Assessment of Remotely Sensed Indices to Estimate Soil Salinity

Soil Salinization is one of the oldest environmental problems and one of the main paths to desertification. Access to information in the shortest time and at low cost is the major factor influencing decision making. The satellite imagery provides information data on salinity and also offers large amount of data that can be analyzed and processed to understand several indices based on the type of the sensor used. In this research, the capability of different indices derived from IRS-P6 data was evaluated to identify saline soils in Mahabad County. The quality of the satellite images was first evaluated and no noticeable radiometric and geometric distortion was detected. The Ortho-rectification of the image was performed using the satellite ephemeris data, digital elevation model, and ground control points. The RMS error was less than a pixel. In this study, the correlation between the bands and used indices, including Salinity1, Salinity2, Salinity3, PCA1 (B2, B3), PCA1 (B4, B5), PCA1 (B1, B2, B3, B4, B5), Fusion (Pan and B2), Fusion (Pan and B3) and Fusion (Pan and B4) with EC were investigated. The highest correlation was related to the Fusion (Pan and B2) with a coefficient 0.76 and the lowest correlation was related to B4 with a coefficient 0.2. The results showed that the indices have a high ability for modeling, mapping and estimating the soil salinity.