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Remote Sens. 2017, 9(3), 188; doi:10.3390/rs9030188

Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation

Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
Key Laboratory of Earth Observation, Sanya 572029, China
International Center for Climate and Environment Sciences (ICCES), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou 519082, China
State Key Laboratory of Earth Surface Process and Resource Ecology, Zhuhai Joint Innovative Center for Climate-Environment-Ecosystem Zhuhai Key Laboratory of Dynamics Urban Climate and Ecology, Beijing Normal University, Beijing 100875, China
State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China
Research Faculty of Agriculture, Hokkaido University, Sapporo 0608589, Japan
Author to whom correspondence should be addressed.
Academic Editors: Jose Moreno and Prasad S. Thenkabail
Received: 19 October 2016 / Revised: 12 February 2017 / Accepted: 20 February 2017 / Published: 23 February 2017
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Quantitative estimation of the magnitude and variability of gross primary productivity (GPP) is required to study the carbon cycle of the terrestrial ecosystem. Using ecosystem models and remotely-sensed data is a practical method for accurately estimating GPP. This study presents a method for assimilating high-quality leaf area index (LAI) products retrieved from satellite data into a process-oriented Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM) to acquire accurate GPP. The assimilation methods, including the Ensemble Kalman Filter (EnKF) and a proper orthogonal decomposition (POD)-based ensemble four-dimensional (4D) variational assimilation method (PODEn4DVar), incorporate information provided by observations into the model to achieve a better agreement between the model-estimated and observed GPP. The LPJ-POD scheme performs better with a correlation coefficient of r = 0.923 and RMSD of 32.676 gC/m2/month compared with the LPJ-EnKF scheme (r = 0.887, RMSD = 38.531 gC/m2/month) and with no data assimilation (r = 0.840, RMSD = 45.410 gC/m2/month). Applying the PODEn4DVar method into LPJ-DGVM for simulating GPP in China shows that the annual amount of GPP in China varied between 5.92 PgC and 6.67 PgC during 2003–2012 with an annual mean of 6.35 PgC/yr. This study demonstrates that integrating remotely-sensed data with dynamic global vegetation models through data assimilation methods has potential in optimizing the simulation and that the LPJ-POD scheme shows better performance in improving GPP estimates, which can provide a favorable way for accurately estimating dynamics of ecosystems. View Full-Text
Keywords: gross primary production; leaf area index; Lund-Potsdam-Jena dynamic global vegetation model; EnKF; PODEn4DVar; China gross primary production; leaf area index; Lund-Potsdam-Jena dynamic global vegetation model; EnKF; PODEn4DVar; China

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ma, R.; Zhang, L.; Tian, X.; Zhang, J.; Yuan, W.; Zheng, Y.; Zhao, X.; Kato, T. Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation. Remote Sens. 2017, 9, 188.

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