日光诱导叶绿素荧光在森林健康监测中的应用

Applications of Sun-Induced Chlorophyll Fluorescence in Forest Health Monitoring

日光诱导叶绿素荧光是指植物叶绿素在吸收太阳辐射后重新发射出的光子，该过程与光合作用密切相关，因此通过测量日光诱导叶绿素荧光（下文简称SIF）能够直接反演植被的光合效率、生理状态及其对环境胁迫的响应。

SIF的核心优势在于它直接来源于光合作用过程，可更准确地反映植被的光合活性与碳吸收能力；同时，其对环境胁迫高度敏感，一旦植物遭受胁迫，光合系统的变化会迅速体现于SIF信号中，使其成为早期胁迫检测的有效指标。

Sun-Induced Chlorophyll Fluorescence (SIF) refers to photons re-emitted by plant chlorophyll after absorbing solar radiation. This process is closely related to photosynthesis; therefore, measuring SIF enables direct retrieval of vegetation's photosynthetic efficiency, physiological status, and responses to environmental stress.

The core advantage of SIF lies in its direct origin from the photosynthetic process, allowing it to more accurately reflect vegetation’s photosynthetic activity and carbon uptake capacity. At the same time, it is highly sensitive to environmental stress. Once plants experience stress, changes in the photosynthetic system are rapidly reflected in the SIF signal, making it an effective indicator for early stress detection.

SIF在森林健康监测中，主要有以下应用：

SIF has the following main applications in forest health monitoring:

1.评估森林光合效率和GPP / Assessing Forest Photosynthetic Efficiency and GPP

通过SIF数据，可以估算森林的GPP。研究表明，基于2000–2015年中国西南地区多生物群系数据发现，在森林、草地、农田、灌丛和荒漠五种生态类型中，SIF与GPP均呈显著线性关系，决定系数r²不低于0.91。

SIF data can be used to estimate forest Gross Primary Productivity (GPP). Research based on multi-biome data from Southwest China between 2000 and 2015 showed that across five ecosystem types—forest, grassland, farmland, shrubland, and desert—SIF and GPP exhibited a significant linear relationship, with a coefficient of determination (r²) no less than 0.91.

2000~2015年间，不同生物群落类型的月平均SIF、月平均NDVI与GPP的线性回归模型

Linear regression models of monthly mean SIF, monthly mean NDVI, and GPP across different biome types during 2000–2015.

2.识别森林病害、干旱胁迫及植被健康状况 / Identifying Forest Diseases, Drought Stress, and Vegetation Health Status

森林健康受到多种因素的影响，包括病虫害、干旱、污染等。病虫害、干旱等胁迫会导致降低叶绿素含量、破坏光合结构或气孔关闭，使SIF信号减弱；健康森林则因光合效率高而SIF值高，衰退或受损森林则相反。

因此，持续监测SIF即可早期捕捉病虫害和干旱的发生，又能综合评估森林的整体健康状况与活力，为精准防控及抗旱管理提供及时依据。

一项研究以棉花黄萎病为案例，发现病害初期，SIF变化主要由光合生理参数驱动（贡献度>70%）。随着病害加重，冠层结构变化（如叶片脱落、冠层结构稀疏）导致的非生理因素贡献度提升47.7%，最终主导SIF变化。

Forest health is affected by various factors, including pests, diseases, drought, and pollution. Stressors such as pests, diseases, and drought can reduce chlorophyll content, damage photosynthetic structures, or cause stomatal closure, thereby weakening the SIF signal. Healthy forests exhibit high SIF values due to high photosynthetic efficiency, while declining or damaged forests show the opposite.

Therefore, continuous SIF monitoring can not only capture the onset of pests and drought at an early stage but also comprehensively assess the overall health and vitality of forests, providing a timely basis for precise prevention, control, and drought management.

A case study on cotton Verticillium wilt found that in the early stage of the disease, changes in SIF were mainly driven by photosynthetic physiological parameters (contribution > 70%). As the disease progressed, the contribution of non-physiological factors (e.g., leaf abscission and canopy thinning) increased by 47.7%, eventually dominating the changes in SIF.

实验期间非生理因子、SIF、SIF_PAR及生理参数的日变化特征；

非生理因子：NIRv、FCVI、RENDVI，生理因子：ΦF；

T1-T2和T2-T3由温降事件划分，T3–T4 由定期灌溉事件划分。

VW1代表急性胁迫，VW3代表整个实验期持续缓慢发病，最终达到重度胁迫水平；VW4代表全程缓慢发病但胁迫程度较轻。

Daily variation characteristics of non-physiological factors, SIF, SIF_PAR, and physiological parameters during the experimental period;
Non-physiological factors: NIRv, FCVI, RENDVI; physiological factor: ΦF;
T1–T2 and T2–T3 are divided by temperature drop events, while T3–T4 are divided by periodic irrigation events.
VW1 represents acute stress; VW3 represents persistent slow progression throughout the experimental period, ultimately reaching severe stress levels; VW4 represents slow progression throughout with relatively mild stress levels.

该发现可直接迁移至森林健康监测：当森林遭受真菌、病原菌或昆虫侵袭时，早期可通过SIF异常降低快速锁定受害区域；中后期结合结构参数（如NIRv）可区分生理衰退与冠层结构破坏的贡献，从而精准评估病害等级并制定针对性防治策略。

These findings can be directly applied to forest health monitoring: when forests are infested by fungi, pathogens, or insects, early SIF reduction can quickly identify affected areas. In mid-to-late stages, combining structural parameters (e.g., NIRv) can help distinguish the contributions of physiological decline and structural damage to the canopy, enabling accurate assessment of disease severity and formulation of targeted control strategies.

3.为长期森林生态动态研究提供数据支持 / Providing Data Support for Long-Term Forest Ecological Dynamics Research

森林生态系统是一个复杂的动态系统，受到气候变化、人为干扰等多种因素的影响。通过长期的SIF数据积累，可以研究森林生态系统的动态变化规律，为森林管理和保护提供科学依据。例如，通过分析SIF的时间序列数据，可以了解森林的物候变化、生长速率、对气候变化的响应等。

Forest ecosystems are complex and dynamic systems influenced by various factors such as climate change and human activities. Long-term SIF data accumulation allows the study of dynamic changes in forest ecosystems, providing a scientific basis for forest management and conservation. For example, analyzing SIF time-series data can reveal forest phenological changes, growth rates, and responses to climate change.

SIF的测量方法 / SIF Measurement Methods

为充分发挥SIF在森林健康监测中的上述应用价值，离不开精准、稳定的测量手段。

爱博能研发生产的日光诱导叶绿素荧光(SIF)监测系统（ABN-SIF系列），利用植物冠层的光谱信息，自动测量日光诱导叶绿素荧光等参数。该系统采用高分辨率、高灵敏度和高稳定性的国产光谱仪，支持在线或机载观测方式，能够提供高频、准确的数据输出，助力植物光合作用状态和长势的实时监测与分析。

To fully leverage the above applications of SIF in forest health monitoring, accurate and stable measurement methods are essential.

The Sun-Induced Chlorophyll Fluorescence (SIF) monitoring system (ABN-SIF series) developed and produced by Aiboneng uses spectral information from the plant canopy to automatically measure parameters such as SIF. Equipped with a high-resolution, high-sensitivity, and high-stability domestically produced spectrometer, the system supports online or airborne observations and delivers high-frequency, accurate data output, facilitating real-time monitoring and analysis of plant photosynthetic status and growth trends.

案例来源 / Sources：

Jia, L., He, Y., Liu, W., Li, Y., & Zhang, Y. (2023). Drought did not change the linear relationship between chlorophyll fluorescence and terrestrial gross primary production under universal biomes. Frontiers in Forests and Global Change, 6, Article 1157340.

Zhou, J., et al. (2024). Roles of physiological and nonphysiological information in sun-induced chlorophyll fluorescence variations for detecting cotton verticillium wilt. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 8835–8850.