The server is under maintenance between 08:00 to 12:00 (GMT+08:00), and please visit later.
We apologize for any inconvenience caused
Login  | Sign Up  |  Oriprobe Inc. Feed
China/Asia On Demand
Journal Articles
Laws/Policies/Regulations
Companies/Products
Bookmark and Share
Modeling water and carbon fluxes above summer maize field in North China Plain with back-propagation neural networks
Author(s): 
Pages: 418-426
Year: Issue:  5
Journal: JOURNAL OF ZHEJIANG UNIVERSITY SCIENCE

Keyword:  Carbon dioxideWater vapor and heat fluxesThree-layer back-propagation neural networks;
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.
Related Articles
No related articles found