基于高光谱的冬小麦叶面积指数估算方法

doi:10.3864/j.issn.0578-1752.2012.10.022

摘要/Abstract

摘要： 【目的】冬小麦叶面积指数是评价其长势和预测产量的重要农学参数，高光谱技术监测叶面积指数的方法能够实现快速无损的监测管理。本文旨在将田间监测和高光谱遥感相结合，探索研究中国南方江汉平原地区冬小麦的最佳波段、光谱参数及监测模型。【方法】研究选取江汉平原的湖北省潜江市后湖管理区，利用ASD地物光谱仪和SunScan冠层分析系统在田间对冬小麦的冠层光谱及叶面积指数的变化进行监测，并探讨高光谱植被指数与冬小麦叶面积指数之间的定量关系。通过相关性分析、回归分析等方法构建6种植被指数与冬小麦叶面积指数的反演模型。【结果】冬小麦冠层光谱反射率中近红外波段870 nm，红光波谷670 nm，绿光波峰550 nm，蓝光450 nm波段对叶面积指数变化最为敏感，通过构建植被指数与叶面积指数模型，相关性均较好，决定系数（R2）为0.675—0.757，其中NDVI反演模型的R2最高为0.757。【结论】经模型精度检验，NDVI植被指数反演模型的精度较其它模型好，较适合对研究样区的冬小麦进行叶面积指数反演。

关键词: 高光谱, 冬小麦, 叶面积指数, 估算

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

夏天, 吴文斌, 周清波, 周勇, 于雷. 基于高光谱的冬小麦叶面积指数估算方法[J]. 中国农业科学, 2012, 45(10): 2085-2092.

XIA Tian, WU Wen-Bin, ZHOU Qing-Bo, ZHOU Yong, YU Lei. An Estimation Method of Winter Wheat Leaf Area Index Based on HyperSpectral Data[J]. Scientia Agricultura Sinica, 2012, 45(10): 2085-2092.

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