## 4 Discussion  
### Prospects of Improving GPP Estimation by SIF Upscaling  
Based on the biome-specific SIF-GPP correlations, the space-based SIF has been successfully used to estimate the GPP at global scales [16]. In most studies that link the SIF to GPP, the SIF occurred at an instantaneous scale but the GPP occurs at a time-integrated scale, which caused uncertainties in the SIF-GPP model and, thus, affected the GPP estimation. It has been demonstrated in Reference [27] that upscaling the GOME-2 monthly product from the instantaneous to daily scale can improve its correlation with the monthly GPP product across different biome types because the effects of latitude on SIF\({}_{\text{ins}}\)-GPP variations have been corrected. Furthermore, this study upscaled the SIF\({}_{\text{inst}}\) to a daily  
Figure 12: Scatter diagrams of the SIF-GPP correlations when using the SIF\({}_{\text{inst}}\) and the predicted SIF using the cos-based and PAR-based methods at half-hour (upper row) and daily (bottom row) intervals at the XTS (red markers) and DM (blue marker) sites.  
scale using PAR as a driving variable. Long-term SIF and GPP measurements have validated that this PAR-based method can reduce not only the latitude dependency but also the weather dependency of the SIF-GPP model, which is likely to improve the accuracy of GPP estimation at the ground and spatial scales.  
Further opportunities are available to evaluate the advantages of PAR-based SIF upscaling for GPP estimation at a global scale using the space-based SIF product (e.g., the GOME-2 monthly SIF product or the TROPOspheric Monitoring Instrument (TROPOMI) SIF product [57]) and the global PAR and GPP products. To date, most global SIF products are gridded monthly averages of valid SIF observations within one month. As satellites can only derive no more than one SIF observation per day, and valid SIF observations mainly occur on sunny days, the monthly SIF averages cannot represent the real monthly SIF to estimate the monthly GPP due to the contributions of photosynthesis and SIF emissions during cloudy weather conditions. Using continuous PAR data for SIF upscaling may solve this problem. If the effects of FPAR and fluorescence efficiency changes on SIF within one month are neglected, the upscaled monthly SIF can be calculated as a product of the instantaneous mean SIF validly observed at 9:30 (local time) and the ratio of the monthly averaged PAR to the corresponding instantaneous PAR. The calculated results will have an additional error due to neglecting the fluorescence efficiency and FPAR changes. Considering the variations in FPAR and fluorescence efficiency with the vegetation growing in one month, SIF products at higher temporal resolutions (e.g., daily or weekly) can be also derived using the data-driven method by integrating reflectance-based products (reflectance, vegetation index, FPAR) and PAR data. Similar to the approach in the studies of References [58; 59], a neural network can first be trained using the instantaneous SIF observations and the instantaneous driven-variables (including PAR, reflectance-based products); then the high-temporal resolution SIF at a daily or weekly resolution can be calculated by using the trained neural network and its key driven variables, including reflectance-based vegetation products (reflectance, vegetation index, FPAR) and the daily or weekly PAR dataset.