## 1 Introduction  
The global forest area, although occupying only 1/3 of the terrestrial area, plays a crucial role in carbon storage within terrestrial ecosystems. Forest vegetation alone accounts for half of the terrestrial carbon pool [1] and stores 60% of the carbon ecosystem. Thus, forest land acts as the largest \"carbon storage pool\" and the most efficient \"carbonabsorber\" [2]. Moreover, forests possess a powerful carbon-sinking ability that is vital for balancing and regulating atmospheric carbon dioxide [3]. Given the complexity and enormity of the global ecosystem mechanisms, even minor changes can have a substantial impact [4]. In forest ecosystems, biomass is multiplied by carbon content to obtain the plant carbon storage, while the distribution of carbon storage in forest ecosystems is basically consistent with the pattern of its biomass [5]. The precise assessments of forest biomass and its variation have provided scientific evidence for carbon storage estimation, which is essential for exploring the carbon cycle of terrestrial ecosystems and moderating the ecosystem processes [6]. Due to the rapid advancement of quantitative remote sensing technology, the findings of remote sensing-based carbon storage estimations are becoming extremely consistent and dependable. Therefore, remote sensing methods have become a major tool for quantifying forest carbon storage on a larger scale [7]. However, many works have illustrated that methods which use optical remote sensing data alone are limited by the saturation effect of optical sensors [8]. Synthetic aperture radar (SAR) can acquire data around the clock at any time, in any weather, and its ability to penetrate vegetation varies with different wavelengths. Vegetation carbon storage can be retrieved by using its backscatter intensity signal and its derivative characteristics [9]. LiDAR is an active remote sensing technology that uses short wavelength laser pulses to penetrate forest canopies and obtain vertical structure information [10]. The ability of LiDAR to detect the vertical structure of forests has proven to be effective in studying forest carbon storage estimation in various forest environments. At present, even in forests with high aboveground biomass (AGB) levels, saturation is rarely observed when using LiDAR methods [11]. Compared to the optical images and SAR, the use of LiDAR significantly improves the accuracy of estimating forest carbon storage. However, LiDAR currently has shortcomings in terms of spatial and temporal coverage, which limit its application in global forest carbon storage estimation. The launch of GEDI, a LiDAR system developed to monitor the vertical structure of forests, presents a unique opportunity for improving vertical forest structure mapping [12]. GEDI's space-based mission is specifically designed to explore vegetation structures and estimate aboveground carbon stocks in temperate and tropical forest and woodlands. To achieve this, GEDI utilizes a 25-meter-diameter footprint of full waveform LiDAR data for estimating vegetation structures between 51.6\({}^{\circ}\) north and south [13]. The GEDI data are divided into four levels, each providing different levels of vegetation information. Many studies have utilized Level 2 canopy height and Level 4 footprint biomass products to evaluate the accuracy of GEDI in estimating forest structural parameters. By obtaining accurate structural measurements of canopy relative height (RH) metrics from GEDI L2A products, [PERSON] et al. [14] estimated the AGB of various forest ecosystems at the laser footprint level by applying ALS-derived biomass models. And the AGB values estimated at different footprint levels of various forest types were applied as independent variables, while relative height metrics (rh60, rh70, rh80, rh90, rh95, rh98, and rh99) extracted from GEDI L2A and canopy profile metrics (cover, pgap_thea, pai, and fhd_normal) extracted from L2B were used as explanatory variables to construct GEDI footprint-level biomass estimation models for each forest type.