## 3 Results
### Verification of HRSIF
#### 3.2.2 Comparison with Other Satellite Products  
To evaluate the consistency of the HRSIF product generated in this study with other satellite products, we also compared the HRSIF product with the TROPOMI SIF and \(\text{SIF}_{\text{Dweiller}}\) products. Spatial correlation analysis was conducted by calculating the time series correlation coefficients between each pair of products at each pixel location. The results show that the HRSIF product exhibits strong global consistency with both satellite products. The spatial distribution of the correlation coefficients between HRSIF and TROPOMI SIF is shown in Figure 5a, with a global correlation exceeding 0.80. The slope of the linear regression, shown in Figure 5c, is 1.2, which is close to one. Additionally, global statistical metrics between HRSIF and \(\text{SIF}_{\text{Dweiller}}\) reveal a high correlation coefficient of 0.9352, with small RMSE and MAE values, as shown in Figure 5b,d. Both HRSIF and \(\text{SIF}_{\text{Dweiller}}\) are based on GOME-2 SIF data and use different downscaling approaches. The high level of agreement between the final products indicates that the machine learning method adopted in this study can achieve comparable results to semi-empirical methods, effectively preserving the physiological information of the original SIF.  
Figure 4: Trends and correlations between HRSIF and ground-based observations at the 8-day scale. The unit of SIF is mW/m\({}^{2}\)/nm/sr. (**a,b**) The US-NRI site; (**c,d**) The CA-OBS site; (**e,f**) The Huailai site; (**g,h**) The Jurong site.  
We also analyzed the errors between HRSIF and the two products under different land cover types and seasons, as shown in Figure 5e,f. Figure 5e randomly selected six different land cover types. The consistency between HRSIF and SIF\({}_{\text{Dweeiller}}\) was generally higher than that between HRSIF and TROPOMI. However, GOME-2, with its coarse resolution, resulted in a relatively high deviation compared to TROPOMI during the pixel averaging process. As shown in Figure 5f, the differences between HRSIF and the two products were relatively consistent across different seasons, indicating that our HRSIF product exhibits good temporal stability.  
It is worth noting that the spatial distribution of the correlation coefficients shows that the correlation between HRSIF and other products is lower in some tropical and arid regions. The primary reason is that persistent cloud cover in tropical rainforests leads to high levels of data noise, while in arid regions (such as deserts and barren lands), sparse vegetation, low photosynthetic activity, and strong soil background interference may result in greater errors [19; 42]. Nevertheless, the strong correlation between HRSIF and the two satellite products in most parts of the world further confirms the validity and reliability of the method proposed in this study.  
Figure 5: Global distribution map of Pearson correlation coefficients and density scatter plots of correlation. (**a**) Correlation between HRSIF and TROPOMI eSIF. (**b**) Correlation between HRSIF and SIF\({}_{\text{Dweeiller}}\). (**c**) The verification scatter plot of the correlation between HRSIF and TROPOMI eSIF for all pixel points worldwide over a one-year period. (**d**) The verification scatter plot of the correlation between HRSIF and SIF\({}_{\text{Dweeiller}}\) for all pixel points worldwide over a one-year period. (**e**) The errors of HRSIF in different ecosystems compared with TROPOMI eSIF and SIF\({}_{\text{Dweeiller}}\). (**f**) The errors of HRSIF compared with TROPOMI eSIF and SIF\({}_{\text{Dweeiller}}\) in different seasons.