## 1 Introduction
[PERSON] _et al._[18] and [PERSON] _et al._[20] compared different vegetation indices over two different sites in central Italy and northwestern USA and found that NDVI provides the most accurate estimation of grass biomass and height. Some studies have used another index known as the Normalized Difference Water Index (NDWI), which is computed using the NIR (near infra-red) and the SWIR (short wave infrared), to estimate vegetation water content [21, 22, 23, 24, 25]. [PERSON] _et al._[21] showed that the NDVI and the NDWI allow for similar precision in soybean and corn VWC estimates. [PERSON] _et al._[24] found that the NDWI is more sensitive to grassland drought conditions than the NDVI. The use of the NDVI and the NDWI for estimating vegetation biophysical parameters is limited due to the saturation of values when vegetation is high or very dense with high values of LAI. [PERSON] _et al._[20] reported that the NDVI saturated when the height of alfalfa exceeded 40 cm. [PERSON] _et al._[26] showed that the NDVI and the NDWI saturate when the LAI of corn and soybean surpassed 3.5 m\({}^{2}\)/m\({}^{2}\) and 4.5 m\({}^{2}\)/m\({}^{2}\), respectively. Synthetic aperture radars (SAR) have shown potential in the estimation of soil surface characteristics, especially surface roughness and soil moisture [27, 28, 29, 30, 31, 32, 33]. Moreover, many studies also assessed the sensitivity of SAR signals at different radar wavelengths (mainly the L-, C- and X-bands) to vegetation conditions [34, 35, 36, 37, 38, 39]. SAR data at L, C and X bands are the configurations most widely used for estimating soil moisture [27, 30, 32, 33, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]. Over bare soil and surfaces with little vegetation, the reflected radar signal depends on soil moisture, roughness, and radar configuration (incidence angle, polarization, and wavelength). The radar signal at C-band is more sensitive to soil moisture at low incidences than at higher incidences (approximately 20 dB/[cm\({}^{3}\)/cm\({}^{3}\)] for incidences between 20\({}^{\circ}\) and 35\({}^{\circ}\): radar signal increases of 2 dB when the soil moisture increases of 0.1 cm\({}^{3}\)/cm\({}^{3}\) and 10 dB/[cm\({}^{3}\)/cm\({}^{3}\)] for incidences higher than 35\({}^{\circ}\)) [45, 52, 53, 54]. The X-band signal at low and high incidences is more sensitive to soil moisture than C-band signals at low incidence angles (approximately 41 dB/[cm\({}^{3}\)/cm\({}^{3}\)] for HH polarization and an incidence of 25\({}^{\circ}\), and approximately 32 dB/[cm\({}^{3}\)/cm\({}^{3}\)] for HH polarization and an incidence of 50\({}^{\circ}\)) [27]. In general, a mean accuracy between 0.03 and 0.06 cm\({}^{3}\)/cm\({}^{3}\) on soil moisture estimates over bare soils can be achieved from the C and X band signals from SAR data [27, 29, 30, 32, 45, 52]. Over vegetated surfaces, radar signals depend on the soil surface characteristics, vegetation, and radar configuration. The penetration depth of the radar wave depends on whether the biophysical parameters of the scatterers within a vegetation layer (e.g., the water content, size and geometry of the scatterers) can enhance or attenuate the interaction between the radar wave and the scatterers. Different theoretical or semi-empirical approaches have been developed to account for the effects of vegetation cover [55, 56, 57, 58]. The most commonly used technique is referred to as the \"water cloud model\" [55]. This model describes the dependence between the radar signal and the vegetated surface parameters. In water-cloud models, the total backscattering signal (\(\sigma_{\rm total}\)) from the surface is the sum of the following signals: (a) the backscattered signal from the soil (\(\sigma_{\rm soil}\)) multiplied by the two-way attenuation (T\({}^{2}\)); and (b) the direct reflected signal from the vegetation (\(\sigma_{\rm veg}\)).