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

This is the authors' abstract. We don't add key points for this paper.

Spaceborne LiDAR provides a promising method for large-scale characterizing LAI. However, the quality of point cloud data from spaceborne LiDAR, especially 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 RMSE by 26.36% compared to all ICESat-2 LAIs. It also showed good agreement with MODIS and 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, but not in evergreen broadleaved forests. ICESat-2 LAI was particularly adept at capturing high LAI values, which had the highest proportion of LAI values over 6.0 compared to 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.