Estimating the crop leaf area index using hyperspectral remote sensing

文献类型： 外文期刊

第一作者： Liu Ke

作者： Liu Ke;Zhou Qing-bo;Wu Wen-bin;Tang Hua-jun;Liu Ke;Zhou Qing-bo;Wu Wen-bin;Tang Hua-jun;Wu Wen-bin;Xia Tian

作者机构：

关键词： hyperspectral;inversion;leaf area index;LAI;retrieval

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：2.848； 五年影响因子：2.979 ）

ISSN：

年卷期：

页码：

收录情况： SCI

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.

分类号： S

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