Sentinel-2 Images for Effective Mapping of Soil Salinity in Agricultural Fields

Abstract

Salinity is a critical feature for the management of agricultural soil, particularly in arid and semi-arid areas. The present study was conducted to develop an effective soil salinity prediction model using Sentinel-2A (S2) satellite data. Initially, the collected soil samples were analysed for soil salinity (EC e ). Subsequently, multiple linear regression analysis was carried out between the obtained EC e values and S2 data, for the prediction of soil salinity models. The relationship between EC e and S2 data, including individual bands, band ratios and spectral indices showed moderate to highly significant correlations (R 2 = 0.43–0.83). A combination of SWIR-1 band and the simplified brightness index was found to be the most appropriate (R 2 = 0.65; P < 0.001) for prediction of soil salinity. The results of this study demonstrate the ability to obtain reliable estimates of EC using S2 data.