12 citations as recorded by crossref.

1. CELNet: A comprehensive efficient learning network for atmospheric plume identification from remotely sensed methane concentration images F. Chen et al.
2. Improved monitoring of methane emissions for the oil and gas sector with Sentinel-2 satellite observations B. Zambrano-Luna et al.
3. Atmospheric Impacts of Oil Well Blowouts: A Synergistic Assessment Using Satellite Remote Sensing and WRF-Chem Modeling J. Biswas et al.
4. The integration of vision transformers and SAM for automated methane super-emitter detection using TROPOMI data M. Marjani et al.
5. S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery A. Radman et al.
6. A data-efficient deep transfer learning framework for methane super-emitter detection in oil and gas fields using the Sentinel-2 satellite S. Zhao et al.
7. Разработка статистических моделей оценки концентрации метана в приземном слое атмосферы по материалам космической съемки в зимний период Н. Попов & В. Малинников
8. Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al.
9. Detecting Methane Emissions from Space Over India: Analysis Using EMIT and Sentinel-5P TROPOMI Datasets A. Siddiqui et al.
10. Exploiting the Matched Filter to Improve the Detection of Methane Plumes with Sentinel-2 Data H. Wang et al.
11. High Spatial and Temporal Resolution Simulations of Methane Column Loadings Due to Routine Emissions and Emission Events in Oil and Gas Regions L. Huang et al.
12. Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy M. Pérez-Carrasco et al.