## 5 Results and Discussion
### Effect of Individual Processing Steps on Soil Moisture Estimation
With an overall R of 0.68, R\({}^{2}\) of 0.46 and uRMSE of 0.056 m\({}^{3}\)/m\({}^{3}\) at the Rur catchment, as well as an overall R of 0.56 (0.63), R\({}^{2}\) of 0.31 (0.40) and uRMSE of 0.065 m\({}^{3}\)/m\({}^{3}\) (0.064 m\({}^{3}\)/m\({}^{3}\)) at the Apulian Tavoliere site at 0.025 m (0.1 m) soil depth, the proposed method for estimating soil moisture achieves consistent results (Figure 12). Here, the  
Figure 11: Comparison between incidence angle normalized and Fourier Series transformed soil moisture estimation and in-situ measured soil moisture (SM) from all eight CRNP stations (**A**), and all six capacitance stations at 0.025 m (**B**) and 0.1 m (**C**) soil depth, grouped into three vegetational periods. highest correlation can be observed at the CRNP stations, followed by the capacitance station at 0.1 m soil depth. The performance for the shallow 0.025 cm soil depth is lower, likely being caused by the higher spatial variability of surface soil moisture. As topsoil measurements tend to be more heterogeneous and may lack spatial representativeness for a 40.000 m\({}^{2}\) pixel, while measurements from deeper soil layers, being less variable, represent more the average dynamics and achieve better correlations with spatial SAR measurements. On the other hand, this could also be caused by the exclusion of high frequencies from the Fourier transformed backscatter time series. As the topsoil shows higher temporal soil moisture dynamics, the low-pass filtering effect could have resulted in lower correlation for this soil moisture depth, even though it is the most comparable in terms of penetration depth of the SAR signal. Reflecting more weekly and seasonal changes, soil moisture measurements from deeper depth could thus have a higher correlation to the low-passed SAR time series. Since at the CRNP station, both the areal measurement of soil moisture and the low-frequency dynamics of soil depth from 0.15-0.70 cm are combined, this could possibly be the reason for the highest correlation and the lowest uRMSE between estimated and in-situ measured soil moisture observed here. For comparison, we also calculated the correlation and error between used GLDAS and in-situ measured soil moisture derived from CRNP stations in the Rur catchment. Evaluating GLDAS for all stations and all years in the Rur catchment, an R value of 0.69 with an uRMSE of 0.061 m\({}^{3}\)/m\({}^{3}\) was achieved, compared to R = 0.68 and uRMSE = 0.056 m\({}^{3}\)/m\({}^{3}\), achieved with the newly proposed approach. In this sense, similar results were obtained, with a significantly higher spatial resolution (GLDAS = 27 km, our approach 0.2 km). The  
Figure 12: Density scatter plot between estimated and in-situ measured soil moisture from all eight CRNP stations (**A**) and all six capacitance stations for 0.025 m (**B**) and 0.1 m soil depth (**C**) for the period 2018 to 2020.  
overall performance at the Apulian Tavoliere site is also comparable to the published SMOSAR soil moisture product validation study over seven soil moisture test sites located across Europe, North America, and Australia for the period between 2015 to 2018 [29]. They were using only a single orbit time series with 1068 individual scenes to estimate soil moisture with a spatial resolution of 1 km and a temporal resolution of 6 to 12 days. Validated against the spatial average of the Apulian site on the network scale, an R value of 0.71 with an RMSE of 0.064 m\({}^{3}\)/m\({}^{3}\) was achieved. As the spatial averaging increases the methods sensitivity to soil moisture, averaging our final soil moisture product over the six capacitance stations further increases the correlation, R, to a value of 0.68 (0.69) with an RMSE = 0.050 m\({}^{3}\)/m\({}^{3}\) (0.053 m\({}^{3}\)/m\({}^{3}\)) for the soil depth of 0.025 m (0.1 m). Spatially averaging across the eight CRNP stations in Rur catchment on a daily basis, R can be further increased to a correlation of R = 0.79 with a RMSE = 0.039 m\({}^{3}\)/m\({}^{3}\), while having 709 averaged soil moisture estimations.