Analyzing canopy structure effects based on LiDAR for GPP-SIF relationship and GPP estimation.

Journal: Frontiers in plant science
Published Date: May 19, 2025

Abstract

The coupling between Gross Primary Productivity (GPP) and Solar-Induced Chlorophyll Fluorescence (SIF) is crucial for understanding terrestrial carbon cycles, with the GPP/SIF ratio regulated by canopy structure, environmental change, and other factors. While studies on canopy structure focus on how internal structure regulates light use efficiency, the impact of remotely sensed canopy structural parameters, particularly Fractional Vegetation Cover (FVC) and Leaf Area Index (LAI), on GPP-SIF coupling remains understudied. Investigating the response of canopy structure to GPP-SIF in large-scale forests supports high-accuracy GPP estimation. LiDAR offers unparalleled advantages in capturing complex vertical canopy structures. In this study, we used multi-source data, particularly LiDAR-derived canopy structure products, to analyze the annual variations in canopy structural parameters and GPP/SIF across different forest types, investigate the response of canopy structure to the GPP-SIF relationship, and employ machine learning models to estimate GPP and assess the contribution of canopy structural factors. We found that LiDAR-derived canopy structure products effectively captured vegetation growth dynamics, exhibiting strong correlation with MODIS products (maximum R²=0.95), but with higher values in densely vegetated areas. GPP/SIF exhibited significant seasonal and forest-type variations, peaking in summer. Its correlation with canopy structural parameters varied seasonally, ranging from 0.21 to 0.75. In summer, the correlation decreased by 5.53% to 30.59% compared to other seasons. In random forest models, incorporating canopy structural parameters improved GPP estimation accuracy (R increasing by 1.30% to 8.07%).

Authors

  • Shuo Shi
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 430079, China.
  • Zixi Shi
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China.
  • Fangfang Qu
    College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
  • Wei Gong
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 430079, China.
  • Lu Xu
    School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Heifei Innovation Research Institute, Beihang University, Hefei, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China; School of Biomedical Engineering, Anhui Medical University, Heifei, China.
  • Chenxi Liu
    Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States of America.

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