Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites

	Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites
	Zhang, TingLong 1; Sun, Rui 1; Peng, ChangHui 1; Zhou, GuoYi 1; Wang, ChunLing 1; Zhu, QiuAn 1; Yang, YanZheng 1
刊名	Science China Earth Sciences
	2016
卷号	59 期号:2 页码:337-348
通讯作者	Peng, ChangHui (cpeng86@gmail.com)
英文摘要	Model simulation and in situ observations are often used to research water and carbon cycles in terrestrial ecosystems, but each of these methods has its own advantages and limitations. Combining these two methods could improve the accuracy of quantifying the dynamics of the water and carbon fluxes of an ecosystem. Data assimilation is an effective means of integrating modeling with in situ observation. In this study, the ensemble Kalman filter (EnKF) and the unscented Kalman filter (UKF) algorithms were used to assimilate remotely sensed leaf area index (LAI) data with the Biome-BGC model to simulate water and carbon fluxes at the Harvard Forest Environmental Monitoring Site (EMS) and the Dinghushan site. After MODIS LAI data from 2000–2004 were assimilated into the improved Biome-BGC model using the EnKF algorithm at the Harvard Forest site, the R2between the simulated and observed results for NEE and evapotranspiration increased by 7.8% and 4.7%, respectively. In addition, the sum of the absolute error (SAE) and the root mean square error (RMSE) of NEE decreased by an average of 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration decreased by 24.5% and 25.5%, respectively. MODIS LAI data of 2003 were assimilated into the Biome-BGC model for the Dinghushan site, and the R2values between the simulated and observed results for NEE and evapotranspiration were increased by 6.7% and 17.3%, respectively. In addition, the SAE values of NEE and ET were decreased by 11.3% and 30.7%, respectively, and the RMSE values of NEE and ET decreased by 10.1% and 30.9%, respectively. These results demonstrate that the accuracy of carbon and water flux simulations can be effectively improved when remotely sensed LAI data are properly integrated with ecosystem models through a data assimilation approach. © 2015, Science China Press and Springer-Verlag Berlin Heidelberg.
收录类别	EI
语种	英语
WOS记录号	WOS:20153301165435
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39669]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. College of Resources and Environmental Science, Northwest A&F University, Yangling, China 2. Laboratory of Ecosystems Forecasting and Global Change, College of Forestry, Northwest A&F University, Yangling, China 3. State Key Laboratory of Remote Sensing Science, Co-sponsored by Beijing Normal University and IRSA, Beijing, China 4. School of Geography and Remote Sensing Sciences, Beijing Normal University, Beijing, China 5. Institute of Environment Sciences, Department of Biological Sciences, University of Quebec at Montreal, Montreal 6.QC, Canada 7. South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
推荐引用方式 GB/T 7714	Zhang, TingLong,Sun, Rui,Peng, ChangHui,et al. Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites[J]. Science China Earth Sciences,2016,59(2):337-348.
APA	Zhang, TingLong.,Sun, Rui.,Peng, ChangHui.,Zhou, GuoYi.,Wang, ChunLing.,...&Yang, YanZheng.(2016).Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites.Science China Earth Sciences,59(2),337-348.
MLA	Zhang, TingLong,et al."Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites".Science China Earth Sciences 59.2(2016):337-348.