# Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation
## 4 Discussion  
The proposed hybrid approaches have allowed more or less strong improvements compared to the initial estimates based on change detection or a separate ANN approach. With an improved change detection method, we observe a negligible contribution of the considered vegetation cover compared to a basic approach directly linking the radar signal to soil moisture. This can be explained by the highly diversified context at the scale of many soil moisture stations with very varied landscape contexts (crops, trees, bare soils, etc.) and different vegetation densities, which can generate significant noise in the modeling of the scattered signal that is difficult to take into account without a more precise description in terms of land use. This noise is particularly observed with the VH/VV index, which is very sensitive to the dynamics of the vegetation cover in a homogeneous context [81], but it could also mix different effects and particularly those of soil roughness [82]. To better analyze proposed results, we examined the time series of the in situ and retrieved soil moisture per station and network. Figure 6 displays the time series of the radar signal (VV), \(NDVI\), and soil moisture \(SSM_{i}\). The in situ soil moisture measurements are illustrated in blue, and the hybrid approach results are drawn in red. The intercomparison between the proposed approach performance within the LasBodegas and Canizal stations reveals \(RMSE\) values of 0.04 m\({}^{3}\)/m\({}^{3}\) and 0.07 m\({}^{3}\)/m\({}^{3}\), respectively. The two stations belong to the same climatic region of the arid steppe and characterize a clay fraction interval of approximately 35%. The performance difference may be induced by the land cover, where the Canizal station is occupied by shrubs and the LasBodegas station is covered by trees. The aforementioned land cover may impact the accuracy of soil moisture retrieval due to the vegetation volume impact on the radar signal in the C-band. Additionally, the measured soil moisture values are lower than 0.3 m\({}^{3}\)/m\({}^{3}\) at the LasBodegas station, and higher values reach 0.4 m\({}^{3}\)/m\({}^{3}\) at the Canizal station. Hence, the soil water content retrieval is more accurate in the first case due to the saturation of the C-band signal at high values of soil moisture. Figure 5: Scatterplots of the estimated soil moisture as a function of ISMN measurements in the REMEDHUS network per considered station. However, the approach has difficulties for certain stations, as shown for FLUXNETAMERFLUX, GROW, SNOTAL, and SMOSMANIA networks, where the _RMSE_ values reach a maximum of 0.09 m\({}^{3}\)/m\({}^{3}\). The analysis of these cases generally leads to contexts of dense vegetation cover that can induce a low sensitivity of the radar signal to soil moisture. In Figure 7, we scatterplot the statistical parameters as a function of _NDVI_ values. According to Figure 7a,b, we observe the increase of _RMSE_ and _Bias_ values as a function of the increase of _NDVI_ values where _RMSE_ can reach 0.10 m\({}^{3}\)/m\({}^{3}\). The vegetation development may induce a _Bias_ between \(-0.06\) m\({}^{3}\)/m\({}^{3}\) and 0.04 m\({}^{3}\)/m\({}^{3}\), where _NDVI_ values exceed 0.5. This behavior may be explained by the C-band potential which is otherwise limited in dense canopies where _NDVI_ values are higher than 0.5. Figure 6: Scatterplots of the temporal evolution of radar signals in VV polarization, NDVI, and the predicted and in situ measurements of soil moisture using the hybrid methods within two stations of the REMEHDUS network: (**a**) LasBodegas station, (**b**) Canizal station. This is difficult to take into account in a general approach based on a neural network trained on stations with different types of surface conditions, such as the case of the FLUXNET-AMERFLUX network.