In scatterometer observations of Earth's land surface, the backscattering coefficient (σ0) is significantly influenced by the satellite instrument's imaging geometry under which the measurements are conducted. It is essential to consider and correct the effects of azimuthal signal dependence to accurately retrieve geophysical parameters such as soil moisture. In this thesis, we introduce and evaluate a dynamic azimuthal correction method against the traditional static correction approach for C-Band backscatter measurements recorded by the Advanced Scatterometer (ASCAT) onboard the Metop satellites. By assessing the Estimated Standard Deviation (ESD) of σ0, we analyse the effectiveness of both correction methods in mitigating azimuthal variability. In addition, we develop three indicators to examine the ESD's temporal changes, as well as the geometric and temporal variability of the correction polynomials. Our analysis demonstrates that the dynamic correction method not only significantly lowers azimuthal noise but also preserves temporal backscatter trends and more accurately corrects signals from areas affected by Radio Frequency Interference (RFI). Therefore, we advocate for its integration into the TU Wien Soil Moisture Retrieval (TUW-SMR) algorithm. Beyond improving soil moisture retrievals, we underscore the potential of ESD as a tool for sand dune mapping and conclude that our newly developed temporal indicators could offer additional insights into changing land surface conditions, from vegetation dynamics in the Sahel to the patterns of urban expansion in China.