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

Abstract

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 (SWR AD ) 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 (SWR AREA ). Airdried 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 SWR AREA was predicted using two approaches. For Approach I, the SWR AREA 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 SWR AREA was calculated from these curves. Results show that vis-NIRS can predict the shape of the SWR-w curves as well as the SWR AREA (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.

Water repellency prediction in high‐organic Greenlandic soils: Comparing vis–NIRS to pedotransfer functions

“…The critical water content also varied considerably ranging from 0.052 to 0.921 kg kg −1 (Table 1). For 71 New Zealand soil samples, w non was found to range between 0.050 and 0.438 kg kg −1 and SWR area to range between 0.12 and 11.57 mN m −1 kg kg −1 (Hermansen et al., 2019a). Similarly, de Jonge et al.…”

“…Further, a study used partial least squares regression (PLS-R) with vis-NIRS to predict the SWR index (measured with the relative sorptivity method) and obtained acceptable results (R 2 > .52 and ratio of performance to interquartile distance > 2.27) for 100 soil samples from Iran (soil organic matter, 0.007-0.136 kg kg −1 ) (Davari et al, 2022). Lastly, the SWR-w curve, as well as SWR area , were predicted using vis-NIRS by Hermansen et al (2019a) on soil samples from the South Island of New Zealand (OC, 0.021-0.147 kg −1 ) using an approach where fitting parameters (maximum SWR, soil water content at maximum SWR and w non ) of a three-parameter moisturedependent SWR model were predicted. The ratio between SWR area and w non , called integrative repellency dynamic index (IRDI), is a measure of the average SWR proposed by Regalado and Ritter (2005).…”

“…The critical water content also varied considerably ranging from 0.052 to 0.921 kg kg −1 (Table 1). For 71 New Zealand T A B L E 2 Pedotransfer functions using linear regression (LR) and multiple linear regression (MLR) for soil water repellency (SWR) indices-SWR after 60 ˚C pretreatment (SWR 60 ), the area under SWR-w curve (SWR area ), critical soil water content (w non ) and the ratio between SWR area and w non (IRDI)-based on organic carbon (OC) in combination with clay content (CL) and pH for 143 samples soil samples, w non was found to range between 0.050 and 0.438 kg kg −1 and SWR area to range between 0.12 and 11.57 mN m −1 kg kg −1 (Hermansen et al, 2019a). Similarly, de Jonge et al ( 2007) found values of w non from 0.03 to 0.095 kg kg −1 but much lower values of SWR area (0-2.9 mN m −1 kg kg −1 ) on Danish soils with different crop types.…”

“…Lastly, the SWR– w curve, as well as SWR area , were predicted using vis–NIRS by Hermansen et al. (2019a) on soil samples from the South Island of New Zealand (OC, 0.021–0.147 kg −1 ) using an approach where fitting parameters (maximum SWR, soil water content at maximum SWR and w non ) of a three‐parameter moisture‐dependent SWR model were predicted. The ratio between SWR area and w non , called integrative repellency dynamic index (IRDI), is a measure of the average SWR proposed by Regalado and Ritter (2005).…”