Remote-Sensing Estimation of Phytoplankton Size Classes From GOCI Satellite Measurements in Bohai Sea and Yellow Sea

Authors

Deyong Sun, Nanjing University of Information Science and Technology
Yu Huan, Nanjing University of Information Science and Technology
Zhongfeng Qiu, Nanjing University of Information Science and Technology
Chuanmin Hu, University of South FloridaFollow
Shengqiang Wang, Nanjing University of Information Science and Technology
Yijun He, Nanjing University of Information Science and Technology

Document Type

Article

Publication Date

2017

Keywords

PSC, remote sensing, HPLC, GOCI, Bohai Sea, Yellow Sea, coastal waters

Digital Object Identifier (DOI)

https://doi.org/10.1002/2017JC013099

Abstract

Phytoplankton size class (PSC), a measure of different phytoplankton functional and structural groups, is a key parameter to the understanding of many marine ecological and biogeochemical processes. In turbid waters where optical properties may be influenced by terrigenous discharge and nonphytoplankton water constituents, remote estimation of PSC is still a challenging task. Here based on measurements of phytoplankton diagnostic pigments, total chlorophyll a, and spectral reflectance in turbid waters of Bohai Sea and Yellow Sea during summer 2015, a customized model is developed and validated to estimate PSC in the two semienclosed seas. Five diagnostic pigments determined through high-performance liquid chromatography (HPLC) measurements are first used to produce weighting factors to model phytoplankton biomass (using total chlorophyll a as a surrogate) with relatively high accuracies. Then, a common method used to calculate contributions of microphytoplankton, nanophytoplankton, and picophytoplankton to the phytoplankton assemblage (i.e., F_m, F_n, and F_p) is customized using local HPLC and other data. Exponential functions are tuned to model the size-specific chlorophyll a concentrations (C_m, C_n, and C_p for microphytoplankton, nanophytoplankton, and picophytoplankton, respectively) with remote-sensing reflectance (R_rs) and total chlorophyll a as the model inputs. Such a PSC model shows two improvements over previous models: (1) a practical strategy (i.e., model C_p and C_n first, and then derive C_m as C-C_p-C_n) with an optimized spectral band (680 nm) for R_rs as the model input; (2) local parameterization, including a local chlorophyll a algorithm. The performance of the PSC model is validated using in situ data that were not used in the model development. Application of the PSC model to GOCI (Geostationary Ocean Color Imager) data leads to spatial and temporal distribution patterns of phytoplankton size classes (PSCs) that are consistent with results reported from field measurements by other researchers. While the applicability of the PSC model together with its parameterization to other optically complex regions and to other seasons is unknown, the findings of this study suggest that the approach to develop such a model may be extendable to other cases as long as local data are used to select the optimal band and to determine the model coefficients.

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Terms of use for work posted in Scholar Commons.

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Yes

Citation / Publisher Attribution

Journal of Geophysical Research: Oceans, v. 122, issue 10, p. 8309-8325

©2017. American Geophysical Union. All Rights Reserved.

Scholar Commons Citation

Sun, Deyong; Huan, Yu; Qiu, Zhongfeng; Hu, Chuanmin; Wang, Shengqiang; and He, Yijun, "Remote-Sensing Estimation of Phytoplankton Size Classes From GOCI Satellite Measurements in Bohai Sea and Yellow Sea" (2017). Marine Science Faculty Publications. 1976.
https://digitalcommons.usf.edu/msc_facpub/1976