Articles | Volume 21, issue 20
https://doi.org/10.5194/acp-21-15589-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-15589-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note: Uncertainties in eddy covariance CO2 fluxes in a semiarid sagebrush ecosystem caused by gap-filling approaches
Jingyu Yao
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou,
China
Zhongming Gao
Laboratory for Atmospheric Research, Department of Civil and
Environmental Engineering, Washington State University, Pullman, Washington,
USA
School of Atmospheric Sciences, Sun Yat-sen University, Southern
Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou, China
Heping Liu
Laboratory for Atmospheric Research, Department of Civil and
Environmental Engineering, Washington State University, Pullman, Washington,
USA
Guoyin Wang
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, China
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Cited
13 citations as recorded by crossref.
- 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
- Quantifying the Spatial Representativeness of Carbon Flux Footprints of a Grassland Ecosystem in the Semi‐Arid Region H. Gong et al. 10.1029/2022JD038269
- 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
- Impact of Shifts in Vegetation Phenology on the Carbon Balance of a Semiarid Sagebrush Ecosystem J. Yao et al. 10.3390/rs14235924
- 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
- The turning of ecological change in the Yellow River Basin T. Gu et al. 10.1002/hyp.15055
- 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
- A ground-independent method for obtaining complete time series of in situ evapotranspiration observations W. Li et al. 10.1016/j.jhydrol.2024.130888
- 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
- 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
- Multiple-Win Effects and Beneficial Implications from Analyzing Long-Term Variations of Carbon Exchange in a Subtropical Coniferous Plantation in China J. Bai et al. 10.3390/atmos15101218
- Upscaling net ecosystem CO2 exchanges in croplands: The application of integrating object-based image analysis and machine learning approaches D. Gao et al. 10.1016/j.scitotenv.2024.173887
- Artificial intelligence and Eddy covariance: A review A. Lucarini et al. 10.1016/j.scitotenv.2024.175406
13 citations as recorded by crossref.
- 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
- Quantifying the Spatial Representativeness of Carbon Flux Footprints of a Grassland Ecosystem in the Semi‐Arid Region H. Gong et al. 10.1029/2022JD038269
- 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
- Impact of Shifts in Vegetation Phenology on the Carbon Balance of a Semiarid Sagebrush Ecosystem J. Yao et al. 10.3390/rs14235924
- 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
- The turning of ecological change in the Yellow River Basin T. Gu et al. 10.1002/hyp.15055
- 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
- A ground-independent method for obtaining complete time series of in situ evapotranspiration observations W. Li et al. 10.1016/j.jhydrol.2024.130888
- 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
- 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
- Multiple-Win Effects and Beneficial Implications from Analyzing Long-Term Variations of Carbon Exchange in a Subtropical Coniferous Plantation in China J. Bai et al. 10.3390/atmos15101218
- Upscaling net ecosystem CO2 exchanges in croplands: The application of integrating object-based image analysis and machine learning approaches D. Gao et al. 10.1016/j.scitotenv.2024.173887
- Artificial intelligence and Eddy covariance: A review A. Lucarini et al. 10.1016/j.scitotenv.2024.175406
Latest update: 06 Jun 2025
Short summary
Gap-filling usually accounts for a large source of uncertainties in the annual CO2 fluxes, though gap-filling CO2 fluxes is challenging at dryland sites due to small fluxes. Using data collected from a semiarid site, four machine learning methods are evaluated with different lengths of artificial gaps. The artificial neural network and random forest methods outperform the other methods. With these methods, uncertainties in the annual CO2 flux at this site are estimated to be within 16 g C m−2.
Gap-filling usually accounts for a large source of uncertainties in the annual CO2 fluxes,...
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