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作物学报 ›› 2017, Vol. 43 ›› Issue (04): 549-557.doi: 10.3724/SP.J.1006.2017.00549

• 耕作栽培·生理生化 • 上一篇    下一篇

基于光谱特征与PLSR结合的叶面积指数拟合方法的无人机画幅高光谱遥感应用

高林1,2,杨贵军1,*,李长春3,冯海宽1,徐波1,王磊1,3,董锦绘1,3,付奎3   

  1. 1北京农业信息技术研究中心 / 国家农业信息化工程技术研究中心 / 农业部农业信息技术重点实验室,北京 100097;2南京大学地理与海洋科学学院,江苏南京 210023;3河南理工大学测绘与国土信息工程学院,河南焦作 454000
  • 收稿日期:2016-05-15 修回日期:2017-01-21 出版日期:2017-04-12 网络出版日期:2017-02-10
  • 通讯作者: 杨贵军, E-mail: yanggj@nercita.org.cn
  • 基金资助:

    本研究由国家重点研发计划项目(2016YFD0300602), 国家自然科学基金项目(61661136003, 41471285, 41271345)和北京市农林科学院科技创新能力建设项目(KJCX20170423)资助。

Application of an Improved Method in Retrieving Leaf Area Index Combined Spectral Index with PLSR in Hyperspectral Data Generated by Unmanned Aerial Vehicle Snapshot Camera

GAO Lin1,2,YANG Gui-Jun1,*,LI Chang-Chun3,FENG Hai-Kuan1,XU Bo 1,WANG Lei1,3,DONG Jin-Hui1,3,FU Kui3   

  1. 1 Beijing Research Center for Information Technology in Agriculture / National Engineering Research Center for Information Technology in Agriculture / Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; 2 School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing in Jiangsu province 210023, China; 3 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo in Henan province 454000, China
  • Received:2016-05-15 Revised:2017-01-21 Published:2017-04-12 Published online:2017-02-10
  • Contact: Yang Guijun, E-mail: yanggj@nercita.org.cn
  • Supported by:

    This study was supported by the National Key Research and Development Program of China (2016YFD0300602), the National Natural Science Foundation of China (61661136003, 41471285, 41271345), and the Innovation Capacity Building Project of Beijing Academy of Agriculture and Forestry Sciences (KJCX20170423).

摘要:

以冬小麦LAI为研究对象,利用孕穗期、开花期和灌浆期获取的无人机UHD185高光谱影像以及同步测定的地面数据(冬小麦冠层ASD反射率和冬小麦LAI),论证光谱特征(红边参数或植被指数)与偏最小二乘回归算法结合的改进型LAI拟合方法在无人机画幅高光谱遥感LAI探测方面的应用价值。首先,从光谱反射率相关性和植被指数相关性两方面比较UHD185与ASD,验证UHD185数据精度;结果表明,第3~第96波段(458~830 nm)的无人机UHD185高光谱数据具有较好的光谱质量,适宜探测冬小麦LAI。其次,分析光谱特征(6种植被指数和4种红边参数)与LAI的相关性,并通过独立验证和交叉验证方法,依次对基于红边参数或植被指数的传统LAI拟合方法和改进型LAI拟合方法的冬小麦LAI预测精度进行评价,相比于传统LAI拟合方法,改进型LAI拟合方法能大幅度提高冬小麦LAI的预测精度,特别是PLSR+REPs。研究结果证实,改进型LAI拟合方法能更加充分地利用无人机UHD185高光谱数据预测冬小麦LAI,可望为无人机高光谱遥感的作物理化参数探测提供几点可借鉴的思路。

关键词: 无人机, 高光谱遥感, 叶面积指数, 偏最小二乘回归, 红边参数, 植被指数

Abstract:

The objective of this study was to demonstrate the value of an improved method of retrieved leaf area index (LAI) based on unmanned aerial vehicle (UAV) hyperspectral data combined spectral characteristics, as red edge parameters (REPs) and vegetation indices, with partial least squares regression (PLSR). We got UAV UHD185 hyperspectral images at booting, anthesis, and filling stages in winter wheat. And synchronously measured ASD hyperspectral data and winter wheat LAI. We compared UHD185 data with ASD data in terms of the correlation between reflectivity and vegetation indices to verify the UAV hyperspectral data accuracy. The band 3 to 96 (458-830 nm) of UHD185 hyperspectral data had better spectral quality and was suitable for detecting winter wheat LAI. We did correlation analysis between spectral characteristics, six kinds of vegetation indices and four kinds of red edge parameters, and LAI, and used two kinds of validation methods, independent validation and cross validation, to analyze the prediction accuracy of winter wheat LAI. Compared with traditional LAI fitting method, the improved LAI fitting method especially PLSR+REPs, greatly improved the prediction accuracy of winter wheat LAI. The above results confirmed that the improved LAI fitting method is able to better utilize UAV UHD185 hyperspectral data to predict LAI of winter wheat. Moreover, it is expected to provide a few new ideas for retrieving crop physical and chemical parameters based on UAV hyperspectral data.

Key words: Unmanned aerial vehicle (UAV), Hyperspectral remote sensing, Leaf area index (LAI), Partial least squares regression, Red edge parameters, Vegetation indices

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