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Particle scattering is an important process that determines both the light penetration through the water column and water‐leaving light. Backscattering, in combination with absorption, determines the remote‐sensing reflectance that is used in ocean color algorithms. Additionally, the wavelength dependence of the backscattering ratio can be related to the particle composition in seawater. Here, we examine the magnitude and the spectral behavior of the backscattering ratio against other bio‐optical properties based on a comprehensive set of continuous measurements collected in coastal waters of the Great Barrier Reef over 3 years. The site is located offshore of a major river, and close to the cross‐shelf transition of bottom sediments from terrigenous muds to marine carbonates. The backscattering ratio measured at 650 nm clearly clustered the data into two types. Type 1, which we identified as terrigenous mud with a low organic fraction, is characterized by backscattering ratio below 0.011 with a mean value of 0.005. Type 2, which we identified as marine carbonate with a higher organic fraction, has backscattering ratio above 0.011 with a mean value of 0.02. Within 3 years, study site was exposed to type 1‐dominated particles 13% of the time, and type 2 87%. The observed changes in the backscattering ratio at this one coastal site are as large as the variability seen throughout the global ocean. This work provides a better understanding of processes determining the optical characteristics and insights into optical parameterizations that can be used in process‐based optical modeling of the Great Barrier Reef.

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.

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