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Wednesday, September 25 • 10:30am - 11:00am
SOMOSPIE: A Modular SOil MOisture SPatial Inference Engine Based on Data-Driven Decisions

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Danny Rorabaugh (University of Tennessee), Mario Guevara (University of Delaware), Ricardo Llamas (University of Delaware), Joy Kitson (University of Delaware), Rodrigo Vargas (University of Delaware), and Michela Taufer (University of Tennessee)

The current availability of soil moisture data over large areas comes from satellite remote sensing technologies (i.e., radar-based systems), but these data have coarse resolution and often exhibit large spatial information gaps. Where data are too coarse or sparse for a given need (e.g., precision agriculture), one can leverage machine-learning techniques coupled with other sources of environmental information (e.g., topography) to generate gap-free information and at a finer spatial resolution (i.e., increased granularity). To this end, we develop a spatial inference engine consisting of modular stages for processing spatial environmental data, generating predictions with machine-learning techniques, and analyzing these predictions. We demonstrate the functionality of this approach and the effects of data processing choices via multiple prediction maps over a United States ecological region with a highly diverse soil moisture profile (i.e., the Middle Atlantic Coastal Plains). The relevance of our work derives from a pressing need to improve the spatial representation of soil moisture for applications in environmental sciences (e.g., ecological niche modeling, carbon monitoring systems, and other Earth system models) and precision agriculture (e.g., optimizing irrigation practices and other land management decisions).


Danny Rorabaugh

University of Tennessee

Wednesday September 25, 2019 10:30am - 11:00am PDT
Macaw Room