Remote‐Sensing Estimation of Phytoplankton Size Classes FromGOCISatellite Measurements in Bohai Sea and Yellow Sea

Abstract

AbstractPhytoplankton 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 chlorophylla, 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 chlorophyllaas 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.,Fm,Fn, andFp) is customized using local HPLC and other data. Exponential functions are tuned to model the size‐specific chlorophyllaconcentrations (Cm,Cn, andCpfor microphytoplankton, nanophytoplankton, and picophytoplankton, respectively) with remote‐sensing reflectance (Rrs) and total chlorophyllaas the model inputs. Such a PSC model shows two improvements over previous models: (1) a practical strategy (i.e., modelCpandCnfirst, and then deriveCmasC‐Cp‐Cn) with an optimized spectral band (680 nm) forRrsas the model input; (2) local parameterization, including a local chlorophyllaalgorithm. 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.