Monitoring of wheat yellow rust with dynamic hyperspectral data

The objective in this study was to develop proper vegetation indices for prediction of soil irrigation demanding under vegetation covering conditions. The traditional method for the winter wheat yellow rust field survey is time consuming. It was discussed of the selection method of characteristic spectral bands and the establishing of inversion model to monitor winter wheat yellow rust using hyperspectral data in this study. The correlation coefficients between selected vegetation index and disease incidence (DI) at infected stages. Inversion models between DI and vegetation index such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), transformed vegetation index (TVI) were used to monitor yellow rust. The multi-temporal hyperspectral airborne images were acquired from winter booting stage to milking stage, and the yellow rust disease of winter wheat was analyzed using hyperspectral images. Compared with healthy wheat, spectral reflectance of disease wheat was higher in 560-670 nm bands but lower in near infrared bands and the absorption depth of chlorophyll in red band and reflectance peak in green band are relatively reduced. A novel spectral index for yellow rust indices was presented, and the degree and area of yellow rust disease were successfully remotely sensed from the multi-temporal hyperspectral data based on spectral index