Using remote sensing technology to develop a framework for improving hydrologic models

Author

• Marissa CookFollow

Date of Award

2026

Degree Name

Natural Resources & The Environment

College

College of Science

Type of Degree

M.S.

Document Type

Thesis

First Advisor

Dr. Yeager-Armstead

Second Advisor

Dr. Rick Gage

Third Advisor

Dr. Aaron Adams

Abstract

With increased storm intensity due to climate change and urbanization, flash flooding has become an increasingly significant issue globally and regionally. Although the factors influencing urban flash flooding are well-known, there is a growing need for technology to accurately and remotely predict the chance of a flash flood occurring from any given rain event to give people time to prepare. This study aims to use multispectral satellite imagery to provide a framework for improving near real-time flood predictions in an urban area of a high gradient, fourth order stream impacted by flooding. Specifically, we utilize satellite imagery to create the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Modified Perpendicular Drought Index (MPDI), and digital number method (DNM), which uses raw spectral band values directly to predict soil moisture, and evaluate the abilities of these indices to predict soil moisture and evapotranspiration within the headwater region of the urbanized watershed. We then use those calculated soil moisture and evapotranspiration values to predict real-time flow. DNM predictions, as single bands or combined, did not surpass the indices tested. Of the remaining indices, the MPDI consistently had the greatest correlations with field soil moisture values, while the NDMI failed to correlate. Further tests examined the relationships between various water balance and SCS-CN equations on predicting runoff (Q) from any given rain event to determine the strength of remote sensing technology in predicting runoff and flood events. From the tests conducted, remote sensing-based Q calculations using the water balance method had a p-value of < 0.05, an R² value of 0.921726, and a Spearman rho of 0.672727, indicating a strong correlation and model fit. Implementing Q and ET values derived from remote sensing has potential to improve current flash flooding and hydrologic models to predict flash flood risk in real time.

Subject(s)

Remote sensing.

Geographic information science.

Floods.

Flood forecasting.

Aerial photography.

Earth sciences -- Remote sensing.

Image processing.

Recommended Citation

Cook, Marissa, "Using remote sensing technology to develop a framework for improving hydrologic models" (2026). Theses, Dissertations and Capstones. 2116.
https://mds.marshall.edu/etd/2116