## 6 Conclusions  
In this study, we used the MCC algorithm to obtain the SSC field in the East China Sea and the Yellow Sea, with the TSM concentration data retrieved by GOCI remote sensing as the tracer. Comparison with the drifting buoy data shows that the method is suitable for SSC inversion offshore of China. The OSU model current field was consistent with the in situ measurement data current field in terms of the current direction and considered the image mismatch in the MCC method in the high turbidity water. We developed a new method, named the \"multi-correlation coefficient optimization algorithm based on the OSU tidal current model\" to improve the accuracy of the GOCI-derived time series data. This method considers the three greatest candidate acquisitions from multi-correlation coefficients as potential vectors. The rotation direction of the vectors within the tidal oscillation is used to identify and substitute for the spurious vector, which improves the inversion accuracy. Compared with the existing spurious vector elimination methods, the AAE of the new method is reduced from 37.82\({}^{\circ}\) to 30.28\({}^{\circ}\), with an improvement of approximately 20% and the AME, approximately 4%. The angle limitation method AAE decreased from 37.82\({}^{\circ}\) to 34.56\({}^{\circ}\), with an improvement of approximately 9%, with no significant change in AME. Based on visual and quantitative results, our new method improves the accuracy and ensures the integrity of the data compared with other state-of-the-art vector filtering methods. In addition, we also count the proportion of accurate vectors after the algorithm processing and conclude that it is better to obtain the three maximum three candidate vectors as potential vectors. For the selection of window size in the MCC method, we \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Time**} & \multicolumn{3}{c}{**Chl-a**} & \multicolumn{3}{c}{**Rrs**} & \multicolumn{3}{c}{**TSM**} \\ \cline{3-10}  & & \begin{tabular}{c} **Number** \\ **of Vectors** \\ \end{tabular} & \begin{tabular}{c} **AME** \\ **AME** \\ \end{tabular} & \begin{tabular}{c} **AAE** \\ **of Vectors** \\ \end{tabular} & \begin{tabular}{c} **Number** \\ **of Vectors** \\ \end{tabular} & \begin{tabular}{c} **AME** \\ **AME** \\ \end{tabular} & \begin{tabular}{c} **AAE** \\ **of Vectors** \\ \end{tabular} & \begin{tabular}{c} **Number** \\ **of Vectors** \\ \end{tabular} & \
\begin{tabular}{c} **AME** \\ **AME** \\ \end{tabular} &
\begin{tabular}{c} **AAE** \\ **(\({}^{\circ}\))** \\ \end{tabular} \\ \hline
27 June & 11:30–12:30 & 1010 & 1.13 & 21.83 & 955 & 1.22 & 27.04 & 1005 & 0.67 & 13.62 \\
12:30–13:30 & 976 & 1.90 & 27.15 & 952 & 0.54 & 16.17 & 981 & 1.59 & 18.31 \\
11 July & 11:30–12:30 & 472 & 0.30 & 13.99 & 448 & 0.62 & 13.51 & 476 & 0.53 & 15.34 \\
12:30–13:30 & 580 & 0.25 & 6.44 & 487 & 0.50 & 16.10 & 553 & 0.52 & 12.54 \\
16 July & 11:30–12:30 & 467 & 0.32 & 24.74 & 464 & 0.76 & 39.16 & 484 & 0.32 & 29.59 \\
12:30–13:30 & 534 & 0.73 & 15.95 & 503 & 1.42 & 24.14 & 541 & 0.99 & 12.34 \\ Average & 673 & 0.77 & 18.35 & 634 & 0.84 & 22.69 & 673 & 0.77 & 16.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics of the GOCI current field inversion results of three different tracers. Figure 15: Comparison of GOCI-derived current field results of three different tracers. evaluated the current characteristics derived under various parameters, and selected the optimal window size. For the selection of tracers, the TSM-derived current field gave higher accuracy and more vectors than the other two products. Conceptualization, J.C. and H.C.; methodology, J.C. and H.C.; software, H.C.; data analysis, J.C. and H.C.; investigation, J.C. and H.C.; resources, Z.C., H.H. and F.G.; writing H.C. and J.C.; supervision, J.C.; project administration, J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.