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

Abstract

Abstract: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 log 10 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, R 2 = 0.77 and MPD = 24%, RMSE = 0.15, R 2 = 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 R 2 (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.

Inherent Optical Properties of the Baltic Sea in Comparison to Other Seas and Oceans

“…An interesting solution is to use methods that train the processor on a representative range of top-of atmosphere radiances with simultaneous Chl a and SPM concentrations as well as CDOM absorption measurements. This has shown to be very effective both for the water properties processor developed by the Free University, Berlin [51]-although it does not quite fully cover the range of all optical variables representative for the Baltic Sea [49]-as well as in the empirical orthogonal functions method described by Craig et al [52] and Wozniak et al [53]. Both approaches work really well in the Baltic Sea [47][48][49]53].…”

Retrieval of Phytoplankton Pigments from Underway Spectrophotometry in the Fram Strait

“…It failed to retrieve the pigments that are mostly absent or below detection limit. Similarly, Soja-Woźniak et al [38] applied this analysis on both hyperspectral and multispectral remote sensing reflectance data and successfully retrieved TChl-a, phycocyanin and phycoerythrin in the Gulf of Gdansk.…”

Bio-optical Properties of Cyanobacteria Blooms in Western Lake Erie

“…While these algorithms were developed from field data collected during cHAB events, measurements were limited in scope in regards to optical properties. Several studies have partial IOP data measurements (Ma et al, 2007(Ma et al, , 2009Campbell et al, 2011;Zhang et al, 2012;Matthews and Bernard, 2013;Li et al, 2015;Soja-Woźniak et al, 2017), but not complete IOP data sets needed to fully understand the light interactions. Radiative transfer models have filled in some of this gap (Metsamaa et al, 2006;Matthews and Bernard, 2013;Robertson Lain et al, 2014), but these studies were based on single cells and did not account for colonial assemblages.…”