Surface water and energy fluxes are essential components of the Earth System. Satellite based techniques to monitor surface energy and water fluxes are beeing developed by combining multiple satellite data.
The research group has generated a harmonized, longterm satellite based climatology of ocean surface fluxes. The HOAPS (Hamburg Ocean Atmosphere Parameters from Satellite) climatology is widely used as observational reference data set for ocean surface fluxes.
Currently an extension of the HOAPS climatology over land is under development. The information from multiple satellite sensors are combined to obtain estimates of land surface water and energy fluxes (e.g surface radiation budget, soil moisture dynamics, precipitation over land).
Remote sensing of soil moisture
The water content of the soil is an important variable, determining the partitioning of the available energy into sensible and latent heat fluxes. It conditions the availability of water for the vegetation and affects the land hydrology as well as land-atmosphere fluxes. Remote sensing techniques are developed to retrieve information on soil moisture dynamics from satellite data. The research group is involved in the evaluation of new satellite sensors like the Soil Moisture and Ocean Salinity Mission (SMOS) and the planned SMAP mission.
Remote sensing sensors provide data at different spatial and temporal scales in the order of meters to tens of kilometers and sub-hourly to yearly frequency. Evaluating the accuracy of remote sensing products using ground measurements requires to bridge the gap between very local in situ measurements and the remote sensing observations.
Climate models are also used at multiple scales. Comparing climate model results against ground observations and/or remote sensing observations therefore also requires techniques to compare these, typically not consistent data sets.
The research group is developing techniques for the scaling and intercomparison of ground observations, satellite data and climate model simulations.
Both, remote sensing observations and model simulations are prone to intrinsic uncertainties. To best combine observational data and model simulations it is important to balance the uncertainties of different data sets.
The research group is therefore developing techniques to retrieve surface parameters from remote sensing data and assessing their uncertainties and use the data for the evaluation and optimization of climate models.