The Relation between Soil Water Repellency and Water Content Can Be Predicted by Vis-NIR Spectroscopy

The severity of soil water repellency (SWR) varies nonlinearly with water content (w), and it is extremely laborious to obtain complete SWR-w curves, which are needed to predict the occurrence of SWR. In this study, we combined a three-parameter moisture-dependent SWR (MD-SWR) model with visible near-infrared spectroscopy (vis-NIRS) as a fast and novel method to estimate the SWR-w curve. The method was applied to a data set of SWR-w curves determined for 71 soil samples (organic carbon [OC] content: 0.021-0.147 kg kg(-1), clay: 0.000-0.520 kg kg(-1)). The degree of SWR was measured on air-dried soil samples (SWRAD) and on soil samples with increasing water contents until the water content at which the soils became wettable (w(NON)) was reached. The three-parameter MD-SWR model was fitted to the measured SWR-w curves between the water content at airdry conditions (w(AD)) and w(NON). The total SWR was then calculated as the trapezoidal integrated area underneath the SWR-w curves (SWRAREA). Air-dried soil samples were scanned with a vis-NIR spectrometer. Each of the three MD-SWR model parameters was correlated to vis-NIRS spectra using partial least squares regression. The SWRAREA was predicted using two approaches. For Approach I, the SWRAREA calculated from the MD-SWR model was predicted with a single vis-NIRS model. Approach II utilized predicted MD-SWR model parameter values to obtain vis-NIRS-predicted SWR-w curves between w(AD) and w(NON), and the SWRAREA was calculated from these curves. Results show that vis-NIRS can predict the shape of the SWR-w curves as well as the SWRAREA (R-2 = 0.58 and 0.56 for Approach I and II, respectively) across a highly variable dataset from a single vis-NIRS scanning and one SWR measurement at air-dried conditions.