## 1 Introduction  
Sun-induced chlorophyll fluorescence (SIF) serves as a valuable byproduct of photosynthesis and has found extensive use in the investigation of processes related to photosynthesis [1; 2; 3; 4]. In recent years, emerging SIF measurement techniques implemented on remote sensing platforms have significantly facilitated the expanded global-scale measurement of SIF, providing a promising prospect for quantifying global gross primary productivity (GPP) based on SIF. Researchers have conducted extensive investigations into satellite SIF and GPP across diverse biomes, indicating a linear relationship between them at largerregional and seasonal scales [5; 6]. Several studies have utilized satellite SIF to estimate GPP at regional and global scales based on this relationship [7; 8; 9]. However, recent research has shed light on the presence of nonlinear SIF-GPP relationships at finer spatial and temporal scales, as well as in the presence of stressful conditions [10; 11; 12]. To gain further insights into the SIF-GPP relationship, researchers have examined finer scales, such as modeling the process from the photosystem emission of chlorophyll fluorescence to the sensor reception signal [1; 3], as well as studying hydrothermal stress [13; 14], herbicide treatment [15], etc. While extensive research has been conducted on the correlation between satellite-obtained SIF and GPP at multiple scales, limited focus has been placed on investigating the influence of the spatial extent of SIF derived from satellites and GPP obtained from eddy covariance (EC) flux towers on their interrelationship. EC flux data have been widely used to evaluate or calibrate carbon flux and its related products from remote sensing methods, as they can provide relatively accurate GPP information. However, challenges arise when estimating GPP using EC flux tower and satellite data due to inconsistencies in the satellite data resolution and the footprint range of EC flux observations [16; 17; 18]. This is mainly because the current SIF products rely on medium- or coarse-resolution satellite datasets, whose spatial extents often have difficulty matching the footprint coverage area of the EC flux. In addition, the footprint coverage area and location of the EC flux vary widely over time due to changes in surface roughness and meteorological elements like wind speed and direction, especially on seasonal scales [18; 19]. To address this limitation, some studies have screened larger areas with homogeneous subsurface landscapes to minimize the impact of spatial heterogeneity on GPP estimates from SIF obtained from satellite data [8; 20]. However, truly homogeneous sites are rare [17], which may constrain the richness and diversity of ecosystem sample selection. Moreover, it may not be reasonable to assume spatial homogeneity of the GPP within the footprint of the EC flux, even when the land cover remains consistent within the footprint [18]. This is especially true for SIF, which is more sensitive to photosynthesis and whose spatial distribution is affected not only by plant physiology but also by canopy structure factors, including the leaf area index (LAI), clumping, and leaf angle distribution [21; 22]. Therefore, spatial-scale mismatches are likely to distort the relationship between SIF and GPP. Several reconstructed SIF products, such as Contiguous SIF (CSIF) [23], Global OCO-2 SIF (GOSIF) [24], SIFnet [25], reconstructed TROPOMI SIF (RTSIF) [26], and enhanced SIF (eSIF) [27], have been developed to overcome the limitations of the current satellite-based SIF products, such as their low spatial and temporal resolution, high noise, and spatial discontinuity. Despite the widespread use of these SIF products globally or in large regional areas, their accuracy in matching GPP data derived from EC flux towers remains limited. Therefore, obtaining high-resolution SIF data is imperative for studying the relationship between satellite SIF and tower-based GPP.