Accurate retrieval of Snow Water Equivalent (SWE) using Synthetic Aperture Radar (SAR) requires effectively decoupling the signal contribution of the snowpack from that of the underlying soil. This study evaluates a multi-frequency soil parameter inversion methodology using Snow Microwave Radiative Transfer (SMRT) model in a temperate, agro-forested environment in Powassan, Ontario. Using multi-frequency observations from Cryospheric SAR (CryoSAR) (L-band), Radarsat Constellation Mission (RCM) (C-band), and TerraSAR-X (TSX) (X-band) acquired during the 2022/2023 winter season, soil roughness and permittivity were jointly inverted to reproduce observed backscatter. The inversion strategy, which optimizes a single time-invariant roughness per site alongside time-varying permittivity, achieved strong agreement between simulated and observed signals across frequencies (Global R<sup>2</sup> = 0.87, RMSE =1.25 dB). Sensitivity analyses reveal a clear frequency-dependant hierarchy of controls: surface roughness dominates L-band backscatter (particularly in VV polarization), soil permittivity governs C- and X-band responses, and extending the analysis to explicitly include snow properties shows that the dominant controls progressively shift to snow microstructure and depth toward Ku-band. Comparisons with in situ measurements indicate that inverted parameters represent effective values at the radar scale; specifically, inverted roughness differs from LiDAR-derived topography, suggesting the influence of basal snow layer properties. Despite complications arising from spatial heterogeneity of soil properties including freeze/thaw cycles, the results demonstrate the feasibility of retrieving soil background parameters to support future multi-frequency snow missions such as Terrestrial Snow Mass Mission (TSMM).