## 4 Discussion  
In previous studies of SIF-scale reconstruction, most have focused on using single models, with limited comparisons among multiple models and a lack of analysis on the driving variables. Furthermore, many studies did not verify the accuracy of reconstructed SIF in terms of the time series and the GPP-SIF mechanism, making it difficult to apply the results in smaller provincial areas [41, 66]. To address these gaps, we chose various driving variables, such as the 1 km scale MODIS reflectance bands (b1, b2, b3, and b4); NDVI; EVI; NIRv; OSAVI; SAVI; LAI; FPAR; and LST for downscaling modeling. Vegetation indices calculated based on reflectance bands reflect the structural properties of SIF, which are associated with the chlorophyll content of vegetation, explaining their relationship with SIF mechanistically, and different vegetation indices provide different vegetation information, reflecting the various data structures of vegetation; among which, the NDVI and EVI can accurately monitor the changes in vegetation [67]. NIRv, as a new development of vegetation index, can reduce the noise effect caused by surface vegetation pollution and retrieve more details of vegetation dynamics compared to the NDVI [68]. The SAVI and OSAVI are particularly useful for monitoring sparsely vegetated areas, as they mitigate  
Figure 16: Correlation analysis between SIF and vegetation index.  
Figure 15: Line chart of the SIF and vegetation indices of wheat.  
the influence of the soil background [69, 70, 71]. Additionally, LST is effective in capturing the growing environment information of SIF, better reflecting the physiological characteristics of vegetation. While the LAI and FPAR show a weaker correlation with SIF, they remain ecologically significant, particularly in agroforestry systems, as they reflect the plant canopy structure and vitality. These indices can improve the characterization of vegetation structure in the model training process [37, 72].  
By incorporating additional variables such as b1, b3, b4, LAI, and FPAR, the inclusion of multiple feature variables significantly improved the model's prediction accuracy. Meanwhile, the accuracy of each downscaled SIF model was significantly improved, to more accurately invert the 1 km SIF, and a month-by-month comparison between the inverted 1 km SIF and the original eSIF image after inputting the high-resolution dataset into the model revealed that the SIF data structure did not change and there was a good correlation between the two. These findings align with similar studies that have demonstrated improvements in downscaling accuracy by adding more comprehensive vegetation data [73, 74, 75]. However, as shown in previous research, the inversion of 1 km SIF is still incomplete due to the lack of some driving variables, which can be overcome by adding more data or interpolating missing values [76, 77]. The comparison of the monthly 1 km SIF with the original eSIF dataset indicated that, while the data structures remained consistent, a complete 1 km SIF reconstruction can be hindered by insufficient driving variables. This observation is consistent with the findings of [PERSON] et al. (2023), who noted that missing variables during reconstruction can lead to inaccuracies in SIF products at a finer scale [78].  
To verify the accuracy of the 1 km SIF, traditional validation methods using other SIF products were employed, as well as a novel approach based on the GOSIF-GPP mechanism. Although temporal and spatial resolution discrepancies between products caused some errors in data synthesis and aggregation, the overall correlation between the two was strong [67, 79]. However, due to the lower resolution of the GPP data, it failed to capture finer details of the vegetation dynamics, as shown in the study by [PERSON] et al. (2021) [80]. Higher-resolution GPP data would provide more accurate validation, as confirmed by recent work in the field [81].