## Discussions
### Uncertainties and Limitations  
Though the proposed SIF-CO\({}_{2}\)-GPP model shows promising potentials to map GPP with a better accuracy, there are still several problems to be solved in the future to reach better performance. The uncertainty and limitation of the proposed method are mainly attributed to the following three aspects. First of all, although the spatial resolution of products of OCO-2 satellite is improved compared with other satellites, the footprint is still large compared with those of EC Flux Towers [18]. We demonstrate the footprint of OCO-2 SIF products on October 2014 in Figure 9. As can be seen from Figure 9, the spatial coverage of OCO-2 SIF products is relatively dense between 30\({}^{\circ}\)S and 30\({}^{\circ}\)N than those in high-latitude regions. That would hinder applications of the proposed method in high-latitude regions where boreal forests locate. Owing to the same reason, there would be a fraction of products of target mode, which would be inappropriate as an input of the proposed model. Even if we had tried our best to select research areas where the vegetation type is uniform, the coarse resolution could still lead to ignorance of heterogeneity in a single pixel, which would eventually reduce the reliability of GPP estimates. The fluorescence detector (Flex) is due to be launched around 2022, which will provide SIF products with finer resolution comparing with those of OCO-2. That would help reduce the error caused by the insufficient resolution of existing SIF products. Moreover, studies have shown that SIF saturates at a relatively low light intensity. This also affects the accuracy of using SIF to estimate GPP [50]. Secondly, the coverage of OCO-2 is sparser than that of MODIS, which results in limited observations for most EC flux towers. This is also a common disadvantage of estimating GPP using satellite-based SIF products with respect to LUE models. As a consequence, the probability of coincidences of the ground stations and the satellite footprints is significantly reduced due to the incomplete coverage of OCO-2. Tackling that problem, we have to lower the criteria for judging coincidences of EC flux towers and SIF products of OCO-2 in this study. Then, such spatial mismatch leads to inevitable errors in SIF, which is the key input of the GPP-estimating model and eventually propagates to estimates of GPP. Consequently, it is of great significance to develop some combined methods to complement the advantages of two different categories of models, namely, SIF-driven models and LUE models. Finally, the SIF data observed in the Target mode is evidently affected by VZA, but in this study, we did not consider this factor. Moreover, we realized that when processing SIF data, the SIF value near 0 should be retained to ensure the accuracy of the experimental results because of the error of the SIF retrieve algorithm. In addition, the impact of CO\({}_{2}\) on vegetation is complex, closely related to the growth state of vegetation and the type of vegetation, which calls for further research in the field of botany.  
This study is a tentative exploration on estimating GPP using XCO\({}_{2}\) and SIF products of OCO-2, due to the limited number of accessible EC flux towers. In future works, we are going to further improve the SIF-CO\({}_{2}\)-GPP model using products of more EC flux towers to realize mapping of GPP globally with higher accuracy.