Mapping soil salinity using spectral remote sensing and GIS in the HABRA plain in Northwest Algeria

Soil salinity is one of the most brutal environmental factors reducing the surface area and productivity of salt-sensitive cultivated plants. This major problem hurts land cover and agricultural productivity, principal to a decline in soil fertility and quality. The objective of this paper is to study the possibility of using an image of the spectral remote sensing data and field survey to sample soil and map salinity by correlating electrical conductivity (EC) field measurements with soil salinity indices derived from the Landsat7 satellite image. In Habra plain (North-western Algeria), the field’s electrical conductivity was measured during the period from 15 to 19 November 1999. These data were used as ground truth for the correlation analysis with different indices of image band values. Landsat 7 images were used to calculate soil salinity indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Salinity Index (NDSI), the Soil Adjusted Vegetation Index (SAVI), and the Soil Salinity Index. A statistical analysis of the electrical conductivity (EC1:5 dS/m) and the environmental indices acquired from the Landsat 7 image was performed. Exponential regression was used to find the best indices, which were NDVI, NDSI, SAVI, and SI5, with a (NDVI R2=0.9, NDSI R2=0.9, SAVI R2=0.68, and SI5 R2=0.77) correlation with field truth data. A salinity map of the Habra plain was generated using this index with an acceptable level of accuracy. This study found that Landsat 7 images can effectively monitor soil salinity levels. The results of this study are relevant for agricultural operations, planners, and farmers by mapping and monitoring the soil salinity contamination. Finally, it is concluded that using remote sensing in salinity detection and mapping is highly significant.