset offers wall-to-wall coverage of all terrestrial ecosystems. Thus, median height values of forest ecosystems \nshould not be interpreted as top of canopy. For comparison with other existing canopy height products access the app\n[GPW Height Comparison Toolkit (GPW-HCT)](https://gpw-lapig.projects.earthengine.app/view/hct).\n\nEstimates of **90% prediction interval values** (5th & 95th percentiles) are available in [OpenLandMap STAC](https://stac.openlandmap.org/openlandmap/collections/gpw_gsvh-30m_go).\n\n**Per-pixel trend analysis** is computed on-the-fly using [GEE App](https://global-pasture-watch.projects.earthengine.app/view/gsvh-30m).\n\n**Limitations:** \n\n- **Vegetation heterogeneity**: The median height is sensitive to shrubs and trees within landscapes dominated by herbaceous cover. \nUsers focused on herbaceous biomass should consider using fractional cover maps to address sub-pixel mixtures. Future versions \nmay incorporate ICESat-2's vertical structure information for a better separation of woody cover.\n\n- **Sub-annual and seasonal patterns**: Current ICESat-2 data density is insufficient to map seasonal height changes common \nin grassy ecosystems. Increased Lidar acquisitions or denser sampling from ongoing measurement are needed to capture \nmore frequent snapshots, which would improve monitoring of intra-annual changes and disturbances, including grazing, fire, and harvesting.\n\n- **Data restrictions and uncertainty**: The training data was restricted to strong-beam, night-time measurements to minimize \nsignal noise, and additional filters were applied, but this does not fully account for all sources of data uncertainty \nin the ICESat-2 mission. Furthermore, cloud cover and smoke hinder signal penetration, increasing uncertainty in some regions. \nSpecifically for 2019, the model presented slightly poorer performance compared to more recent years, which might be \nrelated to differences in data availability and quality at the beginning of the ICESat-2 mission.\n\n- **Difficulty detecting very short vegetation**: Lidar sensors, including ICESat-2, struggle to accurately measure very \nlow canopies, especially with sparse cover or partial ground occlusion. The training data does not identify vegetation \nless than 50 cm from the ground surface, which may lead to overestimation of height in the shortest or most sparse land \ncovers, such as wetland saltpans.\n\n- **Underestimation of maximum heights**: The trained machine learning model exhibited a tendency toward the mean, which \nresulted in overly narrow and optimistic [prediction intervals](https://stac.openlandmap.org/openlandmap/collections/gpw_gsvh-30m_go). \nDue to the underestimation of vegetation height in the upper extremes, care should be taken when interpreting absolute \nvalues in regions with very tall forests, but relative patterns and trends are still informative.\n\n- **Limited independent validation**: While validated against ICESat-2 data (testing set), broader validation using airborne \nor drone Lidar would be necessary to fully assess the quality of the dataset at regional level.\t\n\n**For more information see [Hunter et al., 2025](https://doi.org/10.1038/s41597-025-05739-6),\n[Zenodo](https://doi.org/10.5281/zenodo.15198654) and \n[Global Pasture Watch GitHub site](https://github.com/wri/global-pasture-watch)**\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/gsvh-30m/v1/short-veg-height_m","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/global-pasture-watch/projects_global-pasture-watch_assets_gsvh-30m_v1_short-veg-height_m","rel":"related","title":"Run the example for projects/global-pasture-watch/assets/gsvh-30m/v1/short-veg-height_m in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/global-pasture-watch/projects_global-pasture-watch_assets_gsvh-30m_v1_short-veg-height_m_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_gsvh-30m_v1_short-veg-height_m#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/gsvh-30m/v1/short-veg-height_m/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-pasture-watch/assets/gsvh-30m/v1/short-veg-height_m/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/global-precipitation-nowcast/assets/global_estimation","title":"Oya: 5km Quasi-Global Precipitation Estimates","description":"\nOya is a quasi-global, high-resolution precipitation estimation dataset\nderived from geostationary (GEO) satellite observations.\n\nThe Oya model utilizes the full spectrum of visible and infrared (VIS-IR)\nchannels from a constellation of GEO satellites — including GOES-16/18,\nMeteosat-9/10, and Himawari-8/9 — to generate precipitation estimates\ncovering 60°N to 60°S.\n\nTo address the inherent data imbalance between rain and no-rain events,\nOya employs a two-stage deep learning approach. It combines two U-Net\nmodels: one specialized for precipitation detection and another for\nQuantitative Precipitation Estimation (QPE). The models are\ntrained using high-resolution GPM Combined Radar-Radiometer Algorithm\n(CORRA) v07 data as ground truth and are pre-trained on IMERG-Final\nretrievals to enhance robustness.\n\nOya demonstrates superior performance compared to existing operational\nGEO-based baselines, such as the PERSIANN DynamicInfrared Rain Rate\n(PDIR-Now) and the Convective Rainfall Rate (CRR), across all precipitation\nintensities. It also outperforms Passive Microwave (PMW) based products like\nthe Integrated Multisatellite Retrievals for GPM (IMERG) Early and is\ncompetitive with research-grade products like IMERG Final, which has a\n3.5-month latency. This dataset includes a half-hourly historical record\ngenerated from 2004 with a spatial resolution of 5km.\n\nLimitations:\n\n* Geographic Decline: Retrieval accuracy is highest in tropical zones and\n  degrades at higher latitudes. This is attributed to geostationary satellite\n  viewing angle effects, specifically limb darkening and parallax shift.\n* Topographic Challenges: The model shows reduced performance in arid and\n  high-altitude regions, notably the Tibetan Plateau.\n* Indirect Observation: As an IR/VIS-based product, Oya infers precipitation\n  from cloud-top properties rather than directly sensing raindrops (as radar\n  or passive microwave instruments do).\n\nGoogle makes no commitment about future updates to this dataset.\n\n**Note: The associated paper, Oya: Deep Learning for Accurate Global\nPrecipitation Estimation, has not yet gone through formal peer review. It\nwould be released on arxiv soon.**\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2004-01-01T00:00:00Z","2026-05-26T05:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/global-precipitation-nowcast/assets/global_estimation","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/global-precipitation-nowcast/projects_global-precipitation-nowcast_assets_global_estimation","rel":"related","title":"Run the example for projects/global-precipitation-nowcast/assets/global_estimation in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/global-precipitation-nowcast/projects_global-precipitation-nowcast_assets_global_estimation_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_global-precipitation-nowcast_assets_global_estimation#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-precipitation-nowcast/assets/global_estimation/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/global-precipitation-nowcast/assets/global_estimation/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11","title":"USFS Landscape Change Monitoring System  v2025-11 (CONUS and OCONUS)","description":"\nThis product is part of the Landscape Change Monitoring System (LCMS) data suite.\nIt shows LCMS-modeled change, land cover, and/or land use classes for each year and\ncovers the Conterminous United States (CONUS) as well as areas outside the CONUS (OCONUS) \nincluding Alaska (AK), Puerto Rico-US Virgin Islands (PRUSVI), and Hawaii (HI).\n\nLCMS is a remote sensing-based system for mapping and monitoring landscape change across the\nUnited States. Its objective is to develop a consistent approach using the latest technology\nand advancements in change detection to produce a \"best available\" map of landscape change. For full LCMS methodology, see Housman et al. (2026).\n\n**Products**\n\nOutputs include three annual products: change, land cover, and land use. The change model output relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS. We apply a ruleset based on ancillary datasets to create the final change product, which is a refinement/reclassification of the modeled change to 15 classes that explicitly provide information on the cause of landscape change (e.g., Tree Removal, Wildfire, Wind). Land cover and land use maps depict life-form level land cover and broad-level land use for each year. \n\nBecause no algorithm performs best in all situations, LCMS uses an ensemble of models as\npredictors, which improves map accuracy across a range of ecosystems and change processes\n(Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer\na holistic depiction of landscape change across the United States since 1985.\n\n**Data and Methods**\n\nPredictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection \nalgorithms, and terrain information. These components are all accessed and processed using Google \nEarth Engine (Gorelick et al., 2017).\n\nTo produce annual composites for LandTrendr, USGS Collection 2 Landsat Tier 1 and Sentinel 2A,\n2B Level-1C top of atmosphere reflectance data were used. The CFmask cloud masking algorithm\n(Foga et al., 2017), which is an implementation of Fmask 2.0 (Zhu and Woodcock, 2012)\n(Landsat-only), cloudScore (Chastain et al., 2019) (Landsat-only), s2cloudless\n(Sentinel-Hub, 2021) and Cloud Score+ (Pasquarella et al., 2023) (Sentinel 2-only) are \nused to mask clouds, while TDOM (Chastain et al., 2019) is used to mask cloud shadows (Landsat and Sentinel\n2). For LandTrendr, the annual medoid is then computed to summarize cloud and cloud shadow-free values \nfrom each year into a single composite. For CCDC, United States Geological Survey (USGS) Collection 2 \nLandsat Tier 1 surface reflectance data were used for the CONUS, and Landsat Tier 1 top of atmosphere \nreflectance data for AK, PRUSVI, and HI. \n\nThe composite time series is temporally segmented using LandTrendr\n(Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018).\n\nAll cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm\n(Zhu and Woodcock, 2014).\n\nPredictor data include raw composite values, LandTrendr fitted values, pair-wise differences,\nsegment duration, change magnitude, and slope, and CCDC sine and\ncosine coefficients (first 3 harmonics), fitted values, and pairwise\ndifferences, along with elevation, slope, sine of aspect, cosine of aspect,\nand topographic position indices (Weiss, 2001) from the 10 m USGS 3D Elevation\nProgram (3DEP) data (U.S. Geological Survey, 2019).\n\nReference data are collected using TimeSync, a web-based tool that helps\nanalysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).\n\nRandom Forest models (Breiman, 2001) were trained using reference data from TimeSync and predictor data from LandTrendr, CCDC, and terrain indices to predict annual change, land cover, and land use classes. Following modeling, we institute a series of probability thresholds and rulesets using ancillary datasets to improve qualitative map outputs and reduce commission and omission. More information can be found in the LCMS Methods Brief included in the Description and in Housman et al. (2026). \n\nMore information can be found in the LCMS Methods Brief and the LCMS companion article included in Additional Resources below.\n\n**Additional Resources**\n\n* [A more detailed code example of using LCMS data](https://github.com/google/earthengine-community/blob/master/datasets/scripts/LCMS_Visualization.js).\n\n* The [LCMS Data Explorer](https://apps.fs.usda.gov/lcms-viewer) is a web-based application that\n  provides users the ability to view, analyze, summarize and download LCMS data.\n\n* The [LCMS Dashboard](https://lcms-dashboard.fs2c.usda.gov/lcms) allows users to quickly summarize areas and download reports.\n\n* Please see the [LCMS Methods Brief](https://data.fs.usda.gov/geodata/rastergateway/LCMS/LCMS_v2025-11_Methods.pdf) and the [LCMS companion article](https://doi.org/10.1038/s41597-026-06743-0) (Housman et al., 2026) for more detailed information regarding methods and accuracy assessment, or the [LCMS Geodata Clearinghouse](https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.php) for data downloads metadata, and support documents.\n\nContact [SM.FS.lcms@usda.gov](mailto:SM.FS.lcms@usda.gov) with any\nquestions or specific data requests.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-135.286387,20.38379,-56.446306,52.459364]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2025-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/gtac-data-publish/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11","rel":"related","title":"Run the example for projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/gtac-data-publish/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_LCMS_v2024-10.json","rel":"predecessor-version","title":"USFS/GTAC/LCMS/v2024-10","type":"application/json"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.php","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/gtac-data-publish/assets/LCMS/Product_Version/2025-11/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/gtac-data-publish/assets/TCC/Product_Version/2025-6","title":"USFS Tree Canopy Cover v2025-6 (CONUS and OCONUS)","description":"\nThe Tree Canopy Cover (TCC) data suite, produced by the United States Department of Agriculture, Forest Service (USFS), are annual remote sensing-based map outputs spanning from 1985-2025. These data support the National Land Cover Database (NLCD) project, which is managed by the US Geological Survey (USGS) as part of the Multi-Resolution Land Characteristics (MRLC) consortium. The project aims to use the latest technology to create a consistent, \"best available\" map of tree canopy cover. Methodology is detailed in Heyer et al. (2025). The geographic scope includes the Conterminous United States (CONUS) and OCONUS regions (Southeast Alaska (SEAK), Hawaii (HI), Puerto Rico, and the US Virgin Islands (PRUSVI)).\n\n**Products**\n\nThe TCC data suite includes three products:\n\n* **Science TCC:** The raw, direct outputs from the model.\n\n* **Science standard error (SE):** The model standard deviation of the predicted values from all regression trees. \n\n* **NLCD TCC:** A refined product derived from the annual Science TCC images. It undergoes post-processing to \nreduce interannual noise, highlight long-term trends, and mask specific features (such as water and non-tree agriculture).  \n\nEach image includes a data mask band that has three values representing areas of no data (0), mapped tree canopy cover (1), and non-processing area (2). The non-processing areas are pixels in the study area with no cloud or cloud shadow-free data. Pixels with no data and non-processing area are masked in TCC and SE images.\n\n**Data and Methods**\n\nWe developed training data and random forest models for CONUS, SEAK, PRUSVI and HI using the USFS Forest Inventory and Analysis (FIA) photo-interpreted TCC as reference data. We leveraged Google Earth Engine (GEE) (Gorelick et al., 2017) to process LandTrendr and terrain predictors. Terrain data from the 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019) include elevation, slope, sine of aspect, and cosine of aspect. For CONUS, we also included the Crop Data Layer (CDL) as a predictor (Lin et al., 2022). \n\nWe utilized USGS Collection 2 Landsat Tier 1 and Sentinel 2A/2B Level-1C top of atmosphere reflectance imagery to produce annual medoid composites. To ensure data quality, we applied various algorithms to mask clouds and shadows, \nincluding cFmask (Foga et al., 2017; Zhu and Woodcock, 2012), cloudScore (Chastain et al., 2019), s2cloudless (Sentinel-Hub, 2021), Cloud Score+ (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019). Once masked, we computed annual medoids to create a single cloud-free composite for each year. Finally, the composite time series was temporally segmented using LandTrendr (Kennedy et al., 2010, 2018; Cohen et al., 2018). \n\nFor CONUS, we used 70% of the reference data for calibration and 30% for independent error assessment. Given the ecological diversity of CONUS, we divided the modeling area into 54 tiles (480 km × 480 km). On local computers, we built a unique random forest model for each tile (Breiman, 2001), training it on reference data intersecting a 5×5 window around the center tile. The models were then deployed in GEE to predict wall-to-wall TCC. For OCONUS regions, we used an 80/20 split, and developed a single random forest model for each region. \n\nFor the full TCC methodology, see Heyer et al. (2025).\n\n**Additional Resources**\n\n* Please see the [TCC Methods Brief](https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/docs/TCC_v2025-6_Methods.pdf) or \n[Science of Remote Sensing journal article](https://www.sciencedirect.com/science/article/pii/S2666017225001075)\nfor detailed information regarding methods and accuracy assessment. \n\n* Please see the [TCC Geodata Clearinghouse](https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/)\nfor data downloads, metadata, and support documents.\n\nContact [SM.FS.TCC@usda.gov] (mailto:SM.FS.TCC@usda.gov) with any\nquestions or specific data requests.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-135.286387,20.38379,-56.446306,52.459364]]},"temporal":{"interval":[["1985-06-01T00:00:00Z","2025-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/gtac-data-publish/assets/TCC/Product_Version/2025-6","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/gtac-data-publish/projects_gtac-data-publish_assets_TCC_Product_Version_2025-6","rel":"related","title":"Run the example for projects/gtac-data-publish/assets/TCC/Product_Version/2025-6 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/gtac-data-publish/projects_gtac-data-publish_assets_TCC_Product_Version_2025-6_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_gtac-data-publish_assets_TCC_Product_Version_2025-6#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_TCC_Product_Version_2025-6.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/TCC/Product_Version/2025-6","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_NLCD_RELEASES_2023_REL_TCC_v2023-5.json","rel":"predecessor-version","title":"USGS/NLCD_RELEASES/2023_REL/TCC/v2023-5","type":"application/json"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/gtac-data-publish/assets/TCC/Product_Version/2025-6/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/gtac-data-publish/assets/TCC/Product_Version/2025-6/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_1_2001_2023","title":"WRI/Google DeepMind Global Drivers of Forest Loss 2001-2023 v1.1","description":"This dataset maps the dominant driver of tree cover loss from 2001-2023 globally at 1 km resolution. \nProduced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using \na global neural network model (ResNet) trained on a set of samples collected through visual \ninterpretation of very high-resolution satellite imagery. The model used satellite imagery \n(Landsat 7 & 8, Sentinel-2) and ancillary data to classify seven driver categories: \npermanent agriculture, hard commodities, shifting cultivation, logging, wildfires, \nsettlements and infrastructure, and other natural disturbances. An independent stratified random \nsample collected through interpretation of very high resolution satellite imagery was used to \nestimate the accuracy of the map. \n\nA *driver* is defined as the direct cause of tree cover loss, and can include both temporary \ndisturbances (natural or anthropogenic) or permanent loss of tree cover due to a change to \na non-forest land use (e.g., deforestation).  The *dominant* driver is defined as the \ndirect driver that caused the majority of tree cover loss within each 1 km cell over the time \nperiod. Classes are defined as follows:\n\n* Permanent agriculture: Long-term, permanent tree cover loss for small- to large-scale agriculture. \nThis includes perennial tree crops, as well as pasture and seasonal crops and cropping systems, \nwhich may include a fallow period. Agricultural activities are considered \"permanent\" if there \nis visible evidence that they persist following the tree cover loss event and are not a part of \na temporary cultivation cycle. \n* Hard commodities: Loss due to the establishment or expansion of mining or energy infrastructure.\n* Shifting cultivation: Tree cover loss due to small- to medium-scale clearing for temporary \ncultivation that is later abandoned and followed by subsequent regrowth of secondary forest or \nvegetation.\n* Logging: Forest management and logging activities occurring within managed, natural or semi-natural \nforests and plantations, often with evidence of forest regrowth or planting in subsequent years. \nIncludes clear-cut and selective logging, establishment of logging roads, forest thinning, and \nsalvage or sanitation logging. \n* Wildfire: Tree cover loss due to fire with no visible human conversion or agricultural activity \nafterward. Fires may be started by natural causes (e.g. lightning) or may be related to human \nactivities (accidental or deliberate).\n* Settlements and infrastructure: Tree cover loss due to expansion and intensification of roads, \nsettlements, urban areas, or built infrastructure (not associated with other classes).\n* Other natural disturbances: Tree cover loss due to other non-fire natural disturbances \n(e.g., landslides, insect outbreaks, river meandering). If loss due to natural causes is followed \nby salvage or sanitation logging, it is classified as logging.\n\n**Limitations**: This product does not distinguish between the loss of *natural forest* and *planted \ntrees* (e.g., plantations, tree crops, or agroforestry systems). While tree cover loss associated \nwith the *permanent agriculture*, *hard commodities*, and *settlements and infrastructure* classes represent \na close approximation of deforestation (permanent conversion of forest to another land use), these \nclasses may sometimes include the clearing of planted trees. For example, clearing and replanting an \norchard would be included in the *permanent agriculture* class, but is not deforestation of a natural forest. \nSimilarly, replacement of natural forest with wood fiber plantations is not distinguished from routine \nharvesting within existing plantations established before 2000, as these are both included in the logging \nclass. \n\nThis product shows the dominant driver in each 1km cell over the entire period. It does not show \nmultiple drivers if they occur in the same cell at smaller scales, nor does it detail the sequence of \ndrivers if multiple occurred at different times within the period. Additionally, these data are limited \nin scope to attributing drivers to tree cover loss as mapped by the \n[Global Forest Change v1.11](https://www.science.org/doi/10.1126/science.1244693) tree cover loss product, \nand therefore the detection of loss is subject to the accuracy of that product. \n\n**For a full description of the methods, technical specifications, definitions, accuracy, and \nlimitations, please see the publication**: \n[https://doi.org/10.1088/1748-9326/add606](https://doi.org/10.1088/1748-9326/add606). \nThe data is also available for download on [Zenodo](https://zenodo.org/records/15225267) and \nthe [WRI Data Explorer](https://datasets.wri.org/datasets/dominant-drivers-of-tree-cover-loss-at-1km-v1-1).  \n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2024-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_1_2001_2023","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/landandcarbon/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_1_2001_2023","rel":"related","title":"Run the example for projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_1_2001_2023 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/landandcarbon/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_1_2001_2023_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_1_2001_2023#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_1_2001_2023/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_2001_2022","title":"WRI/Google DeepMind Global Drivers of Forest Loss 2001-2022 v1.0","description":"This dataset maps the dominant driver of tree cover loss from 2001-2022 globally at 1 km resolution. \nProduced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using \na global neural network model (ResNet) trained on a set of samples collected through visual \ninterpretation of very high-resolution satellite imagery. The model used satellite imagery \n(Landsat 7 & 8, Sentinel-2) and ancillary data to classify seven driver categories: \npermanent agriculture, hard commodities, shifting cultivation, logging, wildfires, \nsettlements and infrastructure, and other natural disturbances. An independent stratified random \nsample collected through interpretation of very high resolution satellite imagery was used to \nestimate the accuracy of the map. \n\nA *driver* is defined as the direct cause of tree cover loss, and can include both temporary \ndisturbances (natural or anthropogenic) or permanent loss of tree cover due to a change to \na non-forest land use (e.g., deforestation).  The *dominant* driver is defined as the \ndirect driver that caused the majority of tree cover loss within each 1 km cell over the time \nperiod. Classes are defined as follows:\n\n* Permanent agriculture: Long-term, permanent tree cover loss for small- to large-scale agriculture. \nThis includes perennial tree crops, as well as pasture and seasonal crops and cropping systems, \nwhich may include a fallow period. Agricultural activities are considered \"permanent\" if there \nis visible evidence that they persist following the tree cover loss event and are not a part of \na temporary cultivation cycle. \n* Hard commodities: Loss due to the establishment or expansion of mining or energy infrastructure.\n* Shifting cultivation: Tree cover loss due to small- to medium-scale clearing for temporary \ncultivation that is later abandoned and followed by subsequent regrowth of secondary forest or \nvegetation.\n* Logging: Forest management and logging activities occurring within managed, natural or semi-natural \nforests and plantations, often with evidence of forest regrowth or planting in subsequent years. \nIncludes clear-cut and selective logging, establishment of logging roads, forest thinning, and \nsalvage or sanitation logging. \n* Wildfire: Tree cover loss due to fire with no visible human conversion or agricultural activity \nafterward. Fires may be started by natural causes (e.g. lightning) or may be related to human \nactivities (accidental or deliberate).\n* Settlements and infrastructure: Tree cover loss due to expansion and intensification of roads, \nsettlements, urban areas, or built infrastructure (not associated with other classes).\n* Other natural disturbances: Tree cover loss due to other non-fire natural disturbances \n(e.g., landslides, insect outbreaks, river meandering). If loss due to natural causes is followed \nby salvage or sanitation logging, it is classified as logging.\n\n**Limitations**: This product does not distinguish between the loss of *natural forest* and *planted \ntrees* (e.g., plantations, tree crops, or agroforestry systems). While tree cover loss associated \nwith the *permanent agriculture*, *hard commodities*, and *settlements and infrastructure* classes represent \na close approximation of deforestation (permanent conversion of forest to another land use), these \nclasses may sometimes include the clearing of planted trees. For example, clearing and replanting an \norchard would be included in the *permanent agriculture* class, but is not deforestation of a natural forest. \nSimilarly, replacement of natural forest with wood fiber plantations is not distinguished from routine \nharvesting within existing plantations established before 2000, as these are both included in the logging \nclass. \n\nThis product shows the dominant driver in each 1km cell over the entire period. It does not show \nmultiple drivers if they occur in the same cell at smaller scales, nor does it detail the sequence of \ndrivers if multiple occurred at different times within the period. Additionally, these data are limited \nin scope to attributing drivers to tree cover loss as mapped by the \n[Global Forest Change v1.10](https://www.science.org/doi/10.1126/science.1244693) tree cover loss product, \nand therefore the detection of loss is subject to the accuracy of that product. \n\n**For a full description of the methods, technical specifications, definitions, accuracy, and \nlimitations, please see the publication**: \n[https://doi.org/10.1088/1748-9326/add606](https://doi.org/10.1088/1748-9326/add606). \nThe data is also available for download on [Zenodo](https://zenodo.org/records/15224684) and \nthe [WRI Data Explorer](https://datasets.wri.org/datasets/dominant-drivers-of-tree-cover-loss-at-1km-v1-0).  \n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2023-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_2001_2022","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/landandcarbon/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2001_2022","rel":"related","title":"Run the example for projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_2001_2022 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/landandcarbon/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2001_2022_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2001_2022#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_2001_2022/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_2_2001_2024","title":"WRI/Google DeepMind Global Drivers of Forest Loss 2001-2024 v1.2","description":"This dataset maps the dominant driver of tree cover loss from 2001-2024 globally at 1 km resolution. \nProduced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using \na global neural network model (ResNet) trained on a set of samples collected through visual \ninterpretation of very high-resolution satellite imagery. The model used satellite imagery \n(Landsat 7 & 8, Sentinel-2) and ancillary data to classify seven driver categories: \npermanent agriculture, hard commodities, shifting cultivation, logging, wildfires, \nsettlements and infrastructure, and other natural disturbances. An independent stratified random \nsample collected through interpretation of very high resolution satellite imagery was used to \nestimate the accuracy of the map. \n\nA *driver* is defined as the direct cause of tree cover loss, and can include both temporary \ndisturbances (natural or anthropogenic) or permanent loss of tree cover due to a change to \na non-forest land use (e.g., deforestation).  The *dominant* driver is defined as the \ndirect driver that caused the majority of tree cover loss within each 1 km cell over the time \nperiod. Classes are defined as follows:\n\n* Permanent agriculture: Long-term, permanent tree cover loss for small- to large-scale agriculture. \nThis includes perennial tree crops, as well as pasture and seasonal crops and cropping systems, \nwhich may include a fallow period. Agricultural activities are considered \"permanent\" if there \nis visible evidence that they persist following the tree cover loss event and are not a part of \na temporary cultivation cycle. \n* Hard commodities: Loss due to the establishment or expansion of mining or energy infrastructure.\n* Shifting cultivation: Tree cover loss due to small- to medium-scale clearing for temporary \ncultivation that is later abandoned and followed by subsequent regrowth of secondary forest or \nvegetation.\n* Logging: Forest management and logging activities occurring within managed, natural or semi-natural \nforests and plantations, often with evidence of forest regrowth or planting in subsequent years. \nIncludes clear-cut and selective logging, establishment of logging roads, forest thinning, and \nsalvage or sanitation logging. \n* Wildfire: Tree cover loss due to fire with no visible human conversion or agricultural activity \nafterward. Fires may be started by natural causes (e.g. lightning) or may be related to human \nactivities (accidental or deliberate).\n* Settlements and infrastructure: Tree cover loss due to expansion and intensification of roads, \nsettlements, urban areas, or built infrastructure (not associated with other classes).\n* Other natural disturbances: Tree cover loss due to other non-fire natural disturbances \n(e.g., landslides, insect outbreaks, river meandering). If loss due to natural causes is followed \nby salvage or sanitation logging, it is classified as logging.\n\n**Limitations**: This product does not distinguish between the loss of *natural forest* and *planted \ntrees* (e.g., plantations, tree crops, or agroforestry systems). While tree cover loss associated \nwith the *permanent agriculture*, *hard commodities*, and *settlements and infrastructure* classes represent \na close approximation of deforestation (permanent conversion of forest to another land use), these \nclasses may sometimes include the clearing of planted trees. For example, clearing and replanting an \norchard would be included in the *permanent agriculture* class, but is not deforestation of a natural forest. \nSimilarly, replacement of natural forest with wood fiber plantations is not distinguished from routine \nharvesting within existing plantations established before 2000, as these are both included in the logging \nclass. \n\nThis product shows the dominant driver in each 1km cell over the entire period. It does not show \nmultiple drivers if they occur in the same cell at smaller scales, nor does it detail the sequence of \ndrivers if multiple occurred at different times within the period. Additionally, these data are limited \nin scope to attributing drivers to tree cover loss as mapped by the \n[Global Forest Change v1.12](https://www.science.org/doi/10.1126/science.1244693) tree cover loss product, \nand therefore the detection of loss is subject to the accuracy of that product. \n\n**For a full description of the methods, technical specifications, definitions, accuracy, and \nlimitations, please see the publication**: \n[https://doi.org/10.1088/1748-9326/add606](https://doi.org/10.1088/1748-9326/add606). \nThe data is also available for download on [Zenodo](https://zenodo.org/records/15366671) and \nthe [WRI Data Explorer](https://datasets.wri.org/datasets/dominant-drivers-of-tree-cover-loss-at-1km-v1-2).  \n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_2_2001_2024","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/landandcarbon/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2_2001_2024","rel":"related","title":"Run the example for projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_2_2001_2024 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/landandcarbon/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2_2001_2024_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2_2001_2024#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_2_2001_2024/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_3_2001_2025","title":"WRI/Google DeepMind Global Drivers of Forest Loss 2001-2025 v1.3","description":"This dataset maps the dominant driver of tree cover loss from 2001-2025 globally at 1 km resolution. \nProduced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using \na global neural network model (ResNet) trained on a set of samples collected through visual \ninterpretation of very high-resolution satellite imagery. The model used satellite imagery \n(Landsat 7 & 8, Sentinel-2) and ancillary data to classify seven driver categories: \npermanent agriculture, hard commodities, shifting cultivation, logging, wildfires, \nsettlements and infrastructure, and other natural disturbances. An independent stratified random \nsample collected through interpretation of very high resolution satellite imagery was used to \nestimate the accuracy of the map. \n\nA *driver* is defined as the direct cause of tree cover loss, and can include both temporary \ndisturbances (natural or anthropogenic) or permanent loss of tree cover due to a change to \na non-forest land use (e.g., deforestation).  The *dominant* driver is defined as the \ndirect driver that caused the majority of tree cover loss within each 1 km cell over the time \nperiod. Classes are defined as follows:\n\n* Permanent agriculture: Long-term, permanent tree cover loss for small- to large-scale agriculture. \nThis includes perennial tree crops, as well as pasture and seasonal crops and cropping systems, \nwhich may include a fallow period. Agricultural activities are considered \"permanent\" if there \nis visible evidence that they persist following the tree cover loss event and are not a part of \na temporary cultivation cycle. \n* Hard commodities: Loss due to the establishment or expansion of mining or energy infrastructure.\n* Shifting cultivation: Tree cover loss due to small- to medium-scale clearing for temporary \ncultivation that is later abandoned and followed by subsequent regrowth of secondary forest or \nvegetation.\n* Logging: Forest management and logging activities occurring within managed, natural or semi-natural \nforests and plantations, often with evidence of forest regrowth or planting in subsequent years. \nIncludes clear-cut and selective logging, establishment of logging roads, forest thinning, and \nsalvage or sanitation logging. \n* Wildfire: Tree cover loss due to fire with no visible human conversion or agricultural activity \nafterward. Fires may be started by natural causes (e.g. lightning) or may be related to human \nactivities (accidental or deliberate).\n* Settlements and infrastructure: Tree cover loss due to expansion and intensification of roads, \nsettlements, urban areas, or built infrastructure (not associated with other classes).\n* Other natural disturbances: Tree cover loss due to other non-fire natural disturbances \n(e.g., landslides, insect outbreaks, river meandering). If loss due to natural causes is followed \nby salvage or sanitation logging, it is classified as logging.\n\n**Limitations**: This product does not distinguish between the loss of *natural forest* and *planted \ntrees* (e.g., plantations, tree crops, or agroforestry systems). While tree cover loss associated \nwith the *permanent agriculture*, *hard commodities*, and *settlements and infrastructure* classes represent \na close approximation of deforestation (permanent conversion of forest to another land use), these \nclasses may sometimes include the clearing of planted trees. For example, clearing and replanting an \norchard would be included in the *permanent agriculture* class, but is not deforestation of a natural forest. \nSimilarly, replacement of natural forest with wood fiber plantations is not distinguished from routine \nharvesting within existing plantations established before 2000, as these are both included in the logging \nclass. \n\nThis product shows the dominant driver in each 1km cell over the entire period. It does not show \nmultiple drivers if they occur in the same cell at smaller scales, nor does it detail the sequence of \ndrivers if multiple occurred at different times within the period. Additionally, these data are limited \nin scope to attributing drivers to tree cover loss as mapped by the \n[Global Forest Change v1.13](https://www.science.org/doi/10.1126/science.1244693) tree cover loss product, \nand therefore the detection of loss is subject to the accuracy of that product. \n\n**For a full description of the methods, technical specifications, definitions, accuracy, and \nlimitations, please see the publication**: \n[https://doi.org/10.1088/1748-9326/add606](https://doi.org/10.1088/1748-9326/add606). \nThe data is also available for download on [Zenodo](https://zenodo.org/records/19485190) and \nthe [WRI Data Explorer](https://datasets.wri.org/datasets/dominant-drivers-of-tree-cover-loss-at-1km).  \n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-01-01T00:00:00Z","2026-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_3_2001_2025","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/landandcarbon/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_3_2001_2025","rel":"related","title":"Run the example for projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_3_2001_2025 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/landandcarbon/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_3_2001_2025_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_3_2001_2025#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/landandcarbon/assets/wri_gdm_drivers_forest_loss_1km/v1_3_2001_2025/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/malariaatlasproject/assets/EVI_v061/1km/8-Daily","title":"EVI: Malaria Atlas Project Gap-Filled Enhanced Vegetation Index (8-Daily 1km)","description":"The underlying dataset for this Enhanced Vegetation Index (EVI)\nproduct is MODIS BRDF-corrected imagery (MCD43B4), which was gap-filled\nusing the approach outlined in Weiss et al. (2014) to eliminate missing\ndata caused by factors such as cloud cover. After gap-filling the data was clipped to thresholds of [0, 1] to ensure valid values. \n\nThe gap-filled 8-daily ~1km outputs are then aggregated temporally to produce monthly and annual products.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-02-01T00:00:00Z","2024-12-26T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/EVI_v061/1km/8-Daily","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/malariaatlasproject/projects_malariaatlasproject_assets_EVI_v061_1km_8-Daily","rel":"related","title":"Run the example for projects/malariaatlasproject/assets/EVI_v061/1km/8-Daily in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/malariaatlasproject/projects_malariaatlasproject_assets_EVI_v061_1km_8-Daily_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_malariaatlasproject_assets_EVI_v061_1km_8-Daily#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/EVI_v061/1km/8-Daily/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/EVI_v061/1km/8-Daily/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/malariaatlasproject/assets/EVI_v061/1km/Annual","title":"EVI: Malaria Atlas Project Gap-Filled Enhanced Vegetation Index (Annual 1km)","description":"The underlying dataset for this Enhanced Vegetation Index (EVI)\nproduct is MODIS BRDF-corrected imagery (MCD43B4), which was gap-filled\nusing the approach outlined in Weiss et al. (2014) to eliminate missing\ndata caused by factors such as cloud cover. After gap-filling the data was clipped to thresholds of [0, 1] to ensure valid values. \n\nThe gap-filled 8-daily ~1km outputs are then aggregated temporally to produce monthly and annual products.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-02-01T00:00:00Z","2024-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/EVI_v061/1km/Annual","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/malariaatlasproject/projects_malariaatlasproject_assets_EVI_v061_1km_Annual","rel":"related","title":"Run the example for projects/malariaatlasproject/assets/EVI_v061/1km/Annual in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/malariaatlasproject/projects_malariaatlasproject_assets_EVI_v061_1km_Annual_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_malariaatlasproject_assets_EVI_v061_1km_Annual#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/EVI_v061/1km/Annual/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/EVI_v061/1km/Annual/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/malariaatlasproject/assets/EVI_v061/1km/Monthly","title":"EVI: Malaria Atlas Project Gap-Filled Enhanced Vegetation Index (Monthly 1km)","description":"The underlying dataset for this Enhanced Vegetation Index (EVI)\nproduct is MODIS BRDF-corrected imagery (MCD43B4), which was gap-filled\nusing the approach outlined in Weiss et al. (2014) to eliminate missing\ndata caused by factors such as cloud cover. After gap-filling the data was clipped to thresholds of [0, 1] to ensure valid values. \n\nThe gap-filled 8-daily ~1km outputs are then aggregated temporally to produce monthly and annual products.\n","license":"CC-BY-NC-SA-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2001-02-01T00:00:00Z","2024-12-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/EVI_v061/1km/Monthly","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/malariaatlasproject/projects_malariaatlasproject_assets_EVI_v061_1km_Monthly","rel":"related","title":"Run the example for projects/malariaatlasproject/assets/EVI_v061/1km/Monthly in the Earth Engine Code 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The underlying\ndatasets used to produce the map include roads (comprising the\nfirst ever global-scale use of Open Street Map and Google roads\ndatasets), railways, rivers, lakes, oceans, topographic conditions\n(slope and elevation), landcover types, and national borders.\n\nThese datasets were each allocated a speed or speeds of travel in terms\nof time to cross each pixel of that type. The datasets were then\ncombined to produce a \"friction surface\": a map where\nevery pixel is allocated a nominal overall speed of travel based\non the types occurring within that pixel. Least-cost-path algorithms\n(running in Google Earth Engine and, for high-latitude areas, in\nR) were used in conjunction with this friction surface to calculate\nthe time of travel from all locations to the nearest city (by travel\ntime). Cities were determined using the high-density-cover product\ncreated by the Global Human Settlement Project.\n\nEach pixel in the resultant accessibility map thus represents the modeled \nshortest time from that location to a city.\n\nSource dataset credits are as described in the accompanying paper.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2016-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/accessibility/accessibility_to_cities/2015_v1_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_accessibility_to_cities_2015_v1_0","rel":"related","title":"Run the example for projects/malariaatlasproject/assets/accessibility/accessibility_to_cities/2015_v1_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_accessibility_to_cities_2015_v1_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_malariaatlasproject_assets_accessibility_accessibility_to_cities_2015_v1_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_accessibility_to_cities_2015_v1_0.json","rel":"latest-version","title":"projects/malariaatlasproject/assets/accessibility/accessibility_to_cities/2015_v1_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/Oxford/Oxford_MAP_accessibility_to_cities_2015_v1_0.json","rel":"predecessor-version","title":"Oxford/MAP/accessibility_to_cities_2015_v1_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/accessibility/accessibility_to_cities/2015_v1_0/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/malariaatlasproject/assets/accessibility/accessibility_to_healthcare/2019","title":"Malaria Atlas Project Accessibility to Healthcare 2019","description":"This global accessibility map enumerates land-based travel time (in\nminutes) to the nearest hospital or clinic for all areas between 85\ndegrees north and 60 degrees south for a nominal year 2019. \n\nMajor data collection efforts underway by OpenStreetMap, Google Maps, and\nacademic researchers have been harnessed to compile the most complete\ncollection of healthcare facility locations to date. This map was\nproduced through a collaboration between MAP (University of Oxford),\nTelethon Kids Institute (Perth, Australia), Google, and the University\nof Twente, Netherlands.\n\nThis project builds on previous work published by Weiss et al 2018\n([doi:10.1038/nature25181](https://doi.org/10.1038/nature25181)).\nWeiss et al (2018) utilising datasets for roads\n(comprising the first ever global-scale use of Open Street Map and Google\nroads datasets), railways, rivers, lakes, oceans, topographic conditions\n(slope and elevation), landcover types, and national borders. These\ndatasets were each allocated a speed or speeds of travel in terms of time\nto cross each pixel of that type. The datasets were then combined to\nproduce a \"friction surface\": a map where every pixel is allocated a\nnominal overall speed of travel based on the types occurring within that\npixel. For the current project, an updated friction surface was created to\nincorporate recent improvements within OSM roads data.\n\nLeast-cost-path algorithms (run in Google Earth Engine and, for\nhigh-latitude areas, in R) were used in conjunction with this friction\nsurface to calculate the time of travel from all locations to the nearest\n(in time) healthcare facility. The healthcare facilities dataset utilised\nlocation data from two of the largest global databases: (1) OSM data that\nwas collated and made available for download at\n[www.healthsites.io](https://www.healthsites.io/); and (2) data\nextracted from Google Maps. The global datasets were augmented with\ncontinental-scale facility locations that were recently published for\nAfrica and Australia. To facilitate comparisons between data sources, only\nfacilities defined as hospitals and clinics were used. Multiple points\nfound within the same pixel were merged to match the resolution of the\nanalysis as defined by the selected gridded representation of the Earth's\nsurface. Each pixel in the resultant accessibility map thus represents the\nmodelled shortest time (in minutes) from that location to a hospital or\nclinic.\n\nSource dataset credits are as described in the accompanying paper.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2019-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/accessibility/accessibility_to_healthcare/2019","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_accessibility_to_healthcare_2019","rel":"related","title":"Run the example for projects/malariaatlasproject/assets/accessibility/accessibility_to_healthcare/2019 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_accessibility_to_healthcare_2019_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_malariaatlasproject_assets_accessibility_accessibility_to_healthcare_2019#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_accessibility_to_healthcare_2019.json","rel":"latest-version","title":"projects/malariaatlasproject/assets/accessibility/accessibility_to_healthcare/2019","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/Oxford/Oxford_MAP_accessibility_to_healthcare_2019.json","rel":"predecessor-version","title":"Oxford/MAP/accessibility_to_healthcare_2019","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/accessibility/accessibility_to_healthcare/2019/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/malariaatlasproject/assets/accessibility/accessibility_to_healthcare/2019_walking_only","title":"Malaria Atlas Project Accessibility to Healthcare 2019 (Walking Only)","description":"This global accessibility map enumerates land-based travel time (in\nminutes) to the nearest hospital or clinic for all areas between 85\ndegrees north and 60 degrees south for a nominal year 2019. This surface\nis based on \"walking-only\" travel time, using non-motorized means of\ntransportation only.\n\nMajor data collection efforts underway by OpenStreetMap, Google Maps, and\nacademic researchers have been harnessed to compile the most complete\ncollection of healthcare facility locations to date. This map was\nproduced through a collaboration between MAP (University of Oxford),\nTelethon Kids Institute (Perth, Australia), Google, and the University\nof Twente, Netherlands.\n\nThis project builds on previous work published by Weiss et al 2018\n([doi:10.1038/nature25181](https://doi.org/10.1038/nature25181)).\nWeiss et al (2018) utilising datasets for roads\n(comprising the first ever global-scale use of Open Street Map and Google\nroads datasets), railways, rivers, lakes, oceans, topographic conditions\n(slope and elevation), landcover types, and national borders. These\ndatasets were each allocated a speed or speeds of travel in terms of time\nto cross each pixel of that type. The datasets were then combined to\nproduce a \"friction surface\": a map where every pixel is allocated a\nnominal overall speed of travel based on the types occurring within that\npixel. For the current project, an updated friction surface was created to\nincorporate recent improvements within OSM roads data.\n\nLeast-cost-path algorithms (run in Google Earth Engine and, for\nhigh-latitude areas, in R) were used in conjunction with this friction\nsurface to calculate the time of travel from all locations to the nearest\n(in time) healthcare facility. The healthcare facilities dataset utilised\nlocation data from two of the largest global databases: (1) OSM data that\nwas collated and made available for download at\n[www.healthsites.io](https://www.healthsites.io/); and (2) data\nextracted from Google Maps. The global datasets were augmented with\ncontinental-scale facility locations that were recently published for\nAfrica and Australia. To facilitate comparisons between data sources, only\nfacilities defined as hospitals and clinics were used. Multiple points\nfound within the same pixel were merged to match the resolution of the\nanalysis as defined by the selected gridded representation of the Earth's\nsurface. Each pixel in the resultant accessibility map thus represents the\nmodelled shortest time (in minutes) from that location to a hospital or\nclinic.\n\nSource dataset credits are as described in the accompanying paper.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2019-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/accessibility/accessibility_to_healthcare/2019_walking_only","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_accessibility_to_healthcare_2019_walking_only","rel":"related","title":"Run the example for projects/malariaatlasproject/assets/accessibility/accessibility_to_healthcare/2019_walking_only in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_accessibility_to_healthcare_2019_walking_only_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_malariaatlasproject_assets_accessibility_accessibility_to_healthcare_2019_walking_only#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/accessibility/accessibility_to_healthcare/2019_walking_only/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/malariaatlasproject/assets/accessibility/friction_surface/2015_v1_0","title":"Malaria Atlas Project Global Friction Surface 2015","description":"Note: this surface is replaced by \"Malaria Atlas Project Global \nFriction Surface 2019\".\n\nThis global friction surface enumerates land-based travel\nspeed for all land pixels between 85 degrees north and 60 degrees\nsouth for a nominal year 2015.\n\nThis map was produced through\na collaboration between the University of Oxford Malaria Atlas\nProject (MAP), Google, the European Union Joint Research Centre\n(JRC), and the University of Twente, Netherlands. The underlying\ndatasets used to produce the map include roads (comprising the\nfirst ever global-scale use of Open Street Map and Google roads\ndatasets), railways, rivers, lakes, oceans, topographic conditions\n(slope and elevation), landcover types, and national borders.\n\nThese datasets were each allocated a speed or speeds of travel in terms\nof time to cross each pixel of that type. The datasets were then\ncombined to produce this \"friction surface\": a map\nwhere every pixel is allocated a nominal overall speed of travel\nbased on the types occurring within that pixel, with the fastest\ntravel mode intersecting the pixel being used to determine the\nspeed of travel in that pixel (with some exceptions such as national\nboundaries, which have the effect of imposing a travel time penalty).\n\nThis map represents the travel speed from this allocation process, expressed\nin units of minutes required to travel one meter. It forms the\nunderlying dataset behind the global accessibility map described\nin the referenced paper.\n\nSource dataset credits are as described in the accompanying paper.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2016-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/accessibility/friction_surface/2015_v1_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_friction_surface_2015_v1_0","rel":"related","title":"Run the example for projects/malariaatlasproject/assets/accessibility/friction_surface/2015_v1_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_friction_surface_2015_v1_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_malariaatlasproject_assets_accessibility_friction_surface_2015_v1_0#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/accessibility/friction_surface/2015_v1_0/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/malariaatlasproject/assets/accessibility/friction_surface/2019_v5_1","title":"Malaria Atlas Project Global Friction Surface 2019","description":"This global friction surface enumerates land-based travel speed for all \nland pixels between 85 degrees north and 60 degrees south for a nominal \nyear 2019. \nThis map was produced through a collaboration between \nMAP (University of Oxford), Telethon Kids Institute (Perth, Australia), \nGoogle, and the University of Twente, Netherlands.\n\nThis project builds on previous work published by Weiss et al 2018 \n([doi:10.1038/nature25181](https://doi.org/10.1038/nature25181)). \nWeiss et al (2018) utilising datasets for roads (comprising the first ever \nglobal-scale use of Open Street Map and Google roads datasets), \nrailways, rivers, lakes, oceans, \ntopographic conditions (slope and elevation), landcover types, \nand national borders. These datasets were each allocated a speed or \nspeeds of travel in terms of time to cross each pixel of that type. \nThe datasets were then combined to produce a \"friction surface\"; \na map where every pixel is allocated a nominal overall speed of \ntravel based on the types occurring within that pixel. \n\nFor the current project, an updated friction surface was created \nto incorporate recent improvements within OSM roads data.\nDifferences between this friction surface and the \n2015 version (Weiss et al. 2018) are not necessarily indicative of \nchanges in infrastructure (e.g., new roads being built). \nSuch discrepancies are far more likely to be associated with \nimproved data quality, in particular updates made to OSM road coverage. \nAs a result, comparisons between the friction surfaces and resulting \ntravel time maps should be done cautiously and generally not interpreted \nas representing changes in access over time.\n\nThis map represents the travel speed from this allocation process, \nexpressed in units of minutes required to travel one meter. \nIt forms the underlying dataset behind the global healthcare \naccessibility map described in the referenced paper.\n\nSource dataset credits are as described in the accompanying paper.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2019-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/accessibility/friction_surface/2019_v5_1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_friction_surface_2019_v5_1","rel":"related","title":"Run the example for projects/malariaatlasproject/assets/accessibility/friction_surface/2019_v5_1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_friction_surface_2019_v5_1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_malariaatlasproject_assets_accessibility_friction_surface_2019_v5_1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_friction_surface_2019_v5_1.json","rel":"latest-version","title":"projects/malariaatlasproject/assets/accessibility/friction_surface/2019_v5_1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/Oxford/Oxford_MAP_friction_surface_2019.json","rel":"predecessor-version","title":"Oxford/MAP/friction_surface_2019","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/accessibility/friction_surface/2019_v5_1/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/malariaatlasproject/assets/accessibility/friction_surface/2019_v5_1_walking_only","title":"Malaria Atlas Project Global Friction Surface 2019 (Walking Only)","description":"This global friction surface enumerates land-based travel speed for all \nland pixels between 85 degrees north and 60 degrees south for a \nnominal year 2019. \nThis surface is based on \"walking-only\" travel speed, using non-motorized \nmeans of transportation only.\nThis map was produced through a collaboration between \nMAP (University of Oxford), Telethon Kids Institute (Perth, Australia), \nGoogle, and the University of Twente, Netherlands.\n\nThis project builds on previous work published by Weiss et al 2018 \n([doi:10.1038/nature25181](https://doi.org/10.1038/nature25181)). \nWeiss et al (2018) utilising datasets for roads (comprising the first \never global-scale use of Open Street Map and Google roads datasets), \nrailways, rivers, lakes, oceans, topographic conditions \n(slope and elevation), landcover types, and national borders. \nThese datasets were each allocated a speed or speeds of travel in terms \nof time to cross each pixel of that type. \nThe datasets were then combined to produce a \"friction surface\"; \na map where every pixel is allocated a nominal overall speed of \ntravel based on the types occurring within that pixel. \n\nFor the current project, an updated friction surface was created to \nincorporate recent improvements within OSM roads data.\nDifferences between this friction surface and the 2015 version \n(Weiss et al. 2018) are not necessarily indicative of changes in \ninfrastructure (e.g., new roads being built). \nSuch discrepancies are far more likely to be associated with improved \ndata quality, in particular updates made to OSM road coverage. \nAs a result, comparisons between the friction surfaces and resulting \ntravel time maps should be done cautiously and generally not \ninterpreted as representing changes in access over time.\n\nThis map represents the travel speed from this allocation process, \nexpressed in units of minutes required to travel one meter. \nIt forms the underlying dataset behind the global healthcare accessibility \nmap described in the referenced paper.\n\nSource dataset credits are as described in the accompanying paper.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,85]]},"temporal":{"interval":[["2019-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/accessibility/friction_surface/2019_v5_1_walking_only","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_friction_surface_2019_v5_1_walking_only","rel":"related","title":"Run the example for projects/malariaatlasproject/assets/accessibility/friction_surface/2019_v5_1_walking_only in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/malariaatlasproject/projects_malariaatlasproject_assets_accessibility_friction_surface_2019_v5_1_walking_only_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_malariaatlasproject_assets_accessibility_friction_surface_2019_v5_1_walking_only#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/malariaatlasproject/assets/accessibility/friction_surface/2019_v5_1_walking_only/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/mangrovedatahub2/assets/CGMD-AFCC30","title":"Global Annual Mangrove Fractional Canopy Cover (1984-2023)","description":"This dataset provides global annual mangrove fractional canopy cover (FCC) at 30-m spatial resolution from 1984 to 2023 to support \nlarge-scale analyses of long-term mangrove dynamics, including canopy degradation and recovery, blue carbon assessment, and coastal ecosystem monitoring. \nFCC quantifies the proportion of each Landsat pixel occupied by mangrove canopy and is expressed as a percentage ranging from 0% to 100%.\n\nThe dataset was generated using time-series Landsat imagery within the Google Earth Engine platform. \nAnnual Landsat composites were produced after strict cloud, cloud-shadow, and low-quality observation filtering. \nFCC was then estimated using a linear spectral unmixing approach and spatially constrained to the global mangrove extent for each year. \n\nThe FCC estimates were evaluated against high-resolution Planet imagery across different regions, with root mean square error (RMSE) ranging from 9% to 22%.\n\nUsers should note that FCC values are derived from annual median Landsat composites. \nAs a result, canopy changes occurring late in a calendar year may be captured in the following year's composite, \nwhich can introduce a one-year lag in the timing of detected FCC change.\nFor quick visualization and exploratory analysis, users are encouraged to access \n[the CGMD Earth Engine App](https://zhenzhang.users.earthengine.app/view/globalmangrovedynamic).   \n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1984-01-01T00:00:00Z","2023-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/mangrovedatahub2/assets/CGMD-AFCC30","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/mangrovedatahub2/projects_mangrovedatahub2_assets_CGMD-AFCC30","rel":"related","title":"Run the example for projects/mangrovedatahub2/assets/CGMD-AFCC30 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/mangrovedatahub2/projects_mangrovedatahub2_assets_CGMD-AFCC30_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_mangrovedatahub2_assets_CGMD-AFCC30#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/mangrovedatahub2/assets/CGMD-AFCC30/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/mangrovedatahub2/assets/CGMD-AFCC30/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/mapbiomas-public/assets/bolivia/lulc/v1","title":"MapBiomas Land Use and Land Cover - Bolivia V1.0","description":"MapBiomas Land Use and Land Cover (LULC) dataset for Bolivia is produced annually\nby the MapBiomas Project using Landsat satellite imagery and machine learning\nclassification techniques. The dataset provides consistent annual land cover maps\nat 30-meter resolution covering the period from 1985 to 2024.\n\nBolivia's territory spans several major biomes, including the Amazon rainforest,\nCerrado, Chaco, Pantanal, and the Andean highlands (Altiplano). This diversity\nis reflected in the classification legend, which includes classes specific to\nAndean ecosystems such as Andean grassland and shrubland and Flooded Andean\ngrassland and shrubland.\n\nEach image in the collection contains one band named \"classification\", with\ninteger values representing categorical land cover classes for that year. The\nclassification is based on Landsat 5 (TM), Landsat 7 (ETM+), Landsat 8 and 9\n(OLI/TIRS) surface reflectance data, preprocessed to generate annual cloud-free\ncomposites. The classification process uses automated decision trees trained with\nreference samples and validated by regional experts.\n\nFor more information, classification legend, methodology, and accuracy assessments,\nvisit the [MapBiomas website](https://mapbiomas.org/en).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-69.6644,-23.3146,-57.3608,-9.6792]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2024-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/mapbiomas-public/assets/bolivia/lulc/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/mapbiomas-public/projects_mapbiomas-public_assets_bolivia_lulc_v1","rel":"related","title":"Run the example for projects/mapbiomas-public/assets/bolivia/lulc/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/mapbiomas-public/projects_mapbiomas-public_assets_bolivia_lulc_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_mapbiomas-public_assets_bolivia_lulc_v1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/mapbiomas-public/assets/bolivia/lulc/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/mapbiomas-public/assets/bolivia/lulc/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/mapbiomas-public/assets/brazil/lulc/v1","title":"MapBiomas Land Use and Land Cover - Brazil V1.0","description":"MapBiomas Land Use and Land Cover (LULC) dataset for Brazil is produced annually by the MapBiomas Project using Landsat satellite imagery and machine learning classification techniques.\nThe dataset provides consistent, thematically detailed maps at 30-meter resolution, covering multiple decades and updated each year.\n\nEach image in the collection contains annual land cover classifications with pixel values representing categorical land cover classes, such as forest, agriculture, pasture, water bodies, and urban areas.\nThe classification legend is standardized and maintained across all years, enabling multi-temporal analyses of land use change, deforestation, reforestation, and other landscape dynamics.\n\nThe classification is based on Landsat 5 (TM), Landsat 7 (ETM+), Landsat 8 and 9 (OLI/TIRS) surface reflectance data, preprocessed and mosaicked to generate cloud-free composites for each year.\nThe classification process includes automated decision trees trained with reference samples, along with manual validation by regional experts.\n\nEach image contains one band named \"classification\", representing the land cover classification for that specific year, version and collection id.\n\nFor more information, classification legend, methodology, and accuracy assessments, visit the [MapBiomas website](https://mapbiomas.org/en).\n\nIt is recommended to consult the classification legend to interpret the class values.\nNote that MapBiomas provides discrete classification maps, not probabilities.\nThese maps are most appropriate for map transitions, time series analysis, and land policy monitoring applications.\n\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-74,-34,-34.7,5.45]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2024-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/mapbiomas-public/assets/brazil/lulc/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/mapbiomas-public/projects_mapbiomas-public_assets_brazil_lulc_v1","rel":"related","title":"Run the example for projects/mapbiomas-public/assets/brazil/lulc/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/mapbiomas-public/projects_mapbiomas-public_assets_brazil_lulc_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_mapbiomas-public_assets_brazil_lulc_v1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/mapbiomas-public/assets/brazil/lulc/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/mapbiomas-public/assets/brazil/lulc/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/mapbiomas-public/assets/ecuador/lulc/v1","title":"MapBiomas Land Use and Land Cover - Ecuador V1.0","description":"MapBiomas Land Use and Land Cover (LULC) dataset for Ecuador is produced annually\nby the MapBiomas Project using Landsat satellite imagery and machine learning\nclassification techniques. The dataset provides consistent annual land cover maps\nat 30-meter resolution covering the period from 1985 to 2024.\n\nEcuador's territory encompasses four distinct natural regions: the Amazon (Oriente),\nthe Andes highlands (Sierra), the Pacific Coast (Costa), and the Galapagos Islands.\nThis geographic diversity is reflected in the classification legend, which includes\nclasses specific to Ecuadorian ecosystems such as Andean herbaceous and shrubby\nvegetation, flooded Andean herbaceous, mangrove, and banana plantations.\n\nEach image in the collection contains one band named \"classification\", with\ninteger values representing categorical land cover classes for that year. The\nclassification is based on Landsat 5 (TM), Landsat 7 (ETM+), Landsat 8 and 9\n(OLI/TIRS) surface reflectance data, preprocessed to generate annual cloud-free\ncomposites. The classification process uses automated decision trees trained with\nreference samples and validated by regional experts.\n\nFor more information, classification legend, methodology, and accuracy assessments,\nvisit the [MapBiomas website](https://mapbiomas.org/en).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-81.1555,-5.1216,-75.1741,2.4547]]},"temporal":{"interval":[["1985-01-01T00:00:00Z","2024-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/mapbiomas-public/assets/ecuador/lulc/v1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/mapbiomas-public/projects_mapbiomas-public_assets_ecuador_lulc_v1","rel":"related","title":"Run the example for projects/mapbiomas-public/assets/ecuador/lulc/v1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/mapbiomas-public/projects_mapbiomas-public_assets_ecuador_lulc_v1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_mapbiomas-public_assets_ecuador_lulc_v1#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/mapbiomas-public/assets/ecuador/lulc/v1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/mapbiomas-public/assets/ecuador/lulc/v1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/nature-trace/assets/farmscapes/england_v1_0","title":"Farmscapes 2020","description":"The Farmscapes 2020 dataset provides high-resolution (25cm) probability maps\nfor three key semi-natural features within England's agricultural\nlandscapes: hedgerows, woodland, and stone walls. This dataset was developed\nin collaboration with the Leverhulme Centre for Nature Recovery at Oxford to\nserve as a baseline for applications including landscape restoration,\nbiodiversity monitoring, and ecological connectivity analysis.\n\nThe dataset was generated by applying a vision transformer model to a mosaic\nof high-resolution aerial imagery, trained using a large corpus of\nhuman-annotated labels. The output consists of three distinct probability\nlayers, one for each feature class. This probabilistic format allows users\nto apply custom thresholds to generate binary feature maps tailored to their\nspecific analytical requirements.\n\n**Limitations**\n\n* Geographic Scope: Model performance is reduced in dense urban\nenvironments and mountainous areas, which were underrepresented in the\ntraining data that focused primarily on rural landscapes.\n* Temporal Accuracy: The source imagery was captured between 2018 and 2020.\nConsequently, the dataset does not account for landscape changes that have\noccurred since this period.\n* Class-Specific performance: The stone wall class exhibits lower\naccuracy compared to woodland and hedgerows, a result of\nsignificant class imbalance in the training data.\n\n**Europe Dataset**\n\nIn addition to the primary dataset for England, a preliminary version has\nbeen generated at a European scale. This dataset has not been subjected to\nthe same quantitative validation and should be considered experimental.\nIt is possible to request access to this dataset by submitting\n[this form](https://docs.google.com/forms/d/e/1FAIpQLSfokil1-UFfTxvVhihLx9W3-K51o900VF4ACqkwG1jYmP0W8g/viewform).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/farmscapes/england_v1_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/nature-trace/projects_nature-trace_assets_farmscapes_england_v1_0","rel":"related","title":"Run the example for projects/nature-trace/assets/farmscapes/england_v1_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/nature-trace/projects_nature-trace_assets_farmscapes_england_v1_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_nature-trace_assets_farmscapes_england_v1_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/farmscapes/england_v1_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/farmscapes/england_v1_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/nature-trace/assets/forest_typology/forest_typology_2020_v1_0_collection","title":"Forest Typology (ForTy) 2020 v1.0","description":"The Forest Typology (ForTy) v1 dataset consists of a global per-class\nprobability map at 10 m resolution covering all land areas between\n65°S and 84°N latitude for the year 2020.\n\nThe six-class typology is aligned with FAO and EU Deforestation\nRegulation (EUDR) definitions:\n\n* Class 1 – Primary Forest: Natural forest of native species with no\nvisible indications of human-caused disturbance.\n* Class 2 – Naturally Regenerating Forest: Forests predominantly\nestablished through natural regeneration, including secondary forests\nrecovering from disturbance and mixed forests where natural\nregeneration dominates.\n* Class 3 – Planted Forest: Forests established predominantly through\nplanting or seeding, with an operational rotation period exceeding\n40 years.\n* Class 4 – Plantation Forest: Intensively managed forests\ncharacterized by one or two species, even age, regular spacing, and\na rotation period of 40 years or less.\n* Class 5 – Tree Crops and Agroforestry: Agricultural plantations\nproducing commodities other than wood (e.g., oil palm, rubber, fruit\norchards), including agroforestry systems. Classified as agricultural\nland use under EUDR.\n* Class 6 – Other Land: All non-tree land uses, sparse woody\nvegetation (canopy cover < 10%), other wooded land, trees in urban\nenvironments, and small tree patches that do not meet forest\nthresholds.\n\nThe primary data record is a five-band raster containing unsigned 8-bit\ninteger values (0–250) representing quantized probability values for\neach of five forest type classes. The probability for the sixth class\n(Other land) is obtained as the complement: p_other = 250 − sum(p_i).\nThe map uses the Universal Transverse Mercator (UTM) coordinate system.\n\nTo get the probability score of the sixth, \"Other land\" class, you to\nsubtract from 250 the sum of the first five probabilities. A categorical\nmap can be derived by assigning each pixel to the class with the highest\nprobability (argmax). The probability\nrepresentation enables users to apply custom thresholds, assess\nprediction confidence, and compute uncertainty estimates for downstream\napplications.\n\nThe source data is provided as Cloud Optimized GeoTIFFs (COGs) stored\non Google Cloud Storage at\n`gs://nature-trace/export_rasters/forest_typology_2020_v1_0/`. The\nEarth Engine ImageCollection is backed by these COGs.\n\nLimitations: While this map provides a valuable global baseline, users\nshould be aware of several limitations:\n\n* Temporal reference date: The map represents the state of land cover\nas of the year 2020, based on multi-temporal satellite composites.\nRapid land-use changes occurring within the compositing period may not\nbe fully captured.\n* Definitional ambiguity: The FAO-based definitions underlying the\nEUDR classification rely on management intent and history, which are\nnot directly observable from satellite imagery. The model infers these\ncategories from proxy indicators (spectral signatures, canopy\nstructure, spatial patterns), introducing irreducible uncertainty at\nclass boundaries. For example, mature naturally regenerating\n(secondary) forests can be spectrally and structurally\nindistinguishable from undisturbed primary forests.\n* Agroforestry ambiguity: Mixed agroforestry systems—where tree crops\nsuch as cocoa or coffee grow beneath a native canopy—are among the\nmost challenging classes to resolve. Shaded cocoa and coffee\nagroforests can still be misclassified as natural forest in some\nregions.\n* Planted forest confusion: Planted forests exhibit the lowest\nclassification accuracy among all forest classes (F1 = 58.2%). This\nclass can be spectrally and structurally similar to naturally\nregenerating forests at one end and to plantation forests at the\nother, resulting in frequent misclassification in both directions.\n* Small patches and threshold canopy coverage: Isolated tree patches\nnear the FAO forest area threshold (0.5 ha) and areas at the canopy\ncover boundary (10%) are inherently ambiguous.\n* Probability calibration: The per-class probabilities represent\nensemble-averaged softmax outputs and have not been post-hoc\ncalibrated. Users requiring well-calibrated probabilities for\nquantitative risk assessment should consider regional recalibration\nusing independent reference data.\n* Wildfires: It is not always possible to determine the land use\nafter a wildfire.\n* Conservative primary forest mapping: The primary forest class\nboundary is conservative near human infrastructure. Due to a spatial\nbuffering effect inherited from the training data, the model\nsystematically excludes forest edges near roads, settlements, or\nagriculture from the primary forest class, often labeling them as\nnaturally regenerating instead.\n* Temporarily unstocked areas: Under FAO definitions, temporarily\nunstocked areas (such as recently harvested plantations awaiting\nreplanting or forests in early post-disturbance recovery) are still\nconsidered forest. However, because this map relies on a single year\nof observations (2020), these areas may appear as non-forest and be\nmisclassified as \"Other Land.\"\n* Planted vs. plantation proxy: The distinction between planted and\nplantation forests uses an operational 40-year rotation threshold as\na proxy. True EUDR/FAO management intensity criteria (e.g., even-aged\nclass, regular spacing) are not directly observable from satellite\nimagery.\n* Other Wooded Land (OWL) omission: The EUDR recognizes \"Other Wooded\nLand\" (OWL) as a distinct category, but in this dataset, OWL is not\nresolved as a separate class and is entirely subsumed into the\n\"Other Land\" category.\n* Sub-pixel heterogeneity: Mixed pixels, particularly in complex\nsmallholder landscapes, can reduce classification precision.\nAdditionally, while the output is at 10 m, the effective resolution\nof the model's input data might be coarser than 10 m.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-65,180,84]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/forest_typology/forest_typology_2020_v1_0_collection","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/nature-trace/projects_nature-trace_assets_forest_typology_forest_typology_2020_v1_0_collection","rel":"related","title":"Run the example for projects/nature-trace/assets/forest_typology/forest_typology_2020_v1_0_collection in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/nature-trace/projects_nature-trace_assets_forest_typology_forest_typology_2020_v1_0_collection_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_nature-trace_assets_forest_typology_forest_typology_2020_v1_0_collection#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/forest_typology/forest_typology_2020_v1_0_collection/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/forest_typology/forest_typology_2020_v1_0_collection/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/nature-trace/assets/forest_typology/natural_forest_2020_v1_0_collection","title":"Natural Forests of the World 2020","description":"Natural Forests of the World 2020, provides a global map of\nnatural forest probability for the year 2020 at a 10-meter resolution. It\nwas developed to support initiatives like the European Union's\nDeforestation Regulation (EUDR) and other efforts for forest conservation\nand monitoring.\nThe map represents the likelihood of an area being a natural forest, which\nincludes primary, naturally regenerating secondary, and managed natural\nforests. It distinguishes these from planted forests, tree crops, and\nother land cover types. The dataset was created using a multi-modal\ntemporal-spatial vision transformer model that analyzed seasonal\nmulti-temporal Sentinel-2 satellite imagery and topographical data\n(elevation, slope, aspect based on TanDEM-X's Copernicus GLO-30 DEM).\nThe data is provided as a probabilistic map, allowing users to apply a\nprobability threshold to create a binary natural forest map tailored to\ntheir specific needs.\n\nLimitations: While this map provides a valuable global baseline, users\nshould be aware of several limitations in our map (assessed at the OA\noptimal probability threshold of 0.52):\n\n* The model can have difficulty distinguishing complex agroforestry systems\n(e.g., with shaded tree crops), and smallholder agricultural mosaics can be\ndifficult to distinguish from natural forests.\n* Differentiating planted forests from naturally regenerating ones can be\nchallenging, especially in boreal and some temperate zones, where natural\nforests have lower species diversity and are harvested with longer rotation\ntimes compared to the tropics.\n* Sparse natural forests, such as savanna, are often at the threshold of\nnatural forest definition for the tree canopy height and coverage ratios.\nForest type assignment immediately after a disturbance event (e.g., fire,\nlogging) is inherently ambiguous. It may not be clear from satellite imagery\nwhether the forest will regenerate naturally or if the land will be\nconverted to another use (e.g., plantation, agriculture).\n* Other areas of potential confusion could include large parks within urban\nareas, or planted tree belts that meet forest definition criteria but are\nnot natural.\n* The accuracy of our natural forest map is intrinsically linked to the\nquality and consistency of the various input datasets used for training\nlabel generation. These datasets were created using different methodologies,\nspatial resolutions, temporal ranges, and definitions. Some label layers\nwere the outputs of other models, and are therefore limited by the quality\nof those models. While our approach aimed to harmonize sources and mitigate\nthe impact of individual dataset errors, inconsistencies and inaccuracies in\nthe underlying data could still influence the final map.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/forest_typology/natural_forest_2020_v1_0_collection","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/nature-trace/projects_nature-trace_assets_forest_typology_natural_forest_2020_v1_0_collection","rel":"related","title":"Run the example for projects/nature-trace/assets/forest_typology/natural_forest_2020_v1_0_collection in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/nature-trace/projects_nature-trace_assets_forest_typology_natural_forest_2020_v1_0_collection_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_nature-trace_assets_forest_typology_natural_forest_2020_v1_0_collection#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/forest_typology/natural_forest_2020_v1_0_collection/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/forest_typology/natural_forest_2020_v1_0_collection/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/nature-trace/assets/ghg/emit/mapl_emit_enhancements_v1_0","title":"MAPL-EMIT: Modeled Methane Enhancement and Plume Probability [deprecated]","description":"The Methane Analysis and Plume Localization with EMIT (MAPL-EMIT) enhancements dataset provides high-resolution (60m) methane (CH₄) enhancement maps and plume probabilities derived from radiance data captured by the Earth Surface Mineral Dust Source Investigation (EMIT) instrument which were generated and released as part of the research paper: \"Publication to be updated, preprint: [https://arxiv.org/abs/2604.10094](https://arxiv.org/abs/2604.10094)\"\n\nMethane point-source emissions are a major driver of near-term climate forcing. Traditional space-based identification methods often rely on manual analyst review or matched-filter techniques that can miss smaller plumes. MAPL-EMIT addresses this using a deep learning vision transformer framework that simultaneously analyzes the full radiance spectrum and the spatial context of neighboring pixels. Validated on millions of synthetic plumes and real-world benchmarks, the model achieves significantly lower detection limits, capturing weaker plumes and more plausible emissions than human analysts.\n\nThis dataset provides model-generated granule-level methane enhancements (in ppm-m) along with plume probability scores for each pixel. You can also explore and visualize the enhancement data interactively through the [MAPL-EMIT EE App](https://nature-trace.projects.earthengine.app/view/mapl-emit). If you are interested in plume level data, please see [MAPL-EMIT Plumes](https://developers.google.com/earth-engine/datasets/catalog/projects_nature-trace_assets_ghg_emit_mapl_emit_plumes_v1_0).\n\nThe data is provided on a per-pixel basis:\n\n* **Enhancements:** The estimated column-integrated methane enhancement (ppm-m).\n* **Plume Probability:** A score (0 to 1) representing the model's confidence that the pixel contains a methane plume.\n\n**Example Uses:**\n\n* **Enhancement based flux inversion:** Using the granule level enhancements paired with winds and other relevant sources to produce flux inversion estimates. \n* **Algorithm Development & Cross-Validation:** Utilizing the raw pixel-level enhancements and probabilities to develop custom plume identification algorithms, or comparing against other retrieval techniques.\n* **Detailed Morphological Studies:** Analyzing the continuous spread and diffusion of a plume without relying on pre-computed discrete bounding polygons.\n\n**Limitations:**\n\n* **Enhancement noise:** The enhancements can be noisy, especially when we are looking at very low enhancements which can either be missed out by the model entirely or incorrectly estimated. We observe that the SNR (signal-to-noise ratio) for MAPL-EMIT enhancements is much higher than Matched Filter or other approaches, however, noise still exists and should be handled accordingly.\n* **Instrument coverage:** Data coverage is constrained by EMIT's orbital sampling and field-of-view; observations are only available for locations and times when the instrument collected measurements from the International Space Station.\n* **Environmental Factors:** Model performance degrades in scenarios with heavy cloud cover, shadows, or low-albedo (dark) surfaces, which reduce the quality of the hyperspectral radiance signal.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-08-10T00:00:00Z","2026-06-09T17:16:18Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/ghg/emit/mapl_emit_enhancements_v1_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/nature-trace/projects_nature-trace_assets_ghg_emit_mapl_emit_enhancements_v1_0","rel":"related","title":"Run the example for projects/nature-trace/assets/ghg/emit/mapl_emit_enhancements_v1_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/nature-trace/projects_nature-trace_assets_ghg_emit_mapl_emit_enhancements_v1_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_nature-trace_assets_ghg_emit_mapl_emit_enhancements_v1_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/climate-and-sustainability/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_enhancements_v1_0.json","rel":"latest-version","title":"projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_enhancements_v1_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/climate-and-sustainability/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_enhancements_v1_0.json","rel":"successor-version","title":"projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_enhancements_v1_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/ghg/emit/mapl_emit_enhancements_v1_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/ghg/emit/mapl_emit_enhancements_v1_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/nature-trace/assets/ghg/emit/mapl_emit_plumes_v1_0","title":"MAPL-EMIT: Modeled Methane Plumes [deprecated]","description":"The Methane Analysis and Plume Localization with EMIT (MAPL-EMIT) dataset provides high-resolution (60m) methane (CH₄) plume complex maps and plume source locations derived from radiance data captured by the Earth Surface Mineral Dust Source Investigation (EMIT) instrument.\n\nMethane point-source emissions are a major driver of near-term climate forcing. Traditional space-based identification methods often rely on manual analyst review or matched-filter techniques that can miss smaller emissions and are laborious to characterize and delineate. MAPL-EMIT addresses this using a deep learning vision transformer framework that simultaneously analyzes the full radiance spectrum and the spatial context of neighboring pixels. Validated on millions of synthetic plumes and real-world benchmarks, the model provides automated plume detection across full-granule radiances, estimating per-plume spatial extents, column integrated enhancements (ppm-m increase over background) and source locations.\n\nThis dataset provides model-predicted outputs for methane plume complexes. For each identified plume, the dataset provides the corresponding methane enhancements, an instance mask delineating the plume's shape, and the most likely source location. You can also explore and visualize the plumes interactively through the [MAPL-EMIT EE App](https://nature-trace.projects.earthengine.app/view/mapl-emit). If you are interested in granule level enhancements and plume probabilities, please see\n[MAPL-EMIT Enhancements](https://developers.google.com/earth-engine/datasets/catalog/projects_nature-trace_assets_ghg_emit_mapl_emit_enhancements_v1_0)\n\nEach plume has a 'confidence' property containing one of the following confidence scores:\n\n* **High confidence:** Plumes detected three or more times across different EMIT observations over time. Human review of a random subset of these plumes yielded a false positive rate of ~3-5%. This low error rate is comparable to existing human-annotated datasets, making high-confidence plumes suitable for direct downstream use.\n* **Medium confidence:** Plumes without a temporal match across multiple EMIT observations. Human review indicates a false positive rate of ~50-55% for this category. Due to the high likelihood of false positives, users should exercise caution and apply secondary filtering before downstream analysis. Recommended filtering strategies include matching plumes with known infrastructure, analyzing spectral signatures, applying d_norm or d_cor spectral scores, and cross-referencing with local wind direction.\n\n**Example Uses:**\n\n* **Automated Plume Delineation & Source Localization:** Identifying the spatial footprint of methane plumes and attributing them to specific infrastructure features (e.g., wells, landfills, compressor stations), even amid overlapping plumes.\n* **Global Methane Tracking:** Scanning the complete EMIT data catalog to automatically construct a comprehensive database of global methane point sources.\n* **Impact Quantification:** Combining instance masks and enhancement values to estimate leak severity from previously undetected or uncaptured sources.\n\n**Limitations:**\n\n* **False Positives:** Automated detection inherently carries a risk of false positives. When utilizing medium-confidence plumes, it is critical to treat detections as candidates and filter them against known locations of physical infrastructure, employ temporal aggregations to confirm persistent sources, or other verification methods.\n* **False Negatives:** The model may miss some plumes, particularly those with low enhancements or small spatial extents. Approximately 16% of expert-annotated NASA EMIT L2B plumes are missed by the model.\n* **Enhancement vs. emission rate:** Reported pixel values represent atmospheric column enhancements (ppm-m), not emission rates (kg/hr). Estimating emission flux requires using methods like IME along with external wind fields and introduces additional uncertainty.\n* **Instrument coverage:** Data coverage is constrained by EMIT's orbital sampling and field-of-view; observations are only available for locations and times when the instrument collected measurements from the International Space Station.\n* **Environmental Factors:** Model performance degrades in scenarios with heavy cloud cover, shadows, or low-albedo (dark) surfaces, which reduce the quality of the hyperspectral radiance signal.\n","deprecated":true,"license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-08-10T00:00:00Z","2026-06-09T08:10:21Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/ghg/emit/mapl_emit_plumes_v1_0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/nature-trace/projects_nature-trace_assets_ghg_emit_mapl_emit_plumes_v1_0","rel":"related","title":"Run the example for projects/nature-trace/assets/ghg/emit/mapl_emit_plumes_v1_0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/nature-trace/projects_nature-trace_assets_ghg_emit_mapl_emit_plumes_v1_0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_nature-trace_assets_ghg_emit_mapl_emit_plumes_v1_0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/climate-and-sustainability/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_plumes_v1_0.json","rel":"latest-version","title":"projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_plumes_v1_0","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/climate-and-sustainability/projects_climate-and-sustainability_assets_ghg_emit_mapl_emit_plumes_v1_0.json","rel":"successor-version","title":"projects/climate-and-sustainability/assets/ghg/emit/mapl_emit_plumes_v1_0","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/ghg/emit/mapl_emit_plumes_v1_0/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/ghg/emit/mapl_emit_plumes_v1_0/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/nature-trace/assets/species_distribution_models/australia_mammals/v0","title":"Species Distribution, Australia Mammals","description":"\nThese species distribution model outputs, developed by Google in\ncollaboration with [QCIF](https://www.qcif.edu.au/) and\n[EcoCommons](https://www.ecocommons.org.au/), represent estimates of\nspecies’ relative occurrence likelihoods (i.e., higher values indicate a\nhigher likelihood that the species would be detected at a location, for a\ngiven survey methodology and given survey effort, compared to lower values).\n\nThey were generated using a multi-species\nheterogenous graph neural network model\ntrained jointly on species observation data from the\n[Global Biodiversity Information Facility](https://www.gbif.org/) for 174\nAustralian mammals from 2017 - 2024 (inclusive), combined with species\n[trait](https://figshare.com/articles/dataset/COMBINE_a_Coalesced_Mammal_Database_of_Intrinsic_and_extrinsic_traits/13028255/4) and taxonomy data for the same species from COMBINE,\nand satellite and environmental as embedded by\n[AlphaEarth Foundations](https://deepmind.google/discover/blog/alphaearth-foundations-helps-map-our-planet-in-unprecedented-detail/).\n[A previous phase of this work and the model architecture are described here](https://ar5iv.labs.arxiv.org/html/2503.11900).\nEstimates of species’ geographical distributions are made at c. 1km resolution (S2 cells level 13).\n\nThese distributions are intended to support conservation initiatives like\nthe [Global Biodiversity Framework](https://www.cbd.int/gbf/targets), which\nincludes [30x30](https://en.wikipedia.org/wiki/30_by_30). It is likely that\ndistributions could be significantly improved via fine tuning with\nadditional species observation data, where this is available.\n\nLimitations:\n\n* Experimental Status: These results are intended as demonstrations of early\n  research and have not yet been peer-reviewed.\n* Aggregate vs. Specific Validation: Model outputs for an earlier run described in the paper have been evaluated\n  collectively against expert-produced Area of Habitation maps (mean [ROC AUC](https://bookdown.org/pjhanly/fw840hanly/week-4-species-distribution-models.html#area-under-the-curve-auc)\n  = 0.89 over all species,  AUC ROC > 0.9 for each of the released\n  species), but have not been vetted by species experts on a\n  species-by-species basis.\n* Presence-Only Data Constraints: These models are trained primarily on\n  presence-only data. Outputs represent a relative likelihood of detection\n  given current survey efforts, rather than true probability of occupancy.\n* Input Data Bias: The accuracy of these maps is intrinsically linked to the\n  input datasets. Biodiversity observation data is known to contain spatial,\n  temporal, and taxonomic biases (often reflecting accessibility near roads\n  or population centers rather than true biological distribution). The\n  availability of species observation data has also been found to [reflect\n  social and political inequities](https://www.science.org/doi/abs/10.1126/science.adh8874).\n  These biases may be propagated into the final model outputs.\n\nThis experimental preview was released November 5, 2025.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[112.9,-43.7,153.7,-10.4]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/species_distribution_models/australia_mammals/v0","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/nature-trace/projects_nature-trace_assets_species_distribution_models_australia_mammals_v0","rel":"related","title":"Run the example for projects/nature-trace/assets/species_distribution_models/australia_mammals/v0 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/nature-trace/projects_nature-trace_assets_species_distribution_models_australia_mammals_v0_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_nature-trace_assets_species_distribution_models_australia_mammals_v0#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/nature-trace/assets/species_distribution_models/australia_mammals/v0/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/neon-prod-earthengine/assets/CHM/001","title":"NEON Canopy Height Model (CHM)","description":"Height of the top of canopy above bare earth (Canopy Height Model; CHM). The CHM\nis derived from the [NEON LiDAR](https://www.neonscience.org/data-collection/lidar)\npoint cloud and is generated by creating a continuous surface of canopy height\nestimates across the entire spatial domain of the LiDAR survey. The point cloud\nis separated into classes representing the ground and vegetation returns. The\nground classified points are used to generate a height-normalized point cloud that\nprovides a relative estimate of vegetation elevation. A pit-free surface is then\ngenerated using the height normalized vegetation points to produce the CHM. Any\ncanopy heights less than 2 m are set to zero for data collected with the Optech\nGemini LiDAR sensors, and less than 0.7 m for the Riegl Q780 and Optech Galaxy\nsensors. Data are mosaicked over the AOP footprint onto a spatially uniform grid\nat 1 m spatial resolution. Availability in GEE may not represent full availability\nin the NEON Data Portal (linked below). Additional sites and years can be added to\nGEE upon request by emailing listaopgee@battelleecology.org.\n\nSee [NEON Data Product DP3.30015.001](\nhttps://data.neonscience.org/data-products/DP3.30015.001) for\nmore details.\n\nDocumentation: [NEON Ecosystem structure (DP3.30015.001) Quick Start Guide](\nhttps://data.neonscience.org/api/v0/documents/quick-start-guides/NEON.QSG.DP3.30015.001v1?inline=true&fallback=html)\n\nGet started by exploring the [Intro to AOP Data in Google Earth Engine Tutorial Series](\nhttps://www.neonscience.org/resources/learning-hub/tutorials/intro-aop-data-google-earth-engine-tutorial-series)\n\nBrowse and interact with AOP data in the [NEON AOP GEE Data Viewer App](\nhttps://neon-prod-earthengine.projects.earthengine.app/view/neon-aop-gee-data-viewer---desktop)\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-170,16,-66,73]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2026-05-14T14:26:01Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/CHM/001","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/neon-prod-earthengine/projects_neon-prod-earthengine_assets_CHM_001","rel":"related","title":"Run the example for projects/neon-prod-earthengine/assets/CHM/001 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/neon-prod-earthengine/projects_neon-prod-earthengine_assets_CHM_001_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_neon-prod-earthengine_assets_CHM_001#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC0-1.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/CHM/001/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/CHM/001/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/neon-prod-earthengine/assets/CNC/002","title":"NEON Canopy Nitrogen Content (CNC)","description":"Modelled canopy nitrogen concentration calibrated with NEON plant foliar chemistry observations and\npredicted with L1 surface directional reflectance, derived from the [NEON Imaging Spectrometer (NIS)](\nhttps://www.neonscience.org/data-collection/imaging-spectrometer). Bands include 1) canopy nitrogen percent,\n2) canopy nitrogen model uncertainty, 3) classification result for needle vs. non-needle model and 4) valid\npixel mask based on NDVI threshold. \n\nThe canopy nitrogen data product provides an estimate of canopy nitrogen percent per pixel. Modelled results\nare generated by training a random forest model which leverages directional reflectance spectra as predictor\nvariables and field collected canopy foliar chemistry measurements for model calibration and validation. Both\na pine-needle and non-needle nitrogen model are used, due to the significant difference in final model parameters\nof these distinct plant functional types. Models were trained with samples collected across the history of the NEON\nobservatory, and appropriately filtered for data quality issues. Values were only trained on vegetative samples,\ntherefore models may be inaccurate in other landscape types. Although all pixels are provided, an NDVI threshold\nmask was derived to isolate pixels with vegetation.\n\nAvailability in GEE may not represent full availability in the NEON Data Portal (linked below). Additional sites\nand years can be added to GEE upon request by emailing listaopgee@battelleecology.org.\n\nSee [NEON Data Product DP3.30018.002](\nhttps://data.neonscience.org/data-products/DP3.30018.002) for more details.\n\nDocumentation: [Canopy nitrogen - mosaic (DP3.30018.002) Quick Start Guide](\nhttps://data.neonscience.org/api/v0/documents/quick-start-guides/NEON.QSG.DP3.30018.002v1?inline=true&fallback=html)\n\nGet started by exploring the [Intro to AOP Data in Google Earth Engine Tutorial Series](\nhttps://www.neonscience.org/resources/learning-hub/tutorials/intro-aop-data-google-earth-engine-tutorial-series)\n\nBrowse and interact with AOP data in the [NEON AOP GEE Data Viewer App](\nhttps://neon-prod-earthengine.projects.earthengine.app/view/neon-aop-gee-data-viewer---desktop)\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-170,16,-66,73]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2021-07-18T16:36:45Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/CNC/002","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/neon-prod-earthengine/projects_neon-prod-earthengine_assets_CNC_002","rel":"related","title":"Run the example for projects/neon-prod-earthengine/assets/CNC/002 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/neon-prod-earthengine/projects_neon-prod-earthengine_assets_CNC_002_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_neon-prod-earthengine_assets_CNC_002#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC0-1.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/CNC/002/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/CNC/002/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/neon-prod-earthengine/assets/DEM/001","title":"NEON Digital Elevation Model (DEM)","description":"Digital models of the surface (DSM) and terrain (DTM) derived from [NEON LiDAR data](\nhttps://www.neonscience.org/data-collection/lidar). DSM: Surface features (topographic\ninformation with vegetation and man-made structures present). DTM: Bare earth elevation\n(topographic information with vegetation and man-made structures removed). Images are\ngiven in meters above mean sea level and mosaicked onto a spatially uniform grid at\n1 m resolution. Availability in GEE may not represent full availability in the NEON\nData Portal (linked below). Additional sites and years can be added to GEE upon request\nby emailing listaopgee@battelleecology.org.\n\nSee [NEON Data Product DP3.30024.001](\nhttps://data.neonscience.org/data-products/DP3.30024.001) for more details.\n\nDocumentation: [Elevation - LiDAR (DP3.30024.001) Quick Start Guide](\nhttps://data.neonscience.org/api/v0/documents/quick-start-guides/NEON.QSG.DP3.30024.001v1?inline=true&fallback=html)\n\nGet started by exploring the [Intro to AOP Data in Google Earth Engine Tutorial Series](\nhttps://www.neonscience.org/resources/learning-hub/tutorials/intro-aop-data-google-earth-engine-tutorial-series)\n\nBrowse and interact with AOP data in the [NEON AOP GEE Data Viewer App](\nhttps://neon-prod-earthengine.projects.earthengine.app/view/neon-aop-gee-data-viewer---desktop)\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-170,16,-66,73]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2026-05-14T14:26:01Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/DEM/001","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/neon-prod-earthengine/projects_neon-prod-earthengine_assets_DEM_001","rel":"related","title":"Run the example for projects/neon-prod-earthengine/assets/DEM/001 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/neon-prod-earthengine/projects_neon-prod-earthengine_assets_DEM_001_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_neon-prod-earthengine_assets_DEM_001#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC0-1.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/DEM/001/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/DEM/001/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/neon-prod-earthengine/assets/HSI_REFL/001","title":"NEON Surface Directional Reflectance","description":"The NEON AOP Surface Directional Reflectance is a hyperspectral VSWIR (visible\nto shortwave infrared) data product, containing 426 bands spanning wavelengths\nfrom ~380 nm to 2510 nm. Reflectance is scaled by a factor of 10000. Wavelengths\nbetween 1340-1445 nm and 1790-1955 nm are set to -100; these are water vapor\nabsorption bands without valid values. The dataset also contains QA raster bands.\nMajor processing steps include orthorectification and atmospheric corrections.\nData are collected by one of three [NEON Imaging Spectrometer (NIS)](\nhttps://www.neonscience.org/data-collection/imaging-spectrometer) sensors,\nAVIRIS-NG spectrometers built by NASA's Jet Propulsion Lab (JPL). Reflectance is\nconverted to a mosaic using the nadir-most pixels from the flight lines acquired\nwith the least cloud cover. Spatial resolution is 1 m and spectral resolution\nis ~5 nm. The center wavelength and full width half max (FWHM) for each band are\nprovided in the Image Properties. Availability in GEE may not represent full\navailability in the NEON Data Portal (linked below). Additional sites and years\ncan be added to GEE upon request by emailing listaopgee@battelleecology.org. This\ndataset will eventually be deprecated and replaced by the [NEON Surface Bidirectional Reflectance](\nhttps://developers.google.com/earth-engine/datasets/catalog/projects_neon-prod-earthengine_assets_HSI_REFL_002)\ndataset.\n\nSee [NEON Data Product\nDP3.30006.001](https://data.neonscience.org/data-products/DP3.30006.001) for\nmore details.\n\nDocumentation: [NEON DP3.30006.001 Spectrometer orthorectified surface directional reflectance - mosaic Quick Start\nGuide](https://data.neonscience.org/api/v0/documents/quick-start-guides/NEON.QSG.DP3.30006.001v1?inline=true&fallback=html)\n\nGet started by exploring the [Intro to AOP Data in Google Earth Engine Tutorial Series](\nhttps://www.neonscience.org/resources/learning-hub/tutorials/intro-aop-data-google-earth-engine-tutorial-series)\n\nBrowse and interact with AOP data in the [NEON AOP GEE Data Viewer App](\nhttps://neon-prod-earthengine.projects.earthengine.app/view/neon-aop-gee-data-viewer---desktop)\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-170,16,-66,73]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2021-09-24T14:56:06Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/HSI_REFL/001","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/neon-prod-earthengine/projects_neon-prod-earthengine_assets_HSI_REFL_001","rel":"related","title":"Run the example for projects/neon-prod-earthengine/assets/HSI_REFL/001 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/neon-prod-earthengine/projects_neon-prod-earthengine_assets_HSI_REFL_001_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_neon-prod-earthengine_assets_HSI_REFL_001#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/HSI_REFL/001/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/HSI_REFL/001/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/neon-prod-earthengine/assets/HSI_REFL/002","title":"NEON Surface Bidirectional Reflectance","description":"The NEON AOP Surface Bidirectional Reflectance is a hyperspectral VSWIR (visible\nto shortwave infrared) data product, containing 426 bands spanning wavelengths\nfrom ~380 nm to 2510 nm. Reflectance is scaled by a factor of 10000. Wavelengths\nbetween 1340-1445 nm and 1790-1955 nm are set to -100; these are water vapor\nabsorption bands without valid values. The dataset also contains QA\nraster bands. Major processing steps include orthorectification, plus atmospheric,\ntopographic, and Bidirecitonal Reflectance Distribution Function (BRDF) corrections.\nData are collected by one of three [NEON Imaging Spectrometer (NIS)](\nhttps://www.neonscience.org/data-collection/imaging-spectrometer) sensors,\nAVIRIS-NG spectrometers built by NASA's Jet Propulsion Lab (JPL). Reflectance is\nconverted to a mosaic using the nadir-most pixels from the flight lines acquired\nwith the least cloud cover. Spatial resolution is 1 m and spectral resolution\nis ~5 nm. The center wavelength and full width half max (FWHM) for each band are\nprovided in the Image Properties. Availability in GEE may not represent full\navailability in the NEON Data Portal (linked below). Additional sites and years\ncan be added to GEE upon request by emailing listaopgee@battelleecology.org.\n\nSee [NEON Data Product\nDP3.30006.002](https://data.neonscience.org/data-products/DP3.30006.002) for\nmore details.\n\nGet started by exploring the [Intro to AOP Data in Google Earth Engine Tutorial Series](\nhttps://www.neonscience.org/resources/learning-hub/tutorials/intro-aop-data-google-earth-engine-tutorial-series)\n\nBrowse and interact with AOP data in the [NEON AOP GEE Data Viewer App](\nhttps://neon-prod-earthengine.projects.earthengine.app/view/neon-aop-gee-data-viewer---desktop)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-170,16,-66,73]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2026-08-08T15:15:56Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/HSI_REFL/002","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/neon-prod-earthengine/projects_neon-prod-earthengine_assets_HSI_REFL_002","rel":"related","title":"Run the example for projects/neon-prod-earthengine/assets/HSI_REFL/002 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/neon-prod-earthengine/projects_neon-prod-earthengine_assets_HSI_REFL_002_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_neon-prod-earthengine_assets_HSI_REFL_002#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/HSI_REFL/002/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/HSI_REFL/002/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/neon-prod-earthengine/assets/RGB/001","title":"NEON RGB Camera Imagery","description":"High resolution Red-Green-Blue (RGB) orthorectified camera images mosaicked\nand output onto a fixed, uniform spatial grid using nearest-neighbor resampling;\nspatial resolution is 0.1 m.  The digital camera is part of a suite of instruments\non the NEON Airborne Observation Platform (AOP) that also includes a\nfull-waveform LiDAR system and the NEON Imaging Spectrometer. In the\northorectification process, the digital imagery is re-mapped to the same\ngeographic projection as the LiDAR and imaging spectrometer data that is\nacquired simultaneously. The resulting images share the same map projection\ngrid space as the orthorectified spectrometer and LiDAR imagery. Since the\ndigital camera imagery is acquired at higher spatial resolution than the\nimaging spectrometer data, it can aid in identifying features in the\nspectrometer images including manmade features (e.g., roads, fence lines,\nand buildings) that are indicative of land-use change. Availability in\nGEE may not represent full availability in the NEON Data Portal (linked below).\nAdditional sites and years can be added to GEE upon request by emailing\nlistaopgee@battelleecology.org.\n\nSee [NEON Data Product\nDP3.30010.001](https://data.neonscience.org/data-products/DP3.30010.001) for\nmore details.\n\nDocumentation: [NEON DP3.30010.001 Camera imagery mosaic Quick Start\nGuide](https://data.neonscience.org/api/v0/documents/quick-start-guides/NEON.QSG.DP3.30010.001v1?inline=true&fallback=html)\n\nGet started by exploring the [Intro to AOP Data in Google Earth Engine Tutorial Series](\nhttps://www.neonscience.org/resources/learning-hub/tutorials/intro-aop-data-google-earth-engine-tutorial-series)\n\nBrowse and interact with AOP data in the [NEON AOP GEE Data Viewer App](\nhttps://neon-prod-earthengine.projects.earthengine.app/view/neon-aop-gee-data-viewer---desktop)\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-170,16,-66,73]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2026-07-04T16:19:34Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/RGB/001","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/neon-prod-earthengine/projects_neon-prod-earthengine_assets_RGB_001","rel":"related","title":"Run the example for projects/neon-prod-earthengine/assets/RGB/001 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/neon-prod-earthengine/projects_neon-prod-earthengine_assets_RGB_001_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_neon-prod-earthengine_assets_RGB_001#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC0-1.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/RGB/001/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/neon-prod-earthengine/assets/RGB/001/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/ngis-cat/assets/DEA/NIDEM","title":"Preview National Intertidal Digital Elevation Model 25m 1.0.0","description":"The National Intertidal Digital Elevation Model (NIDEM; Bishop-Taylor et\nal. 2018, 2019) is a continental-scale elevation dataset for Australia's\nexposed intertidal zone. NIDEM provides the first three-dimensional\nrepresentation of Australia's intertidal sandy beaches and shores, tidal\nflats and rocky shores and reefs at 25 m spatial resolution, addressing a\nkey gap between the availability of sub-tidal bathymetry and terrestrial\nelevation data. NIDEM was generated by combining global tidal modelling with\na 30-year time series archive of spatially and spectrally calibrated Landsat\nsatellite data managed within the Digital Earth Australia (DEA) platform.\nNIDEM complements existing intertidal extent products, and provides data to\nsupport a new suite of use cases that require a more detailed understanding\nof the three-dimensional topography of the intertidal zone, such as\nhydrodynamic modelling, coastal risk management and ecological habitat\nmapping.\n\nFor more information, please see the\n[DEA Intertidal Elevation](https://cmi.ga.gov.au/data-products/dea/325/dea-intertidal-elevation-landsat#basics)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[108.81,-44.41,157.82,-9.13]]},"temporal":{"interval":[["1986-08-16T00:00:00Z","2017-07-31T23:59:59Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/ngis-cat/assets/DEA/NIDEM","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ngis-cat/projects_ngis-cat_assets_DEA_NIDEM","rel":"related","title":"Run the example for projects/ngis-cat/assets/DEA/NIDEM in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/ngis-cat/projects_ngis-cat_assets_DEA_NIDEM_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_ngis-cat_assets_DEA_NIDEM#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://doi.org/10.1016/j.ecss.2019.03.006","rel":"cite-as","type":"text/html"},{"href":"https://dapds00.nci.org.au/thredds/catalog/fk4/datacube/002/NIDEM/geotiff/catalog.html","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/ngis-cat/assets/DEA/NIDEM/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/planet-nicfi/assets/basemaps/africa","title":"NICFI Satellite Data Program Basemaps for Tropical Forest Monitoring - Africa","description":"This image collection provides access to high-resolution satellite\nmonitoring of the tropics for the primary purpose of reducing and reversing the\nloss of tropical forests, contributing to combating climate change, conserving\nbiodiversity, contributing to forest regrowth, restoration and enhancement, and\nfacilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how to access the Basemaps, follow the\n[sign up instructions here](https://docs.planet.com/platform/integrations/google-earth-engine/nicfi-gee/).\n\nThe NICFI Satellite Data Program mosaics (also referred to as Planet-NICFI\nmosaics) contain both monthly and biannual collections generated every\n6 months.  The type of the mosaic is stored in the image metadata field\n'cadence'. Use that field along with the start and end date for each mosaic\nto find the desired imagery.\n\nFull details about the Basemaps are available in\n[NICFI Satellite Data Program Basemap spec](https://assets.planet.com/docs/NICFI_Basemap_Spec_Addendum.pdf).\n\nFor more information about the NICFI (Norway's International Climate\nand Forest Initiative) Satellite Data Program and the data offered,\nplease visit\n[the Program's website](https://assets.planet.com/docs/NICFI_General_FAQs.pdf).\n\nIn support of NICFI's mission, you can use this data for a number of projects\nincluding, but not limited to:\n\n* Advance scientific research about the world's tropical forests and the\n  critical services they provide.\n* Implement and improve policies for sustainable forest management and land\n  use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities\n  in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global\n  commodities and financial markets.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-25.5,-30.4,58,25.6]]},"temporal":{"interval":[["2015-12-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/planet-nicfi/assets/basemaps/africa","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/planet-nicfi/projects_planet-nicfi_assets_basemaps_africa","rel":"related","title":"Run the example for projects/planet-nicfi/assets/basemaps/africa in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/planet-nicfi/projects_planet-nicfi_assets_basemaps_africa_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_planet-nicfi_assets_basemaps_africa#terms-of-use","rel":"license","type":"text/html"},{"href":"https://planet.widen.net/s/zfdpf8qxwk/participantlicenseagreement_nicfi_2024","rel":"license","type":"application/pdf"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/planet-nicfi/assets/basemaps/africa/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/planet-nicfi/assets/basemaps/africa/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/planet-nicfi/assets/basemaps/americas","title":"NICFI Satellite Data Program Basemaps for Tropical Forest Monitoring - Americas","description":"This image collection provides access to high-resolution satellite\nmonitoring of the tropics for the primary purpose of reducing and reversing the\nloss of tropical forests, contributing to combating climate change, conserving\nbiodiversity, contributing to forest regrowth, restoration and enhancement, and\nfacilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how to access the Basemaps, follow the\n[sign up instructions here](https://docs.planet.com/platform/integrations/google-earth-engine/nicfi-gee/).\n\nThe NICFI Satellite Data Program mosaics (also referred to as Planet-NICFI\nmosaics) contain both monthly and biannual collections generated every\n6 months.  The type of the mosaic is stored in the image metadata field\n'cadence'. Use that field along with the start and end date for each mosaic\nto find the desired imagery.\n\nFull details about the Basemaps are available in\n[NICFI Satellite Data Program Basemap spec](https://assets.planet.com/docs/NICFI_Basemap_Spec_Addendum.pdf).\n\nFor more information about the NICFI (Norway's International Climate\nand Forest Initiative) Satellite Data Program and the data offered,\nplease visit\n[the Program's website](https://assets.planet.com/docs/NICFI_General_FAQs.pdf).\n\nIn support of NICFI's mission, you can use this data for a number of projects\nincluding, but not limited to:\n\n* Advance scientific research about the world's tropical forests and the\n  critical services they provide.\n* Implement and improve policies for sustainable forest management and land\n  use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities\n  in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global\n  commodities and financial markets.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-118.5,-30.4,-34.5,30.4]]},"temporal":{"interval":[["2015-12-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/planet-nicfi/assets/basemaps/americas","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/planet-nicfi/projects_planet-nicfi_assets_basemaps_americas","rel":"related","title":"Run the example for projects/planet-nicfi/assets/basemaps/americas in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/planet-nicfi/projects_planet-nicfi_assets_basemaps_americas_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_planet-nicfi_assets_basemaps_americas#terms-of-use","rel":"license","type":"text/html"},{"href":"https://planet.widen.net/s/zfdpf8qxwk/participantlicenseagreement_nicfi_2024","rel":"license","type":"application/pdf"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/planet-nicfi/assets/basemaps/americas/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/planet-nicfi/assets/basemaps/americas/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/planet-nicfi/assets/basemaps/asia","title":"NICFI Satellite Data Program Basemaps for Tropical Forest Monitoring - Asia","description":"This image collection provides access to high-resolution satellite\nmonitoring of the tropics for the primary purpose of reducing and reversing the\nloss of tropical forests, contributing to combating climate change, conserving\nbiodiversity, contributing to forest regrowth, restoration and enhancement, and\nfacilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how to access the Basemaps, follow the\n[sign up instructions here](https://docs.planet.com/platform/integrations/google-earth-engine/nicfi-gee/).\n\nThe NICFI Satellite Data Program mosaics (also referred to as Planet-NICFI\nmosaics) contain both monthly and biannual collections generated every\n6 months.  The type of the mosaic is stored in the image metadata field\n'cadence'. Use that field along with the start and end date for each mosaic\nto find the desired imagery.\n\nFull details about the Basemaps are available in\n[NICFI Satellite Data Program Basemap spec](https://assets.planet.com/docs/NICFI_Basemap_Spec_Addendum.pdf).\n\nFor more information about the NICFI (Norway's International Climate\nand Forest Initiative) Satellite Data Program and the data offered,\nplease visit\n[the Program's website](https://assets.planet.com/docs/NICFI_General_FAQs.pdf).\n\nIn support of NICFI's mission, you can use this data for a number of projects\nincluding, but not limited to:\n\n* Advance scientific research about the world's tropical forests and the\n  critical services they provide.\n* Implement and improve policies for sustainable forest management and land\n  use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities\n  in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global\n  commodities and financial markets.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-27.5,180,30.2]]},"temporal":{"interval":[["2015-12-01T00:00:00Z","2026-10-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/planet-nicfi/assets/basemaps/asia","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/planet-nicfi/projects_planet-nicfi_assets_basemaps_asia","rel":"related","title":"Run the example for projects/planet-nicfi/assets/basemaps/asia in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/planet-nicfi/projects_planet-nicfi_assets_basemaps_asia_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_planet-nicfi_assets_basemaps_asia#terms-of-use","rel":"license","type":"text/html"},{"href":"https://planet.widen.net/s/zfdpf8qxwk/participantlicenseagreement_nicfi_2024","rel":"license","type":"application/pdf"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/planet-nicfi/assets/basemaps/asia/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/planet-nicfi/assets/basemaps/asia/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a","title":"PML_V2.2a: Coupled Evapotranspiration and Gross Primary Product (MODIS Edition)","description":"The dataset is produced by the Large Scale Hydrology Lab, which specializes in advancing global and \nregional water cycle research by synthesizing multi-source Earth observations with process-based modeling. \n\nThe PML-V2.2a (MODIS Edition) product provides global terrestrial evapotranspiration (ET) and gross \nprimary production (GPP) at a 500-m, 8-day resolution from 2000 to 2024. Forced by MSWEP and MSWX data, \nthis version features an expanded bottom-up calibration using 208 flux sites and refined parameterizations \nthat explicitly distinguish irrigated from rainfed croplands.\n\nValidation confirms high accuracy across plant functional types (NSE > 0.60, absolute bias < 5%) \nand reliable basin-scale water-balance performance (NSE: 0.89–0.91). Optimized for high-resolution \neco-hydrological monitoring, this MODIS-based record provides a robust historical baseline. Due to \nsatellite orbital drift, this asset will no longer be updated post-2024. For near-present monitoring \nand data extending to 2025, please refer to the companion VIIRS Edition asset, which ensures seamless data continuity.\n\nThis dataset is part of the broader PML-V2.2 suite, where the description paper is available at [Earth System Science Data](https://doi.org/10.5194/essd-18-5663-2026). \nFor the long-term and consolidated record (1982-near present, PML-V2.2a/b/c at 0.1° resolution with different remote sensing forcings), \nplease visit the [TPDC data repository](https://doi.org/10.11888/Terre.tpdc.303314).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["2000-03-05T00:00:00Z","2024-12-26T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/pml_evapotranspiration/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a","rel":"related","title":"Run the example for projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/pml_evapotranspiration/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/pml_evapotranspiration/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a.json","rel":"latest-version","title":"projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/CAS/CAS_IGSNRR_PML_V2_v018.json","rel":"predecessor-version","title":"CAS/IGSNRR/PML/V2_v018","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a_VIIRS","title":"PML_V2.2a: Coupled Evapotranspiration and Gross Primary Product (VIIRS Edition)","description":"The dataset is produced by the Large Scale Hydrology Lab, which specializes in advancing global and \nregional water cycle research by synthesizing multi-source Earth observations with process-based modeling. \n\nThe PML-V2.2a (VIIRS Edition) product provides global terrestrial evapotranspiration (ET) and gross \nprimary production (GPP) at a 500-m, 8-day resolution from 2012 to 2025. Forced by MSWEP and MSWX data, \nthis version features an expanded bottom-up calibration using 208 flux sites and refined parameterizations \nthat explicitly distinguish irrigated from rainfed croplands.\n\nValidation confirms high accuracy across plant functional types (NSE > 0.60, absolute bias < 5%) \nand reliable basin-scale water-balance performance (NSE: 0.89–0.91). Optimized for high-resolution, \nnear-present monitoring, this VIIRS-based record ensures seamless data continuity. For the 2000–2024 \nperiod, please refer to the companion MODIS Edition asset, which will no longer be updated due to satellite orbital drift.\n\nThis dataset is part of the broader PML-V2.2 suite, where the description paper is available at [Earth System Science Data](https://doi.org/10.5194/essd-18-5663-2026).\nplease visit the [TPDC data repository](https://doi.org/10.11888/Terre.tpdc.303314).\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-60,180,90]]},"temporal":{"interval":[["2012-01-25T00:00:00Z","2025-12-27T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a_VIIRS","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/pml_evapotranspiration/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a_VIIRS","rel":"related","title":"Run the example for projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a_VIIRS in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/pml_evapotranspiration/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a_VIIRS_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a_VIIRS#terms-of-use","rel":"license","type":"text/html"},{"href":"https://spdx.org/licenses/CC-BY-4.0.html","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a_VIIRS/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a_VIIRS/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/sat-io/open-datasets/GLOBathy/GLOBathy_bathymetry","title":"GLOBathy Global lakes bathymetry dataset","description":"The GLObal Bathymetric (GLOBathy) dataset, comprising data on over 1.4 million waterbodies globally, has been meticulously developed to harmonize with the widely recognized HydroLAKES dataset. Utilizing a sophisticated Geographic Information System (GIS)-based framework, GLOBathy constructs detailed bathymetric maps by integrating maximum depth estimates and geometric/geophysical attributes sourced from HydroLAKES. Ensuring data accuracy and reliability, GLOBathy undergoes stringent validation procedures involving 1,503 waterbodies and a diverse range of observed data sources. Consequently, GLOBathy stands as a robust and comprehensive dataset for hydrography and aquatic sciences, offering invaluable resources for researchers and professionals in these fields.\n","license":"CC0-1.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2022-01-26T00:00:00Z","2022-01-26T23:59:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/sat-io/open-datasets/GLOBathy/GLOBathy_bathymetry","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/sat-io/projects_sat-io_open-datasets_GLOBathy_GLOBathy_bathymetry","rel":"related","title":"Run the example for projects/sat-io/open-datasets/GLOBathy/GLOBathy_bathymetry in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/sat-io/projects_sat-io_open-datasets_GLOBathy_GLOBathy_bathymetry_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_sat-io_open-datasets_GLOBathy_GLOBathy_bathymetry#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/sat-io/open-datasets/GLOBathy/GLOBathy_bathymetry/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL","title":"LandScan Population Data Global 1km","description":"The LandScan dataset, provided by the Oak Ridge National Laboratory (ORNL), offers a comprehensive and high-resolution global population distribution dataset that serves as a valuable resource for a wide range of applications. Leveraging state-of-the-art spatial modeling techniques and advanced geospatial data sources, LandScan provides detailed information on population counts and density at a 30 arc-second resolution, enabling precise and up-to-date insights into human settlement patterns across the globe. With its accuracy and granularity, LandScan supports diverse fields such as urban planning, disaster response, epidemiology, and environmental research, making it an essential tool for decision-makers and researchers seeking to understand and address various societal and environmental challenges on a global scale.\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2023-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/sat-io/projects_sat-io_open-datasets_ORNL_LANDSCAN_GLOBAL","rel":"related","title":"Run the example for projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/sat-io/projects_sat-io_open-datasets_ORNL_LANDSCAN_GLOBAL_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_sat-io_open-datasets_ORNL_LANDSCAN_GLOBAL#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/sat-io/open-datasets/us-drought-monitor","title":"United States Drought Monitor","description":"The U.S. Drought Monitor is a map released every Thursday, showing parts of the U.S. that are in drought. The map uses five classifications: abnormally dry (D0), showing areas that may be going into or are coming out of drought, and four levels of drought: moderate (D1), severe (D2), extreme (D3) and exceptional (D4). The Drought Monitor has been a team effort since its inception in 1999, produced jointly by the National Drought Mitigation Center (NDMC) at the University of Nebraska-Lincoln, the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Department of Agriculture (USDA). The NDMC hosts the web site of the drought monitor and the associated data, and provides the map and data to NOAA, USDA and other agencies. It is freely available at droughtmonitor.unl.edu.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-04T00:00:00Z","2026-10-27T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/sat-io/open-datasets/us-drought-monitor","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/sat-io/projects_sat-io_open-datasets_us-drought-monitor","rel":"related","title":"Run the example for projects/sat-io/open-datasets/us-drought-monitor in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/sat-io/projects_sat-io_open-datasets_us-drought-monitor_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_sat-io_open-datasets_us-drought-monitor#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/sat-io/open-datasets/us-drought-monitor/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/sat-io/open-datasets/us-drought-monitor/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/gtac-data-publish/assets/TreeMap/Product_Version/2023-1","title":"USFS TreeMap 2023-1","description":"TreeMap datasets provide detailed spatial information on forest characteristics \nacross the entire forested extent of the Conterminous United States (CONUS) for \nthe years 2016, 2020, 2022, and 2023. TreeMap 2014 is also available for CONUS but \nnot included in this data archive. TreeMap 2024 is projected to be released in fall 2026. \n\nFor each year, TreeMap contains 22-band 30 x 30m resolution gridded map images of \nthe forests of the United States, with each band representing an attribute derived \nfrom select Forest Inventory Analysis (FIA) data and one band representing the TreeMap ID \n(TM_ID). Some commonly used attributes have already been generated for users and are \nrepresented here, including forest type, canopy cover percent, live tree stocking, \nlive/dead tree biomass, and carbon in live/dead trees. \n\nTreeMap datasets use [LANDFIRE](https://www.landfire.gov/) data as key input layers and \nare constrained to years when LANDFIRE data are available. TreeMap includes only pixels \ndefined by LANDFIRE as at least 10% forest cover.\n\nTreeMap is produced using a Random Forest machine learning imputation algorithm that \nassigns the most similar FIA plot to each pixel of gridded LANDFIRE input data. The \nobjective is to combine the complementary strengths of detailed-but-spatially-sparse \nFIA data with less-detailed-but-spatially-comprehensive LANDFIRE data to produce better \nestimates of forest characteristics at a variety of scales. \n\nTreeMap is distinct from other imputed forest vegetation products in that it assigns an FIA \nplot identifier to each pixel, whereas other datasets use separate models for each individual \nforest characteristic such as live basal area (e.g., Ohmann and Gregory 2002; Pierce Jr et al. \n2009; Wilson, Lister, and Riemann 2012). The FIA plot identifier can be linked to the hundreds \nof variables and attributes recorded for each tree and plot in the FIA DataMart, FIA's public \nrepository of plot information (Forest Inventory Analysis 2022a). While we provide 21 attribute \nrasters in this data package, users can generate other attributes by linking the TM_ID \nto the FIA plot control number (PLT_CN), which corresponds to a unique plot visit. For \nmore information and a lookup table linking TM_ID to FIA PLT_CN, see the Forest Service \nResearch Data Archive (DOIs given below in “Additional Resources” section).\n\nTreeMap is used in both the private and public sectors for projects including fuel \ntreatment planning, snag hazard mapping, and estimation of terrestrial carbon resources.\n\nThe TreeMap 2016 dataset was produced using the methods described in [Riley et al. (2022)](https://research.fs.usda.gov/treesearch/65597). \nThe TreeMap 2020, 2022, and 2023 datasets were produced using the methods described in \nRiley et al. (2026, In Prep), \nwhich are similar to those used for TreeMap 2016, but differ in that: 1) biophysical \nvariables were derived by overlay of the plot coordinates with Daymet rasters rather \nthan LANDFIRE biophysical variables and included average precipitation, average \nmaximum temperature, average minimum temperature, average vapor pressure, average soil \nwater equivalent, average vapor pressure deficit, average daily shortwave radiation; \nand 2) plots available for imputation in each LANDFIRE zone were limited to those plots \nwith tree species that were present either in the FIA plots found within the LANDFIRE \nzone, or in the zones immediately bordering it. This reduced the number of plots with \ntrees that were imputed outside of their observed range, an issue which affected a small number of pixels in previous TreeMap versions.\n\nThe results for all years showed good correspondence between the target LANDFIRE data \nand the imputed plot data. Values of overall within-class agreement for forest cover, \nforest height, vegetation group, and disturbance code can be found in each vintage's \nmetadata in the USFS Research Data Archive and USFS Raster Data Gateway, as well as in \nRiley et al (In Prep). \n\n**Additional Resources**\n\n* See TreeMap in the Forest Service Research Data Archive for CONUS-wide datasets, the TM_ID to FIA \nPLT_CN lookup table, plot-level tree data (the Tree Table), and additional information on\n[TreeMap 2014](https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0026), [TreeMap 2016](https://www.fs.usda.gov/rds/archive/catalog/RDS-2021-0074), [TreeMap 2020](https://www.fs.usda.gov/rds/archive/catalog/RDS-2025-0031), [TreeMap 2022](https://www.fs.usda.gov/rds/archive/catalog/RDS-2025-0032), and [TreeMap 2023](https://www.fs.usda.gov/rds/archive/catalog/RDS-2026-0038).\n\n* See the [TreeMap 2016 Publication, Riley et al. 2022](https://www.fs.usda.gov/research/treesearch/65597)\nfor more detailed information regarding methods and accuracy assessment.\n\n* The [TreeMap Data Explorer](https://apps.fs.usda.gov/lcms-viewer/treemap.html)\nis a web-based application that provides users with the ability to view and download \nTreeMap attribute data.\n\n* Visit the [TreeMap Raster Data Gateway](https://data.fs.usda.gov/geodata/rastergateway/treemap/) for TreeMap attribute data downloads, metadata, and support documents.\n\n* The [TreeMap GitHub site](https://github.com/firelab/gtac-treemap) contains scripts and documentation for generating TreeMap datasets, including the Random Forest imputation algorithm, and is intended for users who want to generate their own TreeMap datasets.\n\n* See the [FIA Database Manual version 9.4](https://research.fs.usda.gov/understory/forest-inventory-and-analysis-database-user-guide-nfi) for more detailed information on the attributes included in TreeMap.\n\nContact sm.fs.treemaphelp@usda.gov with any questions or specific data requests.\n\n\n\n","license":"proprietary","extent":{"spatial":{"bbox":[[-128.97722,22.76862,-65.25445,51.64968]]},"temporal":{"interval":[["2016-01-01T00:00:00Z","2023-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/gtac-data-publish/assets/TreeMap/Product_Version/2023-1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/gtac-data-publish/projects_gtac-data-publish_assets_TreeMap_Product_Version_2023-1","rel":"related","title":"Run the example for projects/gtac-data-publish/assets/TreeMap/Product_Version/2023-1 in the Earth Engine Code 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Aqua Surface Reflectance Daily Global 1km and 500m","description":"The MODIS Surface Reflectance products provide an estimate\nof the surface spectral reflectance as it would be measured at\nground level in the absence of atmospheric scattering or absorption.\nLow-level data are corrected for atmospheric gases and aerosols.\nMYD09GA version 6.1 provides bands 1-7 in a daily gridded L2G product\nin the sinusoidal projection, including 500m reflectance values\nand 1km observation and geolocation statistics.\n\nDocumentation:\n\n* [User's Guide](https://lpdaac.usgs.gov/documents/306/MOD09_User_Guide_V6.pdf)\n\n* [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/305/MOD09_ATBD.pdf)\n\n* [General Documentation](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD09GA)\n","license":"proprietary","extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-07-04T00:00:00Z","2026-11-08T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09GA","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/MODIS/MODIS_061_MYD09GA","rel":"related","title":"Run the example for MODIS/061/MYD09GA in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/MODIS/MODIS_061_MYD09GA_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GA#terms-of-use","rel":"license","type":"text/html"},{"href":"https://doi.org/10.5067/MODIS/MYD09GA.061","rel":"cite-as"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GA.json","rel":"latest-version","title":"MODIS/061/MYD09GA","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_006_MYD09GA.json","rel":"predecessor-version","title":"MODIS/006/MYD09GA","type":"application/json"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09GA/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/MODIS/061/MYD09GA/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"Finland/SMK/VV/50cm","title":"Finland NRG NLS orthophotos 50 cm by SMK","description":"NLS Orthophotos are an aerial photo dataset covering the whole of Finland.\nThis data is provided by SMK([The Energy Agency](https://energiavirasto.fi/etusivu),\nformerly abbreviated SMK). An orthophoto is a combination of several\nindividual aerial photos. The aerial photo dataset in orthophoto format is\navailable as the most recent dataset consisting of the most recent aerial\nphotos available. The most recent data is usually 1-3 years old. NLS\nOrthophotos are updated every 3 years (in Northern Lapland 12 years).\n\nIn these images, the bands are near-infrared, red, and green.\n\n(In Dataset id, VV stands for \"v&auml;&auml;r&auml;v&auml;ri\", false color)\n\nFor more information, please see the\n[NLS orthophotos documentation](https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/product-descriptions/orthophotos)\n","license":"CC-BY-4.0","extent":{"spatial":{"bbox":[[18,59,29.2,69.4]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2022-01-01T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/Finland/SMK/VV/50cm","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Finland/Finland_SMK_VV_50cm","rel":"related","title":"Run the example for Finland/SMK/VV/50cm in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/Finland/Finland_SMK_VV_50cm_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/Finland_SMK_VV_50cm#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/Finland/SMK/VV/50cm/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/Finland/SMK/VV/50cm/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"projects/gtac-data-publish/assets/TreeMap/Product_Version/2026-1","title":"USFS TreeMap 2026-1","description":"TreeMap datasets provide detailed spatial information on forest characteristics \nacross the entire forested extent of the Conterminous United States (CONUS) for \nthe years 2016, 2020, 2022, and 2023. TreeMap 2014 is also available for CONUS but \nnot included in this data archive. TreeMap 2024 is projected to be released in fall 2026. \n\nFor each year, TreeMap contains 22-band 30 x 30m resolution gridded map images of \nthe forests of the United States, with each band representing an attribute derived \nfrom select Forest Inventory Analysis (FIA) data and one band representing the TreeMap ID \n(TM_ID). Some commonly used attributes have already been generated for users and are \nrepresented here, including forest type, canopy cover percent, live tree stocking, \nlive/dead tree biomass, and carbon in live/dead trees. \n\nTreeMap datasets use [LANDFIRE](https://www.landfire.gov/) data as key input layers and \nare constrained to years when LANDFIRE data are available. TreeMap includes only pixels \ndefined by LANDFIRE as at least 10% forest cover.\n\nTreeMap is produced using a Random Forest machine learning imputation algorithm that \nassigns the most similar FIA plot to each pixel of gridded LANDFIRE input data. The \nobjective is to combine the complementary strengths of detailed-but-spatially-sparse \nFIA data with less-detailed-but-spatially-comprehensive LANDFIRE data to produce better \nestimates of forest characteristics at a variety of scales. \n\nTreeMap is distinct from other imputed forest vegetation products in that it assigns an FIA \nplot identifier to each pixel, whereas other datasets use separate models for each individual \nforest characteristic such as live basal area (e.g., Ohmann and Gregory 2002; Pierce Jr et al. \n2009; Wilson, Lister, and Riemann 2012). The FIA plot identifier can be linked to the hundreds \nof variables and attributes recorded for each tree and plot in the FIA DataMart, FIA's public \nrepository of plot information (Forest Inventory Analysis 2022a). While we provide 21 attribute \nrasters in this data package, users can generate other attributes by linking the TM_ID \nto the FIA plot control number (PLT_CN), which corresponds to a unique plot visit. For \nmore information and a lookup table linking TM_ID to FIA PLT_CN, see the Forest Service \nResearch Data Archive (DOIs given below in “Additional Resources” section).\n\nTreeMap is used in both the private and public sectors for projects including fuel \ntreatment planning, snag hazard mapping, and estimation of terrestrial carbon resources.\n\nThe TreeMap 2016 dataset was produced using the methods described in [Riley et al. (2022)](https://research.fs.usda.gov/treesearch/65597). \nThe TreeMap 2020, 2022, and 2023 datasets were produced using the methods described in \nRiley et al. (2026, In Prep), \nwhich are similar to those used for TreeMap 2016, but differ in that: 1) biophysical \nvariables were derived by overlay of the plot coordinates with Daymet rasters rather \nthan LANDFIRE biophysical variables and included average precipitation, average \nmaximum temperature, average minimum temperature, average vapor pressure, average soil \nwater equivalent, average vapor pressure deficit, average daily shortwave radiation; \nand 2) plots available for imputation in each LANDFIRE zone were limited to those plots \nwith tree species that were present either in the FIA plots found within the LANDFIRE \nzone, or in the zones immediately bordering it. This reduced the number of plots with \ntrees that were imputed outside of their observed range, an issue which affected a small number of pixels in previous TreeMap versions.\n\nThe results for all years showed good correspondence between the target LANDFIRE data \nand the imputed plot data. Values of overall within-class agreement for forest cover, \nforest height, vegetation group, and disturbance code can be found in each vintage's \nmetadata in the USFS Research Data Archive and USFS Raster Data Gateway, as well as in \nRiley et al (In Prep). \n\n**Additional Resources**\n\n* See TreeMap in the Forest Service Research Data Archive for CONUS-wide datasets, the TM_ID to FIA \nPLT_CN lookup table, plot-level tree data (the Tree Table), and additional information on\n[TreeMap 2014](https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0026), [TreeMap 2016](https://www.fs.usda.gov/rds/archive/catalog/RDS-2021-0074), [TreeMap 2020](https://www.fs.usda.gov/rds/archive/catalog/RDS-2025-0031), [TreeMap 2022](https://www.fs.usda.gov/rds/archive/catalog/RDS-2025-0032), and [TreeMap 2023](https://www.fs.usda.gov/rds/archive/catalog/RDS-2026-0038).\n\n* See the [TreeMap 2016 Publication, Riley et al. 2022](https://www.fs.usda.gov/research/treesearch/65597)\nfor more detailed information regarding methods and accuracy assessment.\n\n* The [TreeMap Data Explorer](https://apps.fs.usda.gov/lcms-viewer/treemap.html)\nis a web-based application that provides users with the ability to view and download \nTreeMap attribute data.\n\n* Visit the [TreeMap Raster Data Gateway](https://data.fs.usda.gov/geodata/rastergateway/treemap/) for TreeMap attribute data downloads, metadata, and support documents.\n\n* The [TreeMap GitHub site](https://github.com/firelab/gtac-treemap) contains scripts and documentation for generating TreeMap datasets, including the Random Forest imputation algorithm, and is intended for users who want to generate their own TreeMap datasets.\n\n* See the [FIA Database Manual version 9.4](https://research.fs.usda.gov/understory/forest-inventory-and-analysis-database-user-guide-nfi) for more detailed information on the attributes included in TreeMap.\n\nContact sm.fs.treemaphelp@usda.gov with any questions or specific data requests.\n\n\n\n","license":"proprietary","extent":{"spatial":{"bbox":[[-128.97722,22.76862,-65.25445,51.64968]]},"temporal":{"interval":[["2016-01-01T00:00:00Z","2023-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/projects/gtac-data-publish/assets/TreeMap/Product_Version/2026-1","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/gtac-data-publish/projects_gtac-data-publish_assets_TreeMap_Product_Version_2026-1","rel":"related","title":"Run the example for projects/gtac-data-publish/assets/TreeMap/Product_Version/2026-1 in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/gtac-data-publish/projects_gtac-data-publish_assets_TreeMap_Product_Version_2026-1_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/projects_gtac-data-publish_assets_TreeMap_Product_Version_2026-1#terms-of-use","rel":"license","type":"text/html"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/gtac-data-publish/projects_gtac-data-publish_assets_TreeMap_Product_Version_2026-1.json","rel":"latest-version","title":"projects/gtac-data-publish/assets/TreeMap/Product_Version/2026-1","type":"application/json"},{"href":"https://storage.googleapis.com/earthengine-stac/catalog/USFS/USFS_GTAC_TreeMap_v2022.json","rel":"predecessor-version","title":"USFS/GTAC/TreeMap/v2022","type":"application/json"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/treemap/","rel":"license","type":"text/html"},{"href":"https://data.fs.usda.gov/geodata/rastergateway/treemap/","rel":"source"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/projects/gtac-data-publish/assets/TreeMap/Product_Version/2026-1/queryables","title":"Queryables","type":"application/schema+json"},{"rel":"items","href":"https://earthengine.openeo.org/v1.0/collections/projects/gtac-data-publish/assets/TreeMap/Product_Version/2026-1/items","type":"application/geo+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Vegetation/ESP/v1_2_0/CONUS","title":"LANDFIRE ESP CONUS (Environmental Site Potential) v1.2.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLANDFIRE (LF) layers are created using predictive landscape models based on\nextensive field-referenced data, satellite imagery and biophysical gradient\nlayers using classification and regression trees.\n\nLANDFIRE's (LF) Environmental Site Potential (ESP) represents the vegetation that could be supported at a given site based on the biophysical environment.\nMap units are based on NatureServe's Ecological Systems classification and represent the natural plant communities that may have been present during the reference period.\nESP map units represent the natural plant communities that would become established at late or climax stages of successional development in the absence of disturbance.\nThey reflect the current climate and physical environment, as well as the competitive potential of native plant species. The ESP concept is similar to that used in classifications of potential vegetation, including habitat types and plant associations.\nIn early versions of LF, ESP was used to inform the existing vegetation and fuel mapping processes.\n\nThe LANDIFRE Vegetation datasets include:\n\n* Biophysical Settings (BPS)\n* Environmental Site Potential (ESP)\n* Existing Vegetation Canopy Cover (EVC)\n* Existing Vegetation Height (EVH).\n* Existing Vegetation Type (EVT)\nThese layers are created using predictive landscape models based on extensive\nfield-referenced data, satellite imagery and biophysical gradient layers using\nclassification and regression trees.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/ESP/v1_2_0/CONUS","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Vegetation_ESP_v1_2_0_CONUS","rel":"related","title":"Run the example for LANDFIRE/Vegetation/ESP/v1_2_0/CONUS in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Vegetation_ESP_v1_2_0_CONUS_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_ESP_v1_2_0_CONUS#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/ESP/v1_2_0/CONUS/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Vegetation/ESP/v1_2_0/AK","title":"LANDFIRE ESP AK (Environmental Site Potential) v1.2.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLANDFIRE (LF) layers are created using predictive landscape models based on\nextensive field-referenced data, satellite imagery and biophysical gradient\nlayers using classification and regression trees.\n\nLANDFIRE's (LF) Environmental Site Potential (ESP) represents the vegetation that could be supported at a given site based on the biophysical environment.\nMap units are based on NatureServe's Ecological Systems classification and represent the natural plant communities that may have been present during the reference period.\nESP map units represent the natural plant communities that would become established at late or climax stages of successional development in the absence of disturbance.\nThey reflect the current climate and physical environment, as well as the competitive potential of native plant species. The ESP concept is similar to that used in classifications of potential vegetation, including habitat types and plant associations.\nIn early versions of LF, ESP was used to inform the existing vegetation and fuel mapping processes.\n\nThe LANDIFRE Vegetation datasets include:\n\n* Biophysical Settings (BPS)\n* Environmental Site Potential (ESP)\n* Existing Vegetation Canopy Cover (EVC)\n* Existing Vegetation Height (EVH).\n* Existing Vegetation Type (EVT)\nThese layers are created using predictive landscape models based on extensive\nfield-referenced data, satellite imagery and biophysical gradient layers using\nclassification and regression trees.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/ESP/v1_2_0/AK","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Vegetation_ESP_v1_2_0_AK","rel":"related","title":"Run the example for LANDFIRE/Vegetation/ESP/v1_2_0/AK in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Vegetation_ESP_v1_2_0_AK_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_ESP_v1_2_0_AK#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/ESP/v1_2_0/AK/queryables","title":"Queryables","type":"application/schema+json"}]},{"stac_version":"1.0.0","stac_extensions":[],"type":"Collection","id":"LANDFIRE/Vegetation/ESP/v1_2_0/HI","title":"LANDFIRE ESP HI (Environmental Site Potential) v1.2.0","description":"LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a\nshared program between the wildland fire management programs of the U.S.\nDepartment of Agriculture's Forest Service, U.S. Department of the Interior's\nGeological Survey, and The Nature Conservancy.\n\nLANDFIRE (LF) layers are created using predictive landscape models based on\nextensive field-referenced data, satellite imagery and biophysical gradient\nlayers using classification and regression trees.\n\nLANDFIRE's (LF) Environmental Site Potential (ESP) represents the vegetation that could be supported at a given site based on the biophysical environment.\nMap units are based on NatureServe's Ecological Systems classification and represent the natural plant communities that may have been present during the reference period.\nESP map units represent the natural plant communities that would become established at late or climax stages of successional development in the absence of disturbance.\nThey reflect the current climate and physical environment, as well as the competitive potential of native plant species. The ESP concept is similar to that used in classifications of potential vegetation, including habitat types and plant associations.\nIn early versions of LF, ESP was used to inform the existing vegetation and fuel mapping processes.\n\nThe LANDIFRE Vegetation datasets include:\n\n* Biophysical Settings (BPS)\n* Environmental Site Potential (ESP)\n* Existing Vegetation Canopy Cover (EVC)\n* Existing Vegetation Height (EVH).\n* Existing Vegetation Type (EVT)\nThese layers are created using predictive landscape models based on extensive\nfield-referenced data, satellite imagery and biophysical gradient layers using\nclassification and regression trees.\n","license":"proprietary","extent":{"spatial":{"bbox":[[-175.1,17.52,-63.66,71.48]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2010-12-31T00:00:00Z"]]}},"links":[{"href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/ESP/v1_2_0/HI","rel":"self","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"parent","type":"application/json"},{"href":"https://earthengine.openeo.org/v1.0/","rel":"root","type":"application/json"},{"code":"JavaScript","href":"https://code.earthengine.google.com/?scriptPath=Examples:Datasets/LANDFIRE/LANDFIRE_Vegetation_ESP_v1_2_0_HI","rel":"related","title":"Run the example for LANDFIRE/Vegetation/ESP/v1_2_0/HI in the Earth Engine Code Editor","type":"text/html"},{"href":"https://developers.google.com/earth-engine/datasets/images/LANDFIRE/LANDFIRE_Vegetation_ESP_v1_2_0_HI_sample.png","rel":"preview","type":"image/png"},{"href":"https://developers.google.com/earth-engine/datasets/catalog/LANDFIRE_Vegetation_ESP_v1_2_0_HI#terms-of-use","rel":"license","type":"text/html"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://earthengine.openeo.org/v1.0/collections/LANDFIRE/Vegetation/ESP/v1_2_0/HI/queryables","title":"Queryables","type":"application/schema+json"}]}],"links":[{"rel":"self","href":"https://earthengine.openeo.org/v1.0/collections","type":"application/json"},{"rel":"root","href":"https://earthengine.openeo.org/v1.0/","type":"application/json"},{"rel":"alternate","href":"https://earthengine.openeo.org/v1.0/stac","title":"STAC API","type":"application/json"},{"rel":"alternate","href":"https://developers.google.com/earth-engine/datasets/catalog/","type":"text/html","title":"Human-readable Earth Engine Data Catalog"},{"href":"https://earthengine-stac.storage.googleapis.com/catalog/catalog.json","rel":"alternate","type":"application/json","title":"Machine-readable Earth Engine Data Catalog"}]}