WK-Bathy: A Modular Development Environment for Advancing Satellite-Image-Based Wave Kinematics Bathymetric Inversion

Project overview

The goal of this project is to provide a development environment for experimenting with algorithmic pipelines by which to perform wave kinematic bathymetric inversion across different areas of interest a processing strategies. Below is an example:

• Step 1: Determine four Areas of Interest (AOI)

  • Select a variety of locations that feature a diverse set of conditions

  • For WKB, they must satisfy the conditions:

    • Publicly accessible hydrographic shallow water survey data
    • Swell-wave regime
      • Negligible effects from currents
    • An extended nearshore region of depths below 100 m

  • And they should vary by...

    • Latitude (turbidity)
    • Exposure to marine processes (depositional/erosional)
    • Seafloor features (reefs, sandbars, canyons, heavy slope)

• Step 2: Find and download usable imagery

  • Initialize each AOI with central latitude and longitude, filename header, link to CRM, and bounding box extents

  • Load CRM, extract important metadata and save in AOI object

  • For a range of days around CRM creation date, use CMEMS Wave Analysis and Forecast to identify times for each AOI when Mean significant wave height (SWH) greater than 1 m

    • Average of the highest one-third (33%) of waves (measured from trough to crest) that occur in a given period
    • Store swell period and direction data from CMEMS in AOI object for image selection and evaluation

  • Look for Sentinel-2 imagery from days when SWH > 1 m, and get image with best combination of factors for optical WKB

    • Higher SWH, low cloud coverage, wave direction toward solar azimuth, preferable solar elevation
      • Store this information for image selection and evaluation

  • Look for Sentinel-1 imagery from days when SWH > 1 m, and get image with best combination of factors for SAR WKB

    • Preference to VV
    • Velocity brunching due to orbital motion of waves parallel to SAR azimuth travel direction is primary mechanism for measuring waves from imagery
      • Swell wavelengths need to be greater than cutoff wavelength given by Lmin = R√H/V, where R is the slant range of the wave, V is the SAR platform velocity, and H is the significant wave height
        • Lmin should be as low as possible

  • Select best images

• Step 3: Prepare data

  • Subset images by bounding box without modifying data

  • Apply Natural Earth shapefile to mask land

• Step 4: Derive bathymetry

  • Apply 2D Fast Fourier Transform

    • Feather mask to avoid high-frequency artifacts
    • Tune parameters for each AOI

  • Wavelength Estimation

    • High-intensity blob centroid to estimate wavelength, period, direction.

  • Linear Dispersion

    • Windowed FFT to derive bathymetry for discrete sections

• Step 5: Evaluation

  • Filter non-physical bathymetric estimations against the Coastal Reference Model

  • Calculate Root Mean Square Error against ground-truth multibeam echosounder data

Setup

Prereqs

• Python 3.11 (recommended)
• Git

Create and activate a virtual environment

python -m venv venv
source venv/bin/activate  # Windows: venv\\Scripts\\activate

Install dependencies

pip install -r requirements.txt

Optional: install dev tooling

pip install pytest ruff black mypy

License

This project is licensed under the MIT License — see the LICENSE file for details. © 2025 Marcel Rodriguez-Riccelli