As covariate layers for producing SoilGrids250m predictions we used an extensive stack of covariates, which are primarily based on remote sensing data. These include (see e.g. Fig 4 ):
These covariates were selected to represent factors of soil formation according to Jenny [40 ]: climate, relief, living organisms, water dynamics and parent material. Out of the five main factors, water dynamics and living organisms (especially vegetation dynamics) are not trivial to represent as these operate over long periods of time and often exhibit chaotic behaviour. Using reflectance bands such as the mid-infrared MODIS bands from a single day, would have little use to soil mapping for areas with dynamic vegetation, i.e. with strong seasonal changes in vegetation cover. To account for seasonal fluctuation and for inter-annual variations in surface reflectance, we instead used long-term temporal signatures of the soil surface derived as monthly averages from long-term MODIS imagery (15 years of data). We assume here that, for each location in the world, long-term average seasonal signatures of surface reflectance or vegetation index provide a better indication of soil characteristics than only a single snapshot of surface reflectance. Computing temporal signatures of the land surface requires a considerable investment of time (comparable to the generation of climatic images vs temporary weather maps), but it is possibly the only way to represent the cumulative influence of living organisms on soil formation.
For processing the covariates we used a combination of Open Source GIS software, primarilySAGA GIS [28 (link)], R packages raster [41 ], sp [42 ], GSIF and GDAL [43 ] for reprojecting, mosaicking and merging tiles. SAGA GIS and GDAL were found to be highly suitable for processing large data as parallelization of computing was relatively easy to implement.
We updated the 1 km global soil mask map using the most detailed 30 m resolution global land cover map from 2010. This was combined with the global water mask [44 (link)] and the global sea mask map based on the SRTM DEM [45 (link)] to produce one consistent global soil mask that includes all land areas, expect for: (a) fresh water bodies such as lakes and rivers, and (b) permanent ice.
These covariates were selected to represent factors of soil formation according to Jenny [40 ]: climate, relief, living organisms, water dynamics and parent material. Out of the five main factors, water dynamics and living organisms (especially vegetation dynamics) are not trivial to represent as these operate over long periods of time and often exhibit chaotic behaviour. Using reflectance bands such as the mid-infrared MODIS bands from a single day, would have little use to soil mapping for areas with dynamic vegetation, i.e. with strong seasonal changes in vegetation cover. To account for seasonal fluctuation and for inter-annual variations in surface reflectance, we instead used long-term temporal signatures of the soil surface derived as monthly averages from long-term MODIS imagery (15 years of data). We assume here that, for each location in the world, long-term average seasonal signatures of surface reflectance or vegetation index provide a better indication of soil characteristics than only a single snapshot of surface reflectance. Computing temporal signatures of the land surface requires a considerable investment of time (comparable to the generation of climatic images vs temporary weather maps), but it is possibly the only way to represent the cumulative influence of living organisms on soil formation.
For processing the covariates we used a combination of Open Source GIS software, primarily
We updated the 1 km global soil mask map using the most detailed 30 m resolution global land cover map from 2010. This was combined with the global water mask [44 (link)] and the global sea mask map based on the SRTM DEM [45 (link)] to produce one consistent global soil mask that includes all land areas, expect for: (a) fresh water bodies such as lakes and rivers, and (b) permanent ice.
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