干旱监测新视角：日光诱导叶绿素荧光（SIF）在干旱胁迫响应中的应用

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

干旱是一种严重的环境压力，会削弱植物的生长和光合作用，进而影响生态系统和粮食安全。有没有一种工具能提前捕捉到植物缺水时的微妙变化，帮助及时监测干旱状况，减轻其影响呢？

答案是有的，这就是日光诱导叶绿素荧光（简称SIF）。可以形象地说，SIF是植物释放的光合作用信号，通过捕捉这束微弱的荧光，我们能实时感知作物的健康状态和干旱胁迫响应。

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.

简单说说SIF

我们之前的文章已经详细介绍过，SIF是在光合作用过程中，叶绿素被激发后释放出的荧光信号。它能直接反映光合作用的活跃度，是监测植物生理状态的黄金指标。

那么，怎么捕捉这种几乎肉眼不可见的荧光呢？研究人员开发了多种技术手段，从地面光谱仪到高空遥感卫星，都能测量SIF。传统的卫星虽然覆盖广，但空间和时间分辨率有限。爱博能推出了在线式和无人机载的日光诱导叶绿素荧光(SIF)观测系统，实现多尺度观测，可直接获得日光诱导叶绿素荧光、光合作用速率、归一化植被指数、增强植被指数等参数。

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 (爱博能) 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.

爱博能SIF系列产品 / EXPONENT SIF Product Series

SIF如何帮我们监测干旱？ / How does SIF help monitor drought?

研究人员为了认识SIF与干旱胁迫之间的关系，可谓使出了浑身解数。其中，一个中国团队搭建了一个实验田和智能灌溉控制系统，结合地面实测与SIF观测，来动态监控作物水分状况。

他们让冬小麦经历4种程度的干旱胁迫，实时采集SIF信号，同时监测光合速率和其他生理指标。结果发现，SIF与光合速率呈高度正相关。更重要的是，SIF对轻度干旱的响应比传统土壤水分测量更早更敏感，提前预警能力较强。这项研究不仅验证了SIF作为干旱监测指标的科学性，还为智能农业灌溉提供了数据支持，具有很高的应用价值。

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.

实验田示意图 / Experimental Field Diagram

不同干旱胁迫下的响应（T1、T2、T3和T4分别代表：浇水充足、轻度干旱、中度干旱和重度干旱）。不同字母的值表示在 p < 0.05 处存在显著差异。图中的空心块代表平均值，使用从种植后177~223天收集的数据计算得到。可以看出SIF对T2轻度干旱的响应更加敏感。

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).

不同水分胁迫下，不同参数的季节变化。T1、T2、T3和T4分别代表：浇水充足、轻度干旱、中度干旱和重度干旱。所有值均从9点到16点的平均值。横坐标DAP是指种植后的天数。可以看出SIF则呈现波动变化，对土壤水分更敏感。

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.

而另一个团队，将新疆地区作为研究对象，由于新疆属于大陆性干旱和半干旱气候区域，年降水稀少，蒸发量大，且农业高度依赖灌溉，因此干旱对作物生长的影响尤为显著。科研人员结合2001年至2020年长达20年的遥感SIF数据与当地实地气象及植被监测数据，利用时间序列分析、空间叠加和Mann-Kendall趋势检验等统计方法，深入剖析SIF信号在不同时空尺度上的变化规律。

研究发现SIF值在干旱初期即显著下降，其响应速度快于传统植被指数，能够第一时间反映出植物光合作用的受损程度；此外，不同干旱类型对SIF的影响存在显著差异，尤其是土壤水分胁迫对SIF的抑制最为明显。从空间视角来看，干旱核心区的SIF波动更为剧烈，表现出显著的区域差异。

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.

技术框架 / Technical Framework

类似地，美国的研究人员通过卫星数据分析2011年德克萨斯州干旱和2012年中部大平原干旱，发现SIF信号都在干旱期间明显减弱，有效反映了干旱对植被光合活性带来的抑制作用。

总的来说，这些研究让我们看到SIF不仅是“植物光合作用的即时屏幕”，更是监测干旱胁迫的“灵敏雷达”，赋能智慧农业提前采取措施，保障作物健康成长。

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.

爱博能的SIF观测系统 — 农业“干旱预警专家”

面对日益严峻的气候挑战，爱博能研发的日光诱导叶绿素荧光(SIF)监测系统具备诸多优势：

• 高精度数据捕获，采用高分辨、高灵敏度、高稳定性温漂的国产化光谱仪；

• 多尺度监测，提供在线式和无人机载式监测系统；

• 全天候监测能力，在线式监测系统打破时间和地理限制，实现连续动态观察；

• 定制服务，满足不同作物和区域需求。

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.

未来展望

随着人工智能技术的发展，未来将实现更精准的作物生理状态诊断和产量预估。

如果你也想了解更多关于SIF技术和爱博能的日光诱导叶绿素荧光(SIF)监测系统，欢迎联系我们，开启智慧农业的新篇章！

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!

案例来源 / 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.