Integrating Remote Sensing and Machine Learning for Dynamic Monitoring of Eutrophication in River Systems: A Case Study of Barato River, Japan

Rivers play a crucial role in nutrient cycling, yet are increasingly affected by
eutrophication due to anthropogenic activities. This study focuses on the Barato River in
Hokkaido, Japan, employing an integrated approach of field measurements and Sentinel-2
satellite remote sensing to monitor eutrophication as the river experiencing huge sewage
effluents. Key parameters such as chlorophyll-a (Chla), dissolved inorganic nitrogen (DIN),
dissolved inorganic phosphorus (DIP), and Secchi Disk Depth (SDD) were analyzed. The
developed empirical models showed a strong predictive capability for water quality, particularly
for Chla (R2 = 0.87), DIP (R2 = 0.61), and SDD (R2 = 0.82). Seasonal analysis
indicated peak Chla concentrations in October, reaching up to 92.4 μg/L, alongside significant
decreases in DIN and DIP, suggesting high phytoplankton activity. Advanced machine
learning models, specifically back propagation neural networks, improved the prediction
accuracy with R2 values up to 0.90 for Chla and 0.83 for DIN. Temporal analyses from 2018
to 2022 consistently revealed the Barato River’s eutrophic state, with severe eutrophication
occurring for 33% of the year and moderate for over 50%, emphasizing the ongoing nutrient
imbalance. The strong correlation between DIP and Chla highlights phosphorus as the main
driver of eutrophication. These findings demonstrate the efficacy of integrating remote
sensing and machine learning for dynamic monitoring of river eutrophication, providing
critical insights for nutrient management and water quality improvement.