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ABSTRACT. Sea-ice drift fields were obtained from sequences of synthetic aperture radar (SAR) images using a method based on pattern recognition. The accuracy of the method was estimated for two image products of the Envisat Advanced SAR (ASAR) with 25 m and 150 m pixel size. For data from the winter season it was found that 99% of the south-north and west-east components of the determined displacement vector are within ±3-5 pixels of a manually derived reference dataset, independent of the image resolution. For an image pair with 25 m resolution acquired during summer, the corresponding value is 12 pixels. Using the same resolution cell dimensions for the displacement fields in both image types, the estimated displacement components differed by 150-300 m. The use of different texture parameters for predicting the performance of the algorithm dependent on ice conditions and image characteristics was studied. It was found that high entropy values indicate a good performance.

Abstract-Sea ice motion is triggered by wind and ocean currents. Its magnitude and direction can be automatically retrieved using pairs of satellite images acquired over the same area. However, external reference data for validation of drift retrievals, such as tracks from buoys, are sparse. Information about the reliability of the retrieved ice drift field is crucial for applications such as operational sea ice mapping or validation of computer models for simulations of sea ice dynamics. In this paper we introduce an intrinsic measure based on the properties of radar image pairs to assess the reliability of the retrieved ice drift vectors. The proposed method combines different parameters, e. g. correlation coefficient and two textural quantities, to provide information about the suitability of subimage regions for pattern matching. In this way, we generate a quality parameter (called confidence factor, CFA) for the calculated ice drift velocities. The CFA is compared to results obtained by 'backmatching'. The latter requires that the drift field is computed twice using the image pair, first in sequential and then in reversed order. For stable ice conditions, the results show that areas regarded as unreliable by the CFA compare well with the areas revealing larger differences from backmatching.

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[1] This study deals with observations and simulations of the evolution of coastal polynias focusing on the Ronne Polynia. We compare differences in polynia extent and ice drift patterns derived from satellite radar images and from simulations with the Finite Element Sea Ice Ocean Model, employing three atmospheric forcing data sets that differ in spatial and temporal resolution. Two polynia events are analyzed, one from austral summer and one from late fall 2008. The open water area in the polynia is of similar size in the satellite images and in the model simulations, but its temporal evolution differs depending on katabatic winds being resolved in the atmospheric forcing data sets. Modeled ice drift is slower than the observed and reveals greater turning angles relative to the wind direction in many cases. For the summer event, model results obtained with high-resolution forcing are closer to the drift field derived from radar imagery than those from coarse resolution forcing. For the late fall event, none of the forcing data yields outstanding results. Our study demonstrates that a dense (1-3 km) model grid and atmospheric forcing provided at high spatial resolution (<50 km) are critical to correctly simulate coastal polynias with a coupled sea-ice ocean model.