Satellite Detection of Surface Water Extent: A Review of Methodology
Water is an imperative part of the Earth and an essential resource in human life and production. Under the effects of climate change and human activities, the spatial and temporal distribution of water bodies has been changing, and the shortage of water resources is becoming increasingly serious wor...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/14/7/1148 |
| _version_ | 1797437418731732992 |
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| author | Jiaxin Li Ronghua Ma Zhigang Cao Kun Xue Junfeng Xiong Minqi Hu Xuejiao Feng |
| author_facet | Jiaxin Li Ronghua Ma Zhigang Cao Kun Xue Junfeng Xiong Minqi Hu Xuejiao Feng |
| author_sort | Jiaxin Li |
| collection | DOAJ |
| description | Water is an imperative part of the Earth and an essential resource in human life and production. Under the effects of climate change and human activities, the spatial and temporal distribution of water bodies has been changing, and the shortage of water resources is becoming increasingly serious worldwide. Therefore, the monitoring of water bodies is indispensable. Remote sensing has the advantages of real time, wide coverage, and rich information and has become a brand-new technical means to quickly obtain water information. This study summarizes the current common methods of water extraction based on optical and radar images, including the threshold method, support vector machine, decision tree, object-oriented extraction, and deep learning, as well as the advantages and disadvantages of each method. These methods were applied to the Huai River Basin in China and Nam Co on the Qinghai-Tibet Plateau. The extraction results show that all the aforementioned approaches can obtain reliable results. Among them, the threshold segmentation method based on normalized difference water index is more robust than others. In the water extraction process, there are still many problems that restrict the accuracy of the results. In the future, researchers will continue to search for more automatic, extensive, and high-precision water extraction methods. |
| first_indexed | 2024-03-09T11:20:02Z |
| format | Article |
| id | doaj.art-3bb468f5a18342e0ae7398744ff4206c |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T11:20:02Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-3bb468f5a18342e0ae7398744ff4206c2023-12-01T00:20:55ZengMDPI AGWater2073-44412022-04-01147114810.3390/w14071148Satellite Detection of Surface Water Extent: A Review of MethodologyJiaxin Li0Ronghua Ma1Zhigang Cao2Kun Xue3Junfeng Xiong4Minqi Hu5Xuejiao Feng6Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, ChinaKey Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, ChinaKey Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, ChinaKey Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, ChinaKey Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, ChinaKey Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, ChinaKey Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, ChinaWater is an imperative part of the Earth and an essential resource in human life and production. Under the effects of climate change and human activities, the spatial and temporal distribution of water bodies has been changing, and the shortage of water resources is becoming increasingly serious worldwide. Therefore, the monitoring of water bodies is indispensable. Remote sensing has the advantages of real time, wide coverage, and rich information and has become a brand-new technical means to quickly obtain water information. This study summarizes the current common methods of water extraction based on optical and radar images, including the threshold method, support vector machine, decision tree, object-oriented extraction, and deep learning, as well as the advantages and disadvantages of each method. These methods were applied to the Huai River Basin in China and Nam Co on the Qinghai-Tibet Plateau. The extraction results show that all the aforementioned approaches can obtain reliable results. Among them, the threshold segmentation method based on normalized difference water index is more robust than others. In the water extraction process, there are still many problems that restrict the accuracy of the results. In the future, researchers will continue to search for more automatic, extensive, and high-precision water extraction methods.https://www.mdpi.com/2073-4441/14/7/1148water extractionremote sensingwater body indexclassificationmachine learning |
| spellingShingle | Jiaxin Li Ronghua Ma Zhigang Cao Kun Xue Junfeng Xiong Minqi Hu Xuejiao Feng Satellite Detection of Surface Water Extent: A Review of Methodology Water water extraction remote sensing water body index classification machine learning |
| title | Satellite Detection of Surface Water Extent: A Review of Methodology |
| title_full | Satellite Detection of Surface Water Extent: A Review of Methodology |
| title_fullStr | Satellite Detection of Surface Water Extent: A Review of Methodology |
| title_full_unstemmed | Satellite Detection of Surface Water Extent: A Review of Methodology |
| title_short | Satellite Detection of Surface Water Extent: A Review of Methodology |
| title_sort | satellite detection of surface water extent a review of methodology |
| topic | water extraction remote sensing water body index classification machine learning |
| url | https://www.mdpi.com/2073-4441/14/7/1148 |
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