As atmospheric CO2  is the main cause of the enhanced greenhouse effect, it is crucial to quantify the amount of carbon emissions taken up by the land to improve climate predictions. However, uncertainties remain high, and many questions remain about how different processes act out across different spatial and temporal scales, and how they act together to result in the actual global land carbon sink. Models bring in process understanding but exhibit uncertainties. Hence, purely model-based quantification of carbon fluxes and dynamics of stocks is difficult. Therefore, there is a need to constrain land surface models with observations and to improve understanding of the processes described by these models. In this project, these challenges are tackled using different data sources as field observations and satellite data.

The goals of the project are to:

  • Demonstrate the synergistic exploitation of satellite observations from active and passive microwave sensors together with optical data for better characterisation of carbon and water cycling on land.
  • Generate or adapt a numerical land surface model for its application in a data assimilation framework both for single columns (local) and spatially distributed (regional scale).
  • Acquire and / or analyse a campaign data set to support the development of the model and the data assimilation scheme on the local scale.
[Translate to English:] Maps of Vegetation Optical Depth over Spain

SMOS Level 2 VOD average from July-September 2015 (“tau”, left) and average uncertainty (right) for one of the study areas (Las Majadas, Spain)