Description: Mass loss around the Antarctic Ice Sheet is driven by basal melting and iceberg calving,which constitute the two dominant paths of freshwater flux into the Southern Ocean. Although of similarmagnitude, icebergs play an important and still not fully understood role in the balance of heat andfreshwater around Antarctica. This lack of understanding is partly due to operational difficulties inlarge-scale monitoring in polar regions, despite observational and remote sensing efforts. In this study, anovel machine learning approach, augmented by visual inspection, was applied to three SyntheticAperture Radar (SAR) mosaics of the whole Antarctic continent and its adjacent coastal zone. Althoughoriginally intended for a mapping of the Antarctic continent, the SAR mosaics allow us to document theevolution and distribution of the size (and mass) of icebergs in the pan-Antarctic near-coastal zone for theyears 1997, 2000, and 2008. Our novel algorithm identified 7,649 icebergs in 1997, 13,712 icebergs in 2000,and 7,246 icebergs in 2008 with surface areas between 0.1 and 4,567.82 km2and total masses of 4,641.53,6,862.81, and 5,263.69 Gt, respectively. Large regional variability was observed, although a zonal patterndistribution is present. This has implications for future climate modeling studies that try to estimate thefreshwater flux from melting icebergs, which demands a realistic representation of the interannuallyvarying near-coastal iceberg pattern to initialize the simulations.

Description: Drifting icebergs represent a significant hazard for polar navigation and are able to impact the ocean environment around them. Freshwater flux and the associated cooling from melting icebergs can locally decrease salinity and temperature and thus affect ocean circulation, biological activity, sea ice, and –on larger spatial scales– the whole climate system. However, despite their potential impact, the large-scale operational monitoring of drifting icebergs in sea ice-covered regions is as of today typically restricted to giant icebergs, larger than 18.5 km in length. This is due to difficulties in accurately identifying and following the motion of much smaller features in the polar ocean from space. So far, tracking of smaller icebergs from satellite imagery thus has been limited to open-ocean regions not covered by sea ice. In this study, a novel automated iceberg tracking method, based on a machine learning-approach for automatic iceberg detection, is presented. To demonstrate the applicability of the method, a case study was performed for the Weddell Sea region, Antarctica, using 1213 Advanced Synthetic Aperture Radar (ASAR) satellite images acquired between 2002 and 2011. Overall, a subset of 414 icebergs (3134 re-detections in total) with surface areas between 3.4 km² and 3612 km² were investigated with respect to their prevalent drift patterns, size variability, and average disintegration. The majority of the tracked icebergs drifted between 1.3 km and 2679.2 km westward around the Antarctic continent, following the Antarctic Coastal Current (ACoC) and the Weddell Gyre, at an average drift speed of 3.6 ± 7.4 km day⁻¹. The method also allowed us to estimate an average daily disintegration (i.e. iceberg area decrease) rate of ~0.13% (~37% year⁻¹) for all icebergs. Using the sum of all detected individual surface area reductions, we estimate a total iceberg mass decrease of ~683 Gt year⁻¹, which can be freshwater input and/or new ‘child’ icebergs calved from larger icebergs. The extension to an automated long-term tracking method for icebergs is challenging as the iceberg shape can vary significantly due to abrupt disintegration or calving of bergy bits. However, our machine learning approach extended by automatic shape-based tracking capabilities proved to be a reliable alternative for automatic detection and tracking of icebergs, even under the ambiguous SAR background signatures often found in the Southern Ocean. In particular, the method works in the challenging near-coastal environment where the presence of sea ice and coastal ocean dynamics such as surface waves usually pose major obstacles for other approaches.

Description: Iceberg distribution, dispersion and melting patterns are fundamental aspects in the balance of heat and freshwater in the Southern Ocean; yet these features are not fully understood. This lack of understanding is, in part, due to the difficulties in accurately identifying icebergs in different environmental conditions. To improve the understanding, reliable iceberg detection tools are necessary to achieve a detailed picture of iceberg drift and disintegration patterns, an thus to gain further information on the freshwater input into the Southern Ocean. Here, we present an accurate automatic large-scale iceberg detection method using an alternative machine learning architecture applied to high resolution Synthetic Aperture Radar (SAR) images. Our method is based on the concept of adaptability and focuses on improving the performance of identifying icebergs in ambiguous environmental contexts with wide radiometric, textural, size and shape variability. The fundamentals of the method are centred on superpixel segmentation, ensemble learning and incremental learning. The method is applied to a dataset containing 586 ENVISAT Advanced SAR images acquired during 2003–2005 (Weddell Sea region) and to the Radarsat-1 Antarctic Mapping Project (RAMP) mosaic, covering the Antarctic wide near-coastal zone. These images cover scenes under heterogenous backscattering signatures for all seasons with variable meteorological, oceanographic and acquisition parameters (e.g. band, polarization). Our method is highly adaptable to distinguish icebergs from ambiguous objects hidden in the images. The average false positive rate and miss rate are 2.3 ± 0.4% and 3.3 ± 0.4%, respectively. Overall, 9512 icebergs with sizes varying from 0.1 to 4567.82 km2 are detected with average classification accuracy of 97.5 ± 0.6%. The results confirm that the method presented here is robust for widespread iceberg detection in the Antarctic seas.