文献类型：期刊文献

英文题名：Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation

作者：Yan, Min[1] Tian, Xin[1] Li, Zengyuan[1] Chen, Erxue[1] Wang, Xufeng[2] Han, Zongtao[1,3] Sun, Hong[1]

第一作者：Yan, Min

通信作者：Tian, X[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Peoples R China;[3]Fuzhou Univ, Minist Educ, Key Lab Spatial Data Min & Informat Sharing, Fuzhou 350002, Peoples R China

年份：2016

卷号：8

期号：7

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20172203707218);Scopus(收录号：2-s2.0-85019380217);WOS:【SCI-EXPANDED(收录号:WOS:000382224800037)】；

基金：This work was supported by the National Basic Research Program of China (973 Program) under grant 2013CB733404. The eddy covariance measurements in this study were obtained from ChinaFlux. We would also like to thank Beijing Normal University for providing the GLASS products. The soil data set has been provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn).

语种：英文

外文关键词：carbon fluxes; model incorporation; data assimilation

摘要：This study improved simulation of forest carbon fluxes in the Changbai Mountains with a process-based model (Biome-BGC) using incorporation and data assimilation. Firstly, the original remote sensing-based MODIS MOD_17 GPP (MOD_17) model was optimized using refined input data and biome-specific parameters. The key ecophysiological parameters of the Biome-BGC model were determined through the Extended Fourier Amplitude Sensitivity Test (EFAST) sensitivity analysis. Then the optimized MOD_17 model was used to calibrate the Biome-BGC model by adjusting the sensitive ecophysiological parameters. Once the best match was found for the 10 selected forest plots for the 8-day GPP estimates from the optimized MOD_17 and from the Biome-BGC, the values of sensitive ecophysiological parameters were determined. The calibrated Biome-BGC model agreed better with the eddy covariance (EC) measurements (R-2 = 0.87, RMSE = 1.583 gC.m(-2).d(-1)) than the original model did (R-2 = 0.72, RMSE = 2.419 gC.m(-2).d(-1)). To provide a best estimate of the true state of the model, the Ensemble Kalman Filter (EnKF) was used to assimilate five years (of eight-day periods between 2003 and 2007) of Global LAnd Surface Satellite (GLASS) LAI products into the calibrated Biome-BGC model. The results indicated that LAI simulated through the assimilated Biome-BGC agreed well with GLASS LAI. GPP performances obtained from the assimilated Biome-BGC were further improved and verified by EC measurements at the Changbai Mountains forest flux site (R-2 = 0.92, RMSE = 1.261 gC.m(-2).d(-1)).