Document Type
Article
Date of Original Version
2021
Department
Oceanography
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 global ocean, and their performance in more productive, optically complex coastal and continental shelf regions warrants evaluation. In this study, several existing PSC models were applied in the Northeast U.S. continental shelf (NES) region and compared with in situ PSC estimates derived from a local 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 model performance was assessed using an independent data set. Abundance-based model re-parameterization alone did not result in significant improvement in model performance 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 performing approach displayed similar performance metrics to the regional SST-dependent abundance-based model. The SST-dependent model and the absorption-based method were applied to monthly composites of the NES region for April and September 2019 and qualitatively compared. The results highlight the benefit of considering SST in abundance-based models and the applicability of absorption-based PSC methods in optically complex regions.
Publication Title, e.g., Journal
Remote Sensing of Environment
Volume
267
Citation/Publisher Attribution
Turner, K. J., Mouw, C. B., Hyde, K. J.W., Morse, R., & Ciochetto, A. B. (2021) Optimization and assessment of phytoplankton size class algorithms for ocean color data on the Northeast U.S. continental shelf. Remote Sensing of Environment, 267: 112729, https://doi.org/10.1016/j.rse.2021.112729
Available at: https://doi.org/10.1016/j.rse.2021.112729
Author Manuscript
This is a pre-publication author manuscript of the final, published article.
Terms of Use
This article is made available under the terms and conditions applicable
towards Open Access Policy Articles, as set forth in our Terms of Use.