NDLI: Characterization of powdery mildew in winter wheat using multi-angular hyperspectral measurements

Content Provider	IEEE Xplore Digital Library
Author	Jinling Zhao Lin Yuan Linsheng Huang Dongyan Zhang Jingcheng Zhang Xiaohe Gu
Copyright Year	2013
Description	Author affiliation: Key Lab. of Intell. Comput. & Signal Process., Anhui Univ., Hefei, China (Linsheng Huang) \|\| Beijing Res. Center for Inf. Technol. in Agric., Beijing Acad. of Agric. & Forestry Sci., Beijing, China (Jinling Zhao; Lin Yuan; Dongyan Zhang; Jingcheng Zhang; Xiaohe Gu)
Abstract	This study aims at characterizing wheat canopies caused by powdery mildew (Blumeria graminis f. sp. tritici) with multi-angular hyperspectral data. The filling stage (23 May, 2012) was chosen to achieve such a goal, considering that the disease can show distinctive symptoms during the months of May and June. A total of 37 sample plots were selected including 32 normal canopies and 5 diseased canopies with varied severity. To minimizing the soil background influences, multi-angular hyperspectral data were acquired at different view angles (0°, 45° and 90°). The results showed that the proportion of wheat vegetation and soil changed greatly and the hyperspectral reflectance values correspondingly changed. Consequently, the reflectance at different viewing angles showed great differences, but the curves had the same change trends. The results showed that, to accurately identify the spectral differences caused by powdery mildew, the optimal angle or a combination of several angles must be firstly found from multi-angular hyperspectral measurements.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	2821
Ending Page	2824
File Size	530745
Page Count	4
File Format	PDF
ISBN	9781479911141
ISSN	21536996
DOI	10.1109/IGARSS.2013.6723411
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Diseases Hyperspectral imaging Reflectivity Agriculture Soil Variable speed drives Winter wheat Hyperspectral remote sensing Multi-angular spectral measurement Powdery mildew Sensitive wave bands
Content Type	Text
Resource Type	Article

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