Abstract
A statistical analysis was performed for nearly simultaneously acquired C band synthetic aperture radar (SAR) images and ice freeboard statistics. The data analyzed were collected during a CryoSat calibration‐validation campaign in March 2005 in the Baltic Sea. The 3‐D ice freeboard topography along transects with a total length about 150 km and width of 300 m was constructed from cross‐track scanning airborne laser scanner measurements. The SAR image data set consisted of two nearly coincident Envisat Advanced Synthetic Aperture Radar alternating polarization precision and image mode precision images at HH polarization with their incidence angle ranges deviating about 20°. The data set represents primarily low‐salinity thin first year ice with high ice concentration and thin snow cover under cold weather conditions. The variance of the mean freeboard increased with increasing mean freeboard with the chosen window size (300 m) up to the mean freeboard of 15 cm. This multiplicative character of sea ice cover forms the basis by which we can expect that the C band backscattering coefficient response, which depends only on the ice surface roughness and the top part of ice medium, can provide information on the sea ice thickness. A nonlinear regression model with three control variables, the backscattering coefficient, the dominant thickness of the level ice which has deformed, and a variable accounting the effect of the SAR incidence angle, was established to predict the variation of the ice freeboard. The applied Bayesian approach also provided means to estimate the uncertainty range for the model fitting and the model predictions. The modeled predictions were mostly in good agreement with the measured values. The predictions for three different test lines accounted for 67%–85% of the measured freeboard variance in 300 m scale. In cold conditions with thin snow cover it was possible to estimate the degree of ice deformation and ice thickness quantitatively from C band SAR images.
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