FASTCON: Food, Ag & Supply Chain in Tech Conference

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. We used a multi-satellite multi-sensor Bayesian methodology for prognostic detection of three 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, (2) the precipitation phase (rainfall or snowfall), and (3) water qualities. The proposed approach relied 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.