## 4 Discussion
### Limitations of This Study  
Most downscaled SIF products rely on various explanatory variables from high spatial resolution remote sensing, such as meteorological information. Because these explanatory variables cannot reflect changes in vegetation, downscaled SIF inevitably loses the physiological information contained in the original SIF data. Therefore, what is provided is an estimation of SIF rather than observed SIF values. There are several uncertainties in downscaled SIF. One significant factor is the uncertainty introduced by meteorological reanalysis data (i.e., PAR, temperature, VPD (vapor pressure deficit)). Another influencing factor is that modeling methods may not be entirely suitable (i.e., data-driven or machine learning approaches). While downscaled SIF may lose some information present in real SIF data, it extends the application of SIF in monitoring GPP. Furthermore, the GOME-2 sensor has experienced noticeable degradation during its operational lifespan, which could lead to potential errors. In this study, spatially downscaled SIF products have been calibrated with parameter adjustments to preserve the original information of the SIF signal as much as possible. Therefore, before using downscaled SIF products for long-term trend analysis, it is essential to consider the impact of instrument degradation on the original SIF values.  
Although SIF has significant advantages over traditional VIs in areas such as drought monitoring and productivity assessment, existing SIF products generally suffer from coarse spatiotemporal resolution issues. Despite the higher spatial resolution of downscaled SIF data, it remains coarse for regions with different vegetation types. Therefore, there is an urgent need for SIF data with higher spatial resolution for scientific research. In recent years, satellites such as OCO-2, OCO-3, and TanSat have provided finer spatial resolution for SIF, but their application is limited due to poor spatiotemporal continuity. Fortunately, new satellites including the Sentinel series, FLEX, and GeoCARB have been launched or planned for launch in the coming years. It is believed that in the near future, we will obtain SIF data with high spatiotemporal resolution. This will play a significant role in early drought detection, accurate monitoring of vegetation productivity, and global carbon cycle monitoring.  
Another limitation of our research results is the lack of differentiation between different vegetation types and different climatic environments. However, drought stress can have different effects on various ecosystem communities and plants in different climatic environments. Therefore, the SIF of vegetation in different ecosystem communities may respond differently to drought. Additionally, although SIF can track changes in GPP under drought conditions, how to use SIF to accurately estimate GPP remains unclear. This often requires the establishment of empirical models relating SIF to flux station GPP. However, the empirical models for SIF and GPP lack universality under different environmental conditions and vegetation types. Our study only used simple correlation analysis to study the relationship between SIF and GPP. To more accurately estimate GPP at different spatial and temporal scales, in different environmental conditions and vegetation types, a comprehensive understanding of the mechanisms of SIF estimating GPP is needed, along with the development of mechanistic models for estimating GPP based on SIF.