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

REMOTE SENSING OF ENVIRONMENT

卷:231

Article

Cannizzaro, Jennifer P.¹  Barnes, Brian B.¹  Hu, Chuanmin¹  Corcoran, Alina A.²  Hubbard, Katherine A.²  Muhlbach, Eric²  Sharp, William C.³  Brand, Larry E.⁴  Kelble, Christopher R.⁵ 

[1] Univ S Florida, Coll Marine Sci, St Petersburg, FL 33701 USA

[2] Florida Fish & Wildlife Conservat Commiss, Fish & Wildlife Res Inst, St Petersburg, FL 33701 USA

[3] Florida Fish & Wildlife Conservat Commiss, Fish & Wildlife Res Inst, Marathon, FL 33050 USA

[4] Univ Miami, Rosenstiel Sch Marine & Atmospher Sci, Miami, FL 33149 USA

[5] US NOAA, Atlantic Oceanog & Meteorol Lab, Miami, FL 33149 USA

关键词: Algal bloom;    Ocean color;    Remote sensing;    MODIS;    Chlorophyll;    Cyanobacteria;    Synechococcus;    Florida Bay;   

DOI  :  10.1016/j.rse.2019.111227

来源: Elsevier

【 摘 要 】

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(lambda)), classification criteria for detecting cyanobacteria blooms with chlorophyll-a concentrations (Chl-a) similar to 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 r(2) = 0.80, n = 32) then allowed cyanobacterial chlorophyll-a concentrations (Chl(cl)) 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.

【 授权许可】

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