A novel cyanobacteria occurrence index derived from optical water types in a tropical lake

Cyanobacteria blooms are a threat to water quality of lakes and reservoirs worldwide, requiring scalable monitoring solutions. Existing approaches for remote sensing of cyanobacteria focus on quantifying (accessory) photosynthetic pigment to map surface accumulations. These approaches have proven challenging to validate against in situ observations, limiting uptake in water quality management. Optical Water Types (OWTs) have been used in inland and ocean waters to dynamically select suitable algorithms over optical gradients, thereby helping to limit out-of-scope application of individual algorithms. Here, we present a proof-of-concept study in Winam Gulf, Lake Victoria, extending an existing OWT framework using a hybrid approach combining in situ and satellite-derived water types. This extended OWT set of 25 water types, obtained from K-means clustering > 18 million Sentinel-3 Ocean and Land Colour Instrument (OLCI) spectra, was found to better capture the optical diversity of cyanobacteria bloom phases compared to the original OWT set. We translate this framework into a novel Cyanobacteria Occurrence Index (COI) by assigning weights to key optical features observed in the OWT set, such as phycocyanin absorption and surface accumulation. COI was strongly correlated with established algorithms for chlorophyll-a (Maximum Peak Height; r = 0.9) and phycocyanin (Simis07; r = 0.84), while potentially capturing various bloom phases in optically mixed conditions. We demonstrate how COI could be mapped onto a three-category risk classification to facilitate communication of cyanobacteria occurrence risk. Initial tests across diverse waterbodies suggest potential for wider application, though further validation across different environmental conditions is needed. This work provides a foundation for improved cyanobacteria monitoring in optically complex waters, particularly where conventional sampling approaches face limitations.