Speaker
Description
In recent years monitoring of earth system dynamics by means of remote sensing satellites has begun to play an important role in the provisioning of spatial distributed input or validation data for earth system and or land surface models. With the different global initiatives such as the essential climate variables (ECV) defined by the World Meteorological Organization (WMO) or the European Space Agency Climate Change Initiative (ESA-CCI), global monitoring programs by means of satellite data are envisaged. Currently, several ECVs are provided by means of unique retrieval schemes which are based on either using optical or microwave data streams such as for the retrieval of LAI, FAPAR or soil moisture. Consequently they tend to show discontinuities or data gaps in the optical domain due to clouds or do not use the combined information content of all available frequency domains (e.g. VIS, NIR, SWIR, TIR, microwave) in a joint retrieval.
In this presentation, we present first results of a joint retrieval of land surface parameters over agricultural landscapes such as LAI, FAPAR, vegetation height and soil moisture by means of a weak constraint variational data assimilation approach. Here we invert two physically consistent radiative transfer models in the microwave and optical domain using Sentinel-1 and Sentinel-2 data respectively and employing prior information about the land surface provided by the Joint UK Land Environment Simulator (JULES). In addition we include a prior term which takes expectations of the temporal evolution of the land surface state into account. Outputs are generated in terms of target variables such as LAI and soil moisture as well as their associated uncertainties.
Results are validated against field measurements over the Munich-North-Isar site, Germany. It will be shown that the retrieval of bio- and geophysical parameters from remote sensing data greatly benefit from a joint retrieval from optical and microwave data especially in terms of uncertainty reduction and by providing a consistent retrieval of the spatiotemporal dynamics of land surface variables such as e.g. LAI and soil moisture.
