Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

Comparative analysis of three regression methods for the winter wheat biomass estimation using hyperspectral measurements

Authors
Yuanyuan Fu, Guijun Yang, Haikuan Feng, Xiaoyu Song, Xingang Xu, Jihua Wang
Corresponding Author
Yuanyuan Fu
Available Online March 2013.
DOI
https://doi.org/10.2991/iccsee.2013.434How to use a DOI?
Keywords
winter wheat biomass,hyperspectral,partial least squares regression,principal component regression, stepwise multiple linear regression,spectral transformation
Abstract

Hyperspectal data contain more useful information for characterizing vegetation biomass, compared with multi-spectral data. However, to make full use of the hyperspectral data, the strong multi-collinearity in the data is supposed to be taken into account. With this study we evaluated three multivariate regression methods which are principal component regression (PCR), partial least square regression (PLSR) and stepwise multiple linear regression (SMLR). They are specifically designed to deal with multi-collinearity problem. Furthermore, to identify reliable winter wheat biomass predictive models different types of spectral transformations (continuum removal, first derivative) were combined with the three regression methods, respectively. The comparative analysis was conducted on the data sets collected in 2008 and 2009 field campaigns in Tongzhou and Shunyi district, Beijing, China. Compared with the other combination, the respective combination of three regression methods and continuum removal got the highest estimation accuracy, especially, the combination of PLSR and continuum removal (R2=0.715, RMSE=0.218kg/m2). The experimental results demonstrated that the use of PLSR is recommended for highly multi-collinear data sets. The combination of continuum removal and PLSR could improve the estimation accuracy of winter wheat biomass.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
Series
Advances in Intelligent Systems Research
Publication Date
March 2013
ISBN
978-90-78677-61-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/iccsee.2013.434How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yuanyuan Fu
AU  - Guijun Yang
AU  - Haikuan Feng
AU  - Xiaoyu Song
AU  - Xingang Xu
AU  - Jihua Wang
PY  - 2013/03
DA  - 2013/03
TI  - Comparative analysis of three regression methods for the winter wheat biomass estimation using hyperspectral measurements
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1733
EP  - 1736
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.434
DO  - https://doi.org/10.2991/iccsee.2013.434
ID  - Fu2013/03
ER  -