Estimation of Arctic Sea Ice Thickness using Passive Microwave
The change in Arctic sea ice is an important measure of global warming. Especially, the sea ice has been melted as well as thinned rapidly in recent years. The thin ice is vulnerable to survive from the summer season and accelerates the decline of the sea ice extent (SIE). This study is to estimate...
| Main Authors: | , , , , , |
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| Format: | Conference Object |
| Language: | unknown |
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American Geophysical Union (AGU)
2020
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| Subjects: | |
| Online Access: | https://scholarworks.unist.ac.kr/handle/201301/49683 https://agu.confex.com/agu/osm20/meetingapp.cgi/Paper/650369 |
| Summary: | The change in Arctic sea ice is an important measure of global warming. Especially, the sea ice has been melted as well as thinned rapidly in recent years. The thin ice is vulnerable to survive from the summer season and accelerates the decline of the sea ice extent (SIE). This study is to estimate the sea ice thickness (SIT) of the first-year ice (FYI) using passive microwave brightness temperature (Tb). The brightness temperature data from the Soil Moisture and Ocean Salinity (SMOS) and the Special Sensor Microwave Imager/Sounder (SSMIS) were used. The sea ice concentration (SIC), snow depth (SD), Radio Frequency Interference (RFI), and location data (longitude and latitude) were additionally used to estimate SITs. This study developed the Random Forest (RF) model using the Ice Mass Balance (IMB) buoy dataset during 2012-2017. The model was compared to different SIT products based on Cryosat-2 and SMOS: Cryosat-2 SIT (CS2), Cryosat-2 and SMOS SIT (CS2SMOS), and SMOS SIT. The estimation accuracy of the RF model showed better root mean squared errors (RMSE) compared to CS2, CS2SMOS, and SMOS SIT (0.13, 1.43, 1.69, and 1.56m respectively). |
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