Remote detection of cyanobacteria blooms in an optically shallow subtropical lagoonal estuary using MODIS data

Jennifer P. Cannizzaro, Brian B. Barnes, Chuanmin Hu, Alina A. Corcoran, Katherine A. Hubbard, Eric Muhlbach, William C. Sharp, Larry E. Brand, Christopher R. Kelble

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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 (Rrc(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, CIMODIS, 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 CIMODIS (r2 = 0.80, n = 32) then allowed cyanobacterial chlorophyll-a concentrations (ChlCI) 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.

Original languageEnglish (US)
Article number111227
JournalRemote Sensing of Environment
Volume231
DOIs
StatePublished - Sep 15 2019

Keywords

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

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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