Accurately assessing the carbon sequestration capacity of forests is crucial for mitigating climate change.Traditional methods for estimating Gross Primary Productivity(GPP)of vegetation involve significant uncertaint...Accurately assessing the carbon sequestration capacity of forests is crucial for mitigating climate change.Traditional methods for estimating Gross Primary Productivity(GPP)of vegetation involve significant uncertainties.As a novel remote sensing approach,Solar-Induced chlorophyll Fluorescence(SIF)is directly related to photosynthesis and has demonstrated strong correlations with GPP across various ecosystems,climate zones,and spatial scales.Current GPP estimation methods based on SIF include Light Use Efficiency(LUE)models,the SCOPE process models,and the latest mechanistic light response(MLR)models.Future research should focus on improving the mechanistic understanding of SIF-related processes and promoting the integration of multi-source remote sensing data with SIF-based modeling to enhance the accuracy and universality of GPP estimation.展开更多
基金sponsored by the National Natural Science Foundation of China(Grant number 42250205,42471510)the Open Found of Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks,MNR(CUG-SRCS-0002)the Open Fund of Hubei Key Laboratory of Regional Ecological Process and Environmental Evolution(REEC-OF-202405).
文摘Accurately assessing the carbon sequestration capacity of forests is crucial for mitigating climate change.Traditional methods for estimating Gross Primary Productivity(GPP)of vegetation involve significant uncertainties.As a novel remote sensing approach,Solar-Induced chlorophyll Fluorescence(SIF)is directly related to photosynthesis and has demonstrated strong correlations with GPP across various ecosystems,climate zones,and spatial scales.Current GPP estimation methods based on SIF include Light Use Efficiency(LUE)models,the SCOPE process models,and the latest mechanistic light response(MLR)models.Future research should focus on improving the mechanistic understanding of SIF-related processes and promoting the integration of multi-source remote sensing data with SIF-based modeling to enhance the accuracy and universality of GPP estimation.
