Using L-band radar data for soil salinity mapping—a case study in Central Iraq

Abstract

Soil salinization is a critical environmental problem for dryland agriculture. Mapping its distribution and severity in space and time is essential for agricultural management and development. Recently, remote sensing technology has been widely applied in such mapping but mostly using optical remote sensing data. In conjunction with the field surveys, this case study was aimed at developing an operational approach for this purpose by employing ALOS (Advanced Land Observing Satellite) L-band radar data with support of Landsat 5 TM (Thematic Mapper) imagery acquired at almost the same time. The test was conducted in the Mussaib site in Central Iraq. The innovative procedure involved was the removal or minimization of the impact of vegetation cover and moisture on the backscattering coefficients by Water Cloud Model. The results revealed a strong correlation between the corrected backscattering coefficients of soil and the measured soil salinity (R2 = 0.565–0.677). The radar-based salinity models developed through multivariate linear regression (MLR) analysis were able to predict salinity with reliability of 70.05%. In conclusion, it is possible to use radar data for soil salinity prediction and mapping in dry environment.