Development and Calibration of Sentinel-2 Spectral Indices for Water Quality Parameter Estimation in Alqueva Reservoir, Southern Portugal

Monitoring water quality in large reservoirs is essential yet challenging, particularly in regions with limited in situ coverage. This study presents a robust methodology for integrating a decade-long in situ dataset (2014–2022) with Sentinel-2 multispectral imagery to develop and validate localized algorithms for water quality assessment in the Alqueva Reservoir, the largest artificial lake in Western Europe. Three atmospheric correction algorithms (C2RCC, C2X, C2X-COMPLEX) were evaluated, with C2RCC-COMPLEX identified as the most suitable for capturing the reservoir’s optical complexity, yielding the lowest RMSE for Total Suspended Solids (TSS: 2.4 g/m3) and Secchi Disk Depth (SDD: 0.85 m). Empirical models using Sentinel-2 bands 7 (783 nm), 6 (740 nm), and 8A (865 nm) demonstrated strong correlations (R2 ≈ 0.69–0.71) for Chlorophyll-a (Chl-a) with a range data of 0.1–65 mg/m3, TSS with a range data of 2–13.1 g/m3, and SDD with a range data of 0.4–8 m. Spatially explicit water quality maps illustrate the models’ capacity to capture distinct gradients and seasonal dynamics, e.g., elevated Chl-a (>30 mg/m3) and TSS (>7.5 g/m3) in the reservoir’s nutrient-rich northern section during drought (August 2022), and more uniform conditions following winter recovery (March 2019), with SDD exceeding 2 m near the dam. These results underscore the utility of Sentinel-2 for resolving spatial and temporal variability in optically complex inland waters. The proposed workflow offers a transferable, cost-effective framework for monitoring eutrophication risks and sediment dynamics under increasing hydrological variability.