Predicted USDA soil great groups at 250 m (probabilities)
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 detai...
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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.1476844 https://zenodo.org/record/1476844 |
| Summary: | 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: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/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, v0.2 = version number: 0.2, : {"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.", "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 peri\u00f3dico indexado (ALICE)."]} |
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