Using hyperspectral remote sensing data for the assessment of topsoil organic carbon from agricultural soils

Abstract

Detecting soil organic carbon (SOC) changes is important for both the estimation of carbon sequestration in soils and the development of soil quality. During a field campaign in May 2011 soil samples were collected from two agricultural fields northwest of Koethen (Saxony-Anhalt, Germany) and the SOC content of the samples was determined in the laboratory afterwards. At the same time image data of the test site was acquired by the hyperspectral airborne scanner AISA-DUAL (450-2500 nm). The image data was corrected for atmospheric and geometric effects and a spectral binning has been performed to improve the signal-to-noise ratio (SNR). For parameter prediction, an empirical model based on partial least squares regression (PLSR) was developed from AISA-DUAL image spectra extracted at the geographic location of the soil samples and analytical laboratory results. The obtained SOC concentrations from the AISA-DUAL data are in accordance with the concentration range of the chemical analysis. For this reason, the PLSR-model has been applied to the AISA-DUAL image data. The predicted SOC concentrations reflect the spatial conditions of the two investigated fields. The results indicate the potential of the used method as a quick screening tool for the spatial assessment of SOC, and therefore an appropriate alternative to time- and cost-intensive chemical analysis in the laboratory. © 2012 SPIE.