Sand Content In % (Kg / Kg) At 6 Standard Depths (0, 10, 30, 60, 100 And 200 Cm) At 250 M Resolution ...
Sand 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 . Antartica is not included. To access and visualize maps us...
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| Format: | Dataset |
| Language: | English |
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Zenodo
2018
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| Online Access: | https://dx.doi.org/10.5281/zenodo.1476852 https://zenodo.org/record/1476852 |
| Summary: | Sand 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 . Antartica is not included. To access and visualize maps use: https://landgis.opengeohub.org All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, sand.wfraction = variable: sand weight fraction, usda.3a1a1a = determination method: laboratory method code, m = mean value, 250m = spatial resolution / block support: 250 m, b10..10cm = vertical reference: 10 cm depth below surface, 1950..2017 = time reference: period 1950-2017, v0.1 = version number: 0.1, ... : {"references": ["USDA-NRCS, (2014) Laboratory Methods Manual (SSIR 42). U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center.", "Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagoti\u0107 A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748."]} ... |
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