Leaf area index retrieval with ICESat-2 photon counting LiDAR

•The first study to estimate leaf area index with photon counting LiDAR data.•Applied a new framework for ICESat-2 to estimate leaf area index.•Our study suggests the feasibility of leaf area index retrieval used ICESat-2 data.•ICESat-2 can partly make up the limitations of MODIS in leaf area index retrieval. This is the first study to inverse leaf area index (LAI) with photon counting LiDAR (i.e., Ice, Cloud, and Land Elevation Satellite-2/Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS). A new framework composed of three major steps was applied: noise removal algorithm, photon classification algorithm, and the LAI estimation model. To evaluate the feasibility and accuracy of ICESat-2 derived LAI, the MODIS (Moderate Resolution Imaging Spectroradiometer) LAI product at the spatial resolution of 500-m over two sites (Amazon rainforest and Daxing’an mountains forests) were selected for comparison purpose. The results show satisfactory agreements (Amazon: R=0.693, RMSE=2.545; Daxing’an: R=0.626, RMSE=1.893; Two sites together: R=0.667, RMSE=2.433) between MODIS LAI and ICESat-2 derived LAI. Moreover, we also selected the LAI derived from Sentinel-2 to further validate the ICESat-2 derived LAI, and got better results than MODIS LAI (Amazon: R=0.752, RMSE=2.329; Daxing’an: R=0.704, RMSE=1.724). Our study found that: (1) The new applied framework can effectively inverse LAI with ICESat-2. (2) The ICESat-2 derived LAI are reliable. (3) ICESat-2 exists many advantages over MODIS in terms of LAI estimation, for example, it not only can alleviate the issue of saturation of MODIS when LAI is high, but also can mitigate the problem of poor inversion effect of MODIS where the vegetation types are diversity.