## 1 Introduction  
Information about distributed surface soil moisture content is important for assessing water availability for plant growth in alpine prairies because it impacts the length of the grazing season, the grass growth rate and nutrient uptake. Unfortunately, the spatial and temporal variations of soil moisture cannot be easily observed at large scales using conventional measurement techniques. Satellite remote sensing provides a uniquely effective and efficient means of monitoring and assessing soil moisture.  
The change in the soil dielectric constant as a result of variation in soil moisture content has more influence than other characteristics [1]. Therefore, radar remote sensing is sensitive to soil moisture because its dielectric constant is one of the most important factors in radar backscattering intensity [2]. A number of models that simulate soil moisture based on synthetic aperture radar (SAR) data have been developed for bare soil [3, 4, 5, 6, 7, 8, 9]. However, these models cannot be applied directly in vegetated areas due to the scattering of vegetation [10]. Vegetation canopies complicate the retrieval of moisture in the underlying soil because canopies contain moisture of their own [11]. Thus, SAR acquisition corresponds to the combined signatures of vegetation and soil water [12, 13]. Due to multiple scattering effects of the canopies, the interaction between the contributions of soil moisture, vegetation and observed backscattering is highly nonlinear [11, 14]. Therefore, the key problem in the quantitative estimation of soil moisture is to separate the contributions of vegetation backscatter and vegetation-covered soil moisture backscattering from the observed backscattering.  
To use SAR data for soil moisture estimation in a vegetated area, vegetation effects have been considered in several studies using vegetation scattering models [11, 15, 16, 17, 18]. The effects of vegetation on the SAR signature are controlled by its biophysical parameters (e.g., vegetation coverage and leaf area index), which can be derived by optical remote sensing. The vegetation parameters can be used to quantify vegetation attenuation of radar signals in radiative transfer function models [19]. To minimize the effect of vegetation, some researchers [14, 20, 21, 22] have attempted to use additional vegetation information provided by optical remote sensing, which has been widely used to derive information of vegetation properties. Furthermore, other studies [14, 23] have suggested that the accuracy of soil moisture estimates was significantly improved when optical and SAR data were combined as compared to estimates from SAR data only.  
Despite many successful retrieval studies and promising results achieved, some issues in mountain areas still remain unexplored and further research is required [24, 25]. The retrieval from remote sensing is much more challenging because of the presence of topography. In this paper, a synergistic method for applying optical and SAR remote sensing data to estimate soil moisture in a vegetated area was developed. The method was based on the vegetation coverage for calculating the contribution of the fraction of vegetation and bare soil at the pixel level to better represent the difference in the scattering mechanisms between vegetation and bare soil. The vegetation information was extracted from optical remote sensing to correct the SAR observations for the vegetation effects based on the Water Cloud Model (WCM) [26]. Thirty meter resolution digital elevation model (DEM) data were used to correct terrain-induced distortions in the SAR data. The vegetation correction procedure was embedded within a soil moisture retrieval algorithm for which the surface scattering component was formulated by the Integral Equation Method (IEM) [6]. Then, the developed method was applied to estimate soil moisture in the alpine grassland.