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Soil salinity mapping using dual-polarized SAR Sentinel-1 imagery

Taghadosi, Mohammad Mahdi, Hasanlou, Mahdi, Eftekhari, Kamran
International journal of remote sensing 2019 v.40 no.1 pp. 237-252
agricultural land, algorithms, electrical conductivity, land management, monitoring, remote sensing, saline soils, salinity, soil productivity, soil salinity, soil sampling, statistics, synthetic aperture radar, texture, theoretical models
Soil salinity is a major environmental threat, which has a negative impact on soil productivity and agricultural fields. One of the most promising methods for monitoring affected areas, which has special importance in land management studies, is through remote sensing technologies. While the potential of optical imagery in detecting saline soils is widely investigated, limited studies have been dedicated to assessing the potential of Synthetic Aperture Radar (SAR) imagery in monitoring soil salinity. Accordingly, this paper deals with soil salinity estimation using Sentinel-1 SAR imagery in an area which is highly affected by salinity hazard. Due to lack of a suitable theoretical model for simulating radar backscatter of soil based on salt contents, we investigated a new method to relate radar intensity to measured in-situ salinity directly. In the first step, Sentinel-1 dual polarized VV (Vertical transmit Vertical receive) and VH (Vertical transmit Horizontal receive) data were acquired from the study site. A field study was also conducted, simultaneously, and the Electrical Conductivity (EC) of several soil samples was measured. We then extracted some features based on the intensity images of both VV and VH polarization. Based on the fact that the target texture affects the radar response, an analysis of the texture was also performed by calculating the first and second order statistics, extracted from the histogram and Gray Level Co-occurrence Matrix (GLCM), respectively. The Support Vector Regression (SVR) technique, with different kernel functions, was used to relate explanatory variables to ground measured salinity. We also applied Feature Selection (FS) algorithms of the Genetic Algorithm (GA) and Sequential Feature Selection (SFS) for optimizing the model and selecting the best explanatory features. The results showed that ε-SVR with Radial Basis Function (RBF) kernel had the most accuracy with the Coefficient of Determination (R²) = 0.9783 and Root Mean Square Error (RMSE) = 0.3561 when the GA FS was applied. Also and had the best performance in salinity detection. It can be concluded that the intensity images of VV and VH polarization of SAR imagery have the potential to discriminate saline surface soils, regardless of the failure of common backscattering models.