Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging Wenjiang Huang Æ David W. Lamb Æ Zheng Niu Æ Yongjiang Zhang Æ Liangyun Liu Æ Jihua Wang Published online: 25 August 2007 Ó Springer Science+Business Media, LLC 2007 Abstract 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 hyper- spectral 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. W. Huang  Y. Zhang  L. Liu  J. Wang State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China W. Huang  Z. Niu National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China D. W. Lamb (&) Precision Agriculture Research Group, School of Science and Technology, University of New England, Armidale, NSW 2351, Australia e-mail: dlamb@une.edu.au 123 Precision Agric (2007) 8:187–197 DOI 10.1007/s11119-007-9038-9