Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

Mareike Ließ; Johannes Schmidt; Bruno Glaser

doi:10.1371/journal.pone.0153673

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Open Access

Peer-reviewed

Research Article

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

Mareike Ließ ,

* E-mail: mareike.liess@ufz.de

Affiliations Department of Soil Physics, Helmholtz Centre for Environmental Research–UFZ, Halle (Saale), Germany, Department of Geosciences/ Soil Physics Division, University of Bayreuth, Bayreuth, Germany

http://orcid.org/0000-0001-9325-199X

⨯
Johannes Schmidt,

Affiliation Department of Physics and Geosciences/ Institute of Geography, University of Leipzig, Leipzig, Germany
⨯
Bruno Glaser

Affiliation Department of Soil Biochemistry, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Germany
⨯

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

Mareike Ließ,
Johannes Schmidt,
Bruno Glaser

Published: April 29, 2016
https://doi.org/10.1371/journal.pone.0153673

About the Authors

Mareike Ließ

* E-mail: mareike.liess@ufz.de

Affiliations Department of Soil Physics, Helmholtz Centre for Environmental Research–UFZ, Halle (Saale), Germany, Department of Geosciences/ Soil Physics Division, University of Bayreuth, Bayreuth, Germany

http://orcid.org/0000-0001-9325-199X

Johannes Schmidt

Affiliation Department of Physics and Geosciences/ Institute of Geography, University of Leipzig, Leipzig, Germany

Bruno Glaser

Affiliation Department of Soil Biochemistry, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Germany

Competing Interests

The authors have declared that no competing interests exist.

Author Contributions

Conceived and designed the experiments: ML. Performed the experiments: ML JS. Analyzed the data: ML JS. Contributed reagents/materials/analysis tools: ML JS BG. Wrote the paper: ML JS BG.

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Terrain
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