ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1561826
Analyzing Canopy Structure Effects Based on LiDAR for GPP-SIF Relationship and GPP Estimation
Provisionally accepted
- 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
- 2Electronic Information School, Wuhan University, Wuhan, China
- 3Tianjin Chengjian University, Tianjin, Tianjin Municipality, China
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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. The correlation with canopy structural parameters ranged from 0.21 to 0.75 and varied by season. In summer, the correlation with GPP/SIF decreased by 5.53% to 30.59% compared to other seasons. In random forest models, incorporating canopy structural parameters improved GPP estimation accuracy (R2 increasing by 1.30% to 8.07%).
Keywords: GPP, lidar, SiF, canopy structure, GEDI
Received: 20 Jan 2025; Accepted: 18 Apr 2025.
Copyright: © 2025 Shi, Shi, Gong, Xu and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zixi Shi, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
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