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 …
    africa aluminium isda soil
  • 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 …
    africa bedrock isda soil
  • 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 artefacts 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 artefacts such as banding (striping) might be …
    africa calcium isda soil
  • 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 artefacts 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 artefacts such as banding (striping) might be …
    africa aluminium isda soil
  • 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 artefacts 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 artefacts such as banding (striping) might be seen. Soil property predictions were made by Innovative …
    africa clay isda soil
  • 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 artefacts 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 artefacts such as banding (striping) might be …
    africa isda magnesium soil
  • 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 artefacts 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 artefacts 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 artefacts such as banding (striping) might be …
    africa isda phosphorus soil
  • 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 artefacts 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 artefacts 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 artefacts such as banding (striping) might be …
    africa isda silt soil
  • 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 artefacts such as banding (striping) might be …
    africa isda soil stone
  • iSDAsoil Extractable Sulphur

    Extractable sulphur 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 artefacts such as banding (striping) might be …
    africa isda soil sulphur
  • 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 artefacts 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 artefacts such as banding (striping) might be …
    africa isda soil zinc
  • GLDAS-2.1: Global Land Data Assimilation System

    Global Land Data Assimilation System (GLDAS) ingests satellite and ground-based observational data products. Using advanced land surface modeling and data assimilation techniques, it generates optimal fields of land surface states and fluxes. GLDAS-2.1 is one of two components of the GLDAS Version 2 (GLDAS-2) dataset, …
    3-hourly climate evaporation forcing geophysical gldas
  • Reprocessed GLDAS-2.0: Global Land Data Assimilation System

    Global Land Data Assimilation System (GLDAS) ingests satellite and ground-based observational data products. Using advanced land surface modeling and data assimilation techniques, it generates optimal fields of land surface states and fluxes. GLDAS-2.0 is one of two components of the GLDAS Version 2 (GLDAS-2) dataset, …
    3-hourly climate evaporation forcing geophysical gldas
  • NASA-USDA Enhanced SMAP Global Soil Moisture Data

    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), soil moisture profile (%), surface and subsurface soil moisture anomalies (-). The dataset is generated by integrating …
    geophysical hsl moisture nasa smap soil
  • 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