Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 Data
Satellite technology is an efficient tool, which can provide valuable observations for coastal areas from space. Compared with conventional bathymetric surveying approaches, remote sensing-based shallow water bathymetry retrieval methods have been widely used in recent years. Various empirical model...
| Main Authors: | , , , , , |
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10306283/ |
| _version_ | 1797376370253234176 |
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| author | Nan Xu Lin Wang Han-Su Zhang Shilin Tang Fan Mo Xin Ma |
| author_facet | Nan Xu Lin Wang Han-Su Zhang Shilin Tang Fan Mo Xin Ma |
| author_sort | Nan Xu |
| collection | DOAJ |
| description | Satellite technology is an efficient tool, which can provide valuable observations for coastal areas from space. Compared with conventional bathymetric surveying approaches, remote sensing-based shallow water bathymetry retrieval methods have been widely used in recent years. Various empirical models have been proposed for deriving bathymetry of coastal shallow water, and prior topographic information is required to construct models. Traditional studies tend to select a cloud-free remote sensing image to map the coastal shallow water topography. As a result, in addition to the selection of empirical models, the high-quality remote sensing image and accurate prior topographic data are also of importance. This study aims to propose a method for mapping coastal shallow water bathymetry from multitemporal remote sensing imagery. Here, Sentinel-2 imagery time series are composited to produce a clear image, which can effectively avoid the contamination of clouds, water turbidity and other noises. ICESat-2 lidar altimeter data that contain accurate underwater elevations are used to provide topographic information. Moreover, Sentinel-2-based multispectral information and ICESat-2-based topographic information are combined for the coastal bathymetry retrieval by five empirical models (i.e., linear band model, ratio band model, support vector machine, neural network, and random forest). This proposed method is tested in Dongsha Atoll in South China Sea, and achieve a good performance [training: root mean square error (RMSE): 0.97 m ± 0.76 m, mean absolute percentage error (MAPE): 4.07% ± 0.046%, R-square (R<sup>2</sup>): 0.90 ± 0.14; validation: RMSE: 1.22 m ± 0.43 m, MAPE: 5.43% ± 0.035%, R<sup>2</sup>: 0.86 ± 0.089]. The comparison confirms that machine learning methods perform better than traditional methods, and the deep learning techniques can be further introduced in estimating shallow water bathymetry in the future, which is expected to achieve an excellent accuracy in bathymetry inversion. |
| first_indexed | 2024-03-08T19:38:43Z |
| format | Article |
| id | doaj.art-ad1cbed562f84315852ec53dbf431efd |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-08T19:38:43Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-ad1cbed562f84315852ec53dbf431efd2023-12-26T00:01:24ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352024-01-01171748175510.1109/JSTARS.2023.332623810306283Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 DataNan Xu0https://orcid.org/0000-0001-9912-2347Lin Wang1https://orcid.org/0000-0001-9469-7512Han-Su Zhang2https://orcid.org/0009-0009-9210-2066Shilin Tang3https://orcid.org/0000-0002-0581-1203Fan Mo4https://orcid.org/0000-0002-5105-846XXin Ma5https://orcid.org/0000-0002-0969-2838College of Geography and Remote Sensing, Hohai University, Nanjing, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaSchool of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, ChinaState Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaSatellite technology is an efficient tool, which can provide valuable observations for coastal areas from space. Compared with conventional bathymetric surveying approaches, remote sensing-based shallow water bathymetry retrieval methods have been widely used in recent years. Various empirical models have been proposed for deriving bathymetry of coastal shallow water, and prior topographic information is required to construct models. Traditional studies tend to select a cloud-free remote sensing image to map the coastal shallow water topography. As a result, in addition to the selection of empirical models, the high-quality remote sensing image and accurate prior topographic data are also of importance. This study aims to propose a method for mapping coastal shallow water bathymetry from multitemporal remote sensing imagery. Here, Sentinel-2 imagery time series are composited to produce a clear image, which can effectively avoid the contamination of clouds, water turbidity and other noises. ICESat-2 lidar altimeter data that contain accurate underwater elevations are used to provide topographic information. Moreover, Sentinel-2-based multispectral information and ICESat-2-based topographic information are combined for the coastal bathymetry retrieval by five empirical models (i.e., linear band model, ratio band model, support vector machine, neural network, and random forest). This proposed method is tested in Dongsha Atoll in South China Sea, and achieve a good performance [training: root mean square error (RMSE): 0.97 m ± 0.76 m, mean absolute percentage error (MAPE): 4.07% ± 0.046%, R-square (R<sup>2</sup>): 0.90 ± 0.14; validation: RMSE: 1.22 m ± 0.43 m, MAPE: 5.43% ± 0.035%, R<sup>2</sup>: 0.86 ± 0.089]. The comparison confirms that machine learning methods perform better than traditional methods, and the deep learning techniques can be further introduced in estimating shallow water bathymetry in the future, which is expected to achieve an excellent accuracy in bathymetry inversion.https://ieeexplore.ieee.org/document/10306283/CoastalICESat-2satellitesentinel-2shallow watertopography |
| spellingShingle | Nan Xu Lin Wang Han-Su Zhang Shilin Tang Fan Mo Xin Ma Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 Data IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Coastal ICESat-2 satellite sentinel-2 shallow water topography |
| title | Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 Data |
| title_full | Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 Data |
| title_fullStr | Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 Data |
| title_full_unstemmed | Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 Data |
| title_short | Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 Data |
| title_sort | machine learning based estimation of coastal bathymetry from icesat 2 and sentinel 2 data |
| topic | Coastal ICESat-2 satellite sentinel-2 shallow water topography |
| url | https://ieeexplore.ieee.org/document/10306283/ |
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