Decadal Changes in Greenland Ice Sheet Firn Aquifers from Radar Scatterometer

Surface meltwater runoff is believed to be the main cause of the alarming mass loss in the Greenland Ice Sheet (GrIS); however, recent research has shown that a large amount of meltwater is not directly drained or refrozen but stored in the form of firn aquifers (FAs) in the interior of the GrIS. Mo...

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Published in:Remote Sensing
Main Authors: Xinyi Shang, Xiao Cheng, Lei Zheng, Qi Liang, Zhaohui Chi
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2022
Subjects:
Online Access:https://doi.org/10.3390/rs14092134
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spelling ftmdpi:oai:mdpi.com:/2072-4292/14/9/2134/ 2023-08-20T04:06:52+02:00 Decadal Changes in Greenland Ice Sheet Firn Aquifers from Radar Scatterometer Xinyi Shang Xiao Cheng Lei Zheng Qi Liang Zhaohui Chi 2022-04-29 application/pdf https://doi.org/10.3390/rs14092134 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs14092134 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 9; Pages: 2134 Greenland firn aquifers random forests radar scatterometer surface snowmelt Text 2022 ftmdpi https://doi.org/10.3390/rs14092134 2023-08-01T04:54:46Z Surface meltwater runoff is believed to be the main cause of the alarming mass loss in the Greenland Ice Sheet (GrIS); however, recent research has shown that a large amount of meltwater is not directly drained or refrozen but stored in the form of firn aquifers (FAs) in the interior of the GrIS. Monitoring the changes in FAs over the GrIS is of great importance to evaluate the stability and mass balance of the ice sheet. This is challenging because FAs are not visible on the surface and the direct measurements are lacking. A new method is proposed to map FAs during the 2010–2020 period by using the C-band Advanced Scatterometer (ASCAT) data based on the Random Forests classification algorithm with the aid of measurements from the NASA Operation IceBridge (OIB) program. Melt days (MD), melt intensity (MI), and winter mean backscatter (WM) parameters derived from the ASCAT data are used as the input vectors for the Random Forests classification algorithm. The accuracy of the classification model is assessed by ten-fold cross-validation, and the overall accuracy and Kappa coefficient are 97.49% and 0.72 respectively. The results show that FAs reached the maximum in 2015, and the accumulative area of FAs from 2010 to 2020 is 56,477 km2, which is 3.3% of the GrIS area. This study provides a way to investigate the long-term dynamics in FAs which have great significance for understanding the state of subsurface firn and subglacial hydrological systems. Text Greenland Ice Sheet NASA Operation IceBridge (OIB) MDPI Open Access Publishing Greenland Remote Sensing 14 9 2134
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic Greenland
firn aquifers
random forests
radar scatterometer
surface snowmelt
spellingShingle Greenland
firn aquifers
random forests
radar scatterometer
surface snowmelt
Xinyi Shang
Xiao Cheng
Lei Zheng
Qi Liang
Zhaohui Chi
Decadal Changes in Greenland Ice Sheet Firn Aquifers from Radar Scatterometer
topic_facet Greenland
firn aquifers
random forests
radar scatterometer
surface snowmelt
description Surface meltwater runoff is believed to be the main cause of the alarming mass loss in the Greenland Ice Sheet (GrIS); however, recent research has shown that a large amount of meltwater is not directly drained or refrozen but stored in the form of firn aquifers (FAs) in the interior of the GrIS. Monitoring the changes in FAs over the GrIS is of great importance to evaluate the stability and mass balance of the ice sheet. This is challenging because FAs are not visible on the surface and the direct measurements are lacking. A new method is proposed to map FAs during the 2010–2020 period by using the C-band Advanced Scatterometer (ASCAT) data based on the Random Forests classification algorithm with the aid of measurements from the NASA Operation IceBridge (OIB) program. Melt days (MD), melt intensity (MI), and winter mean backscatter (WM) parameters derived from the ASCAT data are used as the input vectors for the Random Forests classification algorithm. The accuracy of the classification model is assessed by ten-fold cross-validation, and the overall accuracy and Kappa coefficient are 97.49% and 0.72 respectively. The results show that FAs reached the maximum in 2015, and the accumulative area of FAs from 2010 to 2020 is 56,477 km2, which is 3.3% of the GrIS area. This study provides a way to investigate the long-term dynamics in FAs which have great significance for understanding the state of subsurface firn and subglacial hydrological systems.
format Text
author Xinyi Shang
Xiao Cheng
Lei Zheng
Qi Liang
Zhaohui Chi
author_facet Xinyi Shang
Xiao Cheng
Lei Zheng
Qi Liang
Zhaohui Chi
author_sort Xinyi Shang
title Decadal Changes in Greenland Ice Sheet Firn Aquifers from Radar Scatterometer
title_short Decadal Changes in Greenland Ice Sheet Firn Aquifers from Radar Scatterometer
title_full Decadal Changes in Greenland Ice Sheet Firn Aquifers from Radar Scatterometer
title_fullStr Decadal Changes in Greenland Ice Sheet Firn Aquifers from Radar Scatterometer
title_full_unstemmed Decadal Changes in Greenland Ice Sheet Firn Aquifers from Radar Scatterometer
title_sort decadal changes in greenland ice sheet firn aquifers from radar scatterometer
publisher Multidisciplinary Digital Publishing Institute
publishDate 2022
url https://doi.org/10.3390/rs14092134
geographic Greenland
geographic_facet Greenland
genre Greenland
Ice Sheet
NASA Operation IceBridge (OIB)
genre_facet Greenland
Ice Sheet
NASA Operation IceBridge (OIB)
op_source Remote Sensing; Volume 14; Issue 9; Pages: 2134
op_relation Remote Sensing in Geology, Geomorphology and Hydrology
https://dx.doi.org/10.3390/rs14092134
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs14092134
container_title Remote Sensing
container_volume 14
container_issue 9
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