Articles | Volume 19, issue 16
https://doi.org/10.5194/bg-19-3739-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-3739-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Aug 2022
Research article |
| 16 Aug 2022
| 16 Aug 2022
Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation
Haiyang Shi
State Key Laboratory of Desert and Oasis Ecology, Xinjiang
Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi,
Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Geping Luo
CORRESPONDING AUTHOR
State Key Laboratory of Desert and Oasis Ecology, Xinjiang
Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi,
Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Research Center for Ecology and Environment of Central Asia,
Chinese Academy of Sciences, Ürümqi, 830011, China
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Olaf Hellwich
CORRESPONDING AUTHOR
Department of Computer Vision & Remote Sensing, Technische
Universität Berlin, 10587 Berlin, Germany
Mingjuan Xie
State Key Laboratory of Desert and Oasis Ecology, Xinjiang
Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi,
Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Chen Zhang
State Key Laboratory of Desert and Oasis Ecology, Xinjiang
Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi,
Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Yu Zhang
State Key Laboratory of Desert and Oasis Ecology, Xinjiang
Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi,
Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Yuangang Wang
State Key Laboratory of Desert and Oasis Ecology, Xinjiang
Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi,
Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Xiuliang Yuan
State Key Laboratory of Desert and Oasis Ecology, Xinjiang
Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi,
Xinjiang, 830011, China
Xiaofei Ma
State Key Laboratory of Desert and Oasis Ecology, Xinjiang
Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi,
Xinjiang, 830011, China
Wenqiang Zhang
State Key Laboratory of Desert and Oasis Ecology, Xinjiang
Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi,
Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Alishir Kurban
State Key Laboratory of Desert and Oasis Ecology, Xinjiang
Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi,
Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Research Center for Ecology and Environment of Central Asia,
Chinese Academy of Sciences, Ürümqi, 830011, China
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Philippe De Maeyer
State Key Laboratory of Desert and Oasis Ecology, Xinjiang
Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi,
Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Tim Van de Voorde
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
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Cited
8 citations as recorded by crossref.
- Potential of constructing all‐encompassing soil–plant‐atmosphere‐continuum stations and datasets from meteorological, flux, soil moisture station networks and plant‐relevant observations H. Shi 10.1002/hyp.15284
- Satellite-based measurements of temporal and spatial variations in C fluxes of irrigated and rainfed cotton grown in India D. Blaise et al. 10.1016/j.rsase.2024.101365
- Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing M. Xie et al. 10.1038/s41597-023-02473-9
- Machine learning-based investigation of forest evapotranspiration, net ecosystem productivity, water use efficiency and their climate controls at meteorological station level H. Shi et al. 10.1016/j.jhydrol.2024.131811
- Evaluation of water flux predictive models developed using eddy-covariance observations and machine learning: a meta-analysis H. Shi et al. 10.5194/hess-26-4603-2022
- Revisiting and attributing the global controls over terrestrial ecosystem functions of climate and plant traits at FLUXNET sites via causal graphical models H. Shi et al. 10.5194/bg-20-2727-2023
- Extrapolability improvement of machine learning-based evapotranspiration models via domain-adversarial neural networks H. Shi & X. Cai 10.1016/j.envsoft.2025.106383
- Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange M. Kämäräinen et al. 10.5194/bg-20-897-2023
8 citations as recorded by crossref.
- Potential of constructing all‐encompassing soil–plant‐atmosphere‐continuum stations and datasets from meteorological, flux, soil moisture station networks and plant‐relevant observations H. Shi 10.1002/hyp.15284
- Satellite-based measurements of temporal and spatial variations in C fluxes of irrigated and rainfed cotton grown in India D. Blaise et al. 10.1016/j.rsase.2024.101365
- Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing M. Xie et al. 10.1038/s41597-023-02473-9
- Machine learning-based investigation of forest evapotranspiration, net ecosystem productivity, water use efficiency and their climate controls at meteorological station level H. Shi et al. 10.1016/j.jhydrol.2024.131811
- Evaluation of water flux predictive models developed using eddy-covariance observations and machine learning: a meta-analysis H. Shi et al. 10.5194/hess-26-4603-2022
- Revisiting and attributing the global controls over terrestrial ecosystem functions of climate and plant traits at FLUXNET sites via causal graphical models H. Shi et al. 10.5194/bg-20-2727-2023
- Extrapolability improvement of machine learning-based evapotranspiration models via domain-adversarial neural networks H. Shi & X. Cai 10.1016/j.envsoft.2025.106383
- Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange M. Kämäräinen et al. 10.5194/bg-20-897-2023
Latest update: 30 Mar 2025
Short summary
A number of studies have been conducted by using machine learning approaches to simulate carbon fluxes. We performed a meta-analysis of these net ecosystem exchange (NEE) simulations. Random forests and support vector machines performed better than other algorithms. Models with larger timescales had a lower accuracy. For different plant functional types (PFTs), there were significant differences in the predictors used and their effects on model accuracy.
A number of studies have been conducted by using machine learning approaches to simulate carbon...
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