MuSRFM: Multiple scale resolution fusion based precise and robust satellite derived bathymetry model for island nearshore shallow water regions using sentinel-2 multi-spectral imagery

Qin, Xiaoming; Wu, Ziyin; Luo, Xiaowen; Shang, Jihong; Zhao, Dineng; Zhou, Jieqiong; Cui, Jiaxin; Wan, Hongyang; Xu, Guochang

doi:10.1016/j.isprsjprs.2024.09.007

ISPRS Journal of Photogrammetry and Remote Sensing

2024

DOI: 10.1016/j.isprsjprs.2024.09.007

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MuSRFM: Multiple scale resolution fusion based precise and robust satellite derived bathymetry model for island nearshore shallow water regions using sentinel-2 multi-spectral imagery

Xiaoming Qin,

Ziyin Wu,

Xiaowen Luo

et al.

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Cited by 4 publications

References 37 publications

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Diffuse attenuation coefficient and bathymetry retrieval in shallow water environments by integrating satellite laser altimetry with optical remote sensing

Liu,

Xie,

et al. 2025

International Journal of Applied Earth Observation and Geoinfor

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Diffuse attenuation coefficient and bathymetry retrieval in shallow water environments by integrating satellite laser altimetry with optical remote sensing

Liu,

Xie,

et al. 2025

International Journal of Applied Earth Observation and Geoinfor

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Enhancing water depth inversion accuracy in the Yangtze River's Nantong Channel using random forest and coordinate attention mechanisms

Wu,

Fang,

Liu

et al. 2024

Opt. Express

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Accurate bathymetry information is crucial for safe navigation and efficient management of the Yangtze River Channel, a vital shipping corridor in China. Traditional bathymetric surveying methods are time-consuming and labor-intensive, limiting their application in large-scale and real-time monitoring. This study proposes a novel approach for bathymetry inversion in the Yangtze River Nantong Channel by integrating geolocational features obtained from the ZY-1E satellite with high-resolution multibeam data using the random forest algorithm. Our approach incorporates geographical coordinates enhancing the predictive capabilities of conventional models. The random forest with longitude/latitude (RF-Lon./Lat.) model, which incorporates geographical information, outperformed conventional methods, achieving an R2 of 0.57, MAE of 1.99 m, and RMSE of 2.96 m. The successful application of the RF-Lon./Lat. model highlights the effectiveness of integrating geolocational features with machine learning algorithms for accurate bathymetry inversion in the complex and turbid waters of the Yangtze River Channel. This innovative approach offers a promising solution for precise and efficient water depth estimation, which is essential for various applications in the Yangtze River Basin, including channel management, waterway maintenance, and hydrological studies. The insights gained from this study contribute to the growing body of knowledge on the application of machine learning and remote sensing techniques for bathymetric mapping in complex river environments, particularly in the context of the Yangtze River Channel.

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Enhancing Water depth inversion accuracy in turbid coastal environments using random forest and coordinate attention mechanisms

Fang,

Wu,

et al. 2024

Front. Mar. Sci.

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This study introduces an innovative water depth estimation method for complex coastal environments, focusing on Yantian Port. By combining Random Forest algorithms with a Coordinate Attention mechanism, we address limitations of traditional bathymetric techniques in turbid waters. Our approach incorporates geographical coordinates, enhancing spatial accuracy and predictive capabilities of conventional models. The Random Forest Lon./Lat. model demonstrated exceptional performance, particularly in shallow water depth estimation, achieving superior accuracy metrics among all evaluated models. It boasted the lowest Root Mean Square Error (RMSE) and highest coefficient of determination (R²), outperforming standard techniques like Stumpf and Log-Linear approaches. These findings highlight the potential of advanced machine learning in revolutionizing bathymetric mapping for intricate coastal zones, opening new possibilities for port management, coastal engineering, and environmental monitoring of coastal ecosystems. We recommend extending this research to diverse coastal regions to validate its broader applicability. Additionally, exploring the integration of additional geospatial features could further refine the model’s accuracy and computational efficiency. This study marks a significant advancement in bathymetric technology, offering improved solutions for accurate water depth estimation in challenging aquatic environments. As we continue to push boundaries in this field, the potential for enhanced coastal management and environmental stewardship grows, paving the way for more sustainable and informed decision-making in coastal zones worldwide.

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MuSRFM: Multiple scale resolution fusion based precise and robust satellite derived bathymetry model for island nearshore shallow water regions using sentinel-2 multi-spectral imagery

Cited by 4 publications

References 37 publications

Diffuse attenuation coefficient and bathymetry retrieval in shallow water environments by integrating satellite laser altimetry with optical remote sensing

Diffuse attenuation coefficient and bathymetry retrieval in shallow water environments by integrating satellite laser altimetry with optical remote sensing

Enhancing water depth inversion accuracy in the Yangtze River's Nantong Channel using random forest and coordinate attention mechanisms

Enhancing Water depth inversion accuracy in turbid coastal environments using random forest and coordinate attention mechanisms

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