Monitoring Sea Ice in Liaodong Bay of Bohai Sea during the Freezing Period of 2017/2018 Using Sentinel-2 Remote Sensing Data
It is of great significance to monitor sea ice for relieving and preventing sea ice disasters. In this paper, the growth and development of sea ice in Liaodong Bay of Bohai Sea in China were monitored using Sentinel-2 remote sensing data during the freezing period from January to March in 2018. Base...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2021/9974845 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832561354801676288 |
|---|---|
| author | Zhiyong Wang Peilei Sun Lihua Wang Mengyue Zhang Zihao Wang |
| author_facet | Zhiyong Wang Peilei Sun Lihua Wang Mengyue Zhang Zihao Wang |
| author_sort | Zhiyong Wang |
| collection | DOAJ |
| description | It is of great significance to monitor sea ice for relieving and preventing sea ice disasters. In this paper, the growth and development of sea ice in Liaodong Bay of Bohai Sea in China were monitored using Sentinel-2 remote sensing data during the freezing period from January to March in 2018. Based on the comprehensive analysis of the spectral characteristics of seawater and sea ice in visible bands, supplemented by the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI), we proposed a new method based on decision tree classification for extracting sea ice types in Liaodong Bay of Bohai Sea. Using the remote sensing data of eight satellite overpasses acquired from Sentinel-2A/B satellites, the distribution and area of the different sea ice types in Liaodong Bay during the freezing period of 2017/2018 were obtained. Compared with the maximum likelihood (ML) classification method and the support vector machine (SVM) classification method, the proposed method has higher accuracy when discriminating the sea ice types, which proved the new method proposed in this paper is suitable for extracting sea ice types from Sentinel-2 optical remote sensing data in Liaodong Bay. And its classification accuracy reaches 88.05%. The whole process of evolution such as the growth and development of sea ice in Liaodong Bay during the freezing period from January to March in 2018 was monitored. The maximum area of sea ice was detected on 27 January 2018, about 10,187 km2. At last, the quantitative relationship model between the sea ice area and the mean near-surface temperature derived by MODIS data in Liaodong Bay was established. Through research, we found that the mean near-surface temperature was the most important factor for affecting the formation and melt of sea ice in Liaodong Bay. |
| format | Article |
| id | doaj-art-5575ed18fa5748e189073f7567a458f8 |
| institution | Kabale University |
| issn | 2314-4939 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-5575ed18fa5748e189073f7567a458f82025-02-03T01:25:14ZengWileyJournal of Spectroscopy2314-49392021-01-01202110.1155/2021/9974845Monitoring Sea Ice in Liaodong Bay of Bohai Sea during the Freezing Period of 2017/2018 Using Sentinel-2 Remote Sensing DataZhiyong Wang0Peilei Sun1Lihua Wang2Mengyue Zhang3Zihao Wang4College of Geodesy and GeomaticsShenyang Survey and Mapping Research Institute Co. Ltd.Changjiang River Scientific Research InstituteCollege of Geodesy and GeomaticsCollege of Geodesy and GeomaticsIt is of great significance to monitor sea ice for relieving and preventing sea ice disasters. In this paper, the growth and development of sea ice in Liaodong Bay of Bohai Sea in China were monitored using Sentinel-2 remote sensing data during the freezing period from January to March in 2018. Based on the comprehensive analysis of the spectral characteristics of seawater and sea ice in visible bands, supplemented by the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI), we proposed a new method based on decision tree classification for extracting sea ice types in Liaodong Bay of Bohai Sea. Using the remote sensing data of eight satellite overpasses acquired from Sentinel-2A/B satellites, the distribution and area of the different sea ice types in Liaodong Bay during the freezing period of 2017/2018 were obtained. Compared with the maximum likelihood (ML) classification method and the support vector machine (SVM) classification method, the proposed method has higher accuracy when discriminating the sea ice types, which proved the new method proposed in this paper is suitable for extracting sea ice types from Sentinel-2 optical remote sensing data in Liaodong Bay. And its classification accuracy reaches 88.05%. The whole process of evolution such as the growth and development of sea ice in Liaodong Bay during the freezing period from January to March in 2018 was monitored. The maximum area of sea ice was detected on 27 January 2018, about 10,187 km2. At last, the quantitative relationship model between the sea ice area and the mean near-surface temperature derived by MODIS data in Liaodong Bay was established. Through research, we found that the mean near-surface temperature was the most important factor for affecting the formation and melt of sea ice in Liaodong Bay.http://dx.doi.org/10.1155/2021/9974845 |
| spellingShingle | Zhiyong Wang Peilei Sun Lihua Wang Mengyue Zhang Zihao Wang Monitoring Sea Ice in Liaodong Bay of Bohai Sea during the Freezing Period of 2017/2018 Using Sentinel-2 Remote Sensing Data Journal of Spectroscopy |
| title | Monitoring Sea Ice in Liaodong Bay of Bohai Sea during the Freezing Period of 2017/2018 Using Sentinel-2 Remote Sensing Data |
| title_full | Monitoring Sea Ice in Liaodong Bay of Bohai Sea during the Freezing Period of 2017/2018 Using Sentinel-2 Remote Sensing Data |
| title_fullStr | Monitoring Sea Ice in Liaodong Bay of Bohai Sea during the Freezing Period of 2017/2018 Using Sentinel-2 Remote Sensing Data |
| title_full_unstemmed | Monitoring Sea Ice in Liaodong Bay of Bohai Sea during the Freezing Period of 2017/2018 Using Sentinel-2 Remote Sensing Data |
| title_short | Monitoring Sea Ice in Liaodong Bay of Bohai Sea during the Freezing Period of 2017/2018 Using Sentinel-2 Remote Sensing Data |
| title_sort | monitoring sea ice in liaodong bay of bohai sea during the freezing period of 2017 2018 using sentinel 2 remote sensing data |
| url | http://dx.doi.org/10.1155/2021/9974845 |
| work_keys_str_mv | AT zhiyongwang monitoringseaiceinliaodongbayofbohaiseaduringthefreezingperiodof20172018usingsentinel2remotesensingdata AT peileisun monitoringseaiceinliaodongbayofbohaiseaduringthefreezingperiodof20172018usingsentinel2remotesensingdata AT lihuawang monitoringseaiceinliaodongbayofbohaiseaduringthefreezingperiodof20172018usingsentinel2remotesensingdata AT mengyuezhang monitoringseaiceinliaodongbayofbohaiseaduringthefreezingperiodof20172018usingsentinel2remotesensingdata AT zihaowang monitoringseaiceinliaodongbayofbohaiseaduringthefreezingperiodof20172018usingsentinel2remotesensingdata |