## 1 Introduction
Additionally, large-scale biomass products can be obtained from L4A footprint biomass measurements. [PERSON] et al. [15] evaluated the AGB prediction results extracted from GEDI L4A products and generated AGB maps using ground survey samples. The results demonstrated that the AGB prediction results from the GEDI L4A products performed poorly in different AFS models in west Africa, with an 8.94-fold higher forecasting error compared to the estimation based on GEDI L4A. It should be noted that this product is only available in the Guineo-Congolian region. However, because GEDI is discretely sampled along the track, its footprint is spatially discontinuous. There existed certain limitations in predicting vegetation structures information solely with GEDI data. It is difficult to directly provide continuous coverage of canopy height or AGB map without using other methods. Consequently, some scholars had combined GEDI data with other remote sensing data such as MODIS, SAR, Sentinel, and SPOT to obtain continuous information, making-up for the lack of GEDI data alone. [PERSON] et al. [16] proposed a method to generate a wall-to-wall AGBD mapping which depended merely on open access earth observation data. The suitability of this approach for a wide range of geographic areas was examined and compared with the current worldwide AGBD results. For this purpose, GEDI data were integrated with Sentinel-1, Sentinel-2, and altitude data to generate the all-weather AGBD maps for Australia and the United States in 2020. This data integration approach used to produce more accurate and less saturated results in high biomass areas compared to using a single data source. [PERSON] et al. [17] developed a multi-sensor data fusion method using GEDI, ICESat-2, and NISAR data to produce wall-to-wall AGB maps. This verification demonstrated that fusion framework provided more accurate AGB estimates than using GEDI or ICESat-2 data alone. [PERSON] et al. [18] discovered a strong correlation between the forest canopy heights represented by rh96 in GEDI L2A and airborne forest canopy height. They combined rh96 with spectral information and vegetation indices from optical remote sensing images (Landsat8 OLI, Sentinel-1, and Sentinel-2), and established a forest canopy height inversion model using the random forest algorithm. This allowed them to achieve a forest height mapping at a resolution of 30 m in the Yunnan Province. [PERSON] et al. [19] discovered that the L-band of ALOS-2/PALSAR-2 deeply penetrated the forest canopy and provided more accurate information about the vertical structure of vegetation. Therefore, he compared the performance of spaceborne LiDAR GEDI and ICESat-2, which generated continuous canopy height maps for Canada in 2020 in combination with ALOS-2/PALSAR-2 and Sentinel data. It can be seen that joint estimation using multi-source remote sensing data not only revealed physical and chemical parameters related to vegetation structure and function from optical and SAR data but also effectively exploited the vegetation vertical structure information provided by GEDI. Although majority of the studies have focused on using various methods of machine learning to estimate forest canopy height and biomass changes, most of these studies relied on GEDI L4A footprint biomass for large-scale continuous biomass or carbon storage estimation [20]. However, there were relatively fewer studies on the estimation of county-scale carbon storage using spaceborne LiDAR GEDI L2B and Landsat 9 data. The radiation accuracy of OLI-2 and TIRS-2 instruments carried by Landsat 9 was improved from 12-bit of Landsat 8 to 14-bit, the radiation resolution was greatly improved, and the signal-to-noise ratio is also improved [21]. As a result, this study integrated the GEDI footprint data with global coverage Landsat 9 data to analyze the feasibility of estimating forest carbon storage in Shangri-La on a county scale.