Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution ...
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. To access and visualize maps u...
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Online Access: | https://dx.doi.org/10.5281/zenodo.1476851 https://zenodo.org/record/1476851 |
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ftdatacite:10.5281/zenodo.1476851 2024-01-28T10:01:01+01:00 Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution ... Hengl, Tomislav 2018 https://dx.doi.org/10.5281/zenodo.1476851 https://zenodo.org/record/1476851 en eng Zenodo https://dx.doi.org/10.5281/zenodo.1476852 https://dx.doi.org/10.5281/zenodo.2525662 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 LandGIS sand fraction soil texture Dataset dataset 2018 ftdatacite https://doi.org/10.5281/zenodo.147685110.5281/zenodo.147685210.5281/zenodo.2525662 2024-01-04T22:40:10Z 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. To access and visualize maps use: OpenLandMap.org 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, 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 ... : {"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, 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."]} ... Dataset Antarc* Antarctica DataCite Metadata Store (German National Library of Science and Technology) Gonzalez ENVELOPE(-58.250,-58.250,-63.917,-63.917) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
LandGIS sand fraction soil texture |
spellingShingle |
LandGIS sand fraction soil texture Hengl, Tomislav Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution ... |
topic_facet |
LandGIS sand fraction soil texture |
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. To access and visualize maps use: OpenLandMap.org 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, 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 ... : {"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, 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."]} ... |
format |
Dataset |
author |
Hengl, Tomislav |
author_facet |
Hengl, Tomislav |
author_sort |
Hengl, Tomislav |
title |
Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution ... |
title_short |
Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution ... |
title_full |
Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution ... |
title_fullStr |
Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution ... |
title_full_unstemmed |
Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution ... |
title_sort |
sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution ... |
publisher |
Zenodo |
publishDate |
2018 |
url |
https://dx.doi.org/10.5281/zenodo.1476851 https://zenodo.org/record/1476851 |
long_lat |
ENVELOPE(-58.250,-58.250,-63.917,-63.917) |
geographic |
Gonzalez |
geographic_facet |
Gonzalez |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
https://dx.doi.org/10.5281/zenodo.1476852 https://dx.doi.org/10.5281/zenodo.2525662 |
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_doi |
https://doi.org/10.5281/zenodo.147685110.5281/zenodo.147685210.5281/zenodo.2525662 |
_version_ |
1789325698779840512 |