# Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation
## 5 Discussion  
GPP estimates obtained from the optimized MOD_17 model performed well in this study, and thus provided the possibility of incorporating the remote sensing-based model with the process-based model (Biome-BGC) to acquire the carbon fluxes with temporal and spatial continuity. Furthermore, this strategy provided the option of circumventing the problem of scarce field data describing the various forest processes in specific biomes. Previous investigations have applied this method in a Mediterranean forest ecosystem [44; 48; 49], but the errors caused by the uncertainties in the remote sensing-based model would there be propagated to the next calibration and the predictions of carbon fluxes; the calibration of the parameters mainly focused on the parameters related to summer drought without considering exhaustive sensitivity analysis of other crucial parameters or a global calibration. Additionally, many studies concentrating on the calibration of Biome-BGC using field measurements (e.g., dendrochronological data, eddy covariance measurements) were carried out without considering temporal continuity or spatial heterogeneity based on reliable remote sensing data [50; 51]. Figure 6: Results of default, calibrated, and assimilated GPP: (**a**) Performances of GPPs from the EC measurement, default, calibrated and assimilated Biome-BGC; (**b**) comparison and validation of GPP values from the EC measurement and default Biome-BGC; (**c**) comparison and validation of GPP values from the EC measurement and calibrated Biome-BGC; (**d**) comparison and validation of GPP values from the EC measurement and assimilated Biome-BGC. The results of sensitivity analysis showed that the total sensitivity indices of the crucial parameters were the main contributors to the annual mean GPP. This means that one parameter may enlarge the effects of other parameters [52]. The interaction effects of FLNR showed the highest influence on the annual mean GPP. FLNR controls the carboxylation capacity (V\({}_{\text{cmax}}\)), which is the first step in the fixing of CO\({}_{2}\) by the ribulose-1, 5-biphosphate protein [53]. V\({}_{\text{cmax}}\) is a state variable in Biome-BGC with high spatial variability, so it is difficult to acquire [54]. The SLA also made contributions to the annual mean GPP by affecting the LAI. G\({}_{\text{max}}\) controlled the leaf conductance and was crucial for regulating water loss and carbon assimilation. The W\({}_{\text{int}}\) coefficient determined the amount of precipitation intercepted by the canopy, which in turn controlled the amount of precipitation infiltrating [50]. With the interactions between the sensitive parameters being so extensive, the correlation between the input parameters should be further considered in a comprehensive calibration of the Biome-BGC. EnKF sequential data assimilation is an efficient way to improve the simulated carbon fluxes, capable of tracing the seasonal estimates of model outputs. As a key indicator of vegetation growth, the time series GLASS LAI was assimilated into the Biome-BGC model. These products of higher quality than the MODIS LAI were validated by [PERSON] et al. [47] at sites worldwide. The estimates of errors from GLASS LAI and model structure were important to the EnKF algorithm as the errors determined the extent to which the model predictions could be corrected. Considering the drawbacks of Biome-BGC in describing the water cycle [55], it is necessary to assimilate a soil moisture parameter into the model to improve the performance of evapotranspiration and soil moisture. The assimilation of long-term remote sensing observations (e.g., LAI, soil moisture) into process-based models could provide information on forest disturbance, an avenue that needs to be explored in further work. The strategy explored in this study could be applied in long-term simulations of forest carbon fluxes over large regions and even facilitate analyzing the response of carbon fluxes to climatic variation.