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CLC number: S1; TP1.18

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Received: 2004-08-03

Revision Accepted: 2004-12-05

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Journal of Zhejiang University SCIENCE B 2005 Vol.6 No.5 P.418~426

http://doi.org/10.1631/jzus.2005.B0418


Modeling water and carbon fluxes above summer maize field in North China Plain with Back-propagation neural networks


Author(s):  QIN Zhong, SU Gao-li, YU Qiang, HU Bing-min, LI Jun

Affiliation(s):  Ecology academy, School of Life Science, Zhejiang University, Hangzhou 310029, China; more

Corresponding email(s):   q_breeze@126.com, bmhu@mail.hz.zj.com

Key Words:  Carbon dioxide, Water vapor and heat fluxes, Three-layer back-propagation neural networks


QIN Zhong, SU Gao-li, YU Qiang, HU Bing-min, LI Jun. Modeling water and carbon fluxes above summer maize field in North China Plain with Back-propagation neural networks[J]. Journal of Zhejiang University Science B, 2005, 6(5): 418~426.

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author="QIN Zhong, SU Gao-li, YU Qiang, HU Bing-min, LI Jun",
journal="Journal of Zhejiang University Science B",
volume="6",
number="5",
pages="418~426",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.B0418"
}

%0 Journal Article
%T Modeling water and carbon fluxes above summer maize field in North China Plain with Back-propagation neural networks
%A QIN Zhong
%A SU Gao-li
%A YU Qiang
%A HU Bing-min
%A LI Jun
%J Journal of Zhejiang University SCIENCE B
%V 6
%N 5
%P 418~426
%@ 1673-1581
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.B0418

TY - JOUR
T1 - Modeling water and carbon fluxes above summer maize field in North China Plain with Back-propagation neural networks
A1 - QIN Zhong
A1 - SU Gao-li
A1 - YU Qiang
A1 - HU Bing-min
A1 - LI Jun
J0 - Journal of Zhejiang University Science B
VL - 6
IS - 5
SP - 418
EP - 426
%@ 1673-1581
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.B0418


Abstract: 
In this work, datasets of water and carbon fluxes measured with eddy covariance technique above a summer maize field in the North China Plain were simulated with artificial neural networks (ANNs) to explore the fluxes responses to local environmental variables. The results showed that photosynthetically active radiation (PAR), vapor pressure deficit (VPD), air temperature (T) and leaf area index (LAI) were primary factors regulating both water vapor and carbon dioxide fluxes. three-layer back-propagation neural networks (BP) could be applied to model fluxes exchange between cropland surface and atmosphere without using detailed physiological information or specific parameters of the plant.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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