## 1 Introduction  
Remote sensing technology has revolutionized the monitoring of Earth's dynamic surface changes through multi-sensor collaborative observations, offering multidimensional data across atmospheric spectral windows to advance Earth system science [1; 2]. As satellite geospatial data's demands evolve toward commensurate observations at the adequate spatiotemporal resolutions for highly dynamic and fine-resolution (scale) features on Earth such as ocean dynamics, sampling resolution remains a central challenge [3]. In remote sensing, \"scale\" refers to an image's spatial resolution on the Earth's surface (e.g., the 500 m pixel size data from the Geostationary Ocean Color Imager (GOCI)), and the temporal sampling frequency is the satellite repeat time (e.g., GOCI's hourly acquisitions) [4]. These observational scales enable the capture of instantaneous snapshots, like hourly GOCI data, and periodic changes, such as tidal-driven diurnal fluctuations in suspended sediment concentration (SSC). Notably, the availability of remote sensing satellites in geostationary orbits (35,786 km above mean sea level, and orbital period of 24 h) enhances the temporal sampling requirements to as fine as <hourly sampling. However, geostationary orbit-based remote sensing sensors could only observe a particular part of the Earth and not globally. Sun-synchronous satellites (650-800 km altitude) have an orbital period of 90-120 min and could allow orbital sensors to observe as promptly as 3-30-day temporal sampling. Despite its advancements, remote sensing technology has been constrained by the inherent limitations of single-sensor resolutions and discrete sampling characteristics, which struggle to fully align with the continuous multi-scale variations in surface phenomena. This mismatch has led to long-standing challenges in product validation due to spatial scale effects, specifically the conflict between sensor resolution and surface heterogeneity, which induces pixel aggregation errors. Such issues are especially evident in dynamic aquatic environments like estuaries and bays [5]. Taking SSC as an example, its spatiotemporal variability is driven by multiple factors, including tidal dynamics and river discharge. Field measurements in regions like Hangzhou Bay reveal intraday SSC fluctuations exceeding 50%, while traditional low-resolution remote sensing products (e.g., GOCI at 500 m) exhibit mean relative validation errors of approximately 15% in high-variability zones [6]. Scale effects, a cornerstone issue in remote sensing science, have seen their theoretical framework continuously refined since [PERSON] and [PERSON] (1987) [7] proposed the \"three-scale selection criteria\" (information requirements, analytical methods, and scene structure). Their seminal work, employing local variance analysis, revealed the intrinsic relationship between sensor resolution and feature extraction, establishing a paradigmatic foundation for subsequent research. As remote sensing applications expanded across disciplines, [PERSON] et al. (2009) [8] systematically demonstrated the multidimensional impacts of scale effects on measurement errors, inversion models, and product validation. They found that nonlinear responses induced by heterogeneous pixels constitute the core mechanism of cross-scale biases. This bias is particularly detrimental in coastal waters, where tidal dynamics drive hourly fluctuations in SSC exceeding 50% [6]. These findings also resonate with subpixel mixing effects identified in land cover classification ([PERSON] et al., 2019) [9] and energy flux estimation ([PERSON] et al., 2003) [10]. Collectively, this body of work forms a methodological consensus: heterogeneous landscapes demand multi-scale validation. While terrestrial studies have developed scale-correction frameworks using vegetation indices ([PERSON] et al., 2003; [PERSON] et al., 2014; [PERSON] et al., 2019; [PERSON] et al., 2022) [11; 12; 13; 14], significant progress has been made in this field. For instance, [PERSON] et al.