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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ftdatacite:10.5281/zenodo.3528062 2023-05-15T13:41:54+02:00 Predicted USDA soil great groups at 250 m (probabilities) Hengl, Tomislav Nauman, Travis 2018 https://dx.doi.org/10.5281/zenodo.3528062 https://zenodo.org/record/3528062 en eng Zenodo https://dx.doi.org/10.5281/zenodo.1476844 Open Access Creative Commons Attribution Share Alike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode cc-by-sa-4.0 info:eu-repo/semantics/openAccess CC-BY-SA LandGIS soil type USDA soil taxonomy dataset Dataset 2018 ftdatacite https://doi.org/10.5281/zenodo.3528062 https://doi.org/10.5281/zenodo.1476844 2021-11-05T12:55:41Z 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)."]} Dataset Antarc* Antarctica DataCite Metadata Store (German National Library of Science and Technology) Gonzalez ENVELOPE(-58.250,-58.250,-63.917,-63.917) Moura ENVELOPE(28.483,28.483,66.450,66.450) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
LandGIS soil type USDA soil taxonomy |
spellingShingle |
LandGIS soil type USDA soil taxonomy Hengl, Tomislav Nauman, Travis Predicted USDA soil great groups at 250 m (probabilities) |
topic_facet |
LandGIS soil type USDA soil taxonomy |
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: 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)."]} |
format |
Dataset |
author |
Hengl, Tomislav Nauman, Travis |
author_facet |
Hengl, Tomislav Nauman, Travis |
author_sort |
Hengl, Tomislav |
title |
Predicted USDA soil great groups at 250 m (probabilities) |
title_short |
Predicted USDA soil great groups at 250 m (probabilities) |
title_full |
Predicted USDA soil great groups at 250 m (probabilities) |
title_fullStr |
Predicted USDA soil great groups at 250 m (probabilities) |
title_full_unstemmed |
Predicted USDA soil great groups at 250 m (probabilities) |
title_sort |
predicted usda soil great groups at 250 m (probabilities) |
publisher |
Zenodo |
publishDate |
2018 |
url |
https://dx.doi.org/10.5281/zenodo.3528062 https://zenodo.org/record/3528062 |
long_lat |
ENVELOPE(-58.250,-58.250,-63.917,-63.917) ENVELOPE(28.483,28.483,66.450,66.450) |
geographic |
Gonzalez Moura |
geographic_facet |
Gonzalez Moura |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
https://dx.doi.org/10.5281/zenodo.1476844 |
op_rights |
Open Access Creative Commons Attribution Share Alike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode cc-by-sa-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY-SA |
op_doi |
https://doi.org/10.5281/zenodo.3528062 https://doi.org/10.5281/zenodo.1476844 |
_version_ |
1766160007185825792 |