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Position: Home > Articles > Estimating the crop leaf area index using hyperspectral remote sensing Journal of Integrative Agriculture 2016,15 (2)

Estimating the crop leaf area index using hyperspectral remote sensing

作  者：
Liu Ke;Zhou Qing-bo;Wu Wen-bin;Xia Tian;Tang Hua-jun
单  位：
Minist Agr, Key Lab Agriinformat, Beijing 100081, Peoples R China;Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China
关键词：
hyperspectral;inversion;leaf area index;LAI;retrieval
摘  要：
The leaf area index (LAI) is an important vegetation parameter, which is used widely in many applications. Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies. During the last two decades, hyperspectral remote sensing has been employed increasingly for crop LAI estimation, which requires unique technical procedures compared with conventional multispectral data, such as denoising and dimension reduction. Thus, we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques. First, we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation. Second, we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types: approaches based on statistical models, physical models (i.e., canopy reflectance models), and hybrid inversions. We summarize and evaluate the theoretical basis and different methods employed by these approaches (e.g., the characteristic parameters of LAI, regression methods for constructing statistical predictive models, commonly applied physical models, and inversion strategies for physical models). Thus, numerous models and inversion strategies are organized in a clear conceptual framework. Moreover, we highlight the technical difficulties that may hinder crop LAI estimation, such as the "curse of dimensionality" and the ill-posed problem. Finally, we discuss the prospects for future research based on the previous studies described in this review.
作  者：
LIU Ke;ZHOU Qing-bo;WU Wen-bin;XIA Tian;TANG Hua-jun;Key Laboratory of Agri-Informatics,Ministry of Agriculture;Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences;College of Urban & Environmental Sciences,Central China Normal University;
关键词：
hyperspectral;;inversion;;leaf area index;;LAI;;retrieval
摘  要：
The leaf area index(LAI) is an important vegetation parameter,which is used widely in many applications.Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies.During the last two decades,hyperspectral remote sensing has been employed increasingly for crop LAI estimation,which requires unique technical procedures compared with conventional multispectral data,such as denoising and dimension reduction.Thus,we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques.First,we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation.Second,we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types:approaches based on statistical models,physical models(i.e.,canopy reflectance models),and hybrid inversions.We summarize and evaluate the theoretical basis and different methods employed by these approaches(e.g.,the characteristic parameters of LAI,regression methods for constructing statistical predictive models,commonly applied physical models,and inversion strategies for physical models).Thus,numerous models and inversion strategies are organized in a clear conceptual framework.Moreover,we highlight the technical difficulties that may hinder crop LAI estimation,such as the "curse of dimensionality" and the ill-posed problem.Finally,we discuss the prospects for future research based on the previous studies described in this review.

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