eo-tides: Tide modelling tools for large-scale satellite earth observation analysis

• ⚙️ Github repository: https://github.com/GeoscienceAustralia/eo-tides/
• 📦 PyPI: https://pypi.org/project/eo-tides/
• ℹ️ Documentation: https://GeoscienceAustralia.github.io/eo-tides/
• 📰 Paper: https://doi.org/10.21105/joss.07786

eo-tides provides powerful parallelized tools for integrating satellite Earth observation data with tide modelling. 🛠️🌊🛰️

eo-tides combines advanced tide modelling functionality from the pyTMD package with pandas, xarray and odc-geo, providing a suite of flexible tools for efficient analysis of coastal and ocean Earth observation data – from regional, continental, to global scale.

These tools can be applied to petabytes of freely available satellite data (e.g. from Digital Earth Australia or Microsoft Planetary Computer) loaded via Open Data Cube's odc-stac or datacube packages, supporting coastal and ocean earth observation analysis for any time period or location globally.

Highlights

• 🌊 Model tide heights and phases (e.g. high, low, ebb, flow) from multiple global ocean tide models in parallel, and return a pandas.DataFrame for further analysis
• 🛰️ "Tag" satellite data with tide heights based on the exact moment of image acquisition
• 🌐 Model tides for every individual satellite pixel through time, producing three-dimensional "tide height" xarray-format datacubes that can be integrated with satellite data
• 📈 Calculate statistics describing local tide dynamics, as well as biases caused by interactions between tidal processes and satellite orbits
• 🛠️ Validate modelled tides using measured sea levels from coastal tide gauges (e.g. GESLA Global Extreme Sea Level Analysis)

Supported tide models

eo-tides supports all ocean tide models supported by pyTMD. These include:

• Empirical Ocean Tide model (EOT20)
• Finite Element Solution tide models (FES2022, FES2014, FES2012)
• TOPEX/POSEIDON global tide models (TPXO10, TPXO9, TPXO8)
• Global Ocean Tide models (GOT5.6, GOT5.5, GOT4.10, GOT4.8, GOT4.7)
• Hamburg direct data Assimilation Methods for Tides models (HAMTIDE11)
• Technical University of Denmark tide models (DTU23)

For instructions on how to set up these models for use in eo-tides, refer to Setting up tide models.

Installing and setting up eo-tides

To get started with eo-tides, follow the Installation and Setting up tide models guides.

Jupyter Notebooks code examples

Interactive Jupyter Notebook usage examples and more complex coastal EO case studies can be found in the docs/notebooks/ directory, or rendered in the documentation here.

Citing eo-tides

To cite eo-tides in your work, please use the following Journal of Open Source Software citation:

Bishop-Taylor, R., Phillips, C., Sagar, S., Newey, V., & Sutterley, T., (2025). eo-tides: Tide modelling tools for large-scale satellite Earth observation analysis. Journal of Open Source Software, 10(109), 7786, https://doi.org/10.21105/joss.07786

@article{Bishop-Taylor2025,
  doi       = {10.21105/joss.07786},
  url       = {https://doi.org/10.21105/joss.07786},
  year      = {2025},
  publisher = {The Open Journal},
  volume    = {10},
  number    = {109},
  pages     = {7786},
  author    = {Robbi Bishop-Taylor and Claire Phillips and Stephen Sagar and Vanessa Newey and Tyler Sutterley},
  title     = {eo-tides: Tide modelling tools for large-scale satellite Earth observation analysis},
  journal   = {Journal of Open Source Software}
}

In addition, please consider also citing the underlying pyTMD Python package which powers the tide modelling functionality behind eo-tides:

Sutterley, T. C., Howard, S. L., Padman, L., & Siegfried, M. R., (2025). pyTMD: Python-based tidal prediction software. Journal of Open Source Software, 10(116), 8566, https://doi.org/10.21105/joss.08566

@article{Sutterley2025,
  doi       = {10.21105/joss.08566},
  url       = {https://doi.org/10.21105/joss.08566},
  year      = {2025},
  publisher = {The Open Journal},
  volume    = {10},
  number    = {116},
  pages     = {8566},
  author    = {Sutterley, Tyler C. and Howard, Susan L. and Padman, Laurie and Siegfried, Matthew R.},
  title     = {pyTMD: Python-based tidal prediction software},
  journal   = {Journal of Open Source Software}
}

Contributing

We welcome contributions to eo-tides, both through posting issues (e.g. bug reports or feature suggestions), or directly via pull requests (e.g. bug fixes and new features). Read the Contributing guide for details about how you can get involved.

Acknowledgements

For a full list of acknowledgements, refer to Citations and Credits. This repository was initialised using the cookiecutter-uv package.