## 4 Discussion
### Driving Factors of \(n_{\rm p}\) Spatiotemporal Variation  
By applying the developed \(n_{\rm p}\) model to time-series GOCI satellite data, the \(n_{\rm p}\) spatiotemporal variations in the study areas could be obtained, with the derived values spanning a range of 1.01-1.22 (Figures 10 and 11). This variation in magnitude is generally consistent with that reported in previous studies [2; 5; 23; 48; 49]. In general, the \(n_{\rm p}\) in the nearshore regions was higher than that offshore, noting that the nearshore regions showed relatively stable high values, whereas the offshore areas changed a lot during different seasons (or months). High-content mineral particles in the nearshore waters (e.g., the Yellow River estuary and Yangtze River estuary) induced by terrestrial discharge, sediment resuspension, and even shore erosion can explain the high \(n_{\rm p}\) (1.12-1.18, see Figure 10) distribution there.  
Regarding the offshore areas, the \(n_{\rm p}\) values in the BS were higher than those in the YS. This is because the BS is prone to sediment resuspension under the action of wind waves due to its relatively shallow depths [50; 51]. [PERSON] et al. (2014) [52] demonstrated that inorganic minerals and silts controlled the suspended particles in the BS. Therefore, those hard mineral and sediment particles may explain the high \(n_{\rm p}\) values in the BS (except those nearshore regions), which were mainly in the range of 1.10-1.15 (Figure 10) [2; 5; 53]. Fortunately, the derived \(n_{\rm p}\) distributions generally agree with the previous  
Figure 13: (**A**) Scatter plots of X8 (i.e., \(-\log_{10}(R_{\rm rs}(680))\)) and \(B_{\rm p}\)(488) using a quadratic function. This function was found to perform best among the other functions. The solid red lines are the fitted function curves, and the dotted red lines are the 95% confidence bounds. (**B**) Scatter plots of the in situ \(n_{\rm p}\) values versus those predicted from the new method with the developed \(B_{\rm p}\) model. (**C**) Spatial distribution of the \(n_{\rm p}\) derived from GOCI satellite observations in January 2017 using this new method.  
report [5]. By contrast, the YS waters (except those nearshore regions) are easily affected by algal particles, where diatoms and dinoflagellates are the main algal species [54; 55; 56]. Importantly, these algal particles generally show a relatively low bulk refraction (approximately 1.01-1.07) due to their high water content [57; 58; 59]. Therefore, the low refraction distribution in most regions of the YS (i.e., \(n_{\rm P}<1.07\), see Figure 10) can be attributed to the dominance of organic algal particles.  
From the perspective of the seasonal variations, the \(n_{\rm P}\) showed the lowest distribution in summer for most areas of the BS and YS, whereas the highest distribution appeared in winter. The seasonal variations are probably related to water column mixing and stratification, as well as phytoplankton growth and extinction during different seasons [60; 61]. Actually, the factors influencing the \(n_{\rm P}\) distribution could be very complex, which may be linked with many marine physical and biogeochemical processes such as wind waves, river discharge, hydrodynamic conditions, water column mixing and stratification, sediment suspension, phytoplankton growth, and zooplankton grazing. In short, the precise understanding on the mechanisms influencing the \(n_{\rm P}\) spatiotemporal variations remains challenging owing to the coupling influence of these multiple factors.