CoSal: A remote sensing and machine learning framework for mapping coastal soil salinity trends around aquaculture in South Asia

Coastal salinity represents a critical global environmental crisis that threatens agricultural productivity and food security. Traditional remote sensing methods to measure soil salinity in coastal areas are confounded by the presence of soil moisture and ubiquitous water-based land uses. This study introduces CoSal, a remote sensing and machine learning framework for mapping long-term coastal soil salinity trends while accounting for soil moisture and aquaculture, a fast-growing land-based practice of fish farming. We apply CoSal in South Asia (CoSal-SA), where salinity and aquaculture acutely impact agriculture, where we integrate Landsat imagery with soil data from coastal India and Bangladesh. Using 28 metrics and a stacked ensemble of nine machine learning models, CoSal-SA identifies saline soils in waterlogged coastal areas with over 91% accuracy. Applying CoSal-SA to a coastal district in India reveals that 10 percent of the area in 2024 had salinity levels unsuitable for rice cultivation. While interior regions showed decreasing salinity between 1995–2024, the coastal belt experienced intensifying salinity alongside increased aquaculture adoption. CoSal can be adopted for diverse coastal contexts and time periods with additional soil data. CoSal enables crucial research on salinity dynamics at different geographical scales that can guide targeted interventions to ultimately address agricultural productivity losses, food insecurity, and poverty in vulnerable coastal regions.