ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2013, Vol. 10, No. 3, pp. 272-285

Regional parametrisation of Dynamic Global Vegetation Model SEVER based on assimilation of remote sensing data derived land cover map for Russia

S.A. Khvostikov 1, S.V. Venevsky2, S.A. Bartalev 1
1 Space Research Institute of RAS, Moscow, Russia
2 Center for Earth System Science, Tsinghua University, Beijing, People’s Republic of China
This article describes methods and results of parametrisation of Dynamic Global Vegetation Model SEVER on the territory of Russia using two-step optimization procedure. This parametrisation was targeted to increase of similarity between modeling results and remote sensing based land cover map. A similarity criterion was based on spatial correlation for different plant functional types. To perform parametrisation modifications of SEVER program implementation were done, including addition of multithreaded execution and asynchronous data loading. Parametrisation of model was done using optimization method EGO applied to simplified model, and quasi-newton method BFGS applied to full model. As a result of optimization a set of model parameters was obtained, that increases similarity between modeling results and remote sensing land cover map.
Keywords: глобальная модель растительного покрова, параметризация, дистанционное зондирование, Dynamic Global Vegetation Model, parametrisation, remote sensing
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  1. Bartalev S.A., Egorov V.A., Ershov D.V., Isaev A.S., Lupyan E.A., Plotnikov D.E., Uvarov I.A., Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 4, pp. 285–302.
  2. Bartalev S.A., Lupyan E.A., Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2013, Vol. 10, No. 1, pp. 197–214.
  3. Lupyan E.A., Mazurov A.A., Nazirov R.R., Proshin A.A., Flitman E.V., Krasheninnikova Yu.P., Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 1, pp.26–43.
  4. Bartholome E., Belward A.P., GLC2000: a new approach to global land cover mapping from Earth observation data, International Journal of Remote Sensing, 2005, Vol. 26, pp. 1959–1977.
  5. Cox P.M., Betts R.A., Jones C.D., Spall S.A., Totterdell I.J., Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature, 2000, Vol. 408, No. 6809, pp. 184–187.
  6. Cox P.M., Description of the TRIFFID dynamic global vegetation model, Hadley Centre Technical Note, 2001, Vol. 24, pp. 11–16.
  7. Cramer W., Bondeau A., Woodward F.I., Prentice I.C., Betts R.A., Brovkin V., Cox P.M., Fisher V., Foley J.A., Friend A.D., Kucharik C., Lomas M.R., Ramankutty N., Sitch S., Smith B., White A., Young-Molling C., Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models, Global Change Biology, 2001, Vol. 7, No. 4, pp. 357–373.
  8. Defourny P., Bontemps S., Obsomer V., Van Bogaert E., Arino O., Accuracy assessment of global land cover maps: lessons learnt from the GlobCover and GlobCorine experiences, Proceedings of the living planet Symposium, SP-686, June 2010.,
  9. Friedl M.A., McIver D.K., Hodges J.C.F., Zhang X.Y., Muchoney D., Strahler A.H., Woodcock C.E., Gopal S., Schneider A., Cooper A., Baccini A., Gao F., Schaaf C., Global land cover mapping from MODIS: Algorithms and early results, Remote Sensing of Environment, 2002, Vol. 83, pp. 287–302.
  10. Jones D.R., Schonlau M., Welch W.J., Efficient global optimization of expensive black-box functions, Journal of Global Optimization, 1998, Vol. 13, No. 4, pp. 455–492.
  11. Krinner G., Viovy N., Noblet-Ducoudré N., Ogée J., Polcher J., Friedlingstein P., Ciais P., Sitch S., Prentice I.C., A dynamic global vegetation model for studies of the coupled atmospherebiosphere system, Global Biogeochemical Cycles, 2005, Vol. 19, GB1015.
  12. Pfeifer M., Disney M., Quaife T., Marchant R., Terrestrial ecosystems from space: a review of earth observation products for macroecology applications, Global Ecology and Biogeography, 2011, Vol. 21, Issue 6, pp. 603–624.
  13. Poulter B., Ciais P., Hodson E., Lischke H., Maignan F., Plummer S., Zimmermann N.E., Plant functional type mapping for earth system models, Geoscientific Model Development, 2011, Vol. 4, No. 4, pp. 993–1010.
  14. Rayner P. J., Scholze M., Knorr W., Kaminski T., Giering R., Widmann H., Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS), Global Biogeochemical Cycles, 2005, Vol. 19, GB2026.
  15. Roustant O., Ginsbourger D., Deville Y., DiceKriging, DiceOptim: two R packages for the analysis of computer experiments by Kriging-based metamodeling and optimization, Journal of Statistical Software, 2012, Vol. 51, No. 1, pp. 1–55.
  16. Simpson T.W., Mausery T.M., Korte J.J., Mistree F., Kriging models for global approximation in simulation-based multidisciplinary design optimization, AIAA Journal, 2001, Vol. 39, No. 12, pp. 2233–2241.
  17. Sitch P., Smith B., Prentice I.C., Arneth A., Bondeau A., Cramer W., Kaplan J., Levis S., Lucht W., Sykes M., Thonicke K., Venevsky S., Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Global Change Biology, 2003, Vol. 9, No. 2, pp. 161–185.
  18. Venevsky S., Maksyutov P., SEVER: A modification of the LPJ global dynamic vegetation model for daily time step and parallel computation, Environmental Modelling & Software, 2007, Vol. 22, No. 1, pp. 104–109.
  19. Vrugt J.A., Diks C., Gupta H.V., Bouten W., Jacobus M., Verstraten J.M., Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation, Water Resources Research, 2005, Vol. 41, W01017.
  20. Woodward F. I., Williams B. G., Climate and plant distribution at global and local scales, Theory and models in vegetation science, Springer Netherlands, 1987, pp. 189–197.
  21. R Development Core Team. R: A language and environment for statistical computing, available at: