## 3 Evaluation of Remote Sensing Products and GPP Simulations  
Figure 2 shows the time series of the observed and the simulated GPP, NEE, and Re at the Dangx, Haibei, and Maqu sites. After revising the establishment and survival parameterizations, the simulated vegetation coverage was about 50-80% from 2007 to 2010 at the Dangx site, 70-90% from 2003 to 2010 at the Haibei site, and 90-100% from 2010 to 2016 at the Maqu site, which corresponds to the actual observations at each site. Compared to the seasonal variation in the daily GPP, NEE, and Re at Dangx, the observed GPP, NEE, and Re showed different characteristics. Both the absolute GPP and NEE increased steadily from the beginning of the growing season until July, then increased rapidly from July and reached a peak value at the end of August and the beginning \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Soil Property Data** & **Parameterization** & **Model** \\ \hline BGCDV\_CTL & BNU soil property data &
\begin{tabular}{c} Balland and Arp; \\ virtual temperature; \\ establishment and survival \\ Balland and Arp; \\ virtual temperature; \\ establishment and survival; \\ seasonal–deciduous phenology \\ \end{tabular} & CLM5.0-BGCDV \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Designs of the regional experiments by CLM. of September. The simulated GPP generally coincided with the observations, and the simulated GPP of the BGCDV_CTL experiment started earlier and ended later than that of the observed GPP. At the Haibei site, GPP, NEE, and Re showed similar variations during the growing season, increasing beginning in May and reaching a peak value in August (Figure 2). Before modifying the establishment and survival parameterizations, the PFTs in dynamic vegetation simulations could not establish at the Haibei site. After modifying the establishment and survival scheme (BGCDV_CTL experiment), the simulated Re generally coincided with the observations, while the dynamic vegetation model underestimated GPP and NEE from July to September. In addition, the simulated start time of the growing season was advanced, and the end time of the growing season was delayed. The increasing trend of the simulated GPP and NEE was faster than that of the observed GPP and NEE. Seasonal variations in GPP, NEE, and Re at the Maqu site showed a similar pattern to those at the Haibei site. The CLMS.0-DGVM model overestimated Re during the growing season and underestimated NEE. In addition, the model underestimated GPP, NEE, and Re in a vigorous-growth period. In the BGCDV_CTL experiment, variations in the ecosystem productivity simulations were consistent with those in the observations, while the end time of the growing season was delayed. After replacing the original seasonal- deciduous phenology scheme (the BGCDV_NEW experiment), the model effectively shortened the growing season length at the Dangx site. The average Bias and RMSE decreased from 0.116 gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) and 0.321 gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) to 0.092 gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) and 0.261 gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\), respectively, and Corr increased from 0.922 to 0.995 (Table 5). At the Dangx site, the simulated NEE tended to underestimate the observed NEE from August to October, mainly caused by overestimating the simulated Re. Compared to the BGCDV_CTL experiment, the simulated NEE in the BGCDV_NEW experiment was smaller. The RMSE decreased from 0.316 gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) to 0.299 gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\), and Corr increased from 0.572 gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\) to 0.640 gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\). Re began to increase gradually when the growing season started and reached a peak value in August. With the leaves beginning dormancy, Re decreased beginning in September. During the growing season, the simulated Re overestimated the observed Re, and there was a dramatic decline in mid-August in the simulated Re.