High-Resolution Snow Depth on Arctic Sea Ice From Low-Altitude Airborne Microwave Radar Data

arttu.jutila [ at ] awi.de


We present new high-resolution snow depth data on Arctic sea ice derived from airborne microwave radar measurements from the IceBird campaigns of the Alfred Wegener Institute (AWI) together with a new retrieval method using signal peakiness based on an intercomparison exercise of colocated data at different altitudes. We aim to demonstrate the capabilities and potential improvements of radar data, which were acquired at a lower altitude (200 ft) and slower speed (110 kn) and had a smaller radar footprint size (2-m diameter) than previous airborne snow radar data. So far, AWI Snow Radar data have been derived using a 2-18-GHz ultrawideband frequency-modulated continuous-wave (FMCW) radar in 2017-2019. Our results show that our method in combination with thorough calibration through coherent noise removal and system response deconvolution significantly improves the quality of the radar-derived snow depth data. The validation against a 2-D grid of in situ snow depth measurements on level landfast first-year ice indicates a mean bias of only 0.86 cm between radar and ground truth. Comparison between the radar-derived snow depth estimates from different altitudes shows good consistency. We conclude that the AWI Snow Radar aboard the IceBird campaigns is able to measure the snow depth on Arctic sea ice accurately at higher spatial resolution than but consistent with the existing airborne snow radar data of NASA Operation IceBridge. Together with the simultaneous measurements of the total ice thickness and surface freeboard, the IceBird campaign data will be able to describe the whole sea-ice column on regional scales.

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DOI 10.1109/TGRS.2021.3063756

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Jutila, A. , King, J. , Paden, J. , Ricker, R. , Hendricks, S. , Polashenski, C. , Helm, V. , Binder, T. and Haas, C. (2021): High-Resolution Snow Depth on Arctic Sea Ice From Low-Altitude Airborne Microwave Radar Data , IEEE Transactions on Geoscience and Remote Sensing, (60), pp. 1-16 . doi: 10.1109/TGRS.2021.3063756

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