Soil moisture is one of the key variables controlling the water and energy exchanges between
Earth’s surface and the atmosphere. Therefore, remote sensing based soil moisture
information has potential applications in many disciplines. Besides numerical weather
forecasting and climate research these include agriculture and hydrologic applications like
flood and drought forecasting.
The first satellite specifically designed to deliver operational soil moisture products, SMOS
(Soil Moisture and Ocean Salinity), was launched 2009 by the European Space Agency
(ESA). SMOS is a passive microwave radiometer working in the L-band of the microwave
domain, corresponding to a frequency of roughly 1.4 GHz and relies on a new concept. The
microwave radiation emitted by the Earth’s surface is measured as brightness temperatures in
several look angles. A radiative transfer model is used in an inversion algorithm to retrieve
soil moisture and vegetation optical depth, a measure for the vegetation attenuation of the
soil’s microwave emission.
For the application of passive microwave remote sensing products a proper validation and
uncertainty assessment is essential. As these sensors have typical spatial resolutions in the
order of 40 – 50 km, a validation that relies solely on ground measurements is costly and
labour intensive. Here, environmental modelling can make a valuable contribution.
Therefore the present thesis concentrates on the question which contribution coupled land
surface and radiative transfer models can make to the validation and analysis of passive
microwave remote sensing products. The objective is to study whether it is possible to explain
known problems in the SMOS soil moisture products and to identify potential approaches to
improve the data quality.
The land surface model PROMET (PRocesses Of Mass and Energy Transfer) and the
radiative transfer model L-MEB (L-band microwave emission of the Biosphere) are coupled
to simulate land surface states, e.g. temperatures and soil moisture, and the resulting
microwave emission. L-MEB is also used in the SMOS soil moisture processor to retrieve soil
moisture and vegetation optical depth simultaneously from the measured microwave
emission. The study area of this work is the Upper Danube Catchment, located mostly in
Southern Germany.
Since model validation is essential if model data are to be used as reference, both models are
validated on different spatial scales with measurements. The uncertainties of the models are
quantified. The root mean squared error between modelled and measured soil moisture at
several measuring stations on the point scale is 0.065 m3/m3. On the SMOS scale it is 0.039
m3/m3. The correlation coefficient on the point scale is 0.84.
As it is essential for the soil moisture retrieval from passive microwave data that the radiative
transfer modelling works under local conditions, the coupled models are used to assess the
radiative transfer modelling with L-MEB on the local and SMOS scales in the Upper Danube
Catchment. In doing so, the emission characteristics of rape are described for the first time
and the soil moisture retrieval abilities of L-MEB are assessed with a newly developed LMEB
parameterization. The results show that the radiative transfer modelling works well
under most conditions in the study area. The root mean squared error between modelled and
airborne measured brightness temperatures on the SMOS scale is less than 6 – 9 K for the
different look angles.
The coupled models are used to analyse SMOS brightness temperatures and vegetation optical
depth data in the Upper Danube Catchment in Southern Germany. Since the SMOS soil
moisture products are degraded in Southern Germany and in different other parts of the world
these analyses are used to narrow down possible reasons for this.
The thorough analysis of SMOS brightness temperatures for the year 2011 reveals that the
quality of the measurements is degraded like in the SMOS soil moisture product. T