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In estuarine-coastal-shelf seas, particulate organic carbon (POC) shows the highest turnover rates of any organic carbon pool on the planet, playing a key role in the biological carbon pump. Compared with open ocean, estuarine and coastal waters are affected by large river inputs and show high hydrodynamic variability, which results in a mixture of diverse particles that includes inorganic mineral particles, living algal particles, and organic detritus. The highly complex and variable particle compositions in estuarine-coastal-shelf waters pose significant challenges in assessing their distinct roles in the carbon cycle and total POC. To overcome challenges, we collected biogeochemical and optical in situ data from 2014 to 2020 in estuarine-coastal-shelf waters of eastern China, which is one of the largest estuarine-coastal-shelf systems in the world, to develop an algorithm that can optically discriminate particle composition and estimate their respective contributions to POC. The algorithm combines the quasi-analytical algorithm and the semi-empirical radiative transfer algorithm to estimate total suspended particle concentrations and the mass fraction of organic particles from which both phytoplankton- and detritus-related POC fractions are derived. Compared to existing POC algorithms, this algorithm shows improved retrievals compared to in situ counterparts, with $r^{2}$ and root mean squared error (RMSE) values of 0.84 and $16.57~\mu \text{g}~\text{L}^{-1}$ , respectively. The algorithm is also applied to Sentinel-3/ocean and land color instrument (OLCI) images for the year of 2020. Applying the particle component discrimination method can enhance our understanding of the roles of different particle compositions in coastal carbon cycling affected by strong land-sea exchange.

The continental shelf seas of China (CSSC) broadly encompass the Bohai Sea, the Yellow Sea, and the East China Sea and exhibit highly variable optical properties. Accurate satellite estimates of particulate organic carbon (POC) remain challenging because optimal proxies for remotely sensed POC are largely obscure in these optically complex coastal waters. In this study, optical and biogeochemical data, including the particulate beam attenuation coefficient (cp), particulate backscattering coefficient (bbp), remote sensing reflectance (Rrs), POC, total suspended matter (TSM), and chlorophyll‐a(Chla), were collected over multiple seasons and years in the CSSC. We first classified the study area into three different water types with three different POC retrieval proxies: the TSM for high‐TSM waters, Chlafor low‐TSM waters, and Rrsratio (Rrs(490)/Rrs(555)) for moderate‐TSM waters. A composite POC algorithm using these three optimal proxies was then developed for Geostationary Ocean Color Imager (GOCI) satellite data (hereafter called the POC_CSSC algorithm). The validation results indicated that the accuracy of GOCI‐derived POC was greatly improved with a mean relative error of 32.08%. Application of the POC_CSSC algorithm to GOCI data over a tidally impacted estuary demonstrated the robustness of the algorithm and that tides play different roles in the broad CSSC. More specifically, tides have the strongest influence on nearshore estuarine waters, regulating the progression of high‐POC water masses from estuary to offshore environments, while offshore waters were the least influenced by tides with less variable, low POC concentrations. Optimal proxies for remotely sensed POC were explored using in situ data of optical and biogeochemical parametersA new POC algorithm for GOCI satellite inversion with improved accuracy was developed based on the optical classification of water typesPOC diurnal variations in different water types were revealed by GOCI satellite observations