## 5 Results and Discussion
### Effect of Individual Processing Steps on Soil Moisture Estimation
In this respect, the proposed method can further improve the short-term change detection approach, by using smaller temporal intervals between each observation. Furthermore, by applying vegetational correction, the influence of the changing scattering contribution of vegetation can also be managed. As shown, major improvements were achieved in the intermediate and full vegetational period, being addressed by the vegetational correction. In this regard, the presented method is able to estimate soil moisture also during the vegetation period, by reducing the effect of varying incidence angles and vegetation scattering using the temporally dense Sentinel-1 co-polarized C-band time series. It enables a continuous soil moisture monitoring of agricultural areas during the whole crop cycle, representing soil moisture patterns on a regional scale, and thus can fill the gap of recent SAR based soil moisture products, regarding their lacking spatial or temporal resolution for a regional soil moisture monitoring. By using the co-polarized backscatter signal as an indicator for the vegetational influence on the related backscatter ratios for agricultural areas, which have a distinct bare soil and vegetation cover period, the limitations of C-band vegetation sensitivity, leading to biased soil moisture information within the backscattering signal, can be addressed. The observation gaps, which occur when masking out pixels dominated by volume backscattering as proposed by the SMOSAR soil moisture method, can be avoided, especially as these gaps occur during the most important phenological stages (shooting, flowering, and ripening of crops) in the growing period. While being able to improve soil moisture estimation under changing vegetation conditions in general, the retrieval of soil surface information using C-band is physically limited. In this regard, depending on the crop type and the vegetation density, for some periods during the growing cycle, C-band can become completely insensitive to soil moisture [42]. Even though the resampling to a coarser resolution of 200 m averages out the influence of vegetation, there might be pixels, where no correlation between a change in backscatter and a change in soil moisture is present. Hence, further research needs to be carried out, developing a (multi-sensor) method more adapted to the individual crop types and having deeper penetration capabilities through the canopy. While the method performs well in both the overall mean and the growing season, the lowest correlations and highest uRMSE are observed in the non-growing season, where wet soil moisture conditions are prevailing. As previous studies show, the alpha approximation method has the highest bias within the soil moisture range from 0.30-0.55 m\({}^{3}\)/m\({}^{3}\), requiring further improvements especially for wet soil moisture conditions [29]. In addition, the assumption, that the initial soil moisture condition is on the field capacity level is not fully matching for the years 2019 and 2020 at the Rur catchment, as the prevailing drought conditions in these two years are causing soil moisture values lower than field capacity in both January months. By using adapted initial soil moisture conditions, the problem of setting appropriate initial soil conditions should be addressed. In this regard, an iterative approach, as proposed by [PERSON] et al., 2011 [81], could mitigate the effect of the influence of pixels not suitable to be reference for the initial soil moisture. Using a moving window across the time series, soil moisture is estimated for a shorter period, while the mean of the resulting soil moisture estimation serves as initial soil moisture values for the further iteration. By limiting the calculated mean values of soil moisture to the maximum value derived from the field capacity when used as initial soil moisture in the next iteration, the high number of outliers can also be addressed. Nevertheless, this comes with an increase in computational costs, reducing the applicability of the method for regional monitoring.