Use of imaging spectroscopy to assess different organic carbon fractions of agricultural soils

Abstract

The site for this study - located in Rhineland-Palatinate, Germany ("Bitburger Gutland") - covered different geological substrates and agro-pedological zones. In total, 42 plots were sampled in the field; soil samples from the top horizon were analysed in the laboratory for total organic carbon (OC), hot water-extractable C (HWE-C) and microbial C (Cmic). In parallel to the ground campaign, a data set of the HyMap™ airborne imaging sensor was acquired on 27th of August 2009. After pre-processing, HyMap spectra were used to assess the contents of OC, HWE-C and Cmic. As calibration method we used partial least squares regression (PLSR), as it allows a handling of large input spaces and noisy patterns. Since calibration quality was poor for HWE-C and Cmic (cross-validated r2values were less than 0.5), we additionally combined PLSR with a genetic algorithm (GA) to preselect an optimum set of spectral features instead of using the full spectrum. With this GA-PLSR approach, results improved considerably for all constituents in the crossvalidation (r2 ≥ 0.72). Very similar GA selection patterns for all carbon fractions suggest that spurious (indirect) correlations may be relevant for assessing HWE-C and Cmic. For the GA approach, some overfitting due to a selection based on chance correlations between C fractions and spectral variables cannot be excluded. © 2011 SPIE.