Three years of near‐coastal Antarctic iceberg distribution from a machine learning approach applied to SAR imagery

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 andfreshwate...

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Published in:Journal of Geophysical Research: Oceans
Main Authors: Barbat, Mauro M., Rackow, Thomas, Hellmer, Hartmut, Wesche, Christine, Mata, Mauricio M.
Format: Article in Journal/Newspaper
Language:unknown
Published: 2019
Subjects:
Online Access:https://epic.awi.de/id/eprint/50401/
https://hdl.handle.net/10013/epic.cf254ff7-db74-4234-bfc0-19bd9e344031
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spelling ftawi:oai:epic.awi.de:50401 2024-09-15T17:42:12+00:00 Three years of near‐coastal Antarctic iceberg distribution from a machine learning approach applied to SAR imagery Barbat, Mauro M. Rackow, Thomas Hellmer, Hartmut Wesche, Christine Mata, Mauricio M. 2019 https://epic.awi.de/id/eprint/50401/ https://hdl.handle.net/10013/epic.cf254ff7-db74-4234-bfc0-19bd9e344031 unknown Barbat, M. M. , Rackow, T. orcid:0000-0002-5468-575X , Hellmer, H. orcid:0000-0002-9357-9853 , Wesche, C. orcid:0000-0002-9786-4010 and Mata, M. M. (2019) Three years of near‐coastal Antarctic iceberg distribution from a machine learning approach applied to SAR imagery , Journal of Geophysical Research: Oceans . doi:10.1029/2019JC015205 <https://doi.org/10.1029/2019JC015205> , hdl:10013/epic.cf254ff7-db74-4234-bfc0-19bd9e344031 EPIC3Journal of Geophysical Research: Oceans Article isiRev 2019 ftawi https://doi.org/10.1029/2019JC015205 2024-06-24T04:23:24Z 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. Article in Journal/Newspaper Antarc* Antarctic Antarctica Ice Sheet Iceberg* Southern Ocean Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) Journal of Geophysical Research: Oceans 124 9 6658 6672
institution Open Polar
collection Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center)
op_collection_id ftawi
language unknown
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.
format Article in Journal/Newspaper
author Barbat, Mauro M.
Rackow, Thomas
Hellmer, Hartmut
Wesche, Christine
Mata, Mauricio M.
spellingShingle Barbat, Mauro M.
Rackow, Thomas
Hellmer, Hartmut
Wesche, Christine
Mata, Mauricio M.
Three years of near‐coastal Antarctic iceberg distribution from a machine learning approach applied to SAR imagery
author_facet Barbat, Mauro M.
Rackow, Thomas
Hellmer, Hartmut
Wesche, Christine
Mata, Mauricio M.
author_sort Barbat, Mauro M.
title Three years of near‐coastal Antarctic iceberg distribution from a machine learning approach applied to SAR imagery
title_short Three years of near‐coastal Antarctic iceberg distribution from a machine learning approach applied to SAR imagery
title_full Three years of near‐coastal Antarctic iceberg distribution from a machine learning approach applied to SAR imagery
title_fullStr Three years of near‐coastal Antarctic iceberg distribution from a machine learning approach applied to SAR imagery
title_full_unstemmed Three years of near‐coastal Antarctic iceberg distribution from a machine learning approach applied to SAR imagery
title_sort three years of near‐coastal antarctic iceberg distribution from a machine learning approach applied to sar imagery
publishDate 2019
url https://epic.awi.de/id/eprint/50401/
https://hdl.handle.net/10013/epic.cf254ff7-db74-4234-bfc0-19bd9e344031
genre Antarc*
Antarctic
Antarctica
Ice Sheet
Iceberg*
Southern Ocean
genre_facet Antarc*
Antarctic
Antarctica
Ice Sheet
Iceberg*
Southern Ocean
op_source EPIC3Journal of Geophysical Research: Oceans
op_relation Barbat, M. M. , Rackow, T. orcid:0000-0002-5468-575X , Hellmer, H. orcid:0000-0002-9357-9853 , Wesche, C. orcid:0000-0002-9786-4010 and Mata, M. M. (2019) Three years of near‐coastal Antarctic iceberg distribution from a machine learning approach applied to SAR imagery , Journal of Geophysical Research: Oceans . doi:10.1029/2019JC015205 <https://doi.org/10.1029/2019JC015205> , hdl:10013/epic.cf254ff7-db74-4234-bfc0-19bd9e344031
op_doi https://doi.org/10.1029/2019JC015205
container_title Journal of Geophysical Research: Oceans
container_volume 124
container_issue 9
container_start_page 6658
op_container_end_page 6672
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