Articles | Volume 16, issue 6
https://doi.org/10.5194/amt-16-1597-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-1597-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Mar 2023
Research article |
| 28 Mar 2023
| 28 Mar 2023
Accounting for surface reflectance spectral features in TROPOMI methane retrievals
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Tobias Borsdorff
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Mari C. Martinez-Velarte
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Jochen Landgraf
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
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31 citations as recorded by crossref.
- Global Methane Budget 2000–2020 M. Saunois et al. https://doi.org/10.5194/essd-17-1873-2025
- Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements Z. Barkley et al. https://doi.org/10.5194/acp-23-6127-2023
- A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases N. Balasus et al. https://doi.org/10.5194/amt-16-3787-2023
- Surface reflectance biases in XCH4 retrievals from the 2.3 µm band are enhanced in the presence of aerosols P. Somkuti et al. https://doi.org/10.5194/amt-18-4647-2025
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al. https://doi.org/10.1109/JSTARS.2024.3482278
- Towards the Optimization of TanSat-2: Assessment of a Large-Swath Methane Measurement S. Zhu et al. https://doi.org/10.3390/rs17030543
- Satellite quantification of methane emissions from South American countries: a high-resolution inversion of TROPOMI and GOSAT observations S. Hancock et al. https://doi.org/10.5194/acp-25-797-2025
- Incorporating methane isotopologues alters tropical and subtropical methane emission estimates X. Yu et al. https://doi.org/10.1038/s41467-026-72668-2
- If the Yedoma thaws, will we notice? Quantifying detection limits of top-down methane monitoring infrastructures M. Pallandt et al. https://doi.org/10.5194/amt-18-7053-2025
- Trends and seasonality of 2019–2023 global methane emissions inferred from a localized ensemble transform Kalman filter (CHEEREIO v1.3.1) applied to TROPOMI satellite observations D. Pendergrass et al. https://doi.org/10.5194/acp-25-14353-2025
- 2019–2024 trends in African livestock and wetland emissions as contributors to the global methane rise N. Balasus et al. https://doi.org/10.5194/acp-26-4601-2026
- Uncertainty and retrieval sensitivity in TROPOMI-based methane inversions over the North Slope of Alaska R. Ward et al. https://doi.org/10.5194/amt-19-813-2026
- Sensitivity of land-type variations across Canada using S-5p products S. Bhatnagar et al. https://doi.org/10.1016/j.geomat.2025.100048
- Methane retrieval from MethaneAIR using the CO2 proxy approach: a demonstration for the upcoming MethaneSAT mission C. Chan Miller et al. https://doi.org/10.5194/amt-17-5429-2024
- Emerging Trends, Sectoral, and Regional Patterns in China’s Methane Emissions: A Satellite-Constrained Perspective D. Chen et al. https://doi.org/10.1021/acsestair.5c00405
- Satellite-Based Monitoring of Methane Emissions from China’s Rice Hub R. Liang et al. https://doi.org/10.1021/acs.est.4c09822
- Quantifying urban and landfill methane emissions in the United States using TROPOMI satellite data X. Wang et al. https://doi.org/10.1126/sciadv.adz9308
- Recent advances in TROPOMI-based methane source detection: a systematic review R. Liu et al. https://doi.org/10.1080/15481603.2026.2650822
- Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations L. Estrada et al. https://doi.org/10.5194/gmd-18-3311-2025
- Evaluation of Sentinel-5P TROPOMI Methane Observations at Northern High Latitudes H. Lindqvist et al. https://doi.org/10.3390/rs16162979
- Integrating Satellite Observations and Hydrological Models to Unravel Large TROPOMI Methane Emissions in South Sudan Wetlands Y. Albuhaisi et al. https://doi.org/10.3390/rs16244744
- Evaluation of satellite-derived methane emissions from coal mines using the Gaussian plume model in a topographically complex area Y. Gao et al. https://doi.org/10.1016/j.atmosenv.2026.122004
- Attributing 2019–2024 methane growth using TROPOMI satellite observations M. He et al. https://doi.org/10.1126/sciadv.adz9007
- Theoretical Potential of TanSat-2 to Quantify China’s CH4 Emissions S. Zhu et al. https://doi.org/10.3390/rs17132321
- Random Forest Classifier for Cloud Clearing of the Operational TROPOMI XCH4 Product T. Borsdorff et al. https://doi.org/10.3390/rs16071208
- Assessing methane emissions from collapsing Venezuelan oil production using TROPOMI B. Nathan et al. https://doi.org/10.5194/acp-24-6845-2024
- 温室气体多平台遥感探测研究（特邀） 刘. Liu Dong et al. https://doi.org/10.3788/AOS251794
- Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach M. Hemati et al. https://doi.org/10.1038/s41598-024-58995-8
- A multi-year global methane data set obtained by merging observations from TROPOMI and IASI K. Shahzadi et al. https://doi.org/10.5194/essd-18-2153-2026
- Global satellite survey reveals uncertainty in landfill methane emissions M. Dogniaux et al. https://doi.org/10.1038/s41586-025-09683-8
- Global near-surface methane distribution and emission hotspots from TROPOMI: A geospatial machine-learning approach D. Lee et al. https://doi.org/10.1016/j.scitotenv.2026.181848
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Short summary
In the TROPOMI methane data, there are few false methane anomalies that can be misinterpreted as enhancements caused by strong emission sources. These artefacts are caused by features of the underlying surfaces that are not well characterized in the retrieval algorithm. Here we improve the representation of the surface reflectance dependency with wavelength in the forward model, removing the artificial localized CH4 enhancements found in several locations like Siberia, Australia and Algeria.
In the TROPOMI methane data, there are few false methane anomalies that can be misinterpreted as...
