Reliable and accurate snow-thickness data from an airborne platform are currently highly sought after especially in Antarctica. Not only in its own right as a means of helping determine precipitation levels (predicted to increase with the effects of climate change), but also for increased accuracy in sea thickness retrieval from altimeter freeboard estimates. Lack of knowledge of the snow cover will lead to an overestimation of the sea-ice thickness by approximately seven times its snow-cover thickness, for example a snow thickness of 300 mm could lead to an error of 2.1 m in sea-ice thickness. Additionally, snow and sea ice have thermal heat conductivity differing by orders of magnitude (~2 W m^–1 K^–1 for sea ice and 0.3 W m^–1 K^–1 for snow). Hence snow on sea ice affects the ocean–sea-ice–atmosphere heat exchange by almost a factor of ten or more than ice thickness. Until now, FMCW radar operation to measure snow depth has been demonstrated to work from a sled-based platform over sea ice. The difficulty in achieving similar measurements from an airborne platform has been hampered by the stringent requirements of linearity imposed on the FMCW radar sweep during higher altitude operation; a problem not present in handheld or sled-operated radar. Validation of the data collected from a helicopter, which consists of snow as a function of time, with in situ measured snow thickness gathered as a function of distance is impossible without precise INS/GPS synchronization, and difficult with one. Finally, unlike terrestrial snow, or snow over ice sheets, which is mostly dry, smooth on the surface and quite thick (>300 mm), the snow over sea ice in Antarctica does not lend itself well to EM sensing. Due to constant motion of the unsheltered sea ice it is rough, often subject to flooding event and hence is often salty and wet. Consequently snow cover over sea ice in Antarctica presents a difficult scenario for remote sensing. This paper presents the first in situ validated snow-thickness estimates over sea ice in Antarctica derived from a FMCW radar on a helicopter-borne platform. Additionally, the radar extracted air/snow and snow/ice interfaces allow for an estimation of the roughness features of the two layers, a critical component in satellite snow-depth algorithms from AMSR-E. Finally, a quality factor is introduced in order to assess the confidence of the airborne snow-thickness retrievals currently not existing but necessary for model inputs and large-scale geophysical studies.