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Remote Sens. 2016, 8(7), 567; doi:10.3390/rs8070567

Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation

1
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
2
Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
3
Key Laboratory of Spatial Data Mining & Information Sharing of Ministry Education, Fuzhou University, Fuzhou 350002, China
*
Author to whom correspondence should be addressed.
Academic Editors: Anu Swatantran, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 1 April 2016 / Revised: 30 May 2016 / Accepted: 29 June 2016 / Published: 5 July 2016
View Full-Text   |   Download PDF [3575 KB, uploaded 5 July 2016]   |  

Abstract

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 (R2 = 0.87, RMSE = 1.583 gC·m−2·d−1) than the original model did (R2 = 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 (R2 = 0.92, RMSE = 1.261 gC·m−2·d−1). View Full-Text
Keywords: carbon fluxes; model incorporation; data assimilation carbon fluxes; model incorporation; data assimilation
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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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MDPI and ACS Style

Yan, M.; Tian, X.; Li, Z.; Chen, E.; Wang, X.; Han, Z.; Sun, H. Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation. Remote Sens. 2016, 8, 567.

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