Identification of important factors for water vapor flux and CO<inf>2</inf> exchange in a cropland

Qin, Z; Su, GL; Zhang, JE; Ouyang, Y; Yu, Q; Li, J

Identification of important factors for water vapor flux and CO<inf>2</inf> exchange in a cropland

Qin, Z Su, GL Zhang, JE Ouyang, Y Yu, Q

Li, J

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Publication Type:: Journal Article
Citation:: Ecological Modelling, 2010, 221 (4), pp. 575 - 581
Issue Date:: 2010-02-24

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Field	Value	Language
dc.contributor.author	Qin, Z	en_US
dc.contributor.author	Su, GL	en_US
dc.contributor.author	Zhang, JE	en_US
dc.contributor.author	Ouyang, Y	en_US
dc.contributor.author	Yu, Q https://orcid.org/0000-0001-6950-1821	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048	en_US
dc.date.issued	2010-02-24	en_US
dc.identifier.citation	Ecological Modelling, 2010, 221 (4), pp. 575 - 581	en_US
dc.identifier.issn	0304-3800	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13687
dc.description.abstract	Water vapor flux and carbon dioxide (CO2) exchange in croplands are crucial to water and carbon cycle research as well as to global warming evaluation. In this study, a standard three-layer feed-forward back propagation neural network technique associated with the Bayesian technique of automatic relevance determination (ARD) was employed to investigate water vapor and CO2 exchange between the canopy of summer maize and atmosphere in responses to variations of environmental and physiological factors. These factors, namely the photosynthetically active radiation (PAR), air temperature (T), vapor pressure deficient (VPD), leaf-area index (LAI), soil water content in root zone (W), and friction velocity (U), were used as inputs in neural network analysis. Results showed that PAR, VPD, T and LAI were the primary factors regulating both water vapor and CO2 fluxes with VPD and W more critical to water vapor flux and PAR and T more crucial to CO2 exchange. Furthermore, two time variables "day of the year (DOY)" and "time of the day (TOD)" could also improve the simulation results of neural network analysis. The important factors identified by the neural network technique used in this study were in the order of PAR > T > VPD > LAI > U > TOD for water vapor flux and in the order of VPD > W > LAI > T > PAR > DOY for CO2 exchange. This study suggests that neural network technique associated with ARD could be a useful tool for identifying important factors regulating water vapor and CO2 fluxes in terrestrial ecosystem. © 2009 Elsevier B.V.	en_US
dc.relation.ispartof	Ecological Modelling	en_US
dc.relation.isbasedon	10.1016/j.ecolmodel.2009.11.007	en_US
dc.subject.classification	Ecology	en_US
dc.title	Identification of important factors for water vapor flux and CO<inf>2</inf> exchange in a cropland	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	221	en_US
utslib.for	0907 Environmental Engineering	en_US
dc.location.activity	ISI:000274844500002	en_US
dc.location.activity	Hanoi, Vietnam
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	221	en_US

Abstract:

Water vapor flux and carbon dioxide (CO2) exchange in croplands are crucial to water and carbon cycle research as well as to global warming evaluation. In this study, a standard three-layer feed-forward back propagation neural network technique associated with the Bayesian technique of automatic relevance determination (ARD) was employed to investigate water vapor and CO2 exchange between the canopy of summer maize and atmosphere in responses to variations of environmental and physiological factors. These factors, namely the photosynthetically active radiation (PAR), air temperature (T), vapor pressure deficient (VPD), leaf-area index (LAI), soil water content in root zone (W), and friction velocity (U*), were used as inputs in neural network analysis. Results showed that PAR, VPD, T and LAI were the primary factors regulating both water vapor and CO2 fluxes with VPD and W more critical to water vapor flux and PAR and T more crucial to CO2 exchange. Furthermore, two time variables "day of the year (DOY)" and "time of the day (TOD)" could also improve the simulation results of neural network analysis. The important factors identified by the neural network technique used in this study were in the order of PAR > T > VPD > LAI > U* > TOD for water vapor flux and in the order of VPD > W > LAI > T > PAR > DOY for CO2 exchange. This study suggests that neural network technique associated with ARD could be a useful tool for identifying important factors regulating water vapor and CO2 fluxes in terrestrial ecosystem. © 2009 Elsevier B.V.

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http://hdl.handle.net/10453/13687