Assimilating earth observation data into land surface models

T. Quaife*, P. Lewis, M. De Kauwe

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Data assimilation techniques such as the ensemble Kalman filter and the sequential Metropolis-Hastings algorithm provide ameans of integrating satellite data with ecosystemmodels to optimally adjust their temporal trajectory. To some extent thesemethods can compensate for poor model parameterisations but a preferable scenario is to calibrate themodelwell in the first instance. This paper explores how a site specific model calibration can be adapted to a different site using only MODIS reflectance data. Results show that, using reflectance data only, estimates of the net carbon budget of a field site can be extended to a nearby site, but that this best facilitated by re-calibration rather than sequential data assimilation.

Original languageEnglish
Title of host publication2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
PagesV445-V448
Edition1
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings - Boston, MA, United States
Duration: 6 Jul 200811 Jul 2008

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Number1
Volume5

Conference

Conference2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
CountryUnited States
CityBoston, MA
Period6/07/0811/07/08

Keywords

  • Bayesian
  • Data assimilation
  • GORT.
  • NEP

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