Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation

Article English OPEN
Min Yan ; Xin Tian ; Zengyuan Li ; Erxue Chen ; Xufeng Wang ; Zongtao Han ; Hong Sun (2016)
  • Publisher: MDPI AG
  • Journal: Remote Sensing (issn: 2072-4292)
  • Related identifiers: doi: 10.3390/rs8070567
  • Subject: carbon fluxes | model incorporation | data assimilation | Science | Q

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).
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