精(jīng)準(zhǔn)預(yù)測(cè)作物產(chǎn)量對農(nóng)業管理(lǐ)和糧食(shí)安(ān)全至(zhì)關重(zhòng)要(yào)。傳統(tǒng)方法依賴(lài)人工(gōng)采(cǎi)樣和(hé)統計(jì)估(gū)算
，不僅(jǐn)耗時耗(hào)力(lì)
，而且(qiě)精(jīng)度(dù)有限。近年來
，隨著(zhuó)遙感(gǎn)技術(shù)的發展
，太(tài)陽誘導葉(yè)綠素熒光(guāng)（SIF）成(chéng)為(wéi)一(yī)種具有(yǒu)潛(qián)力的(dí)新型作物(wù)監測(cè)指(zhǐ)標(biāo)。

那麼
，SIF是(shì)如(rú)何(hé)反映作(zuò)物(wù)生(shēng)長狀(zhuàng)況的？它在(zài)產量預(yù)測(cè)中有哪些優勢？又受到哪(nǎ)些(xiē)因素影響？南(nán)京(jīng)農業(yè)大(dà)學農(nóng)學(xué)院智慧農業團(tuán)隊(duì)對(duì)這(zhè)些(xiē)問題(tí)展開了係統分(fēn)析(xī)。

「實驗設計與方法」

研究(jiū)人員在(zài)中(zhōng)國(guó)進行(háng)了連(lián)續(xù)兩年的小麥田間試(shì)驗
，設置(zhì)了(liǎo)不(bù)同(tóng)氮肥施用(yòng)水(shuǐ)平(píng)
，種植(zhí)密度(dù)和品(pǐn)種(zhǒng)的(dí)小麥(mài)小區(qū)。在(zài)不同(tóng)時(shí)間尺度（包括關鍵生育期
，日變化(huà)和(hé)全生(shēng)長(cháng)季時間(jiān)序列）采集冠層(céng)光(guāng)譜(pǔ)數據
，並同步(bù)測定(dìng)葉(yè)麵(miàn)積指數(shù)（LAI）
，葉綠素(sù)含量（Cab）等參數
，最終結(jié)合收(shōu)獲實測(cè)產量進行分析(xī)。

圖：小麥農(nóng)學(xué)參數(shù)（LAI和(hé)Cab）
，日均VIs
，日(rì)均(jūn)SIF參數和(hé)PPFD的季節(jié)變化(huà)

「主要發現」

1. 時間(jiān)尺度很關(guān)鍵

研(yán)究發(fā)現
，在較大的時(shí)間尺度上
，累積(jī)的SIF數據(jù)通(tōng)常在小麥產(chǎn)量估計(jì)方(fāng)麵表現(xiàn)更(gēng)好
，多個生長(cháng)時期的(dí)累(léi)積(jī)SIF值顯(xiǎn)示出更(gēng)強的(dí)相關性。

2. 非綫(xiàn)性(xìng)模(mó)型更優

非綫(xiàn)性模型通常能更準確地描述SIF與(yǔ)小(xiǎo)麥產量(liáng)的(dí)關(guān)係。尤其在(zài)瞬時測(cè)量(liáng)尺度下(xià)
，非綫性(xìng)模型比綫性模型擬合效果更(gēng)好。但隨著時間尺(chǐ)度增大
，兩者(zhě)差異逐漸縮小。

3. optimal觀測時(shí)期是(shì)開花期(qī)

在開花期(qī)測量的(dí)總(zǒng)近(jìn)紅(hóng)外SIF（SIFNIR_tot）與產量(liáng)相關(guān)性(xìng)zui高
，是該(gāi)時(shí)期optimal產量(liáng)預測(cè)指標。

圖：關(guān)鍵(jiàn)生(shēng)育(yù)期下SIF參(cān)數(shù)和植被(bèi)指(zhǐ)數(shù)與產量(liáng)的相關(guān)性

4. 冠層(céng)結構影(yǐng)響(xiǎng)顯著(zhù)

研究應用主成(chéng)分分(fēn)析(xī)法(fǎ)和偏最(zuì)小(xiǎo)二(èr)乘法(fǎ)的(dí)變量投影(yǐng)重(zhòng)要(yào)性分析(xī)
，發(fā)現葉麵積指(zhǐ)數(shù)（LAI）和葉綠素含量(liáng)（Cab）對SIF–產(chǎn)量(liáng)關係有(yǒu)重要影響。其中
，LAI的(dí)影響更大。兩(liǎng)者(zhě)都存在(zài)一個(gè)“optimal範圍"。

表：偏最(zuì)小(xiǎo)二乘(chéng)回(huí)歸(guī)（PLSR）模(mó)型(xíng)中影(yǐng)響(xiǎng)因(yīn)變量（yield/SIFypNIR或(huò)yield/SIFypNIR_tot的(dí)PC1）的(dí)因(yīn)素（自(zì)變(biàn)量的PC1：LAI
，Cab和PAR）的(dí)變量(liáng)投影重要(yào)性（VIP）得分(fēn)

5. NDFI敏(mǐn)感(gǎn)但(dàn)不強

歸一化差異熒光指數（NDFI）對(duì)LAI和Cab的(dí)變化(huà)十分敏感
，但(dàn)在(zài)直接(jiē)預測(cè)產量(liáng)方(fāng)麵(miàn)表現不如SIFNIR_tot。

「對農(nóng)業的(dí)啟示(shì)」

這項研究不僅(jǐn)明確了(liǎo)SIF在(zài)產(chǎn)量預測中的實用(yòng)性
，也指(zhǐ)出了其受(shòu)時間尺(chǐ)度(dù)
，冠(guān)層(céng)結構和環境條件的(dí)綜合(hé)影(yǐng)響。未來
，通(tōng)過多(duō)源(yuán)數(shù)據融合與(yǔ)模(mó)型(xíng)優化
，SIF有(yǒu)望(wàng)成(chéng)為(wéi)區域(yù)乃至global尺度(dù)作物產量監(jiān)測(cè)的核心(xīn)手(shǒu)段。

拓展(zhǎn)閱讀：如何(hé)獲取高(gāo)質(zhì)量(liáng)SIF數據(jù)？

想要開展SIF相關(guān)研究(jiū)或(huò)應用
，高精度
，可(kě)定(dìng)製(zhì)的地(dì)麵或無人機載監(jiān)測設備是關鍵。愛博能(néng)研(yán)發(fā)生產的(dí)日光(guāng)誘(yòu)導葉綠素(sù)熒光監測係統（ABN-SIF-2）
，配(pèi)備雙波(bō)段(duàn)光譜儀(yí)
，可同步(bù)獲取(qǔ)SIF信(xìn)號與多種植(zhí)被(bèi)指數
，支持在綫監(jiān)測與無人機(jī)載(zǎi)監(jiān)測。相比衛星遙(yáo)感
，該係(xì)統具(jù)備(bèi)更高的空間分(fēn)辨率(shuài)
，適(shì)合田間尺(chǐ)度精準(zhǔn)監(jiān)測(cè)
，為農情研判與(yǔ)作物模(mó)型驗證提供(gōng)可靠數據(jù)支持。

案例來(lái)源：The Relationship between Wheat Yield and Sun-Induced Chlorophyll Fluorescence from Continuous Measurements over the Growing Season.

How Does Vegetation Fluorescence Predict Wheat Yield?

Accurate prediction of crop yield is crucial for agricultural management and food security. Traditional methods rely on manual sampling and statistical estimation, which are not only time-consuming and labor-intensive but also have limited accuracy. In recent years, with the development of remote sensing technology, Solar-Induced Chlorophyll Fluorescence (SIF) has emerged as a promising new indicator for crop monitoring.

So, how does SIF reflect crop growth status? What are its advantages in yield prediction? And what factors influence it? The team from the College of Agriculture at Nanjing Agricultural University conducted a systematic analysis of these questions.

「Experimental Design and Methods」

Researchers conducted a two-year field experiment on wheat in China, establishing plots with different nitrogen application levels, planting densities, and wheat varieties. Canopy spectral data were collected at different temporal scales (including key growth stages, diurnal variations, and full-growth-season time series). Parameters such as the Leaf Area Index (LAI) and Chlorophyll Content (Cab) were measured synchronously, and the data were ultimately analyzed in conjunction with the actual yield measured at harvest.

Figure: Seasonal variations in wheat agronomic parameters (LAI and Cab), daily average Vegetation Indices (VIs), daily average SIF parameters, and Photosynthetic Photon Flux Density (PPFD).

「Key Findings」

1) Temporal Scale is Crucial:

The study found that on larger temporal scales, cumulative SIF data generally performed better for wheat yield estimation. Cumulative SIF values across multiple growth stages showed a stronger correlation with yield.

2) Nonlinear Models are Superior:

Nonlinear models generally described the relationship between SIF and wheat yield more accurately. This was especially true at the instantaneous measurement scale, where nonlinear models provided a better fit than linear models. However, the difference between the two model types diminished as the temporal scale increased.

3) The Optimal Observation Period is the Flowering Stage:

The total near-infrared SIF (SIFNIR_tot) measured at the flowering stage had the highest correlation with yield, making it the best yield predictor for that period.

Figure: Correlation between SIF parameters/Vegetation Indices and yield during key growth stages.

4) Canopy Structure Has a Significant Impact:

Using Principal Component Analysis and Variable Importance in Projection (VIP) scores from Partial Least Squares Regression (PLSR) analysis, the study found that Leaf Area Index (LAI) and Chlorophyll Content (Cab) significantly influenced the SIF-yield relationship. Among these, LAI had a greater impact. An "optimal range" was observed for both parameters.

Table: Variable Importance in Projection (VIP) scores from the Partial Least Squares Regression (PLSR) model, showing the influence of factors (PC1 of independent variables: LAI, Cab, and PAR) on the dependent variable (PC1 of yield/SIFypNIR or yield/SIFypNIR_tot).

5) NDFI is Sensitive but Not Strong for Direct Prediction:

The Normalized Difference Fluorescence Index (NDFI) was highly sensitive to changes in LAI and Cab. However, it was less effective than SIFNIR_tot for directly predicting yield.

「Implications for Agriculture」

This research not only confirms the practicality of SIF for yield prediction but also highlights that its effectiveness is influenced by a combination of temporal scale, canopy structure, and environmental conditions. In the future, through multi-source data fusion and model optimization, SIF is expected to become a core tool for crop yield monitoring at regional and even global scales.

「Further Reading: How to Obtain High-Quality SIF Data?」

Conducting SIF-related research or applications requires high-precision, customizable ground-based or UAV-borne monitoring equipment. The ABN-SIF-2 Solar-Induced Chlorophyll Fluorescence Monitoring System, developed by ExponentSci, features a dual-band spectrometer capable of simultaneously acquiring SIF signals and various vegetation indices. It supports online monitoring and UAV-based monitoring. Compared to satellite remote sensing, this system offers higher spatial resolution, making it suitable for precise monitoring at the field scale and providing reliable data support for agricultural condition assessment and crop model validation.

Sources:

The Relationship between Wheat Yield and Sun-Induced Chlorophyll Fluorescence from Continuous Measurements over the Growing Season.