Abstract： Zn contamination of rice with different concentrations of stress was identified by the remote sensing diagnosis method from the potential Hyperspectral index and the representative spectral reflectance. At the spectral index level, the authors systematically analyzed the responsive relationships of the Hyperspectral index and four important physiological parameters under the stress of Zn pollution，which include chlorophyll content， water content，cell structure and leaf area index. Through the experiments，the authors extracted Hyperspectral remote sensing indexes which reflect the change of ecological parameters and their interactive reglarity，thus establishing the three-dimensional identification model of Hyperspectral remote sensing indexes which reflect the change of Zn contamination. At the spectral reflectance level，spectral reflectance of representative bands in visible and near infrared spectral bands were decomposed using the method of independent component analysis (ICA)，and the independent components which reflect the change of Zn contamination concentration were found. Thus the visible-near infrared independent component space was established. Zn contamination with different concentrations exhibits different laws in the Hyperspectral index and independent component space，Zn contamination of rice with different concentrations can be determined combined with Hyperspectral index and independent component space，the reliability and sensitivity is improved.
林婷, 刘湘南, 谭正. 基于ICA和高光谱指数的水稻Zn污染监测模型[J]. 国土资源遥感, 2011, 23(2): 59-64.
LIN Ting, LIU Xiang-Nan, TAN Zheng. Zn Contamination Monitoring Model of Rice Based on ICA and Hyperspectral Index. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(2): 59-64.
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2011, Vol. 23 
