1. 中国科学院对地观测与数字地球科学中心，北京 100094 2. 武汉大学测绘学院，湖北 武汉 430072 3. 长江大学地球科学学院，湖北 荆州 434023 4. USGS/EROS Data Center, Sioux Falls, South Dakota 57198，USA
Progress in Leaf Area Index Retrieval Based on Hyperspectral Remote Sensing and Retrieval Models
ZHANG Jia-hua1, 3, DU Yu-zhang2, LIU Xu-feng3, HE Zhen-ming3, Yang Li-min4
1. Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China 2. School of Geodesy and Geometrics, Wuhan University, Wuhan 430072, China 3. Yangtze University, Jingzhou 434023, China 4. USGS/EROS Data Center, Sioux Falls, South Dakota 57198, USA
摘要： 植被叶面积指数(Leaf Area Index , LAI)是陆面过程中影响陆-气交换的重要参数，也是表征植被冠层结构最基本的参量之一。准确而快速地获取LAI是植被-气候相互作用、植被生态和农作物估产研究不可缺少的工作。本文首先针对LAI和高光谱遥感进行概述，然后从不同平台高光谱传感器数据和不同反演方法两个角度总结了国内外近些年来高光谱遥感LAI的研究进展，最后分析了高光谱遥感反演LAI的未来发展方向。
关键词：高光谱遥感;叶面积指数;反演模型;误差
Abstract：The leaf area index (LAI) is a very important parameter affecting land-atmosphere exchanges in land-surface processes; LAI is one of the basic feature parameters of canopy structure, and one of the most important biophysical parameters for modeling ecosystem processes such as carbon and water fluxes. Remote sensing provides the only feasible option for mapping LAI continuously over landscapes, but existing methodologies have significant limitations. To detect LAI accurately and quickly is one of tasks in the ecological and agricultural crop yield estimation study, etc. Emerging hyperspectral remote sensing sensor and techniques can complement existing ground-based measurement of LAI. Spatially explicit measurements of LAI extracted from hyperspectral remotely sensed data are component necessary for simulation of ecological variables and processes. This paper firstly summarized LAI retrieval method based on different level hyperspectral remote sensing platform (i.e., airborne, satellite-borne and ground-based); and secondly different kinds of retrieval model were summed up both at home and abroad in recent years by using hyperspectral remote sensing data; and finally the direction of future development of LAI remote sensing inversion was analyzed.
张佳华1, 3，杜育璋2，刘学锋3，何贞铭3，Yang Li-min4 . 基于高光谱数据和模型反演植被叶面积指数的进展 [J]. 光谱学与光谱分析, 2012, 32(12): 3319-3323.
ZHANG Jia-hua1, 3, DU Yu-zhang2, LIU Xu-feng3, HE Zhen-ming3, Yang Li-min4 . Progress in Leaf Area Index Retrieval Based on Hyperspectral Remote Sensing and Retrieval Models . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(12): 3319-3323.
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