Automatic High-Accuracy Sea Ice Mapping in the Arctic Using MODIS Data
The sea ice cover is changing rapidly in polar regions, and sea ice products with high temporal and spatial resolution are of great importance in studying global climate change and navigation. In this paper, an ice map generation model based on Moderate-Resolution Imaging Spectroradiometer (MODIS) r...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/4/550 |
| _version_ | 1827603804986540032 |
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| author | Liyuan Jiang Yong Ma Fu Chen Jianbo Liu Wutao Yao Erping Shang |
| author_facet | Liyuan Jiang Yong Ma Fu Chen Jianbo Liu Wutao Yao Erping Shang |
| author_sort | Liyuan Jiang |
| collection | DOAJ |
| description | The sea ice cover is changing rapidly in polar regions, and sea ice products with high temporal and spatial resolution are of great importance in studying global climate change and navigation. In this paper, an ice map generation model based on Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance bands is constructed to obtain sea ice data with a high temporal and spatial resolution. By constructing a training sample library and using a multi-feature fusion machine learning algorithm for model classification, the high-accuracy recognition of ice and cloud regions is achieved. The first product provided by this algorithm is a near real-time single-scene sea ice presence map. Compared with the photo-interpreted ground truth, the verification shows that the algorithm can obtain a higher recognition accuracy for ice, clouds, and water, and the accuracy exceeds 98%. The second product is a daily and weekly clear sky map, which provides synthetic ice presence maps for one day or seven consecutive days. A filtering method based on cloud motion is used to make the product more accurate. The third product is a weekly fusion of clear sky optical images. In a comparison with the Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration products performed in August 2019 and September 2020, these composite images showed spatial consistency over time, suggesting that they can be used in many scientific and practical applications in the future. |
| first_indexed | 2024-03-09T05:45:52Z |
| format | Article |
| id | doaj.art-2b6b59178d914aa5a52cb5253a3abcaa |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:45:52Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2b6b59178d914aa5a52cb5253a3abcaa2023-12-03T12:20:49ZengMDPI AGRemote Sensing2072-42922021-02-0113455010.3390/rs13040550Automatic High-Accuracy Sea Ice Mapping in the Arctic Using MODIS DataLiyuan Jiang0Yong Ma1Fu Chen2Jianbo Liu3Wutao Yao4Erping Shang5Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaThe sea ice cover is changing rapidly in polar regions, and sea ice products with high temporal and spatial resolution are of great importance in studying global climate change and navigation. In this paper, an ice map generation model based on Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance bands is constructed to obtain sea ice data with a high temporal and spatial resolution. By constructing a training sample library and using a multi-feature fusion machine learning algorithm for model classification, the high-accuracy recognition of ice and cloud regions is achieved. The first product provided by this algorithm is a near real-time single-scene sea ice presence map. Compared with the photo-interpreted ground truth, the verification shows that the algorithm can obtain a higher recognition accuracy for ice, clouds, and water, and the accuracy exceeds 98%. The second product is a daily and weekly clear sky map, which provides synthetic ice presence maps for one day or seven consecutive days. A filtering method based on cloud motion is used to make the product more accurate. The third product is a weekly fusion of clear sky optical images. In a comparison with the Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration products performed in August 2019 and September 2020, these composite images showed spatial consistency over time, suggesting that they can be used in many scientific and practical applications in the future.https://www.mdpi.com/2072-4292/13/4/550sea iceMODIScloudArcticmapping |
| spellingShingle | Liyuan Jiang Yong Ma Fu Chen Jianbo Liu Wutao Yao Erping Shang Automatic High-Accuracy Sea Ice Mapping in the Arctic Using MODIS Data Remote Sensing sea ice MODIS cloud Arctic mapping |
| title | Automatic High-Accuracy Sea Ice Mapping in the Arctic Using MODIS Data |
| title_full | Automatic High-Accuracy Sea Ice Mapping in the Arctic Using MODIS Data |
| title_fullStr | Automatic High-Accuracy Sea Ice Mapping in the Arctic Using MODIS Data |
| title_full_unstemmed | Automatic High-Accuracy Sea Ice Mapping in the Arctic Using MODIS Data |
| title_short | Automatic High-Accuracy Sea Ice Mapping in the Arctic Using MODIS Data |
| title_sort | automatic high accuracy sea ice mapping in the arctic using modis data |
| topic | sea ice MODIS cloud Arctic mapping |
| url | https://www.mdpi.com/2072-4292/13/4/550 |
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