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

Science China Earth Sciences

Volumn 59, Issue 2, 2016, Pages 337-348

  Zhang, TingLong ^a,b,c   Sun, Rui ^b,c   Peng, ChangHui ^a,d   Zhou, GuoYi ^e   Wang, ChunLing ^e   Zhu, QiuAn ^a   Yang, YanZheng ^a  

^a NORTHWEST A F UNIVERSITY   (China)

^b STATE KEY LABORATORY OF REMOTE SENSING SCIENCE   (China)

^c BEIJING NORMAL UNIVERSITY   (China)

^d UNIVERSITÉ DU QUÉBEC À MONTRÉAL   (Canada)

^e SOUTH CHINA INSTITUTE OF BOTANY   (China)

Author keywords

Biome BGC model; ensemble Kalman filter; leaf area index; net ecosystem exchange; unscented Kalman filter

Indexed keywords

ECOLOGY; ECOSYSTEMS; ENVIRONMENTAL ENGINEERING; EVAPOTRANSPIRATION; FORESTRY; KALMAN FILTERS; MEAN SQUARE ERROR; NONLINEAR FILTERING; RADIOMETERS; REMOTE SENSING;

BIOME-BGC; ENSEMBLE KALMAN FILTER; LEAF AREA INDEX; NET ECOSYSTEM EXCHANGE; UNSCENTED KALMAN FILTER;

DATA INTEGRATION;

EID: 84955749202     PISSN: 16747313     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11430-015-5160-0     Document Type: Article

References (45)

1. Aubinet M, Grelle A, Ibrom A, Rannik Ü, Moncrieff J, Foken T, Vesala T. 1999. Estimates of the annual net carbon and water exchange of forests: The EUROFLUX methodology. Adv Ecol Res, 30: 113–175
2. Bouttier F, Courtier P. 2002. Data Assimilation Concepts and Methods March 1999. Meteorological Training Course Lecture Series. ECMWF
3. Burgers G, van Leeuwen P J, Evensen G. 1998. Analysis scheme in the ensemble Kalman Filter. Mon Weather Rev, 126: 1719–1724
4. Chapin III F S, Matson P A, Vitousek P M. 2012. Principles of Terrestrial Ecosystem Ecology. New York: Springer
5. Chen M, Liu S, Tieszen L L, Hollinger D Y. 2008. An improved state-parameter analysis of ecosystem models using data assimilation. Ecol Model, 219: 317–326
6. Chen J M, Rich P M, Gower T, Norman J M, Plummer S. 1997. Leaf area index of boreal forests: Theory, techniques, and measurements. J Geophys Res, 102: 29429–29443
7. Pury D, Farquhar G D. 1997. Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models. Plant Cell Environ, 20: 537–557
8. Dorigo W A, Zurita-Milla R, de Wit A J W, Brazile J, Singh R, Schaepman M E. 2006. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int J Appl Earth Obs, 9: 165–193
9. Evensen G. 1994. Sequential data assimilation with a non-linear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res, 99: 10143–10162
10. 2 assimilation in leaves of species. Planta, 149: 78–90
11. Fang H L, Liang S L, Kuuusk A. 2003. Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sens Environ, 85: 257–270
12. Fang H, Wei S, Liang S. 2012. Validation of MODIS and CYCLOPES LAI products using global field measurement data. Remote Sens Environ, 119: 43–54
13. Gove J H, Hollinger D Y. 2006. Application of a dual unscented Kalman filter for simultaneous state and parameter estimation in problems of surface-atmosphere exchange. J Geophys Res, 111: 7
14. Ito A, Motoko I. 2012. Water-use efficiency of the terrestrial biosphere: A model analysis focusing on interactions between the global carbon and water cycles. J Hydrometeor, 13: 681–694
15. Jarlan L, Mangiarotti S, Mougin E, Mazzega P, Hiernaux P, Dantec V L. 2008. Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation dynamics model. Remote Sens Environ, 112: 1381–1394
16. Julier S J, Uhlmann J K. 1994. A general method for approximating nonlinear transformations of probability distributions. Technical Report. Robotics Research Group, Department of Engineering Science, University of Oxford
17. Kenneth L K. 1989. Geographic information systems in regional archaeological research and data management. Archaecol Method Theor, 1: 139–203
18. Knyazikhin Y, Martonchik J V, Diner D J, Myneni R B, Verstraete M, Pinty B, Gobron N. 1998a. Estimation of vegetation leaf area index and fraction of absorbed photosynthetically-active radiation from atmosphere- corrected MISR data. J Geophys Res, 103: 32239–32256
19. Knyazikhin Y, Martonchik J V, Myneni R B, Diner D J, Running S W. 1998b. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J Geophys Res, 103: 32257–32274
20. Luo Y Q, Weng E S, Wu X W, Gao C, Zhou X H, Zhang L. 2009. Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models. Ecol Appl, 19: 571–574
21. Luo Y Q, Ogle K, Tucker C, Fei S F, Gao C, LaDeau S, Clark J S, Schimel D S. 2011. Ecological forecasting and data assimilation in a data-rich era. Ecol Appl, 21: 1429–1442
22. Migliavacca M, Meroni M, Busetto L, Colombo R, Zenone T, Matteucci G, Iovanni Manca G, Seufert G. 2009. Modeling gross primary production of agro-forestry ecosystems by assimilation of satellite-derived information in a process-based model. Sensors, 9: 922–942
23. Mitchell K E, Lohmann D, Houser P R, Wood E F, SchaakeJ C, Robock A, Cosgrove B A, Sheffield J, Duan Q Y, Lu L F, Higgins R W, Pinker R T, Tarpley J D, Lettenmaier D P, Marshall C H, Entin J K, Pan M, Shi W, Koren V, Meng J, Ramsay B H, Bailey A A. 2004. The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J Geophys Res, 109: 90
24. Mo X G, Chen J M, Ju W M, Black T A. 2008. Optimization of ecosystem model parameters through assimilating eddy covariance flux data with an ensemble Kalman filter. Ecol Model, 217: 157–173
25. Myneni R B, Asrar G, Gerstl S. 1990. Radiative transfer in threedimensional leaf canopies. Transport Theor Stat, 19: 205–250
26. Peng C H, Joel J, Wu H B, Jiang H, Luo Y Q. 2011. Integrating models with data in ecology and paleoecology: Advances toward a model-data fusion approach. Ecol Lett, 14: 522–536
27. Pipunic R C, Walker J P, Western A. 2008. Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: A comparative synthetic study. Remote Sens Environ, 112: 1295–1305
28. Pisek J, Chen J M. 2007. Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America. Remote Sens Environ, 109: 81–94
29. Quaife T, Lewis P, De kauwe M, Williams M, Law B E, Disney M, Bowyer P. 2008. Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter. Remote Sens Environ, 112: 1347–1364
30. Rayner P. 2010. The current state of carbon cycle data assimilation. Curr Opin Env Sust, 2: 289–296
31. Rayner P J, Scholze M, Knorr W, Kaminski T, Giering R, Widmann H. 2005. Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS). Glob Biogeochem Cycl, 19: GB2026
32. Running S W, Gower S T. 1991. FOREST-BGC, a general model of forest ecosystem processes for regional applications. II. Dynamic carbon allocation and nitrogen budgets. Tree Physiol, 9: 147–160
33. Running S W, Hunt E R. 1993. 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 level. In: Ehleringe J R, Field C B, eds. Scaling Physiological Process: Leaf of Globe. San Diego: Academic Press. 141–158
34. Tatarinov F A, Cienciala E. 2006. Application of BIOME-BGC model to managed forests. 1. Sensitivity analysis. Forest Ecol Manag, 237: 267–279
35. Thornton P E. 1998. Regional Ecosystem Simulation: Combining Surfaceand Satellite-based Observations to Study Linkages between Terrestrial Energy and Mass Budgets. Doctoral Dissertation. Missoula The University of Montana. 56–80
36. Tian Y, Woodcock C E, Wang Y, Privette J L, Shabanov N V, Zhou L, Myneni R B. 2002. Multiscale analysis and validation of the MODIS LAI product: I. Uncertainty assessment. Remote Sens Environ, 83: 414–430
37. Urbanski S, Barford C, Wofsy S, Kucharik C, Pyle E, Budney J, Mckain K, Fitzjarrald D, Czikowsky M, Munger J W. 2007. Factors controlling CO2 exchange on timescales from hourly to decadal at Harvard Forest. J Geophys Res, 112: 1–25
38. 2 flux evaluation over the evergreen coniferous and broad-leaved mixed forest in Dinghushan. Sci China Ser D-Earth Sci, 49(Suppl II): 127–138
39. Wang G X, Weng Q H. 2013. Remote Sensing of Natural Resources. CRC Press. 235
40. White M A, Thornton P E, Running S W. 1997. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob Biogeochem Cycle, 11: 217–234
41. Williams M, Schwarz P A, Law B E, Irvine J, Kurpiu M R. 2005. An improved analysis of forest carbon dynamics using data assimilation. Glob Change Biol, 11: 89–105
42. Xiao Z Q, Liang S L, Wang J D, Jiang B, Li X J. 2011. Real-time retrieval of leaf area index from MODIS time series data. Remote Sens Environ, 115: 97–106
43. Wan E A, van der Merwe R. 2001. The unscented Kalman filter. In: Haykin S, ed. Kalman Filtering and Neural Networks. New York: Wiley
44. Zhang T L, Sun R, Hu B, Feng L C. 2011a. Using simulated annealing algorithm to optimize the parameters of Biome-BGC model (in Chinese). Chin J Ecology, 30: 408–414
45. Zhang T L, Sun R, Hu B, Feng L C, Zhang R H. 2011b. Simulation of water and carbon fluxes in Harvard forest area by using improved Biome- BGC model (in Chinese). Chin J Ecol, 30: 2099–2106