Articles | Volume 18, issue 2
https://doi.org/10.5194/amt-18-533-2025
https://doi.org/10.5194/amt-18-533-2025
Research article
 | 
29 Jan 2025
Research article |  | 29 Jan 2025

Optimal selection of satellite XCO2 images for urban CO2 emission monitoring

Alexandre Danjou, Grégoire Broquet, Andrew Schuh, François-Marie Bréon, and Thomas Lauvaux

Related authors

Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev., 16, 3997–4016, https://doi.org/10.5194/gmd-16-3997-2023,https://doi.org/10.5194/gmd-16-3997-2023, 2023
Short summary

Related subject area

Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Retrieval of NO2 profiles from 3 years of Pandora MAX-DOAS measurements in Toronto, Canada
Ramina Alwarda, Kristof Bognar, Xiaoyi Zhao, Vitali Fioletov, Jonathan Davies, Sum Chi Lee, Debora Griffin, Alexandru Lupu, Udo Frieß, Alexander Cede, Yushan Su, and Kimberly Strong
Atmos. Meas. Tech., 18, 2397–2423, https://doi.org/10.5194/amt-18-2397-2025,https://doi.org/10.5194/amt-18-2397-2025, 2025
Short summary
A channel selection methodology for enhancing volcanic SO2 monitoring using FY-3E/HIRAS-II hyperspectral data
Xinyu Li, Lin Zhu, Hongfu Sun, Jun Li, Ximing Lv, Chengli Qi, and Huanhuan Yan
Atmos. Meas. Tech., 18, 2333–2352, https://doi.org/10.5194/amt-18-2333-2025,https://doi.org/10.5194/amt-18-2333-2025, 2025
Short summary
Predictions of failed satellite retrieval of air quality using machine learning
Edward Malina, Jure Brence, Jennifer Adams, Jovan Tanevski, Sašo Džeroski, Valentin Kantchev, and Kevin W. Bowman
Atmos. Meas. Tech., 18, 1689–1715, https://doi.org/10.5194/amt-18-1689-2025,https://doi.org/10.5194/amt-18-1689-2025, 2025
Short summary
Deep transfer learning method for seasonal TROPOMI XCH4 albedo correction
Alexander C. Bradley, Barbara Dix, Fergus Mackenzie, J. Pepijn Veefkind, and Joost A. de Gouw
Atmos. Meas. Tech., 18, 1675–1687, https://doi.org/10.5194/amt-18-1675-2025,https://doi.org/10.5194/amt-18-1675-2025, 2025
Short summary
Global retrieval of TROPOMI tropospheric HCHO and NO2 columns with improved consistency based on the updated Peking University OMI NO2 algorithm
Yuhang Zhang, Huan Yu, Isabelle De Smedt, Jintai Lin, Nicolas Theys, Michel Van Roozendael, Gaia Pinardi, Steven Compernolle, Ruijing Ni, Fangxuan Ren, Sijie Wang, Lulu Chen, Jos Van Geffen, Mengyao Liu, Alexander M. Cede, Martin Tiefengraber, Alexis Merlaud, Martina M. Friedrich, Andreas Richter, Ankie Piters, Vinod Kumar, Vinayak Sinha, Thomas Wagner, Yongjoo Choi, Hisahiro Takashima, Yugo Kanaya, Hitoshi Irie, Robert Spurr, Wenfu Sun, and Lorenzo Fabris
Atmos. Meas. Tech., 18, 1561–1589, https://doi.org/10.5194/amt-18-1561-2025,https://doi.org/10.5194/amt-18-1561-2025, 2025
Short summary

Cited articles

Broquet, G., Bréon, F.-M., Renault, E., Buchwitz, M., Reuter, M., Bovensmann, H., Chevallier, F., Wu, L., and Ciais, P.: The potential of satellite spectro-imagery for monitoring CO2 emissions from large cities, Atmos. Meas. Tech., 11, 681–708, https://doi.org/10.5194/amt-11-681-2018, 2018. a
Center For International Earth Science Information Network-CIESIN-Columbia University and International Food Policy Research Institute-IFPRI and The World Bank and Centro Internacional De Agricultura Tropical-CIAT: Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extents Grid, https://doi.org/10.7927/H4GH9FVG, 2011. a
Chevallier, F., Broquet, G., Zheng, B., Ciais, P., and Eldering, A.: Large CO2 emitters as seen from satellite: Comparison to a gridded global emission inventory, Geophys. Res. Lett., 49, e2021GL097540, https://doi.org/10.1029/2021gl097540, 2022. a
Danjou, A., Broquet, G., Lian, J., Bréon, F.-M., and Lauvaux, T.: Evaluation of light atmospheric plume inversion methods using synthetic XCO2 satellite images to compute Paris CO2 emissions, Remote Sens. Environ., 305, 113900, https://doi.org/10.1016/j.rse.2023.113900, 2024. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z
Ehret, T., Truchis, A. D., Mazzolini, M., Morel, J.-M., d’Aspremont, A., Lauvaux, T., Duren, R., Cusworth, D., and Facciolo, G.: Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery, Environ. Sci. Technol., 56, 10517–10529, https://doi.org/10.1021/acs.est.1c08575, 2022. a
Download
Short summary
We study the capacity of XCO2 spaceborne imagery to estimate urban CO2 emissions with synthetic data. We define automatic and standard methods and objective criteria for image selection. The wind variability and urban emission budget guide the emission estimation error. Images with low wind variability and high urban emissions account for 47 % of images and give a bias in the emission estimation of −7 % and a spread of 56 %. Other images give a bias of −31 % and a spread of 99 %.
Share