Remote Detection of Cyanobacteria Blooms in an Optically Shallow Subtropical Lagoonal Estuary Using MODIS Data

Authors

Jennifer P. Cannizzaro, University of South FloridaFollow
Brian B. Barnes, University of South FloridaFollow
Chuanmin Hu, University of South FloridaFollow
Alina A. Corcoran, Fish and Wildlife Research Institute
Katherine A. Hubbard, Fish and Wildlife Research Institute
Eric Muhlbach, Fish and Wildlife Research Institute
William C. Sharp, Fish and Wildlife Research Institute
Larry E. Brand, University of Miami
Christopher R. Kelble, U.S. NOAA Atlantic Oceanographic & Meteorological Laboratory, Miami, FL 33149, USA

Document Type

Article

Publication Date

2019

Keywords

Algal bloom, Ocean color, Remote sensing, MODIS, Chlorophyll, Cyanobacteria, Synechococcus, Florida Bay

Digital Object Identifier (DOI)

https://doi.org/10.1016/j.rse.2019.111227

Abstract

Widespread and persistent Ecosystem Disruptive Algal Blooms dominated by marine picocyanobacteria (Synechococcus) commonly occur in the subtropical lagoonal estuary of Florida Bay (U.S.A). These blooms have been linked to a decline in natural sheet flow over the past century from upstream Everglades National Park. Remote sensing algorithms for monitoring cyanobacteria blooms are highly desired but have been mainly developed for freshwater and coastal systems with minimal bottom reflectance contributions in the past. Examination of in situ optical properties revealed that Synechococcus blooms in Florida Bay exhibit unique spectral absorption and reflectance features that form the basis for algorithm development. Using a large, multi-year match-up dataset (2002–2012; n = 682) consisting of in situ pigment concentrations and Moderate Resolution Imaging Spectroradiometer (MODIS) Rayleigh-corrected reflectance (R_rc(λ)), classification criteria for detecting cyanobacteria blooms with chlorophyll-a concentrations (Chl-a) ~5–40 mg m⁻³ were determined based on a new approach to combine the MODIS Cyanobacteria Index, CI_MODIS, and spectral shape around 488 nm, SS(488). The inclusion of SS(488) was required to prevent false positive classifications in seagrass-rich, non-bloom waters with high bottom reflectance contributions. 75% of cyanobacteria blooms were classified accurately based on this modified CI approach with <1% false positives. A strong correlation observed between cyanobacteria bloom in situ Chl-a and CI_MODIS (r² = 0.80, n = 32) then allowed cyanobacterial chlorophyll-a concentrations (Chl_CI) to be estimated. Model simulations and image-based analyses showed that this technique was insensitive to variable aerosol properties and sensor viewing geometry. Application of the approach to the entire MODIS time-series (2000–present) may help identify factors controlling blooms and system responses to ongoing management efforts aimed at restoring flow to pre-drainage conditions. The method may also provide insights for algorithm development for other lagoonal estuaries that experience similar blooms.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

Remote Sensing of Environment, v. 231, art. 111227

Scholar Commons Citation

Cannizzaro, Jennifer P.; Barnes, Brian B.; Hu, Chuanmin; Corcoran, Alina A.; Hubbard, Katherine A.; Muhlbach, Eric; Sharp, William C.; Brand, Larry E.; and Kelble, Christopher R., "Remote Detection of Cyanobacteria Blooms in an Optically Shallow Subtropical Lagoonal Estuary Using MODIS Data" (2019). Marine Science Faculty Publications. 2049.
https://digitalcommons.usf.edu/msc_facpub/2049