Development and validation of a red-edge-based index for improving satellite detection of algal blooms in inland waters

The increasing frequency of freshwater algal blooms threatens aquatic ecosystems globally. While remote sensing algorithms are widely used for bloom detection, conventional methods frequently exhibit degraded performance under optically complex conditions (e.g., sunglint, thin clouds, and high turbidity) and lack sensitivity to sparse blooms. To address these limitations, this study introduces the Red-Edge Floating Algae Index (RFAI). Structured as a baseline-height model, the RFAI mathematically isolates the scattering-induced spectral curvature of algal blooms using three critical Sentinel-2 Multispectral Instrument (MSI) bands: the green reflectance peak (∼560 nm), the red absorption trough (∼665 nm), and the red-edge reflectance peak (∼704 nm). This formulation effectively decouples genuine target signals from confounding background noise. Comparative validation against 17 widely used indices across a diverse set of inland lakes demonstrated the statistically significant overall effectiveness of the RFAI ( p < 0.0001), yielding precision and F1-score improvements of up to 42.8 % and 26.6 %, respectively, under locally optimized parameterizations. Crucially, the RFAI demonstrates robust comprehensive stability across highly variable, optically challenging conditions, effectively mitigating the severe performance fluctuations often observed in individual conventional indices. Supported by a novel optical-type-stratified thresholding strategy, the RFAI establishes a scalable and versatile framework for operational bloom monitoring using compatible red-edge satellite systems, enabling the generation of reliable observational records essential for the sustainable management of aquatic ecosystems.