中文摘要：

叶面积指数（leaf area index,LAI）是评价作物长势和预测产量的重要依据。光谱特征信息作为高光谱遥感的突出优势在追踪LAI动态变化方面极其重要;然而,围绕光谱特征信息所开展的无人机高光谱遥感反演作物LAI的相关研究鲜有报道。该文利用ASD Field Spec FR Pro 2500光谱辐射仪（ASD Field Spec FR Pro 2500 spectroradiometer,ASD）和Cubert UHD185 Firefly成像光谱仪（Cuber UHD185 Firefly imaging spectrometer,UHD185）在冬小麦试验田进行空地联合试验,基于获取的孕穗期、开花期以及灌浆期地面数据和无人机高光谱遥感数据,估测冬小麦LAI。该文选择同步获取的冬小麦冠层ASD光谱反射率数据作为评价无人机UHD185高光谱数据质量的标准,依次从光谱曲线变化趋势、光谱相关性以及目标地物光谱差异三方面展开分析,结果表明458~830 nm（第3~96波段）的UHD185光谱数据可靠,可使用其探测冬小麦LAI,这为今后无人机UHD185高光谱数据的使用提供了参考。该文研究对比分析了UHD185数据计算的红边参数和光谱指数与冬小麦LAI的相关性,结果表明：12种参数中比值型光谱指数RSI（494,610）与LAI高度正相关,是估测LAI的最佳参数;基于比值型光谱指数的对数形式lg（RSI）构建的线性模型展现出lg（RSI）与lg（LAI）较优的线性关系（决定系数R2=0.737,参与建模的样本个数n=103）,且lg（LAI）预测值和lg（LAI）实测值高度拟合性（R2=0.783,均方根误差RMSE=0.127,n=41,P〈0.001）;该研究为利用无人机高光谱遥感数据开展相关研究积累了经验,也为发展无人机高光谱遥感的精准农业应用提供了参考。

英文摘要：

Leaf area index （LAI） is the important vegetation parameter, which is the basis to monitor crop growth and predict crop yield and used widely in many applications. Remote sensing techniques are known to be effective, inexpensive and non-destructive methods for estimating the LAI of crop canopies. And unmanned aerial vehicle （UAV） hyperspectral remote sensing technology as a new method for obtaining agricultural resources, crop growth and other information, can bring new opportunities for LAI dynamic monitoring. As is known to all, spectral characteristic parameters are extremely important for hyperspectral remote sensing in tracking the LAI of crop. However, the research about retrieving the LAI based on UAV hyperspectral remote sensing is rarely reported. And few studies based on spectral characteristic parameters in estimating LAI by using UAV hyperspectral data have been done. In this paper, we carried out the sky-to-ground remote sensing experiments used ASD Field SpecFR Pro 2500 spectroradiometer （ASD） and Cuber UHD185 Firefly imaging spectrometer （UHD185） in winter wheat experimental field. And we acquired two synchronal data including UAV hyperspectral remote sensing data and in-field data for 3 critical growth stages i.e. booting stage, anthesis stage and filling stage. So we retrieved the winter wheat LAI by using the acquired data. Firstly, we regarded the ASD hyperspectral data as the standard spectral data for evaluating UHD185 hyperspectral data. And we evaluated UHD185 data from 3 aspects, i.e. the spectral curve trend, the spectral correlation and the target objects’ spectral differences. It demonstrated that the UHD185 spectrums from 458 to 830 nm were reliable data, which could be used to monitor LAI and provide the basis for using UHD185 hyperspectral data in future. Furthermore, we calculated 12 kinds of spectral characteristic parameters, which were 4 red edge parameters including the red edge position （REP）, the red edge amplitude （Dr）, the red edge area （SDr）, a