36 citations as recorded by crossref.

1. Modelling decadal trends and the impact of extreme events on carbon fluxes in a temperate deciduous forest using a terrestrial biosphere model T. Thum et al. 10.5194/bg-22-1781-2025
2. Full phenology cycle carbon flux dynamics and driving mechanism of Moso bamboo forest C. Xu et al. 10.3389/fpls.2024.1359265
3. A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates H. Vekuri et al. 10.1038/s41598-023-28827-2
4. Multiple gap-filling for eddy covariance datasets A. Lucas-Moffat et al. 10.1016/j.agrformet.2022.109114
5. Improved uncertainty estimates for eddy covariance-based carbon dioxide balances using deep ensembles for gap-filling H. Vekuri et al. 10.1016/j.agrformet.2025.110558
6. Atmospheric water demand constrains net ecosystem production in subtropical mangrove forests R. Gou et al. 10.1016/j.jhydrol.2024.130651
7. Spatiotemporal variation in carbon use efficiency derived from eddy-covariance measurement of global terrestrial biomes C. Jin et al. 10.1016/j.agrformet.2024.110318
8. Missing values imputation in ocean buoy time series data S. Chakraborty et al. 10.1016/j.oceaneng.2024.120145
9. Estimating the methane flux of the Dajiuhu subalpine peatland using machine learning algorithms and the maximal information coefficient technique X. Li et al. 10.1016/j.scitotenv.2024.170241
10. Artificial intelligence and Eddy covariance: A review A. Lucarini et al. 10.1016/j.scitotenv.2024.175406
11. Testing the suitability of Marginal Distribution Sampling as a gap‐filling method using some meteorological data from seven sites in West Africa D. Koukoui et al. 10.1002/met.2152
12. Spatial and temporal variation of three Eddy-Covariance flux footprints in a Tropical Dry Forest M. Abdaki et al. 10.1016/j.agrformet.2023.109863
13. 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. 10.1016/j.envsoft.2025.106611
14. Air–sea interactions in stable atmospheric conditions: lessons from the desert semi-enclosed Gulf of Eilat (Aqaba) S. Abir et al. 10.5194/acp-24-6177-2024
15. A comparative analysis of machine learning approaches to gap filling meteorological datasets B. Lalic et al. 10.1007/s12665-024-11982-8
16. Gap-filling carbon dioxide, water, energy, and methane fluxes in challenging ecosystems: Comparing between methods, drivers, and gap-lengths S. Zhu et al. 10.1016/j.agrformet.2023.109365
17. A physically consistent dataset of water-energy-carbon fluxes across the Soil-Plant-Atmosphere Continuum Y. Wang et al. 10.1038/s41597-025-05386-x
18. A gap filling method for daily evapotranspiration of global flux data sets based on deep learning L. Qian et al. 10.1016/j.jhydrol.2024.131787
19. Robust filling of extra-long gaps in eddy covariance CO2 flux measurements from a temperate deciduous forest using eXtreme Gradient Boosting Y. Liu et al. 10.1016/j.agrformet.2025.110438
20. Coupling Interpretable Feature Selection with Machine Learning for Evapotranspiration Gap Filling L. Wang et al. 10.3390/w17050748
21. Eddy Covariance CO2 Flux Gap Filling for Long Data Gaps: A Novel Framework Based on Machine Learning and Time Series Decomposition D. Gao et al. 10.3390/rs15102695
22. Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange M. Kämäräinen et al. 10.5194/bg-20-897-2023
23. A physical full-factorial scheme for gap-filling of eddy covariance measurements of daytime evapotranspiration Y. Jiang et al. 10.1016/j.agrformet.2022.109087
24. Providing a comprehensive understanding of missing data imputation processes in evapotranspiration-related research: a systematic literature review E. Başakın et al. 10.1080/02626667.2023.2249456
25. Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods D. Garcia-Rodriguez et al. 10.1016/j.ecoinf.2024.102638
26. Machine Learning-Based Prediction of Ecosystem-Scale CO2 Flux Measurements J. Uyekawa et al. 10.3390/land14010124
27. Improving gap-filling performance for CH4 fluxes of eddy covariance data by combining marginal distribution sampling and machine learning algorithm over paddy fields L. Ma et al. 10.1016/j.jclepro.2025.145615
28. Multiple Gap-Filling for Eddy Covariance Datasets A. Lucas-Moffat et al. 10.2139/ssrn.4065277
29. Dynamic and high methane emission flux in pond and lake aquaculture J. Zhao et al. 10.1016/j.jhydrol.2025.132765
30. Trading a little water for substantial carbon gains during the first years of a Eucalyptus globulus plantation M. Silva et al. 10.1016/j.agrformet.2022.108910
31. A ground-independent method for obtaining complete time series of in situ evapotranspiration observations W. Li et al. 10.1016/j.jhydrol.2024.130888
32. Temporally dynamic carbon dioxide and methane emission factors for rewetted peatlands A. Kalhori et al. 10.1038/s43247-024-01226-9
33. Commercial forest carbon protocol over-credit bias delimited by zero-threshold carbon accounting B. Marino & N. Bautista 10.1016/j.tfp.2021.100171
34. Drainage effects on carbon budgets of degraded peatlands in the north of the Netherlands T. Nijman et al. 10.1016/j.scitotenv.2024.172882
35. Technical note: Uncertainties in eddy covariance CO2 fluxes in a semiarid sagebrush ecosystem caused by gap-filling approaches J. Yao et al. 10.5194/acp-21-15589-2021
36. A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000 to 2022 W. Li et al. 10.5194/essd-17-3835-2025