soilgrids function

SoilGrids data layers

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)

Source

https://www.isric.org/explore/soilgrids

get_soilgrids(layers, depths, stats)

Arguments

• layers: A character vector indicating the layers to download from soilgrids
• depths: A character vector indicating the depths to download
• stats: A character vector indicating the statistics to download.

Returns

A function that returns an sf footprint object.

Details

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. tools:::Rd_expr_doi("https://doi.org/10.1371/journal.pone.0169748")