The presence of vegetation complicates the retrieval of soil moisture from microwave remote sensing. Canopies contribute to total amount scattering and attenuate soil surface scattering, which reduces the sensitivity of microwave observations to soil moisture. On the other hand, microwave scattering along soil-vegetation pathways may enhance the soil moisture sensitivity of microwave observations. Most recently, detailed crop backscattering models have been developed to investigate the sensitivity of microwave observations to the land surface properties, such as soil moisture, theoretically. Interpretation of microwave response is a complex task, since it is affected by many parameters. Apart from soil moisture, also the soil roughness, plant moisture content and morphology (i.e., shape, dimensions, orientation, and relative location of vegetation elements) play an important role in the backscatter simulations. For this research the discrete medium scattering model developed by the Tor Vergata University (Rome, Italy) has been applied to simulate the L-band (1.6 GHz) backscattering from a corn field throughout a growth cycle. This model, hereafter referred to as the Tor Vergata model, is based on radiative transfer theory and solves the relevant equations via the matrix doubling algorithm, which includes multiple scattering effects. The model simulations are compared against measurements by the NASA-George Washington University truck mounted scatterometer collected throughout a corn growth cycle. This thesis contributes towards the development of the Tor Vergata modelling system by including recently proposed dielectric mixing models and evaluating a new version of the Tor Vergata model that considers partially covered canopies. A satisfactory agreement is observed between simulated and measured backscattering when fraction vegetation cover in included. At HH polarization, however, a systematic backscatter overestimation remains. Part of the overestimations is resolved by including the more recently developed dielectric mixing models and varying the stem inclination angle over a broader range. However, also the field measured vegetation morphology may include uncertainties.
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