## 4 Discussion  
### Parameter Sensitivity of the SCOPE Model  
We first identified the sensitive parameters of the SCOPE model (v1.7) based on the OCO-2 SIF observations and flux tower GPP using both the GSA (i.e., [PERSON]'s method) and OAT approaches. Sensitivity analysis often serves as a screening tool to reduce the parametric dimensionality and to identify the most important parameters on model performance for parameter optimization [31]. To accurately simulate and better understand the canopy fluorescence and photosynthesis dynamics, we identified and optimized the key physiological parameters in the SCOPE model (V\({}_{\text{cmax}}\), Rd, fqe). SIF is linked to the maximum carboxylation capacity, V\({}_{\text{cmax}}\), an important parameter that determines the capacity of photosynthesis [15; 42], while their complex relationship is still not well understood. It is debatable to what extent SIF is sensitive to V\({}_{\text{cmax}}\) across biomass [41; 45]. A  
Figure 13: Relationships of flux tower GPP (GPP\({}_{\text{EC}}\)) with the SIF (by (**a**) OCO-2 at the 757 nm; (**b**) OCO-2 at the 771 nm; (**c**) GOME-2 at the 740 nm). (**d**) MODIS EVI, and GPP simulated by (**(e)** SIF at 757 nm, (**f**) SIF at 757 nm, (**g**) SIF at 757 nm, and (**h**) EVI) predicted by the optimized SCOPE model for the period 2003–2017.  
previous study [41] showed strong linear relationship between SIF and V\({}_{\rm{cmax}}\) using the SCOPE model and estimated seasonally-varying V\({}_{\rm{cmax}}\) based on the GOME-2 SIF product. In contrast, other studies have shown that the relationship between V\({}_{\rm{cmax}}\) and SIF within the SCOPE model was not stable and varied between different versions [19; 20; 39; 45]. In this study, we used [PERSON]'s method and OAT approach to quantify the sensitivity of SIF and GPP to key parameters in SCOPE at two temperate forest sites. Both approaches showed that SIF had low sensitivity to V\({}_{\rm{cmax}}\), while GPP had high sensitivity to this parameter. Our results are inconsistent with the finding of a previous study [41] based on the old version of the SCOPE model that there is a strong linear relationship between SIF and V\({}_{\rm{cmax}}\). Our finding is instead consistent with the previous results of SIF simulation using SCOPE v1.7 [19; 44; 62], showing a relatively low sensitivity of SIF to photosynthesis in the global parameter space of the model.  
SIF is a signal that is mainly related to the electron transport rate in the light reactions of photosynthesis and only partly related to the carboxylation rates [3; 43], while carboxylation rates mainly depend on the leaf nitrogen content and fraction of nitrogen in RuBisCO as well as the internal CO\({}_{2}\) concentration [65]. It is noticed that the V\({}_{\rm{cmax}}\) cannot be constrained by the optical data alone, and the GPP simulation was improved by combined use of optical and thermal infrared data [66]. Previous studies have demonstrated that V\({}_{\rm{cmax}}\) only partly affected SIF [16; 43]; our study also confirmed that SIF was less sensitive to V\({}_{\rm{cmax}}\).  
In addition, our study also suggested the SIF simulation was more sensitive to fqe, a parameter that directly influences the SIF emissions and is often based on empirical values [46], while V\({}_{\rm{cmax}}\) is the most important parameter for GPP simulation. The physiological parameters such as Cab and LAI are also sensitive parameters in SIF and GPP simulations, which is similar to the finding of a previous study [62]. Meanwhile, we also found that the SIF and GPP were constrained well by the concurrent use of OCO-2 SIF and flux tower GPP data. V\({}_{\rm{cmax}}\) is a parameter that directly influences the photosynthetic capacity and indirectly influences the SIF simulation.