Optical classification and characterization of marine particle assemblages within the western Arctic Ocean

Neukermans, G, Reynolds RA, Stramski D.  2016.  Optical classification and characterization of marine particle assemblages within the western Arctic Ocean. Limnology and Oceanography. 61(4):1472–1494.


We develop an optical classification of marine particle assemblages from an extensive dataset of particle optical properties collected in the Chukchi and Beaufort Seas. Hierarchical cluster analysis of the spectral particulate backscattering-to-absorption ratio partitioned the dataset into seven optically-distinct clusters of particle assemblages, each associated with different characteristics of particle concentration, composition, and phytoplankton taxonomic composition and size. Three phytoplankton-dominated clusters were identified. One was characterized by small-sized phytoplankton that are typically associated with regenerated production, and comprised samples from the subsurface chlorophyll-a maximum in oligotrophic waters of the Beaufort Sea. The other two clusters represented diatom-dominated particle assemblages in turbid shelf waters with differing contributions of photoprotective pigments. Such situations are generally associated with significant new production. Two clusters were dominated by organic nonalgal material, one representing clear waters off the shelf, the other representative of post-diatom bloom conditions in the Chukchi Sea. Another distinct cluster represented mineral-dominated particle assemblages that were observed in the Colville and Mackenzie River plumes and near the seafloor. Finally, samples in a cluster of mixed particle composition were scattered throughout all locations. Optical classification improved performance of predictive bio-optical relationships. These results demonstrate a capability to discriminate distinct assemblages of suspended particles associated with specific ecological conditions from hyperspectral measurements of optical properties, and the potential for identification of ecological provinces at synoptic time and space scales from optical sensors. Analogous analysis of multispectral optical data strongly reduced this capability.