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Phytoplankton composition, abundance and carbon biomass were investigated at monthly intervals during 2006–2007 at a coastal site, “Acqua Alta” an oceanographic tower, in the northern Adriatic Sea. Results were compared with chlorophyll a concentrations of phytoplankton classes attributed by HPLC-CHEMTAX analysis. Changes in the taxonomic structure were associated with environmental parameters. The total carbon biomass of phytoplankton was positively correlated with the temperature and negatively correlated with silicate concentrations. Nutrient concentrations were higher in the winter–spring period than in the summer-autumn period. The highest carbon biomass and abundance of phytoplankton were observed during summer–autumn months. Diatoms were the group that had the highest contribution to the total carbon biomass during the sampling period. Small flagellates, which were the major contributors to the total cell counts were dominant during the summer period. There was a significant correlation between carbon biomass and CHEMTAX-derived Chl a values of diatoms and dinoflagellates. However, the total carbon biomass of phytoplankton was not correlated with Chl a, which seemed to be related to seasonal changes in the ratios of C:Chl a of all taxonomic classes. This ratio was higher during the summer-autumn period (73 ± 33) than during the winter–spring period (17 ± 20).

The upwelling spectral radiance distribution is polarized, and this polarization varies with the optical properties of the water body. Knowledge of the polarized, upwelling, bidirectional radiance distribution function (BRDF) is important for generating consistent, long-term data records for ocean color because the satellite sensors from which the data are derived are sensitive to polarization. In addition, various studies have indicated that measurement of the polarization of the radiance leaving the ocean can used to determine particle characteristics (Tonizzo et al., 2007; Ibrahim et al., 2016; Chami et al., 2001). Models for the unpolarized BRDF (Morel et al., 2002; Lee et al., 2011) have been validated (Voss et al., 2007; Gleason et al., 2012), but variations in the polarization of the upwelling radiance due to the sun angle, viewing geometry, dissolved material, and suspended particles have not been systematically documented. In this work, we simulated the upwelling radiance distribution using a Monte Carlo-based radiative transfer code and measured it using a set of fish-eye cameras with linear polarizing filters. The results of model-data comparisons from three field experiments in clear and turbid coastal conditions showed that the degree of linear polarization (DOLP) of the upwelling light field could be determined by the model with an absolute error of ±0.05 (or 5% when the DOLP was expressed in %). This agreement was achieved even with a fixed scattering Mueller matrix, but did require in situ measurements of the other inherent optical properties, e.g., scattering coefficient, absorption coefficient, etc. This underscores the difficulty that is likely to be encountered using the particle scattering Mueller matrix (as indicated through the remote measurement of the polarized radiance) to provide a signature relating to the properties of marine particles beyond the attenuation/absorption coefficient.

Novelty detection is used to identify the range of applicability of empirical ocean color algorithms. This method is based on the assumption that the level of accuracy of the algorithm output depends on the representativeness of inputs in the training dataset. The effectiveness of the novelty detection method is assessed using two datasets: one representative of the northern Adriatic Sea coastal waters and the other representative of open sea waters. The two datasets are independently used to develop neural network algorithms for the retrieval of chlorophyll-a concentration (Chl-a). The range of applicability of the individual algorithms is presented using remote sensing data derived from the Sea-viewing Wide-Field-of-view Sensor (Sea WiFS) for three selected regions: the central Mediterranean Sea, the North Sea, and the Baltic Sea. An extension of the novelty detection technique is also proposed to blend the individual algorithms and to avoid discontinuities in the resulting Chl-a maps. [ABSTRACT FROM AUTHOR]