Shi Shuo, Shi Zixi, Qu Fangfang, Gong Wei, Xu Lu, Liu Chenxi
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China.
Collaborative Innovation Center of Geospatial Technology, Wuhan Hubei, China.
Front Plant Sci. 2025 May 19;16:1561826. doi: 10.3389/fpls.2025.1561826. eCollection 2025.
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%).
总初级生产力（GPP）与太阳诱导叶绿素荧光（SIF）之间的耦合对于理解陆地碳循环至关重要，GPP/SIF 比值受冠层结构、环境变化和其他因素的调节。虽然关于冠层结构的研究侧重于内部结构如何调节光利用效率，但遥感冠层结构参数，特别是植被覆盖度（FVC）和叶面积指数（LAI）对 GPP-SIF 耦合的影响仍未得到充分研究。研究大规模森林中冠层结构对 GPP-SIF 的响应有助于高精度估算 GPP。激光雷达在捕捉复杂的垂直冠层结构方面具有无与伦比的优势。在本研究中，我们使用多源数据，特别是激光雷达衍生的冠层结构产品，分析不同森林类型中冠层结构参数和 GPP/SIF 的年变化，研究冠层结构对 GPP-SIF 关系的响应，并使用机器学习模型估算 GPP 并评估冠层结构因素的贡献。我们发现，激光雷达衍生的冠层结构产品有效地捕捉了植被生长动态，与 MODIS 产品具有很强的相关性（最大 R²=0.95），但在植被茂密地区的值更高。GPP/SIF 表现出显著的季节和森林类型差异，在夏季达到峰值。其与冠层结构参数的相关性随季节变化，范围从 0.21 到 0.75。与其他季节相比，夏季的相关性下降了 5.53%至 30.59%。在随机森林模型中，纳入冠层结构参数提高了 GPP 估算精度（R 提高了 1.30%至 8.07%）。
