Solar-Induced Chlorophyll Fluorescence (SIF) Applications in Agriculture, Forestry, and Ecological Monitoring

上一期文章中，我們介紹了日光誘導葉綠素熒光（SIF）是什么，本篇將介紹SIF的主要應用。SIF不僅僅是一個科研概念，它正在成為植物健康的“監測儀"，為精準農業、生態監測等領域帶來新的機遇。

This article will introduce the main applications of Solar-Induced Chlorophyll Fluorescence (SIF). SIF is not merely a scientific concept; it is becoming a "plant health monitor," bringing new opportunities to fields such as precision agriculture and ecological monitoring.

SIF：植物光合作用的“晴雨表"

SIF: A Barometer of Plant Photosynthetic Activity

簡單來說，日光誘導葉綠素熒光（SIF）是植物在進行光合作用時發射出的一種微弱光信號。SIF直接反映植物實時進行光合作用的強度。當植物感到“壓力"（如缺水、高溫、病蟲害）時，它們的光合作用會減弱，SIF信號也會隨之變化。因此，SIF就像是植物光合作用的“晴雨表"，能夠靈敏地捕捉植物的生理狀態變化。

Simply put, Solar-Induced Chlorophyll Fluorescence (SIF) is a weak light signal emitted by plants during photosynthesis. SIF directly indicates the real-time intensity of plant photosynthesis. When plants experience "stress" (such as water deficiency, high temperature, pests, or diseases), their photosynthetic activity weakens, and the SIF signal changes accordingly. Therefore, SIF acts like a "barometer" of plant photosynthetic activity, capable of sensitively capturing changes in plant physiological status.

SIF的主要應用介紹 / Introduction to Key SIF Applications

1. 農業領域 / Agriculture

• 監測作物生長：SIF能夠實時反映作物的生長活力和光合效率，為作物生長監測提供更直接的生理信息，幫助種植者了解作物的生長狀態，及時調整管理措施。

• 診斷病蟲害和脅迫： 在作物表現出肉眼可見的病蟲害或水分脅迫癥狀之前，SIF信號可能已經發生變化，為早期預警和精準施策提供依據。

• 化施肥灌溉： 通過監測SIF數據評估作物對養分和水分的需求，可以指導精準施肥和灌溉，提高資源利用效率，降低生產成本。

• 產量預估： SIF與作物最終產量之間存在良好的相關性，利用SIF數據可以更準確地預估作物產量。

? Monitoring Crop Growth: SIF can real-time reflect crop vitality and photosynthetic efficiency, providing more direct physiological information for crop growth monitoring. This helps growers understand crop growth status and adjust management practices promptly.

? Diagnosing Pests, Diseases, and Stress: Before visible symptoms of pests, diseases, or water stress appear in crops, the SIF signal may have already changed, providing a basis for early warning and precise interventions.

? Optimizing Fertilization and Irrigation: By monitoring SIF data to assess crop nutrient and water requirements, precision fertilization and irrigation can be guided, improving resource utilization efficiency and reducing production costs.

? Yield Estimation: There is a good correlation between SIF and final crop yield. Utilizing SIF data can lead to more accurate crop yield estimations.

例如，2012年，美國大平原經歷了一次嚴重的干旱事件。研究人員通過對SIF與干旱指數（如SPI和PDSI）的比較分析，發現SIF比傳統的NDVI能更早、更敏感地反映作物因干旱造成的脅迫。在干旱高峰期，SIF的變化幅度明顯大于NDVI，這意味著SIF可以更早地檢測到農業干旱對作物造成的生理影響。

For example, in 2012, the Great Plains of the United States experienced an extreme drought event. Researchers compared SIF with drought indices (such as SPI and PDSI) and found that SIF reflected crop stress caused by drought earlier and more sensitively than traditional NDVI. During the peak of the drought, the magnitude of SIF change was significantly greater than that of NDVI, indicating that SIF can detect the physiological impact of agricultural drought on crops sooner.

2012年5月至10月期間，日光誘導葉綠素熒光（SIF）的下降

The reduction of solar-induced chlorophyll fluorescence (SIF) from May to October in 2012.

2012年5月至10月期間，歸一化植被指數（NDVI）的下降

The reduction of the normalized difference vegetation index (NDVI) from May to October in 2012.

2. 林業領域 / Forestry

• 干旱脅迫和監測森林火災風險：SIF對植物水分脅迫非常敏感。干旱會導致植物光合作用下降，SIF信號隨之變化。利用SIF數據可以評估森林的干旱程度，輔助進行森林火險預警。

• 評估森林健康：通過監測森林冠層的SIF，可以評估森林的光合能力和健康狀況，及時發現森林退化或病蟲害侵襲的區域。

? Drought Stress Monitoring and Forest Fire Risk Assessment: SIF is highly sensitive to plant water stress. Drought causes a decrease in plant photosynthesis, and the SIF signal changes accordingly. SIF data can be used to assess forest drought levels and assist in forest fire risk early warning.

? Assessing Forest Health: By monitoring forest canopy SIF, the photosynthetic capacity and health status of forests can be assessed, allowing for timely identification of areas experiencing forest degradation or pest and disease infestation.

在一項研究中，科研人員通過對香港地區森林進行SIF信號分析，成功捕獲了因季節變化而產生的植被光合作用動態。在不同季節中，SIF信號明顯呈現出冬季低、春夏增高的趨勢，與植被綠度（NDVI）的變化形成互補關系。研究中利用FLD方法，從690nm和740nm波段準確提取SIF信號，并對比各季節NDVI值，證明了SIF在生態健康和脅迫診斷中的好的表現。

In one study, researchers analyzed SIF signals from forests in Hong Kong and successfully captured the dynamics of vegetation photosynthesis driven by seasonal changes. Across different seasons, the SIF signal clearly showed a trend of low values in winter and increasing values in spring and summer, complementing the changes in vegetation greenness (NDVI). The study used the FLD method to accurately extract SIF signals from the 690nm and 740nm bands and compared them with NDVI values across seasons, demonstrating the excellent performance of SIF in ecological health and stress diagnosis.

利用氧氣A吸收帶數據，反演得到不同季節的葉綠素熒光強度。（DJI：冬天，MAM：春天，JJA：夏天，SON：秋天）

Inverted chlorophyll fluorescence intensity for different seasons using oxygen-A absorption band data. (DJF: Winter, MAM: Spring, JJA: Summer, SON: Autumn)

3. 生態研究 / Ecological Research

• 研究生態系統對環境變化的響應：利用SIF監測氣候事件（如干旱、熱浪）或人為干擾對不同生態系統的影響，深入了解生態系統的脆弱性和恢復能力。

• 研究生態系統碳循環：通過長時間序列的SIF觀測，可以更好地理解生態系統在不同時間尺度上的碳吸收動態，為氣候變化模型提供更準確的參數，同時對評估陸地生態系統的碳匯功能具有重要意義。

• 監測植被生產力：SIF是估算生態系統總初級生產力（GPP）的有力工具，比傳統的基于反射率的植被指數更直接地反映植被的光合固碳能力。

? Studying Ecosystem Response to Environmental Change: Using SIF to monitor the impact of extreme climate events (such as drought, heatwaves) or anthropogenic disturbances on different ecosystems provides a deeper understanding of ecosystem vulnerability and resilience.

? Studying Ecosystem Carbon Cycle: Long-term SIF observations allow for a better understanding of ecosystem carbon uptake dynamics across different time scales, providing more accurate parameters for climate change models, and is also of significant importance for evaluating terrestrial ecosystem carbon sink function.

? Monitoring Vegetation Productivity: SIF is a powerful tool for estimating ecosystem Gross Primary Production (GPP), reflecting the photosynthetic carbon fixation capacity of vegetation more directly than traditional reflectance-based vegetation indices.

一項研究利用全球不同區域的SIF數據，經過處理和校準后，將其與對應區域和時間的實際農作物產量統計數據進行對比分析。研究發現，SIF數據能夠有效地反映農作物光合作用的強度，并與農作物產量呈現出高度的相關性。引入直接反映光合過程的SIF數據，不僅大幅提高了GPP估算精度，還為全球碳循環模型中環境變量的敏感性問題提供了修正依據。

下圖是該研究中美國玉米帶農田通量塔站點和西歐草原站點的數據圖表?；谕克?span>GPP、SIF（A, B）和植被增強指數 EVI（C, D）的時間序列以及時空平均值，SIF和EVI都以相同的垂直比例繪制。改圖直觀地體現SIF和GPP的相關性及較高的相關系數。

One study utilized SIF data from different regions globally, and after processing and calibration, compared it with actual crop yield statistics for corresponding regions and times. The study found that SIF data could effectively reflect the intensity of crop photosynthesis and showed a high correlation with crop yield. The introduction of SIF data, which directly reflects the photosynthetic process, not only significantly improved GPP estimation accuracy but also provided a basis for correcting the sensitivity issues of environmental variables in global carbon cycle models.

The figure below shows data charts from a cropland flux tower site in the US Corn Belt and a grassland site in Western Europe from this study. Time series and spatiotemporally averaged values of flux tower based GPP, SIF (A, B), and Enhanced Vegetation Index (EVI) (C, D) are presented, with SIF and EVI plotted to the same vertical scale. This figure intuitively demonstrates the correlation between SIF and GPP and their high correlation coefficients.

結語 / Conclusion

日光誘導葉綠素熒光（SIF）技術正以更直接更深入的角度，為我們揭示植物光合作用的奧秘，其在精準農業、林業、生態研究等領域的應用前景廣闊。

要充分發揮SIF技術的價值，高精度、高可靠性的觀測系統是關鍵。從塔基到無人機遙感，我們提供多尺度的專業日光誘導葉綠素熒光（SIF）監測解決方案，能夠為科研、農林、環保等領域的客戶提供精準、穩定的SIF數據，助您深入了解植物健康狀況，做出科學決策。

Solar-Induced Chlorophyll Fluorescence (SIF) technology is revealing the mysteries of plant photosynthesis with unprecedented precision, and its application prospects in precision agriculture, forestry, and ecological research are vast.

To fully leverage the value of SIF technology, high-precision and high-reliability observation systems are key. From ground-based towers to drone remote sensing, we offer multi-scale professional Solar-Induced Chlorophyll Fluorescence (SIF) monitoring solutions, providing accurate and stable SIF data to clients in scientific research, agriculture and forestry, environmental protection, and other fields, helping you gain a deeper understanding of plant health and make scientific decisions.

案例來源 / Source

1. L. Guanter et al., Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence, Proc. Natl. Acad. Sci. U.S.A. 111 (14) E1327-E1333,

2. Joiner, J et al., First observations of global and seasonal terrestrial chlorophyll fluorescence from space, Biogeosciences, 8, 637–651, 2011.

3. Wang, S. et al., Monitoring and Assessing the 2012 Drought in the Great Plains: Analyzing Satellite-Retrieved Solar-Induced Chlorophyll Fluorescence, Drought Indices, and Gross Primary Production. Remote Sens. 2016, 8, 61

4. Irteza, S. M. and Nichol, J. E.: MEASUREMENT OF SUN INDUCED CHLOROPHYLL FLUORESCENCE USING HYPERSPECTRAL SATELLITE IMAGERY, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 911–913.

5. Y. Sun et al., OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science358, eaam5747(2017).