Phytoplankton size classes (PSCs) is critically important for community structure and function. It is considered to be the most important trait to determine the spatial and temporal distribution pattern of phytoplankton community, and their responses to environmental changes as well. Using satellite algorithm to obtain large-scale PSCs is the most advanced research content, which is also considered as an effective means to promote biogeochemical integration and model research. Here, based on an extensive HPLC dataset (2002-2015, 50 cruises, 1646 samples) from most area of the China seas covering various seasons and environmental conditions, a regional sea surface temperature (SST) -dependent abundance-based model was tuned to estimate the PSCs in the China seas. Results showed more effective in capturing the trends in PSCs, which demonstrated the potential underestimation of dynamic PSCs variation when using a single set of abundance-based model parameters in the marginal seas. Moreover, the “S” type of PSCs model parameters varied with SST presented an insight that severe and worsening effect on PSCs changes may awaits the continental shelf marginal seas or other middle-latitude regions as global warming tightens. Application of this PSCs model to the MODIS-Aqua data from 2002 to 2017 yielded the temporal and spatial distributions of the PSCs in the China Seas and six representative regional boxes, including the central YS, the estuary of Changjiang River, continental shelf of the ECS, continental shelf of the SCS, the southwest of the Luzon strait, and the middle of the SCS basin, were selected to investigate the monthly climatological PSCs. These results were consistent with in situ investigation but provided more high-resolution information. Associated with environmental factors, this study suggested that the PSCs distributions were highly heterogeneous in both temporal and spatial scales and illustrated that remote-sensed distributions of PSCs are effective in China seas.
 

Phytoplankton,Size Classes,Remote Sensing,China Seas,pigments