A physics-enhanced deep-learning model for estimating turbid shallow water depth from SAR images

Tian MA; Qing XU; Xiaobin YIN; Yan LI; Letian LÜ; Kaiguo FAN

doi:10.1007/s00343-025-5008-1

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A physics-enhanced deep-learning model for estimating turbid shallow water depth from SAR images

Physics | Updated：2026-03-26

- A physics-enhanced deep-learning model for estimating turbid shallow water depth from SAR images
- Journal of Oceanology and Limnology Vol. 44, Issue 1, Pages: 36-49(2026)
- Affiliations：
  
  1.College of Marine Technology/Sanya Oceanographic Institution, Ocean University of China, Qingdao 266100, China
  2.Laboratory for Regional Oceanography and Numerical Modeling, Qingdao Marine Science and Technology Center, Qingdao 266100, China
  3.National Key Laboratory of Intelligent Spatial Information, Beijing 100094, China
- Author bio：
  
  xuqing@ouc.edu.cn
- Funds：
- DOI：10.1007/s00343-025-5008-1
  CLC：
- Received：11 January 2025，
  
  Accepted：04 March 2025，
  
  Online First：17 April 2025，
  
  Published：01 January 2026
- Accepted：
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MA Tian,XU Qing,YIN Xiaobin,et al.A physics-enhanced deep-learning model for estimating turbid shallow water depth from SAR images[J].Journal of Oceanology and Limnology,2026,44(01):36-49.
DOI：

MA Tian,XU Qing,YIN Xiaobin,et al.A physics-enhanced deep-learning model for estimating turbid shallow water depth from SAR images[J].Journal of Oceanology and Limnology,2026,44(01):36-49. DOI： 10.1007/s00343-025-5008-1.

摘要

Abstract

Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management. However

there are still great challenges in estimating water depth using satellite observations in turbid coastal waters. In this paper

we developed a physics-enhanced deep neural network to estimate bathymetry of highly turbid waters of the Changjiang (Yangtze) River estuary from dual-polarized synthetic aperture radar (SAR) images. Sentinel-1A/B SAR images with a spatial resolution of 20 m×22 m were collected and matched with water depth data from nautical charts during 2017–2023. For the input parameters of the model

in addition to the normalized radar backscatter cross section (NRCS) at single polarization and incidence angle

the impacts of both polarimetric characteristics and physical environmental factors on model performance were discussed in detail. Results of feature importance analysis and sensitivity experiments indicate that the polarization ratio and NRCS after removing the influence of background sea surface wind field make significant contributions to the bathymetry retrieval model. The root mean square error (RMSE) of SAR derived water depth decreases from 1.44 to 0.78 m within 0–30-m depth

and the mean relative error (MRE) is reduced from 15.6% to 8.6%. Compared with other machine learning models such as ResNet

XGBoost

and Random Forest

the MRE is reduced by 3.9%

5.7%

and 7.4%

respectively. The spatial distribution of SAR derived water depth also exhibits a high degree of consistency with observations

demonstrating the great potential of the model in estimating the depth of turbid shallow waters.

关键词

Keywords

references

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