Abstract
This work comprises combining the passive radiometer (SMAP) and active scatterometer (ASCAT) remotely sensed surface soil moisture datasets by employing cumulative distributive frequency matching algorithm using second-order polynomial regression for the Indo-Gangetic basin by keeping GLDAS-NOAH as the reference dataset for the period 2015–2016. In order to evaluate the quality, utility and applicability of the combined soil moisture product for a macro river basin, it is further downscaled to 1 km spatial resolution using the universal triangle algorithm. The acceptable ranges of correlation coefficients (0.6–0.75 and 0.5–0.75 between derived soil moisture product with precipitation and the ground soil moisture data, respectively) indicate an interdependency between the surface soil moisture and precipitation and validate the data product too. The results have also shown satisfactory correlation coefficient and RMSE in the range 0.7–0.85 between derived downscaled product and the active-passive soil moisture product, SMAP/Sentinel-1.
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