Estimating Soil Water Retention Parameters Using Remote Sensing Platforms and Data Assimilation Tools

Using air and space-borne remote sensing soil moisture data, insitu profile soil moisture at discrete depths, a physics-based soil hydrology model, and Genetic Algorithm (GA) we have developed (1) near-surface and (2) layer-specific soil-moisture assimilation schemes. This study quantifies the soil hydraulic properties of the near surface (0-5 cm) and root zone (0-200 cm) in a homogeneous/layered soil column (for individual layers) under various scenarios of vegetation type, bottom boundary conditions, and soil layering. We have conducted numerical (synthetic) studies and field experimental validation using simulation-optimization with genetic algorithm (SWAP-GA). In this study, it is demonstrated that soil texture, bottom boundary conditions, soil layers and heterogeneity of various soil types and vertical arrangements influence uncertainties for quantifying the soil hydraulic parameters in the layered soil domain. We envisage that our findings will help in the estimations of effective soil hydraulic parameters at large (remote sensing) footprints for land surface models under soil layering, vertical heterogeneity sequence, different vegetation and land covers, and ground water table depths.