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Soil moisture plays a predominant role in driving the hydrological cycle, being the initial terrestrial variable to interact with precipitation. Its inherent temporal and spatial variability leads to complexity in estimation. Numerous hydrological and climate models adopt a simplified approach by treating soil moisture as a constant parameter in certain regions. While this simplification is intended to streamline complexity, it often introduces inaccuracies and uncertainties into these models. Conventional methods of measuring soil moisture are point-based, requiring labor-intensive, time-consuming, and often destructive procedures. The Soil Moisture Active Passive (SMAP) satellite, designed for soil moisture estimation, operates with a temporal resolution of 3 days and fails to capture the occurrences of extreme events. This study tries to overcome those limitations by estimating soil moisture daily using GNSS-Reflectrometry mission CYGNSS, which has a temporal resolution of 7 hours. The primary observable is the surface reflectance which depends on the surface property of the ground. Ulaby developed a water cloud model to estimate soil moisture using surface reflectance irrespective of LULC. An extended water cloud model was proposed that includes the Leaf Area Index of the land cover in the Chambal sub-basin of Ganga Basin, India. Using SMAP as a reference, The extended water cloud model achieved a correlation of 76.16 % in barren land (RMSE of 0.014) which is higher than in vegetated land with a correlation of 74.42 %.
Somaskandan et al. (Sat,) studied this question.
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