EGU25-7313, updated on 14 Mar 2025
https://doi.org/10.5194/egusphere-egu25-7313
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Poster | Thursday, 01 May, 14:00–15:45 (CEST), Display time Thursday, 01 May, 08:30–18:00
 
vPoster spot A, vPA.2
Assessing the effectiveness of remote sensing indices for predicting E. coli concentrations in an irrigation pond
Seokmin Hong1,2, Billie Morgan1, Matthew Stocker1, Jaclyn Smith1, Moon Kim1, Kyung Hwa Cho3, and Yakov Pachepsky1
Seokmin Hong et al.
  • 1USDA-ARS, Environmental Microbial and Food Safety Laboratory, College Park, United States of America
  • 2Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulsan, 44919, Republic of Korea
  • 3School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 02841, Republic of Korea

Escherichia coli (E. coli) is a key marker for monitoring microbial water quality, with significant consequences for both public health and agricultural practices. To address the challenges of traditional water quality assessments, remote sensing offers a promising alternative. In this research, we implemented the random forest (RF) algorithm to forecast E. coli levels in irrigation ponds using three distinct data sources: (1) conventional water quality measurements, (2) multispectral reflectance values from drones, and (3) remote sensing indices derived from these reflectance values. To enhance the model’s accuracy, a linear transformation was applied during postprocessing. The RF model achieved strong performance (R² = 0.74) with conventional water quality variables, while moderate results were obtained using multispectral reflectance values alone (R² = 0.56). The best outcomes were observed when remote sensing indices were used as inputs, yielding an R² of 0.76. Shapley additive explanations (SHAP) were employed to evaluate the importance of individual variables. Dissolved oxygen, pH, and Chlorophyll-a emerged as critical predictors among water quality parameters. Meanwhile, the visible atmospherically resistant index (VARI) and normalized difference turbidity index (NDTI) were the most significant remote sensing indices. Furthermore, location-based comparisons highlighted differences in the impact of VARI and NDTI between interior and nearshore sampling sites. These findings suggest that remote sensing indices effectively capture water quality features crucial for E. coli persistence. This study underscores the potential of using drone-derived multispectral data to enhance predictions of E. coli concentrations in irrigation ponds.

How to cite: Hong, S., Morgan, B., Stocker, M., Smith, J., Kim, M., Cho, K. H., and Pachepsky, Y.: Assessing the effectiveness of remote sensing indices for predicting E. coli concentrations in an irrigation pond, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7313, https://doi.org/10.5194/egusphere-egu25-7313, 2025.