Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands
The occurrence of Supraglacial Lakes (SGLs) may influence the signals acquired with microwave radiometers, which may result in a degree of uncertainty when employing microwave radiometer data for the detection of surface melt. Accurate monitoring of surface melting requires a reasonable assessment o...
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ftdoajarticles:oai:doaj.org/article:d1c0167bb30d47f489ec727ab208c10b 2024-09-15T18:09:33+00:00 Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands Qian Li Che Wang Lu An Minghu Ding 2024-05-01T00:00:00Z https://doi.org/10.3390/rs16101673 https://doaj.org/article/d1c0167bb30d47f489ec727ab208c10b EN eng MDPI AG https://www.mdpi.com/2072-4292/16/10/1673 https://doaj.org/toc/2072-4292 doi:10.3390/rs16101673 2072-4292 https://doaj.org/article/d1c0167bb30d47f489ec727ab208c10b Remote Sensing, Vol 16, Iss 10, p 1673 (2024) Greenland Ice Sheet surface melt Ka-band L-band Supraglacial Lakes Science Q article 2024 ftdoajarticles https://doi.org/10.3390/rs16101673 2024-08-05T17:49:20Z The occurrence of Supraglacial Lakes (SGLs) may influence the signals acquired with microwave radiometers, which may result in a degree of uncertainty when employing microwave radiometer data for the detection of surface melt. Accurate monitoring of surface melting requires a reasonable assessment of this uncertainty. However, there is a scarcity of research in this field. Therefore, in this study, we computed surface melt in the vicinity of Automatic Weather Stations (AWSs) by employing Defense Meteorological Satellite Program (DMSP) Ka-band data and Soil Moisture and Ocean Salinity (SMOS) satellite L-band data and extracted SGL pixels by utilizing Sentinel-2 data. A comparison between surface melt results derived from AWS air temperature estimates and those obtained with remote sensing inversion in the two different bands was conducted for sites below the mean snowline elevation during the summers of 2016 to 2020. Compared with sites with no SGLs, the commission error (CO) of DMSP morning and evening data at sites where these water bodies were present increased by 36% and 30%, respectively, and the number of days with CO increased by 12 and 3 days, respectively. The omission error (OM) of SMOS morning and evening data increased by 33% and 32%, respectively, and the number of days with OM increased by 17 and 21 days, respectively. Identifying the source of error is a prerequisite for the improvement of surface melt algorithms, for which this study provides a basis. Article in Journal/Newspaper Greenland Ice Sheet Directory of Open Access Journals: DOAJ Articles Remote Sensing 16 10 1673 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Greenland Ice Sheet surface melt Ka-band L-band Supraglacial Lakes Science Q |
spellingShingle |
Greenland Ice Sheet surface melt Ka-band L-band Supraglacial Lakes Science Q Qian Li Che Wang Lu An Minghu Ding Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands |
topic_facet |
Greenland Ice Sheet surface melt Ka-band L-band Supraglacial Lakes Science Q |
description |
The occurrence of Supraglacial Lakes (SGLs) may influence the signals acquired with microwave radiometers, which may result in a degree of uncertainty when employing microwave radiometer data for the detection of surface melt. Accurate monitoring of surface melting requires a reasonable assessment of this uncertainty. However, there is a scarcity of research in this field. Therefore, in this study, we computed surface melt in the vicinity of Automatic Weather Stations (AWSs) by employing Defense Meteorological Satellite Program (DMSP) Ka-band data and Soil Moisture and Ocean Salinity (SMOS) satellite L-band data and extracted SGL pixels by utilizing Sentinel-2 data. A comparison between surface melt results derived from AWS air temperature estimates and those obtained with remote sensing inversion in the two different bands was conducted for sites below the mean snowline elevation during the summers of 2016 to 2020. Compared with sites with no SGLs, the commission error (CO) of DMSP morning and evening data at sites where these water bodies were present increased by 36% and 30%, respectively, and the number of days with CO increased by 12 and 3 days, respectively. The omission error (OM) of SMOS morning and evening data increased by 33% and 32%, respectively, and the number of days with OM increased by 17 and 21 days, respectively. Identifying the source of error is a prerequisite for the improvement of surface melt algorithms, for which this study provides a basis. |
format |
Article in Journal/Newspaper |
author |
Qian Li Che Wang Lu An Minghu Ding |
author_facet |
Qian Li Che Wang Lu An Minghu Ding |
author_sort |
Qian Li |
title |
Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands |
title_short |
Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands |
title_full |
Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands |
title_fullStr |
Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands |
title_full_unstemmed |
Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands |
title_sort |
influence of supraglacial lakes on accuracy of inversion of greenland ice sheet surface melt data in different passive microwave bands |
publisher |
MDPI AG |
publishDate |
2024 |
url |
https://doi.org/10.3390/rs16101673 https://doaj.org/article/d1c0167bb30d47f489ec727ab208c10b |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_source |
Remote Sensing, Vol 16, Iss 10, p 1673 (2024) |
op_relation |
https://www.mdpi.com/2072-4292/16/10/1673 https://doaj.org/toc/2072-4292 doi:10.3390/rs16101673 2072-4292 https://doaj.org/article/d1c0167bb30d47f489ec727ab208c10b |
op_doi |
https://doi.org/10.3390/rs16101673 |
container_title |
Remote Sensing |
container_volume |
16 |
container_issue |
10 |
container_start_page |
1673 |
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1810447123091554304 |