Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 Images
Surface water is a highly dynamical object on the earth’s surface. At present, satellite remote sensing is the most effective way to accurately depict the temporal and spatial variation characteristics of surface water on a large scale. In this study, a region-adaptive random forest algorithm is des...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/14/9/1454 |
| _version_ | 1797502476578979840 |
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| author | Hailong Tang Shanlong Lu Muhammad Hasan Ali Baig Mingyang Li Chun Fang Yong Wang |
| author_facet | Hailong Tang Shanlong Lu Muhammad Hasan Ali Baig Mingyang Li Chun Fang Yong Wang |
| author_sort | Hailong Tang |
| collection | DOAJ |
| description | Surface water is a highly dynamical object on the earth’s surface. At present, satellite remote sensing is the most effective way to accurately depict the temporal and spatial variation characteristics of surface water on a large scale. In this study, a region-adaptive random forest algorithm is designed on the Google Earth Engine (GEE) for automatic surface water mapping by using data from multi-sensors such as Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-1 SAR images as source data, and China as a case study region. The visual comparison of the mapping results with the original images under different landform areas shows that the extracted water body boundary is consistent with the water range in the image. The cross-validation with the JRC GSW validation samples shows a very high precision that the average producer’s accuracy and average user’s accuracy of water is 0.933 and 0.998, respectively. The average overall accuracy and average kappa is 0.966 and 0.931, respectively. The independent verification results of lakes with different areas also prove the high accuracy for our method, with a maximum average error of 3.299%. These results show that the method is an ideal way for large-scale surface water mapping with a high spatial–temporal resolution. |
| first_indexed | 2024-03-10T03:33:38Z |
| format | Article |
| id | doaj.art-d071a2a0fcc54854b007743fc82b3b6c |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T03:33:38Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-d071a2a0fcc54854b007743fc82b3b6c2023-11-23T09:36:01ZengMDPI AGWater2073-44412022-05-01149145410.3390/w14091454Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 ImagesHailong Tang0Shanlong Lu1Muhammad Hasan Ali Baig2Mingyang Li3Chun Fang4Yong Wang5Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaInstitute of Geo-Information & Earth-Observation (IGEO), PMAS Arid Agriculture University, Rawalpindi 46300, PakistanCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaSurface water is a highly dynamical object on the earth’s surface. At present, satellite remote sensing is the most effective way to accurately depict the temporal and spatial variation characteristics of surface water on a large scale. In this study, a region-adaptive random forest algorithm is designed on the Google Earth Engine (GEE) for automatic surface water mapping by using data from multi-sensors such as Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-1 SAR images as source data, and China as a case study region. The visual comparison of the mapping results with the original images under different landform areas shows that the extracted water body boundary is consistent with the water range in the image. The cross-validation with the JRC GSW validation samples shows a very high precision that the average producer’s accuracy and average user’s accuracy of water is 0.933 and 0.998, respectively. The average overall accuracy and average kappa is 0.966 and 0.931, respectively. The independent verification results of lakes with different areas also prove the high accuracy for our method, with a maximum average error of 3.299%. These results show that the method is an ideal way for large-scale surface water mapping with a high spatial–temporal resolution.https://www.mdpi.com/2073-4441/14/9/1454surface water mappingremote sensingrandom forestGoogle Earth Engine |
| spellingShingle | Hailong Tang Shanlong Lu Muhammad Hasan Ali Baig Mingyang Li Chun Fang Yong Wang Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 Images Water surface water mapping remote sensing random forest Google Earth Engine |
| title | Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 Images |
| title_full | Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 Images |
| title_fullStr | Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 Images |
| title_full_unstemmed | Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 Images |
| title_short | Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 Images |
| title_sort | large scale surface water mapping based on landsat and sentinel 1 images |
| topic | surface water mapping remote sensing random forest Google Earth Engine |
| url | https://www.mdpi.com/2073-4441/14/9/1454 |
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