Quantitative prediction of toxin-producing Aphanizomenon cyanobacteria in freshwaters using Sentinel-2 satellite imagery

This study aimed to develop an empirical model to predict the spatial distribution of Aphanizomenon using the Ridiyagama reservoir in Sri Lanka with a dual-model strategy. In December 2020, a bloom was detected with a high density of Aphanizomenon and chlorophyll-a concentration. We generated a set of algorithms using in situ chlorophyll-a data with surface reflectance of Sentinel-2 bands on the same day using linear regression analysis. The in situ chlorophyll-a concentration was better regressed to the reflectance ratio of (1 + R665)/(1–R705) derived from B4 and B5 bands of Sentinel-2 with high reliability (R2 = 0.81, p < 0.001). The second regression model was developed to predict Aphanizomenon cell density using chlorophyll-a as the proxy and the relationship was strong and significant (R2 = 0.75, p