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Optical proxies based on light scattering measurements have potential to improve the study and monitoring of aquatic environments. In this study, we evaluated several optical proxies for characterization of particle mass concentration, composition, and size distribution of suspended particulate matter from two contrasting coastal marine environments. We expanded upon our previous study of Southern California coastal waters, which generally contained high proportions of organic particles, by conducting angle-resolved polarized light scattering measurements in predominantly turbid and inorganic-particle dominated Arctic coastal waters near Prudhoe Bay, Alaska. We observed that the particulate backscattering coefficient b was the most effective proxy for the mass concentration of suspended particulate matter (SPM) when compared with particulate scattering and attenuation coefficients bp was the most effective proxy for the mass concentration of suspended particulate matter (SPM) when compared with particulate scattering and attenuation coefficients b p and c p . Improvements were seen with b bp as a proxy for the concentration of particulate organic carbon (POC), although only if particulate assemblages were previously classified in terms of particle composition. We found that the ratio of polarized-light scattering measurements at 110º and 18º was superior in performance as a proxy for the composition parameter POC/SPM in comparison to the particulate backscattering ratio b bp / b p . The maximum value of the degree of linear polarization D o L P p , m a x observed within the range of scattering angles 89°-106° was found to provide a reasonably good proxy for a particle size parameter (i.e., 90th percentile of particle volume distribution) which characterizes the proportions of small- and large-sized particles. These findings can inform the development of polarized light scattering sensors to enhance the capabilities of autonomous platforms.

We have examined several approaches for estimating the surface concentration of particulate organic carbon, POC, from optical measurements of remote-sensing re-flectance, Rrs(λ), using field data collected in tropical and subtropical waters of the eastern South Pacific and eastern Atlantic Oceans. These approaches include a direct empirical relationship between POC and the blue-to-green band ratio of re-flectance, Rrs(λB)/Rrs(555), and two-step algorithms that consist of relationships linking reflectance to an inherent optical property IOP (beam attenuation or backscattering coefficient) and POC to the IOP. We considered two-step empirical algorithms that exclusively include pairs of empirical relationships and two-step hybrid algorithms that consist of semianalytical models and empirical relationships. The surface POC in our data set ranges from about 10mgm-3 within the South Pacific Subtropical Gyre to 270mgm-3 in the Chilean upwelling area, and data on phytoplankton pigments, suspended particulate matter, and the backscattering ratio suggest a considerable variation in the composition of particulate assemblages in the investigated waters. The POC algorithm based on the direct relationship between POC and Rrs(λB)/Rrs(555) promises reasonably good performance in the vast areas of the open ocean covering different provinces from hyperoligotrophic and oligotrophic waters within subtropical gyres to eutrophic coastal upwelling regimes characteristic of eastern ocean boundaries. The best error statistics were found for power function fits to the data of POC vs. Rrs(443)/Rrs(555) and POC vs. Rrs(490)/Rrs(555). For our data set that includes over 50 data pairs, these relationships are characterized by the mean normalized bias of about 2% and the normalized root mean square error of about 20%. We recommend that these algorithms be implemented for routine processing of ocean color satellite data to produce maps of surface POC with the status of an evaluation data product for continued work on algorithm development and refinements. The two-step algorithms also deserve further attention because they can utilize various models for estimating IOPs from reflectance, offer advantages for developing an understanding of bio-optical variability underlying the algorithms, and provide flexibility for regional or seasonal parameterizations of the algorithms. [ABSTRACT FROM AUTHOR]