## 4 SSC Spatial Variability and CV Analysis  
Using the SSC inversion results from both GOCI and HJ CCD imagery, the standard deviation and mean within a 3 \(\times\) 3-pixel window were computed to derive the coefficient of variation (CV) of SSC. The CV is a normalized metric for quantifying data dispersion and is defined as the ratio of the standard deviation (\(\sigma\)) to the arithmetic mean (\(\upmu\)), effectively eliminating the influence of units and reflecting the relative intensity of spatial heterogeneity. High CV values indicate pronounced fluctuations in SSC within a region and serve as a key parameter for evaluating scale effects. Figure 6 presents the spatial distribution of the CV calculated from a 3 \(\times\) 3 window for the 10:39 HJ CCD (500 m) image and the 10:29 GOCI (500 m) image on 12 December 2011, revealing that the spatial patterns of the CV for the two sensor datasets are consistent. In particular, Figure 6a (HJ-CCD) and 6b (GOCI) show that the highest CV values (>0.20) are concentrated around the Giantang River mouth and neighboring intertidal shoals--zones well known for intense tidal shear and vigorous bottom-sediment resuspension. By contrast, the central navigation channel (121\({}^{\circ}\)10\({}^{\prime}\)-121\({}^{\circ}\)30\({}^{\prime}\) E) consistently exhibits low CV (<0.08), indicating more uniform SSC dynamics in the deeper main channel. Crucially, both sensors identify the same high- and low-variability hotspots, demonstrating that the CV-based heterogeneity index remains directly comparable across different spatial resolutions. Intermediate CV bands (\(\approx\)0.12-0.18), depicted as pale-yellow patches along both banks, correspond to rapidly changing optical properties in the tidal flats. This spatial pattern underscores how strongly tidal forcings and bathymetric gradients control SSC variability, and it confirms the utility of CV as a robust metric for cross-sensor scale comparisons. Figure 5: Retrieval results of suspended sediment concentration in Hangzhou Bay on 12 December 2011: (**a**) HJ CCD image at 10:39 (500 m); (**b**) GOCI image at 10:29 (500 m). The CV calculation in this study is based on the following equations:  
\[\mu=\frac{1}{N}{\sum_{i=1}^{N}{x_{i}}} \tag{3}\]  
\[\sigma=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}{(x_{i}-\mu)}^{2}}} \tag{4}\]  
\[CV=\frac{\sigma}{\mu} \tag{5}\]  
where \(x_{i}\) represents the SSC value (in mg/L) of the \(i\)-th pixel within the 3 \(\times\) 3 window, \(N\) is the total number of pixels in the window (\(N\) = 9 in this study), and \(\sigma\) and \(\mu\) denote the standard deviation and the arithmetic mean of SSC, respectively. Table 2 summarizes the minimum, maximum, mean, and standard deviation of the spatial variability of SSC retrieved from the HJ CCD and GOCI imagery. The mean CV for the HJ CCD SSC is 0.0638, whereas that for the GOCI SSC is 0.0564, indicating a greater spatial variability in the HJ CCD data compared to the GOCI data. Furthermore, Figure 7 displays the frequency distribution of the CV calculated from the 3 \(\times\) 3 window SSC results for both sensors, providing an intuitive visualization of the variability distribution. Specifically, the distributions for HJ-CCD and GOCI share a pronounced primary mode at CV \(\approx\) 0.04-0.06, indicating that most pixels experience only moderate local variability. The HJ-CCD curve also exhibits a longer right-hand tail beyond CV > 0.10, reflecting its finer spatial resolution's superior ability to capture extreme SSC fluctuations in highly dynamic zones. In contrast, the GOCI shows a slightly elevated frequency in the CV \(\approx\) 0.08-0.12 range, likely because its broader instantaneous field of view averages over more heterogeneous conditions spanning both deeper channels and adjacent shallows.