The monitoring of vegetation growth conditions is a very important problem in proper agricultural management and in yield forecasting. Spectral reflectance signatures taken in the optical spectrum are useful for such applications, and a variety of information from optical sensors can be applied for estimating soil moisture values and conditions of vegetation growth. However, the acquisition of optical sensor data is often hampered by unfavorable weather conditions. Radar sensors are able to collect the data despite of clouds. This is the major reason why they are much more likely to provide useful information. The microwave backscatter value depends on sensor configuration such as incydent angle and polarization (Allen and Ulaby, 1984; Le Toan et al., 1984; Paloscia, 1998; Ulaby, 1978). Furthermore, it is affected by soil and plant dielectric and geometric properties (Ulaby et al., 1986; Dabrowska-Zielinska et al., 1994; Gruszczynska et al., 1998). Thus, it has been suggested that the combination of microwave signatures at different frequencies and/or polarizations may provide information on soil and vegetation conditions (Le Toan, 1982). The strong interaction of backscatter signal with soil and vegetation cannot be expressed by a simple linear function. It seems that mechanistic models may better characterize the contribution of various parameters on the observed backscattering signatures. The simulation of backscattering coefficient using the Water Cloud Model (WCM) was presented in the paper of Atema and Ulaby (1978) and modified by Prevot et al. (1993) and Champion (2000). The analysis based on the WCM uses two different SAR frequencies. Numeric inversion of the model may be useful in estimating soil moisture and vegetation descriptors. Thus, the goal of this study was to extract from dual-frequency and incidence angle satellite SAR signatures consistent information about moisture in soils and about various features of plants by implementation of the water-cloud model. The study was carried out on Polish agricultural regions but it is hoped that it will be applicable anywhere on the planet. During satellite overpasses, the ground-based measurements required such as soil moisture, Leaf Area Index, and biomass were collected. The backscattering coefficients were collected from ERS-2.SAR (C-VV band at 5.3 GHz, incidence angle 23o, spatial resolution 30 m) and from JERS-SAR (L-HH band at 1.275 GHz, incidence angle 35o, spatial resolution 18 m). The applicability of three different vegetation descriptors to the semi-empirical water-cloud model was investigated. The contribution to the backscatter values of vegetation features such as leaf area expressed in the Leaf Area Index (LAI) and the dielectric properties of leaf surface expressed in the Leaf Water Area Index (LWAI) and the Vegetation Water Mass (VWM) was examined in order to reveal the best fit of the model. It was found that in C-band, which had an incidence angle of 23o, the soil moisture contribution to the sigma value was predominant over the vegetation contribution. When the kanopy cover increases, the sensitivity of a radar signal to dry soil conditions (SM<0.1) decreased. The sigma value was the most sensitive to vegetation descriptor VWM which described the amount of water in vegetation. Attenuation of soil signal by the canopy was found in all three vegetation descriptor types; the strongest attenuation effect was observed in the case of VWM. In L-band (where the incidence angle was 35o), the dominant signal to total s° value comes from volume scattering of vegetation for LAI>3. When LAI<3 the vegetation contribution to total s° value appeared in two-way attenuation. The results gave us the possibility of comparing the modelled parameters with the measured soil and vegetationparameters. The study will continue using L band with different polarization from future ALOS PALSAR satellite and C band from ENVISAT ASAR various polarization. The combination of model simulating backscattering coefficient from canopy as Michigan Microwave Canopy Scattering model (Ulaby et al., 1990) and semi empirical model will be applied for different vegetation types not only for narrow leaf crops but also for broad leafs in order to verify possibility to increase the precision in obtaining soil vegetation descriptors.