Crevasses are formed by glacier movement and the stresses within glacier ice. Knowledge of the crevasses’ distribution is critical for understanding the glacier and ice shelf stability. In this study, we propose an automated crevasse extraction framework based on Sentinel-1 SAR imagery and an improved U-Net network. The spatial distribution of crevasses on Antarctic ice shelves in 2020 was mapped with a spatial resolution of ~40 m, and the characteristics of crevasses on the Nickerson Ice Shelf, Jelbart Ice Shelf, Amery Ice Shelf, Thwaites Glacier, and Shackleton Ice Shelf were analyzed. The results indicated the extraction accuracy of our method was 84.2% and the F1 score was 72.5%. Compared with previous published studies, the identification of the crevasse areas had good visual consistency. However, in some scenes, the recall rate was relatively lower due to the quality of the SAR image, terrain surrounding the crevasses, and observation geometry. The crevasses on different ice shelves had different characteristics in terms of length, density, type, and spatial pattern, implying the different stress structures of ice shelves. The Thwaites Glacier and the Nickerson Ice Shelf in the West Antarctica Ice Sheet (WAIS) had shorter ice crevasses, whereas the lengths of ice crevasses on the Jelbart Ice Shelf and the Amery Ice Shelf in the East Antarctica Ice Sheet (EAIS) were relatively long. Nevertheless, there are more closely spaced crevasses on the ice shelf in WAIS compared to that in the EAIS. For the distribution of crevasse types, the Nickerson Ice Shelf and the Shackleton Ice Shelf had various forms of crevasses. There were mainly transverse crevasses developed on the Jelbart Ice Shelf and the Amery Ice Shelf. This study provides a helpful reference and guidance for automated crevasse extraction. The method proposed by this study manifests great application potential and the efficacy of producing a time-series crevasse data set with higher spatial resolution and larger coverage. In the future, more Sentinel-1 SAR imagery will be applied and the effect of temporal and spatial variations in crevasses on the stability of ice shelves will be investigated, which will contribute to project the ice shelf stability and explore the sea level rise implications of recent and future cryosphere changes.

Snow depth is a fundamental parameter in hydrological and climate models, which is crucial for studying climate change, hydrological cycles, and ecosystem changes. Polarimetric synthetic aperture radar interferometry (PolInSAR) is one of the most promising snow depth retrieval methods, which is sensitive to the shape, direction, and vertical distribution of targets. The dense medium random-volume-over-ground (DM-RVoG) model for PolInSAR has been shown to be workable for snow depth retrieval, it still suffers from the inaccuracy of the parameters representing the phase center and decorrelation. In this study, based on the backscattering mechanism of snow, a novel snow depth retrieval method is proposed to improve the DM-RVoG model using polarization decomposition and decorrelation optimization. First, the polarization decomposition is extended to obtain the ground scattering phase. Then, the coherence region boundary estimation method is put forward to obtain pure volume decorrelation. Finally, the proposed method is validated using Ku-band UAV SAR data, and the accuracy is assessed using in-situ data. The correlation coefficient, root mean square error, and mean absolute error of the proposed method are 0.88, 4.98 cm, and 4.08 cm, respectively, demonstrating significant improvements compared with the original method.

Sea ice concentration (SIC) plays a significant role in climate change research and ship’s navigation in polar regions. Satellite-based SIC products have become increasingly abundant in recent years; however, the uncertainty of these products still exists and needs to be further investigated. To comprehensively evaluate the consistency of the SIC derived from different SIC algorithms in long time series and the whole polar regions, we compared four passive microwave (PM) satellite SIC products with the ERA-Interim sea ice fraction dataset during the period of 2015–2018. The PM SIC products include the SSMIS/ASI, AMSR2/BT, the Chinese FY3B/NT2, and FY3C/NT2. The results show that the remotely sensed SIC products derived from different SIC algorithms are generally in good consistency. The spatial and temporal distribution of discrepancy among satellite SIC products for both Arctic and Antarctic regions are also observed. The most noticeable difference for all the four SIC products mostly occurs in summer and at the marginal ice zone, indicating that large uncertainties exist in satellite SIC products in such period and areas. The SSMIS/ASI and AMSR2/BT show relatively better consistency with ERA-Interim in the Arctic and Antarctic, respectively, but they exhibit opposite bias (dry/wet) relative to the ERA-Interim data. The sea ice extent (SIE) and sea ice area (SIA) derived from PM and ERA-Interim SIC were also compared. It is found that the difference of PM SIE and SIA varies seasonally, which is in line with that of PM SIC, and the discrepancy between PM and ERA-Interim data is larger in Arctic than in Antarctic. We also noticed that different algorithms have different performances in different regions and periods; therefore, the hybrid of multiple algorithms is a promising way to improve the accuracy of SIC retrievals. It is expected that our findings can contribute to improving the satellite SIC algorithms and thus promote the application of these useful products in global climate change studies.