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Synthetic Aperture Radar (SAR) satellite images are used to monitor Arctic sea ice, with systematic data records dating back to 1991. We propose a semi-supervised classification method that separates open water from sea ice and can utilise ERS-1/2, Envisat ASAR, RADARSAT-2 and Sentinel-1 SAR images. The classification combines automatic segmentation with a manual segment selection stage. The segmentation algorithm requires only the backscatter intensities and incidence angle values as input, therefore can be used to establish a consistent decadal sea ice record. In this study we investigate the sea ice conditions in two Svalbard fjords, Kongsfjorden and Rijpfjorden. Both fjords have a seasonal ice cover, though Rijpfjorden has a longer sea ice season. The satellite image dataset has weekly to daily records from 2002 until now, and less frequent records between 1991 and 2002. Time overlap between different sensors is investigated to ensure consistency in the reported sea ice cover. The classification results have been compared to high-resolution SAR data as well as in-situ measurements and sea ice maps from Ny-Ålesund. For both fjords the length of the sea ice season has shortened since 2002 and for Kongsfjorden the maximum sea ice coverage is significantly lower after 2006. [ABSTRACT FROM AUTHOR]

In this paper we investigate the potential of melt pond fraction retrieval from X-band polarimetric synthetic aperture radar (SAR) on drifting first-year sea ice. Melt pond fractions retrieved from a helicopter-borne camera system were compared to polarimetric features extracted from four dual-polarimetric X-band SAR scenes, revealing significant relationships. The correlations were strongly dependent on wind speed and SAR incidence angle. Copolarisation ratio was found to be the most promising SAR feature for melt pond fraction estimation at intermediate wind speeds (6:2ms-1), with a Spearman's correlation coefficient of 0:46. At low wind speeds (0:6ms-1), this relation disappeared due to low backscatter from the melt ponds, and backscatter VV-polarisation intensity had the strongest relationship to melt pond fraction with a correlation coefficient of -0:53. To further investigate these relations, regression fits were made both for the intermediate (Rfit2 D 0:21) and low (Rfit2 D 0:26) wind case, and the fits were tested on the satellite scenes in the study. The regression fits gave good estimates of mean melt pond fraction for the full satellite scenes, with less than 4% from a similar statistics derived from analysis of low-altitude imagery captured during helicopter ice-survey flights in the study area. A smoothing window of 51×51 pixels gave the best reproduction of the width of the melt pond fraction distribution. A considerable part of the backscatter signal was below the noise floor at SAR incidence angles above ~40°, restricting the information gain from polarimetric features above this threshold. Compared to previous studies in C-band, limitations concerning wind speed and noise floor set stricter constraints on melt pond fraction retrieval in X-band. Despite this, our findings suggest new possibilities in melt pond fraction estimation from X-band SAR, opening for expanded monitoring of melt ponds during melt season in the future. [ABSTRACT FROM AUTHOR]

In this study, we investigate the influence of Arctic sea ice surface roughness on full polarimetric C-band SAR features in the late summer season. Surface roughness measured from a helicopter-borne laser altimeter is compared to 14 individual SAR features holding a documented theoretical or empirical relation to surface roughness. Three C-band Radarsat-2 SAR scenes, covering a study area with both first-year ice and old ice surfaces with a variety of roughnesses are included in the study. Nine of the individual investigated SAR features were, in at least one of the scenes, correlated to laser altimeter measured root-mean-square height with a Spearman’s correlation coefficient better than $\pm 0.6$. The strength of the correlation varied from scene to scene, and the between-scene variation was explained by differences in incidence angle, changes in the meteorological conditions, and changes in the microscale surface roughness of both the sea ice and the melt pond surfaces. We highlight that knowledge of temperature and weather history could improve the quality of surface roughness interpretation in SAR scenes in the late summer. The findings are important for improved interpretation of satellite SAR sea ice imagery, relevant to, e.g., shipping and detection of sea ice changes in the context of climate research and monitoring. [ABSTRACT FROM PUBLISHER]