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Acquiring high quality Snow Water Equivalent (SWE) data presents a challenge that emanates from the intrinsic spatial variability of snow properties and complex alpine terrains. These challenges necessitate the development of precise remote sensing technologies that can accurately acquire snow data at both temporal and spatial scales. Here, we introduce machine learning (ML) algorithms to estimate distributed SWE from Ku-and X-band microwave radar frequency from the Snow Water Equivalent Synthetic Aperture Radar and Radiometer (SWESARR) instrument. In this study, the data was acquired at Grand Mesa, Colorado, during the peak snow accumulation period, by NASA's proprietary SWESARR instrument deployed during the Feb 2020 NASA SnowEx campaign. The instrument measures radar backscatter at 9.65 𝐺𝐻𝑧 (𝑋‑𝑏𝑎𝑛𝑑), 13.6 𝐺𝐻𝑧 (𝐾𝑢𝑙𝑜‑𝑏𝑎𝑛𝑑), and 17.25 𝐺𝐻𝑧 (𝐾𝑢ℎ𝑖‑𝑏𝑎𝑛𝑑) radar …