Comparative Research on Estimating the Severity of Yellow Rust in Winter Wheat

	Comparative Research on Estimating the Severity of Yellow Rust in Winter Wheat
	Wang Jing 1; Jing Yuan-shu 1; Huang Wen-jiang 1; Zhang Jing-cheng 1; Zhao Juan 1; Zhang Qing 1; Wang Li 1
刊名	SPECTROSCOPY AND SPECTRAL ANALYSIS
	2015
卷号	35 期号:6 页码:192-201
关键词	Hyper-spectral Yellow rust Partial Least Square BP neural network Disease index
通讯作者	Huang, WJ (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China.
英文摘要	In order to improve the accuracy of wheat yellow rust disease severity using remote sensing and to find the optimum inversion model of wheat diseases, the canopy reflectance and disease index (DI) of winter wheat under different severity stripe rust were acquired. The three models of PLS (Partial Least Square), BP neural network using seven hyperspectral vegetation indices which have significant relationship with the occurrence of disease and vegetation index (PRI) were adopted to build a feasible regression model for detecting the disease severity. The results showed that PLS performed much better. The inversion accuracy of PLS method is best than of the VI (PRI, Photochemical Reflectance Index) and BP neural network models. The coefficients of determination (R-2) of three methods to estimate disease severity between predicted and measured values are 0.936, 0.918 and 0.767 respectively. Evaluation was made between the estimated DI and the measured DI, indicating that the model based on PLS is suitable for monitoring wheat disease. In addition, to explore the different contributions of diverse types of vegetation index to the models, the paper attempts to use NDVI, GNDVI and MSR which on behalf of vegetation greenness and ND-WI and MSI that represents the moisture content to be input variables of PLS model. The results showed that, for the wheat yellow rust disease, changes in chlorophyll content is more sensitive to the disease severity than the changes in water content of the canopy. However, the accuracy of the two models are both lower than predicted when participating in all seven vegetation indices, namely using several species of vegetation indices tends to be more accurate than that using single category. It indicated that it has great potential for evaluating wheat disease severity by using hyper-spectral remote sensing.
研究领域[WOS]	Spectroscopy
收录类别	SCI ; EI
语种	中文
WOS记录号	WOS:000355883400038
内容类型	期刊论文
源URL	[http://ir.ceode.ac.cn/handle/183411/38432]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1.[Wang Jing 2.Jing Yuan-shu 3.Zhao Juan] Nanjing Univ Informat Sci & Technol, Sch Appl Meteorol, Nanjing 210044, Jiangsu, Peoples R China 4.[Wang Jing 5.Huang Wen-jiang 6.Zhang Qing 7.Wang Li] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 8.[Zhang Jing-cheng] Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
推荐引用方式 GB/T 7714	Wang Jing,Jing Yuan-shu,Huang Wen-jiang,et al. Comparative Research on Estimating the Severity of Yellow Rust in Winter Wheat[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2015,35(6):192-201.
APA	Wang Jing.,Jing Yuan-shu.,Huang Wen-jiang.,Zhang Jing-cheng.,Zhao Juan.,...&Wang Li.(2015).Comparative Research on Estimating the Severity of Yellow Rust in Winter Wheat.SPECTROSCOPY AND SPECTRAL ANALYSIS,35(6),192-201.
MLA	Wang Jing,et al."Comparative Research on Estimating the Severity of Yellow Rust in Winter Wheat".SPECTROSCOPY AND SPECTRAL ANALYSIS 35.6(2015):192-201.