## 1 Introduction
The sensitivity analysis (SA) can be used to evaluate the relative importance of the various explanatory variables in a model and can be employed to identify the most important variables that influence model outputs [31]. Two main types of SA methods have been commonly used in the screening of model parameters. The local SA methods only change one input variable at a time, and thus are not adequate for the sensitivity analysis of complex models that have many variables. In contrast, the global SA (GSA) methods explore the relative importance of each variable to model outputs with full input variable space [31,32], and are suitable to analyze the full parameter space of a complex model like SCOPE. The model-data fusion (MDF) (or data assimilation) method has been increasingly used to optimize the parameters in carbon cycle models [26,27,29,33,34]. The MDF technique is a useful tool to constrain model parameters with various observations [27,33]. With this technique, various types of observations and prior knowledge of parameters can be used to estimate parameters and to reduce their uncertainties by minimizing the cost functions that compare observations and simulations [33]. The main optimization techniques of the MDF method can be either batch or sequential methods, which depends on whether the data are processed all at once or at a time [28]. The batch methods that process all data and observations simultaneously include gradient-based methods and global optimization algorithms such as the genetic algorithm [26] and shuffled complex evolution (SCE) approaches [35]. The sequential methods process data sequentially, and the most widely used sequential method is perhaps the Kalman filter [28]. Since the conventional optimization methods are computationally intensive, particularly for complex models with a large number of parameters, efforts have been made to reduce the computational burden of parameter optimization. The surrogate-based optimization, replacing the original expensive models with simplified statistical models during the optimization process, is one of the commonly used ways to efficiently optimize complex dynamic models [36; 37; 38]. Some previous studies have used surrogate models or emulators of SCOPE (e.g., [39; 40]). The performance of the emulators was typically determined by the machine learning algorithm, dimensionality reduction method, and the number of components during the modeling [37; 40]. Given the efficiency and effectiveness of the surrogate-based optimization, here we used the surrogate-based method in our study. Meanwhile, previous studies have demonstrated the importance of optimizing the key parameters in simulating photosynthesis process using the SCOPE model (e.g., [19; 41]). The maximum carboxylation capacity (V\({}_{\text{cmax}}\), \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) of RuBisCO is one of the most important parameters in the photosynthesis submodel [42]. SIF is related to the electron transport rate in the process of photosynthesis [3], and there are complex and intrinsic linkages among the absorbed photosynthetically active radiation (APAR), fluorescence radiance, and the rate of photochemistry [16; 43]. Declining V\({}_{\text{cmax}}\) reduces the saturated rate of photosynthesis and switches the light-limiting condition to the light-saturating condition at lower PAR, leading to the maximum photochemistry rate at which photochemical yield declines [43]. With the increase in PAR and nonphotochemical quenching, fluorescence yield first increases and then declines, while the fluorescence radiance (expected SIF) rises first nonlinearly and then linearly [43]. At a midday satellite overpass with high PAR, SIF tends to be higher for leaves with high V\({}_{\text{cmax}}\) than for leaves with low V\({}_{\text{cmax}}\), leading to a positive relationship between SIF and V\({}_{\text{cmax}}\)[43].