Silt 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 not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = t...
Soil water content (weight percentage of water) in percent for 33 kPa and 1500 kPa suctions predicte...
iSDAsoil dataset soil silt content in % predicted at 30 m resolution for 0–20 and 20–50 cm depth int...
Distribution of the USDA orders (12) based on machine learning predictions of great groups (https://...
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...
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 pH in H2O in × 10 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Pro...
Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard ...
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,...
Distribution of the USDA soil great groups based on machine learning predictions from global compila...
Soil texture classes (USDA system) for 6 standard soil depths (0, 10, 30, 60, 100 and 200 cm) at 250...
Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard ...
Available Water Capacity (in mm) derived by calculating Water Retention Difference (difference betwe...
Soil water content (weight percentage of water) in percent for 33 kPa and 1500 kPa suctions predicte...
iSDAsoil dataset soil silt content in % predicted at 30 m resolution for 0–20 and 20–50 cm depth int...
Distribution of the USDA orders (12) based on machine learning predictions of great groups (https://...
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...
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 pH in H2O in × 10 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Pro...
Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard ...
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,...
Distribution of the USDA soil great groups based on machine learning predictions from global compila...
Soil texture classes (USDA system) for 6 standard soil depths (0, 10, 30, 60, 100 and 200 cm) at 250...
Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard ...
Available Water Capacity (in mm) derived by calculating Water Retention Difference (difference betwe...
Soil water content (weight percentage of water) in percent for 33 kPa and 1500 kPa suctions predicte...
iSDAsoil dataset soil silt content in % predicted at 30 m resolution for 0–20 and 20–50 cm depth int...
Distribution of the USDA orders (12) based on machine learning predictions of great groups (https://...