# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 4 Discussion  
### Comparison and Analysis of Phenological Characteristics at Different Scales  
The phenological metrics extracted from SIF data with three different spatial resolutions revealed the significant influence of spatial scale in the phenological tracking process of deciduous forests. Specifically, the use of 1-km resolution SIF products in tracking phenological metrics of deciduous forests, such as the SOS and EOS, showed significant improvement. These products demonstrated better consistency with results extracted from the GPP sites compared to the 5-km and 50-km resolution SIF products (Table S2). This can be attributed to the fact that phenological data obtained from satellite imagery incorporate details from all features within a pixel. As high-resolution data, the 1-km SIF captures detailed vegetation information because its smaller pixel coverage allows more precise detection of localized changes and better differentiation of growth stages. In contrast, coarse-resolution surface phenology inversions consistently exhibit the mixing effects of different vegetation components [36, 70, 71]. Compared to the 1 km window, the content of deciduous forest pixels within the 5 km and 50 km windows decreased by 27.48% and 36.99%, respectively. The coarse-resolution SIF data may not be able to effectively distinguish vegetation heterogeneity, resulting in more ambiguous phenology information extracted (Figure 7). Prior studies have indicated that when vegetation in a proportion of high-resolution pixels begins greening, the detected coarse-resolution SOS corresponds to the earlier stages of high-resolution SOS pixels [37]. This may explain why coarse-resolution SIF tends to underestimate phenological metrics. The source area footprint of the flux tower is less than a 1000-m radius, which better matches the 1 km spatial scale and shows significant scale differences with 5 km/50 km. This is another key driver of the scale effect: coarse-resolution pixels, containing a large amount of heterogeneous land surface (e.g., cropland, bare soil) outside the flux tower source area, intrinsically exhibit a weakened correlation between their SIF signal and tower-based GPP. In addition, the differences in phenological metrics between SIF data at different spatial resolutions may be influenced by the degree of landscape fragmentation [30]. Landscape fragmentation may lead to the disruption of the continuity of deciduous forest ecosystems, thus affecting the measurement of phenological metrics. Future research should focus on improving algorithms for generating such high-resolution SIF products and conducting comprehensive assessments of their effectiveness for application to a wider range of vegetation types and environmental conditions [72].  
When comparing and analyzing the phenological data across scales, we recognize that scale selection is critical to capture trends in phenological changes. At a 1-km resolution, SIF data can accurately capture complex geographic details and local ecological shifts, facilitating the identification of subtle variations in phenological metrics (Figure 8). However, at coarser resolutions, such as those of the GOSIF and GOME-2 SIF datasets, the trend of phenological advancement becomes more evident (Figures 9 and 10). This is because coarser-scale data integrate more spatial information and reduce local noise and variability [73]. As a result, coarse-scale data may ignore local fluctuations, thus providing a clearer perspective on ecological patterns and trends at large scales. The comparative analysis underscores the significant role of scale effects in phenology research: the fine scale facilitates in-depth exploration of local ecological processes, while the coarse scale is more suitable for identifying trends in macroscopic ecological change. Therefore, researchers must carefully consider the spatial scale of their research questions and select the appropriate data resolution to ensure that the trends of phenological change can be accurately captured and interpreted.