REMOTE SENSING OF PHYTOPLANKTON SIZE CLASSES ON THE NORTHEAST U.S. CONTINENTAL SHELF

Abstract

The size structure of phytoplankton communities influences important ecological and biogeochemical processes, including the transfer of energy through marine food webs. A variety of algorithms have been developed to estimate phytoplankton size classes (PSCs) from satellite ocean color data. However, many of these algorithms were developed for application to the open ocean, and their performance in more productive, optically complex continental shelf systems has not been fully evaluated. In this study, several existing PSC algorithms were applied in the Northeast U.S. continental shelf (NES) and assessed by comparison to in situ PSC estimates derived from a regional HPLC pigment data set. The effect of regional re-parameterization and incorporation of sea surface temperature (SST) into existing abundance-based model frameworks was investigated, and the models were validated using an independent data set of in situ and satellite matchups. Abundance-based model re-parameterization alone did not result in significant improvement in performance in the NES compared with other models, however, the inclusion of SST led to a consistent reduction in model error for all size classes. Of two absorption-based algorithms tested, the best validating approach displayed similar performance metrics to the regional abundance-based model that included SST. The SST-dependent model was applied to monthly imagery composites of the NES region for April and September 2019, and qualitatively compared with imagery from the absorption-based approach. The results indicate the benefit of considering SST in abundance-based models and the applicability of absorption-based approaches in optically dynamic regions.