The Aboveground Biomass Estimation of the Grain for Green Program Stands Using UAV-LiDAR and Sentinel-2 Data  
[PERSON]  
1 School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China; [EMAIL_ADDRESS] (G.Y. ); [EMAIL_ADDRESS] (C.J. );  
2 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] (T.W. ); [EMAIL_ADDRESS] (T.S. ); [EMAIL_ADDRESS] (Y.Z. ); checha  
vegetation established under the program remain unstable, and the economic benefits are not yet evident, necessitating urgent efforts to consolidate the achievements of farmland restoration. Inner Mongolia is the province with the largest area of farmland converted to forests and grasslands under the GGP, with a cumulative total of 3.03 million hectares by the end of 2019 [3]. As of now, this figure has increased to 3.12 million hectares, primarily focused on forests. The project area spans a vast east-west range, with the widespread use of tree and shrub species such as Mongolian Scots pine (_Pinus sylvestris_ var. _mongholica_), poplar (_Populus_ spp. ), and korshinsk peashrub (_Caragana korshinskii_). Aboveground biomass (AGB) is a key indicator of the GGP's effectiveness, which not only reflects the growth status of vegetation but is also closely linked to the carbon stock and ecosystem services of forested areas [4]. Therefore, accurately estimating the AGB of GGP stands is essential not only for evaluating the ecological benefits of the program but also for providing critical data to support China's carbon sink monitoring. Destructive sampling is the most accurate and reliable method for measuring AGB [5]. However, its extensive application is impractical due to the significant destruction of forests that it entails, as well as its time-consuming, labor-intensive, and costly nature [6]. With advancements in remote sensing technology, remote sensing has become a widely used tool for estimating forest canopy structure and AGB, as it can provide accurate and rapid vegetation cover information over large areas [7]. Among these technologies, light detection and ranging (LiDAR), as an active remote sensing method, has demonstrated strong penetration capabilities, allowing for the rapid and accurate acquisition of three-dimensional vegetation data, as well as horizontal and vertical structural information on forest canopies [8]. The point cloud data obtained from LiDAR can be used to derive precise structural attributes such as tree height, DBH, canopy size, and more [9; 10], making it an effective tool for forest AGB estimation. The integration of LiDAR-derived structural attributes with other remote sensing data has become a prominent approach for estimating AGB across various forest ecosystems. Studies combining LiDAR with optical satellite data, such as Sentinel-2 and Landsat, have utilized UAV or airborne LiDAR's high-resolution canopy data alongside the extensive spatial coverage of optical imagery to monitor forest restoration and large-scale biomass distribution [11; 12]. Similarly, pairing LiDAR with hyperspectral data has enabled the detailed mapping of AGB at individual tree and regional scales by merging structural information with vegetation spectral signatures [13; 14]. Furthermore, integrating LiDAR with synthetic aperture radar (SAR) data, such as Sentinel-1, has improved AGB estimates in complex environments by leveraging radar's sensitivity to forest structure [15]. Currently, the estimation of the aboveground biomass of forests mainly focuses on typical forests with dense canopy cover and tall trees [16; 17]. However, GGP stands possess unique characteristics that distinguish them from such forests. Taking the new round of GGP in Inner Mongolia as an example, which began implementation in 2015, these stands are characterized by younger tree age, lower canopy cover, and relatively shorter tree height.