Chlorophyll-a Mapping and Prediction in the Mar Menor Lagoon Using C2RCC-Processed Sentinel 2 Imagery

Abstract

The Mar Menor, Europe's largest hypersaline coastal lagoon, located in southeastern Spain, has undergone severe eutrophication crises, with devastating impacts on biodiversity and water quality. Monitoring chlorophyll-a, a proxy for phytoplankton biomass, is essential to anticipate harmful algal blooms and guide mitigation strategies. Traditional in situ measurements, while precise, are spatially and temporally limited. Satellite-based approaches provide a more comprehensive view, enabling scalable and long-term monitoring. This study aims to overcome limitations of chlorophyll monitoring, often restricted to surface estimates or limited temporal coverage, by developing a reliable methodology to predict and map chlorophyll-a concentrations across the water column of the Mar Menor. This work integrates Sentinel 2 imagery with buoy-based ground truth to create models capable of high-resolution, depth-specific monitoring, enhancing early-warning capabilities for eutrophication. Sentinel 2 images were atmospherically corrected using C2RCC processors. Buoy data were aggregated by depth. Multiple ML algorithms, including CatBoost, XGBoost, SVMs, and MLPs, were trained and validated using a cross-validation scheme with multi-objective optimization functions. Band-combination experiments and spatial aggregation strategies were tested to optimize prediction. The results show depth-dependent performance. The Root Mean Squared Logarithmic Error (RMSLE) obtained ranges from 0.34 at the surface to 0.39 at 3-4 m, while the R2 value was 0.76 at the surface, 0.76 at 1-2 m, 0.70 at 2-3 m, and 0.60 at 3-4 m. Generated maps successfully reproduced known eutrophication events. The study delivers an end-to-end, validated methodology chlorophyll mapping. Its integration of multispectral band combinations, buoy calibration, and modeling offers a transferable framework for other turbid coastal systems.