260 Advances in Geospatial Applications for Environmental and Engineering Geology (Posters)

Recent advancements in geospatial technologies have enhanced our ability to analyze, model, and predict geological and environmental processes with high precision. This session will explore the latest innovations in remote sensing, GIS, and data analytics applied to environmental engineering geosciences.

Wednesday, 22 October 2025
8:00 AM - 5:30 PM
HBGCC, Hall 1

High-resolution Mapping of Soil Moisture Variation using UAS thermal and Multispectral Imagery

Paper number: 260-3
Program: Technical Sessions
Time: 8:00 AM – 8:00 AM

Authors

Abstract

Traditional soil moisture monitoring methods, such as in situ sensors and satellite imagery, face limitations in operational efficiency and spatial resolution. To overcome these challenges, this study employs Unmanned Aerial Systems (UAS)-based thermal and multispectral data and machine learning to generate high-resolution soil moisture maps within Saturated Riparian Buffers (SRBs). The primary goal is to improve the understanding of how soil moisture variation influences nutrient cycling processes and water quality within the SRBs. The research aims to answer the following questions: (1) How reliable are UAS-based measured land surface characteristics (e.g., vegetation cover (Indices), land surface temperature, and slope) in mapping soil moisture variability? and (2) Which of the listed land surface characteristics is the most reliable predictor of soil moisture variability using UAS data? We hypothesized that (1) the reliability of UAS-based measurements in mapping soil moisture variability will decrease during low periods of land surface temperatures due to reduced thermal contrast and diminished UAS sensor sensitivity to soil moisture variations and (2) Soil moisture variability is primarily influenced by vegetation cover, Areas with lower vegetation cover are associated with higher soil moisture variability, while those areas with higher vegetation cover are associated with lower variability. We also expect that water content, temperatures, and slopes will exhibit more significant soil moisture variability, while areas with more vegetation, higher water content, and lower temperatures will show reduced soil moisture variability. The study will collect UAS-based imagery and in situ soil moisture data at the T-3 site in central Illinois. We will process and analyze these data using machine learning to assess reliability and identify the key factors driving soil moisture variability, bridging the gap between traditional and modern monitoring methods. The findings will help optimize SRB management practices for better nutrient retention and water quality outcomes while also advancing the use of UAS-based remote sensing in environmental monitoring.

Geological Society of America Abstracts with Programs. Vol. 57, No. 6

doi: 10.1130/abs/2025AM-413155

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