Deriving Large-Scale Coastal Bathymetry from Sentinel-2 Images Using an HIGH-Performance Cluster: A Case Study...

Area of Science:

• Earth Observation
• Oceanography
• Geosciences

Background:

• Coastal zones are vital for global activities but lack detailed bathymetric data due to sparse measurements and high costs.
• Conventional methods for coastal observation are expensive and limited in scope, hindering comprehensive monitoring.
• Existing remote sensing techniques show potential for deriving bathymetry, but large-scale application faces computational challenges.

Purpose of the Study:

• To develop and implement a methodology for deriving global coastal bathymetry using remote sensing data.
• To leverage high-performance computing (HPC) to overcome the computational costs associated with large-scale satellite image processing.
• To provide a new perspective for mapping coastal bathymetry, addressing the scarcity of accurate measurements.

Main Methods:

• Utilized Sentinel-2A/B satellite imagery for remote sensing data acquisition.
• Developed and implemented a novel methodology on a High-Performance cluster (HPC) for bathymetry derivation.
• Processed large-scale S-2 image coverage to generate global coastal bathymetry maps.

Main Results:

• Successfully derived coastal bathymetry from Sentinel-2 images on a global scale using an HPC implementation.
• Generated a bathymetry map for North America (NA) and compared it with the GEBCO global bathymetric product.
• Identified specific 'hotspots' by closely examining the outputs of the generated bathymetry map.

Conclusions:

• The integration of remote sensing (Sentinel-2) and HPC offers a viable solution for large-scale coastal bathymetry mapping.
• This approach significantly enhances our ability to monitor and understand coastal areas, overcoming traditional data limitations.
• The developed methodology provides a valuable tool for global coastal zone management and scientific research.