Published November 2, 2018 | Version v0.2
Dataset Open

Predicted USDA soil great groups at 250 m (probabilities)

  • 1. Envirometrix Ltd
  • 2. US Geological Survey Ecosystems

Description

Distribution of the USDA soil great groups based on machine learning predictions from global compilation of soil profiles (>350,000 training points). To learn more about soil great groups please refer to the Illustrated Guide to Soil Taxonomy - NRCS - USDA. Processing steps are described in detail here. Antarctica is not included.

To access and visualize maps use: OpenLandMap.org

A back-up copy of all predictions (>65GB) can be downloaded from: http://gofile.me/6J25n/mQ3cHOOMr

If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:

All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:

  • sol = theme: soil,
  • grtgroup = variable: USDA great group,
  • usda.argiustolls = determination method: USDA soil taxonomy class Argiustolls,
  • p = probability,
  • 250m = spatial resolution / block support: 250 m,
  • s0..0cm = vertical reference: soil surface,
  • 1950..2017 = time reference: period 1950-2017,
  • v0.2 = version number: 0.2,

Files

landgis_soil_types_point_query.jpg

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Additional details

References

  • USDA-NRCS, (2014). Illustrated Guide to Soil Taxonomy: Version 1.0, September 20014, 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.
  • Silva, B. P. C., Silva, M. L. N., Avalos, F. A. P., de Menezes, M. D., & Curi, N. (2019). Digital soil mapping including additional point sampling in Posses ecosystem services pilot watershed, southeastern Brazil. Scientific reports, 9(1), 1-12.
  • Samuel-Rosa, A., Dalmolin, R. S. D., MOURA-BRUNO, J. M., Teixeira, W. G., & FILIPPINI ALBA, J. M. (2018). Open legacy soil survey data in Brazil: geospatial data quality and how to improve it. Embrapa Clima Temperado-Artigo em periódico indexado (ALICE).