High-Resolution Mapping of Shallow Water Bathymetry Based on the Scale-Invariant Effect Using Sentinel-2 and GF-1 Satellite Remote Sensing Data

High-resolution water depth data are of great significance in island research and coastal ecosystem monitoring. However, the acquisition of high-resolution imagery has been a challenge due to the difficulties and high costs associated with obtaining such data. To address this issue, this study proposes a water depth inversion method based on Gaofen-1 (GF-1) satellite data, which integrates multi-source satellite data to obtain high-resolution bathymetric data. Specifically, the research utilizes bathymetric data derived from Sentinel-2 and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) as prior information, combined with high-resolution imagery obtained from the GF-1 satellite constellation (GF-1B/C/D). Then, it employs a scale-invariant effect to map bathymetry with a spatial resolution of 2 m, applied to four study areas in the Pacific Islands. The results are further evaluated using ICESat-2 data, which demonstrate that the water depth inversion results from this study possess high accuracy, with R2 values exceeding 0.85, root mean square error (RMSE) ranging from 0.56 to 0.90 m, with an average of 0.7125 m, and mean absolute error (MAE) ranging from 0.43 to 0.76 m, with an average of 0.55 m. Additionally, this paper discusses the applicability of the scale-invariant assumption in this research and the improvements of the quadratic polynomial ratio model (QPRM) method compared to the classical linear ratio model (CLRM) method. The findings indicate that the integration of multi-source satellite remote sensing data based on the scale-invariant effect can effectively obtain high-precision, high-resolution bathymetric data, providing significant reference value for the application of GF-1 satellites in high-resolution bathymetry mapping.

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Publication Details

Journal
Remote Sensing
Published
2025-02-13
DOI
https://doi.org/10.3390/rs17040640
Citations
5
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
4.00

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article

High-Resolution Mapping of Shallow Water Bathymetry Based on the Scale-Invariant Effect Using Sentinel-2 and GF-1 Satellite Remote Sensing Data

Dongling Li, Ju Guan, Huaguo Zhang, Wenting Cao et al.
5 citations
Remote Sensing
Remote Sensing and LiDAR Applications
4.00
article

High-Resolution Mapping of Shallow Water Bathymetry Based on the Scale-Invariant Effect Using Sentinel-2 and GF-1 Satellite Remote Sensing Data

Dongling Li, Ju Guan, Huaguo Zhang, Wenting Cao, Juan Wang, Tong Han
article en
5 citations

Abstract

High-resolution water depth data are of great significance in island research and coastal ecosystem monitoring. However, the acquisition of high-resolution imagery has been a challenge due to the difficulties and high costs associated with obtaining such data. To address this issue, this study proposes a water depth inversion method based on Gaofen-1 (GF-1) satellite data, which integrates multi-source satellite data to obtain high-resolution bathymetric data. Specifically, the research utilizes bathymetric data derived from Sentinel-2 and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) as prior information, combined with high-resolution imagery obtained from the GF-1 satellite constellation (GF-1B/C/D). Then, it employs a scale-invariant effect to map bathymetry with a spatial resolution of 2 m, applied to four study areas in the Pacific Islands. The results are further evaluated using ICESat-2 data, which demonstrate that the water depth inversion results from this study possess high accuracy, with R2 values exceeding 0.85, root mean square error (RMSE) ranging from 0.56 to 0.90 m, with an average of 0.7125 m, and mean absolute error (MAE) ranging from 0.43 to 0.76 m, with an average of 0.55 m. Additionally, this paper discusses the applicability of the scale-invariant assumption in this research and the improvements of the quadratic polynomial ratio model (QPRM) method compared to the classical linear ratio model (CLRM) method. The findings indicate that the integration of multi-source satellite remote sensing data based on the scale-invariant effect can effectively obtain high-precision, high-resolution bathymetric data, providing significant reference value for the application of GF-1 satellites in high-resolution bathymetry mapping.

Remote SensingVol. 17(4)
Ministry of Natural Resources (CN), Zhejiang Ocean University (CN), Second Institute of Oceanography (CN), Zhejiang University (CN)
National Aeronautics and Space Administration, European Space Agency, Ministry of Science and Technology of the People's Republic of China, Centre National de la Recherche Scientifique, National Key Research and Development Program of China
Openalex Percentile: Top 8%
Remote Sensing and LiDAR Applications
4.00
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