## 6 Conclusions  
The study presents a methodology for estimating volumetric soil moisture content [m\({}^{3}\)/m\({}^{3}\)] under changing vegetation using multi-orbit co-polarized Sentinel-1 C-band time series. The method was tested using TERENO sites in the Rur catchment, Germany (Figure 13) as well as the Apulian Tavoliere site, Italy. The high revisit time of the Sentinel-1 A and B satellites enables a temporal resolution of the resulting soil moisture product of one to two days when using all available orbits. The method was evaluated using a spatial resolution of 200 m in accordance with the footprint radius of the eight in-situ soil moisture sites collecting cosmic ray neutron probe (CRNP) measurements for the years 2018 to 2020. In addition to the SAR recordings, auxiliary data of soil texture and field capacity were used as initial starting value and soil moisture to dielectric constant conversion. The presented method is based on a change in detection approach, using the short-term change between two consecutive SAR recordings caused solely by changes in soil moisture. The influence of other surface parameters such as soil roughness, vegetation biomass, plant height, or phenological stage can be disentangled from the change in backscattering signal to some extent, as they are associated with a slower rate of change. In this regard, establishing a temporal high resolution time series from multiple orbits increases the validity of this algorithm pre-requisite. To reduce the effect of alternating incidence angles on the backscattering signal introduced by using the multi-orbit time series, an incidence angle normalization was applied, and an additional Fourier Series transformation was developed. Furthermore, the effect of changing vegetation scattering contribution on the backscattering signal was addressed. The backscatter ratios were corrected for vegetation influence based on the related co-polarized backscatter time series as an indicator for the vegetation backscatter contribution at pixel scale. In the study, the effect on the individual processing steps on the backscattering time series as well as on the resulting soil moisture estimation are analyzed and discussed. Furthermore, the final soil moisture product was validated with in-situ measurements for two different sites with two different in-situ measuring techniques as well as three different soil depths. Each of the applied preprocessing steps significantly increased the overall fitness of the estimated soil moisture, both in terms of correlation coefficient as well as regarding uRMSE. While the incidence angle normalization led to an alignment of the mean backscattering values between individual orbits, the effect of incidence angle is still present in the individual backscattering signal distribution. By using the Fourier Series transformation, dismissing frequencies higher than the revisit time of the Sentinel-1A and Sentinel-1B, the change in backscattering signal caused from varying incident angles, can be excluded, resulting in higher correlation as well as reduced uRMSE at most individual CRNP and capacitance stations. Adding the newly developed vegetation correction further increase the correlation between estimated and in-situ measured soil moisture, especially at the crop dominated stations. To provide an outlook, further research should be carried out adapting the initial soil moisture condition for drought periods as well as evaluating other globally available datasets for this purpose. Especially, the assumption that soil moisture conditions in the Rur catchment in January are near the field capacity level is not true throughout the drought years of 2019 and 2020. Furthermore, the criteria for applying the vegetational adaption was chosen empirically based on the characteristics of the study areas. For a global application, further research should be carried out on establishing a more generalized criterion.