Stefanie Linow

Inhomogenities in the sea ice motion field cause deformation zones, such as leads, cracks and pressure ridges. Due to their long and often narrow shape, those structures are referred to as Linear Kinematic Features (LKFs). In this paper we specifically address the identification and characterization of variations and discontinuities in the spatial distribution of the total deformation, which appear as LKFs. The distribution of LKFs in the ice cover of the polar oceans is an important factor influencing the exchange of heat and matter at the ocean-atmosphere interface. Current analyses of the sea ice deformation field often ignore the spatial/geographical context of individual structures, e.g., their orientation relative to adjacent deformation zones. In this study, we adapt image processing techniques to develop a method for LKF detection which is able to resolve individual features. The data are vectorized to obtain results on an object-based level. We then apply a semantic postprocessing step to determine the angle of junctions and between crossing structures. The proposed object detection method is carefully validated. We found a localization uncertainty of 0.75 pixel and a length error of 12% in the identified LKFs. The detected features can be individually traced to their geographical position. Thus, a wide variety of new metrics for ice deformation can be easily derived, including spatial parameters as well as the temporal stability of individual features.

The local mass balance of sea ice is dependent on the advection of ice into and out of an area and on the deformation processes in that area. Sea-ice motion can be observed from space by synthetic aperture radar (SAR) and quantified by drift-detection algorithms. Due to the scarcity of field observations, it remains a challenging task to validate the resulting motion fields. We analyse the quality of sea-ice motion fields derived from SAR data, using an example dataset from the Weddell Sea region. We apply a quality indicator for sea-ice motion fields which is independent of field data and evaluate it with reference data obtained from visual analysis of the SAR images. Together with the motion field, sea-ice deformation can also be retrieved from SAR data. Similarly to ice motion, it is very difficult to obtain field data to evaluate the quality of the results. Based on a manually derived reference dataset, we introduce a method to validate the retrieved deformation rates. This procedure requires no additional field data. Our analysis shows that deformation rates derived from SAR data are consistent with results obtained from buoy analysis by previous studies.

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.

The sensitivity of radar measurements to snow accumulation rate is determined by the firn volume characteristics of the ice sheets. Here we present a new approach for calculating the volume backscattering of dry firn, which is combined with recently developed empirical parameterizations of firn grain size and density as functions of depth, surface temperature, and accumulation rate. To this end, dense medium radiative transfer theory is applied to calculate the volume scattering and absorption coefficients. The coefficients for the density transition between snow and dense firn are evaluated using polynomial interpolation. For testing the method, we used measured accumulation rates, Envisat ASAR C‐band wide‐swath mode images, and QuikSCAT Ku‐band backscattering data from Dronning Maud Land, Antarctica. The comparison between measured and simulated backscattering coefficients shows that no tuning parameter is necessary to obtain the correct absolute level of scattering intensity. The robustness of accumulation rate retrievals depends on the consideration of technical and environmental factors. Due to the presence of sastrugi on the ice sheet surface the measured intensities are sensitive to the radar look direction. Wind compaction of snow and depth hoar formation change the depth‐dependent snow density and grain size profiles. Theoretical simulations revealed that the backscattering coefficient at C‐band is more sensitive to changes of accumulation rates than at Ku‐band. Penetration depths can vary significantly, dependent on radar frequency and firn characteristics. This has to be taken into account when comparing accumulation rates from different locations.

Firn microstructure properties from six different sites in Greenland and Antarctica are investigated by means of X-ray microcomputer tomography. The optical effective radius is calculated from the specific surface area (SSA) and used as a measure of grain size. It is shown that the recently introduced spherical approximation of firn grains using the effective radius R_eff is representative of grain size in the microwave frequency region. The measured profiles show the well-known increase of grain size with depth at all sites, where the increase is largest at near-surface depths. A large variability in grain size on the decimeter-to-centimeter scale as a result of different grain properties of single layers is superimposed on the overall trend at each site. A simple empirical parameterization of grain-size evolution is developed which allows the rapid grain growth in the uppermost layers of the firn to be predicted. The growth is driven by strong seasonal and diurnal temperature gradients. The model can be used to simulate grain-size profiles required by models of firn/microwave interaction (e.g. for retrieval of accumulation rates from satellite microwave sensors) in a more realistic fashion.