SoilGrids is a project combining global observation data with machine learning to map the spatial distribution of soil properties across the globe. It is produced at a spatial resolution of 250 meters and each parameters is mapped at different depths. In order to be able to assess prediction uncertainty, besides the mean and median prediction, the 0.05 and 0.95 percentile predictions are available. The following parameters are available:
- bdod
Bulk density of the fine earth fraction (kg/dm3)
- cec
Cation Exchange Capacity of the soil (cmol(c)/kg)
- cfvo
Volumetric fraction of coarse fragments > 2 mm (cm3/100cm3 (volPerc))
- clay
Proportion of clay particles < 0.002 mm in the fine earth fraction (g/100g)
- nitrogen
Total nitrogen (g/kg)
- phh2o
Soil pH (pH)
- sand
Proportion of sand particles > 0.05 mm in the fine earth fraction (g/100g)
- silt
Proportion of silt particles >= 0.002 mm and <= 0.05 mm in the fine earth fraction (g/100g)
- soc
Soil organic carbon content in the fine earth fraction (g/kg)
- ocd
Organic carbon density (kg/m3)
- ocs
Organic carbon stocks (kg/m²)
Details
Users can specify the following arguments:
- layer
The soil parameter as a single character
- depth
The requested depth as a single character
- stat
The predicted statistic as a single character
Except for ocs, which is only available for a depth of "0-30cm",
all other parameters are available at the following depths:
"0-5cm"
"5-15cm"
"15-30cm"
"30-60cm"
"60-100cm"
"100-200cm"
Each parameter and depth is available for the following statistics:
"Q0.05"
"Q0.50"
"mean"
"Q0.95"
References
Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE 12(2): e0169748. doi:10.1371/journal.pone.0169748