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Fusion of Multiple Models for Improving Gross Primary Production Estimation With Eddy Covariance Data Based on Machine Learning
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2023
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Source: Journal of Geophysical Research: Biogeosciences, 128(3)
[PDF-3.33 MB]
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Journal Title:Journal of Geophysical Research: Biogeosciences
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Description:Terrestrial gross primary production (GPP) represents the magnitude of CO2 uptake through vegetation photosynthesis, and is a key variable for carbon cycles between the biosphere and atmosphere. Light use efficiency (LUE) models have been widely used to estimate GPP for its physiological mechanisms and availability of data acquisition and implementation, yet each individual GPP model has exhibited large uncertainties due to input errors and model structure, and further studies of systematic validation, comparison, and fusion of those models with eddy covariance (EC) site data across diverse ecosystem types are still needed in order to further improve GPP estimation. We here compared and fused five GPP models (VPM, EC-LUE,
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Source:Journal of Geophysical Research: Biogeosciences, 128(3)
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ISSN:2169-8953;2169-8961;
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Rights Information:Other
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