## 3 Results
### Characteristics of the Spatial and Temporal Distribution of HRSIF  
Using the XGBoost-trained model, we generated a high-resolution SIF dataset (HRSIF) with a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) and an 8-day temporal resolution for the period from 2007 to 2020, presented in TIFF format. Taking the data from 14 September 2018 as an example, the grid-distributed pixel-level results are shown in Figure 6a. Figure 6b shows the 8-day average of the original GOME-2 SIF data (OSIF_8 day), and Figure 6c presents the original daily GOME-2 SIF data (OSIF). By comparison, HRSIF demonstrates significantly enhanced spatial detail and can reflect the internal heterogeneity of local ecosystems. For instance, Figure 6d reveals high SIF values over a small section of the U.S. Corn Belt, which are not discernible in Figure 6f,h. Similarly, in the detailed maps of Australia shown in Figure 6e,g,i, HRSIF clearly distinguishes differences in SIF values between agricultural areas and forests, as well as the high values in the eastern Great Dividing Range region--features that are typically averaged out or obscured in the original lower-resolution data.  
Figure 6: Spatial distribution and detailed views of HRSIF, OSIF_8 day, and OSIF. (**a,d,e**) show the downscaled HRSIF data; (**b,f,g**) represent the 8-day averaged original GOME-2 SIF data (OSIF_8 day); (**c,h,i**) display the original daily GOME-2 SIF data (OSIF).  
To validate the correlation between the generated HRSIF and the original OSIF, six regions with different land cover types were randomly selected, as indicated by the red boxes in Figure 1. These regions span six continents, excluding Antarctica. Within each region, pixel-wise covariance and Pearson correlation coefficient matrices were calculated. As shown in Table 3, the results demonstrate a strong overall correlation between the downscaled HRSIF and the original SIF, with an average Pearson correlation of 0.76. In most regions, the correlation exceeds 0.6. However, lower correlation values were observed in Regions 2 and 6, which include arid and semi-arid areas.  
This study also analyzes the distribution and variation in HRSIF from both spatial and temporal perspectives. Temporally, Figure 6(a)-6 illustrate the time series trends of HRSIF and OSIF_8 day at different sites, representing six types of land cover: grassland, open shrulands, cropland, mixed forest, deciduous broadleaf forests, and evergreen needleleaf forests. The results show that HRSIF exhibits clear seasonal variation patterns that closely align with vegetation growth cycles. The SIF value continues to rise during the spring green-up period, reaches a clear peak during the summer growth season, and decreases during the autumn senescence period. This main seasonal rhythm proves that this dataset can effectively capture the basic dynamics of vegetation photosynthetic activity. Moreover, compared with the OSIF_8 day product, the time series of HRSIF shows a significantly smoother profile. This smoothness effectively filters out high-frequency noise while retaining the main seasonal and interannual trends, highlighting the robustness and clarity of the HRSIF dataset in long-term vegetation dynamic monitoring. Spatially, Figure 6(g) presents the latitudinal averages of HRSIF and OSIF_8 day. In the Northern Hemisphere, high SIF values are concentrated in mid-latitude regions, while in the Southern Hemisphere, they are more pronounced in low-latitude regions. Overall, the results in Figure 6 demonstrate strong consistency between HRSIF and OSIF_8 day across both spatial and temporal scales. This indicates that the downscaling algorithm preserves the original characteristics of SIF emissions, which is crucial for the reliability and application of the resulting data product.