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Remote Sens. 2017, 9(4), 343;

A Novel Statistical Approach for Ocean Colour Estimation of Inherent Optical Properties and Cyanobacteria Abundance in Optically Complex Waters

Institute of Oceanography, University of Gdansk, Al. Piłsudskiego 46, 81-378 Gdynia, Poland
Department of Earth and Space Sciences, Chalmers University of Technology, 412 96 Gothenburg, Sweden
Department of Oceanography, Dalhousie University, 1355 Oxford Street, P.O. Box 15000, Halifax, NS B3H 4R2, Canada
Department of Ecology, Environment and Plant Sciences, Stockholm University, 10961 Stockholm, Sweden
CSIRO Oceans & Atmosphere, Crawley, WA 6009, Australia
Institute of Oceanology, Polish Academy of Sciences, Powstanców Warszawy 55, 81-712 Sopot, Poland
Department of Mathematics and Statistics, Dalhousie University, Halifax, NS B3H 4R2, Canada
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Xiaofeng Li and Prasad S. Thenkabail
Received: 7 February 2017 / Revised: 17 March 2017 / Accepted: 23 March 2017 / Published: 4 April 2017
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Eutrophication is an increasing problem in coastal waters of the Baltic Sea. Moreover, algal blooms, which occur every summer in the Gulf of Gdansk can deleteriously impact human health, the aquatic environment, and economically important fisheries, tourism, and recreation industries. Traditional laboratory-based techniques for water monitoring are expensive and time consuming, which usually results in limited numbers of observations and discontinuity in space and time. The use of hyperspectral radiometers for coastal water observation provides the potential for more detailed remote optical monitoring. A statistical approach to develop local models for the estimation of optically significant components from in situ measured hyperspectral remote sensing reflectance in case 2 waters is presented in this study. The models, which are based on empirical orthogonal function (EOF) analysis and stepwise multilinear regression, allow for the estimation of parameters strongly correlated with phytoplankton (pigment concentration, absorption coefficient) and coloured detrital matter abundance (absorption coefficient) directly from reflectance spectra measured in situ. Chlorophyll a concentration, which is commonly used as a proxy for phytoplankton biomass, was retrieved with low error (median percent difference, MPD = 17%, root mean square error RMSE = 0.14 in log10 space) and showed a high correlation with chlorophyll a measured in situ (R = 0.84). Furthermore, phycocyanin and phycoerythrin, both characteristic pigments for cyanobacteria species, were also retrieved reliably from reflectance with MPD = 23%, RMSE = 0.23, R2 = 0.77 and MPD = 24%, RMSE = 0.15, R2 = 0.74, respectively. The EOF technique proved to be accurate in the derivation of the absorption spectra of phytoplankton and coloured detrital matter (CDM), with R2 (λ) above 0.83 and RMSE around 0.10. The approach was also applied to satellite multispectral remote sensing reflectance data, thus allowing for improved temporal and spatial resolution compared with the in situ measurements. The EOF method tested on simulated Medium Resolution Imaging Spectrometer (MERIS) or Ocean and Land Colour Instrument (OLCI) data resulted in RMSE = 0.16 for chl-a and RMSE = 0.29 for phycocyanin. The presented methods, applied to both in situ and satellite data, provide a powerful tool for coastal monitoring and management. View Full-Text
Keywords: remote sensing reflectance; EOF; phytoplankton pigments; cyanobacteria; CDM; MERIS; OLCI remote sensing reflectance; EOF; phytoplankton pigments; cyanobacteria; CDM; 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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Soja-Woźniak, M.; Craig, S.E.; Kratzer, S.; Wojtasiewicz, B.; Darecki, M.; Jones, C.T. A Novel Statistical Approach for Ocean Colour Estimation of Inherent Optical Properties and Cyanobacteria Abundance in Optically Complex Waters. Remote Sens. 2017, 9, 343.

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