- |---| | [### BLM AIM TerrADat TerrestrialAIM Point v1 ![](https://developers.google.com/earth-engine/datasets/images/BLM/BLM_AIM_v1_TerrADat_TerrestrialAIM_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/BLM_AIM_v1_TerrADat_TerrestrialAIM) | | Since 2011, the Bureau of Land Management (BLM) has collected field information to inform land health through its Assessment Inventory and Monitoring (AIM) strategy. To date, more than 6,000 terrestrial AIM field plots have been collected over BLM lands. The BLM AIM data archive is ... | | [blm](https://developers.google.com/earth-engine/datasets/tags/blm) [ecosystems](https://developers.google.com/earth-engine/datasets/tags/ecosystems) [hydrology](https://developers.google.com/earth-engine/datasets/tags/hydrology) [range](https://developers.google.com/earth-engine/datasets/tags/range) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) [table](https://developers.google.com/earth-engine/datasets/tags/table) | - |---| | [### FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System ![](https://developers.google.com/earth-engine/datasets/images/NASA/NASA_FLDAS_NOAH01_C_GL_M_V001_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001) | | The FLDAS dataset (McNally et al. 2017), was designed to assist with food security assessments in data-sparse, developing country settings. It includes information on many climate-related variables including moisture content, humidity, evapotranspiration, average soil temperature, total precipitation rate, etc. There are multiple different FLDAS datasets; ... | | [climate](https://developers.google.com/earth-engine/datasets/tags/climate) [cryosphere](https://developers.google.com/earth-engine/datasets/tags/cryosphere) [evapotranspiration](https://developers.google.com/earth-engine/datasets/tags/evapotranspiration) [humidity](https://developers.google.com/earth-engine/datasets/tags/humidity) [ldas](https://developers.google.com/earth-engine/datasets/tags/ldas) [monthly](https://developers.google.com/earth-engine/datasets/tags/monthly) | - |---| | [### GLDAS-2.1: Global Land Data Assimilation System ![](https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GLDAS_V021_NOAH_G025_T3H_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V021_NOAH_G025_T3H) | | NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model ... | | [3-hourly](https://developers.google.com/earth-engine/datasets/tags/3-hourly) [climate](https://developers.google.com/earth-engine/datasets/tags/climate) [cryosphere](https://developers.google.com/earth-engine/datasets/tags/cryosphere) [evaporation](https://developers.google.com/earth-engine/datasets/tags/evaporation) [forcing](https://developers.google.com/earth-engine/datasets/tags/forcing) [geophysical](https://developers.google.com/earth-engine/datasets/tags/geophysical) | - |---| | [### GLDAS-2.2: Global Land Data Assimilation System ![](https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GLDAS_V022_CLSM_G025_DA1D_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V022_CLSM_G025_DA1D) | | NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model ... | | [3-hourly](https://developers.google.com/earth-engine/datasets/tags/3-hourly) [climate](https://developers.google.com/earth-engine/datasets/tags/climate) [cryosphere](https://developers.google.com/earth-engine/datasets/tags/cryosphere) [evaporation](https://developers.google.com/earth-engine/datasets/tags/evaporation) [forcing](https://developers.google.com/earth-engine/datasets/tags/forcing) [geophysical](https://developers.google.com/earth-engine/datasets/tags/geophysical) | - |---| | [### MERRA-2 M2T1NXLND: Land Surface Diagnostics V5.12.4 ![](https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GSFC_MERRA_lnd_2_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/NASA_GSFC_MERRA_lnd_2) | | M2T1NXLND (or tavg1_2d_lnd_Nx) is an hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface diagnostics, such a baseflow flux, runoff, surface soil wetness, root zone soil wetness, water at surface layer, water at ... | | [climate](https://developers.google.com/earth-engine/datasets/tags/climate) [cryosphere](https://developers.google.com/earth-engine/datasets/tags/cryosphere) [evaporation](https://developers.google.com/earth-engine/datasets/tags/evaporation) [ice](https://developers.google.com/earth-engine/datasets/tags/ice) [merra](https://developers.google.com/earth-engine/datasets/tags/merra) [precipitation](https://developers.google.com/earth-engine/datasets/tags/precipitation) | - |---| | [### NLDAS-2: North American Land Data Assimilation System Forcing Fields ![](https://developers.google.com/earth-engine/datasets/images/NASA/NASA_NLDAS_FORA0125_H002_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/NASA_NLDAS_FORA0125_H002) | | Land Data Assimilation System (LDAS) combines multiple sources of observations (such as precipitation gauge data, satellite data, and radar precipitation measurements) to produce estimates of climatological properties at or near the Earth''s surface. This dataset is the primary (default) forcing file (File A) for Phase ... | | [climate](https://developers.google.com/earth-engine/datasets/tags/climate) [evaporation](https://developers.google.com/earth-engine/datasets/tags/evaporation) [forcing](https://developers.google.com/earth-engine/datasets/tags/forcing) [geophysical](https://developers.google.com/earth-engine/datasets/tags/geophysical) [hourly](https://developers.google.com/earth-engine/datasets/tags/hourly) [humidity](https://developers.google.com/earth-engine/datasets/tags/humidity) | - |---| | [### OpenLandMap Clay Content ![](https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02) | | Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail here. Antarctica is ... | | [clay](https://developers.google.com/earth-engine/datasets/tags/clay) [envirometrix](https://developers.google.com/earth-engine/datasets/tags/envirometrix) [opengeohub](https://developers.google.com/earth-engine/datasets/tags/opengeohub) [openlandmap](https://developers.google.com/earth-engine/datasets/tags/openlandmap) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) [usda](https://developers.google.com/earth-engine/datasets/tags/usda) | - |---| | [### OpenLandMap Predicted Hapludalfs Probability ![](https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P_v01_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX-HAPLUDALFS_P_v01) | | Predicted USDA soil great groups at 250 m (probabilities). Distribution of the USDA soil great groups based on machine learning predictions from global compilation of soil profiles. To learn more about soil great groups please refer to the Illustrated Guide to Soil Taxonomy - NRCS ... | | [envirometrix](https://developers.google.com/earth-engine/datasets/tags/envirometrix) [opengeohub](https://developers.google.com/earth-engine/datasets/tags/opengeohub) [openlandmap](https://developers.google.com/earth-engine/datasets/tags/openlandmap) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### OpenLandMap Sand Content ![](https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_SAND-WFRACTION_USDA-3A1A1A_M_v02_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_SAND-WFRACTION_USDA-3A1A1A_M_v02) | | Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail here. Antarctica is ... | | [envirometrix](https://developers.google.com/earth-engine/datasets/tags/envirometrix) [opengeohub](https://developers.google.com/earth-engine/datasets/tags/opengeohub) [openlandmap](https://developers.google.com/earth-engine/datasets/tags/openlandmap) [sand](https://developers.google.com/earth-engine/datasets/tags/sand) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) [usda](https://developers.google.com/earth-engine/datasets/tags/usda) | - |---| | [### OpenLandMap Soil Bulk Density ![](https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_BULKDENS-FINEEARTH_USDA-4A1H_M_v02_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_BULKDENS-FINEEARTH_USDA-4A1H_M_v02) | | Soil bulk density (fine earth) 10 x kg / m3 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Processing steps are described in detail here. Antarctica is not included. To access and visualize maps outside of Earth ... | | [density](https://developers.google.com/earth-engine/datasets/tags/density) [envirometrix](https://developers.google.com/earth-engine/datasets/tags/envirometrix) [opengeohub](https://developers.google.com/earth-engine/datasets/tags/opengeohub) [openlandmap](https://developers.google.com/earth-engine/datasets/tags/openlandmap) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### OpenLandMap Soil Organic Carbon Content ![](https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02) | | Soil organic carbon content in x 5 g / kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution Predicted from a global compilation of soil points. Processing steps are described in detail here. Antarctica is not included. ... | | [carbon](https://developers.google.com/earth-engine/datasets/tags/carbon) [envirometrix](https://developers.google.com/earth-engine/datasets/tags/envirometrix) [opengeohub](https://developers.google.com/earth-engine/datasets/tags/opengeohub) [openlandmap](https://developers.google.com/earth-engine/datasets/tags/openlandmap) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### OpenLandMap Soil Texture Class (USDA System) ![](https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_TEXTURE-CLASS_USDA-TT_M_v02_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_TEXTURE-CLASS_USDA-TT_M_v02) | | Soil texture classes (USDA system) for 6 soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m Derived from predicted soil texture fractions using the soiltexture package in R. Processing steps are described in detail here. Antarctica is not included. To access ... | | [envirometrix](https://developers.google.com/earth-engine/datasets/tags/envirometrix) [opengeohub](https://developers.google.com/earth-engine/datasets/tags/opengeohub) [openlandmap](https://developers.google.com/earth-engine/datasets/tags/openlandmap) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) [usda](https://developers.google.com/earth-engine/datasets/tags/usda) | - |---| | [### OpenLandMap Soil Water Content at 33kPa (Field Capacity) ![](https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01) | | Soil water content (volumetric %) for 33kPa and 1500kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution Training points are based on a global compilation of soil profiles: USDA NCSS AfSPDB ISRIC WISE EGRPR SPADE ... | | [envirometrix](https://developers.google.com/earth-engine/datasets/tags/envirometrix) [opengeohub](https://developers.google.com/earth-engine/datasets/tags/opengeohub) [openlandmap](https://developers.google.com/earth-engine/datasets/tags/openlandmap) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### OpenLandMap Soil pH in H2O ![](https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02) | | Soil pH in H2O at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution Processing steps are described in detail here. Antarctica is not included. To access and visualize maps outside of Earth Engine, use this page. If you ... | | [envirometrix](https://developers.google.com/earth-engine/datasets/tags/envirometrix) [opengeohub](https://developers.google.com/earth-engine/datasets/tags/opengeohub) [openlandmap](https://developers.google.com/earth-engine/datasets/tags/openlandmap) [ph](https://developers.google.com/earth-engine/datasets/tags/ph) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### OpenLandMap USDA Soil Taxonomy Great Groups ![](https://developers.google.com/earth-engine/datasets/images/OpenLandMap/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01) | | Predicted USDA soil great group probabilities at 250m. Distribution of the USDA soil great groups based on machine learning predictions from global compilation of soil profiles. To learn more about soil great groups please refer to the Illustrated Guide to Soil Taxonomy - NRCS - ... | | [envirometrix](https://developers.google.com/earth-engine/datasets/tags/envirometrix) [opengeohub](https://developers.google.com/earth-engine/datasets/tags/opengeohub) [openlandmap](https://developers.google.com/earth-engine/datasets/tags/openlandmap) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) [usda](https://developers.google.com/earth-engine/datasets/tags/usda) | - |---| | [### Reprocessed GLDAS-2.0: Global Land Data Assimilation System ![](https://developers.google.com/earth-engine/datasets/images/NASA/NASA_GLDAS_V20_NOAH_G025_T3H_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V20_NOAH_G025_T3H) | | NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model ... | | [3-hourly](https://developers.google.com/earth-engine/datasets/tags/3-hourly) [climate](https://developers.google.com/earth-engine/datasets/tags/climate) [cryosphere](https://developers.google.com/earth-engine/datasets/tags/cryosphere) [evaporation](https://developers.google.com/earth-engine/datasets/tags/evaporation) [forcing](https://developers.google.com/earth-engine/datasets/tags/forcing) [geophysical](https://developers.google.com/earth-engine/datasets/tags/geophysical) | - |---| | [### SLGA: Soil and Landscape Grid of Australia (Soil Attributes) ![](https://developers.google.com/earth-engine/datasets/images/CSIRO/CSIRO_SLGA_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/CSIRO_SLGA) | | The Soil and Landscape Grid of Australia (SLGA) is a comprehensive dataset of soil attributes across Australia at 3 arc-second resolution (\~90m pixels). The surfaces are the outcomes from modelling that describe the spatial distribution of the soil attributes using existing soil data and environmental ... | | [australia](https://developers.google.com/earth-engine/datasets/tags/australia) [csiro](https://developers.google.com/earth-engine/datasets/tags/csiro) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) [tern](https://developers.google.com/earth-engine/datasets/tags/tern) | - |---| | [### SOLUS: Soil properties of the conterminous United States at 100-m resolution ![](https://developers.google.com/earth-engine/datasets/images/USDA/USDA_SOLUS100_V0_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/USDA_SOLUS100_V0) | | SOLUS (Soil Landscapes of the United States) is a national map product developed by the National Cooperative Soil Survey that is focused on providing a consistent set of spatially continuous soil property maps to support large scope soil investigations and land use decisions. SOLUS maps ... | | [soil](https://developers.google.com/earth-engine/datasets/tags/soil) [usda](https://developers.google.com/earth-engine/datasets/tags/usda) | - |---| | [### SPL3SMP_E.005 SMAP L3 Radiometer Global Daily 9 km Soil Moisture ![](https://developers.google.com/earth-engine/datasets/images/NASA/NASA_SMAP_SPL3SMP_E_005_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_005) | | Data starting from 2023-12-04 are available in the NASA/SMAP/SPL3SMP_E/006 collection. This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) L-Band radiometer. The daily data here were collected from the descending (local ... | | [drought](https://developers.google.com/earth-engine/datasets/tags/drought) [nasa](https://developers.google.com/earth-engine/datasets/tags/nasa) [smap](https://developers.google.com/earth-engine/datasets/tags/smap) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) [soil-moisture](https://developers.google.com/earth-engine/datasets/tags/soil-moisture) [surface](https://developers.google.com/earth-engine/datasets/tags/surface) | - |---| | [### SPL3SMP_E.006 SMAP L3 Radiometer Global Daily 9 km Soil Moisture ![](https://developers.google.com/earth-engine/datasets/images/NASA/NASA_SMAP_SPL3SMP_E_006_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_006) | | Data prior from 2023-12-04 are available in the older NASA/SMAP/SPL3SMP_E/005 collection. They will eventually be reprocessed and added to this collection. This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) L-Band ... | | [drought](https://developers.google.com/earth-engine/datasets/tags/drought) [nasa](https://developers.google.com/earth-engine/datasets/tags/nasa) [smap](https://developers.google.com/earth-engine/datasets/tags/smap) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) [soil-moisture](https://developers.google.com/earth-engine/datasets/tags/soil-moisture) [surface](https://developers.google.com/earth-engine/datasets/tags/surface) | - |---| | [### SPL4SMGP.008 SMAP L4 Global 3-hourly 9-km Surface and Root Zone Soil Moisture ![](https://developers.google.com/earth-engine/datasets/images/NASA/NASA_SMAP_SPL4SMGP_008_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL4SMGP_008) | | The SMAP Level-4 (L4) Soil Moisture product includes surface soil moisture (0-5 cm vertical average), root-zone soil moisture (0-100 cm vertical average), and additional research products (not validated), including surface meteorological forcing variables, soil temperature, evapotranspiration, and net radiation. This dataset, formally known as the ... | | [drought](https://developers.google.com/earth-engine/datasets/tags/drought) [nasa](https://developers.google.com/earth-engine/datasets/tags/nasa) [smap](https://developers.google.com/earth-engine/datasets/tags/smap) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) [soil-moisture](https://developers.google.com/earth-engine/datasets/tags/soil-moisture) [surface](https://developers.google.com/earth-engine/datasets/tags/surface) | - |---| | [### SoilGrids250m 2.0 - Volumetric Water Content ![](https://developers.google.com/earth-engine/datasets/images/ISRIC/ISRIC_SoilGrids250m_v2_0_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISRIC_SoilGrids250m_v2_0) | | Volumetric Water Content at 10kPa, 33kPa, and 1500kPa suction in 10\^-3 cm\^3/cm\^3 (0.1 v% or 1 mm/m) at 6 standard depths (0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm). Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global ... | | [soil](https://developers.google.com/earth-engine/datasets/tags/soil) [soil-moisture](https://developers.google.com/earth-engine/datasets/tags/soil-moisture) [water](https://developers.google.com/earth-engine/datasets/tags/water) | - |---| | [### iSDAsoil Bulk Density, \<2mm Fraction ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_bulk_density_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_bulk_density) | | Bulk density, \<2mm fraction at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with x/100. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Clay Content ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_clay_content_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_clay_content) | | Clay content at soil depths of 0-20 cm and 20-50 cm,\\npredicted mean and standard deviation. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be seen. Soil property predictions were made by Innovative ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [clay](https://developers.google.com/earth-engine/datasets/tags/clay) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Depth to Bedrock ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_bedrock_depth_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_bedrock_depth) | | Depth to bedrock at 0-200 cm depth, predicted mean and standard deviation. Due to the potential cropland mask that was used for generating the data, many areas of exposed rock (where depth to bedrock would be 0 cm) have been masked out and therefore appear ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [bedrock](https://developers.google.com/earth-engine/datasets/tags/bedrock) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Effective Cation Exchange Capacity ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_cation_exchange_capacity_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_cation_exchange_capacity) | | Effective Cation Exchange Capacity predicted mean and standard deviation at soil depths of 0-20 cm and 20-50 cm, Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [aluminium](https://developers.google.com/earth-engine/datasets/tags/aluminium) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Extractable Calcium ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_calcium_extractable_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_calcium_extractable) | | Extractable calcium at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Extractable Iron ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_iron_extractable_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_iron_extractable) | | Extractable iron at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Extractable Magnesium ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_magnesium_extractable_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_magnesium_extractable) | | Extractable magnesium at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Extractable Phosphorus ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_phosphorus_extractable_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_phosphorus_extractable) | | Extractable phosphorus at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Extractable Potassium ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_potassium_extractable_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_potassium_extractable) | | Extractable potassium at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Extractable Sulfur ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_sulphur_extractable_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_sulphur_extractable) | | Extractable sulfur at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Extractable Zinc ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_zinc_extractable_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_zinc_extractable) | | Extractable zinc at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Fertility Capability Classification ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_fcc_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_fcc) | | Soil fertility capability classification derived using slope, chemical, and physical soil properties. For more information about this layer, please visit this page. The classes for the 'fcc' band apply to pixel values that must be back-transformed with x modulo 3000. In areas of dense jungle ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Organic Carbon ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_carbon_organic_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_carbon_organic) | | Organic carbon at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [carbon](https://developers.google.com/earth-engine/datasets/tags/carbon) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Sand Content ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_sand_content_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_sand_content) | | Sand content at soil depths of 0-20 cm and 20-50 cm,\\npredicted mean and standard deviation. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be seen. Soil property predictions were made by Innovative ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [sand](https://developers.google.com/earth-engine/datasets/tags/sand) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Silt Content ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_silt_content_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_silt_content) | | Silt content at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Stone Content ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_stone_content_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_stone_content) | | Stone content at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Total Carbon ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_carbon_total_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_carbon_total) | | Total carbon at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [aluminium](https://developers.google.com/earth-engine/datasets/tags/aluminium) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil Total Nitrogen ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_nitrogen_total_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_nitrogen_total) | | Total nitrogen at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/100)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [nitrogen](https://developers.google.com/earth-engine/datasets/tags/nitrogen) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil USDA Texture Class ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_texture_class_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_texture_class) | | USDA Texture Class at soil depths of 0-20 cm and 20-50 cm. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be seen. Soil property predictions were made by Innovative Solutions for Decision ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [aluminium](https://developers.google.com/earth-engine/datasets/tags/aluminium) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil extractable Aluminium ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_aluminium_extractable_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_aluminium_extractable) | | Extractable aluminium at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. Soil property predictions were made by Innovative Solutions for Decision Agriculture Ltd. (iSDA) at 30 m pixel size using machine learning coupled ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [aluminium](https://developers.google.com/earth-engine/datasets/tags/aluminium) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) | - |---| | [### iSDAsoil pH ![](https://developers.google.com/earth-engine/datasets/images/ISDASOIL/ISDASOIL_Africa_v1_ph_sample.png)](https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_ph) | | pH at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with x/10. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be seen. ... | | [africa](https://developers.google.com/earth-engine/datasets/tags/africa) [isda](https://developers.google.com/earth-engine/datasets/tags/isda) [ph](https://developers.google.com/earth-engine/datasets/tags/ph) [soil](https://developers.google.com/earth-engine/datasets/tags/soil) |