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https://github.com/eradiate/eradiate

Eradiate: a next-generation radiative transfer model for Earth observation applications
https://github.com/eradiate/eradiate

earth-observation radiative-transfer

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Eradiate: a next-generation radiative transfer model for Earth observation applications

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![Eradiate logo](docs/fig/eradiate-logo.svg "Eradiate — A new-generation radiative transfer simulation package")

# Eradiate Radiative Transfer Model

[![pypi][pypi-badge]][pypi-url]
[![docs][rtd-badge]][rtd-url]
[![black][black-badge]][black-url]
[![ruff][ruff-badge]][ruff-url]
[![zenodo][zenodo-badge]][zenodo-url]

[pypi-badge]: https://img.shields.io/pypi/v/eradiate?style=flat-square
[pypi-url]: https://pypi.org/project/eradiate/
[rtd-badge]: https://img.shields.io/readthedocs/eradiate?logo=readthedocs&logoColor=white&style=flat-square
[rtd-url]: https://eradiate.readthedocs.io/en/latest/
[black-badge]: https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square
[black-url]: https://github.com/psf/black/
[ruff-badge]: https://img.shields.io/badge/%E2%9A%A1%EF%B8%8F-ruff-red?style=flat-square
[ruff-url]: https://ruff.rs
[zenodo-badge]: https://img.shields.io/badge/doi-10.5281/zenodo.7224314-blue.svg?style=flat-square
[zenodo-url]: https://zenodo.org/records/7224314

Eradiate is a modern radiative transfer simulation software package for Earth
observation applications. Its main focus is accuracy, and for that purpose, it
uses the Monte Carlo ray tracing method to solve the radiative transfer
equation.

## Detailed list of features



  • Spectral computation



    Solar reflective spectral region

    Eradiate ships spectral data within from 280 nm to 2400 nm. This range can be
    extended with additional data (just ask for it!).



    Line-by-line simulation

    These are true monochromatic simulations (as opposed to narrow band
    simulations).
    Eradiate provides monochromatic absorption datasets spanning the wavelength
    range [250, 3125] nm.
    It also supports user-defined absorption data provided it complies with the
    dataset format specifications.



    Band simulation

    These simulations computes results in spectral bands.
    The correlated k-distribution (CKD) method with configurable
    quadrature rule is used. This method achieves a trade-off between performance
    and accuracy for the simulation of absorption by gases.
    Eradiate ships with absorption datasets suitable for use within the CKD
    method in spectral bands of variable width (including 1 nm and 10 nm
    wavelength bands and 100 cm^-1 wavenumber bands), from 250 nm up to 3125 nm.
    It also supports user-defined absorption data provided it complies with the
    dataset format specifications.


  • Atmosphere



    One-dimensional atmospheric profiles

    Both standard profiles, e.g. the AFGL (1986) profiles, and customized
    profiles are supported.



    Plane-parallel and spherical-shell geometries

    This allows for more accurate results at high illumination and viewing
    angles.


  • Surface



    Lambertian and RPV reflection models

    Model parameters can be varied against the spectral dimensions.



    Detailed surface geometry

    Add a discrete canopy model (either disk-based abstract models, or more
    realistic mesh-based models).



    Combine with atmospheric profiles

    Your discrete canopy can be integrated within a scene featuring a 1D
    atmosphere model in a fully coupled simulation.


  • Illumination



    Directional illumination model

    An ideal illumination model with a Delta angular distribution.



    Many irradiance datasets

    Pick your favourite—or bring your own.


  • Measure



    Top-of-atmosphere radiance and BRF computation

    An ideal model suitable for satellite data simulation.



    Perspective camera sensor

    Greatly facilitates scene setup: inspecting the scene is very easy.



    Many instrument spectral response functions

    Our SRF data is very close to the original data, and we provide advice to
    further clean up the data, trading off accuracy for performance.


  • Monte Carlo ray tracing



    Mitsuba renderer as radiometric kernel

    We leverage the advanced Python API of this cutting-edge C++ rendering
    library.



    State-of-the-art volumetric path tracing algorithm

    Mitsuba ships a null-collision-based volumetric path tracer which performs
    well in the cases Eradiate is used for.


  • Traceability



    Documented data and formats

    We explain where our data comes from and how users can build their own data
    in a format compatible with Eradiate's input.



    Transparent algorithms

    Our algorithms are researched and documented, and their implementation is
    open-source.



    Thorough testing

    Eradiate is shipped with a large unit testing suite and benchmarked
    periodically against community-established reference simulation software.


  • Interface



    Comprehensive Python interface

    Abstractions are derived from computer graphics and Earth observation and
    are designed to feel natural to EO scientists.



    Designed for interactive usage

    Jupyter notebooks are now an essential tool in the digital scientific
    workflow.



    Integration with Python scientific ecosystem

    The implementation is done using the Scientific Python stack.



    Standard data formats (mostly NetCDF)

    Eradiate uses predominantly xarray data structures for I/O.


## Installation and usage

For build and usage instructions, please refer to the
[documentation](https://eradiate.readthedocs.org).

## Support

Got a question? Please visit our
[discussion forum](https://github.com/eradiate/eradiate/discussions).

## Authors and acknowledgements

Eradiate is developed by a core team consisting of Vincent Leroy,
Sebastian Schunke, Nicolas Misk and Yves Govaerts.

Eradiate uses the
[Mitsuba 3 renderer](https://github.com/mitsuba-renderer/mitsuba3), developed by
the [Realistic Graphics Lab](https://rgl.epfl.ch/),
taking advantage of its Python interface and proven architecture, and extends it
with components implementing numerical methods and models used in radiative
transfer for Earth observation. The Eradiate team acknowledges Mitsuba creators
and contributors for their work.

The development of Eradiate is funded by the
[Copernicus programme](https://www.copernicus.eu/) through a project managed by
the [European Space Agency](http://www.esa.int/) (contract no
40000127201/19/I‑BG).
The design phase was funded by the [MetEOC-3 project](http://www.meteoc.org/)
(EMPIR grant 16ENV03).

## Citing Eradiate

The most general citation is as follows:

```bibtex
@software{Eradiate,
author = {Leroy, Vincent and Nollet, Yvan and Schunke, Sebastian and Misk, Nicolas and Govaerts, Yves},
license = {LGPL-3.0},
title = {Eradiate radiative transfer model},
url = {https://github.com/eradiate/eradiate},
doi = {10.5281/zenodo.7224314},
year = {2024}
}
```

If you want to reference a specific version, you can update the previous citation
with `doi`, `year` and `version` fields populated with metadata retrieved from our
[Zenodo records](https://zenodo.org/search?q=parent.id%3A7224314&f=allversions%3Atrue&l=list&p=1&s=10&sort=version).
Example:

```bibtex
@software{Eradiate,
author = {Leroy, Vincent and Nollet, Yvan and Schunke, Sebastian and Misk, Nicolas and Govaerts, Yves},
license = {LGPL-3.0},
title = {Eradiate radiative transfer model},
url = {https://github.com/eradiate/eradiate},
doi = {10.5281/zenodo.10411036},
year = {2023},
version = {0.25.0},
}
```

## License

Eradiate is free software licensed under the
[GNU Lesser General Public License (v3)](./LICENSE).

## Project status

Eradiate is actively developed. It is beta software.