Predicting soil salinity with Vis-NIR spectra after removing the effects of soil moisture using external parameter orthogonalization

Abstract

Robust models for predicting soil salinity that use visible and near-infrared (vis-NIR) reflectancespectroscopy are needed to better quantify soil salinity in agricultural fields. Currently available models are not sufficiently robust for variable soil moisture contents. Thus, weused external parameter orthogonalization (EPO), which effectively projects spectra onto the subspace orthogonal to unwanted variation, to remove the variations caused by anexternal factor, e.g., the influences of soil moisture on spectral reflectance. In this study, 570 spectra between 380 and 2400 nm were obtained from soils with various soil moisturecontents and salt concentrations in the laboratory; 3 soil types × 10 salt concentrations × 19 soil moisture levels were used. To examine the effectiveness of EPO, we compared the partialleast squares regression (PLSR) results established from spectra with and without EPO correction. The EPO method effectively removed the effects of moisture, and the accuracyand robustness of the soil salt contents (SSCs) prediction model, which was built using the EPO-corrected spectra under various soil moisture conditions, were significantly improvedrelative to the spectra without EPO correction. This study contributes to the removal of soil moisture effects from soil salinity estimations when using vis-NIR reflectance spectroscopyand can assist others in quantifying soil salinity in the future.