Using Radar and Optical Data for Soil Salinity Modeling and Mapping in Central Iraq

Abstract

As one of the environmental calamities, soil salinization has become a key concern in agricultural management, especially, in irrigated areas in dryland systems. How to quantify and map soil salinity in space and time to provide relevant advices for decision-makers and land managers for their agricultural development has become a pressing issue. Based on our previous works, this study was aimed to develop rather simple and operational approaches for such quantification and mapping taking the Mussaib site in Central Iraq as an example. In conjunction with the field samples, ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array L-band Synthetic Aperture Radar) data and Landsat 5 TM (Thematic Mapper) imagery acquired at almost the same time were employed for this purpose. After derivation of different biophysical indicators from the TM images and removal of the impacts of vegetation water content (VWC) on the L-band radar backscattering coefficients, a multivariate linear regression (MLR) modeling was applied to establish the combined and radar-based soil salinity models. Results revealed that VWC removal procedure could significantly improve the correlation between the measured apparent soil salinity (ECa) and radar backscattering coefficients by 7.5–25.6%. The optical-radar combined models can reliably predict soil salinity with an accuracy of 77.0–83.7% (R2 = 0.770–0.837). Merely, further improvement in reducing the impacts of vegetation cover and soil moisture by radar data themselves is still recommended. In conclusion, the optical-radar combined approaches and models developed in this chapter shall be operational for soil salinity modeling and mapping; and radar-based approach has great potential for this purpose.