Predicted USDA soil great groups at 250 m (probabilities)

• Main Authors: Hengl, Tomislav, Nauman, Travis
• Format: Dataset
• Language: English
• Published: Zenodo 2018
• Subjects: LandGIS; soil type; USDA soil taxonomy; Gonzalez; antartic*
• Online Access: https://dx.doi.org/10.5281/zenodo.2578103; https://zenodo.org/record/2578103

Distribution of the USDA soil great groups based on machine learning predictions from global compilation of soil profiles. 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 . Antartica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the LandGIS maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://github.com/Envirometrix/LandGISmaps/issues General questions and comments: https://disqus.com/home/forums/landgis/ 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, v0.1 = version number: 0.1, : {"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, Blagoti\u0107 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."]}