## 1 Introduction
corrected the hotspot effect of the MODIS RossThick-LiSparseReciprocal (RTLSR) model with greater accuracy, which has the potential to improve the accuracy of CI inversion [30]. They developed a backup algorithm based on the AFX and BRDF archetypes to limit outliers in CI inversion results, producing global MODIS CI products with 500 m resolution for monthly and 8-day intervals from 2001 to 2020 [31]. These products provide valuable references and data support for applications in land surface and ecological models, as well as for studying the spatiotemporal characteristics of CI. As seasons change, vegetation undergoes a series of phenological variations, including sprouting, leaf development, flowering, fruiting, and defoliation, which is not only accompanied by changes in vegetation greenness but also by changes in canopy structure. Currently, vegetation phenological remote sensing monitoring technology has become highly advanced after decades of development. Due to its ability to significantly eliminate noise caused by observation conditions such as solar zenith angle, atmospheric and soil background, NDVI and EVI (Enhanced Vegetation Index), which characterize vegetation greenness, have become the primary parameters used in vegetation phenological remote sensing monitoring. As a crucial structural parameter reflecting the degree of nonrandomness in the spatial distribution of canopy foliage, the CI theoretically exhibits distinct phenological variations. However, owing to the limited data available and the relatively recent initiation of quantitative research on CI, there has been some debate regarding its phenological characteristics. Some researchers argue that the CI does not exhibit seasonal variations and thus is considered constant throughout the year [32; 33; 34], which impacts LAI estimation and the accuracy of ecological modeling [35]. However, more researchers believe and have started studying the seasonal variations in CI. Previous studies have shown that the timing of maximum clumping generally occurs earlier than the timing of maximum greenness of vegetation leaves [36]. This is likely related to the gradual increase in chlorophyll concentration, which represents greenness, during vegetation growth, while canopy leaves tend to reach their clumping state earlier [37; 38]. Long-term field measurements of CI indicate that driven by vegetation phenology, CI in vegetation types such as evergreen needleleaf forests [39; 40], deciduous broadleaf forests [2; 41], savannas [42], grasslands [41], and crops [43; 44] exhibit pronounced seasonal variations. Additionally, some researchers have studied the seasonal variations in CI via multiangle remote sensing data. Studies indicate that the CI exhibits significant seasonal variations at both regional and global scales. For example, [PERSON] et al. [45] demonstrated that CI in China clearly exhibits seasonal variations. [PERSON] et al. [46] reported that while the interannual variation pattern of the CI in the Sanjiang Plain is not pronounced, the seasonal variations are significant. [PERSON] et al. [27] reported significant seasonal variations in CI on a global scale. [PERSON] et al. [47] compared the temporal and spatial characteristics of various global CI products and reported that both types of CI data exhibit seasonal variations. In summary, despite numerous studies that have analyzed CI over time to capture its seasonal and interannual variations, most research has been limited to examining the fluctuation characteristics of the average CI at regional or global scales. In general, CI values with distinct seasonal variation characteristics that are useful for ecological modeling have yet to be developed. Therefore, there is an urgent need to develop a CI product that more precisely reflects seasonal variations, providing direct and effective support for modeling various ecological, meteorological, and other surface processes.