CryoSat-2 waveform classification for melt event monitoring
Measuring the mass balance of ice sheets is important with respect to understanding among others sea level rise, glacier dynamics, global ocean circulation and marine ecosystems. One important parameter of the mass balance is surface melt, which can be estimated from different satellite data sources...
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Septentrio Academic Publishing
2022
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ftunitroemsoe:oai:ojs.henry.ub.uit.no:article/6284 2023-05-15T16:21:22+02:00 CryoSat-2 waveform classification for melt event monitoring Vermeer, Martijn Völgyes, David McMillan, Malcolm Fantin, Daniele 2022-03-28 application/pdf https://septentrio.uit.no/index.php/nldl/article/view/6284 https://doi.org/10.7557/18.6284 eng eng Septentrio Academic Publishing https://septentrio.uit.no/index.php/nldl/article/view/6284/6556 https://septentrio.uit.no/index.php/nldl/article/view/6284 doi:10.7557/18.6284 Copyright (c) 2022 Martijn Vermeer, David Völgyes, Malcolm McMillan, Daniele Fantin https://creativecommons.org/licenses/by/4.0 CC-BY Proceedings of the Northern Lights Deep Learning Workshop; Vol. 3 (2022): Proceedings of the Northern Lights Deep Learning Workshop 2022 2703-6928 deep learning surface mass balance melt dynamics Greenland ice sheet Cryosat-2 MODIS info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article 2022 ftunitroemsoe https://doi.org/10.7557/18.6284 2022-03-31T01:48:58Z Measuring the mass balance of ice sheets is important with respect to understanding among others sea level rise, glacier dynamics, global ocean circulation and marine ecosystems. One important parameter of the mass balance is surface melt, which can be estimated from different satellite data sources. In this study we investigate the potential of utilizing machine learning techniques for CryoSat-2 (CS2) radar altimeter waveform classification in order to derive melt information. Training data is derived by spatio-temporally matching of CS2 measurements with MODIS land surface temperature measurements. We propose a time convolution network with a fully connected classifier tail for CS2 waveform classifcation. In addition a non-deep learning model is implemented, providing a baseline. One of the main challenges is the high class imbalance, as surface temperatures on the interior of Greenland rarely reach the freezing point. The model performance is measured by several metrics: F1 score, average recall and Matthews correlation coefficient. The results of this proof of concept study indicate feasibility. Article in Journal/Newspaper glacier Greenland Ice Sheet University of Tromsø: Septentrio Academic Publishing Greenland Proceedings of the Northern Lights Deep Learning Workshop 3 |
institution |
Open Polar |
collection |
University of Tromsø: Septentrio Academic Publishing |
op_collection_id |
ftunitroemsoe |
language |
English |
topic |
deep learning surface mass balance melt dynamics Greenland ice sheet Cryosat-2 MODIS |
spellingShingle |
deep learning surface mass balance melt dynamics Greenland ice sheet Cryosat-2 MODIS Vermeer, Martijn Völgyes, David McMillan, Malcolm Fantin, Daniele CryoSat-2 waveform classification for melt event monitoring |
topic_facet |
deep learning surface mass balance melt dynamics Greenland ice sheet Cryosat-2 MODIS |
description |
Measuring the mass balance of ice sheets is important with respect to understanding among others sea level rise, glacier dynamics, global ocean circulation and marine ecosystems. One important parameter of the mass balance is surface melt, which can be estimated from different satellite data sources. In this study we investigate the potential of utilizing machine learning techniques for CryoSat-2 (CS2) radar altimeter waveform classification in order to derive melt information. Training data is derived by spatio-temporally matching of CS2 measurements with MODIS land surface temperature measurements. We propose a time convolution network with a fully connected classifier tail for CS2 waveform classifcation. In addition a non-deep learning model is implemented, providing a baseline. One of the main challenges is the high class imbalance, as surface temperatures on the interior of Greenland rarely reach the freezing point. The model performance is measured by several metrics: F1 score, average recall and Matthews correlation coefficient. The results of this proof of concept study indicate feasibility. |
format |
Article in Journal/Newspaper |
author |
Vermeer, Martijn Völgyes, David McMillan, Malcolm Fantin, Daniele |
author_facet |
Vermeer, Martijn Völgyes, David McMillan, Malcolm Fantin, Daniele |
author_sort |
Vermeer, Martijn |
title |
CryoSat-2 waveform classification for melt event monitoring |
title_short |
CryoSat-2 waveform classification for melt event monitoring |
title_full |
CryoSat-2 waveform classification for melt event monitoring |
title_fullStr |
CryoSat-2 waveform classification for melt event monitoring |
title_full_unstemmed |
CryoSat-2 waveform classification for melt event monitoring |
title_sort |
cryosat-2 waveform classification for melt event monitoring |
publisher |
Septentrio Academic Publishing |
publishDate |
2022 |
url |
https://septentrio.uit.no/index.php/nldl/article/view/6284 https://doi.org/10.7557/18.6284 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
glacier Greenland Ice Sheet |
genre_facet |
glacier Greenland Ice Sheet |
op_source |
Proceedings of the Northern Lights Deep Learning Workshop; Vol. 3 (2022): Proceedings of the Northern Lights Deep Learning Workshop 2022 2703-6928 |
op_relation |
https://septentrio.uit.no/index.php/nldl/article/view/6284/6556 https://septentrio.uit.no/index.php/nldl/article/view/6284 doi:10.7557/18.6284 |
op_rights |
Copyright (c) 2022 Martijn Vermeer, David Völgyes, Malcolm McMillan, Daniele Fantin https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.7557/18.6284 |
container_title |
Proceedings of the Northern Lights Deep Learning Workshop |
container_volume |
3 |
_version_ |
1766009370549682176 |