Wenqiang Zhang
- ORCID iD
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0000-0001-7316-6456
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Machine learning-based investigation of forest evapotranspiration, net ecosystem productivity, water use efficiency and their climate controls at meteorological station level
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- Journal Article
- A1
- open access
Quantitative evaluation of the impact of band optimization methods on the accuracy of the hyperspectral metal element inversion models
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The increasing rate of net carbon uptake in Eurasia has been declining since the early 2000s
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Changes in carbon fluxes and driving mechanisms in terrestrial ecosystems based on meteorological stations
(2024) -
- Journal Article
- A1
- open access
New data-driven estimation of metal element in rocks using a hyperspectral data and geochemical data
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Comparing the use of all data or specific subsets for training machine learning models in hydrology : a case study of evapotranspiration prediction
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- Journal Article
- A1
- open access
Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing
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- Journal Article
- A1
- open access
New data‐driven method for estimation of net ecosystem carbon exchange at meteorological stations effectively increases the global carbon flux data
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- Journal Article
- A1
- open access
Can gross Primary Productivity Products be effectively evaluated in regions with few observation data?
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- Journal Article
- A1
- open access
Evaluation of water flux predictive models developed using eddy-covariance observations and machine learning : a meta-analysis