Measurement of Cyanobacterial Bloom Magnitude Using Satellite Remote Sensing

• Published in: Scientific reports Vol. 9; no. 1; pp. 18310 - 17
• Main Authors: Mishra, Sachidananda, Stumpf, Richard P, Schaeffer, Blake, Werdell, P Jeremy, Loftin, Keith A, Meredith, Andrew
• Format: Journal Article
• Language: English
• Published: Goddard Space Flight Center Nature Research 04.12.2019; Nature Publishing Group UK; Nature Publishing Group
• Subjects: 704/172; 704/286; Algal blooms; Algorithms; Biomass; Cyanobacteria - growth & development; Data acquisition; Decision making; Drinking water; Earth Resources And Remote Sensing; Environmental Monitoring; Eutrophication; Florida; Harmful Algal Bloom; Humanities and Social Sciences; Image processing; Lakes; Lakes - microbiology; multidisciplinary; Ohio; Public health; Remote sensing; Remote Sensing Technology; Science; Science (multidisciplinary); Toxins; Water Quality; Water quality management; Florida; Ohio
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-54453-y

Cyanobacterial harmful algal blooms (cyano HABs) are a serious environmental, water quality and public health issue worldwide because of their ability to form dense biomass and produce toxins. Models and algorithms have been developed to detect and quantify cyanoHABs biomass using remotely sensed data but not for quantifying bloom magnitude,information that would guide water quality management decisions. We propose a method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs. The magnitude is the spatio temporal mean of weekly or biweekly maximum cyanobacteria biomass for the season or year. CyanoHAB biomass is quantified using a standard reflectance spectral shape based algorithm that uses data from Medium Resolution Imaging Spectrometer (MERIS). We demonstrate the method to quantify annual and seasonal cyanoHAB magnitude in Florida and Ohio (USA) respectively during 2003-2011 and rank the lakes based on median magnitude over the study period. The new method can be applied to Sentinel-3 Ocean Land Color Imager (OLCI) data for assessment of cyanoHABs and the change over time, even with issues such as variable data acquisition frequency or sensor calibration uncertainties between satellites. CyanoHAB magnitude can support monitoring and management decision making for recreational and drinking water sources.