Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression
The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightne...
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ftmdpi:oai:mdpi.com:/2072-4292/13/12/2283/ 2023-08-20T04:03:47+02:00 Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression Hyangsun Han Sungjae Lee Hyun-Cheol Kim Miae Kim agris 2021-06-10 application/pdf https://doi.org/10.3390/rs13122283 EN eng Multidisciplinary Digital Publishing Institute Ocean Remote Sensing https://dx.doi.org/10.3390/rs13122283 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 12; Pages: 2283 summer sea ice concentration Pacific Arctic Ocean AMSR2 ERA-5 Random Forest regression Text 2021 ftmdpi https://doi.org/10.3390/rs13122283 2023-08-01T01:55:42Z The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), ... Text Arctic Arctic Ocean Climate change Northern Sea Route Pacific Arctic Sea ice MDPI Open Access Publishing Arctic Arctic Ocean Pacific Remote Sensing 13 12 2283 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
summer sea ice concentration Pacific Arctic Ocean AMSR2 ERA-5 Random Forest regression |
spellingShingle |
summer sea ice concentration Pacific Arctic Ocean AMSR2 ERA-5 Random Forest regression Hyangsun Han Sungjae Lee Hyun-Cheol Kim Miae Kim Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression |
topic_facet |
summer sea ice concentration Pacific Arctic Ocean AMSR2 ERA-5 Random Forest regression |
description |
The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), ... |
format |
Text |
author |
Hyangsun Han Sungjae Lee Hyun-Cheol Kim Miae Kim |
author_facet |
Hyangsun Han Sungjae Lee Hyun-Cheol Kim Miae Kim |
author_sort |
Hyangsun Han |
title |
Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression |
title_short |
Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression |
title_full |
Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression |
title_fullStr |
Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression |
title_full_unstemmed |
Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression |
title_sort |
retrieval of summer sea ice concentration in the pacific arctic ocean from amsr2 observations and numerical weather data using random forest regression |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13122283 |
op_coverage |
agris |
geographic |
Arctic Arctic Ocean Pacific |
geographic_facet |
Arctic Arctic Ocean Pacific |
genre |
Arctic Arctic Ocean Climate change Northern Sea Route Pacific Arctic Sea ice |
genre_facet |
Arctic Arctic Ocean Climate change Northern Sea Route Pacific Arctic Sea ice |
op_source |
Remote Sensing; Volume 13; Issue 12; Pages: 2283 |
op_relation |
Ocean Remote Sensing https://dx.doi.org/10.3390/rs13122283 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13122283 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
12 |
container_start_page |
2283 |
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1774714215135707136 |