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| Modeling water and carbon fluxes above summer maize field in North China Plain with Back-propagation neural networks* |
| QIN Zhong, SU Gao-li, YU Qiang, HU Bing-min, LI Jun |
| Ecology academy, School of Life Science, Zhejiang University, Hangzhou 310029, China; Centre of Climatology, Zhejiang Meteorological Bureau, Hangzhou 310029, China; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; School of Science, Zhejiang University, Hangzhou 310029, China |
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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.
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Received: 03 August 2004
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