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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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 |
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Open Polar |
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Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) |
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ftawi |
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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 |
_version_ |
1810488702092181504 |