The key objective of the HGF Alliance Remote Sensing and Earth System Dynamics is to explore how data and products provided by the next generation of remote sensing systems can be used for achieving a new level in understanding and modelling global environmental processes and climate change. It is further closely related to the development of DLR's next generation radar satellite mission Tandem-L, as the 8 HGF centres are building the core of the satellite science team.
The Alliance is divided into four research topics, of which each one is dedicated to a specific Earth Sphere:
- Biosphere: Global forest structure and biomass dynamics are evaluated for forest and biodiversity monitoring and the quantification of the global carbon cycle
- Geosphere: The ability to measure topographic variations with millimetre accuracy is explored for improving the understanding of earthquake and volcano activities
- Hydrosphere: the quantification of soil moisture and its variations at high spatial resolution is assessed with respect to hydrological models and the global water cycle
- Cryosphere: The estimation of melting processes in snow, ice and permafrost regions is adressed in terms of global climate change
Within the Hydrosphere, the project specifically aims at the three overarching research questions:
- Retrival and sensor data fusion: How can we optimally retrieve and synergistically merge different remote sensing technologiesin order to improve hydrological variables and parameters?
- Validation: How precise are hydrological variables observed using remote sensing technologies at different spatial and temporal scales?
- Data assimilation: How can remotely sensed hydrological products be further used in order to better understand and predict the hydrological cycle?
The specific role of our team at KIT/IMK-IFU in Garmisch-Partenkirchen within the Hydrosphere Cluster is the combination of modelled and satellite derived soil moisture by using Copula-based data assimilation concepts. The spatio-temporal dependence structure between different observations and models is derived and analyzed. This dependence structure can be used to derive the complete multivariate distribution function between e.g. modeled and observed soil moisture at different locations, but also between different variables, e.g. precipitation, temperature, and soil moisture. By using this distribution function, it will be possible to perform a Copula-based data assimilation between different data sources, but also between different indicator variables.