Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy

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成果类型：

期刊论文

作者：

Hong, Yongsheng;Chen, Songchao;Liu, Yaolin;Zhang, Yong;Yu, Lei;...

通讯作者：

Chen, Yiyun;Liu, Yanfang

作者机构：

[Hong, Yongsheng; Chen, Yiyun; Liu, Yanfang; Cheng, Hang; Liu, Yaolin; Liu, Yi] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China.

[Hong, Yongsheng; Cheng, Hang; Chen, Yiyun; Liu, Yi] Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Jiangsu, Peoples R China.

[Chen, Songchao] INRA, Unite InfoSol, F-45075 Orleans, France.

[Chen, Songchao] INRA, Agrocampus Ouest, UMR SAS, F-35042 Rennes, France.

[Zhang, Yong] Anhui Univ Finance & Econ, Sch Publ Finance & Adm, Bengbu 233030, Peoples R China.

通讯机构：

[Chen, YY; Liu, YF] W

Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China.

语种：

英文

关键词：

Visible and near infrared spectroscopy;Soil organic matter;Fractional order derivative;Local modeling;Memory-based learning

期刊：

CATENA

ISSN：

0341-8162

年：

2019

卷：

174

页码：

104-116

DOI：

10.1016/j.catena.2018.10.051

基金类别：

We acknowledge the editors and anonymous reviewers for their constructive comments and suggestions. This work was supported by the National Natural Science Foundation of China (grant numbers: 41771440 and 41501444 ).

机构署名：

本校为其他机构

院系归属：

城市与环境科学学院

摘要：

Visible and near-infrared (Vis–NIR) spectroscopy is used to estimate soil organic matter (SOM). Spectral preprocessing techniques and multivariate modeling methods play important roles in the quantitative analysis of SOM. First and second derivatives (i.e., the conventional integer order derivatives) are commonly used spectral derivatives, which, however, may ignore some detailed spectral information regarding SOM. Here, we presented a fractional order derivative (FOD) method to preprocess the reflectance spectra. Robust modeling methods are s...

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Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy

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