Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study
The accurate monitoring and measurement of sea ice thickness (SIT) is crucial for understanding climate change and preventing economic losses caused by sea ice disasters near coastal regions. In this study, a new method is developed to retrieve the SIT in Liaodong Bay (LDB) based on the Rayleigh-cor...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/5/936 |
| _version_ | 1797415540406353920 |
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| author | Fengguan Gu Rui Zhang Xiangshan Tian-Kunze Bo Han Lei Zhu Tingwei Cui Qinghua Yang |
| author_facet | Fengguan Gu Rui Zhang Xiangshan Tian-Kunze Bo Han Lei Zhu Tingwei Cui Qinghua Yang |
| author_sort | Fengguan Gu |
| collection | DOAJ |
| description | The accurate monitoring and measurement of sea ice thickness (SIT) is crucial for understanding climate change and preventing economic losses caused by sea ice disasters near coastal regions. In this study, a new method is developed to retrieve the SIT in Liaodong Bay (LDB) based on the Rayleigh-corrected reflectance from Geostationary Ocean Color Imager (GOCI) images in the winters of 2012 and 2013. Compared with previously developed SIT retrieval methods (e.g., the method based on the thermodynamic principle of sea ice) using remote sensing data, our method has significant advantages with respect to the inversion accuracy (achieving retrieval skill scores as high as 0.86) and spatiotemporal resolution. Moreover, there is no significant increase in the computational cost with this method, which makes the method suitable for operational SIT retrieval in the global ocean. |
| first_indexed | 2024-03-09T05:50:06Z |
| format | Article |
| id | doaj.art-535ee344c63748bcafcd03eb5783d2c6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:50:06Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-535ee344c63748bcafcd03eb5783d2c62023-12-03T12:18:05ZengMDPI AGRemote Sensing2072-42922021-03-0113593610.3390/rs13050936Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case StudyFengguan Gu0Rui Zhang1Xiangshan Tian-Kunze2Bo Han3Lei Zhu4Tingwei Cui5Qinghua Yang6Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaSchool of Marine Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaAlfred-Wegener-Institut Helmholtz-Zentrum für Polar-und Meeresforschung, 27570 Bremerhaven, GermanySouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaSchool of Marine Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaThe accurate monitoring and measurement of sea ice thickness (SIT) is crucial for understanding climate change and preventing economic losses caused by sea ice disasters near coastal regions. In this study, a new method is developed to retrieve the SIT in Liaodong Bay (LDB) based on the Rayleigh-corrected reflectance from Geostationary Ocean Color Imager (GOCI) images in the winters of 2012 and 2013. Compared with previously developed SIT retrieval methods (e.g., the method based on the thermodynamic principle of sea ice) using remote sensing data, our method has significant advantages with respect to the inversion accuracy (achieving retrieval skill scores as high as 0.86) and spatiotemporal resolution. Moreover, there is no significant increase in the computational cost with this method, which makes the method suitable for operational SIT retrieval in the global ocean.https://www.mdpi.com/2072-4292/13/5/936sea ice thicknessinversion accuracyLiaodong BayGOCIremote sensing retrieval |
| spellingShingle | Fengguan Gu Rui Zhang Xiangshan Tian-Kunze Bo Han Lei Zhu Tingwei Cui Qinghua Yang Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study Remote Sensing sea ice thickness inversion accuracy Liaodong Bay GOCI remote sensing retrieval |
| title | Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study |
| title_full | Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study |
| title_fullStr | Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study |
| title_full_unstemmed | Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study |
| title_short | Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study |
| title_sort | sea ice thickness retrieval based on goci remote sensing data a case study |
| topic | sea ice thickness inversion accuracy Liaodong Bay GOCI remote sensing retrieval |
| url | https://www.mdpi.com/2072-4292/13/5/936 |
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