Applications of Sun-Induced Chlorophyll Fluorescence in Forest Health Monitoring

日光誘導葉綠素熒光是指植物葉綠素在吸收太陽輻射后重新發射出的光子，該過程與光合作用密切相關，因此通過測量日光誘導葉綠素熒光（下文簡稱SIF）能夠直接反演植被的光合效率、生理狀態及其對環境脅迫的響應。

SIF的核心優勢在于它直接來源于光合作用過程，可更準確地反映植被的光合活性與碳吸收能力；同時，其對環境脅迫高度敏感，一旦植物遭受脅迫，光合系統的變化會迅速體現于SIF信號中，使其成為早期脅迫檢測的有效指標。

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在森林健康監測中，主要有以下應用：

SIF has the following main applications in forest health monitoring:

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

通過SIF數據，可以估算森林的GPP。研究表明，基于2000–2015年中國西南地區多生物群系數據發現，在森林、草地、農田、灌叢和荒漠五種生態類型中，SIF與GPP均呈顯著線性關系，決定系數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年間，不同生物群落類型的月平均SIF、月平均NDVI與GPP的線性回歸模型

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

2.識別森林病害、干旱脅迫及植被健康狀況 / Identifying Forest Diseases, Drought Stress, and Vegetation Health Status

森林健康受到多種因素的影響，包括病蟲害、干旱、污染等。病蟲害、干旱等脅迫會導致降低葉綠素含量、破壞光合結構或氣孔關閉，使SIF信號減弱；健康森林則因光合效率高而SIF值高，衰退或受損森林則相反。

因此，持續監測SIF即可早期捕捉病蟲害和干旱的發生，又能綜合評估森林的整體健康狀況與活力，為精準防控及抗旱管理提供及時依據。

一項研究以棉花黃萎病為案例，發現病害初期，SIF變化主要由光合生理參數驅動（貢獻度>70%）。隨著病害加重，冠層結構變化（如葉片脫落、冠層結構稀疏）導致的非生理因素貢獻度提升47.7%，最終主導SIF變化。

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.

實驗期間非生理因子、SIF、SIF_PAR及生理參數的日變化特征；

非生理因子：NIRv、FCVI、RENDVI，生理因子：ΦF；

T1-T2和T2-T3由溫降事件劃分，T3–T4 由定期灌溉事件劃分。

VW1代表急性脅迫，VW3代表整個實驗期持續緩慢發病，最終達到重度脅迫水平；VW4代表全程緩慢發病但脅迫程度較輕。

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.

該發現可直接遷移至森林健康監測：當森林遭受真菌、病原菌或昆蟲侵襲時，早期可通過SIF異常降低快速鎖定受害區域；中后期結合結構參數（如NIRv）可區分生理衰退與冠層結構破壞的貢獻，從而精準評估病害等級并制定針對性防治策略。

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.為長期森林生態動態研究提供數據支持 / Providing Data Support for Long-Term Forest Ecological Dynamics Research

森林生態系統是一個復雜的動態系統，受到氣候變化、人為干擾等多種因素的影響。通過長期的SIF數據積累，可以研究森林生態系統的動態變化規律，為森林管理和保護提供科學依據。例如，通過分析SIF的時間序列數據，可以了解森林的物候變化、生長速率、對氣候變化的響應等。

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的測量方法 / SIF Measurement Methods

為充分發揮SIF在森林健康監測中的上述應用價值，離不開精準、穩定的測量手段。

愛博能研發生產的日光誘導葉綠素熒光(SIF)監測系統（ABN-SIF系列），利用植物冠層的光譜信息，自動測量日光誘導葉綠素熒光等參數。該系統采用高分辨率、高靈敏度和高穩定性的國產光譜儀，支持在線或機載觀測方式，能夠提供高頻、準確的數據輸出，助力植物光合作用狀態和長勢的實時監測與分析。

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.

案例來源 / 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.