Modeling soil salinity using direct and indirect measurement techniques: A comparative analysis

Abstract Soil salinization is a major problem for low-elevation countries like Bangladesh and is expected to worsen due to global warming and associated sea level rise. A constant monitoring of salinity affected areas is imperative to prevent land degradation, agricultural and livelihood losses. With this aim, we developed three soil salinity models with direct, indirect and a combination of both of these soil salinity measurement techniques, for south-western Bangladesh. The five salinity indexes (direct) and eleven environmental variables (indirect) were integrated using Principal Component Analysis, and the predictability of the models was evaluated against ground-based soil salinity measurements from soil survey and land cover maps generated during the model development. Delineation based solely on salinity indexes yielded results that contradicted with models developed from indirect variables and combining all the variables. Results suggest that the salinity model developed by combining direct and indirect techniques has the highest prediction capacity and can also be explained in terms of land cover changes. Therefore, an integrated approach yielded better delineation of salt-affected areas, characterized by active hydrological processes and vegetation cover. The findings and maps produced from this study would provide a new contextual planning tool to policymakers, for devising adaptation strategies in affected areas of Bangladesh.