## 4 Discussion
Therefore, by incorporating the optimized MOD_17 model and the calibrated Biome-BGCMuSo model, it was effective to suppress the simulation of different forest types and it was possible to simulate carbon flux with spatial and temporal continuity. Although we have reduced the uncertainty of the carbon flux simulation of different forest types in the Greater Khingan Mountains forest area of Inner Mongolia through the model-model coupling strategy, the simulation scale of the Biome-BGCMuSo model is 1 km, so the problem of pixel mixing cannot be avoided. The key to solving this problemis to use high-resolution remote sensing products. Although the classification products used in this study are fine classification products with a resolution of 30 m, the problem of pixel mixing cannot be avoided after resampling to 1 km. In addition, due to differences in model structure, parameter values, and driving data among different models, even if the coupling between models can improve the global representation of sensitivity parameter calibration, there are still errors in the simulation of forest ecological processes in large-area time series. In the model simulation, state parameters (such as LAI, soil moisture, freeze-thaw state, etc.) have large uncertainties, which will cause error transmission during the simulation process. Data assimilation is an objective and quantitative analysis method that integrates observations with heterogeneous, irregular distributions and different precisions on various types of spatiotemporal scales into the model, and obtains the best guesses for model parameters and state variables as much as possible [49]. The response of forest NPP to climate change is complex, and different forest types vary greatly. Climate controls the formation of forest NPP by changing environmental conditions to affect the physiological structure and process of forests, so changes in NPP can reflect the response of forest ecosystems to the environmental climate [50]. Generally, forest NPP has a positive response to precipitation changes, and has both positive and negative responses to temperature changes, but the response to temperature is stronger than that to precipitation. This study found that from 2012 to 2018, the forest NPP in the Greater Khingan Mountains, Inner Mongolia, had a positive response to the average temperature and average saturated vapor pressure, which was consistent with the results of [PERSON] et al. [51] and [PERSON] et al. [52]. However, the difference is that we found a negative response between forest NPP and precipitation, which may have the following two reasons: First, the response of temperature to climate in the study area is much stronger than that of precipitation, especially in cold places. The annual average temperature in our selected study area is \(-3.5\)\({}^{\circ}\)C and the extreme temperature reaches \(-50.2\)\({}^{\circ}\)C, so the forest NPP may be more sensitive to temperature. Second, it may be due to the mutual interference of different climatic factors and the joint action of multiple factors. As shown in Figure 10, the minimum value of forest NPP in the study area was in 2013, and the average temperature, average saturated vapor pressure and annual shortwave radiation were the minimum values in 7 years, while the annual precipitation was the highest value in 7 years. Through the joint action of multiple factors, the stomatal conductance of plants is reduced or even closed, and both plant transpiration and photosynthesis are significantly reduced, thereby reducing the accumulation of dry matter. From 2012 to 2018, the annual mean value of forest NPP ranged from 26.83 g to 718.27 g/C/m\({}^{2}\)/year, which was positively and strongly correlated with VPD and T\({}_{\rm mean}\): 0.82 and 0.81, respectively. The weak correlation with Sard was 0.18, and the correlation with Pre was negatively correlated at \(-0.66\).