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INTEGRATING EDDY COVARIANCE INFORMATION WITH BEPS MODEL USING A VARIATIONAL ASSIMILATION SCHEME FOR IMPROVING TEMPORALLY CONTINUOUS GPP ESTIMATION
语种英语
Xinyao Xie1,2; Ainong Li1; Gaofei Yin1; Jinhu Bian1
2018
会议录名IEEE International Symposium on Geoscience and Remote Sensing IGARSS
会议录编者/会议主办者IEEE
页码9048-9051
会议名称38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
会议日期2018
会议地点Valencia, SPAIN
会议举办国SPAIN
产权排序1
摘要The gross primary productivity (GPP) is an essential parameter of terrestrial carbon cycle, and simulation of GPP through terrestrial ecosystem process model usually needs a specification of model parameter. However, estimating model parameters in situ field or laboratory is a laborious and tedious work, causing a general lack of data. In this study, a reliable variational data assimilation scheme integrating Boreal Ecosystem Productivity Simulator (BEPS) with eddy covariance fluxes, was proposed to account for the seasonal variations of model parameters and improve temporally continuous GPP estimation Results suggested that the proposed variational assimilation scheme in our study could effectively track the seasonal variations of model parameters. With optimal temporally continuous values of parameters, BEPS model had a better performance and potential ability for the GPP estimation.
关键词GPP BEPS model Data assimilation Eddy covariance fluxes remote sensing
ISBN号978-1-5386-7150-4
ISSN号2153-6996
收录类别CPCI
WOS记录号WOS:000451039808155
引用统计
文献类型会议论文
条目标识符http://ir.imde.ac.cn/handle/131551/24502
专题中国科学院水利部成都山地灾害与环境研究所
通讯作者Ainong Li
作者单位1.Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, China, 610041;
2.University of Chinese Academy of Sciences, Beijing, China, 100049
第一作者单位中国科学院水利部成都山地灾害与环境研究所
通讯作者单位中国科学院水利部成都山地灾害与环境研究所
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Xinyao Xie,Ainong Li,Gaofei Yin,et al. INTEGRATING EDDY COVARIANCE INFORMATION WITH BEPS MODEL USING A VARIATIONAL ASSIMILATION SCHEME FOR IMPROVING TEMPORALLY CONTINUOUS GPP ESTIMATION[C]//IEEE,2018:9048-9051.
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