Contributor

Stuart Stothoff, Osvalso Pensado, Ginger Alford, Eric Larson

Abstract

Measuring moisture dynamics in soil and overlying vegetation is key to understanding ecosystem and agricultural dynamics in many contexts. For many applications, moisture information is demanded at high temporal frequency over large areas. Sentinel-1 C-band radar backscatter satellite images provide a repeating sequence of fine-resolution (10-m) observations that can be used to infer soil and vegetation moisture, but the 12-day interval between satellite observations is infrequent relative to the sensed moisture dynamics. Machine learning approaches have been used to predict soil moisture at higher spatial resolutions than the original satellite images, but little effort has been made to increase the temporal resolution of the images. This study extends machine learning approaches to infer fine-resolution backscatter between observations relying on auxiliary data observations, including elevation and daily gridded weather. Several variations of Multi-modal Fully Convolutional Neural Network architectures, problem setup, and training methods are explored for a predominantly rural area in southwest Oklahoma near the transition between humid subtropical and semiarid climates. The training area lies in the overlap zone for adjacent Sentinel-1 satellite tracks, allowing for training with several different temporal offsets. We find that the UNET architecture produced the most accurate and robust estimated backscatter patterns, with superior prediction compared to a prior observation baseline in nearly all cases investigated when geography was included in the training data. This superior performance also generalized to nearby areas when training data for a given geography was not available, where 86% of predictions performed superior compared to a prior observation baseline.

Degree Date

Fall 2021

Document Type

Thesis

Department

Computer Science

Advisor

Eric Larson

Second Advisor

Ginger Alford

Subject Area

Computer Science

Format

.pdf

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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