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Monday, December 9 • 3:00pm - 3:30pm
Multi-Satellite Remote Sensing of Land-Atmosphere Interactions: Advanced Data-Driven Methodologies for Passive Microwave Retrievals

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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.

avatar for Zeinab Takbiri, MS, PhD

Zeinab Takbiri, MS, PhD

Data Scientist, Cargill
During her Ph.D. at the University of Minnesota, Zeinab developed models using multi-satellite observations to improve disaster and water resources management. At Cargill, she develops computer vision models across different businesses.

Monday December 9, 2019 3:00pm - 3:30pm CST
Discovery Hall (Floor 4) Science Museum of Minnesota, 120 W Kellogg Blvd, St. Paul, MN 55102