| 光谱学与光谱分析 |
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| 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 |
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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.
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Received: 2012-06-11
Accepted: 2012-09-10
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Corresponding Authors:
ZHANG Jia-hua
E-mail: jhzhangcma@gmail.com
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