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Predicting gross primary productivity of poplar plantations based on solar-induced chlorophyll fluorescence using an improved machine learning model

Yiheng Wang Zhipeng Li Jinsong Zhang Joanna Simms Xin Wang

森林生态系统（英文版）Issue(6)：1097-1109,13.

森林生态系统（英文版）Issue(6)：1097-1109,13.DOI:10.1016/j.fecs.2025.100368

Predicting gross primary productivity of poplar plantations based on solar-induced chlorophyll fluorescence using an improved machine learning model

Yiheng Wang ¹Zhipeng Li ¹Jinsong Zhang ¹Joanna Simms ²Xin Wang¹

作者信息

1. Key Laboratory of Tree Breeding and Cultivation,Research Institute of Forestry,Chinese Academy of Forestry,Beijing 100091,China||Collaborative Innovation Center of Sustainable Forestry in Southern China,Nanjing Forest University,Nanjing 210037,China||Henan Xiaolangdi Forest Ecosystem National Observation and Research Station,Jiyuan 454650,China
2. Department of Forest Sciences,University of Helsinki,PO Box 3,Helsinki FI-00014,Finland
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摘要

关键词

Gross primary productivity/Solar-induced chlorophyll fluorescence(SIF)/Integrated learning/Particle swarm optimization(PSO)/Predictive modeling

Key words

Gross primary productivity/Solar-induced chlorophyll fluorescence(SIF)/Integrated learning/Particle swarm optimization(PSO)/Predictive modeling

引用本文复制引用

Yiheng Wang,Zhipeng Li,Jinsong Zhang,Joanna Simms,Xin Wang..Predicting gross primary productivity of poplar plantations based on solar-induced chlorophyll fluorescence using an improved machine learning model[J].森林生态系统（英文版）,2025,(6):1097-1109,13.

基金项目

This work was supported by the National Key R & D Program of China(No.2023YFD2200400-01)and the Fundamental Scientific Research Operation of Central-level Public Welfare Scientific Research Institutes(No.CAFYBB2023MA001). （No.2023YFD2200400-01）

森林生态系统（英文版）

ISSN：2095-6355

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