Author(s): Mario Suaza-Medina; Javier Lacasta; Sergio Martin-Segura; F. Javier Zarazaga-Soria

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Keywords: Artificial intelligence; Eutrophication; Remote sensing

Abstract: This work presents a satellite-based early warning system for eutrophication in large reservoirs used for drinking water, recreation, and biodiversity protection. Current monitoring still depends mainly on in-situ sampling of nutrient and cyanobacteria concentrations, accurate yet resource-intensive and slow, often catching problems too late. The study first determines which spectral bands from the Sentinel-2 satellite are most sensitive to eutrophication. Based on historical reports from the Ebro River Basin Authority in Spain, a reservoir with alternating eutrophic and non-eutrophic years was chosen to detect signal differences. Bands B09 and B11 proved the most discriminative. To train a predictive model, a far larger labeled dataset was needed. An open U.S. dataset with over 13,700 in-situ cyanobacteria measurements since 2017 was therefore used. Several Machine Learning models were trained with Sentinel-2 data as predictors. GradientBoost, AdaBoost, and XGBoost delivered the best performance, with a weighted average of F1 score of ~0.71 despite class imbalance. The results confirm the feasibility of issuing early alerts so authorities can deploy field teams proactively, laying the basis for scalable, cost-efficient eutrophication management tools.

DOI:

Year: 2026