Synthetic Aperture Radar (SAR) images offer crucial information for studying and monitoring sea ice in the Arctic. Sentinel-1 captures images of the area using an extremely wide swath for reduced revisit time. The backscattered signal from sea ice and open water is often very weak, making it difficult to distinguish from the sensor thermal noise floor. Thermal noise impacts the images by generating a bias and increasing the fluctuations related to speckle phenomenon. Analyzing these images requires both correcting this bias and reducing fluctuations without blurring out the image content. The acquisition of several sub-swaths in a single pass using Terrain Observation with Progressive Scans (TOPS) produces images that exhibit, after compensation for antenna gains, a non-uniform thermal noise floor and strong discontinuities between sub-swaths. Denoising techniques must take these specificities into account to restore the images. This paper introduces a joint approach to remove the thermal noise offset and suppress fluctuations due to speckle and thermal noise. Compensating at once for all these effects largely reduces artifacts at the boundary between sub-swaths. We demonstrate using both numerical simulations and actual Sentinel-1 images that debiased polarimetric reflectivities can be recovered and fluctuations strongly reduced while preserving fine spatial structures.

Separation between deformed sea ice and high-backscatter young ice (YI) areas is one of the remaining challenges for automatic classification of sea ice types in synthetic aperture radar (SAR) images. While high-backscatter areas are usually interpreted and classified as deformed sea ice or multi-year ice, they may at times be new ice or YI areas with a rough surface due to frost flowers, snow crusts, saline snow cover from brine wicking, or finger rafting and small-scale deformation. Here we investigate the usefulness of the polarization difference (PD: VV-HH) for the detection, separation, and characterization of YI areas in L- (ALOS-2) and C-band (RADARSAT-2) SAR images.

The CIRFA-Cruise 2022 with RV Kronprins Haakon to the north-eastern coast of Greenland in the period April 22nd to May 9th 2022 was organised to perform measurements and make observations which allow for validation of sea ice remote sensing information and forecast products resulting from work in the Centre for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA), a Centre for Research-based Innovation at UiT the Arctic University of Norway. This paper uses data collected during the cruise to investigate questions related to the interpretation and temporal consistency of polarimetric features computed from a series of quad-pol Radarsat-2 (RS-2) images, which was collected over a fast ice site in the Belgica Bank area in the western Fram Strait. The CIRFA-2022 Cruise team visited this fast ice site in the end of April 2022. The time series covers the transition from cold winter conditions in April to melting in mid June. This transition impacts radar backscat-tering, as can be clearly seen in the Pauli decomposition of quad-pol images.

This paper presents some results obtained during the 2022 CIRFA arctic cruise concerning the measurements of the radar response of different types of sea-ice using a high-resolution Ground-Based radar system operating at C band. This MIMO device was able to directly measure tomograms, or slices of reflectivity in the elevation-ground plane, allowing to quantitatively appreciate the penetration of radar waves into sea-ice types having complex geometrical features, and to assess the dominant contributions measured by radar devices at C band.

Monitoring sea ice in polar regions is critical for understanding global climate change and supporting marine navigation. Recently, researchers started to utilize machine/deep learning methodologies to automate the separation of sea ice and open water in synthetic aperture radar imagery. However, this requires a large amount of reliably labeled training data. We here propose an augmentation routine for Sentinel-1 data which incorporates physical principles of radar backscatter into the augmentation procedure. Firstly, we apply an incidence angle aware algorithm to segment Sentinel-1 images into separate clusters. We compute the corresponding slopes of backscatter intensity with the incidence angle for each cluster. Secondly, the slopes are used as prior information to project labeled pixels and segments to different incidence angles and thus further enrich the labeled data. We then apply a simplified U-Net for pixel-wise classification of Sentinel-1 images into sea ice or open water. The performance of our model is evaluated by visual inspection as well as comparison with an available product from the Chinese Academy of Science (CAS). The results indicate that the physics-based data augmentation improves the model performance compared to training with data without augmentation. The inferred ice edge is in line with the inference for other available data sets (CAS), but with a finer spatial resolution. Finally, we also found the inference of our model is highly correlated with the visual interpretation of overlapping optical observations. Overall, the proposed methodology provides an alternative for the automated separation of sea ice/open water at fine spatial resolution.