Applications of Sun-Induced Chlorophyll Fluorescence in Forest Health Monitoring

日(rì)光誘導葉(yè)綠素熒(yíng)光(guāng)是指(zhǐ)植物(wù)葉綠素在(zài)吸收太(tài)陽(yáng)輻射(shè)後重(zhòng)新發射出(chū)的光子，該過(guò)程與(yǔ)光(guāng)合作用(yòng)密切相關(guān)，因此通(tōng)過(guò)測量日(rì)光誘(yòu)導葉(yè)綠素(sù)熒光(guāng)（下文(wén)簡稱SIF）能夠直接反(fǎn)演(yǎn)植被的(dí)光合效(xiào)率，生理(lǐ)狀(zhuàng)態及其對環(huán)境(jìng)脅(xié)迫的響(xiǎng)應
。

SIF的核心優(yōu)勢(shì)在於它直接(jiē)來源於(yú)光合(hé)作(zuò)用(yòng)過程(chéng)，可更準確地反映植(zhí)被的光合活性與碳(tàn)吸(xī)收能力；同(tóng)時，其對環境(jìng)脅迫(pò)高度(dù)敏感，一(yī)旦植物遭(zāo)受(shòu)脅迫，光合(hé)係(xì)統(tǒng)的變(biàn)化會迅速體現於(yú)SIF信號中，使其(qí)成(chéng)為早期脅(xié)迫檢(jiǎn)測的有效指(zhǐ)標(biāo)
。

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(zài)森林健(jiàn)康監測(cè)中(zhōng)，主要有以下(xià)應(yīng)用：

SIF has the following main applications in forest health monitoring:

1.評估(gū)森林光合效率和(hé)GPP / Assessing Forest Photosynthetic Efficiency and GPP

通(tōng)過SIF數(shù)據(jù)，可(kě)以估(gū)算(suàn)森(sēn)林(lín)的(dí)GPP
。研(yán)究表(biǎo)明(míng)，基於2000–2015年(nián)中(zhōng)國(guó)西南(nán)地區(qū)多生物群(qún)係(xì)數據發現，在森(sēn)林(lín)，草地(dì)，農(nóng)田，灌(guàn)叢(cóng)和(hé)荒漠(mò)五(wǔ)種(zhǒng)生態類(lèi)型中(zhōng)，SIF與(yǔ)GPP均呈顯著綫(xiàn)性(xìng)關係，決定(dìng)係數r²不(bù)低於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(tóng)生物群落(là)類型的月平均SIF，月平(píng)均NDVI與GPP的(dí)綫性回(huí)歸模(mó)型(xíng)

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

2.識別(bié)森林(lín)病(bìng)害，幹(gān)旱(hàn)脅(xié)迫及植被健康(kāng)狀況 / Identifying Forest Diseases, Drought Stress, and Vegetation Health Status

森林健康受到多(duō)種(zhǒng)因素的影響，包(bāo)括病蟲(chóng)害，幹(gān)旱(hàn)，汙(wū)染等
。病蟲(chóng)害(hài)，幹旱等(děng)脅(xié)迫(pò)會導致(zhì)降(jiàng)低葉(yè)綠素(sù)含(hán)量，破(pò)壞(huài)光合(hé)結(jié)構或氣(qì)孔關閉，使(shǐ)SIF信號減弱；健(jiàn)康森林則(zé)因(yīn)光(guāng)合(hé)效率高(gāo)而SIF值高(gāo)，衰(shuāi)退或受(shòu)損(sǔn)森林(lín)則(zé)相反
。

因此，持續監測SIF即可(kě)早(zǎo)期(qī)捕(bǔ)捉病(bìng)蟲害和(hé)幹(gān)旱(hàn)的發生(shēng)，又能綜合評(píng)估森林(lín)的整體健(jiàn)康狀況(kuàng)與活(huó)力(lì)，為精準防(fáng)控(kòng)及(jí)抗旱管理提供及時(shí)依據(jù)
。

一項(xiàng)研究(jiū)以(yǐ)棉(mián)花(huā)黃(huáng)萎(wěi)病(bìng)為(wéi)案例，發現(xiàn)病(bìng)害初期，SIF變化主(zhǔ)要(yào)由(yóu)光合生理參數(shù)驅動(dòng)（貢獻度(dù)>70%）
。隨(suí)著病害加重，冠(guān)層(céng)結(jié)構變化(huà)（如(rú)葉片脫(tuō)落，冠層結(jié)構(gòu)稀(xī)疏）導(dǎo)致的非(fēi)生理因素(sù)貢(gòng)獻(xiàn)度提(tí)升47.7%，最(zuì)終主導SIF變(biàn)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(fēi)生理因(yīn)子，SIF，SIF_PAR及生理(lǐ)參(cān)數的日變化(huà)特征；

非生理因(yīn)子：NIRv，FCVI，RENDVI，生(shēng)理(lǐ)因(yīn)子：ΦF；

T1-T2和T2-T3由(yóu)溫降(jiàng)事件劃分(fēn)，T3–T4 由(yóu)定期灌溉事件劃分
。

VW1代表(biǎo)急性(xìng)脅(xié)迫(pò)，VW3代(dài)表(biǎo)整(zhěng)個實驗(yàn)期(qī)持(chí)續(xù)緩(huǎn)慢發病(bìng)，最(zuì)終達(dá)到重(zhòng)度脅迫水(shuǐ)平；VW4代(dài)表(biǎo)全程(chéng)緩(huǎn)慢發病但脅迫(pò)程(chéng)度較(jiào)輕(qīng)
。

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(kě)直接遷(qiān)移至(zhì)森(sēn)林健康監(jiān)測：當森林遭(zāo)受真(zhēn)菌，病原菌或(huò)昆(kūn)蟲侵襲(xí)時，早(zǎo)期可(kě)通過SIF異常降低(dī)快(kuài)速鎖(suǒ)定(dìng)受害(hài)區(qū)域(yù)；中(zhōng)後期(qī)結合結(jié)構(gòu)參數(shù)（如NIRv）可區分生(shēng)理衰退(tuì)與冠層結(jié)構(gòu)破壞(huài)的(dí)貢(gòng)獻，從(cóng)而精準評估病(bìng)害等(děng)級並製定(dìng)針對性(xìng)防(fáng)治策(cè)略(lüè)
。

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(cháng)期森林生態動態研(yán)究提供數據支持 / Providing Data Support for Long-Term Forest Ecological Dynamics Research

森林生態係統是一(yī)個(gè)復(fù)雜(zá)的動態係統，受到(dào)氣(qì)候變化，人為幹(gān)擾(rǎo)等多(duō)種(zhǒng)因(yīn)素的影(yǐng)響
。通(tōng)過長期(qī)的SIF數(shù)據(jù)積累(léi)，可以研究森林生(shēng)態(tài)係統的(dí)動(dòng)態(tài)變化(huà)規(guī)律(lǜ)，為森林管理和(hé)保(bǎo)護(hù)提(tí)供科(kē)學(xué)依據
。例如，通過(guò)分(fēn)析SIF的時間序列數(shù)據，可以(yǐ)了解(jiě)森林的(dí)物候變(biàn)化，生長速率(shuài)，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(dí)測量方法 / SIF Measurement Methods

為充分發揮SIF在森(sēn)林(lín)健(jiàn)康監測中的上述(shù)應用(yòng)價(jià)值，離不(bù)開精準(zhǔn)，穩定(dìng)的(dí)測量手(shǒu)段(duàn)
。

愛(ài)博能研(yán)發(fā)生產(chǎn)的(dí)日光誘(yòu)導(dǎo)葉(yè)綠(lǜ)素熒光(SIF)監測(cè)係(xì)統（ABN-SIF係列），利用(yòng)植(zhí)物冠層(céng)的(dí)光譜信(xìn)息，自動(dòng)測(cè)量(liáng)日光(guāng)誘導葉綠素熒光等參數(shù)
。該係統(tǒng)采用高分(fēn)辨率(shuài)，高靈(líng)敏度(dù)和(hé)高穩(wěn)定性的國產光(guāng)譜儀(yí)，支(zhī)持在(zài)綫或機載觀測方式，能(néng)夠提供(gōng)高頻，準確(què)的數(shù)據輸出，助力植物光(guāng)合作用狀態和(hé)長勢的(dí)實時監(jiān)測(cè)與(yǔ)分(fēn)析(xī)
。

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(yuán) / 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.