Comparative Assessment of Soil Salinity Using Sentinel-2 and Landsat-7 Remote Sensing Data

Mohamed A. Elshewy (Department of Civil Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11751)
Mohamed Freeshah (2) Civil and Environmental Engineering Department, College of Engineering, UAE University, Al Ain 15551, United Arab Emirates. 3) Geomatics Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11629)
Mostafa H. A. Mohamed (Department of Civil Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11751)
Mahmoud M. E. Gad (Civil Engineering Department, Canadian International College, Cairo)
Mervat M. Refaat (3) Geomatics Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt. 4) Civil Engineering Department, Canadian International College, Cairo)

Civil Engineering Journal
Vol. 12 No. 5 (2026): May

This study evaluates the performance of the Sentinel-2 multi-spectral instrument (MSI) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) for soil salinity mapping across contrasting agroecosystems in Egypt, with particular emphasis on subsurface salinity conditions (>0.5 m). A multi-stage calibration framework was implemented, in which historical Landsat-5 imagery (1995) was first integrated with field-measured electrical conductivity (EC) data to establish a spectral baseline. This baseline was subsequently applied to Sentinel-2 and Landsat-7 imagery acquired in 2015 and validated using in-situ total dissolved solids (TDS) measurements. Among the evaluated spectral indices, Salinity Index 5 (SI5) demonstrated the strongest relationship with field data and was selected for salinity mapping. Comparative analysis revealed that Sentinel-2 significantly outperforms Landsat-7, achieving a higher predictive accuracy (R² = 0.89) compared to Landsat-7 (R² = 0.72), primarily due to its finer spatial resolution (10 m) and reduced mixed-pixel effects. In addition, the application of second-degree polynomial regression substantially improved model performance relative to linear approaches, confirming the non-linear nature of soil salinity–spectral relationships. The results further indicate that surface spectral indices can provide meaningful estimates of subsurface salinity under specific environmental conditions. Overall, the integration of multi-temporal satellite data, robust spectral indices, and non-linear modeling provides an effective framework for soil salinity assessment in arid environments. This approach enhances the reliability of remote sensing-based monitoring and supports sustainable land management in salinity-affected regions.