Methane Alert and Response System Matched Filters retrievals for EMIT, PRISMA and EnMAP
This repository provides an implementation with georeader of the matched filters methods of Roger et al. 2024 for EMIT, PRISMA and EnMAP. It also includes an adaptation of mag1c of Foote et al. 2020 for EMIT.
Installation
pip install marshsi
Examples
- EMIT example 👉 emit_example.ipynb
- PRISMA example 👉 prisma_example.ipynb
- EnMAP example 👉 enmap_example.ipynb
Citation
If you use this repo please cite:
@article{ruzicka_2025,
author = {Růžička, V. and Mateo-García, G. and Irakulis-Loitxate, I. and Johnson, J. E. and Montesino San Martín, M. and Allen, A. and Guanter, L. and Thompson, D. R.},
title = {Operational machine learning for remote spectroscopic detection of CH₄ point sources},
journal = {arXiv},
year = {2025},
doi = {10.48550/arXiv.2511.07719},
url = {https://doi.org/10.48550/arXiv.2511.07719}
}
@Article{roger_2024,
AUTHOR = {Roger, J. and Guanter, L. and Gorroño, J. and Irakulis-Loitxate, I.},
TITLE = {Exploiting the entire near-infrared spectral range to improve the detection of methane plumes with high-resolution imaging spectrometers},
JOURNAL = {Atmospheric Measurement Techniques},
VOLUME = {17},
YEAR = {2024},
NUMBER = {4},
PAGES = {1333--1346},
URL = {https://amt.copernicus.org/articles/17/1333/2024/},
DOI = {10.5194/amt-17-1333-2024}
}
@article{ruzicka_starcop_2023,
title = {Semantic segmentation of methane plumes with hyperspectral machine learning models},
volume = {13},
issn = {2045-2322},
url = {https://www.nature.com/articles/s41598-023-44918-6},
doi = {10.1038/s41598-023-44918-6},
number = {1},
journal = {Scientific Reports},
author = {Růžička, Vít and Mateo-Garcia, Gonzalo and Gómez-Chova, Luis and Vaughan, Anna, and Guanter, Luis and Markham, Andrew},
month = nov,
year = {2023},
pages = {19999}
}
@ARTICLE{foote_2020,
author={M. D. {Foote} and P. E. {Dennison} and A. K. {Thorpe} and D. R. {Thompson} and S. {Jongaramrungruang} and C. {Frankenberg} and S. C. {Joshi}},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Fast and Accurate Retrieval of Methane Concentration From Imaging Spectrometer Data Using Sparsity Prior},
year={2020},
volume={},
number={},
pages={1-13},
keywords={Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG);greenhouse gas emissions;methane mapping;plume detection.},
doi={10.1109/TGRS.2020.2976888},
ISSN={1558-0644},
month={},
}
Metadata
Release files for marshsi 0.1.8
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| marshsi-0.1.8.tar.gz | 2.0 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| marshsi-0.1.8-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 3.9 MB
Release files / marshsi-0.1.8.tar.gz
| Download URL | marshsi-0.1.8.tar.gz |
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| Size | 2.0 MB |
| Tags | Source |
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Release files / marshsi-0.1.8-py3-none-any.whl
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| Tags | Python 3 |
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