Climatic Change

, Volume 103, Issue 1–2, pp 117–136 | Cite as

Remotely sensed soil moisture integration in an ecosystem carbon flux model. The spatial implication

  • Willem W. Verstraeten
  • Frank Veroustraete
  • Wolfgang Wagner
  • Tom Van Roey
  • Walter Heyns
  • Sara Verbeiren
  • Jan Feyen


While remote sensing is able to provide spatially explicit datasets at regional to global scales, extensive application to date has been found only in the reporting and verification of ecosystem carbon fluxes under the Kyoto Protocol. One of the problems is that new remote sensing datasets can be used only with models or data assimilation schemes adapted to include a data input interface dedicated to the type and format of these remote sensing datasets. In this study, soil water index data (SWI), derived from the ERS scatterometer (10-daily time period with a spatial resolution of 50 km), are integrated into the ecosystem carbon balance model C-Fix to assess 10-daily Net Ecosystem Productivity (NEP) patterns of Europe from the remote sensing perspective on an approximate 1-by-1 km2 pixel scale using NDVI-AVHRR data. The modeling performance of NEP obtained with and without the assimilation of remotely sensed soil moisture data in the carbon flux model C-Fix is evaluated with EUROFLUX data. Results show a general decrease of the RRMSE of up to 11 with an average of 3.46. C-Fix is applied at the European scale to demonstrate the potential of this ecosystem carbon flux model, based on remote sensing inputs. More specifically, the strong impact of soil moisture on the European carbon balance in the context of the Kyoto Protocol (anthropogenic carbon emissions) is indicated at the country level. Results suggest that several European countries shift from being a carbon sink (i.e., NEP > 1) to being a carbon source (i.e., NEP < 0) whether or not short-term water availability (i.e., soil moisture) is considered in C-Fix NEP estimations.


Normalize Difference Vegetation Index Soil Respiration Gross Primary Productivity Ecosystem Respiration Relative Root Mean Square Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Willem W. Verstraeten
    • 1
  • Frank Veroustraete
    • 2
  • Wolfgang Wagner
    • 3
  • Tom Van Roey
    • 2
  • Walter Heyns
    • 2
  • Sara Verbeiren
    • 2
  • Jan Feyen
    • 4
  1. 1.Geomatics EngineeringKatholieke Universiteit Leuven (K.U. Leuven)HeverleeFlanders
  2. 2.Flemish Institute for Technological Research (VITO)MolFlanders
  3. 3.Institute of Photogrammetry and Remote SensingVienna University of Technology (T.U. Wien)ViennaAustria
  4. 4.Laboratory for Soil and Water ManagementKatholieke Universiteit Leuven (K.U. Leuven)HeverleeFlanders

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