Research

Cyanobacteria Index (CI) is widely used to detect and quantify cyanobacteria density. However, only a few studies evaluated the algorithm for its detection accuracy. In this study, we validated CI algorithm (Fig. 1) for its effectiveness in identifying lakes with toxin-producing blooms using MERIS and OLCI data in 11 states across the United States over 11 years (2005–2011, 2016–2019) (Fig. 2). 

We used in situ Microcystins (MCs) data as a proxy of the presence of CyanoHABs in water bodies. Although we are aware that the satellite sensors cannot detect toxins, we used MCs as the indicator of cyanobacterial health risk and as a confirmation of cyanoHAB presence in a lake. Another reason for using MCs is that they are the most common laboratory measurement researchers or water quality managers make during CyanoHABs. 

How accurate is the CI algorithm?

We evaluated algorithm performance by its ability to detect a CyanoHAB 'Presence' or 'Absence.' We found the overall accuracy of CyanoHAB detection to be 84% with same-day matchups. Precision - the percentage of algorithm-detected bloom events that are 'true' bloom events of the bloom detection was 87%, and recall - the portion of 'true' bloom events detected by the algorithm was 90%. Based on a bootstrapping simulation, the overall accuracy of the algorithm can vary between 77% and 87% (95% confidence) (Fig. 3). 

These findings demonstrate that the CI algorithm has utility for routine monitoring of toxic cyanobacteria blooms in lakes across the United States on a synoptic scale.

Citation

Mishra, S., Stumpf, R.P., Schaeffer, B.A., Schaeffer, B., Werdell, J., Loftin and Meredith, A. (2021). Evaluation of satellite-based cyanobacteria blooms detection algorithm using field-measured Microcystin data. Science of The Total Environment, p.145462.