## 4 Discussion
### Parameter Estimation and Model Evaluation  
Given the complexity of the SCOPE model, the parameter estimation strategy is of great importance for photosynthesis and SIF simulations. Previous studies used simplified approaches (e.g., using the linear relationship between SIF and V\({}_{\rm{cmax}}\) or using a module of the SCOPE model) to optimize the parameters of the SCOPE model (e.g., [19; 41]). As above-mentioned, we found that the relationship between SIF and V\({}_{\rm{cmax}}\) was not statistically significant in version 1.70 of the SCOPE model, while the simplified SCOPE module (i.e., only using a module of the SCOPE model) cannot reflect the linkages between SIF and photosynthesis processes. Therefore, we utilized the surrogate modeling-based approach to optimize the parameters of the model because this approach requires a limited number of model runs and is more computationally efficient. The surrogate approach has been proven to be efficient and effective in searching the approximate optimal parameters in previous studies (e.g., [37; 40]). Our study showed that the surrogate approach is able to estimate parameters of a relatively complex model like SCOPE. However, it should be noted that the surrogate model is not the \"real\" model, and it is designed to use cheaper, approximate solutions to a simulation model. Its performance depends on multiple factors such as the surrogate method choice, optimization methods, and the initial sample size and complexity of the problems [37; 40]. Previous studies have used inversion approaches to retrieve key parameters in controlling photosynthetic activity (e.g., [19; 26; 27; 44]), and have demonstrated that optimizing the biochemical parameters such as the V\({}_{\rm{cmax}}\) can effectively improve the estimation of photosynthetic capacity [19]. Previous studies have mainly utilized coarse-resolution SIF observations from GOME-2 [12; 13; 47], and few studies have used finer-resolution OCO-2 SIF to constrain parameters in the SCOPE model (e.g., [44]). We used both OCO-2 SIF and flux tower GPP data to constrain the SIF and GPP simulation by optimizing photosynthetic parameters and SIF related parameters. The optimized SCOPE model had satisfactory performance in simulating SIF and GPP. Similar to two previous studies [44; 45], our study demonstrated that the OCO-2 SIF measurements constrained the SIF related parameters such as fqe well but not V\({}_{\text{Cmax}}\). Our study demonstrated that the OCO-2 SIF observations could improve the SIF simulation of the SCOPE model by optimizing uncertain parameters. The optimized SCOPE model was evaluated using flux tower GPP data and SIF observations from both OCO-2 and GOME-2. Our results showed that the optimized model captured the magnitude, seasonality, and interannual variability of both SIF and GPP fairly well. This implies that the model can be used to effectively predict SIF and GPP. The simulated SIF and GPP can be potentially used to examine the responses of fluorescence and photosynthesis to extreme events like drought. Unlike satellite data (e.g., GOME-2, OCO-2), the SCOPE model can be to simulate SIF and GPP for the past, present, and future. In addition, the predicted SIF by the SCOPE model can potentially be used to fill the data gaps for the OCO-2 SIF products to generate spatially and temporally continuous SIF products over the globe. The uncertainty/bias in the meteorological data used by the SCOPE model will certainly introduce uncertainty to the SIF estimates. The model can be run without the satellite input, which makes it possible to scale from instantaneous data into diurnal cycles, and calculate the SIF and carbon fluxes in cloudy and rainy days [15]. The produced SIF can also be further used to improve carbon cycle modeling at large scales based on the SIF-GPP relationships. Previous studies showed significant relationships between satellite-derived SIF measurements (GOSAT, GOME-2, and OCO-2) and gridded GPP data (e.g., [5; 12; 13]).