title: [Using canopy hyperspectral ratio index to retrieve relative water content of wheat under yellow rust stress].
abstract: The aim of this paper is to estimate canopy relative water contents (RWC) of winter wheat under yellow rust stress by using hyperspectral remote sensing. The canopy reflectance of winter wheat that infected different severity yellow rust was collected and the disease index (DI) of the wheat was investigated respectively in the fields, whereafter the wheat was sampled corresponding to the canopy reflectance measurements and the RWC of the whole wheat were measured in the Laboratory. The research showed that the canopy spectra reflectance gradually decreased in the near-infrared (NIR) region (900-1,300 nm) with RWC reduction, however, canopy spectra reflectance gradually increased in the short-wave-infrared (SWIR) region (1,300-2,500 nm), and there was just higher minus correlation between RWC and DI. Smoothing the canopy spectra, the ratio indices were built by using the sensitive bands for water in NIR and SWIR, and then the estimation RWC linear models were built by using ratio indices as variables, and the model inversion precision and stability were analyzed and compared for estimation RWC. The result indicated that the inversion precision and the stability of the model with ratio index R1,300/R1,200 as variable excel other models, the linear model's RMSE is 3.43, and the relative error is 4.78%. So, this study results not only can provide assistant information for diagnosing wheat disease but also can supply theories and methods for inversion vegetation RWC by using hyperspectral images in the future.
authors.affiliation: College of Geoscience and Surveying Engeneering, China Univeristy of Mine and Technology, Beijing 100083, China. ahdsjjb@126.com
authors.forename: Jin-bao
authors.initials: JB
authors.lastname: Jiang
authors.forename: Wen-jiang
authors.initials: WJ
authors.lastname: Huang
authors.forename: Yun-hao
authors.initials: YH
authors.lastname: Chen
mesh_terms: D001487:Basidiomycota; D016014:Linear Models; D008962:Models, Theoretical; D010935:Plant Diseases; D012621:Seasons; D019265:Spectroscopy, Near-Infrared; D013312:Stress, Physiological; D014908:Triticum / Q000737:chemistry* / Q000382:microbiology*; D014867:Water
chemical_list: D014867:Water