Quantifying moisture and roughness with Support Vector Machines improves spectroscopic soil organic carbon prediction.

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. [ABSTRACT FROM AUTHOR]