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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ftdoajarticles:oai:doaj.org/article:f493a8e7942f4d4380e3d592cfa9fb83 2023-05-15T14:46:42+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 2021-06-01T00:00:00Z https://doi.org/10.3390/rs13122283 https://doaj.org/article/f493a8e7942f4d4380e3d592cfa9fb83 EN eng MDPI AG https://www.mdpi.com/2072-4292/13/12/2283 https://doaj.org/toc/2072-4292 doi:10.3390/rs13122283 2072-4292 https://doaj.org/article/f493a8e7942f4d4380e3d592cfa9fb83 Remote Sensing, Vol 13, Iss 2283, p 2283 (2021) summer sea ice concentration Pacific Arctic Ocean AMSR2 ERA-5 Random Forest regression Science Q article 2021 ftdoajarticles https://doi.org/10.3390/rs13122283 2022-12-31T15:11:19Z 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 (T B ) 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 T B 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 T B values of AMSR2 channels, the ratios of T B 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 ... Article in Journal/Newspaper Arctic Arctic Ocean Climate change Northern Sea Route Pacific Arctic Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Arctic Ocean Pacific Remote Sensing 13 12 2283 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
summer sea ice concentration Pacific Arctic Ocean AMSR2 ERA-5 Random Forest regression Science Q |
spellingShingle |
summer sea ice concentration Pacific Arctic Ocean AMSR2 ERA-5 Random Forest regression Science Q 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 Science Q |
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 (T B ) 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 T B 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 T B values of AMSR2 channels, the ratios of T B 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 ... |
format |
Article in Journal/Newspaper |
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 |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13122283 https://doaj.org/article/f493a8e7942f4d4380e3d592cfa9fb83 |
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, Vol 13, Iss 2283, p 2283 (2021) |
op_relation |
https://www.mdpi.com/2072-4292/13/12/2283 https://doaj.org/toc/2072-4292 doi:10.3390/rs13122283 2072-4292 https://doaj.org/article/f493a8e7942f4d4380e3d592cfa9fb83 |
op_doi |
https://doi.org/10.3390/rs13122283 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
12 |
container_start_page |
2283 |
_version_ |
1766317895727448064 |