Monitoring cyanobacteria occurrence in freshwater reservoirs using semi-analytical algorithms and orbital remote sensing

Abstract

Cyanobacterial blooms pose a serious threat to the multiple uses of inland waters because of their adverse effects on the environment and human health. Monitoring cyanobacteria concentrations using traditional methods can be expensive and impractical. Recently, alternative efforts using remote sensing techniques have been successful. In particular, semi-analytical modelling approaches have been used to successfully predict chlorophyll (Chl)-a concentrations from remote sensing reflectance. The aims of this study were to test the performance of different semi-analytical algorithms in the estimation of Chl-a concentrations and the applicability of Sentinel-2 multispectral instrument (MSI) imagery, and its atmospheric correction algorithms, in the estimation of Chl-a concentrations. For our dataset, phycocyanin concentration was strongly correlated with Chl-a concentration and the inversion model of inland waters (IIMIW) semi-analytical algorithm was the best performing model, achieving a root mean square error of 4.6mgm–3 in the prediction of Chl-a. When applying the IIMIW model to MSI data, the use of top-of-atmosphere reflectance performed better than the atmospheric correction algorithm tested. Overall, the results were satisfactory, demonstrating that even without an adequate atmospheric correction pipeline, the monitoring of cyanobacteria can be successfully achieved by applying a semi-analytical bio-optical model to MSI data.

Cyanobacteria in inland waters: new monitoring, reporting, modelling and ecological research

“…Semi-analytical modelling techniques, where waterleaving radiance is linked to inherent optical properties and inwater photopigment concentrations, have also been developed (Mishra et al 2013;Li et al 2015). In this special issue, Borges et al (2020) explore the performance of different semianalytical algorithms for chlorophyll (Chl)-a. Using Sentinel-2 Multispectral Instrument imagery and its atmospheric correction algorithms, Borges et al (2020) found that phycocyanin concentration is strongly correlated to Chl-a concentration and that the Inversion Model of Inland Waters semi-analytical algorithm was the best performing.…”

“…In this special issue, Borges et al (2020) explore the performance of different semianalytical algorithms for chlorophyll (Chl)-a. Using Sentinel-2 Multispectral Instrument imagery and its atmospheric correction algorithms, Borges et al (2020) found that phycocyanin concentration is strongly correlated to Chl-a concentration and that the Inversion Model of Inland Waters semi-analytical algorithm was the best performing. Interestingly, Borges et al (2020) found that an algorithm using top-of-atmosphere reflectance performs better than an algorithm using atmospheric correction, showing that even without an adequate atmospheric correction, monitoring of cyanobacteria can be achieved using semi-analytical bio-optical models.…”

Hydrological Modelling and Evaluation of Detention Ponds to Improve Urban Drainage System and Water Quality

“…Furthermore, it is essential to propose alternatives to retaining the diffuse pollution. In this lake, algal blooms sporadically occur, especially at the beginning the rainy season, turning several spots unsuitable for swimming and potentially hampering the use of the lake's water for human consumption [20].…”