Satellite chlorophyll a (Chl) is an essential parameter for large-scale studies on changes in the marine ecosystem and biogeochemistry. However, in the eastern China seas (ECSs), the coexistence and interaction of turbid and clear water pose challenges to the application of standard satellite Chl products to studies on phytoplankton dynamics. Based on the empirical modification of the OC-CCI (Ocean Colour Climate Change Initiative) standard products, we herein provide a locally modified chlorophyll a (LMC) dataset and compare the large-scale Chl distributions with satellite and in-situ Chl datasets. Compared with standard Chl, LMC has a higher correlation with in-situ data (R-2 = 0.58) and lower Bias (0.08) and is more consistent with the ship-measured Chl distributions. Using the LMC dataset, we analyzed the climatological Chl distributions in the ECSs and classified the Chl annual cycles into four types. Our results suggest that high CM patterns not only rely on nutrient supply, but also relate to light conditions controlled by vertical mixing and suspended matter. The different Chl annual cycles are mainly caused by differences in the duration and range of nutrients and light limitation, and thus their distributions tend to vary with water depth. We believe that this work presents a more accurate dataset in the ECSs and can improve the understanding of local biogeochemical cycles.
1.State Ocean Adm, Inst Oceanog 2, Minist Nat Resources, Key Lab Marine Ecosyst & Biogeochem, Hangzhou 310012, Zhejiang, Peoples R China 2.Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, 36 Baochubei Rd, Hangzhou 310012, Zhejiang, Peoples R China 3.Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, Guangzhou 510301, Guangdong, Peoples R China 4.Univ Maine, Sch Marine Sci, Orono, ME 04469 USA
Recommended Citation:
Hao, Qiang,Chai, Fei,Xiu, Peng,et al. Spatial and temporal variation in chlorophyll a concentration in the Eastern China Seas based on a locally modified satellite dataset[J]. ESTUARINE COASTAL AND SHELF SCIENCE,2019-01-01,220:220-231