## 4 Discussion  
### High Accuracy Can Be Realized Based on the Forest Age Data from Landsat  
The 3-PG model has been widely used to estimate forest growth parameters such as DBH, H, biomass, and NPP. In addition, the model can also output other parameters, such as forest volume, stand basal area, and stand density, which are required by forest managers. In this study, we estimated and evaluated the simulated DBH, H, and ACS of eucalyptus at the SP level and FSC level, and analyzed their sensitivity to the forest age data. During the simulation, we adopted most of the default parameters that have been established for eucalyptus (except the allometric growth equations) [51]. Both the measured data and estimated data from UAV lidar show that the 3-PG model has high simulation accuracy as long as high-quality forest age is provided. The management information can further improve the simulation accuracy. Our study shows that the forest age data from Landsat data have similar simulation accuracy with the scenario of using surveyed forest age and thinning data together. The reasons might be the uncertainties of surveyed forest age data and the minor impact of thinning measures on final ACS. In fact, it is difficult to acquire the exact planting time of eucalyptus (e.g., month), especially under the condition of the coexistence of coppice and planting. Eucalyptus has a very rapid growth in the early stage and reaches canopy closure within 2-3 years [19]. It is very difficult to obtain such high precision planting time. The forest planting time in the model was needed to be set at month, which might be difficult to simulate the early growth process of eucalyptus. The assimilation of more dense time series remote sensing data or products (e.g., LAI from Landsat or Sentinel) might improve these processes. As an important parameter of forest carbon cycle model, forest age represents the planting time of trees/stands and reflects the current growth stage. For physiological-ecological process models, changes in stand age inevitably affect factors such as stomatal conductivity and hydraulic conductivity, which, in turn, affect physiological processes in trees, such as photosynthesis and root turnover rates [27; 52]. In addition, trees at different ages have different sensitivities to parameters [53], e.g., trees have a high sensitivity to parameters such as soil fertility in the young stage and a low sensitivity to stand density in the mature stage. Therefore, it is necessary to obtain accurate and reliable information on the age of the forest during the carbon cycle, and will be the fundamental to optimize and parameterize the regional carbon models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & \multicolumn{2}{c}{**H**} & \multicolumn{3}{c}{**ACS**} \\ \hline  & \(\mathbf{R^{2}}\) & **RMSE (m)** & \begin{tabular}{c} **Change** \\ **Degree of** \\ **RMSE** \\ \end{tabular} & \(\mathbf{R^{2}}\) & \
\begin{tabular}{c} **RMSE** \\ **(Mg/ha)** \\ \end{tabular} &
\begin{tabular}{c} **Change** \\ **Degree of** \\ **RMSE** \\ \end{tabular} \\ \hline \(-3\) months & 0.74 & 3.04 & \(-10.53\%\) & 0.75 & 9.22 & 1.1\% \\ \(-6\) months & 0.73 & 3.04 & \(-10.53\%\) & 0.74 & 9.27 & 1.64\% \\ \(-12\) months & 0.72 & 3.19 & 5.06\% & 0.71 & 10.33 & 13.27\% \\ No change & 0.68 & 3.36 & 0 & 0.70 & 9.12 & 0 \\ +3 months & 0.74 & 3.25 & \(-3.27\%\) & 0.77 & 10.25 & 12.39\% \\ +6 months & 0.74 & 3.4 & \(1.19\%\) & 0.77 & 11 & 20.61\% \\ +12 months & 0.74 & 3.77 & 12.2\% & 0.77 & 12.88 & 41.23\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of model predicted height (H) and aboveground carbon stocks (ACS) with lidar inverse H and ACS under different stand age conditions based on FSC scale. The constant (aWS) and power (nWS) in the allometric equation of stem biomass play an important role in the biomass allocation sub-model. Previous studies show that the main reason for the poor simulation of the 3-PG model is not using the local biomass allocation and allometric growth parameters [54].