###### Abstract
Satellite remote sensing has the advantage of continuous monitoring of land surface change information over long distances and large areas. It has become an important and effective means to obtain the spatial distribution of forest age [14]. There are two main strategies to retrieve forest age from remote sensing data. One is to establish a forest age estimation model based on single or multi-period remote sensing data (e.g., spectral, vegetation index, tree height product), combining with ground survey, and meteorological and other data. This method has been used to extract the spatial distribution of forest age at global, national, and regional scales [8; 11; 14; 15; 16]. The second is to extract forest disturbance year based on time-series remote sensing data change detection [17; 18]. Recently, [PERSON] et al. [19] proposed a random localization segmentation-based method to map the spatial distribution of successive plantation generation and forest age for these short-rotation eucalyptus plantations based on time series Landsat data. These remote-sensing-based forest age products provided the valuable input data for forest ecosystem carbon models. However, due to the limitations of remote sensing data and algorithms, such as cloud snow, noise, spatial and temporal resolution, and saturation of remote sensing signals [20], there are often some errors and uncertainties in the obtained forest age data, with R\({}^{2}\) ranging from 0.7 to 0.92 and RMSE ranging from 1.2 to 2.91 years, especially in tropical and subtropical regions [3; 19; 21]. The carbon cycle model based on tree growth and ecological process is an effective method to simulate forest growth, biomass, and carbon stocks [22; 23]. It can be grouped into two categories: patch-scale carbon cycle model and regional-scale carbon cycle model, according to the simulation spatial scale [24]. The patch-scale carbon cycle model can be further divided into individual tree-based and stand-based carbon cycle models. The prior can simulate the growth and mortality of each tree, and predict the diameter distribution of the stand. These models usually require lots of input data, computationally intensive, and most are conducted locally [23]. The stand-based patch carbon model can simulate the forest carbon cycle at different time scales (day, month, or year) by assuming that the trees are spatially uniformly distributed in a stand [23]. The stand-based carbon models, such as spatial production allocation model (SPAM), the individual-based forest landscape and disturbance (iLand) model, and 3-PG model [25; 26; 27] are widely used to simulate forest growth and carbon cycle, and make management measures plans [28; 29; 30]. These models can be easily extended to the regional scale [24]. The roles of forest age in the forest carbon cycle research mainly focus on the use of forest age to analyze the impact of forest management on carbon sinks and to improve carbon estimates in the terrestrial carbon models [1]. The utilization of forest age data can effectively improve the accuracy of the simulations, but the uncertainty in the forest age might also bring much ambiguity to the carbon cycle model, and few studies have assessed the impact of such uncertainties on the carbon simulation results, especially for the remote-sensing-based forest age data. For example, many researchers conducted sensitivity analyses on different parameters in 3-PG models for variety research purposes, including soil fertility (FR), age at canopy closure (fullCanAge), maximum canopy quantum efficiency, maximum canopy conductance, aWS (constant in stem mass v diam. relationship), and nWS (power in stem mass v diam. relationship) [22,31]. Few studies selected stand age parameters for sensitivity analysis, because most of the studies performed their research at plot level with accurate and known forest age. Eucalyptus is a globally important plantation tree species with fast growth rate, short harvest rotation, and strong carbon sequestration capacity [32].