Optimal selection of satellite XCO<sub>2</sub> images for urban CO<sub>2</sub> emission monitoring
<p>There is a growing interest in estimating urban CO<span class="inline-formula"><sub>2</sub></span> emission from spaceborne imagery of the CO<span class="inline-formula"><sub>2</sub></span> column-average dry-air mole fract...
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Copernicus Publications
2025-01-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/18/533/2025/amt-18-533-2025.pdf |
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| author | A. Danjou G. Broquet A. Schuh F.-M. Bréon T. Lauvaux T. Lauvaux |
| author_facet | A. Danjou G. Broquet A. Schuh F.-M. Bréon T. Lauvaux T. Lauvaux |
| author_sort | A. Danjou |
| collection | DOAJ |
| description | <p>There is a growing interest in estimating urban CO<span class="inline-formula"><sub>2</sub></span> emission from spaceborne imagery of the CO<span class="inline-formula"><sub>2</sub></span> column-average dry-air mole fraction (XCO<span class="inline-formula"><sub>2</sub></span>). Emission estimation methods have been widely tested and applied to actual or synthetic images. However, there is still a lack of objective criteria for selecting images that are worth processing. This study analyzes the performances of an automated method for estimating urban emissions as a function of the targeted cities and of the atmospheric conditions. It uses synthetic data experiments with synthetic truth and 9920 synthetic satellite images of XCO<span class="inline-formula"><sub>2</sub></span> over 31 of the largest cities across the world generated with a global adaptive-mesh model, the Ocean–Land–Atmosphere Model (OLAM), zoomed in at high resolution over these cities. We use a decision tree learning method applied to this ensemble of synthetic images to define criteria based on these emission and atmospheric conditions for the selection of suitable satellite images.</p>
<p>We show that our automated method for the emission estimation, based on a Gaussian plume model, manages to produce estimates for 92 % of the synthetic images. Our learning method identifies two criteria, the wind direction's spatial variability and the targeted city's emission budget, that discriminate images whose processing yields reasonable emission estimates from those whose processing yields large errors. Images corresponding to low spatial variability in wind direction (less than 12°) and to high urban emissions (greater than 2.1 kt CO<span class="inline-formula"><sub>2</sub></span> h<span class="inline-formula"><sup>−1</sup></span>) account for 47 % of the images, and their processing yields relative errors in the emission estimates with a median value of <span class="inline-formula">−</span>7 % and an interquartile range (IQR) of 56 %. Images corresponding to a high spatial variability in wind direction or to low urban emissions account for 53 % of our images, and their processing yield relative errors in the emission estimates with a median value of <span class="inline-formula">−</span>31 % and an IQR of 99 %. Despite such efficient filtering, the accuracy of the estimates corresponding to the former group of images varies widely from city to city.</p> |
| format | Article |
| id | doaj-art-a1572d854c1049b491630ac8a1766f3e |
| institution | Kabale University |
| issn | 1867-1381 1867-8548 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj-art-a1572d854c1049b491630ac8a1766f3e2025-01-29T05:04:13ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482025-01-011853355410.5194/amt-18-533-2025Optimal selection of satellite XCO<sub>2</sub> images for urban CO<sub>2</sub> emission monitoringA. Danjou0G. Broquet1A. Schuh2F.-M. Bréon3T. Lauvaux4T. Lauvaux5Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceLaboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceCooperative Institute for Research in the Atmosphere (CIRA), Colorado State University, Fort Collins, Colorado, USALaboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceLaboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceMolecular and Atmospheric Spectrometry Group (GSMA) – UMR 7331, University of Reims Champagne-Ardenne, 51687 Reims, France<p>There is a growing interest in estimating urban CO<span class="inline-formula"><sub>2</sub></span> emission from spaceborne imagery of the CO<span class="inline-formula"><sub>2</sub></span> column-average dry-air mole fraction (XCO<span class="inline-formula"><sub>2</sub></span>). Emission estimation methods have been widely tested and applied to actual or synthetic images. However, there is still a lack of objective criteria for selecting images that are worth processing. This study analyzes the performances of an automated method for estimating urban emissions as a function of the targeted cities and of the atmospheric conditions. It uses synthetic data experiments with synthetic truth and 9920 synthetic satellite images of XCO<span class="inline-formula"><sub>2</sub></span> over 31 of the largest cities across the world generated with a global adaptive-mesh model, the Ocean–Land–Atmosphere Model (OLAM), zoomed in at high resolution over these cities. We use a decision tree learning method applied to this ensemble of synthetic images to define criteria based on these emission and atmospheric conditions for the selection of suitable satellite images.</p> <p>We show that our automated method for the emission estimation, based on a Gaussian plume model, manages to produce estimates for 92 % of the synthetic images. Our learning method identifies two criteria, the wind direction's spatial variability and the targeted city's emission budget, that discriminate images whose processing yields reasonable emission estimates from those whose processing yields large errors. Images corresponding to low spatial variability in wind direction (less than 12°) and to high urban emissions (greater than 2.1 kt CO<span class="inline-formula"><sub>2</sub></span> h<span class="inline-formula"><sup>−1</sup></span>) account for 47 % of the images, and their processing yields relative errors in the emission estimates with a median value of <span class="inline-formula">−</span>7 % and an interquartile range (IQR) of 56 %. Images corresponding to a high spatial variability in wind direction or to low urban emissions account for 53 % of our images, and their processing yield relative errors in the emission estimates with a median value of <span class="inline-formula">−</span>31 % and an IQR of 99 %. Despite such efficient filtering, the accuracy of the estimates corresponding to the former group of images varies widely from city to city.</p>https://amt.copernicus.org/articles/18/533/2025/amt-18-533-2025.pdf |
| spellingShingle | A. Danjou G. Broquet A. Schuh F.-M. Bréon T. Lauvaux T. Lauvaux Optimal selection of satellite XCO<sub>2</sub> images for urban CO<sub>2</sub> emission monitoring Atmospheric Measurement Techniques |
| title | Optimal selection of satellite XCO<sub>2</sub> images for urban CO<sub>2</sub> emission monitoring |
| title_full | Optimal selection of satellite XCO<sub>2</sub> images for urban CO<sub>2</sub> emission monitoring |
| title_fullStr | Optimal selection of satellite XCO<sub>2</sub> images for urban CO<sub>2</sub> emission monitoring |
| title_full_unstemmed | Optimal selection of satellite XCO<sub>2</sub> images for urban CO<sub>2</sub> emission monitoring |
| title_short | Optimal selection of satellite XCO<sub>2</sub> images for urban CO<sub>2</sub> emission monitoring |
| title_sort | optimal selection of satellite xco sub 2 sub images for urban co sub 2 sub emission monitoring |
| url | https://amt.copernicus.org/articles/18/533/2025/amt-18-533-2025.pdf |
| work_keys_str_mv | AT adanjou optimalselectionofsatellitexcosub2subimagesforurbancosub2subemissionmonitoring AT gbroquet optimalselectionofsatellitexcosub2subimagesforurbancosub2subemissionmonitoring AT aschuh optimalselectionofsatellitexcosub2subimagesforurbancosub2subemissionmonitoring AT fmbreon optimalselectionofsatellitexcosub2subimagesforurbancosub2subemissionmonitoring AT tlauvaux optimalselectionofsatellitexcosub2subimagesforurbancosub2subemissionmonitoring AT tlauvaux optimalselectionofsatellitexcosub2subimagesforurbancosub2subemissionmonitoring |