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ESTIMATION OF LEAF AREA INDEX BY USING MULTI-ANGULAR HYPERSPECTRAL IMAGING DATA BASED ON THE TWO-LAYER CANOPY REFLECTANCE MODEL

文献类型： 外文期刊

作者： Liao, Qinhong 1 ; Zhao, Chunjiang 1 ; Yang, Guijun 1 ; Coburn, Craig 4 ; Wang, Jihua 1 ; Zhang, Dongyan 1 ; Wang, Zhij 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Zhejiang Univ, Coll Environm & Nat Resources, Hangzhou 310058, Zhejiang, Peoples R China

3.Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Peoples R China

4.Univ Lethbridge, Dept Geog, Lethbridge, AB T1K 3M4, Canada

关键词： Leaf Area Index;Multi-Angular Hyperspectral Image;Canopy Reflectance Model;Vegetation Index;Partial Least Square Regression

期刊名称：INTELLIGENT AUTOMATION AND SOFT COMPUTING （ 影响因子：1.647； 五年影响因子：1.469 ）

ISSN： 1079-8587

年卷期： 2013 年 19 卷 3 期

页码：

收录情况： SCI

摘要： This study aims to investigate the effects of observation angle on the estimation of leaf area index (LAI) by using multi-angular hyperspectral imaging data. First, the bidirectional reflectance was simulated with a two-layer canopy reflectance model (ACRM), the obvious bell-shaped and bowl-shaped pattern can be found in the blue, red and NIR wavebands. Subsequently, the three most commonly used vegetation indexes, the normalized difference vegetation index (NDVI), the simple ratio index (SRI) and enhanced vegetation index (EVI) were used to exploit the effect of different observation angles. Through the analysis of simulated data, SRI and EVI displayed a greater potential for estimating LAI due to the fact that they are more sensitive to the variation of observation angle, thus the partial least square regression (PLS) based on the cross validation was applied both to the single observation angle and to various combinations of multiple observation angles. The result shows that SRI has obtained the highest estimation accuracy (R-2 = 0.47, RMSE = 0.30) by the combination of six observation angles, which agreed well with the simulated result, indicating that multi-angular observation can improve the estimation accuracy of LAI.

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