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

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2017-04, Vol.9 (4), p.343
• Main Authors: Soja-Woźniak, Monika, Craig, Susanne, Kratzer, Susanne, Wojtasiewicz, Bożena, Darecki, Miroslaw, Jones, Chris
• Format: Article
• Language: English
• Subjects: Absorption; Absorption spectra; Absorptivity; Abundance; Algae; Algal blooms; Animal models; Aquatic environment; Biomass; CDM; Chlorophyll; Coastal waters; Coefficients; Color; Cyanobacteria; Detection; Economic importance; Environmental monitoring; EOF; Eutrophication; Fisheries; In situ measurement; Management; Mean square values; MERIS; OLCI; Optical properties; Phytoplankton; Phytoplankton pigments; Pigments; Plankton; Radiometers; Recreation; Reflectance; Regression analysis; Remote sensing; Remote sensing reflectance; Simulation; Spatial discrimination; Spatial resolution; Statistical analysis; Statistics; Tourism; Water monitoring
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs9040343

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.