Abstract
Soil salinization is one of the most common forms of land degradation. The detection and assessment of soil salinity is critical for the prevention of environmental deterioration especially in arid and semi-arid areas. This study introduced the fractional derivative in the pretreatment of visible and near infrared (VIS–NIR) spectroscopy. The soil samples (n = 400) collected from the Ebinur Lake Wetland, Xinjiang Uyghur Autonomous Region (XUAR), China, were used as the dataset. After measuring the spectral reflectance and salinity in the laboratory, the raw spectral reflectance was preprocessed by means of the absorbance and the fractional derivative order in the range of 0.0–2.0 order with an interval of 0.1. Two different modeling methods, namely, partial least squares regression (PLSR) and random forest (RF) with preprocessed reflectance were used for quantifying soil salinity. The results showed that more spectral characteristics were refined for the spectrum reflectance treated via fractional derivative. The validation accuracies showed that RF models performed better than those of PLSR. The most effective model was established based on RF with the 1.5 order derivative of absorbance with the optimal values of R2 (0.93), RMSE (4.57 dS m−1), and RPD (2.78 ≥ 2.50). The developed RF model was stable and accurate in the application of spectral reflectance for determining the soil salinity of the Ebinur Lake wetland. The pretreatment of fractional derivative could be useful for monitoring multiple soil parameters with higher accuracy, which could effectively help to analyze the soil salinity.
Highlights
Soil salinization is one of the most common forms and drivers of land degradation, and entails significant environmental, social, and economic consequences, especially in arid and semi-arid areas (Akramkhanov et al, 2011; Ding & Yu, 2014; Nawar, Buddenbaum & Hill, 2015)
The relative high mean salinity indicated that the surface soils were salt-affected in the Ebinur Lake wetland
The results showed that the distribution of the soil salinity of all datasets was left-skewed in contrast to the standardized normal distribution
Summary
Soil salinization is one of the most common forms and drivers of land degradation, and entails significant environmental, social, and economic consequences, especially in arid and semi-arid areas (Akramkhanov et al, 2011; Ding & Yu, 2014; Nawar, Buddenbaum & Hill, 2015). How to cite this article Wang et al (2018), Quantitative estimation of soil salinity by means of different modeling methods and visiblenear infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China. Oasis ecosystem is the material and ecological base of arid and semi-arid areas (Abliz et al, 2016). With the rapidly increasing population densities and drastic land use changes over the past few decades, soil salinization has become the main restraint for a sustainable development of oasis agriculture, and for the stability of regional ecosystems (Scudiero, Skaggs & Corwin, 2014). Detection as well as assessment of soil salinity are essential to regional ecological stability, and these problems have attracted considerable attention worldwide in recent years
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