Quantitative prediction of toxin-producing Aphanizomenon cyanobacteria in freshwaters using Sentinel-2 satellite imagery. (9th September 2022) - Gunawardana, Menik Hitihami M. A. S. V.; Sanjaya, Kelum; Atapaththu, Keerthi S. S.; Yapa Mudiyanselage, Ajith L. W. Y.; Masakorala, Kanaji; Widana Gamage, Shirani M. K.
Abstract: 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 ( R 2 = 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 ( R 2 = 0.75, p <0.001). Coupling the former regression models, an empirical model was derived to predict Aphanizomenon cell density in the same reservoir with high reliability ( R 2 = 0.71, p <0.001). Furthermore, the predicted and observed spatial distribution of Aphanizomenon was fairly agreed. Our results highlight that the present empirical model has a high capability for an accurate prediction of Aphanizomenon cell density and their spatial distribution in freshwaters, which helps in the management of toxic algal blooms and associated health impacts. HIGHLIGHTS: The Chl-a and Aphanizomenon prediction model was derived using Sentinel-2 imagery. Modified reflectance ratios well regressed with in situ c hl-a concentration. Model predicts chl-a with high reliability ( R 2 = 0.83, p < 0.001). Spatial distribution of Aphanizomenon cell density fairly agrees with field observations. Graphical Abstract