Takbiri, Zeinab2018-11-282018-11-282018-08https://hdl.handle.net/11299/201100University of Minnesota Ph.D. dissertation. August 2018. Major: Civil Engineering. Advisors: Efi Foufoula-Georgiou, Ardeshir Ebtehaj. 1 computer file (PDF); xii, 183 pages.Satellite Earth observations are increasing at an unprecedented rate, not even conceivable three decades ago, as new satellites have been launched and planned. However, the past quarter-century of outstanding progress in the fundamental technology of remote sensing has not translated into comparable advances in remote sensing of the water cycle. First, this dissertation presents a multi-satellite multi-sensor Bayesian methodology for prognostic detection of two key components in the terrestrial water cycle: (1) the extent of flooded regions at a sub-daily basis, which improves the flood forecasting by identifying the soil saturated zones, and (2) the precipitation phase (rainfall or snowfall). Remote sensing of snowfall is still very new and challenging despite that snowfall accounts for the majority of total precipitation events over mid- to high latitudes and its spatial distribution conditions the snowpack dynamics and hydrological responses. The proposed approach relies on a nearest-neighbor search based on a weighted distance metric and a modern sparsity-promoting inversion method using observations from optical, short-infrared, and microwave bands, thereby allowing the detection under all-sky (clear and cloudy) conditions. Last, this dissertation quantifies the effects of snow cover, particularly the snow depth, on the radiometric signal of snowfall in an attempt to mitigate challenges in passive microwave detection and estimation of snowfall.enActive MicrowavesFlood RetrievalsGPMPassive MicrowavesSnowfall RetrievalsThesis or Dissertation