title: [Disease index inversion of wheat stripe rust on different wheat varieties with hyperspectral remote sensing].
abstract: It is becoming more and more important to use mixed wheat varieties to control wheat stripe rust. Different wheat varieties were planted in field and stripe rust was caused by artificial inoculation. Disease index (DI) was assessed and the canopy reflection data of wheat canopy were obtained by ASD FieldSpec HandHeld FR(325-1 075 nm) made by ASD Company. The correlation analysis between DI and spectral data (reflectance and the first derivative) was conducted, and the estimation models between DI and reflection data (reflectance at 690 and 850 nm, SDr, NDVI and RVI) were built using linear regression method. The results showed that different combinations of wheat varieties had the similar variation at different disease index. DI has positive correlation with reflectance of wheat canopy in visible region, and has significant negative correlation in the near infrared region. DI has stable negative correlation with the first derivative in the region of 700-760 nm and with big fluctuation in other regions. The correlation was compared between DI and hyperspectral derivative index, and SDr has the best correlation with DI. DI estimation models were built based on the canopy reflectance at 690 and 850 nm, SDr, NDVI and RVI. The determinant coefficient of the models is between 0.588 and 0.855, 0.669 and 0.911, 0.534 and 0.773, and 0.587 and 0.751, respectively, and all the models were fit well. The results indicated that DI of wheat stripe rust could be inverted using hyperspectral remote sensing technique and that the inversion effect was hardly influenced by the different combinations of wheat varieties.
authors.affiliation: State Key Lab of Plant Pathology, Ministry of Agriculture, Department of Plant Pathology, China Agricultural University, Beijing 100094, China. guojiebin1@163.com
authors.forename: Jie-Bin
authors.initials: JB
authors.lastname: Guo
authors.forename: Chong
authors.initials: C
authors.lastname: Huang
authors.forename: Hai-Guang
authors.initials: HG
authors.lastname: Wang
authors.forename: Zhen-Yu
authors.initials: ZY
authors.lastname: Sun
authors.forename: Zhan-Hong
authors.initials: ZH
authors.lastname: Ma
mesh_terms: D001487:Basidiomycota / Q000472:pathogenicity*; D016014:Linear Models; D010935:Plant Diseases; D058998:Remote Sensing Technology; D013057:Spectrum Analysis; D014908:Triticum / Q000145:classification / Q000382:microbiology*