Articles | Volume 17, issue 12
https://doi.org/10.5194/gmd-17-4773-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-4773-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
18 Jun 2024
Model description paper |
| 18 Jun 2024
| 18 Jun 2024
The ddeq Python library for point source quantification from remote sensing images (version 1.0)
Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Erik Koene
Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Sandro Meier
Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Remote Sensing Laboratories, University of Zurich, Zurich, Switzerland
Diego Santaren
Laboratoire des Sciences du Climat et de l'Environment, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
Grégoire Broquet
Laboratoire des Sciences du Climat et de l'Environment, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
Frédéric Chevallier
Laboratoire des Sciences du Climat et de l'Environment, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
Janne Hakkarainen
Earth Observation Research, Finnish Meteorological Institute (FMI), Helsinki, Finland
Janne Nurmela
Earth Observation Research, Finnish Meteorological Institute (FMI), Helsinki, Finland
Laia Amorós
Earth Observation Research, Finnish Meteorological Institute (FMI), Helsinki, Finland
Johanna Tamminen
Earth Observation Research, Finnish Meteorological Institute (FMI), Helsinki, Finland
Dominik Brunner
Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
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Cited
10 citations as recorded by crossref.
- Temporal variability of NOx emissions from power plants: a comparison of satellite- and inventory-based estimates G. Kuhlmann et al. https://doi.org/10.5194/acp-26-4405-2026
- Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods J. Dumont Le Brazidec et al. https://doi.org/10.5194/gmd-18-3607-2025
- NOx emissions constraints from GEMS NO2 retrievals: inversion methodology and air quality model evaluation in Bangkok using ASIA-AQ multi-platform observations J. Christopoulos et al. https://doi.org/10.5194/acp-26-8021-2026
- Deep Learning Methods for Inferring Industrial CO2 Hotspots from Co-Emitted NO2 Plumes E. Sun et al. https://doi.org/10.3390/rs17071167
- Evidence of successful methane mitigation in one of Europe's most important oil production region G. Kuhlmann et al. https://doi.org/10.5194/acp-25-5371-2025
- A lightweight NO2-to-NOx conversion model for quantifying NOx emissions of point sources from NO2 satellite observations S. Meier et al. https://doi.org/10.5194/acp-24-7667-2024
- Benchmarking data-driven inversion methods for the estimation of local CO2 emissions from synthetic satellite images of XCO2 and NO2 D. Santaren et al. https://doi.org/10.5194/amt-18-211-2025
- Quantifying CH4 point source emissions with airborne remote sensing: first results from AVIRIS-4 S. Meier et al. https://doi.org/10.5194/amt-19-333-2026
- Monitoring Persistent Methane Emissions from the Secunda CTL Synthetic Fuel Plant Using Satellite Observations H. Virta et al. https://doi.org/10.1021/acs.estlett.5c01140
- Linear integrated mass enhancement: A method for estimating hotspot emission rates from space-based plume observations J. Hakkarainen et al. https://doi.org/10.1016/j.rse.2025.114623
10 citations as recorded by crossref.
- Temporal variability of NOx emissions from power plants: a comparison of satellite- and inventory-based estimates G. Kuhlmann et al. https://doi.org/10.5194/acp-26-4405-2026
- Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods J. Dumont Le Brazidec et al. https://doi.org/10.5194/gmd-18-3607-2025
- NOx emissions constraints from GEMS NO2 retrievals: inversion methodology and air quality model evaluation in Bangkok using ASIA-AQ multi-platform observations J. Christopoulos et al. https://doi.org/10.5194/acp-26-8021-2026
- Deep Learning Methods for Inferring Industrial CO2 Hotspots from Co-Emitted NO2 Plumes E. Sun et al. https://doi.org/10.3390/rs17071167
- Evidence of successful methane mitigation in one of Europe's most important oil production region G. Kuhlmann et al. https://doi.org/10.5194/acp-25-5371-2025
- A lightweight NO2-to-NOx conversion model for quantifying NOx emissions of point sources from NO2 satellite observations S. Meier et al. https://doi.org/10.5194/acp-24-7667-2024
- Benchmarking data-driven inversion methods for the estimation of local CO2 emissions from synthetic satellite images of XCO2 and NO2 D. Santaren et al. https://doi.org/10.5194/amt-18-211-2025
- Quantifying CH4 point source emissions with airborne remote sensing: first results from AVIRIS-4 S. Meier et al. https://doi.org/10.5194/amt-19-333-2026
- Monitoring Persistent Methane Emissions from the Secunda CTL Synthetic Fuel Plant Using Satellite Observations H. Virta et al. https://doi.org/10.1021/acs.estlett.5c01140
- Linear integrated mass enhancement: A method for estimating hotspot emission rates from space-based plume observations J. Hakkarainen et al. https://doi.org/10.1016/j.rse.2025.114623
Saved (final revised paper)
Latest update: 19 Jul 2026
Short summary
We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter notebooks included in the library.
We present a Python software library for data-driven emission quantification (ddeq). It can be...
