###### Abstract  
Eucalyptus plantations play an important role in the timber supply and global warming mitigation around the world. Forest age is a critical factor for evaluating and modeling forest structure (e.g., diameter at breast height (DBH), height (H), aboveground carbon stocks (ACS)) and their dynamics. Recently, the spatial distribution of forest age at different scales based on time series remote sensing data has been widely investigated. However, it is unclear whether such data can effectively support the simulation and assessment of forest structure, especially in fast-growing plantation forests. In this study, the physiological principles in predicting growth (3-PG) model was firstly optimized and calibrated using survey and UAV lidar data at the sample plot (SP) scale, and was then applied at the forest sub-compartment (FSC) scale by designing different simulation scenarios driven by different forest age data sources and adjustments. The sensitivity of the simulated forest structure parameters to forest age was assessed at the SP and FSC levels. The results show that both the survey forest age data and the remote-sensing-derived forest age data could accurately estimate the DBH, H, and ACS of eucalyptus plantations with the coefficients of determination (R\({}^{2}\)) ranging from 0.87 to 0.94, and the relative root mean square error (RRMSE) below 20% at SP level. At the FSC level, the simulation results based on remotely sensed forest age data are significantly better than FSC forest age data from surveys by forestry bureaus, with R\({}^{2}\) of ACS 0.7, RMSE 9.12 Mg/ha, and RRMSE 28.24%. The results of the sensitivity analysis show that the DBH, H, and ACS show different degrees of variation under different adjusted forest ages at SP and FSC level. The maximum difference in ACS is 82.91% at the SP scale if the forest age decreases 12 months and 41.23% at the FSC scale if the forest age increases 12 months. This study provides an important reference for future studies using forest age data obtained by remote sensing to drive the forest carbon model in a large spatial scale. 3-PG model; eucalyptus; forest age; forest structure; remote sensing; sensitivity Article  
Forests Structure Simulation of Eucalyptus Plantation Using Remote-Sensing-Based Forest Age Data and 3-PG Model. Remote Sens.202315183.10.10. with the increase in stand age in the early stage of the forest ecosystems, reaches the maximum in the middle stage after canopy closure, and gradually declines in the later stage [5]. This relationship makes most carbon cycle variables, such as biomass, carbon stocks, gross primary productivity (GPP), and net ecosystem productivity (NEP), closely related to forest age [6; 7]. Therefore, forest age is the key data to accurately estimate and simulate the carbon uptake dynamics of the forest ecosystem [8], and many carbon cycle models take forest age as known information [9; 10]. However, there is often a lack of accurate, timely, and high-spatial-resolution information on the spatial distribution of forest age in regional forest carbon cycle research, which makes it difficult for the models to conduct forest carbon stock simulation and estimation [11]. Traditionally, the way to obtain forest age was mainly through forest inventory at sample plot (SP) by inquiring, professional experience, or tree cones [12], which were very costly, long cycle, and easily subject to geographical restrictions. It is difficult to obtain large-scale and long-term spatial forest age data. Taking China as an example, a three-level forest resources inventory system has been established: national forest continuous inventory (NFCI), forest management planning inventory (FMPI), and forest operation design inventory (FODI) [13]. FODI is a very detailed survey conducted at the smallest forest management compartment (FSC), and is the only spatial data of forest age from a manual survey. However, the survey is conducted every five years, meaning the forest age information is relatively lagged and full of uncertainties.