Application of Backpropagation (BP) Neural Networks for Satellite-Derived Bathymetry Using Sentinel-2 Data: Accurate Depth Estimation in Coastal Environments.
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| Title: | Application of Backpropagation (BP) Neural Networks for Satellite-Derived Bathymetry Using Sentinel-2 Data: Accurate Depth Estimation in Coastal Environments. |
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| Authors: | Heng, Chen1 (AUTHOR), Liu, Guohao1 (AUTHOR), Wu, Zhongqiang1 (AUTHOR) wuzhongqiang@hainnu.edu.cn |
| Source: | Marine Geodesy. Mar2026, Vol. 49 Issue 2, p231-257. 27p. |
| Subjects: | Back propagation, Bathymetry, Oceanography, Oceanographic maps, Remote sensing, Coastal zone management, Bathymetric maps |
| Abstract: | Accurate bathymetry estimation is essential for coastal management, resource exploration, and ecological conservation. However, traditional depth measurement methods are often time-consuming, expensive, and logistically challenging, particularly for large-scale marine surveys. These limitations have driven the development of Satellite-Derived Bathymetry (SDB), which leverages high-resolution satellite imagery and advanced computational techniques to provide a more efficient, cost-effective, and scalable solution. This study utilizes Sentinel-2 satellite data combined with various algorithmic approaches to estimate bathymetry in the Nanshan Port area. Among these methods, the Backpropagation (BP) neural network demonstrates outstanding performance in shallow water and coastal environments, effectively addressing light attenuation and water scattering challenges in turbid waters. The results indicate that, compared to traditional depth measurement techniques, BP neural network predictions are smoother, more detailed, and highly accurate, offering a refined and comprehensive representation of underwater topography. Beyond validating the feasibility of satellite remote sensing for bathymetric estimation, this study highlights key future directions, including algorithm optimization, multi-source data fusion, and enhanced deep learning models. The integration of high-resolution optical and LiDAR data, along with improved AI-driven models, holds great potential to further enhance the precision and reliability of bathymetric mapping. These advancements are expected to revolutionize marine research and coastal zone management, enabling more effective environmental monitoring, disaster risk assessment, and sustainable development. As remote sensing technologies continue to evolve, their integration with adaptive deep learning techniques, such as backpropagation neural networks, will further enhance the accuracy and automation of oceanographic studies, unlocking new possibilities for global marine exploration [ABSTRACT FROM AUTHOR] |
| Copyright of Marine Geodesy is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 191833860 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Application of Backpropagation (BP) Neural Networks for Satellite-Derived Bathymetry Using Sentinel-2 Data: Accurate Depth Estimation in Coastal Environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Heng%2C+Chen%22">Heng, Chen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Guohao%22">Liu, Guohao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Zhongqiang%22">Wu, Zhongqiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wuzhongqiang@hainnu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Marine+Geodesy%22">Marine Geodesy</searchLink>. Mar2026, Vol. 49 Issue 2, p231-257. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Back+propagation%22">Back propagation</searchLink><br /><searchLink fieldCode="DE" term="%22Bathymetry%22">Bathymetry</searchLink><br /><searchLink fieldCode="DE" term="%22Oceanography%22">Oceanography</searchLink><br /><searchLink fieldCode="DE" term="%22Oceanographic+maps%22">Oceanographic maps</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Coastal+zone+management%22">Coastal zone management</searchLink><br /><searchLink fieldCode="DE" term="%22Bathymetric+maps%22">Bathymetric maps</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate bathymetry estimation is essential for coastal management, resource exploration, and ecological conservation. However, traditional depth measurement methods are often time-consuming, expensive, and logistically challenging, particularly for large-scale marine surveys. These limitations have driven the development of Satellite-Derived Bathymetry (SDB), which leverages high-resolution satellite imagery and advanced computational techniques to provide a more efficient, cost-effective, and scalable solution. This study utilizes Sentinel-2 satellite data combined with various algorithmic approaches to estimate bathymetry in the Nanshan Port area. Among these methods, the Backpropagation (BP) neural network demonstrates outstanding performance in shallow water and coastal environments, effectively addressing light attenuation and water scattering challenges in turbid waters. The results indicate that, compared to traditional depth measurement techniques, BP neural network predictions are smoother, more detailed, and highly accurate, offering a refined and comprehensive representation of underwater topography. Beyond validating the feasibility of satellite remote sensing for bathymetric estimation, this study highlights key future directions, including algorithm optimization, multi-source data fusion, and enhanced deep learning models. The integration of high-resolution optical and LiDAR data, along with improved AI-driven models, holds great potential to further enhance the precision and reliability of bathymetric mapping. These advancements are expected to revolutionize marine research and coastal zone management, enabling more effective environmental monitoring, disaster risk assessment, and sustainable development. As remote sensing technologies continue to evolve, their integration with adaptive deep learning techniques, such as backpropagation neural networks, will further enhance the accuracy and automation of oceanographic studies, unlocking new possibilities for global marine exploration [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Marine Geodesy is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/01490419.2025.2506408 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 231 Subjects: – SubjectFull: Back propagation Type: general – SubjectFull: Bathymetry Type: general – SubjectFull: Oceanography Type: general – SubjectFull: Oceanographic maps Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Coastal zone management Type: general – SubjectFull: Bathymetric maps Type: general Titles: – TitleFull: Application of Backpropagation (BP) Neural Networks for Satellite-Derived Bathymetry Using Sentinel-2 Data: Accurate Depth Estimation in Coastal Environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Heng, Chen – PersonEntity: Name: NameFull: Liu, Guohao – PersonEntity: Name: NameFull: Wu, Zhongqiang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01490419 Numbering: – Type: volume Value: 49 – Type: issue Value: 2 Titles: – TitleFull: Marine Geodesy Type: main |
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