Quantitative prediction of toxin-producing Aphanizomenon cyanobacteria in freshwaters using Sentinel-2 satellite imagery. (9th September 2022)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: Journal of water and health. Volume 20:Number 9(2022)
• Page Start: 1364
• Page End: 1379
• Publication Date: 2022-09-09
• Subjects: Aphanizomenon -- chlorophyll -- empirical model -- remote sensing -- Sentinel-2 -- satellite images; Water quality management -- Periodicals; Water -- Pollution -- Environmental aspects -- Periodicals; Environmental health -- Periodicals; Water quality -- Health aspects -- Periodicals; Water -- Health aspects -- Periodicals; Water -- Pollution -- Health aspects -- Periodicals; Public Health; Water Pollution -- prevention & control; Quality Control; Water Microbiology; Water Supply -- standards; Health & Medicine (General); Hydrology; Environmental health; Water -- Health aspects; Water -- Pollution -- Environmental aspects; Water -- Pollution -- Health aspects; Water quality -- Health aspects; Water quality management; Water; Gezondheid; Periodical; Periodicals; 363.61
• DOI: 10.2166/wh.2022.093
• Languages: English
• ISSNs: 1477-8920

Abstract

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