## 1 Introduction
However, soil moisture estimates could be inaccurate for plots with well-developed vegetation cover because the vegetation contribution dominates the soil contribution in some cases of vegetation. [PERSON] et al. [26] showed from the C-band data collected mainly on wheat and grasslands that the NDVI threshold from which the soil contribution dominates the vegetation contribution depends mainly on the soil moisture condition and the SAR parameters (polarization and incidence angle). This threshold on the NDVI increases with the increase of soil moisture or with the decrease of SAR incidence angle. In addition, this threshold is lower with the VH polarization than with the VV polarization. In the VV polarization, the soil contribution dominates the vegetation contribution until the NDVI values are high (approximately 0.8) in the case of the incidence angle lower than 40\({}^{\circ}\) and soil moisture higher than 10 vol %. For dry soil conditions (\(mv\) lower than 10 vol %) and high incidence angles (40\({}^{\circ}\) for example), the threshold value in the VV polarization at which soil contribution dominates the vegetation contribution corresponds to an NDVI lower than approximately 0.6. For the VH polarization, the soil contribution quickly becomes lower than the vegetation contribution, especially for a high incidence angle and low soil moisture values. The NDVI threshold is approximately 0.25 for low \(mv\) values (5 vol %) and 0.5 for high \(mv\) values (30 vol %). The arrival of Sentinel-1 (S1) and Sentinel-2 (S2) satellites have encouraged the development of an operational algorithm for soil moisture mapping over agricultural areas with high revisit time and high spatial resolution (up to plot scale). Indeed, the coupling of the S1 and S2 data through the WCM allows for the reduction of uncertainty caused by vegetation cover in soil moisture estimates. However, the main difficulty for accurate soil moisture estimates lies on the fact that for a given vegetation condition, many combination of soil characteristics (soil moisture and roughness) leads to the same radar backscattering coefficient. Thus, inaccurate soil moisture estimates could be obtained in the case where roughness value is unknown. To overcome the roughness effects on the soil moisture estimates, multi-temporal change detection approaches were adopted for soil moisture mapping at a low spatial resolution [34; 35; 36]. At a low spatial resolution, the change in radar signal with time could be related to the change of soil moisture only. In the change detection approach, the radar signal on a given date is compared to the radar signal acquired in very wet and very dry periods to provide a relative surface soil moisture index, ranging between 0 and 1 (0 for the driest conditions, 1 for the wettest conditions) [37; 38; 36]. Using the change detection approach, [PERSON] et al. [37] operationally mapped the soil moisture over Calabria (Italy) using ASAR Wide Swath images in VV polarization with an accuracy of 7.3 vol %. [PERSON] et al. [36] operationally mapped the soil moisture over the northern and central parts of Tunisia using ENVISAT/ASAR data with a precision of approximately 3.5 vol %. At the plot scale and in agricultural areas, the change detection algorithm could not always be applied because the surface roughness frequently changes as a result of agricultural practices. Recently, [PERSON] et al. [39] developed a novel approach based on a new optical trapezoid model to estimate the soil moisture from Sentinel-2 images. This approach that uses a linear relationship between soil moisture and shortwave infrared transformed reflectance provides volumetric moisture content estimation errors about 5 vol %. Several studies tend to use the neural networks technique to invert backscattering models and estimate the soil moisture. [PERSON] et al. [40] developed an algorithm to retrieve the soil moisture content over smooth bare soils from ERS-SAR data (VV-23\({}^{\circ}\)).