7 citations as recorded by crossref.

1. Variability and uncertainty in net ecosystem carbon exchange modeling: Systematic estimates at global flux sites via ensemble machine learning N. Wang et al. https://doi.org/10.1016/j.agrformet.2025.110784
2. Modeling carbonyl sulfide and carbon dioxide fluxes in a northern boreal coniferous forest using memory-based deep learning S. Liu et al. https://doi.org/10.1016/j.ecolmodel.2025.111283
3. Improved uncertainty estimates for eddy covariance-based carbon dioxide balances using deep ensembles for gap-filling H. Vekuri et al. https://doi.org/10.1016/j.agrformet.2025.110558
4. Random Forest Spatial-Temporal and State Space Models to Assess the Impact of Bushfire-Induced Aerosol Events on Ozone Depletion in Australia I. Hudson et al. https://doi.org/10.3390/app14219825
5. High-resolution remote sensing and machine-learning-based upscaling of methane fluxes: a case study in the Western Canadian tundra K. Ivanova et al. https://doi.org/10.5194/bg-23-233-2026
6. Explainable machine learning for modeling of net ecosystem exchange in boreal forests E. Ezhova et al. https://doi.org/10.5194/bg-22-257-2025
7. Multi-Source Remote Sensing and GIS for Forest Carbon Monitoring Toward Carbon Neutrality X. Liang et al. https://doi.org/10.3390/f16060971