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Remote Sens. 2016, 8(3), 212; doi:10.3390/rs8030212

Empirical Model for Phycocyanin Concentration Estimation as an Indicator of Cyanobacterial Bloom in the Optically Complex Coastal Waters of the Baltic Sea

1
Institute of Oceanography, University of Gdansk, Al. Pilsudskiego 46, 81−378 Gdynia, Poland
2
Department of Earth and Space Sciences, Chalmers University of Technology, 412 96 Gothenburg, Sweden
3
Institute of Oceanology Polish Academy of Sciences, Powstancow Warszawy 55, 81–712 Sopot, Poland
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Raphael M. Kudela, Xiaofeng Li and Prasad S. Thenkabail
Received: 11 December 2015 / Revised: 9 February 2016 / Accepted: 29 February 2016 / Published: 5 March 2016
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Abstract

Commonly used parameters to assess cyanobacteria blooms are chlorophyll a concentration and cyanobacterial cell counts. Chlorophyll a is contained in all phytoplankton groups and therefore it is not a good estimator when only detection of cyanobacteria is desired. Moreover, laboratory determination of cyanobacterial cell counts is difficult and it requires a well-trained specialist. Instead of that, cyanobacterial blooms can be assessed using phycocyanin, a marker pigment for cyanobacteria, which shows a strong correlation with the biomass of cyanobacteria. The objective of this research is to develop a simple, remote sensing reflectance-based spectral band ratio model for the estimation of phycocyanin concentration, optimized for the waters of the Baltic Sea. The study was performed using hyperspectral remote sensing reflectance data and reference pigment concentration obtained in the optically complex coastal waters of the Baltic Sea, where cyanobacteria bloom occur regularly every summer, often causing severe damages. The presented two-band model shows good estimation results, with root-mean-square error (RMSE) 0.26 and determination coefficient (R2) 0.73. Moreover, no correlation with chlorophyll a concentration is observed, which makes it accurate in predicting cyanobacterial abundance in the presence of other chlorophyll-containing phytoplankton groups as well as for the waters with high colored dissolved organic matter (CDOM) concentration. The developed model was also adapted to spectral bands of the recently launched Sentinel-3 Ocean and Land Color Imager (OLCI) radiometer, and the estimation accuracy was comparable (RMSE = 0.28 and R2 = 0.69). The presented model allows frequent, large-scale monitoring of cyanobacteria biomass and it can be an effective tool for the monitoring and management of coastal regions. View Full-Text
Keywords: cyanobacteria; phycocyanin; band ratio; Baltic Sea; hyperspectral remote sensing reflectance; MERIS; OLCI cyanobacteria; phycocyanin; band ratio; Baltic Sea; hyperspectral remote sensing reflectance; MERIS; OLCI
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Woźniak, M.; Bradtke, K.M.; Darecki, M.; Krężel, A. Empirical Model for Phycocyanin Concentration Estimation as an Indicator of Cyanobacterial Bloom in the Optically Complex Coastal Waters of the Baltic Sea. Remote Sens. 2016, 8, 212.

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