Methods to Improve the Accuracy and Robustness of Satellite-Derived Bathymetry through Processing of Optically Deep Waters
Selecting a representative optical deep-water area is crucial for accurate satellite-derived bathymetry (SDB) based on semi-theoretical and semi-empirical models. This study proposed a deep-water area selection method where potential areas were identified by integrating remote sensing imagery with e...
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| Format: | Article |
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MDPI AG
2023-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/22/5406 |
| _version_ | 1797457810890424320 |
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| author | Dongzhen Jia Yu Li Xiufeng He Zhixiang Yang Yihao Wu Taixia Wu Nan Xu |
| author_facet | Dongzhen Jia Yu Li Xiufeng He Zhixiang Yang Yihao Wu Taixia Wu Nan Xu |
| author_sort | Dongzhen Jia |
| collection | DOAJ |
| description | Selecting a representative optical deep-water area is crucial for accurate satellite-derived bathymetry (SDB) based on semi-theoretical and semi-empirical models. This study proposed a deep-water area selection method where potential areas were identified by integrating remote sensing imagery with existing global bathymetric data. Specifically, the effects of sun glint correction for deep-water areas on SDB estimation were investigated. The results indicated that the computed SDB had significant instabilities when different optical deep-water areas without sun glint correction were used for model training. In comparison, when sun glint correction was applied, the SDB results from different deep-water areas had greater consistency. We generated bathymetric maps for the Langhua Reef in the South China Sea and Buck Island near the U.S. Virgin Islands using Sentinel-2 multispectral images and 70% of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data. Additionally, 30% of the ICESat-2 bathymetry data and NOAA NGS Topo-bathy Lidar data served as the validation data to evaluate the qualities of the computed SDB, respectively. The results showed that the average quality of the SDB significantly improved with sun glint correction application by a magnitude of 0.60 m in terms of the root mean square error (<i>RMSE</i>) for two study areas. Moreover, an evaluation of the SDB data computed from different deep-water areas showed more consistent results, with <i>RMSE</i>s of approximately 0.4 and 1.4 m over the Langhua Reef and Buck Island, respectively. These values were consistently below 9% of the maximum depth. In addition, the effects of the optical image selection on SDB inversion were investigated, and the SDB calculated from the images over different time periods demonstrated similar results after applying sun glint correction. The results showed that this approach for optical deep-water area selection and correction could be used for improving the SDB, particularly in challenging scenarios, thereby enhancing the accuracy and robustness of SDB. |
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| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T16:29:19Z |
| publishDate | 2023-11-01 |
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| series | Remote Sensing |
| spelling | doaj.art-4a207c9998284d6582bac705620b99912023-11-24T15:04:46ZengMDPI AGRemote Sensing2072-42922023-11-011522540610.3390/rs15225406Methods to Improve the Accuracy and Robustness of Satellite-Derived Bathymetry through Processing of Optically Deep WatersDongzhen Jia0Yu Li1Xiufeng He2Zhixiang Yang3Yihao Wu4Taixia Wu5Nan Xu6School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, ChinaSchool of Earth Sciences and Engineering, Hohai University, Nanjing 211100, ChinaSchool of Earth Sciences and Engineering, Hohai University, Nanjing 211100, ChinaChina Railway Water Conservancy & Hydropower Planning and Design Group, Nanchang 330029, ChinaSchool of Earth Sciences and Engineering, Hohai University, Nanjing 211100, ChinaSchool of Earth Sciences and Engineering, Hohai University, Nanjing 211100, ChinaSchool of Earth Sciences and Engineering, Hohai University, Nanjing 211100, ChinaSelecting a representative optical deep-water area is crucial for accurate satellite-derived bathymetry (SDB) based on semi-theoretical and semi-empirical models. This study proposed a deep-water area selection method where potential areas were identified by integrating remote sensing imagery with existing global bathymetric data. Specifically, the effects of sun glint correction for deep-water areas on SDB estimation were investigated. The results indicated that the computed SDB had significant instabilities when different optical deep-water areas without sun glint correction were used for model training. In comparison, when sun glint correction was applied, the SDB results from different deep-water areas had greater consistency. We generated bathymetric maps for the Langhua Reef in the South China Sea and Buck Island near the U.S. Virgin Islands using Sentinel-2 multispectral images and 70% of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data. Additionally, 30% of the ICESat-2 bathymetry data and NOAA NGS Topo-bathy Lidar data served as the validation data to evaluate the qualities of the computed SDB, respectively. The results showed that the average quality of the SDB significantly improved with sun glint correction application by a magnitude of 0.60 m in terms of the root mean square error (<i>RMSE</i>) for two study areas. Moreover, an evaluation of the SDB data computed from different deep-water areas showed more consistent results, with <i>RMSE</i>s of approximately 0.4 and 1.4 m over the Langhua Reef and Buck Island, respectively. These values were consistently below 9% of the maximum depth. In addition, the effects of the optical image selection on SDB inversion were investigated, and the SDB calculated from the images over different time periods demonstrated similar results after applying sun glint correction. The results showed that this approach for optical deep-water area selection and correction could be used for improving the SDB, particularly in challenging scenarios, thereby enhancing the accuracy and robustness of SDB.https://www.mdpi.com/2072-4292/15/22/5406satellite-derived bathymetryremote sensingLanghua ReefBuck Islandshallow coastal waterssun glint |
| spellingShingle | Dongzhen Jia Yu Li Xiufeng He Zhixiang Yang Yihao Wu Taixia Wu Nan Xu Methods to Improve the Accuracy and Robustness of Satellite-Derived Bathymetry through Processing of Optically Deep Waters Remote Sensing satellite-derived bathymetry remote sensing Langhua Reef Buck Island shallow coastal waters sun glint |
| title | Methods to Improve the Accuracy and Robustness of Satellite-Derived Bathymetry through Processing of Optically Deep Waters |
| title_full | Methods to Improve the Accuracy and Robustness of Satellite-Derived Bathymetry through Processing of Optically Deep Waters |
| title_fullStr | Methods to Improve the Accuracy and Robustness of Satellite-Derived Bathymetry through Processing of Optically Deep Waters |
| title_full_unstemmed | Methods to Improve the Accuracy and Robustness of Satellite-Derived Bathymetry through Processing of Optically Deep Waters |
| title_short | Methods to Improve the Accuracy and Robustness of Satellite-Derived Bathymetry through Processing of Optically Deep Waters |
| title_sort | methods to improve the accuracy and robustness of satellite derived bathymetry through processing of optically deep waters |
| topic | satellite-derived bathymetry remote sensing Langhua Reef Buck Island shallow coastal waters sun glint |
| url | https://www.mdpi.com/2072-4292/15/22/5406 |
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