In this study, we examined the potential of vis-NIR reflectance spectroscopy, coupled with partial least squares regression (PLSR) analysis, for the evaluation and prediction of soil water retention at field capacity (FC) and permanent wilting point (PWP) and related basic soil properties [organic carbon (OC), sand, silt, and clay contents] in an agricultural irrigated land of southern Italy. Soil properties were determined in the laboratory with reference to the Italian Official Methods for Soil Analysis. Vis-NIR reflectance spectra were measured in the laboratory, using a high-resolution spectroradiometer. All soil variables, with the exception of silt, evidently affected some specific spectral features. Multivariate calibrations were performed to predict the soil properties from reflectance spectra. PLSR was used to calibrate the spectral data using two-thirds of samples for calibration and one-third for validation. Spectroscopic data were pre-processed [multiplicative scatter correction (MSC), standard normal variance (SNV), wavelet detrending (WD), first and second derivative transformation, and filtering] prior to multivariate calibration. The results revealed very good models (2.0 < RPD < 2.5) for the prediction of FC, PWP and sand, and excellent (RPD > 2.5) models for the prediction of clay and OC, whereas a poor (RPD < 1.4) prediction model was obtained for silt.

This study attempts to gain an areal understanding of potential soil loss due to water erosion in the lower Calore River valley (Southern Italy). To this end, the Universal Soil Loss Equation (USLE) was applied to data obtained from the processing of multitemporal satellite images acquired by the Sentinel 2A system, the elaboration of a digital terrain model and that of weather station records, and the re-elaboration of pre-existing information regarding soil characteristics. The information acquired regarding each USLE factor (i.e., rain erosivity, R; soil erodibility, K; slope and length steepness, LS; and cover management, C), was combined in a GIS environment to obtain a Map of the potential risk of soil erosion. The results showed that the potential soil losses due to water erosion are quite worrying, particularly in hilly and foothill agricultural areas, dominated by valuable crops such as vines and olives.