Using Canopy Hyperspectral Ratio Index to Retrieve Relative Water Content of Wheat Under Yellow Rust Stress
JIANG Jin-bao1, HUANG Wen-jiang2, CHEN Yun-hao3*
1. College of Geoscince and Surveying Ingeneering, China Univeristy of Mine and Technology, Beijing 100083, China 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China 3. College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
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.
Key words：Wheat;Canopy spectral;Yellow rust;Relative water content(RWC);Inversion models
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