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Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy

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Abstract

Aims

This study aimed to compare stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and support vector machine regression (SVMR) for estimating soil total nitrogen (TN) contents with laboratory visible/near-infrared reflectance (Vis/NIR) of selected coarse and heterogeneous soils. Moreover, the effects of the first (1st) vs. second (2nd) derivative of spectral reflectance and the importance wavelengths were explored.

Methods

The TN contents and the Vis/NIR were measured in the laboratory. Several methods were employed for Vis/NIR data pre-processing. The SMLR, PLSR and SVMR models were calibrated and validated using independent datasets.

Results

Results showed that the SVMR and the PLSR models had similar performances, and better performances than the SMLR. The spectral bands near 1450, 1850, 2250, 2330 and 2430 nm in the PLSR model were important wavelengths. In addition, the 1st derivative was more appropriate than the 2nd derivative for spectral data pre-processing.

Conclusions

PLSR was the most suitable method for estimating TN contents in this study. SVMR may be a promising technique, and its potential needs to be further explored. Moreover, the future studies using outdoor and airborne/satellite hyperspectral data for estimating TN content are necessary for testing the findings.

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Abbreviations

TN:

Total nitrogen

SOM:

Soil organic matter

Vis/NIR:

Visible/near-infrared reflectance

SMLR:

Stepwise multiple linear regression

PLSR:

Partial least squares regression

SVMR:

Support vector machine regression

SG:

Savitzky-Golay smoothing

SNV:

Standard normal variate

MSC:

Multiplicative scatter correction

MC:

Mean centering

PCR:

Principal component regression

ANN:

Artificial neural network

MCD:

Minimum covariance determinant

R2 :

Coefficient of determination

RMSE:

Root mean square error

RPD:

Residual estimation deviation

PCs:

Principal components

AIC:

Akaike information criterion

VIP:

Variable importance in the projection

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Acknowledgements

This study was supported by the Special Foundation of the Ministry of Finance of China for Nonprofit Research of Forestry Industry (Grant No. 200904001) and the National Natural Science Foundation of China (Grant No. 41171290).

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Authors and Affiliations

  1. School of Resource and Environmental Science & Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, 430079, Wuhan, China

    Tiezhu Shi, Junjie Wang, Teng Fei, Yiyun Chen & Guofeng Wu

  2. Institute of Wetland Research, Chinese Academy of Forestry, 100091, Beijing, China

    Lijuan Cui

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  1. Tiezhu Shi
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  2. Lijuan Cui
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  3. Junjie Wang
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  4. Teng Fei
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  5. Yiyun Chen
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  6. Guofeng Wu
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Corresponding author

Correspondence to Guofeng Wu.

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Responsible Editor: Catherine Henault.

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Shi, T., Cui, L., Wang, J. et al. Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy. Plant Soil 366, 363–375 (2013). https://doi.org/10.1007/s11104-012-1436-8

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