## 3 Results
### Correlation of HRSIF and GPP Under Different Land Use Types  
As a novel remote sensing parameter, SIF exhibits a strong global linear relationship with GPP [44], making it an effective indicator of photosynthetic activity and terrestrial productivity. The high-resolution HRSIF product developed in this study can be further applied in climate-driven vegetation monitoring, carbon budgeting, and ecosystem stress assessment. Previous studies have statistically demonstrated the strong linear relationship between SIF and vegetation gross primary productivity (GPP) at both global and regional scales [45; 46]. To validate the reliability of the downscaled HRSIF data generated in this study, we compared it with GPP derived from flux tower observations. In addition, to highlight the advantages of HRSIF in representing GPP, we also compared its relationship with that of SIF\({}_{\text{Duvuiller}}\) and GPP. We used the FluxNet 2015 dataset [47], deriving GPP \begin{table}
\begin{tabular}{c c} \hline \hline
**Region** & **Pearson** \\ \hline Region 1 & 0.67 \\ Region 2 & 0.35 \\ Region 3 & 0.65 \\ Region 4 & 0.72 \\ Region 5 & 0.78 \\ Region 6 & 0.51 \\
**All regions** & **0.76** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The list of Pearson correlation coefficients between HRSIF and OSIF in the selected regions. from observed NEE data collected by global eddy covariance flux towers, combined with ecosystem respiration. In this study, the average of daytime and nighttime-based calculations (GPP_DT_VUT_REF and GPP_NT_VUT_REF) was used as the final GPP value for each site, and aggregated into 8-day intervals to match the temporal resolution of HRSIF. The dataset also includes the land cover type of each site. By comparing with MODIS land cover data, we selected sites with homogeneous land cover types to ensure spatial consistency and reduce comparison errors. Ultimately, six flux tower sites with distinct vegetation types were selected, as shown in Table 4. The selected sites represent grassland (GRA), evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF), cropland (CRO), mixed forest (MF), and open shrubland (OSH). Considering the coverage period of satellite SIF data and site-based GPP observations, the long-term trends of SIF and GPP were extracted from each selected site. At the 8-day temporal scale, HRSIF, SIF\({}_{\text{Duvellier}}\), and GPP exhibit strong linear correlations and consistent \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sites** & **IGBP Type** & **Latitude** & **Longitude** \\ \hline CN-CNG & GRA & 44.59 & 123.51 \\ CA-OBS & ENF & 53.99 & \(-\)105.12 \\ US-UMB & DBF & 45.56 & \(-\)84.71 \\ CH-OE2 & CRO & 47.29 & 7.73 \\ BE-VIE & MF & 50.30 & 5.99 \\ RU-COK & OSH & 70.83 & 147.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of flux observation stations. Figure 7: Spatiotemporal distribution of HRSIF and OSIF_8 day. (**a–f**) Temporal variations in HRSIF and OSIF_8 day at different land cover type sites from 2007 to 2010; (**g**) Latitudinal distribution of HRSIF and OSIF_8 day. annual trends at MF, DBF, ENF, and GRA sites (Figure 8). However, the correlation is relatively weaker at CRO and OSH sites, as shown in Figure 8j. This weaker correlation can be attributed to the need for long-term field management in croplands, where human activities such as irrigation and fertilization can significantly alter vegetation dynamics and physiological processes, introducing additional uncertainty in SIF-GPP estimation [48,49]. Most shrublands are located in arid regions, where vegetation is subject to strong drought stress and highly dynamic changes, which reconstructed SIF products may not capture effectively [50]. This can impair the correlation between SIF and GPP to some extent. Overall, HRSIF shows higher correlation with GPP than SIF\({}_{\text{Dweiller}}\), indicating that the downscaled HRSIF product generated in this study provides a more accurate representation of GPP.