25 citations as recorded by crossref.

1. Daily detection and quantification of methane leaks using Sentinel-3: a tiered satellite observation approach with Sentinel-2 and Sentinel-5p S. Pandey et al. 10.1016/j.rse.2023.113716
2. A critical analysis of challenges and opportunities for upcycling food waste to animal feed to reduce climate and resource burdens Z. Dou et al. 10.1016/j.resconrec.2024.107418
3. Development of Artificial Intelligence/Machine Learning (AI/ML) Models for Methane Emissions Forecasting in Seaweed C. Louime & T. Raza 10.3390/methane3030028
4. 2024 ESA-ECMWF workshop report: current status, progress and opportunities in machine learning for Earth system observation and prediction P. Ebel et al. 10.1038/s41612-024-00757-4
5. Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data S. Vanselow et al. 10.5194/acp-24-10441-2024
6. S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery A. Radman et al. 10.1016/j.rse.2023.113708
7. CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery A. Vaughan et al. 10.5194/amt-17-2583-2024
8. High-resolution satellite estimates of coal mine methane emissions from local to regional scales in Shanxi, China S. Bai et al. 10.1016/j.scitotenv.2024.175446
9. Report on Landsat 8 and Sentinel-2B observations of the Nord Stream 2 pipeline methane leak M. Dogniaux et al. 10.5194/amt-17-2777-2024
10. The methane imperative D. Shindell et al. 10.3389/fsci.2024.1349770
11. Current Status of Satellite Remote Sensing-Based Methane Emission Monitoring Technologies M. Kim et al. 10.9719/EEG.2024.57.5.513
12. Evidence of animal productivity outcomes when fed diets including food waste: A systematic review of global primary data Y. Wang et al. 10.1016/j.resconrec.2024.107411
13. Long-term investigation of methane and carbon dioxide emissions in two Italian landfills L. Brilli et al. 10.1016/j.heliyon.2024.e29356
14. Developing unbiased estimation of atmospheric methane via machine learning and multiobjective programming based on TROPOMI and GOSAT data K. Li et al. 10.1016/j.rse.2024.114039
15. Artificial intelligence‐driven insights: Precision tracking of power plant carbon emissions using satellite data Z. Zhang et al. 10.1049/enc2.12129
16. Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio Q. Xu et al. 10.1080/01431161.2024.2421946
17. A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B Z. He et al. 10.5194/amt-17-2937-2024
18. Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer B. Rouet-Leduc & C. Hulbert 10.1038/s41467-024-47754-y
19. Tracking methane super-emitters from space J. O’Callaghan 10.1038/d41586-024-03594-w
20. Detecting Methane Emissions from Space Over India: Analysis Using EMIT and Sentinel-5P TROPOMI Datasets A. Siddiqui et al. 10.1007/s12524-024-01925-y
21. First validation of high-resolution satellite-derived methane emissions from an active gas leak in the UK E. Dowd et al. 10.5194/amt-17-1599-2024
22. Assessing methane emissions from collapsing Venezuelan oil production using TROPOMI B. Nathan et al. 10.5194/acp-24-6845-2024
23. Multisatellite Data Depicts a Record-Breaking Methane Leak from a Well Blowout L. Guanter et al. 10.1021/acs.estlett.4c00399
24. Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. 10.1007/s11783-025-1922-x
25. Assessing the Relative Importance of Satellite-Detected Methane Superemitters in Quantifying Total Emissions for Oil and Gas Production Areas in Algeria S. Naus et al. 10.1021/acs.est.3c04746