11 citations as recorded by crossref.

1. Revisiting the quantification of power plant CO2 emissions in the United States and China from satellite: A comparative study using three top-down approaches C. He et al. https://doi.org/10.1016/j.rse.2024.114192
2. Assessing the accuracy of meteorological transport model ensemble towards estimating Indian carbon fluxes T. Anna Mathew et al. https://doi.org/10.1088/2752-5295/adffa7
3. Using satellite-based Sun-induced chlorophyll fluorescence and spectral reflectance for improving terrestrial CO2 flux estimates of India A. Ravi et al. https://doi.org/10.1088/2752-664X/adabed
4. Anthropogenic carbon dioxide origin tracing study in Anmyeon-do, South Korea: Based on STILT-footprint and emissions data J. Kim et al. https://doi.org/10.1016/j.scitotenv.2023.164677
5. Spatiotemporal variations of near-surface CO2 concentrations in China and their multiple drivers: Modeling pathways towards the “dual carbon” goals T. Yang et al. https://doi.org/10.1016/j.jclepro.2026.149166
6. Leveraging TROPOMI observations and WRF-GHG modeling towards improving methane emission assessments in India T. Mathew et al. https://doi.org/10.5194/acp-26-4453-2026
7. High-resolution GEOS-Chem model for Indian monsoon region: Seasonal cycle and budget of tropospheric CO2 S. Allahudheen et al. https://doi.org/10.1016/j.atmosenv.2023.119913
8. Potential of using CO2 observations over India in a regional carbon budget estimation by improving the modelling system V. Thilakan et al. https://doi.org/10.5194/acp-24-5315-2024
9. Extratropical storms induce carbon outgassing over the Southern Ocean M. Carranza et al. https://doi.org/10.1038/s41612-024-00657-7
10. Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO2 Emissions S. Jeong et al. https://doi.org/10.1021/acs.est.4c09395
11. Spatiotemporal CO₂ emission prediction modeling methods: a comprehensive literature review Z. Omar & L. Liyanage https://doi.org/10.1007/s40808-026-02899-1