山地森林叶面积指数（LAI）遥感估算研究进展

贺敏; 闻建光; 游冬琴; 唐勇; 吴胜标; 郝大磊; 林兴稳; 龚张融

doi:10.11834/jrs.20210244

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山地森林叶面积指数（LAI）遥感估算研究进展
Review of forest Leaf Area Index retrieval over rugged terrain based on remotely sensed data
2022年26卷第12期页码：2451-2472
纸质出版日期： 2022-12-07 ，
DOI： 10.11834/jrs.20210244

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贺敏，闻建光，游冬琴，唐勇，吴胜标，郝大磊，林兴稳，龚张融.2022.山地森林叶面积指数（LAI）遥感估算研究进展.遥感学报，26（12）： 2451-2472

He M，Wen J G，You D Q，Tang Y，Wu S B，Hao D L，Lin X W and Gong Z R. 2022. Review of forest Leaf Area Index retrieval over rugged terrain based on remotely sensed data. National Remote Sensing Bulletin， 26（12）：2451-2472
贺敏，闻建光，游冬琴，唐勇，吴胜标，郝大磊，林兴稳，龚张融.2022.山地森林叶面积指数（LAI）遥感估算研究进展.遥感学报，26（12）： 2451-2472 DOI： 10.11834/jrs.20210244.

He M，Wen J G，You D Q，Tang Y，Wu S B，Hao D L，Lin X W and Gong Z R. 2022. Review of forest Leaf Area Index retrieval over rugged terrain based on remotely sensed data. National Remote Sensing Bulletin， 26（12）：2451-2472 DOI： 10.11834/jrs.20210244.

摘要

叶面积指数LAI（Leaf Area Index）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，因此在地形复杂的地区精度较差。这是因为在山地中崎岖的地表不仅会导致严重的辐射失真现象，还会因邻近的地形对地物目标造成遮挡，因此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，在中国占了近2/3，在这些复杂区域中估算LAI考虑地形因素十分必要。在本文中，我们首先系统地总结了现有LAI反演算法和全球遥感产品的分辨率、精度等信息，并讨论了将这些算法和产品应用于崎岖地形LAI反演的主要挑战。然后，针对山地植被场景中存在的地形效应、尺度效应，总结出山地植被冠层LAI反演的策略主要包括地形校正方法和山地辐射传输模型，并讨论了不同策略的优缺点。接着，文章讨论了野外观测的LAI数据在崎岖地形上存在的地形效应和尺度效应，以及这些效应对反演结果验证的影响程度。最后，综合总结和展望表明，遥感观测、山地辐射传输建模、机器学习技术等方面的协调使用将来可以为崎岖地表的LAI精准估算和可靠验证提供了一条有希望的途径。

Abstract

Leaf Area Index (LAI)

an essential climate variable that characterizes vegetation canopy structure

is essential 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

thereby demonstrating good performance in a homogeneous land surface. However

many studies have demonstrated that neglecting the influence of topography may cause large biases and uncertainties of the estimated LAI in a mountain area. A rugged terrain can not only distort radiation in different slopes and aspects but also cause shadows due to neighboring topographic effects. Forest occupies a large proportion of the land surface and has the most complex structure over rugged terrain

attracting greater attention to estimate accurate LAI due to its great contributions to the ecological environment. In this work

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. Thereafter

we reviewed the main LAI retrieval methods

including topographic correction methods and the mountain radiative transfer models. Finally

the topographic and scale effects of the field in situ LAI data over a 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 toward accurate LAI estimations and reliable validation over a rugged terrain.

关键词

叶面积指数地形校正遥感反演统计模型冠层反射率模型验证

Keywords

Leaf Area Indextopographyremote sensingretrievelstatistical modelcanopy reflectance modelvalidation

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