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 is not included. To access and visualize maps use: https://landgis.opengeohub.org All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, sand.wfraction = variable: sand weight fraction, usda.3a1a1a = determination method: laboratory method code, m = mean value, 250m = spatial resolution / block support: 250 m, b10..10cm = vertical reference: 10 cm depth below surface, 1950..2017 = time reference: period 1950-2017, ...
Soil texture classes (USDA system) for 6 standard soil depths (0, 10, 30, 60, 100 and 200 cm) at 250...
iSDAsoil dataset soil texture classes derived from sand, silt and clay fractions at 30 m resolution ...
Distribution of the USDA suborders based on machine learning predictions of great groups (https://do...
Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution...
Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution...
Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution...
Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolu...
Soil bulk density (fine earth) 10 x kg / m3 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at ...
Soil bulk density (fine earth) 10 x kg / m3 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at ...
Soil organic carbon content in × 5 g / kg (to convert to % divide by 2) at 6 standard depths (0, 10,...
Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environ...
iSDAsoil dataset soil sand content in % predicted at 30 m resolution for 0–20 and 20–50 cm depth int...
Distribution of the USDA soil great groups based on machine learning predictions from global compila...
Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard ...
Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard ...
Soil texture classes (USDA system) for 6 standard soil depths (0, 10, 30, 60, 100 and 200 cm) at 250...
iSDAsoil dataset soil texture classes derived from sand, silt and clay fractions at 30 m resolution ...
Distribution of the USDA suborders based on machine learning predictions of great groups (https://do...
Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution...
Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution...
Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution...
Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolu...
Soil bulk density (fine earth) 10 x kg / m3 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at ...
Soil bulk density (fine earth) 10 x kg / m3 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at ...
Soil organic carbon content in × 5 g / kg (to convert to % divide by 2) at 6 standard depths (0, 10,...
Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environ...
iSDAsoil dataset soil sand content in % predicted at 30 m resolution for 0–20 and 20–50 cm depth int...
Distribution of the USDA soil great groups based on machine learning predictions from global compila...
Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard ...
Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard ...
Soil texture classes (USDA system) for 6 standard soil depths (0, 10, 30, 60, 100 and 200 cm) at 250...
iSDAsoil dataset soil texture classes derived from sand, silt and clay fractions at 30 m resolution ...
Distribution of the USDA suborders based on machine learning predictions of great groups (https://do...