## 6 Conclusions  
This study addresses the long-standing challenge of scale effects in validating suspended sediment concentration (SSC) retrievals for highly dynamic coastal waters by developing a coefficient of variation (CV)-driven multi-scale framework. Using HJ CCD (30 m, sun-synchronous orbit) and GOCI (500 m, geostationary orbit) imagery acquired  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**CV Range**} & \multirow{2}{*}{**Mean CV**} & \multicolumn{3}{c}{**HJ Center**} & \multicolumn{3}{c}{**HJ Avg**} \\ \cline{3-8}  & & **R\({}^{2}\)** & **RMSE (mg/L)** & **MRE (\%)** & **R\({}^{2}\)** & **RMSE (mg/L)** & **MRE (\%)** \\ \hline
0.00–0.07 & 0.064 & 0.924 & 67.924 & 12.03 & 0.933 & 63.585 & 10.27 \\
0.07–0.08 & 0.076 & 0.848 & 85.320 & 11.41 & 0.903 & 68.153 & 10.21 \\
0.08–0.10 & 0.091 & 0.806 & 95.559 & 11.84 & 0.879 & 75.629 & 9.49 \\
0.10–0.15 & 0.114 & 0.711 & 98.200 & 14.89 & 0.830 & 75.368 & 11.63 \\
0.15–0.20 & 0.169 & 0.353 & 112.589 & 24.63 & 0.623 & 85.980 & 17.61 \\
0.20–0.25 & 0.222 & 0.147 & 122.628 & 32.14 & 0.558 & 88.293 & 19.60 \\
0.25–0.30 & 0.270 & 0.024 & 156.261 & 41.26 & 0.487 & 113.309 & 22.37 \\
0.30–0.74 & 0.377 & 0.017 & 179.461 & 64.10 & 0.496 & 128.564 & 23.62 \\ \hline \end{tabular}
\end{table}
Table 6: R\({}^{2}\) and MRE values of regression relationships between different scales of HJ CCD SSC and GOCI SSC for eight variation intervals.  
Figure 12: R\({}^{2}\) and MRE of regression relationships between different scales of HJ CCD SSC and GOCI SSC for eight variation intervals: (**a**) the trend of R\({}^{2}\); (**b**) the trend of MRE.  
nearly simultaneously in Hangzhou Bay (within approximately 10 min), three critical findings emerged:  
1. Spatial averaging outperforms point-based validation: In regions with high spatial heterogeneity (CV > 0.10), the spatial averaging of HJ-CCD data (HJ-Avg) improved the correlation (R\({}^{2}\)) between HJ-CCD and GOCI retrievals by 17% and reduced the mean relative error (MRE) by 22% compared to central-pixel validation (HJ-Center). In zones where CV exceeds 0.15, HJ-Avg's R\({}^{2}\) decreases to approximately 0.80 and its MRE rises to around 13.5% (Table 6 and Figure 12). Nevertheless, this modest decline is much more gradual than the sharp degradation shown by HJ-Center, which falls to an R\({}^{2}\) of 0.71 and an MRE of 14.89% under the same conditions. Such relative stability confirms the robustness of the spatial-averaging approach in highly dynamic areas.
2. The CV threshold defines scale effect dominance: A critical CV threshold of 0.10 was identified, beyond which scale effects dominate validation errors. This threshold aligns with [PERSON]'s scale theory [7], confirming that sensor resolution must match feature heterogeneity. Subpixel analysis revealed that HJ CCD resolved finer variability (CV up to 0.0944) compared to the GOCI (CV = 0.0528), directly addressing the first question posed in the Introduction on quantifying SSC spatial heterogeneity.
3. Uncertainty reduction through multi-scale synergy: The framework reduced SSC validation uncertainty by 22%, lowering the RMSE from 107.06 mg/L (18.9% of the mean SSC) for traditional methods to 82.15 mg/L (14.7% of the mean) (Figure 10). This empirically answers the second question in the Introduction, demonstrating that spatial averaging--not single-point validation--is essential for mitigating scale effects in dynamic waters.