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Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging

文献类型： 外文期刊

作者： Huang, Wenjiang 1 ; Lamb, David W. 2 ; Niu, Zheng 2 ; Zhang, Yongjiang 2 ; Liu, Liangyun 2 ; Wang, Jihua 2 ;

作者机构： 1.Univ New England, Sch Sci & Technol, Precis Agr Res Grp, Armidale, NSW 2351, Australia

2.Univ New England, Sch Sci & Technol, Precis Agr Res Grp, Armidale, NSW 2351, Australia; Beijing Normal Univ, Chinese Acad Sci, Inst Remote Sensing Applicat, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： winter wheat; disease index (DI); canopy reflectance; remote sensing; pushbroom hyperspectral imaging spectrometer (PHI); photochemical reflectance index (PRI)

期刊名称：PRECISION AGRICULTURE （ 影响因子：5.385； 五年影响因子：5.004 ）

ISSN： 1385-2256

年卷期： 2007 年 8 卷 4-5 期

页码：

收录情况： SCI

摘要： The aim of this study was to evaluate the accuracy of the spectro-optical, photochemical reflectance index (PRI) for quantifying the disease index (DI) of yellow rust (Biotroph Puccinia striiformis) in wheat (Triticum aestivum L.), and its applicability in the detection of the disease using hyperspectral imagery. Over two successive seasons, canopy reflectance spectra and disease index (DI) were measured five times during the growth of wheat plants (3 varieties) infected with varying amounts of yellow rust. Airborne hyperspectral images of the field site were also acquired in the second season. The PRI exhibited a significant, negative, linear, relationship with DI in the first season (r(2) = 0.91, n = 64), which was insensitive to both variety and stage of crop development from Zadoks stage 3-9. Application of the PRI regression equation to measured spectral data in the second season yielded a coefficient of determination of r(2) = 0.97 (n = 80). Application of the same PRI regression equation to airborne hyperspectral imagery in the second season also yielded a coefficient of determination of DI of r(2) = 0.91 (n = 120). The results show clearly the potential of PRI for quantifying yellow rust levels in winter wheat, and as the basis for developing a proximal, or airborne/spaceborne imaging sensor of yellow rust in fields of winter wheat.

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