## 7 Conclusions  
The present study aimed to present an operational approach for soil moisture estimates in agricultural areas at a high spatial resolution (up to plot scale). Our approach is based on a synergic use of radar and optical images. With the use of Sentinel-1 and Sentinel-2 data, the developed approach will provide soil moisture estimates with a high temporal frequency. For the French territory, a temporal frequency of 6 days is possible (revisit time of Sentinel-1 SAR data). Our approach for soil moisture estimates used the neural networks technique to invert the radar signal and estimate the soil moisture. First, a parameterized Water Cloud Model [26] combined with the Integral Equation Model (IEM) were used to generate a synthetic database of the backscattering coefficient in the VV and VH (incidence angle between 20\({}^{\circ}\) and 45\({}^{\circ}\)) for a wide range of soil moisture (\(2<mv\) (vol %) < 40), soil roughness (0.5 < _Hrms_ (cm) < 3.8), and vegetation conditions (0 < NDVI < 0.75). Second, the simulated backscattering coefficients and the NDVI values were noisy to obtain a more realistic synthetic database. Third, the noisy synthetic database was divided into a noisy training database to train the neural networks, and a noisy validation database to validate the trained neural networks. Finally, the trained neural networks were applied to the real database to evaluate their robustness for the estimation of soil moisture. Three SAR inversion configurations were developed (VV alone, VH alone and both VV and VH) with and without the constraint on the soil moisture values. For each inversion configuration, the neural networks were trained using all noisy synthetic database elements (no a priori information  
Figure 10: Soil moisture maps. Blank areas correspond to zones where the algorithm was not applied (no agricultural areas). on the \(mv\)), noisy synthetic database elements with \(mv\) between 2 and 30 vol % (a priori information on the \(mv\) and dry to slightly wet soils), and using the noisy synthetic database elements with \(mv\) between 20 and 40 vol % (a priori information on the \(mv\) and very wet). The neural networks with a priori information on the \(mv\) for dry to slightly wet soils shall be applied if the SAR image was acquired far from a rainy episode or in context of fast drying out of soil (high temperatures). The neural networks with a priori information on the \(mv\) and very wet soils shall be applied if the SAR image is acquired after an intense rainy episode. The developed neural networks were validated using the synthetic validation database and the real database. From the synthetic database, the results showed that the use of VV alone allows for better accuracy on the \(mv\) estimates than VH alone, and gives a similar accuracy when compared to the use of both VV and VH. In addition, the results showed that the use of a priori information improves the estimation of \(mv\) by reducing the errors (bias and RMSE) on the \(mv\) estimates. For dry to slightly wet soils (\(mv\) between 2 and 25 vol %) and using VV alone or both VV and VH, the accuracy (RMSE) on the \(mv\) estimates is approximately 5.5 vol % with no a priori information on the \(mv\) and a 4.0 vol % with a priori information on the \(mv\). For very wet soil conditions (\(mv\) between 25 and 40 vol %) and using VV alone or both VV and VH, the accuracy (RMSE) on the \(mv\) estimates is 6.9 vol % with no a priori information and 5.1 vol % with a priori information on the \(mv\). Using VH alone, the RMSE on the \(mv\) estimates is 7.6 vol % with no a priori information on the \(mv\) and 5.8 vol % with a priori information on the \(mv\) in the case of dry to slightly wet soils. For an \(mv\) between 25 and 40 vol %, the RMSE on the \(mv\) estimates is 9.7 vol % with no a priori information and 5.3 vol % with a priori information on the \(mv\). In addition, the results showed that the errors on the \(mv\) depend mainly on \(Hrms\) and NDVI values.