USE OF VISIBLE AND NEAR INFRARED REFLECTANCE SPECTROSCOPY TO ESTIMATE SOIL PROPERTIES IN ARGENTINA

Abstract

The aim of this study was to develop visible and near-infrared spectroscopy (Vis-NIRS) calibration models for predicting the content of organic carbon (OC), total nitrogen (N), clay + silt, and pH values in soils from Argentina, employing different mathematical preprocessing techniques for spectral data. A total of 154 soil samples with contrasting physicochemical characteristics was selected, dried, and sieved to 2 mm prior to the analysis of OC, N, pH, and clay + silt using reference methods. Subsequently, the Vis-NIR spectrum (400 to 2500 nm) of each sample was obtained in reflectance mode. The sample set was randomly divided into two groups: one for calibration model development (80%) and the other for model validation (20%). Eight preprocessing techniques for spectral information were employed, and the best one for each parameter studied was selected based on the criteria of achieving the minimum standard error of cross-validation (EECV), maximum coefficient of determination of cross-validation (R²cv), and maximum residual predictive deviation (RPD). Calibration models were obtained using modified partial least squares regression (MPLS) and five-fold cross-validation. The best models for predicting OC and N were obtained with the raw spectra (RPD=4.69 and 3.65 respectively); for pH, the best model was obtained using the standard normal variate preprocessing technique with the second derivative (RPD=2.27), and for clay + silt, the best model was obtained with multiple scattering correction and second derivative (RPD=2.83). The performance of the models applied to the calibration and testing groups was similar. The results indicate that the appropriate choice of spectral data preprocessing technique can optimize calibration for the prediction. Vis-NIRS is a valuable tool for soil monitoring in Argentina, complementing traditional analysis methods.