## 5 Discussion
For each grid cell, the LPJ-DGVM simulated potential vegetation distribution according to several bioclimatic limits [5], which would bring deviations if simulated vegetation compositions are different from actual composition of the FFTs in the cell. For example, the potential vegetation types simulated by the LPJ-DGVM of the CN-Dan (FPT: GRA) are C\({}_{3}\) perennial grass and boreal broadleaved summergreen tree. The simulated GPP of CN-Dan showed sizable higher values due to a large percentage of C\({}_{3}\) perennial grass was simulated as summergreen tree. Moreover, it is indicated that default leaf longevity may cause significant differences of terrestrial carbon flux estimation, and other parameters, such as turnover times of the leaf, sapwood, and fine root, also need to be revised to reduce uncertainties [75]. Second, previous research has found that plant photosynthesis and runoff are particularly sensitive to air temperature, which will be affected by the interpolation of monthly temperature to \"quasi-daily\" values [78; 79]. Third, empirical or simplified algorithms were adopted in the LPJ model to simulate the ecosystem carbon cycle, which also induced uncertainties in model outputs [5]. Considering the poor performances of the LPJ model in calculating the water cycle process, it is necessary to assimilate soil moisture into the model to constrain some model parameters such as sensible and latent heat fluxes [15] and, hence, decrease the mismatch between simulation results and observations. Our GPP estimates also have other sources of uncertainty. Though GLASS LAI is widely used for modeling research, it cannot be the same as the ground-measured LAI. Moreover, the LPJ model is usually influenced by uncertainty of several crucial parameters, such as soil texture and atmospheric driving data, including temperature, precipitation, and cloud cover [7]. These errors could propagate to the final GPP values and eventually cause deviation of the model estimates from the observed data. To optimize the model outputs, incorporating the remote sensing model into the LPJ-POD scheme by calibrating sensitive parameters of the LPJ model [71; 80] will be also considered in further work. In this study, GPP simulated by the optimal scheme (LPJ-POD) in China was comparable to previous studies. At the national scale, the GPP estimates exhibited strong discrepancies from various ecosystem models (Table 4; [63; 64; 81; 82; 83; 84]). A higher terrestrial annual productivity estimate of 12.26 PgC in China was obtained from a remote sensing model [83], which is almost the equivalent of twice the GPP estimates of this study and [PERSON] et al. [63]. [PERSON] et al. [82] constructed a daily driving Terrestrial Ecosystem Production process model (TEPC) and gave a relative higher vegetation production of 7.356 PgC/yr in China based on the Boreal Ecosystem Productivity Simulator (BEPS) and the bio-geochemical model (Biome-BGC). Based on the analysis of published terrestrial productivity in China, lower productivity focused mostly on Carnegie-Ames-Stanford Approach (CASA), BIOME-BGC and BEPS, and the results revealed that the averaged GPP in China was 5.656 Pg/yr according to the 16 studies on terrestrial productivity of China [85], which is 10% lower than GPP estimates in our study. Different model performance of GPP estimates in China owing to different definition of vegetation dynamic, parameter settings, and simulation algorithms. Our developed LPJ-POD system provides an effective way to constrain parameter values and makes model outputs closer to the observation data, which can be a comparative method when estimating China's annual GPP.