Optimizing an Abundance-Based Model for Satellite Remote Sensing of Phytoplankton Size Classes in the Bohai and Yellow Seas of China

Phytoplankton size structure is crucial for the functionality of the ocean food web and biogeochemical cycling, serving as a key indicator for assessing the state of marine ecosystems quantitatively. Despite the development of various algorithms to quantify phytoplankton size classes (PSCs) using satellite ocean color data, their reliability in optically complex coastal regions remains uncertain and requires regional optimization. In this study, we optimized and compared typical abundance-based three-component PSC models for the Bohai and Yellow Seas (BYS) of China using an extensive in situ PSC dataset derived from pigment samples analyzed via high-performance liquid chromatography (HPLC). The optimized sea surface temperature (SST)-dependent abundance-based PSC model of the BYS (SA-PSC-BYS) generated the most accurate remotely sensed PSC datasets for the BYS and effectively reproduced the PSC patterns observed in previous in situ studies. These patterns highlight the prevalence of micro- and pico-phytoplankton at higher and lower total biomass levels, respectively, and the dominance of nanophytoplankton at the mid-range of total biomass. This reliable PSC model will be invaluable for future remote sensing studies aiming to understand the detailed spatiotemporal variability of PSCs in the BYS. Additionally, it has the potential to enhance our understanding of the trophic connections between phytoplankton size structure and fisheries, thereby facilitating the development of effective marine management strategies.