- |---| | [### Cocoa Probability model 2026a ![](https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_cocoa_model_2026a_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_cocoa_model_2026a) | | This image collection provides estimated per-pixel probability that the underlying area is occupied by the commodity. The probability estimates are provided at 10 meter resolution, and have been generated by a machine learning model. For details, see the technical documentation on the Forest Data Partnership ... | | [agriculture](https://developers.google.com/earth-engine/datasets/tags/agriculture) [alphaearth-derived](https://developers.google.com/earth-engine/datasets/tags/alphaearth-derived) [biodiversity](https://developers.google.com/earth-engine/datasets/tags/biodiversity) [conservation](https://developers.google.com/earth-engine/datasets/tags/conservation) [crop](https://developers.google.com/earth-engine/datasets/tags/crop) [eudr](https://developers.google.com/earth-engine/datasets/tags/eudr) | - |---| | [### Coffee Probability model 2026a ![](https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_coffee_model_2026a_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_coffee_model_2026a) | | This image collection provides estimated per-pixel probability that the underlying area is occupied by the commodity. The probability estimates are provided at 10 meter resolution, and have been generated by a machine learning model. For details, see the technical documentation on the Forest Data Partnership ... | | [agriculture](https://developers.google.com/earth-engine/datasets/tags/agriculture) [alphaearth-derived](https://developers.google.com/earth-engine/datasets/tags/alphaearth-derived) [biodiversity](https://developers.google.com/earth-engine/datasets/tags/biodiversity) [conservation](https://developers.google.com/earth-engine/datasets/tags/conservation) [crop](https://developers.google.com/earth-engine/datasets/tags/crop) [eudr](https://developers.google.com/earth-engine/datasets/tags/eudr) | - |---| | [### DESS China Terrace Map v1 ![](https://developers.google.com/earth-engine/datasets/images/Tsinghua/Tsinghua_DESS_ChinaTerraceMap_v1_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/Tsinghua_DESS_ChinaTerraceMap_v1) | | This dataset is a China terrace map at 30 m resolution in 2018. It was developed through supervised pixel-based classification using multisource and multi-temporal data based on the Google Earth Engine platform. The overall accuracy and kappa coefficient achieved 94% and 0.72, respectively. This first ... | | [agriculture](https://developers.google.com/earth-engine/datasets/tags/agriculture) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse) [landuse-landcover](https://developers.google.com/earth-engine/datasets/tags/landuse-landcover) [tsinghua](https://developers.google.com/earth-engine/datasets/tags/tsinghua) | - |---| | [### Dynamic World V1 ![](https://developers.google.com/earth-engine/datasets/images/GOOGLE/GOOGLE_DYNAMICWORLD_V1_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1) | | Dynamic World is a 10m near-real-time (NRT) Land Use/Land Cover (LULC) dataset that includes class probabilities and label information for nine classes. Dynamic World predictions are available for the Sentinel-2 L1C collection from 2015-06-27 to present. The revisit frequency of Sentinel-2 is between 2-5 days ... | | [global](https://developers.google.com/earth-engine/datasets/tags/global) [google](https://developers.google.com/earth-engine/datasets/tags/google) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse) [landuse-landcover](https://developers.google.com/earth-engine/datasets/tags/landuse-landcover) [nrt](https://developers.google.com/earth-engine/datasets/tags/nrt) | - |---| | [### ESA WorldCover 10m v100 ![](https://developers.google.com/earth-engine/datasets/images/ESA/ESA_WorldCover_v100_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100) | | The European Space Agency (ESA) WorldCover 10 m 2020 product provides a global land cover map for 2020 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes and has been generated in the framework of ... | | [esa](https://developers.google.com/earth-engine/datasets/tags/esa) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse) [landuse-landcover](https://developers.google.com/earth-engine/datasets/tags/landuse-landcover) [sentinel1-derived](https://developers.google.com/earth-engine/datasets/tags/sentinel1-derived) [sentinel2-derived](https://developers.google.com/earth-engine/datasets/tags/sentinel2-derived) | - |---| | [### ESA WorldCover 10m v200 ![](https://developers.google.com/earth-engine/datasets/images/ESA/ESA_WorldCover_v200_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v200) | | The European Space Agency (ESA) WorldCover 10 m 2021 product provides a global land cover map for 2021 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes and has been generated in the framework of ... | | [esa](https://developers.google.com/earth-engine/datasets/tags/esa) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse) [landuse-landcover](https://developers.google.com/earth-engine/datasets/tags/landuse-landcover) [sentinel1-derived](https://developers.google.com/earth-engine/datasets/tags/sentinel1-derived) [sentinel2-derived](https://developers.google.com/earth-engine/datasets/tags/sentinel2-derived) | - |---| | [### GPW Annual Dominant Class of Grasslands v1 ![](https://developers.google.com/earth-engine/datasets/images/global-pasture-watch/projects_global-pasture-watch_assets_ggc-30m_v1_grassland_c_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_grassland_c) | | This dataset provides global annual dominant class maps of grasslands (cultivated and natural/semi-natural) from 2000 to 2022 at 30-m spatial resolution. Produced by the Land \& Carbon Lab Global Pasture Watch initiative, the mapped grassland extent includes any land cover type, which contains at least ... | | [global](https://developers.google.com/earth-engine/datasets/tags/global) [global-pasture-watch](https://developers.google.com/earth-engine/datasets/tags/global-pasture-watch) [land](https://developers.google.com/earth-engine/datasets/tags/land) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse) [landuse-landcover](https://developers.google.com/earth-engine/datasets/tags/landuse-landcover) | - |---| | [### GPW Annual Probabilities of Cultivated Grasslands v1 ![](https://developers.google.com/earth-engine/datasets/images/global-pasture-watch/projects_global-pasture-watch_assets_ggc-30m_v1_cultiv-grassland_p_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_cultiv-grassland_p) | | This dataset provides global annual probability maps of cultivated grassland from 2000 to 2022 at 30-m spatial resolution. Produced by the Land \& Carbon Lab Global Pasture Watch initiative, the mapped grassland extent includes any land cover type, which contains at least 30% of dry ... | | [global](https://developers.google.com/earth-engine/datasets/tags/global) [global-pasture-watch](https://developers.google.com/earth-engine/datasets/tags/global-pasture-watch) [land](https://developers.google.com/earth-engine/datasets/tags/land) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse) [landuse-landcover](https://developers.google.com/earth-engine/datasets/tags/landuse-landcover) | - |---| | [### GPW Annual Probabilities of Natural/Semi-natural Grasslands v1 ![](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)](https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_nat-semi-grassland_p) | | This dataset provides global annual probability maps of natural/semi-natural grassland from 2000 to 2022 at 30-m spatial resolution. Produced by the Land \& Carbon Lab Global Pasture Watch initiative, the mapped grassland extent includes any land cover type, which contains at least 30% of dry ... | | [global](https://developers.google.com/earth-engine/datasets/tags/global) [global-pasture-watch](https://developers.google.com/earth-engine/datasets/tags/global-pasture-watch) [land](https://developers.google.com/earth-engine/datasets/tags/land) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse) [landuse-landcover](https://developers.google.com/earth-engine/datasets/tags/landuse-landcover) | - |---| | [### GPW Annual livestock headcount FAOSTAT-adjusted v1 ![](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)](https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_gld-1km_v1_livestock-headcount-faostat_m) | | This dataset provides global livestock headcount from 2000 to 2022 at 1-km spatial resolution. Produced by the Land \& 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 ... | | [agriculture](https://developers.google.com/earth-engine/datasets/tags/agriculture) [global](https://developers.google.com/earth-engine/datasets/tags/global) [global-pasture-watch](https://developers.google.com/earth-engine/datasets/tags/global-pasture-watch) [gpw](https://developers.google.com/earth-engine/datasets/tags/gpw) [land](https://developers.google.com/earth-engine/datasets/tags/land) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse) | - |---| | [### GPW Annual uncalibrated Gross Primary Productivity (uGPP) v1 ![](https://developers.google.com/earth-engine/datasets/images/global-pasture-watch/projects_global-pasture-watch_assets_ggpp-30m_v1_ugpp_m_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggpp-30m_v1_ugpp_m) | | This dataset provides global uncalibrated EO-based Gross Primary Productivity from 2000 at 30-m spatial resolution. Produced by the Land \& Carbon Lab Global Pasture Watch initiative, the current dataset provides Gross Primary Productivity (GPP) values globally at 30-m spatial resolution from 2000 onwards. GPP values ... | | [global](https://developers.google.com/earth-engine/datasets/tags/global) [global-pasture-watch](https://developers.google.com/earth-engine/datasets/tags/global-pasture-watch) [land](https://developers.google.com/earth-engine/datasets/tags/land) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse) [plant-productivity](https://developers.google.com/earth-engine/datasets/tags/plant-productivity) | - |---| | [### Global Oil Palm Extent and Planting Year (1990-2021) v1.2 ![](https://developers.google.com/earth-engine/datasets/images/BIOPAMA/BIOPAMA_GlobalOilPalm_v1_2_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/BIOPAMA_GlobalOilPalm_v1_2) | | Global oil palm extent for 2016-2021 at 10 m resolution and oil palm planting year from 1990 to 2021 at 30 m resolution. The dataset consists of 609 100x100 km grid tiles covering tropical and subtropical oil-palm-growing regions worldwide, mapping 23.98 Mha of closed-canopy oil ... | | [agriculture](https://developers.google.com/earth-engine/datasets/tags/agriculture) [biodiversity](https://developers.google.com/earth-engine/datasets/tags/biodiversity) [conservation](https://developers.google.com/earth-engine/datasets/tags/conservation) [crop](https://developers.google.com/earth-engine/datasets/tags/crop) [global](https://developers.google.com/earth-engine/datasets/tags/global) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) | - |---| | [### Google Global Landsat-based CCDC Segments (1999-2019) ![](https://developers.google.com/earth-engine/datasets/images/GOOGLE/GOOGLE_GLOBAL_CCDC_V1_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_GLOBAL_CCDC_V1) | | This collection contains precomputed results from running the Continuous Change Detection and Classification (CCDC) algorithm on 20 years of Landsat surface reflectance data. CCDC is a break-point finding algorithm that uses harmonic fitting with a dynamic RMSE threshold to detect breakpoints in time-series data. The ... | | [change-detection](https://developers.google.com/earth-engine/datasets/tags/change-detection) [google](https://developers.google.com/earth-engine/datasets/tags/google) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) [landsat-derived](https://developers.google.com/earth-engine/datasets/tags/landsat-derived) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse) [landuse-landcover](https://developers.google.com/earth-engine/datasets/tags/landuse-landcover) | - |---| | [### LUCAS Copernicus (Polygons with attributes, 2018) V1 ![](https://developers.google.com/earth-engine/datasets/images/JRC/JRC_LUCAS_HARMO_COPERNICUS_POLYGONS_V1_2018_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/JRC_LUCAS_HARMO_COPERNICUS_POLYGONS_V1_2018) | | The Land Use/Cover Area frame Survey (LUCAS) in the European Union (EU) was set up to provide statistical information. It represents a triennial in-situ landcover and land-use data-collection exercise that extends over the whole of the EU's territory. LUCAS collects information on land cover and ... | | [copernicus](https://developers.google.com/earth-engine/datasets/tags/copernicus) [eu](https://developers.google.com/earth-engine/datasets/tags/eu) [jrc](https://developers.google.com/earth-engine/datasets/tags/jrc) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse) [landuse-landcover](https://developers.google.com/earth-engine/datasets/tags/landuse-landcover) | - |---| | [### LUCAS Harmonized (Theoretical Location, 2006-2018) V1 ![](https://developers.google.com/earth-engine/datasets/images/JRC/JRC_LUCAS_HARMO_THLOC_V1_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/JRC_LUCAS_HARMO_THLOC_V1) | | The Land Use/Cover Area frame Survey (LUCAS) in the European Union (EU) was set up to provide statistical information. It represents a triennial in-situ landcover and land-use data-collection exercise that extends over the whole of the EU's territory. LUCAS collects information on land cover and ... | | [eu](https://developers.google.com/earth-engine/datasets/tags/eu) [jrc](https://developers.google.com/earth-engine/datasets/tags/jrc) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse) [landuse-landcover](https://developers.google.com/earth-engine/datasets/tags/landuse-landcover) [lucas](https://developers.google.com/earth-engine/datasets/tags/lucas) | - |---| | [### LUCAS THLOC (Points with attributes, 2022) V1 ![](https://developers.google.com/earth-engine/datasets/images/JRC/JRC_LUCAS_THLOC_V1_2022_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/JRC_LUCAS_THLOC_V1_2022) | | The Land Use/Cover Area frame Survey (LUCAS) in the European Union (EU) was set up to provide statistical information. It represents a triennial in-situ landcover and land-use data-collection exercise that extends over the whole of the EU's territory. LUCAS collects information on land cover and ... | | [eu](https://developers.google.com/earth-engine/datasets/tags/eu) [jrc](https://developers.google.com/earth-engine/datasets/tags/jrc) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) [landuse](https://developers.google.com/earth-engine/datasets/tags/landuse) [landuse-landcover](https://developers.google.com/earth-engine/datasets/tags/landuse-landcover) [lucas](https://developers.google.com/earth-engine/datasets/tags/lucas) | - |---| | [### Palm Probability model 2026a ![](https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2026a_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_palm_model_2026a) | | This image collection provides estimated per-pixel probability that the underlying area is occupied by the commodity. The probability estimates are provided at 10 meter resolution, and have been generated by a machine learning model. For details, see the technical documentation on the Forest Data Partnership ... | | [agriculture](https://developers.google.com/earth-engine/datasets/tags/agriculture) [alphaearth-derived](https://developers.google.com/earth-engine/datasets/tags/alphaearth-derived) [biodiversity](https://developers.google.com/earth-engine/datasets/tags/biodiversity) [conservation](https://developers.google.com/earth-engine/datasets/tags/conservation) [crop](https://developers.google.com/earth-engine/datasets/tags/crop) [eudr](https://developers.google.com/earth-engine/datasets/tags/eudr) | - |---| | [### Rubber Probability model 2026a ![](https://developers.google.com/earth-engine/datasets/images/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2026a_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_rubber_model_2026a) | | This image collection provides estimated per-pixel probability that the underlying area is occupied by the commodity. The probability estimates are provided at 10 meter resolution, and have been generated by a machine learning model. For details, see the technical documentation on the Forest Data Partnership ... | | [agriculture](https://developers.google.com/earth-engine/datasets/tags/agriculture) [alphaearth-derived](https://developers.google.com/earth-engine/datasets/tags/alphaearth-derived) [biodiversity](https://developers.google.com/earth-engine/datasets/tags/biodiversity) [conservation](https://developers.google.com/earth-engine/datasets/tags/conservation) [crop](https://developers.google.com/earth-engine/datasets/tags/crop) [eudr](https://developers.google.com/earth-engine/datasets/tags/eudr) | - |---| | [### USFS Landscape Change Monitoring System v2025-11 (CONUS and OCONUS) ![](https://developers.google.com/earth-engine/datasets/images/gtac-data-publish/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/projects_gtac-data-publish_assets_LCMS_Product_Version_2025-11) | | This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS-modeled change, land cover, and/or land use classes for each year and covers the Conterminous United States (CONUS) as well as areas outside the CONUS (OCONUS) including Alaska (AK), Puerto ... | | [change-detection](https://developers.google.com/earth-engine/datasets/tags/change-detection) [forest](https://developers.google.com/earth-engine/datasets/tags/forest) [fsic-go](https://developers.google.com/earth-engine/datasets/tags/fsic-go) [gtac](https://developers.google.com/earth-engine/datasets/tags/gtac) [gtac-data-publish](https://developers.google.com/earth-engine/datasets/tags/gtac-data-publish) [landcover](https://developers.google.com/earth-engine/datasets/tags/landcover) | - |---| | [### WRI/Google DeepMind Global Drivers of Forest Loss 2001-2022 v1.0 ![](https://developers.google.com/earth-engine/datasets/images/landandcarbon/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2001_2022_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2001_2022) | | This dataset maps the dominant driver of tree cover loss from 2001-2022 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected ... | | [agriculture](https://developers.google.com/earth-engine/datasets/tags/agriculture) [deforestation](https://developers.google.com/earth-engine/datasets/tags/deforestation) [forest](https://developers.google.com/earth-engine/datasets/tags/forest) [forest-biomass](https://developers.google.com/earth-engine/datasets/tags/forest-biomass) [google](https://developers.google.com/earth-engine/datasets/tags/google) [landandcarbon](https://developers.google.com/earth-engine/datasets/tags/landandcarbon) | - |---| | [### WRI/Google DeepMind Global Drivers of Forest Loss 2001-2023 v1.1 ![](https://developers.google.com/earth-engine/datasets/images/landandcarbon/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_1_2001_2023_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_1_2001_2023) | | This dataset maps the dominant driver of tree cover loss from 2001-2023 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected ... | | [agriculture](https://developers.google.com/earth-engine/datasets/tags/agriculture) [deforestation](https://developers.google.com/earth-engine/datasets/tags/deforestation) [forest](https://developers.google.com/earth-engine/datasets/tags/forest) [forest-biomass](https://developers.google.com/earth-engine/datasets/tags/forest-biomass) [google](https://developers.google.com/earth-engine/datasets/tags/google) [landandcarbon](https://developers.google.com/earth-engine/datasets/tags/landandcarbon) | - |---| | [### WRI/Google DeepMind Global Drivers of Forest Loss 2001-2024 v1.2 ![](https://developers.google.com/earth-engine/datasets/images/landandcarbon/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2_2001_2024_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_2_2001_2024) | | This dataset maps the dominant driver of tree cover loss from 2001-2024 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected ... | | [agriculture](https://developers.google.com/earth-engine/datasets/tags/agriculture) [deforestation](https://developers.google.com/earth-engine/datasets/tags/deforestation) [forest](https://developers.google.com/earth-engine/datasets/tags/forest) [forest-biomass](https://developers.google.com/earth-engine/datasets/tags/forest-biomass) [google](https://developers.google.com/earth-engine/datasets/tags/google) [landandcarbon](https://developers.google.com/earth-engine/datasets/tags/landandcarbon) | - |---| | [### WRI/Google DeepMind Global Drivers of Forest Loss 2001-2025 v1.3 ![](https://developers.google.com/earth-engine/datasets/images/landandcarbon/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_3_2001_2025_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_3_2001_2025) | | This dataset maps the dominant driver of tree cover loss from 2001-2025 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected ... | | [agriculture](https://developers.google.com/earth-engine/datasets/tags/agriculture) [deforestation](https://developers.google.com/earth-engine/datasets/tags/deforestation) [forest](https://developers.google.com/earth-engine/datasets/tags/forest) [forest-biomass](https://developers.google.com/earth-engine/datasets/tags/forest-biomass) [google](https://developers.google.com/earth-engine/datasets/tags/google) [landandcarbon](https://developers.google.com/earth-engine/datasets/tags/landandcarbon) |