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In the last decades microwave remote sensing has proven its capability to provide\nvaluable information about the land surface. New sensor generations as e.g.\nENVISAT ASAR are capable to provide frequent imagery with an high information\ncontent. To make use of these multiple imaging capabilities, sophisticated\nparameter inversion and assimilation strategies have to be applied. A profound\nunderstanding of the microwave interactions at the land surface is therefore\nessential.\nThe objective of the presented work is the analysis and quantitative description of\nthe backscattering processes of vegetated areas by means of microwave\nbackscattering models. The effect of changing imaging geometries is investigated\nand models for the description of bare soil and vegetation backscattering are\ndeveloped. Spatially distributed model parameterisation is realized by synergistic\ncoupling of the microwave scattering models with a physically based land surface\nprocess model. This enables the simulation of realistic SAR images, based on bioand\ngeophysical parameters.\nThe adequate preprocessing of the datasets is crucial for quantitative image\nanalysis. A stringent preprocessing and sophisticated terrain geocoding and\ncorrection procedure is therefore suggested. It corrects the geometric and\nradiometric distortions of the image products and is taken as the basis for further\nanalysis steps.\nA problem in recently available microwave backscattering models is the inadequate\nparameterisation of the surface roughness. It is shown, that the use of classical\nroughness descriptors, as the rms height and autocorrelation length, will lead to\nambiguous model parameterisations. A new two parameter bare soil backscattering\nmodel is therefore recommended to overcome this drawback. It is derived from\ntheoretical electromagnetic model simulations. The new bare soil surface scattering\nmodel allows for the accurate description of the bare soil backscattering coefficients.\nA new surface roughness parameter is introduced in this context, capable to\ndescribe the surface roughness components, affecting the backscattering\ncoefficient. It is shown, that this parameter can be directly related to the intrinsic\nfractal properties of the surface.\nSpatially distributed information about the surface roughness is needed to derive\nland surface parameters from SAR imagery. An algorithm for the derivation of the\nnew surface roughness parameter is therefore suggested. It is shown, that it can be\nderived directly from multitemporal SAR imagery.\nStarting from that point, the bare soil backscattering model is used to assess the\nvegetation influence on the signal. By comparison of the residuals between\nmeasured backscattering coefficients and those predicted by the bare soil\nbackscattering model, the vegetation influence on the signal can be quantified.\nSignificant difference between cereals (wheat and triticale) and maize is observed in\nthis context.\nIt is shown, that the vegetation influence on the signal can be directly derived from\nalternating polarisation data for cereal fields. It is dependant on plant biophysical\nvariables as vegetation biomass and water content.\nThe backscattering behaviour of a maize stand is significantly different from that of\nother cereals, due to its completely different density and shape of the plants. A\ndihedral corner reflection between the soil and the stalk is identified as the major\nsource of backscattering from the vegetation. A semiempirical maize backscattering\nmodel is suggested to quantify the influences of the canopy over the vegetation\nperiod.\nThus, the different scattering contributions of the soil and vegetation components\nare successfully separated. The combination of the bare soil and vegetation\nbackscattering models allows for the accurate prediction of the backscattering\ncoefficient for a wide range of surface conditions and variable incidence angles.\nTo enable the spatially distributed simulation of the SAR backscattering coefficient,\nan interface to a process oriented land surface model is established, which provides\nthe necessary input variables for the backscattering model. Using this synergistic,\ncoupled modelling approach, a realistic simulation of SAR images becomes possible\nbased on land surface model output variables. It is shown, that this coupled\nmodelling approach leads to promising and accurate estimates of the backscattering\ncoefficients. The remaining residuals between simulated and measured backscatter\nvalues are analysed to identify the sources of uncertainty in the model. A detailed\nfield based analysis of the simulation results revealed that imprecise soil moisture\npredictions by the land surface model are a major source of uncertainty, which can\nbe related to imprecise soil texture distribution and soil hydrological properties.\nThe sensitivity of the backscattering coefficient to the soil moisture content of the\nupper soil layer can be used to generate soil moisture maps from SAR imagery. An\nalgorithm for the inversion of soil moisture from the upper soil layer is suggested\nand validated. It makes use of initial soil moisture values, provided by the land\nsurface process model. Soil moisture values are inverted by means of the coupled\nland surface backscattering model. The retrieved soil moisture results have an RMSE\nof 3.5 Vol %, which is comparable to the measurement accuracy of the reference\nfield data.\nThe developed models allow for the accurate prediction of the SAR backscattering\ncoefficient. The various soil and vegetation scattering contributions can be\nseparated. The direct interface to a physically based land surface process model\nallows for the spatially distributed modelling of the backscattering coefficient and\nthe direct assimilation of remote sensing data into a land surface process model.\nThe developed models allow for the derivation of static and dynamic landsurface\nparameters, as e.g. surface roughness, soil texture, soil moisture and biomass from\nremote sensing data and their assimilation in process models. They are therefore\nreliable tools, which can be used for sophisticated practice oriented problem\nsolutions in manifold manner in the earth and environmental sciences.