TY - JOUR
T1 - Delta-Scale 10-m Tidal Flat Topography Reconstruction
T2 - A Transferable Deep Learning Approach Using Sentinel-2 Time Series
AU - Li, Peng
AU - Zhang, Jiahan
AU - Yu, Fengling
AU - Tan, Kai
AU - Xu, Nan
AU - Chen, Chunpeng
AU - Tang, Pengfei
AU - Liang, Cunren
AU - Li, Zhenhong
AU - Wang, Houjie
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2026
Y1 - 2026
N2 - 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.
AB - 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.
KW - Coastal topography
KW - deep learning (DL)
KW - digital elevation model (DEM)
KW - river delta
KW - satellite remote sensing
UR - https://www.scopus.com/pages/publications/105036877778
U2 - 10.1109/TGRS.2026.3686116
DO - 10.1109/TGRS.2026.3686116
M3 - 文章
AN - SCOPUS:105036877778
SN - 0196-2892
VL - 64
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
M1 - 4206212
ER -