A robust large-scale surface water mapping framework with high spatiotemporal resolution based on the fusion of multi-source remote sensing data
Large-scale and dynamic surface water mapping is crucial for understanding the impact of global climate change and human activities on the distribution of surface water resources. Remote sensing imagery has become the primary data source for surface water mapping due to its high spatiotemporal resol...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223001103 |
| _version_ | 1797842777744080896 |
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| author | Junjie Li Linyi Li Yanjiao Song Jiaming Chen Zhe Wang Yi Bao Wen Zhang Lingkui Meng |
| author_facet | Junjie Li Linyi Li Yanjiao Song Jiaming Chen Zhe Wang Yi Bao Wen Zhang Lingkui Meng |
| author_sort | Junjie Li |
| collection | DOAJ |
| description | Large-scale and dynamic surface water mapping is crucial for understanding the impact of global climate change and human activities on the distribution of surface water resources. Remote sensing imagery has become the primary data source for surface water mapping due to its high spatiotemporal resolution and wide coverage. However, the reliability of current water products during flood seasons is limited due to the influence of clouds on optical remote sensing images. Moreover, annual and seasonal surface water mapping cannot capture intra-month variations of water bodies. To address these challenges, we proposed a high spatiotemporal surface water mapping framework on Google Earth Engine that combines multi-source remote sensing data. Our framework can generate 10 m spatial resolution surface water maps at a 15-day time step. We classified water bodies using Sentinel-2 images and a classification tree algorithm, and then used Sentinel-1 data to compensate for cloudy and missing data areas in Sentinel-2 images, resulting in seamless cloud-unaffected surface water maps. We evaluated the effectiveness of our proposed framework in six floodplains around the world, and experimental results demonstrate that the water maps generated by our framework outperform existing public datasets and our framework has great potential for hydrological applications. Our proposed framework can capture the details of surface water dynamics with higher spatial and temporal resolution and is free from cloud influence, which is necessary for water resources management, flood monitoring, and disaster response. |
| first_indexed | 2024-04-09T16:54:50Z |
| format | Article |
| id | doaj.art-783abb3592134bb6a2db32fc8070aed1 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-09T16:54:50Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-783abb3592134bb6a2db32fc8070aed12023-04-21T06:43:03ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-04-01118103288A robust large-scale surface water mapping framework with high spatiotemporal resolution based on the fusion of multi-source remote sensing dataJunjie Li0Linyi Li1Yanjiao Song2Jiaming Chen3Zhe Wang4Yi Bao5Wen Zhang6Lingkui Meng7School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaCorresponding authors at: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China.; School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaCorresponding authors at: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China.; School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaLarge-scale and dynamic surface water mapping is crucial for understanding the impact of global climate change and human activities on the distribution of surface water resources. Remote sensing imagery has become the primary data source for surface water mapping due to its high spatiotemporal resolution and wide coverage. However, the reliability of current water products during flood seasons is limited due to the influence of clouds on optical remote sensing images. Moreover, annual and seasonal surface water mapping cannot capture intra-month variations of water bodies. To address these challenges, we proposed a high spatiotemporal surface water mapping framework on Google Earth Engine that combines multi-source remote sensing data. Our framework can generate 10 m spatial resolution surface water maps at a 15-day time step. We classified water bodies using Sentinel-2 images and a classification tree algorithm, and then used Sentinel-1 data to compensate for cloudy and missing data areas in Sentinel-2 images, resulting in seamless cloud-unaffected surface water maps. We evaluated the effectiveness of our proposed framework in six floodplains around the world, and experimental results demonstrate that the water maps generated by our framework outperform existing public datasets and our framework has great potential for hydrological applications. Our proposed framework can capture the details of surface water dynamics with higher spatial and temporal resolution and is free from cloud influence, which is necessary for water resources management, flood monitoring, and disaster response.http://www.sciencedirect.com/science/article/pii/S1569843223001103Surface waterGoogle Earth EngineRemote sensingSentinel-1Sentinel-2 |
| spellingShingle | Junjie Li Linyi Li Yanjiao Song Jiaming Chen Zhe Wang Yi Bao Wen Zhang Lingkui Meng A robust large-scale surface water mapping framework with high spatiotemporal resolution based on the fusion of multi-source remote sensing data International Journal of Applied Earth Observations and Geoinformation Surface water Google Earth Engine Remote sensing Sentinel-1 Sentinel-2 |
| title | A robust large-scale surface water mapping framework with high spatiotemporal resolution based on the fusion of multi-source remote sensing data |
| title_full | A robust large-scale surface water mapping framework with high spatiotemporal resolution based on the fusion of multi-source remote sensing data |
| title_fullStr | A robust large-scale surface water mapping framework with high spatiotemporal resolution based on the fusion of multi-source remote sensing data |
| title_full_unstemmed | A robust large-scale surface water mapping framework with high spatiotemporal resolution based on the fusion of multi-source remote sensing data |
| title_short | A robust large-scale surface water mapping framework with high spatiotemporal resolution based on the fusion of multi-source remote sensing data |
| title_sort | robust large scale surface water mapping framework with high spatiotemporal resolution based on the fusion of multi source remote sensing data |
| topic | Surface water Google Earth Engine Remote sensing Sentinel-1 Sentinel-2 |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223001103 |
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