Methods to Quantify Salinity Problems Using Remote Sensing Technologies

Nearly 10% of additional land is becoming salinized each year, and by 2050, it is estimated that approximately 50% of the world’s agricultural land will be affected by salinity. This development highlights the critical need for timely monitoring and precise mapping of soil salinization, particularly in arid, semi-arid, and coastal regions. This study discusses methods for analyzing the salinization process. It also explores salinity’s primary causes and consequences, along with strategies for reclamation and sustainable soil management. A significant focus is placed on the role of remote sensing technologies in tracking and evaluating soil salinity. This chapter has also discussed various salinity and vegetation indices such as normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), and canopy response salinity index (CRSI). Widely used satellites like Landsat (Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Operational Land Imager (OLI)), Sentinel-2 (Multi Spectral Instrument (MSI)), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) are integral to these efforts, offering multispectral and thermal data critical for detecting salinity-related changes in soil and vegetation. In addition, we also conducted a case study to observe the change in salinity in Mathura from 2015 to 2020. We found that the salinity in Mathura has reduced due to natural leaching by increased average rainfall during the given period. Overall, this case study indicates that Quantum Geographic Information System (QGIS) and Google Earth Engine (GEE) can efficiently extract soil salinity and enhance the mapping of its spatial distribution. This work has significant implications for enhancing satellite imagery for salinity modeling in Mathura, Uttar Pradesh.