11 citations as recorded by crossref.

1. Methane intensity and emissions across major oil and gas basins and individual jurisdictions using MethaneSAT observations J. Williams et al. https://doi.org/10.5194/acp-26-5961-2026
2. Enhancing the Detection of Potential Anthropogenic Methane Emission Sources in China Using Machine Learning and TROPOMI Observations S. Yu et al. https://doi.org/10.1021/acs.estlett.6c00115
3. 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
4. What is causing the methane surge? E. Nisbet & M. Manning https://doi.org/10.1126/science.aee6226
5. Enhanced methane monitoring: a globally harmonized daily 0.1° XCH4 through machine learning-based fusion of GOSAT, GOSAT-2, and TROPOMI J. Keya et al. https://doi.org/10.5194/amt-19-4313-2026
6. Worldwide inference of national methane emissions by inversion of satellite observations with UNFCCC prior estimates J. East et al. https://doi.org/10.1038/s41467-025-67122-8
7. Incorporating methane isotopologues alters tropical and subtropical methane emission estimates X. Yu et al. https://doi.org/10.1038/s41467-026-72668-2
8. Tropical Wetland Methane Emissions and Trends (2004–2023) Inferred from Landsat-Based Inundated Vegetation Data Z. Chen et al. https://doi.org/10.1021/acs.est.6c05412
9. Quantifying national, state, and oil/gas field methane emissions and trends in the US (2019–2024) through high resolution inversion of satellite observations L. Estrada et al. https://doi.org/10.5194/acp-26-10629-2026
10. Attributing 2019–2024 methane growth using TROPOMI satellite observations M. He et al. https://doi.org/10.1126/sciadv.adz9007
11. The added value of new ground-based observations in improving China's methane emission quantification H. Zhong et al. https://doi.org/10.5194/amt-19-4759-2026