Modeling water and carbon fluxes above summer maize field in North China Plain with back-propagation neural networks.
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.
Qin
Zhong
Z
Ecology academy, School of Life Science, Zhejiang University, Hangzhou 310029, China; q_breeze@126.com.
Su
Gao-Li
GL
Yu
Qiang
Q
Hu
Bing-Min
BM
Li
Jun
J
D000383:Agriculture; D002244:Carbon / Q000378:metabolism*; D002245:Carbon Dioxide / Q000378:metabolism; D002681:China; D008954:Models, Biological; D016571:Neural Networks, Computer; D012621:Seasons; D014835:Volatilization; D014867:Water / Q000378:metabolism*; D003313:Zea mays / Q000378:metabolism*
D014867:Water; D002245:Carbon Dioxide; D002244:Carbon