## 7 Conclusions
In using VV alone, or VV and VH together, an underestimation and overestimation of estimated \(mv\) was observed respectively for an \(Hrms\) lower and higher than 1.5 cm in the case of dry to slightly wet soil conditions (\(mv\) between 2 and 25 vol %). In addition, the RMSE on the \(mv\) was the lowest for a soil roughness between 1 and 2 cm for all SAR inversion configurations. For very wet soils (\(mv\) between 25 and 40 vol %), the neural networks underestimate the reference \(mv\). Both the difference between the estimated \(mv\) and the reference \(mv\) as well as the RMSE on the \(mv\) estimates decreases when the \(Hrms\) increases. Moreover, the results showed that for dry to slightly wet soils, the RMSE on the \(mv\) increases with the NDVI. For an \(mv\) between 25 and 40 vol % (very wet soils), the RMSE on the estimated \(mv\) does not depend on the NDVI. Finally, the results showed that errors (RMSE and bias) on the \(mv\) estimates do not depend on the incidence angle. The use of a real database confirms that VV alone with the consideration of a priori information on the \(mv\) achieves the best accuracy on the soil moisture estimates (bias = 0.2 vol % and RMSE = 5.0 vol %). However, poor \(mv\) estimates were observed (errors approximately 10 vol %) for smooth (\(Hrms\) lower than 1 cm) and rough soils (\(Hrms\) higher than 3 cm)  
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