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Improving Satellite-Soil Moisture Estimates Using Geomorphometry And Machine Learning
Friday, 04 October 2019, 2:30
Friday, October 4, 2019. 2:30 PM. Improving Satellite-Soil Moisture Estimates Using Geomorphometry And Machine Learning. Rodrigo Vargas, University of Delaware. Sponsored by Department of Environmental Sciences. More information.
Soil moisture is a critical variable that links climate dynamics with water and food security. It plays an important role in land evapotranspiration processes, and its variability is an indicator of the water cycle intensification. However, detailed information is required to accurately delineate the spatial extent of droughts, quantify the intensity of drought stress, better parameterize climate models, and inform policy decisions. This talk focuses on how topography can be used to downscale satellite-derived soil moisture to identify spatial patterns and temporal trends across the USA and the world. We propose that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture.
Location ENVIRONMENTAL & NATURAL RESOURCE SCIENCES BLDG. DEPT. OF ENVIRONMENTAL SCIENCES - 14 COLLEGE FARM ROAD, ROOM 223, NEW BR