Soil salinity prediction in the Lower Cheliff plain (Algeria) based on remote sensing and topographic feature analysis

Abstract

Soil salinity and ground surface morphology in the Lower Cheliff plain (Algeria) can directly or indirectly impact the stability of environments. Soil salinization in this area is a major pedological problem related to several natural factors, and the topography appears to be important in understanding the spatial distribution of soil salinity. In this study, we analyzed the relationship between topographic parameters and soil salinity, giving their role in understanding and estimating the spatial distribution of soil salinity in the Lower Cheliff plain. Two satellite images of Landsat 7 in winter and summer 2013 with reflectance values and the digital elevation model (DEM) were used. We derived the elevation and slope gradient values from the DEM corresponding to the sampling points in the field. We also calculated the vegetation and soil indices (i.e. NDVI (normalized difference vegetation index), RVI (ratio vegetation index), BI (brightness index) and CI (color index)) and soil salinity indices, and analyzed the correlations of soil salinity with topography parameters and the vegetation and soil indices. The results showed that soil salinity had no correlation with slope gradient, while it was significantly correlated with elevation when the EC (electrical conductivity) values were less than 8 dS/m. Also, a good relationship between the spectral bands and measured soil EC was found, leading us to define a new salinity index, i.e. soil adjusted salinity index (SASI). SASI showed a significant correlation with elevation and measured soil EC values. Finally, we developed a multiple linear regression for soil salinity prediction based on elevation and SASI. With the prediction power of 45%, this model is the first one developed for the study area for soil salinity prediction by the combination of remote sensing and topographic feature analysis.