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叶面积指数(Leaf Area Index, LAI)是表征植被几何结构及生长状态的重要生物物理参数，也是陆表过程模型的重要输入参数，如何获取高精度LAI一直备受关注。近年来，随着遥感数据的不断丰富， LAI遥感估算算法得到了快速发展，全球尺度的LAI产品已被广泛应用于气候与生态环境变化研究。然而，当前主流的LAI遥感产品生成算法基本上基于平坦地表假设而忽略了地形的影响。但实际上山地中地表反射失真现象严重，尤其是森林多样的冠层结构和山地复杂地形的相互影响给LAI遥感反演带来了较大的不确定性。山地作为一种特殊的地貌，约占全球陆地表面的1/4，在我国占了近2/3，在这些复杂区域中估算LAI考虑地形因素十分必要。本文在总结分析现有LAI反演算法以及主要产品基础上，分析了山地LAI反演中存在的主要问题，系统性综述了当前山地LAI遥感估算在模型、算法、验证等方面的研究进展，为山地LAI等地表植被参数定量化高精度反演提供参考。
LAI (Leaf Area Index), an essential climate variable that characterizes vegetation canopy structure, plays an important role in ecological and hydrological processes. Global scale LAI remote sensing products had been generated and widely used in the research of ecological environment. Most existing LAI retrieval algorithms assume that the land surface is flat and homogeneous, which shows good performance in homogeneous land surface. However, many researches have demonstrated that neglecting the influence of topography may lead to large biases and uncertainties of estimated LAI in mountain area. Actually, rugged terrain can not only distort radiation in different slopes and aspects, but also cause shadows due to neighboring topography effects. Over rugged terrain, forest occupies a large proportion of the land surface and has the most complex structure, which attracts more attention to estimate accurate LAI due to its great contributions to ecological environment. In this paper, we systematically summarized the LAI retrieval algorithms and global remote sensing products, and investigated the major challenges when applying those algorithms to LAI inversion over rugged terrain. Then, we reviewed the main LAI retrieval methods including topographic correction methods and the mountain radiative transfer models. Finally, the topographic effects and scale effects of field in situ LAI data over rugged terrain were discussed. The comprehensive summary and prospects show that great advances in remote sensing observations, radiation transfer modeling, machine learning techniques, etc. provide a promising way towards accurate LAI estimations and reliable validation over rugged terrain.