Accurate estimation of gross primary production(GPP)of terrestrial vegetation is crucial for comprehending the carbon dynamics.To date,there is still no consensus on the magnitude and seasonality of global GPP among t...Accurate estimation of gross primary production(GPP)of terrestrial vegetation is crucial for comprehending the carbon dynamics.To date,there is still no consensus on the magnitude and seasonality of global GPP among the major global GPP products,underscoring the necessity to improve GPP models for higher accuracy of global GPP estimates.Here,we introduce an improved Vegetation Photosynthesis Model(VPM v3.0),which incorporates site-specific apparent optimum temperature for photosynthesis,leaf-trait-based light absorption(flat leaf vs.needle leaf),and improved water stress estimation.The global VPM simulation is driven by Moderate Resolution Imaging Spectroradiometer images and the ERA5-Land climate dataset.We evaluate VPM v3.0 using GPP from 205 eddy flux tower sites across 11 land cover types(1,658 site-years)(GPPEC),as well as the TROPOspheric monitoring instrument(TROPOMI)solar-induced fluorescence(SIF)product for 2018 to 2021.The slope,R^(2),and root mean square error between GPP from VPM v3.0(GPPVPM-v3)and GPPEC are 0.97,0.78,and 1.46 gC m^(−2) day^(−1),respectively.GPPVPM-v3 shows high temporal consistency with TROPOMI SIF.VPM v3.0 provides higher accuracy of GPP estimates at most evaluated sites than VPM v2.0.Comparisons of global GPP from VPM v3.0 with other major global GPP products reveal both spatial-temporal consistency and discrepancies.These findings clearly indicate the improved accuracy of VPM v3.0 in estimating GPP,making it suitable for generating global GPP datasets.展开更多
基金supported by research grants from the U.S.National Science Foundation(OIA-1946093 and OIA-1920946)NASA(80NSSC24K0118)USDA National Institute of Food and Agriculture(2020-67014-30935).
文摘Accurate estimation of gross primary production(GPP)of terrestrial vegetation is crucial for comprehending the carbon dynamics.To date,there is still no consensus on the magnitude and seasonality of global GPP among the major global GPP products,underscoring the necessity to improve GPP models for higher accuracy of global GPP estimates.Here,we introduce an improved Vegetation Photosynthesis Model(VPM v3.0),which incorporates site-specific apparent optimum temperature for photosynthesis,leaf-trait-based light absorption(flat leaf vs.needle leaf),and improved water stress estimation.The global VPM simulation is driven by Moderate Resolution Imaging Spectroradiometer images and the ERA5-Land climate dataset.We evaluate VPM v3.0 using GPP from 205 eddy flux tower sites across 11 land cover types(1,658 site-years)(GPPEC),as well as the TROPOspheric monitoring instrument(TROPOMI)solar-induced fluorescence(SIF)product for 2018 to 2021.The slope,R^(2),and root mean square error between GPP from VPM v3.0(GPPVPM-v3)and GPPEC are 0.97,0.78,and 1.46 gC m^(−2) day^(−1),respectively.GPPVPM-v3 shows high temporal consistency with TROPOMI SIF.VPM v3.0 provides higher accuracy of GPP estimates at most evaluated sites than VPM v2.0.Comparisons of global GPP from VPM v3.0 with other major global GPP products reveal both spatial-temporal consistency and discrepancies.These findings clearly indicate the improved accuracy of VPM v3.0 in estimating GPP,making it suitable for generating global GPP datasets.
