## 1 Introduction
(2003) [11] demonstrated that the Enhanced Vegetation Index (EVI), through its spectral design incorporating blue-band correction and a soil adjustment factor, effectively reduces saturation effects in dense vegetation compared to AVHRR-NDVI, thereby enhancing sensitivity to canopy structural variations in high-biomass ecosystems. Further, [PERSON] et al. (2022) [14] combined hyperspectral imaging with UAV platforms to evaluate spectral indices for agricultural phenotyping, revealing that modified indices--such as the Modified Soil-AdjustedVegetation Index (MSAVI)--exhibit reduced sensitivity to subpixel soil-vegetation mixing compared to conventional vegetation indices. In contrast, analogous progress in aquatic remote sensing remains limited due to two unique challenges: (1) the coupling of high-frequency temporal variability with sharp spatial gradients, and (2) the absence of physical constraints in data-driven SSC retrieval models [15, 16, 17, 18]. Global efforts have contributed to diverse retrieval and validation techniques. Studies in regions such as Canada's Peace-Athabasca Delta and the Mississippi River in the U.S. [19] have advanced our understanding of SSC dynamics. In China, research has focused on typical areas like Hangzhou Bay and the Pearl River Estuary. For example, [PERSON] et al. (2013) [20] developed an HJ CCD-based algorithm tailored for turbid waters (RMSE < 20 mg/L), and [PERSON] et al. (2019) [21] achieved hourly SSC monitoring in Lake Taihu, located at the southern part of the Yangtze River delta and the third largest freshwater lake in China, using GOCI data. While these technological advancements demonstrate progress in regional SSC monitoring, they inadvertently expose two systemic limitations when confronting scale effects in highly dynamic aquatic systems [22]. First, despite machine learning breakthroughs--exemplified by ELM models achieving R\({}^{2}\) > 0.9 in major rivers [15]--predominant single-sensor approaches neglect cross-resolution synergies: GOCI's 500 m hourly data lack spatial detail, while HJ CCD's 30 m imagery suffers temporal sparsity. Second, current scale-correction methods remain reactive (e.g., GAN-based super-resolution [23]) rather than proactively designing scale-aware validation frameworks. These limitations persist despite theoretical consensus from fractal studies [24] and multi-scale validation principles [25], suggesting an implementation gap between terrestrial and aquatic applications. To bridge this gap, this study proposes a CV-partitioned validation framework grounded in two empirical findings from Hangzhou Bay: (1) HJ CCD-derived SSC heterogeneity maps reveal subpixel CVs up to 0.0944 within GOCI pixels, whereas the corresponding CV of SSC calculated within a 3 \(\times\) 3-pixel window for the 500 m GOCI image is 0.0528, and (2) spatial averaging (compared to single-point validation) enhances R\({}^{2}\) by approximately 17% and reduces MRE by approximately 22% in areas with moderate heterogeneity (CV = 0.10-0.15). In regions with high heterogeneity (CV > 0.15, which corresponds to the commonly accepted homogeneity screening threshold [26]), even greater improvements are observed. This study innovatively develops a CV-partitioned framework. By conducting a synergistic scale analysis of HJ CCD and GOCI imagery in Hangzhou Bay--a representative high-dynamic estuarine system--we address two core questions: (1) How can high-resolution HJ CCD data characterize SSC spatial heterogeneity within GOCI pixels? (2) Which scale-transition method (mean vs. central pixel) minimizes validation errors across different CV intervals?