The Russian and Norwegian Arctic are gaining notoriety as an alternative maritime route connecting the Atlantic and Pacific Oceans and as sources of natural resources. The renewed interest in the Northeast Passage or the Northern Sea Route is fueled by a recession of Arctic sea ice coupled with the discovery of new natural resources at a time when emerging and global markets are in growing demand for them. Driven by the expectation of potential future economic importance of the region, political interest and governance has been rapidly developing, mostly within the Arctic Council. However, this paper argues that optimism regarding the potential of Arctic routes as an alternative to the Suez Canal is overstated. The route involves many challenges: jurisdictional disputes create political uncertainties; shallow waters limit ship size; lack of modern deepwater ports and search and rescue (SAR) capabilities requires ships to have higher standards of autonomy and safety; harsh weather conditions and free-floating ice make navigation more difficult and schedules more variable; and more expensive ship construction and operation costs lessen the economic viability of the route. Technological advances and infrastructure investments may ameliorate navigational challenges, enabling increased shipping of natural resources from the Arctic to global markets.

An updated algorithm for the removal of thermal noise in Sentinel-1 synthetic aperture radar (SAR) level-1 ground range detected (GRD) data in cross-polarization is presented. The algorithm is comprised of two steps: correction of the annotated thermal noise magnitude (previously proposed in Park et al. , 2018) and a novel correction of the annotated thermal noise range dependence. The magnitude of the annotated thermal noise is corrected by applying scale and offset coefficients tuned on a few hundred of Sentinel-1 data acquired over surfaces with low backscatter in interferometric wide and extra-wide swath modes in HV and VH polarizations. The values of coefficients for all modes and polarizations for data processing with the Instrument Processing Facility (IPF) version 3.1–3.3 are provided. The range dependence is corrected by minimizing a cost function between the annotated range profiles of thermal noise and antenna pattern gain (APG). An objective validation metric based on a comparison of averaged backscatter at interswath boundaries is proposed. Validation is performed on hundreds of Sentinel-1 scenes acquired over the open ocean, doldrums, deserts, and sea ice. It shows that the new algorithm outperforms the standard thermal noise removal algorithm proposed by European Space Agency in almost all cases. Analysis shows that the new algorithm worsens noise correction in cases when the range dependence of the annotated APG does not match with the observed signal, indicating either problems with signal processing on IPF or imprecise annotation of APG.

High-resolution aerial photographs of Arctic region are a great source for different sea ice feature recognition, which are crucial to validate, tune and improve climate models. Melt ponds on the surface of melting Arctic sea ice are of particular interest as they are sensitive and valuable indicators and are proxy to the processes in the Arctic climate system. Manual analysis of this remote sensing data is extremely difficult and time-consuming due to the complex shapes and unpredictable boundaries of the melt ponds, and that leads to the necessity for automatizing the processes. In this study, we propose a robust and efficient automatic method for melt pond region segmentation and boundary extraction from high-resolution aerial photographs. The proposed algorithm is based on a swin transformer U-Net in which we introduce novel cross-channel attention mechanisms into the decoder design. The framework operates with optical data and allows for classifying imagery into four classes: sea ice/snow, open water, melt pond, and submerged ice. We use aerial photographs collected during the Healy-Oden Trans Arctic Expedition (HO-TRAX) expedition over Arctic sea ice in the summer season of 2005 as test data. The experimental results show that the proposed method is suitable for precise automatic extraction of melt pond geometry and it can also be extended for other optical data sources that involve melt ponds. The approach has a promising potential to be used to analyze melt ponds' corresponding changes between years.

Discussed is the hydrodynamic model of iceberg drift. Presented are the examples ofits use in operational practice for predicting iceberg drift in the Kara Sea and for enhancing ice monitoring in the Barents Sea.

Permafrost tundra contains more than twice as much carbon as is currently in the atmosphere, and it is warming six times as fast as the global mean. Tundra lakes dynamics is a robust indicator of global climate processes, and is still not well understood. Satellite data, particularly, from synthetic aperture radar (SAR) is a suitable tool for tundra lakes recognition and monitoring of their changes. However, manual analysis of lake boundaries can be slow and inefficient; therefore, reliable automated algorithms are required. To address this issue, we propose a two-stage approach, comprising instance deep-learning-based segmentation by U-Net, followed by semantic segmentation based on a watershed algorithm for separating touching and overlapping lakes. Implementation of this concept is essential for accurate sizes and shapes estimation of an individual lake. Here, we evaluated the performance of the proposed approach on lakes, manually extracted from tens of C-band SAR images from Sentinel-1, which were collected in the Yamal Peninsula and Alaska areas in the summer months of 2015–2022. An accuracy of 0.73, in terms of the Jaccard similarity index, was achieved. The lake’s perimeter, area and fractal sizes were estimated, based on the algorithm framework output from hundreds of SAR images. It was recognized as lognormal distributed. The evaluation of the results indicated the efficiency of the proposed approach for accurate automatic estimation of tundra lake shapes and sizes, and its potential to be used for further studies on tundra lake dynamics, in the context of global climate change, aimed at revealing new factors that could cause the planet to warm or cool.

A feature tracking techniques for sea ice drift retrieval from a pair of sequential satellite synthetic aperture radar (SAR) images are discussed. The Scale Invariant Feature Transform (SIFT), its alternative called ORB and A-KAZE features are selected for the intercomparison. The experimental results obtained for dual polarized Sentinel-1 C-SAR Extended Wide Swath mode data showed high relevance of feature tracking using nonlinear scale-spaces: A-KAZE. The approach exploits the benefits of nonlinear multi-scale image representations using A-KAZE features, which is a method that detects and describes image features in an anisotropic scale space that preserves important object boundaries while adaptively removing noise and small image details. It found that feature tracking using nonlinear scale-spaces on SAR images is preferable compared to other existing feature tracking alternatives that make use of Gaussian or linear scale spaces.