Sophisticated snowpack models are required to provide accurate estimation of snowpack properties across the forest-tundra ecotone where in situ measurements are sparse. As snowpack properties strongly influence radar scattering signals, accurate simulation is crucial for the success of spaceborne radar missions to retrieve snow water equivalent (SWE). In this study, we evaluate the ability of default and Arctic versions of Crocus embedded within the Soil, Vegetation and Snow version 2 (SVS2-Crocus) land surface model to simulate snowpack properties (e.g. depth, density, SWE, specific surface area; SSA) across a 40-km transect of the Northwest Territories, Canada, using two winter seasons (2021–22 & 2022–23) of in situ measurements. An ensemble of simulated snowpack properties (120 members from default and Arctic SVS2-Crocus) was used in the Snow Microwave Radiative Transfer (SMRT) model to simulate Ku-band (13.5 GHz) backscatter. Modelled backscatter using multi-layer SVS2-Crocus snowpack simulations were compared to backscatter using a simplified 3-layer radar-equivalent snowpack. Results highlight that Arctic SVS2-Crocus wind-induced compaction modifications were spatially transferable across the forest-tundra ecotone, reducing the RMSE of surface density by an average of 29 %. Basal vegetation modifications were less effective in simulating low-density basal snow layers at all sites (2022 & 2023; default RMSE: 67 kg m^-3; Arctic RMSE: 69 kg m^-3) but were necessary to simulate a physically representative Arctic density profile. SVS2-Crocus underestimated SSA leading to high errors in the simulation of snow backscatter (2022 & 2023; default RMSE 3.5 dB; Arctic RMSE: 4.8 dB). RMSE of backscatter was reduced by implementing a minimum SSA value (8.7 m² kg^-1; 2022 & 2023; default RMSE: 1.5 dB; Arctic RMSE: 1.5 dB). A radar-equivalent snowpack was effective in retaining the scattering behaviour of the multi-layer snowpack (RMSE < 1 dB) providing a means to estimate SWE with increased computational efficiency.