Water Quality Trends and Remote Sensing Model Development in Portuguese Reservoirs Using Sentinel-2 Imagery

Iberian reservoirs are highly vulnerable to droughts, warming temperatures, and agricultural runoff, which accelerate eutrophication. Monitoring these dynamics is crucial for sustainable management. This study investigated long-term trends in chlorophyll-a (Chl-a) and water transparency Secchi depth and developed empirical models for the Alto Rabagão (Rb) and Aguieira (Ag) reservoirs in Portugal. We used Sentinel-2 Level-2A reflectance data coupled with 153 in situ observations (2014–2024) for model calibration (n = 95) and validation (n = 58). Temporal trends were assessed using linear regression and Mann–Kendall analyses. Empirical models based on spectral indices (TBDO1, TBDO, MCI, NDWI) were evaluated using walk-forward time-series cross-validation. Results revealed a significant Chl-a increase (0.38 µg L−1 year−1; p = 0.016) and a simultaneous decline in transparency (p < 0.001) in Rb, indicating progressive eutrophication. In contrast, no significant trends were detected in Ag. Reservoir-specific models achieved moderate-to-high predictive performance, particularly for Chl-a (R2 up to 0.75; cross-validated R2 = 0.67–0.68, RMSE = 1.1 µg L−1, MAE = 0.82 µg L−1). Models using combined datasets showed lower accuracy, highlighting the importance of site-specific calibration. Wilcoxon signed-rank tests confirmed the absence of systematic bias between observed and predicted values. Ultimately, Sentinel-2 imagery combined with time-series cross-validation provides a reliable and cost-effective framework for the long-term monitoring of inland water quality.