Abstract
Robust models for predicting soil salinity that use visible and near-infrared (vis–NIR) reflectance spectroscopy are needed to better quantify soil salinity in agricultural fields. Currently available models are not sufficiently robust for variable soil moisture contents. Thus, we used external parameter orthogonalization (EPO), which effectively projects spectra onto the subspace orthogonal to unwanted variation, to remove the variations caused by an external 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 moisture contents 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 partial least squares regression (PLSR) results established from spectra with and without EPO correction. The EPO method effectively removed the effects of moisture, and the accuracy and robustness of the soil salt contents (SSCs) prediction model, which was built using the EPO-corrected spectra under various soil moisture conditions, were significantly improved relative 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 spectroscopy and can assist others in quantifying soil salinity in the future.
Highlights
Soil salinization is a primary ecological degradation process, especially in arid and semi-arid areas [1], that limits water uptake by crops [2], inhibits crop growth and reduces crop yield [1]
This paper investigated an improved method for predicting soil salinity by using visible and near-infrared (vis—NIR) spectra after removing the effects of soil moisture by using external parameter orthogonalization (EPO)
The reflectance value across the entire wavelength domain decreased as the soil moisture content increased; the soil moisture content had a large influence on reflectance, which masked the subtle responses of various salt contents on reflectance and should not be ignored
Summary
Soil salinization is a primary ecological degradation process, especially in arid and semi-arid areas [1], that limits water uptake by crops (by reducing the osmotic potential and the total soil water potential) [2], inhibits crop growth and reduces crop yield [1]. Timely detection of the extent and magnitude of soil salinity is important for agriculture practices [3,4,5]. It is difficult to obtain up-to-date soil salinity information by using conventional techniques to identify and monitor soil salinity because these techniques are time consuming and expensive and require high sampling densities and frequencies [6, 7]. Efforts are being made to obtain more cost-effective methods for mapping soil salinity. During the last two decades, PLOS ONE | DOI:10.1371/journal.pone.0140688 October 15, 2015
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