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Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS-NIR Spectroscopy

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WOS被引频次:2
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成果类型:
期刊论文
作者:
Hong, Yongsheng;Chen, Yiyun;Yu, Lei;Liu, Yanfang;Liu, Yaolin;Zhang, Yong;Liu, Yi;Cheng, Hang
通讯作者:
Chen, Yiyun(chenyy@whu.edu.cn)
作者机构:
[Hong, Yongsheng; Chen, Yiyun; Liu, Yanfang; Liu, Yi; Cheng, Hang; Liu, Yaolin] School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China
[Hong, Yongsheng; Chen, Yiyun; Liu, Yi; Cheng, Hang] Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, 430079, China
[Hong, Yongsheng; Chen, Yiyun; Liu, Yi; Cheng, Hang] State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing, 210008, China
[Liu, Yaolin; Liu, Yanfang] Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, Wuhan, 430079, China
[Yu, Lei] Key Laboratory for Geographical Process Analysis and Simulation of Hubei Province, Central China Normal University, Wuhan, 430079, China
通讯机构:
[Chen, Yiyun; Liu, Yanfang] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China.
[Chen, Yiyun] Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Jiangsu, Peoples R China.
[Chen, Yiyun] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China.
[Liu, Yanfang] Wuhan Univ, Minist Educ, Key Lab Geog Informat Syst, Wuhan 430079, Hubei, Peoples R China.
语种:
英文
关键词:
Accurate estimation - Fractional order derivatives - Reflectance spectrum - Soil organic matters - Variable selection - Vis-NIR spectroscopy - Visible and near infrared - Visible and near-infrared spectroscopy
期刊:
REMOTE SENSING
ISSN:
2072-4292
年:
2018
卷:
10
期:
3
文献类别:
WOS:Article;EI:Journal article (JA)
所属学科:
ESI学科类别:地球科学;WOS学科类别:Remote Sensing
入藏号:
WOS:000428280100125;EI:20181204934975
基金类别:
National Natural Science Foundation of China [41771440, 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 has been extensively applied to estimate soil organic matter (SOM) in the laboratory. However, if field/moist VIS-NIR spectra can be directly applied to estimate SOM, then much of the time and labor would be avoided. Spectral derivative plays an important role in eliminating unwanted interference and optimizing the estimation model. Nonetheless, the conventional integer order derivatives (i.e., the first and second derivatives) may neglect some detailed information related to SOM. Besides, the full-spectrum generally contains redundant spectral variables, which would affect the model accuracy. This study aimed to investigate different combinations of fractional order derivative (FOD) and spectral variable selection techniques (i.e., competitive adaptive reweighted sampling (CARS), elastic net (ENET) and genetic algorithm (GA)) to optimize the VIS-NIR spectral model of moist soil. Ninety-one soil samples were collected from Central China, with their SOM contents and reflectance spectra measured. Support vector machine (SVM) was applied to estimate SOM. Results indicated that moist spectra differed greatly from dried ground spectra. With increasing order of derivative, the spectral resolution improved gradually, but the spectral strength decreased simultaneously. FOD could provide a better tool to counterbalance the contradiction between spectral resolution and spectral strength. In full-spectrum SVM models, the most accurate estimation was achieved by SVM model based on 1.5-order derivative spectra, with validation R<sup>2</sup>= 0.79 and ratio of the performance to deviation (RPD) = 2.20. Of all models studied (different combinations of FOD and variable selection techniques), the highest validation model accuracy for SOM was achieved when applying 1.5 derivative spectra and GA method (validation R<sup>2</sup>= 0.88 and RPD = 2.89). Among the three variable selection techniques, overall, the GA method yielded the optimal predictability. However, due to its long computation time, one alternative was to use CARS method. The results of this study confirm that a suitable combination of FOD and variable selection can effectively improve the model performance of SOM in moist soil.<br/> &copy;2018 by the authors.
参考文献:
Ackerson JP, 2017, GEODERMA, V286, P131, DOI 10.1016/j.geoderma.2016.10.018
Benkhettou N, 2015, SIGNAL PROCESS, V107, P230, DOI 10.1016/j.sigpro.2014.05.026
Chang C.-C., 2011, ACM T INTELL SYST TE, V2
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