Snow is a vital component of the global land surface energy and water budget. In this study, we investigate the how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use an Observation System Simulation Experiment, specifically investigating how much snow simulated using popular models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24°-by-37° domain in the Western United States and Canada, simulating snow at approximately 250 m resolution and hourly timesteps in water-year 2019. We perform two data assimilation experiments, including: 1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals, and 2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that assimilating synthetic SWE observations improved average SWE biases at peak snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14%, to within 1%. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at peak snowpack were 111 mm, and average SWE biases were on the order of 150%. Here, the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18%) and the SWE mean absolute error (27 mm). Data assimilation also improved estimates of the temporal evolution of both SWE, even in spring snowmelt periods when melting snow and high snow liquid water content block the synthetic SWE retrievals. In fact, in the Upper Colorado River basin, melt-season SWE biases were improved from 63% to within 1%, and the Nash Sutcliffe Efficiency of runoff improved from –2.59 to 0.22. These results demonstrate the value of a snow-focused globally relevant remote sensing platform, and data assimilation for improving the characterization of SWE and associated water availability.