Quantifying moisture and roughness with Support Vector Machines improves spectroscopic soil organic carbon prediction |
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| Authors: | Christoph Römer Andrei Rodionov Jan Behmann Stefan Pätzold Gerhard Welp Lutz Plümer |
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| Affiliation: | 1. University of Bonn, Institute of Geodesy and Geoinformation (IGG), Geoinformation, Meckenheimer Allee 271, 53113 Bonn, Germany;2. University of Bonn, Institute of Crop Science and Resource Conservation (INRES), Soil Science and Soil Ecology, Nussallee 13, 53115 Bonn, Germany |
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| Abstract: | The challenges of Vis‐NIR spectroscopy are permanent soil surface variations of moisture and roughness. Both disturbance factors reduce the prediction accuracy of soil organic carbon (SOC) significantly. For improved SOC prediction, both disturbance effects have to be determined from Vis‐NIR spectra, which is especially challenging for roughness. Thus, an approach for roughness quantification under varying moisture and its impact on SOC assessment using Support Vector Machines is presented here. |
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| Keywords: | ordinal classification moisture roughness soil organic carbon (SOC) Vis‐NIR spectroscopy |
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