ACTA AGRICULTURAE UNIVERSITATIS JIANGXIENSIS, Volume 41 , Issue 6 : 1227-1234(2019) https://doi.org/10.13836/j.jjau.2019143

Evaluation of Accuracy of Bootstrap-PLSR Model based on Vis-NIR Spectra in Predicting Soil Organic Matter

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  • ReceivedMay 29, 2019


Soil organic matter (SOM) is an essential soil fertility indictor of paddy soil in the middle-lower Yangtze plain.Rapid,non-destructive and accurate determination of SOM is vital to preventing soil degradation caused by inappropriate land management practice.Visible-near infrared (vis-NIR) spectroscopy with PLSR can be used to effectively estimate soil properties.In this study,523 soil samples were collected from paddy fields in the provinces of Zhejiang,Jiangxi and Hunan,China.Partial least squares regression (PLSR) and PLSR combined with Bootstrap sampling were used to compare the prediction accuracy of SOM based on vis–NIR full bands.The coefficient of determination (R 2),root mean square error (RMSE),and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy.The generalization ability and stability of the Bootstrap-PLSR model were analyzed by using the distribution of the 95% confidence interval of the predicted value and the measured value,the difference in the coefficients between the PLSR and the Bootstrap-PLSR could produce higher accuracy (R 2=0.76,RMSE=5.82,RPIQ=2.51) compared with that of PLSR (R 2=0.72,RMSE=6.27,RPIQ=2.33).The performance of the PLSR and Bootstrap-PLSR in SOM prediction did not differ significantly in the range of higher and lower values but a slight increase could be found in the middle SOM value.The Bootstrap-PLSR could provide the uncertainty of the models and their predictions.Therefore,PLSR coupled with Bootstrap sampling is recommended for prediction of SOM in the middle-lower Yangtze plain.

Funded by



the National Nature Science Foundation(41361049)

the Science and Technology Research Project of Jiangxi Provincial Department of Education(GJJ181150)


[1] 杨梅花,王芳东,赵小敏,等. 基于综合指数的吉安县耕地质量监测[J]. 江西农业大学学报,2014,36(4):911-917.. Google Scholar

[2] Vohland M,Besold J,Hill J,et al. Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy[J]. Geoderma,2011,166(1):198-205.. Google Scholar

[3] Morra M J,Hall M H,Freeborn L L. Carbon and nitrogen analysis of soil fractions using near-infrared reflectance spectroscopy[J]. Soil Science Society of America Journal,1991,55:288-291.. Google Scholar

[4] Stenberg B,Viscarra Rossel R A,Mouazen A M,et al. Chapter five-visible and near infrared spectroscopy in soil science [M]. SPARKS D L. Adv Agron. Academic Press,2010:163-215.. Google Scholar

[5] Word S,Sjösröm M,Eriksson L. PLS-regression:a basic tool of chemometrics[J]. Chemometrics and Intelligent Laboratory Systems,2001,58(2):109-130.. Google Scholar

[6] Wold S. Cross-validatory estimation of the number of components in factor and principal components models[J]. Technometrics,1978,20(4):397-405.. Google Scholar

[7] Hastie T,Tibshirani R,Friedman J. The elements of statistical learning:data mining,inference,and prediction[J]. The Mathematical Intelligencer,2005,27(2),83-85.. Google Scholar

[8] Hancock T,Put R,Coomans D,et al. A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies[J]. Chemometrics and Intelligent Laboratory Systems,2005,76(2):185-196.. Google Scholar

[9] Mevik B H,Segtnan V H,Næs T. Ensemble methods and partial least squares regression[J]. Journal of Chemometrics,2004,18(11):498-507.. Google Scholar

[10] Rossel R a V. Robust modelling of soil diffuse reflectance spectra by bagging-partial least squares regression[J]. Journal of Near Infrared Spectroscopy,2007,15(1):39-47.. Google Scholar

[11] Stoner E R,Baumgardner M F. Characteristic variations in reflectance of surface soils[J]. Soil Science Society of America Journal,1981,45:1161-1165.. Google Scholar

[12] Clark R N,Roush T L. Reflectance spectroscopy:quantitative analysis techniques for remote sensing applications[J]. Journal of Geophysical Research:Solid Earth,1984,89(B7):6329-6340.. Google Scholar

[13] 杨梅花,赵小敏,王芳东,等. 基于主成分分析的最小数据集的肥力指数构建[J]. 江西农业大学学报,2016,38(6):1188-1195.. Google Scholar

[14] 李曦. 基于高光谱遥感的土壤有机质预测建模研究[D]. 杭州:浙江大学,2013.. Google Scholar

[15] Viscarra Rossel R A. ParLeS:software for chemometric analysis of spectroscopic data[J]. Chemometr IntellLab,2008,90(1):72-83.. Google Scholar

[16] 杨梅花,赵小敏,方倩,等. 基于可见-近红外光谱变量选择的土壤全氮含量估测研究[J]. 中国农业科学,2014,47(12):2374-2383.. Google Scholar

[17] Yang M,Xu D,Chen S,et al. Evaluation of machine learning approaches to predict soil organic matter and pH using vis-NIR spectra[J]. Sensors,2019,19(2):263.. Google Scholar

[18] Bellon-Maurel V,Fernandez-Ahumada E,Palagos B,et al. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy[J]. TRAC Trends in Analytical Chemistry,2010,29(9):1073-1081.. Google Scholar

[19] Wilding L P. Spatial variability:its documentation,accommodation and implication to soil surveys[J]. Soil Spatial Variability,1985:166-194.. Google Scholar

[20] Abdi D,Tremblay G F,Ziadi N,et al. Predicting soil phosphorous and other properties using near infrared spectroscopy[J]. 2012,76:2318-2326.. Google Scholar

[21] Leardi R,Seasholtz M B,Pell R J. Variable selection for multivariate calibration using a genetic algorithm:prediction of additive concentrations in polymer films from Fourier transform-infrared spectral data[J]. Analytica Chimica Acta,2002,461(2):189-200.. Google Scholar

[22] Almeida M R,Fidelis C H,Barata L E,et al. Classification of amazonian rosewood essential oil by Raman spectroscopy and PLS-DA with reliability estimation[J]. Talanta,2013,117:305-311.. Google Scholar