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Noise-Resistant Spectral Features for Retrieving Foliar Chemical Parameters

文献类型： 外文期刊

作者： Zhang, Jingcheng 1 ; Huang, Yanbo 2 ; Li, Zhenhai 3 ; Liu, Peng 1 ; Yuan, Lin 4 ;

作者机构： 1.Hangzhou Dianzi Univ, Coll Life Informat Sci & Instrument Engn, Hangzhou 310018, Zhejiang, Peoples R China

2.ARS, Crop Prod Syst Res Unit, USDA, Stoneville, MS 38776 USA

3.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

4.Zhejiang Univ Water Resources & Elect Power, Sch Informat Engn & Art & Design, Hangzhou 310018, Zhejiang, Peoples R China

关键词： Noise;parameter estimation;vegetation;wavelet transforms

期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （ 影响因子：3.784； 五年影响因子：3.734 ）

ISSN： 1939-1404

年卷期： 2017 年 10 卷 12 期

页码：

收录情况： SCI

摘要： Foliar chemical constituents are important indicators for understanding vegetation growing status and ecosystem functionality. Provided the noncontact and nondestructive traits, the hyperspectral analysis is a superior and efficient method for deriving these parameters. In practice, thespectral noise issue significantly impacts the performance of the hyperspectral retrieving system. To systematically investigate this issue, by introducing varying levels of noise to spectral signals, an assessment on noise-resistant capability of spectral features and models for retrieving concentrations of chlorophyll, carotenoids, and leaf water content was conducted. Given the continuous waveletanalysis (CWA) showed superior performance in extracting critical information associating plants biophysical and biochemical status in recent years, both wavelet features (WFs) and some conventional features (CFs) were chosen for the test. Two datasets including a leaf optical properties experiment dataset (n = 330), and a corn leaf spectral experiment dataset (n = 213) were used for analysis and modeling. The results suggested that the WFs had stronger correlations with all leaf chemical parameters than the CFs. According to an evaluation by decay rate of retrieving error that indicates noise-resistant capability, both WFs and CFs exhibited strong resistance to spectral noise. Particularly for WFs, the noise-resistant capability is relevant to the scale of the features. Based on the identified spectral features, both univariate and multivariate retrieving models were established and achieved satisfactory accuracies. Synthesizing the retrieving accuracy, noise resistivity, and model's complexity, the optimal univariate WF models were recommended in practice for retrieving leaf chemical parameters.

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