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

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 R2 between 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 R2 values 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.

[1]  R. Colwell.  Remote sensing of natural resources. , 1968 .

[2]  R. Myneni,et al.  Radiative transfer in three dimensional leaf canopies , 1990 .

[3]  S. Running,et al.  FOREST-BGC, A general model of forest ecosystem processes for regional applications. II. Dynamic carbon allocation and nitrogen budgets. , 1991, Tree physiology.

[4]  S. Running,et al.  Generalization of a forest ecosystem process model for other biomes, Biome-BGC, and an application for global-scale models. Scaling processes between leaf and landscape levels , 1993 .

[5]  S. Running,et al.  8 – Generalization of a Forest Ecosystem Process Model for Other Biomes, BIOME-BGC, and an Application for Global-Scale Models , 1993 .

[6]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[7]  S. Julier,et al.  A General Method for Approximating Nonlinear Transformations of Probability Distributions , 1996 .

[8]  S. T. Gower,et al.  Leaf area index of boreal forests: theory, techniques, and measurements , 1997 .

[9]  D. Pury,et al.  Simple scaling of photosynthesis from leaves to canopies without the errors of big‐leaf models , 1997 .

[10]  S. Running,et al.  A continental phenology model for monitoring vegetation responses to interannual climatic variability , 1997 .

[11]  S. Running,et al.  Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active , 1998 .

[12]  G. Evensen,et al.  Analysis Scheme in the Ensemble Kalman Filter , 1998 .

[13]  D. Diner,et al.  Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere‐corrected MISR data , 1998 .

[14]  Peter E. Thornton,et al.  Regional ecosystem simulation: Combining surface- and satellite-based observations to study linkages between terrestrial energy and mass budgets , 1998 .

[15]  Ü. Rannik,et al.  Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology , 2000 .

[16]  S. Haykin.  Kalman Filtering and Neural Networks , 2001 .

[17]  C. Woodcock,et al.  Multiscale analysis and validation of the MODIS LAI product: II. Sampling strategy , 2002 .

[18]  F. Chapin,et al.  Principles of Terrestrial Ecosystem Ecology , 2002, Springer New York.

[19]  Rudolph van der Merwe,et al.  The Unscented Kalman Filter , 2002 .

[20]  C. Woodcock,et al.  Multiscale analysis and validation of the MODIS LAI product: I. Uncertainty assessment , 2002 .

[21]  S. Liang,et al.  Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model , 2003 .

[22]  J. D. Tarpley,et al.  The multi‐institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system , 2004 .

[23]  J. Berry,et al.  A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.

[24]  B. Law,et al.  An improved analysis of forest carbon dynamics using data assimilation , 2005 .

[25]  R. Giering,et al.  Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS) , 2005 .

[26]  J. Gove,et al.  Application of a dual unscented Kalman filter for simultaneous state and parameter estimation in problems of surface‐atmosphere exchange , 2006 .

[27]  Emil Cienciala,et al.  Application of BIOME-BGC model to managed forests: 2. Comparison with long-term observations of stand production for major tree species , 2006 .

[28]  Xiaomin Sun,et al.  CO2 flux evaluation over the evergreen coniferous and broad-leaved mixed forest in Dinghushan, China , 2006 .

[29]  Emil Cienciala,et al.  Application of BIOME-BGC model to managed forests: 1. Sensitivity analysis , 2006 .

[30]  J. Pisek,et al.  Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America , 2007 .

[31]  M. Frezzotti,et al.  Spatial and temporal variability of surface mass balance near Talos Dome, East Antarctica , 2007 .

[32]  S. Wofsy,et al.  Factors controlling CO2 exchange on timescales from hourly to decadal at Harvard Forest , 2007 .

[33]  Michael E. Schaepman,et al.  A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[34]  Philip Lewis,et al.  Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter , 2008 .

[35]  Jeffrey P. Walker,et al.  Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: A comparative synthetic study , 2008 .

[36]  Lionel Jarlan,et al.  Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation dynamics model , 2008 .

[37]  D. Hollinger,et al.  An improved state-parameter analysis of ecosystem models using data assimilation , 2008 .

[38]  T. A. Black,et al.  Optimization of ecosystem model parameters through assimilating eddy covariance flux data with an ensemble Kalman filter , 2008 .

[39]  Li Zhang,et al.  Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models. , 2009, Ecological applications : a publication of the Ecological Society of America.

[40]  Michele Meroni,et al.  Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model , 2009, Sensors.

[41]  Peter J. Rayner,et al.  The current state of carbon-cycle data assimilation , 2010 .

[42]  S. Liang,et al.  Real-time retrieval of Leaf Area Index from MODIS time series data , 2011 .

[43]  Hong Jiang,et al.  Integrating models with data in ecology and palaeoecology: advances towards a model-data fusion approach. , 2011, Ecology letters.

[44]  Feng Li-chao.  Using simulated annealing algorithm to optimize the parameters of Biome-BGC model , 2011 .

[45]  Zhang Rong-hua.  Simulation of water and carbon fluxes in Harvard forest area by using improved Biome-BGC model , 2011 .

[46]  Shenfeng Fei,et al.  Ecological forecasting and data assimilation in a data-rich era. , 2011, Ecological applications : a publication of the Ecological Society of America.

[47]  Motoko Inatomi,et al.  Water-Use Efficiency of the Terrestrial Biosphere: A Model Analysis Focusing on Interactions between the Global Carbon and Water Cycles , 2012 .

[48]  S. Liang,et al.  Validation of MODIS and CYCLOPES LAI products using global field measurement data , 2012 .

[49]  Qihao Weng,et al.  Automated individual tree-crown delineation and treetop detection with very-high-resolution aerial imagery , 2013 .