A New Perspective on Drought Monitoring: Application of Solar-Induced Chlorophyll Fluorescence (SIF) in Drought Stress Response

干旱是一種嚴重的環境壓力，會削弱植物的生長和光合作用，進而影響生態系統和糧食安全。有沒有一種工具能提前捕捉到植物缺水時的微妙變化，幫助及時監測干旱狀況，減輕其影響呢？

答案是有的，這就是日光誘導葉綠素熒光（簡稱SIF）。可以形象地說，SIF是植物釋放的光合作用信號，通過捕捉這束微弱的熒光，我們能實時感知作物的健康狀態和干旱脅迫響應。

Drought is a severe environmental stress that impairs plant growth and photosynthesis, thereby affecting ecosystems and food security. Is there a tool that can detect subtle changes in plants during water deficits early, enabling timely drought monitoring and mitigation?

The answer lies in solar-induced chlorophyll fluorescence (SIF), a signal emitted by plants during photosynthesis. By capturing this faint fluorescence, we can monitor crop health and drought stress responses in real time.

簡單說說SIF

我們之前的文章已經詳細介紹過，SIF是在光合作用過程中，葉綠素被激發后釋放出的熒光信號。它能直接反映光合作用的活躍度，是監測植物生理狀態的黃金指標。

那么，怎么捕捉這種幾乎肉眼不可見的熒光呢？研究人員開發了多種技術手段，從地面光譜儀到高空遙感衛星，都能測量SIF。傳統的衛星雖然覆蓋廣，但空間和時間分辨率有限。愛博能推出了在線式和無人機載的日光誘導葉綠素熒光(SIF)觀測系統，實現多尺度觀測，可直接獲得日光誘導葉綠素熒光、光合作用速率、歸一化植被指數、增強植被指數等參數。

To briefly explain SIF, it is the fluorescent signal released when chlorophyll molecules are excited during photosynthesis. Because SIF directly reflects photosynthetic activity, it serves as a valuable indicator for assessing plant physiological status.

How do we capture this nearly invisible fluorescence? Researchers have developed various technologies ranging from ground-based spectrometers to airborne and satellite remote sensing platforms capable of measuring SIF. Conventional satellites offer broad coverage but are limited in spatial and temporal resolution. The company EXPONENT (愛博能) has developed both online and drone-mounted SIF Monitoring systems that enable multi-scale monitoring and provide real-time measurements of SIF, photosynthetic rate, normalized vegetation index (NDVI), enhanced vegetation index (EVI), and other parameters.

愛博能SIF系列產品 / EXPONENT SIF Product Series

SIF如何幫我們監測干旱？ / How does SIF help monitor drought?

研究人員為了認識SIF與干旱脅迫之間的關系，可謂使出了渾身解數。其中，一個中國團隊搭建了一個實驗田和智能灌溉控制系統，結合地面實測與SIF觀測，來動態監控作物水分狀況。

他們讓冬小麥經歷4種程度的干旱脅迫，實時采集SIF信號，同時監測光合速率和其他生理指標。結果發現，SIF與光合速率呈高度正相關。更重要的是，SIF對輕度干旱的響應比傳統土壤水分測量更早更敏感，提前預警能力較強。這項研究不僅驗證了SIF作為干旱監測指標的科學性，還為智能農業灌溉提供了數據支持，具有很高的應用價值。

To better understand the relationship between SIF and drought stress, researchers have employed comprehensive methods. One Chinese team established an experimental field with an intelligent irrigation control system, combining in-situ measurements with SIF observations to dynamically monitor crop water status.

They subjected winter wheat to four levels of drought stress, collecting real-time SIF data along with photosynthetic rate and other physiological metrics. The results showed a strong positive correlation between SIF and photosynthetic rate. Importantly, SIF responded earlier and more sensitively to mild drought than traditional soil moisture measurements, providing effective early warning capability. This study confirmed the scientific validity of SIF as a drought monitoring indicator and provided valuable data support for precision irrigation in smart agriculture.

實驗田示意圖 / Experimental Field Diagram

不同干旱脅迫下的響應（T1、T2、T3和T4分別代表：澆水充足、輕度干旱、中度干旱和重度干旱）。不同字母的值表示在 p < 0.05 處存在顯著差異。圖中的空心塊代表平均值，使用從種植后177~223天收集的數據計算得到。可以看出SIF對T2輕度干旱的響應更加敏感。

Responses under Different Drought Stress Levels (T1, T2, T3, and T4 represent well-watered, mild drought, moderate drought, and severe drought, respectively). Different letters indicate significant differences at p < 0.05. Hollow squares in the figure represent the mean values, calculated from data collected between 177 and 223 days after planting. It can be observed that SIF shows greater sensitivity to mild drought (T2).

不同水分脅迫下，不同參數的季節變化。T1、T2、T3和T4分別代表：澆水充足、輕度干旱、中度干旱和重度干旱。所有值均從9點到16點的平均值。橫坐標DAP是指種植后的天數。可以看出SIF則呈現波動變化，對土壤水分更敏感。

Seasonal Variation of Different Parameters under Various Water Stress Conditions. T1, T2, T3, and T4 represent well-watered, mild drought, moderate drought, and severe drought, respectively. All values are averages from 9 a.m. to 4 p.m. The x-axis DAP refers to Days After Planting. SIF exhibits fluctuations and is more sensitive to soil moisture changes.

而另一個團隊，將新疆地區作為研究對象，由于新疆屬于大陸性干旱和半干旱氣候區域，年降水稀少，蒸發量大，且農業高度依賴灌溉，因此干旱對作物生長的影響尤為顯著。科研人員結合2001年至2020年長達20年的遙感SIF數據與當地實地氣象及植被監測數據，利用時間序列分析、空間疊加和Mann-Kendall趨勢檢驗等統計方法，深入剖析SIF信號在不同時空尺度上的變化規律。

研究發現SIF值在干旱初期即顯著下降，其響應速度快于傳統植被指數，能夠第一時間反映出植物光合作用的受損程度；此外，不同干旱類型對SIF的影響存在顯著差異，尤其是土壤水分脅迫對SIF的抑制最為明顯。從空間視角來看，干旱核心區的SIF波動更為劇烈，表現出顯著的區域差異。

Another research group focused on Xinjiang, a region characterized by a continental arid and semi-arid climate with low annual precipitation, high evaporation, and heavy agricultural reliance on irrigation. Using 20 years (2001–2020) of remote sensing SIF data combined with local meteorological and vegetation monitoring records, they applied time series analysis, spatial overlay, and Mann-Kendall trend tests to examine SIF variations across different spatial and temporal scales.

They found that SIF values declined significantly early in drought events, with a faster response than traditional vegetation indices, thereby promptly indicating reductions in photosynthesis. Different drought types had distinct effects on SIF, with soil moisture stress showing the strongest suppression. Spatially, the drought core areas exhibited greater SIF variability, reflecting pronounced regional differences.

技術框架 / Technical Framework

類似地，美國的研究人員通過衛星數據分析2011年德克薩斯州干旱和2012年中部大平原干旱，發現SIF信號都在干旱期間明顯減弱，有效反映了干旱對植被光合活性帶來的抑制作用。

總的來說，這些研究讓我們看到SIF不僅是“植物光合作用的即時屏幕”，更是監測干旱脅迫的“靈敏雷達”，賦能智慧農業提前采取措施，保障作物健康成長。

Similarly, researchers in the United States analyzed satellite data from the 2011 Texas drought and the 2012 Central Great Plains drought, observing notable SIF declines during these events that effectively represented drought-induced reductions in vegetation photosynthetic activity.

Overall, these studies demonstrate that SIF serves not only as an “instantaneous screen” of plant photosynthesis but also as a highly sensitive “radar” for detecting drought stress, empowering smart agriculture to take proactive measures to safeguard crop health.

愛博能的SIF觀測系統 — 農業“干旱預警專家”

面對日益嚴峻的氣候挑戰，愛博能研發的日光誘導葉綠素熒光(SIF)監測系統具備諸多優勢：

• 高精度數據捕獲，采用高分辨、高靈敏度、高穩定性溫漂的國產化光譜儀；

• 多尺度監測，提供在線式和無人機載式監測系統；

• 全天候監測能力，在線式監測系統打破時間和地理限制，實現連續動態觀察；

• 定制服務，滿足不同作物和區域需求。

EXPONENT’s SIF Monitoring System — An Agricultural “Drought Early Warning Expert”

In response to increasing climate challenges, EXPONENT has developed a solar-induced chlorophyll fluorescence (SIF) monitoring system featuring:

• high-precision data acquisition using domestically produced spectrometers with high resolution, sensitivity, and stable temperature drift;

• multi-scale monitoring with both online and drone-mounted systems;

• all-weather continuous monitoring that overcomes temporal and geographical constraints;

• customizable solutions to meet diverse crop and regional needs.

未來展望

隨著人工智能技術的發展，未來將實現更精準的作物生理狀態診斷和產量預估。

如果你也想了解更多關于SIF技術和愛博能的日光誘導葉綠素熒光(SIF)監測系統，歡迎聯系我們，開啟智慧農業的新篇章！

Future Outlook

With advances in artificial intelligence, future developments will enable even more accurate diagnosis of crop physiological states and yield prediction.

If you want to learn more about SIF technology and EXPONENT’s SIF monitoring system, feel free to contact us and join the new era of smart agriculture!

案例來源 / Source

1. Zhao et al., Exploring the Ability of Solar-Induced Chlorophyll Fluorescence for Drought Monitoring Based on an Intelligent Irrigation Control System. Remote Sens. 2022, 14, 6157.

2. Xue et al., 2024. Response of solar-induced chlorophyll fluorescence-based spatial and temporal evolution of vegetation in Xinjiang to multiscale drought. Front. Plant Sci. 15:1418396.

3. Sun et al., 2015, Drought onset mechanisms revealed by satellite solar-induced chlorophyll fluorescence: Insights from two contrasting extreme events, J. Geophys. Res. Biogeosci., 120, 2427–2440.