## 2 Materials and Methods
### 3-PG Model and Parameter Setting
#### 2.3.2 Simulation Scheme Design  
Figure 3 shows the overall flowchart of the study. We used SP data, FSC data, Landsat age data, meteorological data, and site conditions to calibrate and drive the 3-PG model. Then, the surveyed forest age and forest age from Landsat were used to drive the 3-PG model and simulate the DBH, H, and ACS of eucalyptus plantations at the plot scale, and 17 sample plots with three sets of investigation data were used for validation. The impacts of historical management information on the accuracy of simulation results were also evaluated. Similarly, the FSC age and forest age from Landsat were used to simulate the DBH, tree height, and carbon storage of the eucalyptus plantations at the FSC scale, and validated by UAV lidar data. Finally, we explored the sensitivity of the simulated forest structure to the forest age on two scales. Simulation Scheme Based on the SP Level  
The carbon stocks for the 17 sample plots were simulated using the parameterized 3-PG model based on the surveyed forest data and forest age data from Landsat (Figure 4). The sample plot was chosen to represent a certain area that had similar plantation history  
Figure 3: The overall work flow chart of the article. and management. The forest age information of the plot was obtained by extracting the age of the pixel where the plot was located in the Landsat pixels. The impacts of historical management information on the simulation results were also evaluated by considering the selective cutting or not (acquired during the survey). It was difficult for Landsat time-series data to accurately detect the thinning activities, so the management was not considered in the forest age from Landsat-based simulation. All the biomass variables from the model were conversed to carbon stock by multiplying the carbon coefficient and obtaining the ACS. The simulation accuracy of three forest structure variables (DBH, H, ACS) were assessed by the survey data. Simulation Scheme Based on the FSC Level  
The simulations were then carried out for the 140 FSC of eucalyptus plantation. The following three scenarios were designed to evaluate the impact of forest age data on the carbon stocks simulation. (a) Simulation based on FSC age information. The forest age from 2017 FSC survey data was used as the input data to drive the 3-PG model. Considering that FSC age was obtained from 2017, and the validation data from lidar were obtained in July 2021, some FSC may have been harvested during the period, but the FSC data may have lagged. Therefore, the FSC were divided into two groups for evaluation: FSC planted before 2015 and FSC planted after 2015 (eucalyptus plantation harvested age mainly \(\geq\) 6 years in the study area);  
(b) Simulation based on forest age data from Landsat. The forest age data (introduced in Section 2.2.3) extracted from Landsat time-series data in January 2021 were used as the input to drive the 3-PG model. It should be noted that the simulation was performed for the 140 FSC, but not for each pixel due to lack of high spatial resolution data of soil, meteorological, tree density, etc. ;  
(c) Simulation based on the adjusted forest age data. As the forest age based on remote sensing data has many uncertainties, we adjusted the forest age by \(\pm\)3 months, \(\pm\)6 months, and \(\pm\)12 months to test the sensitivity of the model simulation results for both the SP and FSC.2.3.3. accuracy evaluation. The simulated ACS, DBH, and H of SP were evaluated by the surveyed data. The simulated ACS and H of FSC were evaluated by the data calculated from UAV lidar (introduced in Section 2.2.4). The coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and relative root mean square error (RRMSE) were used to evaluate the simulation accuracy of the model.