Hyperspectral remote sensing monitoring of cyanobacteria blooms in a large South American reservoir: high- and medium-spatial resolution satellite algorithm simulation

Abstract

We used hyperspectral remote sensing with the aim of establishing a monitoring program for cyanobacteria in a South American reservoir. We sampled at a wide temporal (2012–16; 10 seasons) and spatial (30km) gradient, and retrieved 111 field hyperspectral signatures, chlorophyll-a, cyanobacteria densities and total suspended solids. The hyperspectral signatures for cyanobacteria-dominated situations (n=75) were used to select the most suitable spectral bands in seven high- and medium-spatial resolution satellites (Sentinel 2, Landsat 5, 7 and 8, SPOT-4/5 and -6/7, WorldView 2), and for the development of chlorophyll and cyanobacteria cell abundance algorithms (λ550 – λ650+λ800) ÷ (λ550+λ650+λ800). The best-performing chlorophyll algorithm was Sentinel 2 ((λ560 – λ660+λ703) ÷ (λ560+λ660+λ703); R2=0.80), followed by WorldView 2 ((λ550 – λ660+λ720) ÷ (λ550+λ660+λ720); R2=0.78), Landsat and the SPOT series ((λ550 – λ650+λ800) ÷ (λ550+λ650+λ800); R2=0.67–0.74). When these models were run for cyanobacteria abundance, the coefficient of determination remained similar, but the root mean square error increased. This could affect the estimate of cyanobacteria cell abundance by ~20%, yet it still enable assessment of the alert level categories for risk assessment. The results of this study highlight the importance of the red and near-infrared region for identifying cyanobacteria in hypereutrophic waters, demonstrating coherence with field cyanobacteria abundance and enabling assessment of bloom distribution in this ecosystem.

Spatial and temporal assessment of the risk associated to bacteria in recreational waters of a large South American Reservoir

“…To prevent confusions between phytoplankton and suspended matter, we also masked pixels where red band reflectance was higher or equal to the green band. In those pixels where the green band was higher than the red band, we computed cyanobacteria abundance and chlorophyll-a concentrations using algorithms built using spectral firms obtained in the study area by Drozd et al (2019) (Table 2).…”

“…Field monitoring approaches are time and labor intensive, and, despite the effort and costs involved, they often fail to represent the spatial and temporal heterogeneity of cyanobacterial blooms, which are very dynamic, particularly in large ecosystems. More recently, cyanobacteria blooms are monitored using remote sensing techniques, by estimating several cyanobacteria proxies such as chlorophyll-a (Tebbs et al 2013; for a review, see Dörnhöfer and Oppelt 2016), phycocyanin (Simis et al 2005; Randolph et al 2008; Li et al 2015) or cell numbers (Lunetta et al 2015; Drozd et al 2019). Indeed, remote sensing is recognized as a tool for providing complete and synoptic geographical coverage of water quality in freshwater systems (Hadjimitsis et al 2010 and references therein).…”

“…The cyanobacteria abundance algorithm can detect average densities of 200 cells mL -1(Drozd et al 2019) and hence is able to map CARU's alert levels. Next, for the mean values obtained during the studied period we classified pixels into the alert categories proposed by WHO and CARU, calculated the areas of each risk category and built maps showing of the distribution of the risk at landscape scale (750 km 2 ).…”

“…Algorithms used for estimating chlorophyll-a concentration and cyanobacteria abundance using Landsat satellites, developed byDrozd et al (2019) for the Salto Grande Reservoir. g=green band, r=red band and nir=near infra-red band.…”

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

“…By using new and diverse scientific tools, various temporal and spatial scales of monitoring and detection of cyanobacterial blooms will be important to water resource managers. In this special issue, Drozd et al (2020) provide a novel analysis of hyperspectral remote sensing data, resulting in semi-empirical models for large spatial-and temporal-scale monitoring of cyanobacteria. Their results show the importance of the red and near-infrared spectral region for identifying cyanobacteria in hypereutrophic waters.…”