Sensing of Soil Organic Carbon Using Visible and Near-Infrared Spectroscopy at Variable Moisture and Surface Roughness

Variations in soil moisture and surface roughness are major obstacles for the proximal sensing of soil organic C (SOC) using visible and near-infrared spectroscopy (VIS-NIRS). We gained a significant improvement of SOC prediction under field conditions with a stepwise approach. This comprised of (i) the estimation of these disturbing factors and (ii) the subsequent use of this information in multivariate SOC prediction. We took 120 surface soil samples (SOC contents 6.55-13.40 g kg(-1)) from a long-term trial near Bonn, Germany. To assess soil moisture, we recorded VIS-NIR spectra on <2-mm sieved disturbed samples at seven different moisture levels (air-dried to 30% w/w). The impact of roughness on VIS-NIRS performance was studied with undisturbed samples (air-dried and at different moisture levels), which were scanned with a laser profiler after fractionation into six aggregate size classes. The results confirmed that it was possible to include VIS-NIRS based assessments of soil moisture [R-adj(2) = 0.96; root mean square error of cross validation (RMSECV) = 1.99% w/w] into the prediction of SOC contents for sieved samples <2 mm (R-adj(2) = 0.81-0.94; RMSEp = 0.41-0.72 g SOC kg(-1)). However, for rough soil surfaces, SOC contents were overestimated, and the prediction of roughness indices using VIS-NIRS failed. Fortunately, surface roughness did not impair the VIS-NIRS assessment of soil moisture. Hence, we could directly estimate moisture via VIS-NIRS in undisturbed field samples and then incorporate this information into a moisture-dependent prediction of SOC contents. This provided accurate SOC estimates for field-moist, undisturbed samples (R-adj(2) = 0.91). Deviations from the reference method (elemental analysis) were below 2 g SOC kg(-1).