Abstract. Snow on sea ice is a key component of the Arctic climate system, strongly regulating the surface energy and mass balances of ice-covered regions. This study presents a long-term wintertime snow depth (SD) dataset on Arctic sea ice spanning the period 1978–2025 derived from multi-channel brightness temperature (TB) observations acquired by the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSMIS). We first analysed the relationship between the spectral gradient ratio (GR) of vertically polarized TBs at 19 and 37 GHz (GRV(37/19)) and altimetric snow depth derived from the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and CryoSat-2 missions. Based on this analysis, we proposed a new SD estimation algorithm using three predictors, including GRV(37/19), bulk snow density from the NASA Eulerian Snow On Sea Ice Model (NESOSIM) and a cumulative time variable to account for the effects of snow metamorphism on passive microwave retrievals. The resulting SYSU SnowDepth dataset was validated against various independent observations, categorized into point-scale and transect-based measurements. While the dataset demonstrates an overall good accuracy, the validation performance was strongly influenced by the spatial representativeness of the reference data. Specifically, root-mean-square error (RMSE) values ranged from ~3 to 7 cm against transect-based measurements from airborne and buoy array observations, but increased to 10 to 17 cm against point-scale measurements from individual buoys, aircraft landing sites and ship-based observations. Furthermore, retrieval accuracy was higher over first-year ice (FYI) than multi-year ice (MYI) and exhibited a seasonal variation with RMSE increasing from October to April as the snowpack thickens. To our knowledge, the SYSU SnowDepth dataset is the longest satellite-based SD record providing pan-Arctic coverage of both FYI and MYI throughout the full winter season (October–April). It is expected to significantly benefit altimetry-based sea ice thickness estimation, the assimilation of snow information into sea ice models, the assessment of light availability for under-ice biota, weather forecasting, and climate monitoring. The newly developed SYSU SnowDepth dataset is available at <a href="https://doi.org/10.5281/zenodo.21472646">https://doi.org/10.5281/zenodo.21472646</a> (He et al., 2026).