## 1 Introduction
The method consists of inverting the IEM model for a restricted roughness range (_Hrms_ between 0.6 and 1.6 cm) by using neural networks. The results indicate that only two soil moisture classes (dry and wet soils) can be retrieved using ERS data. To estimate soil moisture and roughness from the C-band polarimetric radar data, [PERSON] et al. [41] first generated a database of backscattering coefficients for a wide range of bare soil conditions (_Hrms_ between 0.3 and 3.6 cm and _mv_ between 5 and 45 vol %) using the IEM. Then, the neural networks were trained using the synthetic database. A priori information on the soil moisture and roughness were considered to improve the accuracy of neural networks. Finally, the trained neural networks were validated using a real database and show an accuracy on the soil moisture estimates of approximately 7 vol % with the use of a priori information. [PERSON] et al. [21] proposed an approach based on the neural network technique for operational soil moisture mapping from Sentinel-1 images. In the study of [PERSON] et al. [21], the neural network was trained using a synthetic database of backscattering coefficients simulated from IEM and WCM for a wide range of soil moisture (5 < _mv_ (vol %) < 45), soil roughness (1 < _Hrms_ (cm) < 3), and vegetation conditions (0.2 < NDVI < 0.8). Inputs to the neural networks were the SAR data (single or dual polarization) and NDVI; the output was the soil moisture only. The inversion of the radar signal for estimating the soil moisture doesn't require roughness measurements. The developed neural networks were validated using a real database (composed of SAR, optical data and in situ measurements) and the results showed that the neural networks give an accuracy on the soil moisture estimates of between 2 and 5 vol %. [PERSON] et al. [30] trained and validated neural networks using a huge dataset of satellite images acquired from RADARSAT-2 and LANDSAT-7/8 and in situ measurements. The results showed that HH polarization is the most relevant for soil moisture estimates (accuracy approximately 6 vol % even for dense grassland cover). [PERSON] et al. [27] developed an inversion technique based on the neural networks technique to invert the WCM for soil moisture estimation. First, the WCM was used to generate synthetic datasets of backscattering coefficients in the X-band (incidence angle \(\sim\)30\({}^{\circ}\)) for a wide range of soil moisture (_mv_ between 5 and 45 vol %) and vegetation conditions (NDVI between 0 and 1). Then, the synthetic database was used to train and validate the neural networks. Finally, the trained neural networks were applied to estimate the soil moisture with an accuracy of approximately 5 vol % (using HH polarization alone and both HH and HV polarizations). The aim of this paper is to develop an operational approach for mapping soil moisture at high spatial resolution (up to the plot scale) in agriculture areas. The proposed approach is based on the inversion of the WCM using the neural network technique. First, a calibrated WCM and the IEM were combined to generate a synthetic database of SAR backscattering coefficients for a wide range of soil and vegetation conditions. Then, the database was noisy and divided into two equal sub-databases, one for the neural networks training (noisy training database) and one for the neural networks validation (noisy validation database). Further, the neural networks were trained using the noisy training database. Finally, to evaluate the neural networks' performance, the trained neural networks were validated using the noisy validation database and the real database. The use of the new Sentinel-1 and Sentinel-2 data for operational soil moisture mapping in agricultural areas with high revisit time and at the plot scale is an innovative use of spatial imageries. The performance of the neural networks was studied when introducing a priori information on the soil moisture. In this paper, the following section presents the study site and database descriptions.