Large-Scale Retrieval and Quality Control of Leaf Area Index Based on ICESat-2 Spaceborne Photon-Counting Laser Altimeter

Spaceborne light detection and ranging (LiDAR) provides a promising method for large-scale characterizing leaf area index (LAI). However, the quality of point cloud data from spaceborne LiDAR, especially Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), is susceptible to atmosphere and background noise, introducing considerable uncertainty in LAI retrieval. Thus, efficiently screening out the high-quality point cloud is a significant guarantee for high-quality LAI retrieval. In this study, we proposed a quality control (QC) method that employed the number of 10-m windows without ground points in the ICESat-2 100-m segment as the QC flag. This method divided segments into 11 QC flags from 0 to 10 and was applied to LAI retrieval across Chinese forests from 2019 to 2020. The field measurements at locations identical to ICESat-2 ground tracks were used to validate the ICESat-2 LAI at different QC flags. The results showed that the proposed method effectively improved point cloud quality recognition and LAI accuracy, with ICESat-2 LAI (QC <3) reducing root mean square error (RMSE) by 26.36% compared with all ICESat-2 LAIs. It also showed good agreement with Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Land Surface Satellite (GLASS) LAI and mitigated saturation issues in passive optical imagery. The ICESat-2 LAI with QC <3 performed better in deciduous broadleaved, evergreen needle-leaved, deciduous needle-leaved, and mixed forests (MFs), but not in evergreen broadleaved forests (EBFs). ICESat-2 LAI was particularly adept at capturing high-LAI values, which had the highest proportion of LAI values over 6.0 compared with MODIS and GLASS LAI. The proposed method has the potential for large-scale and high-quality LAI retrieval using ICESat-2 data on a global scale.