# Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation
## 3 Results
### Neural Network Hybrid Approach  
The different combinations of input metrics are tested to estimate soil moisture. Figure 3 illustrates the results of validations applied for 30% of the database, for each case of combination with the statistical parameters \(RMSE\) and \(R\).  
Figure 2: Scatterplots of the retrieved surface soil moisture (SSM) as a function of in situ SSM measurements colored according to NDVI value variation using two change detection approaches: (**a**) classic approach and (**b**) new approach expressed in Equation (7), where \(V1\) is the \(NDVI\).  
Figure 3: Scatterplots of the retrieved SSM as a function of in situ SSM measurements using the ANN approach using multiple combinations of features: (**a**) VV, _NDVI_, (**b**) VV, _NDVI_, the incidence angle, (**c**) _NDVI_, the incidence angle, \(I_{SSM}\), (**d**) VV, _NDVI_, the incidence angle, \(\Delta_{VV}\), (**e**) VV, _NDVI_, the incidence angle, \(\Delta_{VV}\), \(I_{SSM}\), (**f**) VV, NDVI, the incidence angle, \(\Delta_{VV}\), \(I_{SSM}\), VH/VV ratio, (**g**) VV, _NDVI_, the incidence angle, \(\Delta_{VV}\), \(SSM_{t}\), (**h**) VV, _NDVI_, the incidence angle, \(\Delta_{VV}\), \(I_{SSM}\), \(SSM_{t}\), (**i**) VV, _NDVI_, the incidence angle, \(\Delta_{VV}\), \(I_{SSM}\), VH/VV ratio, \(SSM_{t}\).  
For the first six predictor combinations (Figure 3a-f), we observe relatively close precision with _RMSE_ values in the range of 0.095 m\({}^{3}\)/m\({}^{3}\) and 0.083 m\({}^{3}\)/m\({}^{3}\) and \(R\) of 0.3-0.6. The introduction of moisture estimated by the classic change detection algorithm (Equation (3)) as input to ANN allows a strong improvement in the accuracy of soil moisture estimation with an _RMSE_ equal to 0.063 m\({}^{3}\)/m\({}^{3}\) and \(R\) = 0.76 when we consider the predictors: VV, _NDVI_, the incidence angle, \(\Delta_{VV}\), and \(SSM_{t}\). By adding the VH/VV ratio and \(I_{SSM}\), the _RMSE_ value decreases to 0.062 m\({}^{3}\)/m\({}^{3}\), and the correlation coefficient reaches a value of approximately 0.79. This result confirms the contribution of the hybrid approach to estimating soil moisture. This first estimated soil moisture strongly contributes to a better estimate of soil moisture by the ANN.  
Figure 4 illustrates the accuracy of intercomparisons between in situ measurements and satellite estimates for the optimal case for different tested networks, where we represent the _RMSE_ and \(R\) parameters by blue and orange boxes. The _RMSE_ values vary from 0.03 m\({}^{3}\)/m\({}^{3}\) to 0.09 m\({}^{3}\)/m\({}^{3}\), and _R_-values fluctuate between 0.37 and 0.84.  
Good consistency is generally observed for networks such as AMMA-CATCH, COSMOS, MAQU, RSMN, HOAL, HOBE, IPE, BIEBRZA S-1, TAHMO, REMEDHUS and RISMA, and _RMSE_ values are under or equal to 0.05 m\({}^{3}\)/m\({}^{3}\). The NAQU network is characterized by the lowest _RMSE_ value of 0.03 m\({}^{3}\)/m\({}^{3}\) and \(R\) value of 0.77.  
Within the same soil moisture in situ network, the accuracy of soil moisture retrieval varies from one station to another. For the REMEDHUS case characterized by an _RMSE_ equal to 0.05 m\({}^{3}\)/m\({}^{3}\), _RMSE_ values per station range between 0.03 m\({}^{3}\)/m\({}^{3}\) and 0.09 m\({}^{3}\)/m\({}^{3}\), and R values vary between 0.34 and 0.69, as represented in Figure 5.  
Figure 4: Boxplots of statistical parameters (_R_ and _RMSE_) of soil moisture retrieval as a function of ISMN-considered networks using the hybrid methodology of change detection and ANN.