A comprehensive characterisation of satellite soil moisture from a hydrological point of view.

Soil moisture (SM) modulates energy, water, and carbon flows between the land surface and the atmosphere, thus crucial in hydrometeorology and agricultural research. Remote sensing techniques, particularly operating at L-band microwave wavelengths, provide quasi-global spatial coverage of SM with adequate accuracy, compared to scarce ground observations. However, there are fundamental complications in its direct application, pertaining to their disparity in measuring depths and large spatial footprints, making them incompatible with the hydrological principles needed for regional studies. In the thesis, firstly, we describe how much SM is traded across the globe, embedded in crop yields. This is driven by socio-economic and anthropogenic demands, such that few regions are experiencing environmental sustainability issues in reserving SM for sustaining nature’s health. Upon describing the importance of SM, we demonstrate two major challenges, i: e., differences in soil drying attributes and spatial representation of satellite SM compared to ground data. All analysis is conducted with passive microwave Soil Moisture Active Passive (SMAP) data. We observed systematic bias in the soil drying rates (due to losses) of SMAP SM that holds hydrological significance. Generally, SMAP drying rates are higher due to a mismatch in the measuring depths of satellite and ground sensors. A bias correction approach along with a SM reconstruction procedure is developed to address the issue, recharacterizing the drying rates and generating estimates in line with ground observations. SM reconstruction procedure maintains diurnal characteristics to preserve the original remotely sensed SM dynamics. To improve on the coarse spatial resolution of SMAP, a disaggregation technique is developed using antecedent precipitation information and validated with ground networks within the continental USA. Satisfactory improvements are achieved in the recharacterization, reconstruction, and disaggregat