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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 01490419 |
| DOI: | 10.1080/01490419.2025.2506408 |
