| 光谱学与光谱分析 |
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| Vegetation Index Estimation by Chlorophyll Content of Grassland Based on Spectral Analysis |
| XIAO Han, CHEN Xiu-wan*, YANG Zhen-yu, LI Huai-yu, ZHU Han |
| Institute of Remote Sensing and Geographic Information System，Peking University，Beijing 100871，China |
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Abstract Comparing the methods of existing remote sensing research on the estimation of chlorophyll content, the present paper confirms that the vegetation index is one of the most practical and popular research methods. In recent years, the increasingly serious problem of grassland degradation. This paper, firstly, analyzes the measured reflectance spectral curve and its first derivative curve in the grasslands of Songpan, Sichuan and Gongger, Inner Mongolia, conducts correlation analysis between these two spectral curves and chlorophyll content, and finds out the regulation between REP (red edge position) and grassland chlorophyll content, that is, the higher the chlorophyll content is, the higher the REIP (red-edge inflection point) value would be. Then, this paper constructs GCI (grassland chlorophyll index) and selects the most suitable band for retrieval. Finally, this paper calculates the GCI by the use of satellite hyperspectral image, conducts the verification and accuracy analysis of the calculation results compared with chlorophyll content data collected from field of twice experiments. The result shows that for grassland chlorophyll content, GCI has stronger sensitivity than other indices of chlorophyll, and has higher estimation accuracy. GCI is the first proposed to estimate the grassland chlorophyll content, and has wide application potential for the remote sensing retrieval of grassland chlorophyll content. In addition, the grassland chlorophyll content estimation method based on remote sensing retrieval in this paper provides new research ideas for other vegetation biochemical parameters’ estimation, vegetation growth status’ evaluation and grassland ecological environment change’s monitoring.
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Received: 2013-11-09
Accepted: 2014-02-25
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Corresponding Authors:
CHEN Xiu-wan
E-mail: xwchen@pku.edu.cn
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