NDLI: Using Hyperspectral Indices to Diagnose Severity of Winter Wheat Stripe Rust

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Using Hyperspectral Indices to Diagnose Severity of Winter Wheat Stripe Rust

Content Provider	IEEE Xplore Digital Library
Author	Jinbao Jiang Yunhao Chen Jianxiong Zhang Jing Li
Copyright Year	2006
Description	Author affiliation: Coll. of Resources Sci. & Technol., Beijing Normal Univ. (Jinbao Jiang; Yunhao Chen)
Abstract	Hyperspectral remote sensing data, the spectral resolution of which is less than 10 nm, include a mass of diagnosing spectrum information and can be used to non-destructively detect disease stresses of vegetation. A field experiment had been done to collect the canopy spectral reflectance of winter wheat. Canopy reflectance spectra were surveyed, and the disease index (DI) were gained by the field survey in the same regions. The results shows that while DI increases, the spectral reflectance of the canopy gradually increases in the red valley region. And the coefficient of vegetation index $SD_{r}/SD_{g$ and DI is the largest. Here, SDg and SDr denote the sum of 1st derivative within green edge and red edge respectively. Linear and non-linear regression methods are used to build the inversion models. Testing results indicate that the models consisted of $SD_{r}/SD$ $_{g}$ has the highest predictable precision. So the conclusion of this paper is that the model is the best one for inversion about severity of winter wheat yellow rust by hyperspectra. The conclusion has important mean to guide people to cure the disease of crops and increase yields of crops and ensure security of food supplies
File Size	121420
File Format	PDF
ISBN	0780397363
DOI	10.1109/ICOSP.2006.344504
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Diseases Reflectivity Hyperspectral sensors Hyperspectral imaging Vegetation Crops Remote sensing Stress Testing Predictive models
Content Type	Text
Resource Type	Article

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