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Discussion of preliminary results from use of machine learning for downscaling satellite-based soil moisture data for groundwater management
Proceedings of the 2023 Mississippi Water Resources Conference

Year: 2023 Authors: Ellepola A., Yarbrough L., Ghaffari Z., Easson G., Yasarer H.

Satellite imagery has become an increasingly valuable tool for watershed management. Remote sensing systems such as multispectral and radar imaging can provide detailed information about soil moisture levels, vegetation cover, and topography. These data can be used to identify areas of high soil moisture, monitor changes in soil moisture over time, and assess the impact of human activities on watersheds. A commonly used system in orbit for monitoring soil moisture is the Soil Moisture Active Passive Mission (SMAP). For SMAP, and all spaceborne systems, one of the major limitations for users to implement satellite-based data is the extreme coarseness of the pixel (~9 km). This talk presents an overview of a machine learning technique that is currently being used in soil moisture monitoring and watershed management in north Mississippi.

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