作物学报2017，Vol.43Issue(4)：549-557,9.DOI:10.3724/SP.J.1006.2017.00549
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
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
摘要
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 hyperspec-tral 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. More-over, it is expected to provide a few new ideas for retrieving crop physical and chemical parameters based on UAV hyperspectral data.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Key words
Unmanned aerial vehicle (UAV)/Hyperspectral remote sensing/Leaf area index (LAI)/Partial least squares regres-sion/Red edge parameters/Vegetation indices引用本文复制引用
高林,杨贵军,李长春,冯海宽,徐波,王磊,董锦绘,付奎..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[J].作物学报,2017,43(4):549-557,9.基金项目
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. This study was supported by the National Key Research and Development Program of China (2016YFD0300602), the National Natural Sci-ence Foundation of China (61661136003, 41471285, 41271345), and the Innovation Capacity Building Project of Beijing Academy of Agri-culture and Forestry Sciences (KJCX20170423). （2016YFD0300602）
