12 citations as recorded by crossref.

1. Implementation of Near-Real-Time Satellite Data Retrieval for CO₂ Concentration Using an Enhanced Transformer Network J. Yu et al. https://doi.org/10.1109/ACCESS.2026.3659201
2. XCO2 Data Full-Coverage Mapping in China Based on Random Forest Models R. Chen et al. https://doi.org/10.3390/rs17010048
3. Transformer-Based Fast Mole Fraction of CO 2 Retrievals from Satellite-Measured Spectra W. Chen et al. https://doi.org/10.34133/remotesensing.0470
4. Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
5. A novel transformer-based CO2 retrieval framework incorporating prior constraint and hierarchical features injection: assessment of transferability for Tansat-2 L. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2026.01.039
6. Spatiotemporal Characteristic of XCO2 and Its Changing Contribution Rate from Different Influencing Indicators in Mongolian Plateau of Central Asia Y. A et al. https://doi.org/10.3390/atmos16050560
7. APPLICATION OF MACHINE LEARNING IN RADIATIVE HEAT TRANSFER W. Chen & T. Ren https://doi.org/10.1615/AnnualRevHeatTransfer.2025059506
8. Deep Neural Emulator of Radiative Transfer Model for Near-Infrared Hyperspectral Simulation: Implications for Fast Satellite Retrieval of XCO2 R. Hu et al. https://doi.org/10.1109/TGRS.2025.3644156
9. Research on a fast XCO2 retrieval method based on one-dimensional convolutional neural networks Y. Jiang et al. https://doi.org/10.1016/j.jqsrt.2026.110074
10. From Deterministic to Probabilistic: A Lightweight Framework for Probabilistic Machine Learning in Trace Gas Remote Sensing W. Chen et al. https://doi.org/10.34133/remotesensing.0881
11. Random Forest-Based Retrieval of XCO2 Concentration from Satellite-Borne Shortwave Infrared Hyperspectral W. Zhang et al. https://doi.org/10.3390/atmos16030238
12. Retrieving the atmospheric concentrations of carbon dioxide and methane from the European Copernicus CO2M satellite mission using artificial neural networks M. Reuter et al. https://doi.org/10.5194/amt-18-241-2025