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. The distribution and variation of phytoplankton size class (PSC) are key to understanding ocean biogeochemical processes and ecosystems. Remote sensing of the PSC in the East China Sea (ECS) remains a challenge, although many algorithms have been developed to estimate PSC. Here based on a local dataset from the ECS, a regional model was tuned to estimate the PSC from the spectral features of normalized phytoplankton absorption (aph) using a principal component analysis approach. Before applying the refined PSC model to MODIS (Moderate Resolution Imaging Spectroradiometer) data, reconstructing satellite remote sensing reflectance (Rrs) at 412 and 443 nm was critical through modeling them from Rrs between 469 and 555 nm using multiple regression analysis. Satellite-derived PSC results compared well with those derived from pigment composition, which demonstrated the potential of satellite ocean color data to estimate PSC distributions in the ECS from space. Application of the refined PSC model to the reconstructed MODIS data from 2003 to 2016 yielded the seasonal distributions of the PSC in the ECS, suggesting that the PSC distributions were heterogeneous in both temporal and spatial scales. Micro-phytoplankton were dominant in coastal waters throughout the year, especially in the Changjiang estuary. For the middle shelf region, the seasonal shifts from the dominance of micro- and nano-phytoplankton in the winter and spring to the dominance of nano- and pico-phytoplankton in the summer and autumn were observed. Pico-phytoplankton were especially dominant in the Kuroshio region in the spring, summer, and autumn. The seasonal variations of the PSC in the ECS were probably affected by a combination of the water column stability, upwelling, sea surface temperature, and the Kuroshio. Additionally, human activity and riverine discharge might also influence the PSC distribution in the ECS, especially in the coastal region.

Phytoplankton size class in the East China Sea derived from MODIS satellite data