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Constructing Representative Calibration Dataset Based on Spectral Transformation and Kennard-Stone Algorithm for VNIR Modeling of Soil Total Nitrogen in Paddy Soil
Chen Yi-yun1,2,3,4; Zhao Rui-ying1,5; Qi Tian-ci1; Qi Lin1,6; Zhang Chao1
2017-07-01
Source PublicationSPECTROSCOPY AND SPECTRAL ANALYSIS
ISSN1000-0593
Volume37Issue:7Pages:2133-2139
Corresponding AuthorZhao Rui-ying(ruiyingzhao@whu.edu.cn)
AbstractVisible and near infrared (VNIR) has been widely used to estimate various soil properties. The construction of calibration dataset during the VNIR estimation of soil properties not only influences the representative of the calibration dataset, but also on prediction accuracy of models. Current strategy for calibration dataset construction combines VNIR spectra with Kennard-Stone (KS) algorithm. However, this strategy neglects the fact that soil reflectance spectra are a comprehensive reflection of soil properties rather than a specific component. As a result, the constructed dataset from this approach may be not good enough to represent the relationships between soil spectra and the target soil component. Given that different spectral transformations could be helpful to highlight the spectral characteristics of target component, they might be also useful in the selection of samples for model calibration. The aim of the study is to explore the potentials of the combined approach of different spectral transformations and KS algorithm in the construction of calibration dataset and the VNIR estimation of soil total nitrogen (TN). It is hypothesized that the proposed approach could help to better select samples for calibration, which are more representative comparing with those selected by KS algorithm using sample reflectance spectra. A total of 100 samples have been collected from paddy soil in Jianghan Plain of Hubei Province. Five transformation methods, namely the first derivative (FD), Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiple Scatter Correction (MSC) and Harr Wavelet transform have been employed for spectral transformations. Thereafter, KS algorithm is used to construct representative calibration datasets based on the differently transformed spectra. Partial least square regression (PLSR) is then used for model calibration. Whether the different spectral transformation methods can improve the representative of the calibration dataset constructed by KS algorithm is examined. The results illustrate that different spectral transformations can exert different effects on the construction of calibration dataset. The SG and Wavelet spectral transformations do not make a difference for the calibration dataset constructed by KS algorithm using reflectance spectra, with ratio of performance to standard deviate (RPD) of 1. 41 and 1. 27 respectively. The spectral transformations of FD, SNV or MSC do improve the calibration dataset constructed by KS algorithm, with the RPDs improve from 0. 95, 1. 48 and 1. 42 to 1. 13, 1. 78 and 2. 20 respectively. The study indicates that such spectral transformations as SNV and MSC could change the way that KS algorithm constructs calibration dataset and improve its representative relationships between soil spectra and soil TN. Therefore, we conclude that the proposed strategy for calibration dataset construction holds great pontentials to improve the model prediction capability in the VNIR estimation of soil TN.
KeywordSpectral transformation Kennard-Stone algorithm Calibration dataset construction Model accuracy Soil total nitrogen
DOI10.3964/j.issn.1000-0593(2017)07-2133-07
Indexed BySCI
Language英语
WOS Research AreaSpectroscopy
WOS SubjectSpectroscopy
WOS IDWOS:000405648400027
PublisherOFFICE SPECTROSCOPY & SPECTRAL ANALYSIS
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/62797
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhao Rui-ying
Affiliation1.Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China
2.Wuhan Univ, Suzhou Inst, Suzhou 215123, Peoples R China
3.Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China
4.Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China
5.Zhejiang Univ, Inst Agr Remote Sensing & Informat Technol Applic, Hangzhou 310058, Zhejiang, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Chen Yi-yun,Zhao Rui-ying,Qi Tian-ci,et al. Constructing Representative Calibration Dataset Based on Spectral Transformation and Kennard-Stone Algorithm for VNIR Modeling of Soil Total Nitrogen in Paddy Soil[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2017,37(7):2133-2139.
APA Chen Yi-yun,Zhao Rui-ying,Qi Tian-ci,Qi Lin,&Zhang Chao.(2017).Constructing Representative Calibration Dataset Based on Spectral Transformation and Kennard-Stone Algorithm for VNIR Modeling of Soil Total Nitrogen in Paddy Soil.SPECTROSCOPY AND SPECTRAL ANALYSIS,37(7),2133-2139.
MLA Chen Yi-yun,et al."Constructing Representative Calibration Dataset Based on Spectral Transformation and Kennard-Stone Algorithm for VNIR Modeling of Soil Total Nitrogen in Paddy Soil".SPECTROSCOPY AND SPECTRAL ANALYSIS 37.7(2017):2133-2139.
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