Datasets tagged soil in Earth Engine

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  • SLGA: Soil and Landscape Grid of Australia (Soil Attributes)
    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 csiro digital-soil-mapping globalsoilmap slga soil

  • iSDAsoil extractable Aluminium
    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 …
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  • iSDAsoil Depth to Bedrock
    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 …
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  • iSDAsoil Bulk Density, <2mm Fraction
    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 bulk-density isda soil

  • iSDAsoil Extractable Calcium
    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 …
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  • iSDAsoil Organic Carbon
    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 carbon carbon-organic isda soil

  • iSDAsoil Total Carbon
    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 …
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  • iSDAsoil Effective 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 aluminium isda soil

  • iSDAsoil 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 …
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  • iSDAsoil Fertility Capability Classification
    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 fcc isda soil

  • iSDAsoil Extractable Iron
    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 iron isda soil

  • iSDAsoil Extractable Magnesium
    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 …
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  • iSDAsoil Total Nitrogen
    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 isda nitrogen soil

  • iSDAsoil 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 isda ph soil

  • iSDAsoil Extractable Phosphorus
    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 …
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  • iSDAsoil Extractable Potassium
    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 isda potassium soil

  • iSDAsoil 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 isda sand soil

  • iSDAsoil 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 …
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  • iSDAsoil 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 …
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  • iSDAsoil Extractable Sulfur
    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 isda soil sulfur

  • iSDAsoil USDA 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 aluminium isda soil

  • iSDAsoil Extractable Zinc
    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 isda soil zinc

  • GLDAS-2.1: Global Land Data Assimilation System
    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 climate evaporation forcing geophysical gldas

  • GLDAS-2.2: Global Land Data Assimilation System
    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 climate evaporation forcing geophysical gldas

  • Reprocessed GLDAS-2.0: Global Land Data Assimilation System
    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 climate evaporation forcing geophysical gldas

  • NASA-USDA Enhanced SMAP Global Soil Moisture Data
    Data production is temporarily interrupted due to upstream source problems. Production is expected to resume in 2023. The NASA-USDA Enhanced SMAP Global soil moisture data provides soil moisture information across the globe at 10-km spatial resolution. This dataset includes: surface and subsurface soil moisture (mm), …
    geophysical hsl nasa smap soil soil-moisture

  • OpenLandMap Soil Bulk Density
    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. Antartica is not included. To access and visualize maps outside of Earth …
    bulk density envirometrix opengeohub openlandmap soil

  • OpenLandMap Clay Content
    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 . Antartica …
    clay envirometrix opengeohub openlandmap soil usda

  • OpenLandMap USDA Soil Taxonomy Great Groups
    Predicted USDA soil great group probablities 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 opengeohub openlandmap soil taxonomy usda

  • OpenLandMap Soil Organic Carbon Content
    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 . Antartica is not …
    carbon envirometrix opengeohub openlandmap organic soil

  • OpenLandMap Soil pH in H2O
    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 . Antartica is not included. To access and visualize maps outside of Earth Engine, use this page. If …
    envirometrix opengeohub openlandmap ph soil

  • OpenLandMap Sand Content
    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 . Antartica …
    envirometrix opengeohub openlandmap sand soil usda

  • OpenLandMap Soil Texture Class (USDA System)
    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 . Antartica is not included. To …
    envirometrix opengeohub openlandmap soil texture usda

  • OpenLandMap Soil Water Content at 33kPa (Field Capacity)
    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 opengeohub openlandmap soil watercontent