Skip to main content

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 in the solar reflective region (at least from 280 nm to 2500 nm).
    Line-by-line simulation These are true monochromatic simulations (as opposed to narrow band simulations). Eradiate provides monochromatic absorption databases covering the [250, 3125] nm interval. User-defined absorption databases are also supported (see the database format).
    Band simulation These simulations compute 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 provides CKD-ready absorption databases for the [250, 3125] nm interval, with various spectral bin sizes (100 cm⁻¹, 1 nm, 10 nm). User-defined absorption databases are also supported (see the database format).
    Polarization Eradiate optionally supports polarized light simulation. This feature can be switched on or off to achieve the best compromise between accuracy and performance.
  • 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, RPV, Ross Thick-Li Sparse, Hapke, Maignan and ocean surface reflection models All models can be parametrized against the spectral dimension.
    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 or finite-size illumination model Eradiate supports both ideal (Delta angular distribution), and realistic (finite angular size) illumination models.
    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 and find the right balance between accuracy and performance.
  • Monte Carlo ray tracing
    Mitsuba renderer as radiometric kernel We leverage the advanced Python API of a cutting-edge C++ rendering system.
    State-of-the-art volumetric path tracing algorithm Mitsuba ships a null-collision-based volumetric path tracer which performs well in many of the cases Eradiate is used for. We also provide a special-purpose path tracing algorithm for plane-parallel geometries that can perform up to 2 orders of magnitude faster than the null-collision algorithm.
  • 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.

Support

Got a question? Please visit our discussion forum.

Authors and acknowledgements

Eradiate is developed by a core team consisting of Vincent Leroy, Claudia Emde, Nicolae Marton, Nicolas Misk and Yves Govaerts. For more information about the Eradiate team, visit our website.

Eradiate uses the Mitsuba 3 renderer, developed by the Realistic Graphics Lab, 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 through a project managed by the European Space Agency (contract no 40000127201/19/I‑BG). The design phase was funded by the MetEOC-3 project (EMPIR grant 16ENV03).

Citing Eradiate

The most general citation is as follows:

@software{Eradiate,
    author = {Leroy, Vincent and Nollet, Yvan and Schunke, Sebastian and Misk, Nicolas and Marton, Nicolae 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. Example:

@software{Eradiate,
    author = {Leroy, Vincent and Nollet, Yvan and Schunke, Sebastian and Misk, Nicolas and Marton, Nicolae and Govaerts, Yves},
    license = {LGPL-3.0},
    title = {Eradiate radiative transfer model},
    url = {https://github.com/eradiate/eradiate},
    doi = {10.5281/zenodo.19186132},
    year = {2026},
    version = {1.1.0},
}

License

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

Project status

Eradiate is actively developed. It is beta software.

Metadata

Release files for eradiate 1.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for eradiate 1.2.0
File Size Uploaded
eradiate-1.2.0.tar.gz 300.3 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for eradiate 1.2.0
File Interpreter ABI Platform
eradiate-1.2.0-py3-none-any.whl Python 3 none any Details

Total release size: 689.6 kB

Release files / eradiate-1.2.0.tar.gz

Download URL eradiate-1.2.0.tar.gz
Size 300.3 kB
Tags Source
SHA-256 checksum
How to use checksums
588bcf50a3b277f6de1655e7037cd0d83bae17d0b2bbfbfe72fc5f92ee966ec3
BLAKE2b-256 checksum
How to use checksums
15bc77e6ab7348c7b92159582dbd987bad1a0e38bae4ecfa8a986a1ebe586847
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jun 10, 2026.

Transparency log

Release files / eradiate-1.2.0-py3-none-any.whl

Download URL eradiate-1.2.0-py3-none-any.whl
Size 389.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
1c3e193177e347daa16a2980db6952729e2e4a7e40fbd739a8f598d3e2c4c85d
BLAKE2b-256 checksum
How to use checksums
c10e1e1783a64e31eff27f47d59d8782147a6c309e4d3718504dd97f91827143
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jun 10, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

1.2.0 This release

2 release files

1.1.0

2 release files

1.0.2

2 release files

1.0.1

2 release files

1.0.0

2 release files

0.30.0

2 release files

0.29.1

2 release files

0.27.2

2 release files

0.27.1

2 release files

0.27.0

2 release files

0.26.3

2 release files

0.26.2

2 release files

0.25.0

2 release files

0.24.4

2 release files

0.24.3

2 release files

0.24.2

2 release files

0.24.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page