SoilGrids is based on about 150000 soil profiles for training and a pile of 158 soil covariates based on remote sensing (mainly from MODIS land products, SRTM DEM derivatives, climate images and global topographic and lithological maps) to fit the aggregated random forests and scales of machine learning methods, and the enhancement and polynomial logical regression realized in the R package, The soil properties (organic carbon, bulk density, nitrogen, cation exchange capacity, pH value, soil texture content and coarse debris) of seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm) were predicted.
File name: soil attribute+soil depth, for example, PH0-5.tif represents soil pH value of 0-5cm depth soil layer.
Data reading mode: can be opened by Arcgis.
Hengl, T. (2022). 250m SoilGrids in the upper reaches of Yangtze River, China (2016). Upper Yangtze River Scientific Data Center, (Download the reference：
Related Literatures:1. Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748. doi:10.1371/journal. pone.0169748( View Details | Bibtex)
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| East: 112.00 | West: 89.00 |
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| South: 24.00 | North: 36.50 |
