1. Introduction
Salt affected soils are caused by excess accumulation of salt which are
pronounced at the soil surface. Salt is often derived from geological
formations featuring shale, marl, limestone, sylvite, gypsum, and
halite, but variability of soil salinity is mostly due to parent
material, soil type, and landscape position (Clay et al., 2001).
Moreover, salts can be transported to the soil surface by capillary
action from brackish water tables and can accumulate due to evaporation;
they can also accumulate as a result of anthropogenic activities such as
fertilization or oil production. Soil salinization is a universal
problem and current estimations of the proportion of salt-affected soils
in irrigated lands for several countries were 27 % in India, 20 % in
Australia, 28 % in Pakistan, 50 % in Iraq and 30 % in Egypt (Stockle,
2013). The accumulation of soluble salts in the root zone greatly affect
plant growth, resulting in lower crop yields and adversely affecting the
soil fertility (Li et al. 2013).
Soil salinity is typically assessed by measuring the soil electrical
conductivity in saturated paste extracts (ECe) or by using extracts with
different soil-to-water ratios (Sonmez et al. , 2008). Developed
in the mid-1950s, ECe is one of the most widely reported soil quality
assessment parameters (Karlen et al., 2008), regular monitoring
of which is essential for efficient soil and water management and
sustainability of agricultural lands (Bilgili et al., 2011).
Electrical conductivity can act as an indirect indicator of important
soil physical properties (Rhoades et al., 1999) and provides
important information about the impact that farm practices, such as
irrigation and soil and crop management, have at both the field and
regional scales. Therefore, reliable information on the nature and
spatial extent of soil salinity is a prerequisite for restoring
fertility and preventing further degradation. Thus, timely detection of
the extent and magnitude of soil salinity is important for agriculture
practices.
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; hence efforts are being made to
obtain more cost-effective methods for mapping soil salinity. During the
last two decades, visible and near-infrared spectroscopy has been used
as a rapid, cost-effective and relatively accurate method for analyzing
conventional soil properties (Nocita et al., 2015). Previously,
several studies indicated that pure sodium chloride is featureless in
Vis-NIR regions because salt is not a strong or direct chromophore
(Metternicht et al., 1997). However, the presence of salts in
soils may result in subtle spectral responses when combined with—OH,
which is common in soils. Therefore, soil salinity can be characterized
by soil spectral reflectance or salinity spectral indices using partial
least squares regression, artificial neural network, and stepwise
multiple linear regression methods (Zhang et al., 2011) and can
be detected using high-resolution spectroscopy (Jin et al.,2015). Accordingly, interest in using reflectance spectroscopy as a
rapid and effective tool for mapping soil salinity has recently grown
and several studies have estimated salt contents of air-dried soils with
reasonable accuracy using reflectance spectroscopy (Yong-Ling et
al., 2010).
Hyperspectral visible and near-infrared reflectance spectroscopy
displays promise as a result of its performance, accuracy and cost
effectiveness in the determination of most soil properties (Shepherd and
Walsh 2002). Various statistical modeling techniques help to correlate a
single reflectance spectrum of soil to a host of physical, chemical,
mineralogical and microbiological attributes of that soil after proper
calibration and validation of models. Principal component regression
(PCR), partial least squares regression (PLSR), multivariate adaptive
regression splines (MARS), artificial neural networks (ANN) are some of
the commonly used diagnostics for calibration and validation of
hyperspectral models (Bilgili et al. , 2010). The reliability of
calibration of spectral data with chemical analysis data needs to be
enhanced by factoring in variations on account of land use and choice of
scale. The calibration process also needs to be made indubitable by
using optimal sample size and sampling strategy. Once the calibration
models between soil reflectance spectra and soil variables have been
established, they can be used to predict unidentified parameters.
Several regression methods based on visible near IR have been used to
estimate soil salinity, and partial least-squares regression is the most
common (Farifteh et al. 2007; Bilgili et al. 2011). The PLSR approach
has inference capabilities that are useful for modelling a probable
linear relationship between the measured reflectance spectra and salt
content in soils (Farifteh et al. 2007). The MARS method is considered a
nonparametric method that estimates complex nonlinear relationships
among independent and dependent variables (Friedman 1991), and it has
been effectively applied in different fields (Bilgili et al. ,
2010; Felicísimo et al., 2012) and generally exhibits high
performance results compared with other linear and non-parametric
regression models, such as principal component regressions,
classification and regression trees and artificial neural networks.
This study was conducted to evaluate multivariate regression models to
predict electrical conductivity using Vis-NIR and MIR Spectra as a
substitute to conventional soil analysis. The specific goals of this
study were to find out sensitive regions of the spectrum for modelling
electrical conductivity and compare the performance of multivariate
regression models PLSR, RF, SVR, and MARS for predicting EC, both in the
Vis-NIR and MIR spectral region.