The <i>ddeq</i> Python library for point source quantification from remote sensing images (version 1.0)

Kuhlmann, Gerrit; Koene, Erik; Meier, Sandro; Santaren, Diego; Broquet, Grégoire; Chevallier, Frédéric; Hakkarainen, Janne; Nurmela, Janne; Amorós, Laia; Tamminen, Johanna; Brunner, Dominik

doi:https://doi.org/10.5194/gmd-17-4773-2024

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.

https://doi.org/10.5194/gmd-17-4773-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 12

Model description paper

|

18 Jun 2024

Model description paper |

| 18 Jun 2024

The ddeq Python library for point source quantification from remote sensing images (version 1.0)

Gerrit Kuhlmann, Erik Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner

Supplement

https://doi.org/10.5194/gmd-17-4773-2024-supplement

Data sets

SMARTCARB wind fields G. Kuhlmann https://doi.org/10.5281/zenodo.10684753

ynthetic XCO2, CO and NO2 observations for the CO2M and Sentinel-5 satellites G. Kuhlmann et al. https://doi.org/10.5281/zenodo.4048228

Model code and software

ddeq G. Kuhlmann https://gitlab.com/empa503/remote-sensing/ddeq

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.