## 1 Introduction
For the MOD17 products, inaccuracies in the parameterization of model parameters (such as maximum light-use efficiency (\(\varepsilon_{\text{max}}\) or LUE\({}_{\text{max}}\))) were found to be one of the most important factors attributed to the bias of MODIS GPP [12; 20]. The current MOD17 algorithm uses the constant maximum LUE and other parameters for one ecosystem [18], which is not suitable for variability of climate conditions and ecosystems. Previous studies found that the LUE parameter in the MOD17 algorithm was underestimated [26]. Several attempts have been made to calibrate the maximum LUE parametersand to improve the performance of MODIS GPP estimation [24; 26; 32]. However, most of these studies overlooked the potential impacts of other model parameters' uncertainty on the estimation of GPP, e.g., water-limited factors, which are important factors for GPP estimation, especially for ecosystems in the arid region. Research community have established that by adjusting the key parameters of the model can improve GPP estimation using MODIS GPP algorithm, which can compensate for the errors introduced by the model structures [23]. A model-data fusion approach provides powerful tools for optimizing the model parameters and quantifying the influence of uncertainties, and is being increasingly used to estimate the parameters of ecological models [33; 34; 35; 36; 37; 38]. Model-data fusion approaches include Bayesian and non-Bayesian approaches. Non-Bayesian approaches, such as global optimization algorithms, can efficiently determine the optimal parameter solutions by minimizing (or maximizing) objective functions [36], but cannot quantify uncertainty. In contrast, the Bayesian approach can be employed to update the parameter distributions when new information becomes available [37], and produce reliable estimates of parameter and predictive uncertainty [38]. Some past studies have strengthened the importance of parameters estimation in carbon cycle models [32; 39], but have mainly focused on single site to constrain the parameters of a given plant functional type (PFT), in addition, few studies have assessed the variability of parameters within a PFT [40]. For MODIS GPP validation, since the PFT parameters in the MOD17 algorithm are obtained from flux towers worldwide, they are not appropriate for specific regions such as the arid regions of China. Thus, this study aims to examine the performance of newly released MODIS GPP C6 products and MOD17 algorithms in predicting GPP in a typical arid region of China. The overall goals of this study are to: (1) Evaluate the model performance of the MODIS GPP Collection 6 products at eight-day to annual time scale across various ecosystem types in a typical arid region of China; (2) analyze the uncertainty of remote sensing models in simulating GPP in typical arid regions; and (3) quantify the parameter uncertainties in GPP estimation for the main ecosystem types in arid regions of China by using a Bayesian approach with calibration of maximum LUE and water and temperature-limited factors. This research will contribute to the development and improvement of GPP estimates in arid regions.