A 10-year record of Arctic summer sea ice freeboard from CryoSat-2

Geoffrey J Dawson; Jack C Landy; Michel Tsamados; Alexander S Komarov; Stephen Howell; Harry Heorton; Thomas Krumpen

doi:10.1016/j.rse.2021.112744

A 10-year record of Arctic summer sea ice freeboard from CryoSat-2

Geoffrey J Dawson^*
, Jack C Landy
, Michel Tsamados
, Alexander S Komarov
, Stephen Howell
, Harry Heorton
, Thomas Krumpen

^*Corresponding author for this work

School of Geographical Sciences

Research output: Contribution to journal › Article (Academic Journal) › peer-review

47 Citations (Scopus)

160 Downloads (Pure)

Abstract

Satellite observations of pan-Arctic sea ice thickness have so far been constrained to winter months. For radar altimeters, conventional methods cannot differentiate leads from meltwater ponds that accumulate at the ice surface in summer months, which is a critical step in the ice thickness calculation. Here, we use over 350 optical and synthetic aperture radar (SAR) images from the summer months to train a 1D convolution neural network for separating CryoSat-2 radar altimeter returns from sea ice floes and leads with an accuracy >80%. This enables us to generate the first pan-Arctic measurements of sea ice radar freeboard for May–September between 2011 and 2020. Results indicate that the freeboard distributions in May and September compare closely to those from a conventional ‘winter’ processor in April and October, respectively. The freeboards capture expected patterns of sea ice melt over the Arctic summer, matching well to ice draft observations from the Beaufort Gyre Exploration Program (BGEP) moorings. However, compared to airborne laser scanner freeboards from Operation IceBridge and airborne EM ice thickness surveys from the Alfred Wegener Institute (AWI) IceBird program, CryoSat-2 freeboards are underestimated by 0.02–0.2 m, and ice thickness is underestimated by 0.28–1.0 m, with the largest differences being over thicker multi-year sea ice. To create the first pan-Arctic summer sea ice thickness dataset we must address primary sources of uncertainty in the conversion from radar freeboard to ice thickness.

Original language	English
Article number	112744
Number of pages	18
Journal	Remote Sensing of Environment
Volume	268
Early online date	29 Oct 2021
DOIs	https://doi.org/10.1016/j.rse.2021.112744
Publication status	Published - 1 Jan 2022

Bibliographical note

Funding Information:
This paper is a contribution to the UK Natural Environment Research Council (NERC) Project ?PRE-MELT? under Grant NE/T000546/1. JL also acknowledges support from the European Space Agency Living Planet Fellowship ?Arctic-SummIT? under Grant ESA/4000125582/18/I-NS and from the Centre for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA) project through the Research Council of Norway (RCN) under Grant #237906. MT acknowledges support from ESA's ?CryoSat+ Antarctic Ocean? under grant ESA AO/1-9156/17/I-BG and MT & JL from the ?EXPRO+ Snow? under grant ESA AO/1-10061/19/I-EF. The authors thank the SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research & Exploitation) service available through ESA Grid Processing on Demand (GPOD) for providing Level 2 CryoSat-2 observations. This free-to-use service was invaluable for completing the objectives of our study. We acknowledge the use of imagery provided by services from RADARSAT-2 data and products ? MDA Geospatial Services Inc. ? All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency. RADARSAT-2 data are available for a fee from the Natural Resources Canada's Earth Observation Data Management System (www.eodms-sgdot.nrcan-rncan.gc.ca). We thank three anonymous reviewers for their comments.

Publisher Copyright:
© 2021 The Authors

Keywords

Sea ice
CryoSat-2
Radar altimetry
Radar freeboard

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title = "A 10-year record of Arctic summer sea ice freeboard from CryoSat-2",

abstract = "Satellite observations of pan-Arctic sea ice thickness have so far been constrained to winter months. For radar altimeters, conventional methods cannot differentiate leads from meltwater ponds that accumulate at the ice surface in summer months, which is a critical step in the ice thickness calculation. Here, we use over 350 optical and synthetic aperture radar (SAR) images from the summer months to train a 1D convolution neural network for separating CryoSat-2 radar altimeter returns from sea ice floes and leads with an accuracy >80\%. This enables us to generate the first pan-Arctic measurements of sea ice radar freeboard for May–September between 2011 and 2020. Results indicate that the freeboard distributions in May and September compare closely to those from a conventional {\textquoteleft}winter{\textquoteright} processor in April and October, respectively. The freeboards capture expected patterns of sea ice melt over the Arctic summer, matching well to ice draft observations from the Beaufort Gyre Exploration Program (BGEP) moorings. However, compared to airborne laser scanner freeboards from Operation IceBridge and airborne EM ice thickness surveys from the Alfred Wegener Institute (AWI) IceBird program, CryoSat-2 freeboards are underestimated by 0.02–0.2 m, and ice thickness is underestimated by 0.28–1.0 m, with the largest differences being over thicker multi-year sea ice. To create the first pan-Arctic summer sea ice thickness dataset we must address primary sources of uncertainty in the conversion from radar freeboard to ice thickness.",

keywords = "Sea ice, CryoSat-2, Radar altimetry, Radar freeboard",

author = "Dawson, \{Geoffrey J\} and Landy, \{Jack C\} and Michel Tsamados and Komarov, \{Alexander S\} and Stephen Howell and Harry Heorton and Thomas Krumpen",

note = "Funding Information: This paper is a contribution to the UK Natural Environment Research Council (NERC) Project ?PRE-MELT? under Grant NE/T000546/1. JL also acknowledges support from the European Space Agency Living Planet Fellowship ?Arctic-SummIT? under Grant ESA/4000125582/18/I-NS and from the Centre for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA) project through the Research Council of Norway (RCN) under Grant \#237906. MT acknowledges support from ESA's ?CryoSat+ Antarctic Ocean? under grant ESA AO/1-9156/17/I-BG and MT \& JL from the ?EXPRO+ Snow? under grant ESA AO/1-10061/19/I-EF. The authors thank the SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research \& Exploitation) service available through ESA Grid Processing on Demand (GPOD) for providing Level 2 CryoSat-2 observations. This free-to-use service was invaluable for completing the objectives of our study. We acknowledge the use of imagery provided by services from RADARSAT-2 data and products ? MDA Geospatial Services Inc. ? All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency. RADARSAT-2 data are available for a fee from the Natural Resources Canada's Earth Observation Data Management System (www.eodms-sgdot.nrcan-rncan.gc.ca). We thank three anonymous reviewers for their comments. Publisher Copyright: {\textcopyright} 2021 The Authors",

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doi = "10.1016/j.rse.2021.112744",

language = "English",

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T1 - A 10-year record of Arctic summer sea ice freeboard from CryoSat-2

AU - Dawson, Geoffrey J

AU - Landy, Jack C

AU - Tsamados, Michel

AU - Komarov, Alexander S

AU - Howell, Stephen

AU - Heorton, Harry

AU - Krumpen, Thomas

N1 - Funding Information: This paper is a contribution to the UK Natural Environment Research Council (NERC) Project ?PRE-MELT? under Grant NE/T000546/1. JL also acknowledges support from the European Space Agency Living Planet Fellowship ?Arctic-SummIT? under Grant ESA/4000125582/18/I-NS and from the Centre for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA) project through the Research Council of Norway (RCN) under Grant #237906. MT acknowledges support from ESA's ?CryoSat+ Antarctic Ocean? under grant ESA AO/1-9156/17/I-BG and MT & JL from the ?EXPRO+ Snow? under grant ESA AO/1-10061/19/I-EF. The authors thank the SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research & Exploitation) service available through ESA Grid Processing on Demand (GPOD) for providing Level 2 CryoSat-2 observations. This free-to-use service was invaluable for completing the objectives of our study. We acknowledge the use of imagery provided by services from RADARSAT-2 data and products ? MDA Geospatial Services Inc. ? All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency. RADARSAT-2 data are available for a fee from the Natural Resources Canada's Earth Observation Data Management System (www.eodms-sgdot.nrcan-rncan.gc.ca). We thank three anonymous reviewers for their comments. Publisher Copyright: © 2021 The Authors

PY - 2022/1/1

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N2 - Satellite observations of pan-Arctic sea ice thickness have so far been constrained to winter months. For radar altimeters, conventional methods cannot differentiate leads from meltwater ponds that accumulate at the ice surface in summer months, which is a critical step in the ice thickness calculation. Here, we use over 350 optical and synthetic aperture radar (SAR) images from the summer months to train a 1D convolution neural network for separating CryoSat-2 radar altimeter returns from sea ice floes and leads with an accuracy >80%. This enables us to generate the first pan-Arctic measurements of sea ice radar freeboard for May–September between 2011 and 2020. Results indicate that the freeboard distributions in May and September compare closely to those from a conventional ‘winter’ processor in April and October, respectively. The freeboards capture expected patterns of sea ice melt over the Arctic summer, matching well to ice draft observations from the Beaufort Gyre Exploration Program (BGEP) moorings. However, compared to airborne laser scanner freeboards from Operation IceBridge and airborne EM ice thickness surveys from the Alfred Wegener Institute (AWI) IceBird program, CryoSat-2 freeboards are underestimated by 0.02–0.2 m, and ice thickness is underestimated by 0.28–1.0 m, with the largest differences being over thicker multi-year sea ice. To create the first pan-Arctic summer sea ice thickness dataset we must address primary sources of uncertainty in the conversion from radar freeboard to ice thickness.

AB - Satellite observations of pan-Arctic sea ice thickness have so far been constrained to winter months. For radar altimeters, conventional methods cannot differentiate leads from meltwater ponds that accumulate at the ice surface in summer months, which is a critical step in the ice thickness calculation. Here, we use over 350 optical and synthetic aperture radar (SAR) images from the summer months to train a 1D convolution neural network for separating CryoSat-2 radar altimeter returns from sea ice floes and leads with an accuracy >80%. This enables us to generate the first pan-Arctic measurements of sea ice radar freeboard for May–September between 2011 and 2020. Results indicate that the freeboard distributions in May and September compare closely to those from a conventional ‘winter’ processor in April and October, respectively. The freeboards capture expected patterns of sea ice melt over the Arctic summer, matching well to ice draft observations from the Beaufort Gyre Exploration Program (BGEP) moorings. However, compared to airborne laser scanner freeboards from Operation IceBridge and airborne EM ice thickness surveys from the Alfred Wegener Institute (AWI) IceBird program, CryoSat-2 freeboards are underestimated by 0.02–0.2 m, and ice thickness is underestimated by 0.28–1.0 m, with the largest differences being over thicker multi-year sea ice. To create the first pan-Arctic summer sea ice thickness dataset we must address primary sources of uncertainty in the conversion from radar freeboard to ice thickness.

KW - Sea ice

KW - CryoSat-2

KW - Radar altimetry

KW - Radar freeboard

U2 - 10.1016/j.rse.2021.112744

DO - 10.1016/j.rse.2021.112744

M3 - Article (Academic Journal)

SN - 0034-4257

VL - 268

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

M1 - 112744

ER -

A 10-year record of Arctic summer sea ice freeboard from CryoSat-2

Abstract

Bibliographical note

Keywords

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