Coupled modelling of land surface microwave interactions using ENVISAT ASAR data

In the last decades microwave remote sensing has proven its
capability to provide valuable information about the land surface.
New sensor generations as e.g. ENVISAT ASAR are capable to provide
frequent imagery with an high information content. To make use of
these multiple imaging capabilities, sophisticated parameter
inversion and assimilation strategies have to be applied. A
profound understanding of the microwave interactions at the land
surface is therefore essential. The objective of the presented work
is the analysis and quantitative description of the backscattering
processes of vegetated areas by means of microwave backscattering
models. The effect of changing imaging geometries is investigated
and models for the description of bare soil and vegetation
backscattering are developed. Spatially distributed model
parameterisation is realized by synergistic coupling of the
microwave scattering models with a physically based land surface
process model. This enables the simulation of realistic SAR images,
based on bioand geophysical parameters. The adequate preprocessing
of the datasets is crucial for quantitative image analysis. A
stringent preprocessing and sophisticated terrain geocoding and
correction procedure is therefore suggested. It corrects the
geometric and radiometric distortions of the image products and is
taken as the basis for further analysis steps. A problem in
recently available microwave backscattering models is the
inadequate parameterisation of the surface roughness. It is shown,
that the use of classical roughness descriptors, as the rms height
and autocorrelation length, will lead to ambiguous model
parameterisations. A new two parameter bare soil backscattering
model is therefore recommended to overcome this drawback. It is
derived from theoretical electromagnetic model simulations. The new
bare soil surface scattering model allows for the accurate
description of the bare soil backscattering coefficients. A new
surface roughness parameter is introduced in this context, capable
to describe the surface roughness components, affecting the
backscattering coefficient. It is shown, that this parameter can be
directly related to the intrinsic fractal properties of the
surface. Spatially distributed information about the surface
roughness is needed to derive land surface parameters from SAR
imagery. An algorithm for the derivation of the new surface
roughness parameter is therefore suggested. It is shown, that it
can be derived directly from multitemporal SAR imagery. Starting
from that point, the bare soil backscattering model is used to
assess the vegetation influence on the signal. By comparison of the
residuals between measured backscattering coefficients and those
predicted by the bare soil backscattering model, the vegetation
influence on the signal can be quantified. Significant difference
between cereals (wheat and triticale) and maize is observed in this
context. It is shown, that the vegetation influence on the signal
can be directly derived from alternating polarisation data for
cereal fields. It is dependant on plant biophysical variables as
vegetation biomass and water content. The backscattering behaviour
of a maize stand is significantly different from that of other
cereals, due to its completely different density and shape of the
plants. A dihedral corner reflection between the soil and the stalk
is identified as the major source of backscattering from the
vegetation. A semiempirical maize backscattering model is suggested
to quantify the influences of the canopy over the vegetation
period. Thus, the different scattering contributions of the soil
and vegetation components are successfully separated. The
combination of the bare soil and vegetation backscattering models
allows for the accurate prediction of the backscattering
coefficient for a wide range of surface conditions and variable
incidence angles. To enable the spatially distributed simulation of
the SAR backscattering coefficient, an interface to a process
oriented land surface model is established, which provides the
necessary input variables for the backscattering model. Using this
synergistic, coupled modelling approach, a realistic simulation of
SAR images becomes possible based on land surface model output
variables. It is shown, that this coupled modelling approach leads
to promising and accurate estimates of the backscattering
coefficients. The remaining residuals between simulated and
measured backscatter values are analysed to identify the sources of
uncertainty in the model. A detailed field based analysis of the
simulation results revealed that imprecise soil moisture
predictions by the land surface model are a major source of
uncertainty, which can be related to imprecise soil texture
distribution and soil hydrological properties. The sensitivity of
the backscattering coefficient to the soil moisture content of the
upper soil layer can be used to generate soil moisture maps from
SAR imagery. An algorithm for the inversion of soil moisture from
the upper soil layer is suggested and validated. It makes use of
initial soil moisture values, provided by the land surface process
model. Soil moisture values are inverted by means of the coupled
land surface backscattering model. The retrieved soil moisture
results have an RMSE of 3.5 Vol %, which is comparable to the
measurement accuracy of the reference field data. The developed
models allow for the accurate prediction of the SAR backscattering
coefficient. The various soil and vegetation scattering
contributions can be separated. The direct interface to a
physically based land surface process model allows for the
spatially distributed modelling of the backscattering coefficient
and the direct assimilation of remote sensing data into a land
surface process model. The developed models allow for the
derivation of static and dynamic landsurface parameters, as e.g.
surface roughness, soil texture, soil moisture and biomass from
remote sensing data and their assimilation in process models. They
are therefore reliable tools, which can be used for sophisticated
practice oriented problem solutions in manifold manner in the earth
and environmental sciences.