1. INTRODUCTION
Soil salinization is one of the major land degradation processes in
almost all parts of the world. About 836 million hectares (Mha) area of
the world (3% of total geographical area) is affected with soil
salinization, of which, 48% is saline and 52% is sodic (Singh, 2016).
The extent of the salt-affected soils (SAS) in India is 6.73 Mha, of
which 1.28 Mha (19%) is in the coastal region (CSSRI, 2018). Unlike the
SAS formed by secondary salinization, the SAS of the coastal region
possesses unique physical and chemical properties. The co-existence of
the soil acidity (low soil pH) with high salinity level (high electrical
conductivity, EC) is a unique feature of these soils. The ingression of
saline water from sea and estuaries either naturally or through
anthropogenic activities is the major reason for salinity in these
soils. The excess amount of the salts affects the physical, chemical and
biological properties of the soils and ultimately the plant growth
(Mahajan et al. , 2016; Yuan et al. , 2007). The
productivity of these soils is often very low. Quantitative assessment
of the soil properties is very crucial to understand, maintain and
improve the soil quality and enhance crop yield (Askari et al. ,
2015; Xu et al. , 2018). But, the soil is a heterogeneous resource
due to complex processes and mechanisms involved in soil formation
(Morellos et al. , 2016). Real-time assessment of soil salinity at
high temporal and spatial resolution and other properties are essential
to manage these soils efficiently for crop production. Soil sampling and
laboratory analysis to adequately assess the spatial and temporal
variability of soil properties are time-consuming and expensive (Xuet al. , 2018) and often limited to smaller areas. Such a
challenge has attracted researchers worldwide in recent years to find
alternate ways to overcome it. Compared to the conventional laboratory
analysis methods, hyperspectral remote sensing (HRS) has been proposed
as one of the modern, valid and alternate techniques for monitoring the
soil properties (Stenberg et al. , 2010). Moreover, multiple soil
properties can be estimated using a single representative spectral scan
of each sample (Vohland et al. , 2011).
Based on the spectral absorption and reflection features in visible
(VIS) and near-infrared (NIR) region, remote sensing technology could be
employed for the estimation of soil properties and salt content (Islamet al. , 2003). Researchers have investigated the use of VIS-NIR
based spectroscopy for estimating different soil attributes in different
soil types. Multivariate statistics is required to build the
relationship between the complex features or patterns of soil spectral
data and the soil properties (Araújo et al. , 2014; Stenberget al. , 2010). Among different multivariate statistical
techniques, commonly used linear techniques are stepwise multiple linear
regression (SMLR), principal component regression (PCR) and partial
least square regression (PLSR), however, use of non-linear techniques
like multivariate adaptive regression splines (MARS), random forest
(RF), support vector machine regression (SVMR) are also becoming popular
as relationships between spectral data and soil attributes are rarely
linear (Araújo et al. , 2014; Xu et al. , 2018). Wanget al. (2018) found that the RF based model with 1.5 order
derivative of absorbance was most effective, stable and accurate to
quantify the soil salinity with coefficient of determination
(R2) = 0.93, root means square error (RMSE) = 4.57 dS
m-1 and ratio of performance to deviation (RPD) =
2.78. Cécillon et al. (2009) and Stenberg et al. (2010)
investigated the applicability of the VIS-NIR remote sensing and
observed absorption bands for NaCl, KCl and MgSO4 at
1930 nm, 1430 nm and 1480 nm, respectively. The use of PLSR and
artificial neural network (ANN) to predict the salt concentration (NaCl,
KCl, MgCl2 and MgSO4) has been
successfully demonstrated by Farifteh et al. (2007). Nawaret al. (2014) recorded good prediction of soil EC based on the
MARS model using soil spectral reflectance (R2 = 0.73,
RMSE = 6.53 dS m-1, and RPD = 1.96) compared to PLSR.
The use of VIS-NIR remote sensing for predicting the soil properties
like soil organic carbon (SOC), soil pH, EC, total nitrogen (N),
available N, total phosphorus (P), Mehlich 1 extractable P, total
potassium (K), cation exchange capacity (CEC), moisture, soil texture,
clay content, etc. has been studied widely (Araújo et al. , 2015;
Cécillon et al. , 2009; Chang et al. , 2001; Christy, 2008;
Morellos et al. , 2016; Schirrmann et al. , 2013; Vasqueset al. , 2008, 2009; Viscarra Rossel et al. , 2006; Wenjunet al. , 2014; Xu et al. , 2018). The non-linear
multivariate techniques (SVMR and Back Propagation Neural Network
(BPNN)) outperformed the linear techniques (PCR and PLSR) to predict the
soil organic matter, total N, total P and total K in soil cores of paddy
fields (Xu et al. , 2018). The use of the VIS-NIR spectra for
characterizing soils gives a large number of predictor variables but
using the full spectra at high-resolution compromises with the
multi-collinearity and noise (Vohland et al. , 2011). Thus, the
selection of a proper multivariate techniques for calibration and
prediction of a variable is an important factor (Mouazen et al. ,
2010; Nawar et al. , 2016; Xu et al. , 2018).
To the best of our knowledge, very few studies have been conducted on
SAS and almost none on SAS having acidic soil reaction. The objectives
of the study were to (1) study effect of the soil salinity on the
VIS-NIR spectral reflectance pattern, (2) investigate potential of
VIS-NIR spectroscopy to estimate the various properties of SAS and (3)
compare the predictive ability of the linear and non-linear multivariate
techniques for estimation of soil properties of SAS.