Date of Award

5-2026

Degree Type

Thesis

Degree Name

M.S.

Degree Program

Civil Engineering

Department

Civil and Environmental Engineering

Major Professor

Dr. Satish Bastola

Second Advisor

Dr. Gianna Marie Cothren

Third Advisor

Dr. Anika Tabassum Sarkar

Abstract

Soil moisture is a key variable in hydrology that controls water and energy exchange between the land surface and the atmosphere. Despite its importance, reliably estimating soil moisture is challenging because different measurement methods measure it at different scales and with varying accuracy. Ground-based sensors provide reliable point-scale data but are costly and spatially limited. In this context, models that can produce spatially and temporally consistent soil moisture are valuable tools. However, such models need calibration, and their use is limited by the unavailability of soil moisture data. Satellite products such as the Soil Moisture Active Passive (SMAP) offer wide spatial coverage but at a coarse resolution, which limits their usefulness for calibrating soil moisture models.

This study evaluated the transferability of soil moisture model parameters across scales using data from 18 stations in the southeastern United States. First, the two different types of soil moisture models were evaluated. The results show that both complex and simple soil moisture models perform comparably in simulating soil moisture dynamics. In this study, we evaluated the transferability of model parameters calibrated with SMAP data. The results show encouraging outcomes in a few locations and poor outcomes in others, indicating opportunities to improve satellite-derived soil moisture data. To explore the potential to improve the satellite-derived dataset, we conducted a laboratory experiment with five soil types at 15 moisture levels and extracted image attributes to develop functional relationships between soil moisture and image attributes using commonly used machine learning algorithms (Linear regression, ANN, and Decision Tree).  These image-to-soil-moisture relationships may be valuable for future drone-based field estimation, thereby supporting satellite downscaling and model calibration across Louisiana.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

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