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Evaluating deep learning model for multispectral feature mapping of fecal coliform contamination in major rivers of South Korea
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DOI：10.1016/j.rse.2026.115458.png)
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Abstract
En 中文
Fecal coliform bacteria are key microbial indicators of water quality and public health risk, yet conventional monitoring methods relying on site-specific sampling and laboratory analysis are spatially limited and lack realtime responsiveness. This study develops a novel framework for large-scale estimation of fecal coliform concentrations by integrating Sentinel-2 multispectral imagery with a convolutional neural network (CNN) model across South Korea's four major river basins: Han, Nakdong, Geum, and Yeongsan. To enhance spectral sensitivity to microbial pollution, backscattering albedo (uT) was derived from reflectance data and incorporated alongside first- and second-order spectral differentiation. A ResNet-18-based CNN model was trained using Sentinel-2 reflectance, backscattering albedo, first and second differentiation combined dataset and in-situ fecal coliform data from 2017 to 2022, achieving performance with R2 values of 0.922 and 0.566 for training and validation datasets, respectively. The model captured low-concentration patterns more consistently, while prediction errors increased in moderate-to-high concentration ranges. Spatial prediction maps revealed contamination hotspots in urban and agricultural zones, particularly downstream of tributary confluences and near regulated flow structures. Model uncertainty was quantified using Maximum Likelihood Estimation, and SHapley Additive exPlanations (SHAP) analysis identified near-infrared and shortwave-infrared backscattering bands as the most influential features, providing a transparent interpretation of the model's behavior. This remote sensing-based approach enables robust, scalable, and explainable estimation of fecal coliform distributions over broad geographic areas, surpassing the spatial and temporal limitations of traditional field-based monitoring. This study presents the large-scale fecal coliform estimation model that directly incorporates backscattering albedo and spectral derivatives, offering a novel remote sensing-based solution that captures microbial contamination dynamics with unprecedented spatial precision and model interpretability.
Keywords:
Remote sensing
Fecal coliform
Backscattering albedo
Machine learning
Non-point source pollution
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11.4
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1.1W
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9.4W
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