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Delta-Scale 10-m Tidal Flat Topography Reconstruction: A Transferable Deep Learning Approach Using Sentinel-2 Time Series

  • Peng Li*
  • , Jiahan Zhang
  • , Fengling Yu
  • , Kai Tan
  • , Nan Xu
  • , Chunpeng Chen
  • , Pengfei Tang
  • , Cunren Liang
  • , Zhenhong Li
  • , Houjie Wang
  • *此作品的通讯作者
  • Ocean University of China
  • Qingdao Marine Science and Technology Center
  • Xiamen University
  • Shenzhen University
  • East China Normal University
  • Hong Kong Polytechnic University
  • Seoul National University
  • Peking University
  • Chang'an University

科研成果: 期刊稿件文章同行评审

摘要

Reliable tidal flat elevation data with a resolution better than 30 m are vital for understanding macroscale sedimentation processes and storm surge erosion in estuarine deltas. However, the highly dynamic and turbid environments of global estuaries pose significant challenges for traditional manual or uncrewed aerial vehicle (UAV) methods in delta-scale monitoring. Insufficient elevation data currently hinder sea-level rise modeling and coastal management for the world's densely populated, flood-prone river deltas. There is an urgent need to characterize and reconstruct the vertical geomorphology of tidal flats through improved observations and modeling. In this study, we propose a robust tidal flat topography mapping framework using freely available satellite imagery and deep learning (DL). First, we developed an enhanced U-Net model to delineate land-water boundaries and generate inundation frequency maps from time-series Sentinel-2 imagery. A regionally adaptive nonlinear inversion model was then constructed using inundation frequency and ICESat-2 elevation data, allowing us to generate a 10-m resolution tidal flat digital elevation model (DEM) of the Yellow River estuary. The DL model achieved an F1-score of 0.96, and the resulting DEM showed strong agreement with UAV LiDAR data, yielding a root mean square error (RMSE) of 0.18 m. The reconstructed elevations range from -0.51 to 0.96 m, exhibiting a spatial gradient of lower elevations in the north and higher elevations in the southern and eastern sectors. To assess transferability, we applied the method to the radial sand ridge system in the South Yellow Sea. The model achieved a comparable RMSE of 0.52 m without retraining, demonstrating robust generalization across different estuarine environments. This study provides a cost-effective framework for monitoring coastal geomorphological changes and helps fill the data gap in delta-scale topographic mapping for global intertidal zones.

源语言英语
文章编号4206212
期刊IEEE Transactions on Geoscience and Remote Sensing
64
DOI
出版状态已出版 - 2026

联合国可持续发展目标

此成果有助于实现下列可持续发展目标:

  1. 可持续发展目标 14 - 水下生物
    可持续发展目标 14 水下生物

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