Effective management of urban coastal pollution is often hindered by an inability to track the spatial and seasonal dynamics of wastewater plumes from creeks and canals. This study addresses this management gap by developing and validating a satellite-based operational monitoring framework for one of the polluted creeks in the Chennai metropolitan city, and adjacent coastal waters of the Bay of Bengal, India. The present study assessed water quality and plume dynamics using monthly one cloud free data from January 2023 to December 2025. Satellite-derived Chlorophyll-a and Total Suspended Matter were processed using the Case 2 Regional Coast Colour processor, and turbidity was estimated using a semi-empirical algorithm. Retrieved products were validated against synchronous in-situ measurements from two different field campaigns, demonstrating a strong positive correlation. Time-series analysis revealed low turbidity or no plume events during the dry season and extensive, highly turbid plumes during the Northeast Monsoon season (October to December) with plume extension observed up to 5 km offshore. A Random Forest classifier was trained using unsupervised K-means++ clustering of > 80,000 remote sensing reflectance spectra from a representative high-discharge (15 th November 2024) event produced 14 spectrally distinct optical water types, ranging from clear marine waters to anomalously polluted types characterized by elevated red and near-infrared reflectance. This hybrid approach provides a robust framework for the spatial detection and monitoring of coastal discharges. This framework enables early warning of pollution events, supports compliance tracking with environmental regulations, and allows targeted interventions to reduce ecological impacts. This approach demonstrates how readily available satellite data and machine learning can be operationalized to improve coastal water quality management in rapidly urbanizing regions facing similar challenges.
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Logesh et al. (2026) studied this question.
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