Machine Learning-Driven Estimation of Fecal Coliform Concentrations Using Sentinel-2 Imagery in South Korea

Fecal coliforms are thermotolerant bacteria excreted from warm-blooded animals into soil and water, contaminating water bodies through runoff and resuspension of sediments. This contamination poses significant public health risks, especially during summer recreational activities, leading to waterborne diseases like diarrhea, typhoid, cholera, and dysentery. Monitoring and managing fecal coliform levels in recreational waters are crucial for public health and environmental safety. However, variability in fecal coliform concentrations due to human and wildlife activities complicates the management. This study aims to enhance water safety and public health by utilizing sentinel-2 band reflectance data and backscattering albedo to understand the relationship between fecal coliform reflectance in the rivers to generalize the fecal coliform management model.

In this study, we constructed Sentinel-2 dataset covering the period from January 2017 to December 2022 for the Han, Nakdong, Geum, and Yeongsan Rivers in South Korea. To accurately align the water quality monitoring stations with the Sentinel-2 data, we ensured that the latitude and longitude coordinates were free from clouds and not located on bridges. Therefore, monitoring stations that did not meet the specified conditions, with an above NDWI (Normalized Difference Water Index) of 0.1, and a below HOT (Hazed-Optimized Transformation) of 0.05 were preprocessed. For the preprocessed data points, this study converted the reflectance values of 10 Sentinel-2 bands (2, 3, 4, 5, 6, 7, 8, 8A, 11, and 12) into backscattering albedo. This approach was taken to account for the characteristics of fecal coliform, which is colorless. Model training was performed using CNN (Convolutional Neural Network), ANN (Artificial Neural Network), Random Forest, and XGBoost. As a result, CNN successfully predicted the trend of fecal coliform in the all the rivers and showed superior performance compared to other models. The results of this study are expected to provide a basis for fecal coliform management using Sentinel-2 band reflectance data in the four major rivers of South Korea and other regions around the world.