Scientia Agricultura Sinica ›› 2012, Vol. 45 ›› Issue (10): 2085-2092.doi: 10.3864/j.issn.0578-1752.2012.10.022

• RESEARCH NOTES • Previous Articles     Next Articles

An Estimation Method of Winter Wheat Leaf Area Index Based on HyperSpectral Data

 XIA  Tian, WU  Wen-Bin, ZHOU  Qing-Bo, ZHOU  Yong, YU  Lei   

  1. 1.中国农业科学院农业资源与农业区划研究所/农业部农业信息技术重点实验室，北京100081
    2.华中师范大学城市与环境科学学院，武汉 430079
  • Received:2011-12-05 Online:2012-05-15 Published:2012-03-29

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Abstract: 【Objective】 Leaf area index (LAI) is one of the important parameters for evaluating winter wheat growth status  and forecasting its yield. Hyperspectral remote sensing is a new technical approach that can be used to acquire the instant information of winter wheat LAI without harm to the growing crops. By integrating hyperspectral remote sensing and traditional field  monitoring, this study aims to explore the best band spectral parameters and monitoring model for winter wheat LAI inversion in south China. 【Method】 The study was carried out at Houhu Management District of Qianjiang city, South China’s Jianghan Plain. At winter wheat growth stage, the winter wheat canopy spectral reflectance and LAI were monitored in field using the ASD FieldSpec 3 and SunScan canopy analysis system. Then the correlation between the Hyperspectral Vegetation Index (HVI) and LAI was analyzed. Six inversion models were constructed for estimating LAI by using correlation analysis, regression analysis and other methods. 【Result】 The results show that winter wheat canopy spectral reflectance in near infrared band of 870nm platform, red waveband of 670 nm, green waveband of 550nm and blue waveband of 450nm are the most sensitive bands to LAI changes. The coefficients of determination (R2) of the constructed HVI/LAI model are between 0.675-0.757. Among them, the NDVI inversion model has the highest R2 (0.757).【Conclusion】 Accuracy test shows that NDVI inversion model has the highest accuracy compared to other models. It is concluded that NDVI model is the most suitable model for inverting winter wheat LAI in the study area. Nevertheless, band selection is also important in adopting the new technical approach for monitoring winter wheat LAI in South China’s Jianghan Plain.

Key words: hyperspectral, winter wheat, leaf area index, estimation

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