Abstract
Helicopter-borne radar backscatter measurements are analyzed with respect to a multifrequency classification approach of sea ice.
Measurements were carried out over the Arctic Ocean during August and
September 2007 and represented unusually warm freeze-up conditions.
Radar cross sections (RCSs) of totally ice-free wind-roughened water are
used in combination with an ocean surface theoretical backscattering
model for the calibration. The calibrated RCS sigma degrees agrees
within 1 dB with nearly simultaneous Envisat Advanced Synthetic Aperture
Radar measurements and literature values. Sea ice was classified using a
Bayesian maximum likelihood approach. By including information from
simultaneous infrared and visible video imagery of sea ice, four
different surface types of sea ice could be identified in the resulting
sigma degrees: old ice, gray ice, nilas, and open water. The most
reliable classification was obtained through combination of copolarized
C-, X-, and Ku-band data. The results degraded by only 7% in the case
where the X-band information was dropped. On the other hand, a
combination of the C- and X-bands or the X- and Ku-bands yielded a
degradation of 13%. Given the remaining uncertainties in the approach,
for sea ice classification during summer/fall conditions, our results
suggest the complementary use of two of these three frequency bands
instead of relying on just one frequency band.
