SoilGrids1km — Global Soil Information Based on Automated Mapping
Identifieur interne : 000563 ( Ncbi/Merge ); précédent : 000562; suivant : 000564SoilGrids1km — Global Soil Information Based on Automated Mapping
Auteurs : Tomislav Hengl [Pays-Bas] ; Jorge Mendes De Jesus [Pays-Bas] ; Robert A. Macmillan [Canada] ; Niels H. Batjes [Pays-Bas] ; Gerard B. M. Heuvelink [Pays-Bas] ; Eloi Ribeiro [Pays-Bas] ; Alessandro Samuel-Rosa [Brésil] ; Bas Kempen [Pays-Bas] ; Johan G. B. Leenaars [Pays-Bas] ; Markus G. Walsh [Tanzanie] ; Maria Ruiperez Gonzalez [Pays-Bas]Source :
- PLoS ONE [ 1932-6203 ] ; 2014.
Abstract
Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail.
We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg−1), soil pH, sand, silt and clay fractions (%), bulk density (kg m−3), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha−1), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%.
SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via
Url:
DOI: 10.1371/journal.pone.0105992
PubMed: 25171179
PubMed Central: 4149475
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PMC:4149475Le document en format XML
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<author><name sortKey="Gonzalez, Maria Ruiperez" sort="Gonzalez, Maria Ruiperez" uniqKey="Gonzalez M" first="Maria Ruiperez" last="Gonzalez">Maria Ruiperez Gonzalez</name>
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</nlm:aff>
<country xml:lang="fr" wicri:curation="lc">Pays-Bas</country>
<wicri:regionArea>ISRIC — World Soil Information, Wageningen</wicri:regionArea>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail.</p>
</sec>
<sec><title>Methodology/Principal Findings</title>
<p>We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg−1), soil pH, sand, silt and clay fractions (%), bulk density (kg m−3), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha−1), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%.</p>
</sec>
<sec><title>Conclusions/Significance</title>
<p>SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via <ext-link ext-link-type="uri" xlink:href="http://soilgrids.org">http://soilgrids.org</ext-link>
under a Creative Commons Non Commercial license.</p>
</sec>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id>
<journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">plosone</journal-id>
<journal-title-group><journal-title>PLoS ONE</journal-title>
</journal-title-group>
<issn pub-type="epub">1932-6203</issn>
<publisher><publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">25171179</article-id>
<article-id pub-id-type="pmc">4149475</article-id>
<article-id pub-id-type="publisher-id">PONE-D-14-21805</article-id>
<article-id pub-id-type="doi">10.1371/journal.pone.0105992</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
<subj-group subj-group-type="Discipline-v2"><subject>Computer and Information Sciences</subject>
<subj-group><subject>Geoinformatics</subject>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v2"><subject>Earth Sciences</subject>
<subj-group><subject>Geography</subject>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v2"><subject>Ecology and Environmental Sciences</subject>
<subj-group><subject>Environmental Geography</subject>
</subj-group>
<subj-group><subject>Natural Resources</subject>
</subj-group>
<subj-group><subject>Soil Science</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group><article-title>SoilGrids1km — Global Soil Information Based on Automated Mapping</article-title>
<alt-title alt-title-type="running-head">SoilGrids1km</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Hengl</surname>
<given-names>Tomislav</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="corresp" rid="cor1"><sup>*</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>de Jesus</surname>
<given-names>Jorge Mendes</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>MacMillan</surname>
<given-names>Robert A.</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Batjes</surname>
<given-names>Niels H.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Heuvelink</surname>
<given-names>Gerard B. M.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ribeiro</surname>
<given-names>Eloi</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Samuel-Rosa</surname>
<given-names>Alessandro</given-names>
</name>
<xref ref-type="aff" rid="aff4"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Kempen</surname>
<given-names>Bas</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Leenaars</surname>
<given-names>Johan G. B.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Walsh</surname>
<given-names>Markus G.</given-names>
</name>
<xref ref-type="aff" rid="aff5"><sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Gonzalez</surname>
<given-names>Maria Ruiperez</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label>
<addr-line>ISRIC — World Soil Information, Wageningen, the Netherlands</addr-line>
</aff>
<aff id="aff2"><label>2</label>
<addr-line>LandMapper Environmental Solutions Inc., Edmonton, Canada</addr-line>
</aff>
<aff id="aff3"><label>3</label>
<addr-line>Wageningen University, Wageningen, the Netherlands</addr-line>
</aff>
<aff id="aff4"><label>4</label>
<addr-line>Federal Rural University of Rio de Janeiro, Rio de Janeiro, Brazil</addr-line>
</aff>
<aff id="aff5"><label>5</label>
<addr-line>The Earth Institute, Columbia University, New York, New York, United States of America, and Selian Agricultural Research Inst., Arusha, Tanzania</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Bond-Lamberty</surname>
<given-names>Ben</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>DOE Pacific Northwest National Laboratory, United States of America</addr-line>
</aff>
<author-notes><corresp id="cor1">* E-mail: <email>tom.hengl@wur.nl</email>
</corresp>
<fn fn-type="conflict"><p><bold>Competing Interests: </bold>
RAM is owner and retired principle of LandMapper Environmental Solutions Inc. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials.</p>
</fn>
<fn fn-type="con"><p>Conceived and designed the experiments: TH. Contributed to the writing of the manuscript: TH BM GH BK MW. Prepared input profile and covariate data: NB JL MG AR. Web mapping services and user interfaces: JJ ER. Cross-validation and quality control: AR GH BK NB RAM. Design of the system, programming and preparation of input data: TH JJ.</p>
</fn>
</author-notes>
<pub-date pub-type="collection"><year>2014</year>
</pub-date>
<pub-date pub-type="epub"><day>29</day>
<month>8</month>
<year>2014</year>
</pub-date>
<volume>9</volume>
<issue>8</issue>
<elocation-id>e105992</elocation-id>
<history><date date-type="received"><day>19</day>
<month>5</month>
<year>2014</year>
</date>
<date date-type="accepted"><day>25</day>
<month>7</month>
<year>2014</year>
</date>
</history>
<permissions><copyright-year>2014</copyright-year>
<copyright-holder>Hengl et al</copyright-holder>
<license><license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>
, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p>
</license>
</permissions>
<abstract><sec><title>Background</title>
<p>Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail.</p>
</sec>
<sec><title>Methodology/Principal Findings</title>
<p>We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg−1), soil pH, sand, silt and clay fractions (%), bulk density (kg m−3), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha−1), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%.</p>
</sec>
<sec><title>Conclusions/Significance</title>
<p>SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via <ext-link ext-link-type="uri" xlink:href="http://soilgrids.org">http://soilgrids.org</ext-link>
under a Creative Commons Non Commercial license.</p>
</sec>
</abstract>
<funding-group><funding-statement>ISRIC is a non-profit organization primarily funded by the Dutch government. The authors are especially thankful for support from the Africa Soil Information Service (AfSIS) project, funded by the Bill and Melinda Gates foundation and the Alliance for a Green Revolution in Africa (AGRA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement>
</funding-group>
<counts><page-count count="17"></page-count>
</counts>
<custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name>
<meta-value>The authors confirm that all data underlying the findings are fully available without restriction. SoilGrids1km are available for download under a Creative Commons non-Commercial license via <ext-link ext-link-type="uri" xlink:href="http://soilgrids.org">http://soilgrids.org</ext-link>
.</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
<notes><title>Data Availability</title>
<p>The authors confirm that all data underlying the findings are fully available without restriction. SoilGrids1km are available for download under a Creative Commons non-Commercial license via <ext-link ext-link-type="uri" xlink:href="http://soilgrids.org">http://soilgrids.org</ext-link>
.</p>
</notes>
</front>
</pmc>
<affiliations><list><country><li>Brésil</li>
<li>Canada</li>
<li>Pays-Bas</li>
<li>Tanzanie</li>
</country>
<region><li>Gueldre (province)</li>
<li>État de New York</li>
<li>État de Rio de Janeiro</li>
</region>
<settlement><li>New York</li>
<li>Rio de Janeiro</li>
<li>Wageningue</li>
</settlement>
<orgName><li>Université Columbia</li>
<li>Université de Wageningue</li>
</orgName>
</list>
<tree><country name="Pays-Bas"><noRegion><name sortKey="Hengl, Tomislav" sort="Hengl, Tomislav" uniqKey="Hengl T" first="Tomislav" last="Hengl">Tomislav Hengl</name>
</noRegion>
<name sortKey="Batjes, Niels H" sort="Batjes, Niels H" uniqKey="Batjes N" first="Niels H." last="Batjes">Niels H. Batjes</name>
<name sortKey="De Jesus, Jorge Mendes" sort="De Jesus, Jorge Mendes" uniqKey="De Jesus J" first="Jorge Mendes" last="De Jesus">Jorge Mendes De Jesus</name>
<name sortKey="Gonzalez, Maria Ruiperez" sort="Gonzalez, Maria Ruiperez" uniqKey="Gonzalez M" first="Maria Ruiperez" last="Gonzalez">Maria Ruiperez Gonzalez</name>
<name sortKey="Heuvelink, Gerard B M" sort="Heuvelink, Gerard B M" uniqKey="Heuvelink G" first="Gerard B. M." last="Heuvelink">Gerard B. M. Heuvelink</name>
<name sortKey="Heuvelink, Gerard B M" sort="Heuvelink, Gerard B M" uniqKey="Heuvelink G" first="Gerard B. M." last="Heuvelink">Gerard B. M. Heuvelink</name>
<name sortKey="Kempen, Bas" sort="Kempen, Bas" uniqKey="Kempen B" first="Bas" last="Kempen">Bas Kempen</name>
<name sortKey="Leenaars, Johan G B" sort="Leenaars, Johan G B" uniqKey="Leenaars J" first="Johan G. B." last="Leenaars">Johan G. B. Leenaars</name>
<name sortKey="Ribeiro, Eloi" sort="Ribeiro, Eloi" uniqKey="Ribeiro E" first="Eloi" last="Ribeiro">Eloi Ribeiro</name>
</country>
<country name="Canada"><noRegion><name sortKey="Macmillan, Robert A" sort="Macmillan, Robert A" uniqKey="Macmillan R" first="Robert A." last="Macmillan">Robert A. Macmillan</name>
</noRegion>
</country>
<country name="Brésil"><region name="État de Rio de Janeiro"><name sortKey="Samuel Rosa, Alessandro" sort="Samuel Rosa, Alessandro" uniqKey="Samuel Rosa A" first="Alessandro" last="Samuel-Rosa">Alessandro Samuel-Rosa</name>
</region>
</country>
<country name="Tanzanie"><region name="État de New York"><name sortKey="Walsh, Markus G" sort="Walsh, Markus G" uniqKey="Walsh M" first="Markus G." last="Walsh">Markus G. Walsh</name>
</region>
</country>
</tree>
</affiliations>
</record>
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