Abstract
Soil salinization is one of the serious environmental problems in arid and semiarid regions. As an effective technique for monitoring soil salinity, remote sensing (RS) technology has been widely used to estimate soil salinity in recent years. Previous studies on soil salinity mapping based on RS images adopted linear regression (LR) between the field measured of electrical conductivity (EC) and the RS data. It is expected that nonlinear regression (NLR) models improve the accuracy of soil salinity mapping over LR. The main objectives of this study are: (1) evaluation the capability of various NLR models for estimating soil salinity based on optical Sentinel-2 RS images, (2) feature selection for soil salinity estimation, and (3) updated and accurate soil salinity map production in the dried lake bed of Urmia Lake. The investigated NLR models include: polynomials, rational functions, powers, exponential, gaussian, logarithmic, and sum of sinusoidal functions with different degrees. All these regression models were calibrated and evaluated separately based on 8 visible and infrared bands of the Sentinel-2 image and 17 salinity indices to estimate soil salinity in the dried lake bed of Urmia Lake (Iran). The evaluation results confirmed the superiority of the NLR models over the LR model for soil salinity estimation. The polynomial degree 3 (Poly-3) based on S3 index (\({\text{S}}3 = \frac{G \times R}{B}\)) could predict EC value with better accuracy than the best LR model (based on narrow NIR band). The R2 and RMSE of the Poly-3 model were 0.98 and 8.16 dS/m while corresponding values of the best LR model were 0.88 and 20.85 dS/m in test samples, respectively. In general, the results show that the NLR models, along with RS data, have enough accuracy to estimate soil salinity. To compare these methods visually and estimate salt’s distribution and concentration in this area, soil salinity maps were predicted by the best NLR model (\({\text{EC}} = 1.63 \times 10^{ - 10} \times {\text{S}}3^{3} - 9.95 \times 10^{ - 6} \times {\text{S}}3^{2} + 0.11 \times {\text{S}}3 - 151.7\)) and the other linear and NLR models in the dried lake bed of Urmia Lake.
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Farahmand, N., Sadeghi, V. Estimating Soil Salinity in the Dried Lake Bed of Urmia Lake Using Optical Sentinel-2 Images and Nonlinear Regression Models. J Indian Soc Remote Sens 48, 675–687 (2020). https://doi.org/10.1007/s12524-019-01100-8
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DOI: https://doi.org/10.1007/s12524-019-01100-8