30 citations as recorded by crossref.

1. Resolving heterogeneous fluxes from tundra halves the growing season carbon budget S. Ludwig et al. https://doi.org/10.5194/bg-21-1301-2024
2. Ideas and perspectives: Enhancing research and monitoring of carbon pools and land-to-atmosphere greenhouse gases exchange in developing countries D. Kim et al. https://doi.org/10.5194/bg-19-1435-2022
3. Insights into terrestrial carbon and water cycling from the global eddy covariance network J. Xiao et al. https://doi.org/10.1038/s43017-025-00743-1
4. Atmospheric effects are stronger than soil moisture in restricting net CO2 uptake of managed grasslands in New Zealand J. Goodrich et al. https://doi.org/10.1016/j.agrformet.2023.109822
5. Carbon–Water Coupling in Forest Ecosystems Under Climate Change: Advances in Water Use Efficiency and Sustainability Perspectives X. Liang et al. https://doi.org/10.3390/su17219501
6. Impact of Climate Warming on Cropland Water Use Efficiency in Northeast China Based on BESS Satellite Data F. Guo et al. https://doi.org/10.3390/rs18081223
7. A Normalization-Calibration Model for Multi-Source Ground-Based FPAR Observations in Mountainous Forests Y. Cai et al. https://doi.org/10.3390/rs17233797
8. Light use efficiency models incorporating diffuse radiation impacts for simulating terrestrial ecosystem gross primary productivity: A global comparison H. Xu et al. https://doi.org/10.1016/j.agrformet.2023.109376
9. Empirical upscaling of OzFlux eddy covariance for high-resolution monitoring of terrestrial carbon uptake in Australia C. Burton et al. https://doi.org/10.5194/bg-20-4109-2023
10. Real-world wintertime CO, N2O, and CO2 emissions of a central European village L. Haszpra et al. https://doi.org/10.5194/amt-15-5019-2022
11. NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area D. Charrière et al. https://doi.org/10.3390/rs16203894
12. A pre-whitening with block-bootstrap cross-correlation procedure for temporal alignment of data sampled by eddy covariance systems D. Vitale et al. https://doi.org/10.1007/s10651-024-00615-9
13. Carbon Flux Measurement Supersites of the Russian Federation: Objectives, Methodology, Prospects A. Olchev & S. Gulev https://doi.org/10.1134/S0001433824700841
14. Global trends in land-atmosphere CO<SUB>2</SUB> exchange fluxes: an analysis of a flux measurement dataset and comparison with terrestrial model simulations A. ITO https://doi.org/10.2480/agrmet.D-21-00015
15. Potential for equation discovery with AI in the climate sciences C. Huntingford et al. https://doi.org/10.5194/esd-16-475-2025
16. Flux Footprints: A Critical Link to Bridge Eddy‐Covariance Measurements With Models, Remote Sensing, and Other Observations H. Chu et al. https://doi.org/10.1111/gcb.70887
17. Intercomparison of methods to estimate gross primary production based on CO2 and COS flux measurements K. Kohonen et al. https://doi.org/10.5194/bg-19-4067-2022
18. Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set S. Walther et al. https://doi.org/10.5194/bg-19-2805-2022
19. Satellite remote sensing and artificial intelligence for livestock greenhouse gas benchmarking: measurement, attribution, and verification challenges P. Prajesh et al. https://doi.org/10.1039/D5VA00425J
20. Disentangling Turbulent Gas Diffusion from Non-diffusive Transport in the Boundary Layer A. Kowalski et al. https://doi.org/10.1007/s10546-021-00605-5
21. Introducing ‘miniRECgap’ R package for simple gap-filling of missing eddy covariance CO2 flux measurements with classic nonlinear environmental response functions via GUI-supported R-scripts (case-study: In-sample gap-filling with ‘miniRECgap’ vs. MDS and an optimised shallow ANN in a ‘challenging’ peatland ecosystem) A. Premrov et al. https://doi.org/10.1016/j.envsoft.2025.106611
22. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands J. Irvin et al. https://doi.org/10.1016/j.agrformet.2021.108528
23. Declining resilience of forest carbon sinks linked to increased atmospheric water deficit during droughts in the Northern Hemisphere X. Wu et al. https://doi.org/10.1088/1748-9326/ada170
24. Paddy rice methane emissions across Monsoon Asia Z. Ouyang et al. https://doi.org/10.1016/j.rse.2022.113335
25. Predictive value of soil moisture and concurrent variables in the multivariate modelling of cereal yields in water-limited environments J. Gaona et al. https://doi.org/10.1016/j.agwat.2023.108280
26. Impact of filtering methods on ultrafine particles turbulent fluxes by eddy covariance G. Pappaccogli et al. https://doi.org/10.1016/j.atmosenv.2022.119237
27. An Ecosystem-Scale Flux Measurement Strategy to Assess Natural Climate Solutions K. Hemes et al. https://doi.org/10.1021/acs.est.0c06421
28. Warming and Drought Weaken the Carbon Sink Capacity of an Endangered Paleoendemic Temperate Rainforest in South America J. Perez‐Quezada et al. https://doi.org/10.1029/2022JG007258
29. AmeriFlux BASE data pipeline to support network growth and data sharing H. Chu et al. https://doi.org/10.1038/s41597-023-02531-2
30. The spatial and environmental distribution of the global eddy covariance tower network P. Stoy et al. https://doi.org/10.1007/s00484-026-03183-8