Mapping spatial variability of soil salinity in a coastal area located in an arid environment using geostatistical and correlation methods based on the satellite data

Saline lakes can increase the soil and water salinity of the coastal areas. The main aim of this study is to distinguish the characteristics of the spectral reflectance of saline soil, analyze the statistical relationship between soil EC and characteristics of the spectral reflectance of saline soil, and to map soil salinity east of the Maharloo Lake. The correlation between field measurements of electrical conductivity and remote sensing spectral indices was evaluated using multiple regression analysis. In this study, Kriging, CoKriging, and multiple regressions were applied for soil salinity mapping and classification using 100 soil samples. After radiometric, geometric, and atmospheric corrections of Landsat OLI images, the statistical correlation between the electrical conductivity of field measurements and spectral reflectance was investigated. According to obtained results, the modified salinity index (MSI) with the highest correlation (R2=0.78) was used as an auxiliary variable for the coKriging method. Kriging with a spherical model was selected for soil salinity mapping (RMSE = 50.5 and R2 = 0.18). The RMSE and R2 values for CoKriging were (43.2 and 0.42), respectively. Because of their acceptable R2 (=0.65) and low standard deviation (33.8) for salinity analysis, MSI and difference vegetation index (DVI) were used to estimate and zonate soil salinity in the study area. The results showed that soil salinity could be estimated via spectral indices with acceptable accuracy, R2 and RMSE. Overall, this method leads to a decrease in the costs involved in the soil mapping of saline soil areas.