Soil Organic Carbon Prediction by Vis-NIR Spectroscopy: Case Study the Kur-Aras Plain, Azerbaijan

Abstract

ABSTRACTVisible and near infrared (Vis-NIR) spectroscopy is a rapid, accurate, cost-effective, and nondestructive alternative method for soil analysis. The aim of this study was to examine the potential of Vis-NIR spectroscopy to predict soil organic carbon (SOC) content. In total, 194 soil samples were collected from 46 soil profiles in the Kur-Aras plain dominated by Calcisols, Gleysols, and Anthrosols, Azerbaijan. SOC content was predicted by partial least squares regression (PLSR) using spectral reflectance data. In the modeling phase, spectral data were involved in preprocessing and transformation techniques. The dataset was randomly separated into two subsets: a calibration subset and a validation subset that was independent of the calibration subset. Modeling results were evaluated based on coefficient of determination (R2), root mean square error of prediction (RMSE), and residual prediction deviation (RPD). Most optimal calibration model was considered when the prediction of the leave-one-out cross-validation showed best performance. This study found that different preprocessing procedures effect model performance. SOC content significantly effected spectral reflectance from soil, where a decrease in reflectance with an increase in SOC was determined through the entire wavelength. We obtained an accurate prediction model for SOC with the R2, RMSE, and RPD values of cross validation 0.85, 3.77 g/kg, and 2.54, respectively. This model is valid over the range 3.80–6.71 g/kg of SOC content. Further attempts need to be given to different calibration strategies to more accurately predict SOC content.