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.

这是第一项使用光子计数 LiDAR（即冰、云和陆地高程卫星 2/高级地形激光高度计系统，ICESat-2/ATLAS）来反演叶面积指数 (LAI) 的研究。应用了由三个主要步骤组成的新框架：噪声去除算法、光子分类算法和 LAI 估计模型。为了评估ICESat-2衍生LAI的可行性和准确性，选择了两个站点（亚马逊雨林和大兴安山森林）空间分辨率为500米的MODIS（中分辨率成像光谱仪）LAI产品进行比较。结果显示 MODIS LAI 和 ICESat-2 导出的 LAI 之间的一致性令人满意（亚马逊：R=0.693，RMSE=2.545；大兴安：R=0.626，RMSE=1.893；两个站点加在一起：R=0.667，RMSE=2.433）。而且，我们还选取了 Sentinel-2 衍生的 LAI 进一步验证了 ICESat-2 衍生的 LAI，得到了比 MODIS LAI 更好的结果（亚马逊：R=0.752，RMSE=2.329；大兴安：R=0.704，RMSE=1.724） . 我们的研究发现：（1）新的应用框架可以有效地将 LAI 与 ICESat-2 反演。(2) ICESat-2 导出的 LAI 是可靠的。(3) ICESat-2在LAI估计方面比MODIS有很多优势，例如，它不仅可以缓解LAI高时MODIS饱和的问题，而且可以缓解MODIS反演效果差的问题，其中植被类型多样。(2) ICESat-2 导出的 LAI 是可靠的。(3) ICESat-2在LAI估计方面比MODIS有很多优势，例如，它不仅可以缓解LAI高时MODIS饱和的问题，而且可以缓解MODIS反演效果差的问题，其中植被类型多样。(2) ICESat-2 导出的 LAI 是可靠的。(3) ICESat-2在LAI估计方面比MODIS有很多优势，例如，它不仅可以缓解LAI高时MODIS饱和的问题，而且可以缓解MODIS反演效果差的问题，其中植被类型多样。