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

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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
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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 %.
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