Published December 24, 2018 | Version v0.2
Dataset Open

Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

  • 1. EnvirometriX Ltd

Description

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. Antarctica is not included.

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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,
  • v0.2 = version number: 0.2,

Files

landGIS_sand_content.jpg

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Additional details

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, Blagotić A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748.
  • Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0.