Abstract
Shallow underwater topography has important practical applications in fisheries, navigation, and pipeline laying. Traditional multibeam bathymetry is limited by the high cost of largescale topographic surveys in large, shallow sand wave areas. Remote sensing inversion methods to detect shallow sand wave topography in Taiwan rely heavily on measured water depth data. To address these problems, this study proposes a largescale remote sensing inversion model of sand wave topography based on long short-term memory network machine learning. Using multi-angle sun glitter remote sensing to obtain sea surface roughness (SSR) information and by learning and training SSR and its corresponding water depth information, the sand wave topography of a largescale shallow sea sand wave region is extracted. The accuracy of the model is validated through its application to a 774 km2 area in the sand wave topography of the Taiwan Banks. The model obtains a root mean square error of 3.31–3.67 m, indicating that the method has good generalization capability and can achieve a largescale topographic understanding of shallow sand waves with some training on measured bathymetry data. Sand wave topography is widely present in tidal environments; our method has low requirements for ground data, with high application value.
Highlights
Extensive sand wave topography is distributed in tidal environments worldwide, such as the North Sea in Europe [1], the South Sea in Korea [2], San Francisco Bay in the Americas [3], and shallow shoals in Taiwan, China [4]
The relationship in the spatial domain was mapped to the temporal domain to establish a bathymetric inversion model
The model was designed based on the characteristic relationship between water depth and roughness and has a low dependence on measured water depth data
Summary
Extensive sand wave topography is distributed in tidal environments worldwide, such as the North Sea in Europe [1], the South Sea in Korea [2], San Francisco Bay in the Americas [3], and shallow shoals in Taiwan, China [4]. The study of sand wave shoal topography forms an important basis for coastal protection [5], navigation safety [6], submarine pipeline laying, and drilling platform construction [7]. There are three methods that can be used for sand wave shallow terrain detection: sonar multibeam bathymetry [10], synthetic aperture radar (SAR) detection, and sun glitter remote sensing. Multibeam bathymetry uses vessels as the detection platform and has high efficiency, accuracy, and resolution in detecting trajectories. The scanning width is extremely limited, the coverage area is small, the measurement period is long, and the manpower and financial requirements are high [11]
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