Spatial variations of snowpack properties are an essential component in flood predictions and water resource management. Satellite microwave remote sensing has shown great potential in retrieving snowpack properties such as: snow depth, snow grain size, and snow density. In this research, we investigate the potential of microwave emissivity which is highly influenced by snowpack properties. Brightness temperature and emissivity data generated from HUT (Helsinki University of Technology) microwave emission of snow model were evaluated with satellite microwave measurements. The comparison of the real measurements (in-situ and satellite) with the modeled results shows that the scattering signature (19GHz-37GHz and 19GHz-85GHz) shows better results in emissivities rather than brightness temperature data. Furthermore, the over the deep snow (>30cm), the emissivities scattering signature of (19GHz- 37GHz) has best performance while over shallow snow (<30cm) the emissivities scattering signature of (19GHz- 85GHz) performs superior. The results indicate the validity of grain growth assumption to some extent but it fails to address it quantitatively as a function of time.