use of visible and near‐infrared spectroscopy for the analysis of soil water repellency

• Published in: European journal of soil science 2014-05, Vol.65 (3), p.360-368
• Main Authors: Kim, I, Pullanagari, R. R, Deurer, M, Singh, R, Huh, K. Y, Clothier, B. E
• Format: Article
• Language: English
• Subjects: Agronomy. Soil science and plant productions; Biological and medical sciences; chemical composition; discriminant analysis; drought; Earth sciences; Earth, ocean, space; ethanol; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; laboratory techniques; least squares; Physical properties; Physics, chemistry, biochemistry and biology of agricultural and forest soils; prediction; reflectance; soil organic carbon; soil sampling; Soil science; soil water; Soils; spectral analysis; spectroscopy; Surficial geology; Water and solute dynamics
• ISSN: 1351-0754; 1365-2389
• DOI: 10.1111/ejss.12138

This study investigated the potential of visible/near‐infrared reflectance spectroscopy (Vis‐NIRS) to predict soil water repellency (SWR). The top 40 mm of soils (n = 288) across 48 sites under pastoral land‐use in the North Island of New Zealand, which represented 10 soil orders and covered five classes of drought proneness, were analysed by standard laboratory methods and Vis‐NIRS. Soil WR was measured by using the molarity of ethanol droplet (MED) and the water drop penetration time (WDPT) tests. Soil organic carbon content (%C) was also measured to examine a possible relationship with SWR. A partial least squares regression (PLSR) model was developed by using Vis‐NIRS spectral data and the reference laboratory data. In addition, we explored the power of discrimination based on WDPT classes using partial least squares discriminant analysis (PLS‐DA). The PLSR of the processed spectra produced moderately accurate prediction for MED (R² ᵥₐₗ = 0.61, RPDᵥₐₗ = 1.60, RMSEᵥₐₗ = 0.59) and good prediction for %C (R² ᵥₐₗ = 0.82, RPDᵥₐₗ = 2.30, RMSEᵥₐₗ = 2.72). When the data from the 10 soil orders were considered separately and based on soil order rather than being grouped, the prediction of MED was further improved except for the Allophanic, Brown, Organic and Ultic soil orders. The PLS‐DA was successful in classifying 60% of soil samples into the correct WDPT classes. Our results indicate clearly that Vis‐NIRS has the potential to predict SWR. Further improvement in the prediction accuracy of SWR is envisaged by increasing the understanding of the relationship between Vis‐NIRS and the SWR of all New Zealand soil orders as a function of their physical properties and chemical constituents such as hydrophobic compounds.