12 citations as recorded by crossref.

1. A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases N. Balasus et al. 10.5194/amt-16-3787-2023
2. Deep learning applied to CO2 power plant emissions quantification using simulated satellite images J. Dumont Le Brazidec et al. 10.5194/gmd-17-1995-2024
3. Understanding the potential of Sentinel-2 for monitoring methane point emissions J. Gorroño et al. 10.5194/amt-16-89-2023
4. Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. 10.3390/atmos15040449
5. 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
6. Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al. 10.1109/JSTARS.2024.3482278
7. Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane D. Jacob et al. 10.5194/acp-22-9617-2022
8. Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods J. Dumont Le Brazidec et al. 10.5194/gmd-18-3607-2025
9. Simulation evaluation of a single-photon laser methane remote sensor for leakage rate monitoring S. Zhu et al. 10.1364/OE.513894
10. Towards Supporting Satellite Design Through the Top-Down Approach: A General Model for Assessing the Ability of Future Satellite Missions to Quantify Point Source Emissions L. Yao et al. 10.3390/rs16234503
11. Accounting for surface reflectance spectral features in TROPOMI methane retrievals A. Lorente et al. 10.5194/amt-16-1597-2023
12. Instrument Performance Analysis for Methane Point Source Retrieval and Estimation Using Remote Sensing Technique Y. Jiang et al. 10.3390/rs17040634