日光诱导叶绿素荧光(SIF)在农林与生态监测的应用

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

上一期文章中，我们介绍了日光诱导叶绿素荧光（SIF）是什么，本篇将介绍SIF的主要应用。SIF不仅仅是一个科研概念，它正在成为植物健康的“监测仪"，为精准农业、生态监测等领域带来新的机遇。

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

简单来说，日光诱导叶绿素荧光（SIF）是植物在进行光合作用时发射出的一种微弱光信号。SIF直接反映植物实时进行光合作用的强度。当植物感到“压力"（如缺水、高温、病虫害）时，它们的光合作用会减弱，SIF信号也会随之变化。因此，SIF就像是植物光合作用的“晴雨表"，能够灵敏地捕捉植物的生理状态变化。

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的主要应用介绍 / Introduction to Key SIF Applications

1. 农业领域 / Agriculture

• 监测作物生长：SIF能够实时反映作物的生长活力和光合效率，为作物生长监测提供更直接的生理信息，帮助种植者了解作物的生长状态，及时调整管理措施。

• 诊断病虫害和胁迫： 在作物表现出肉眼可见的病虫害或水分胁迫症状之前，SIF信号可能已经发生变化，为早期预警和精准施策提供依据。

• 化施肥灌溉： 通过监测SIF数据评估作物对养分和水分的需求，可以指导精准施肥和灌溉，提高资源利用效率，降低生产成本。

• 产量预估： SIF与作物最终产量之间存在良好的相关性，利用SIF数据可以更准确地预估作物产量。

▷ 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，美国大平原经历了一次严重的干旱事件。研究人员通过对SIF与干旱指数（如SPI和PDSI）的比较分析，发现SIF比传统的NDVI能更早、更敏感地反映作物因干旱造成的胁迫。在干旱高峰期，SIF的变化幅度明显大于NDVI，这意味着SIF可以更早地检测到农业干旱对作物造成的生理影响。

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月期间，日光诱导叶绿素荧光（SIF）的下降

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

2012年5月至10月期间，归一化植被指数（NDVI）的下降

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

2. 林业领域 / Forestry

• 干旱胁迫和监测森林火灾风险：SIF对植物水分胁迫非常敏感。干旱会导致植物光合作用下降，SIF信号随之变化。利用SIF数据可以评估森林的干旱程度，辅助进行森林火险预警。

• 评估森林健康：通过监测森林冠层的SIF，可以评估森林的光合能力和健康状况，及时发现森林退化或病虫害侵袭的区域。

▷ 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.

在一项研究中，科研人员通过对香港地区森林进行SIF信号分析，成功捕获了因季节变化而产生的植被光合作用动态。在不同季节中，SIF信号明显呈现出冬季低、春夏增高的趋势，与植被绿度（NDVI）的变化形成互补关系。研究中利用FLD方法，从690nm和740nm波段准确提取SIF信号，并对比各季节NDVI值，证明了SIF在生态健康和胁迫诊断中的好的表现。

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.

利用氧气A吸收带数据，反演得到不同季节的叶绿素荧光强度。（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. 生态研究 / Ecological Research

• 研究生态系统对环境变化的响应：利用SIF监测气候事件（如干旱、热浪）或人为干扰对不同生态系统的影响，深入了解生态系统的脆弱性和恢复能力。

• 研究生态系统碳循环：通过长时间序列的SIF观测，可以更好地理解生态系统在不同时间尺度上的碳吸收动态，为气候变化模型提供更准确的参数，同时对评估陆地生态系统的碳汇功能具有重要意义。

• 监测植被生产力：SIF是估算生态系统总初级生产力（GPP）的有力工具，比传统的基于反射率的植被指数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.

一项研究利用全球不同区域的SIF数据，经过处理和校准后，将其与对应区域和时间的实际农作物产量统计数据进行对比分析。研究发现，SIF数据能够有效地反映农作物光合作用的强度，并与农作物产量呈现出高度的相关性。引入直接反映光合过程的SIF数据，不仅大幅提高了GPP估算精度，还为全球碳循环模型中环境变量的敏感性问题提供了修正依据。

下图是该研究中美国玉米带农田通量塔站点和西欧草原站点的数据图表。基于通量塔的GPP、SIF（A, B）和植被增强指数 EVI（C, D）的时间序列以及时空平均值，SIF和EVI都以相同的垂直比例绘制。改图直观地体现SIF和GPP的相关性及较高的相关系数。

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.

结语 / Conclusion

日光诱导叶绿素荧光（SIF）技术正以更直接更深入的角度，为我们揭示植物光合作用的奥秘，其在精准农业、林业、生态研究等领域的应用前景广阔。

要充分发挥SIF技术的价值，高精度、高可靠性的观测系统是关键。从塔基到无人机遥感，我们提供多尺度的专业日光诱导叶绿素荧光（SIF）监测解决方案，能够为科研、农林、环保等领域的客户提供精准、稳定的SIF数据，助您深入了解植物健康状况，做出科学决策。

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.

案例来源 / 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).