12 citations as recorded by crossref.

1. The impact of permafrost on carbon dioxide and methane fluxes in Siberia: A meta-analysis O. Masyagina & O. Menyailo https://doi.org/10.1016/j.envres.2019.109096
2. Effects of wetland disturbance on methane emissions and influential factors: A global meta-analysis of field studies S. Xu et al. https://doi.org/10.1016/j.scitotenv.2024.178325
3. Effects of hummock-hollow microtopography on CO2 and CH4 emissions from sedge peatlands in the Changbai Mountains, Northeast China X. Li et al. https://doi.org/10.1016/j.agrformet.2026.111220
4. Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions M. Ueyama et al. https://doi.org/10.1111/gcb.16594
5. Winter CH4 and CO2 Accumulation from a Permafrost Peatland Pond is Critical to Spring thaw Carbon Emissions J. Xue et al. https://doi.org/10.1007/s13157-024-01852-1
6. Interpreting eddy covariance data from heterogeneous Siberian tundra: land-cover-specific methane fluxes and spatial representativeness J. Tuovinen et al. https://doi.org/10.5194/bg-16-255-2019
7. FLUXNET-CH4: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands K. Delwiche et al. https://doi.org/10.5194/essd-13-3607-2021
8. Optimized wetland rewetting strategies can control methane, carbon dioxide, and oxygen responses to water table fluctuations B. Zhao et al. https://doi.org/10.1038/s43247-025-03163-7
9. Modeled Microbial Dynamics Explain the Apparent Temperature Sensitivity of Wetland Methane Emissions S. Chadburn et al. https://doi.org/10.1029/2020GB006678
10. Evaluating two soil carbon models within the global land surface model JSBACH using surface and spaceborne observations of atmospheric CO2 T. Thum et al. https://doi.org/10.5194/bg-17-5721-2020
11. Predicting CO2 and CH4 fluxes and their seasonal variations in a subarctic wetland under two shared socioeconomic pathway climate scenarios B. Zhao et al. https://doi.org/10.1016/j.agrformet.2024.110359
12. Causality guided machine learning model on wetland CH4 emissions across global wetlands K. Yuan et al. https://doi.org/10.1016/j.agrformet.2022.109115