## 6 Conclusions
### Limitations and Future Directions  
While this study successfully addresses the scale effect challenges in SSC validation, several limitations highlight avenues for further research. The reliance on temporally proximal datasets, though effective in isolating spatial variability, poses constraints for long-term analysis due to their scarcity. Future investigations should prioritize temporal scale effects driven by tidal dynamics or seasonal sediment fluxes, which remain unaccounted for in the current framework. In particular, different tidal phases can modulate spatial heterogeneity by altering sediment resuspension and transport patterns, thereby shifting CV distributions and potentially modifying the optimal CV threshold for separating low-versus high-variability zones. Additionally, water depth variations within this relatively shallow estuary influence light attenuation and bottom-sediment mixing, impacting the observed CV values and scale effect metrics; integrating bathymetric data into the framework could further refine the identification of heterogeneity hotspots and inform depth-specific threshold adjustments.  
The empirical CV threshold of 0.10, while validated in Hangzhou Bay's high-turbidity environment, requires rigorous testing across diverse estuarine systems--such as low-sediment or microtidal regions--to establish universally applicable criteria. Beyond spatial heterogeneity, the framework's adaptability to other ocean color parameters (e.g., CDOM or chlorophyll) remains unexplored, necessitating evaluations under varying spectral and hydrodynamic conditions to ensure robustness. Addressing these gaps will enhance the framework's operational utility and theoretical relevance for global coastal monitoring.  
In conclusion, this study directly answers the two questions posed in the Introduction: (1) HJ CCD data effectively resolve SSC spatial heterogeneity within GOCI pixels, revealing subpixel variability (CV up to 0.0944) invisible to coarser sensors; (2) spatial averaging (mean SSC) minimizes validation errors across CV intervals, particularly in high-heterogeneity zones (CV > 0.10), where it reduces MRE by 11.63% compared to central pixel methods. These findings establish a replicable framework for scale-aware validation, bridging the gap between theoretical principles and practical applications in coastal remote sensing. In conclusion, the CV-driven framework not only resolves the \"scale mismatch\" problem in SSC validation but also establishes a paradigm for cross-resolution data fusion. By addressing the core challenges of spatial heterogeneity and validation biases, it advances the operational monitoring of sediment dynamics in critical coastal ecosystems, offering actionable insights to advance scientific research through sensor design and multi-scale modeling and to enhance coastal management via real-time sediment transport assessment.  
Conceptualization, Z.Y., B.W. and B.Z.; data curation, Y.D., W.L. and Z.Y.; investigation, W.L. and Z.Y.; methodology, B.Z. and Z.Y.; writing--original draft, Y.D. and Z.Y.; writing--review and editing, C.K.S., X.Y. and J.W.; funding, Z.Y. and B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"Pioneer and Leading Goose + X\" S&T Program of Zhejiang (#2025C02230), the Natural Science Foundation of Zhejiang Province of China (#LHZY24C140002), and the National Natural Science Foundation of China (#40971193, 41206169).  
**Data Availability Statement**: Enquiries regarding in situ data availability should be directed to the authors.  
The authors declare no conflicts of interest.