Quantitative prediction of toxin-producing Aphanizomenon cyanobacteria in freshwaters using Sentinel-2 satellite imagery - Menik Hitihami M. A. S. V. Gunawardana; Kelum Sanjaya; Keerthi S. S. Atapaththu; Ajith L. W. Y. Yapa Mudiyanselage; Kanaji Masakorala; Shirani M. K. Widana Gamage (IWA Publishing, 2022)
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<0.001). Coupling the former regression models, an empirical model was derived to predict Aphanizomenon cell density in the same reservoir with high reliability (R2 = 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 chl-a concentration.; Model predicts chl-a with high reliability (R2 = 0.83, p < 0.001).; Spatial distribution of Aphanizomenon cell density fairly agrees with field observations.;