精準預測作物產量對農業管理和糧食安全至關重要。傳統方法依賴人工采樣和統計估算，不僅耗時耗力，而且精度有限。近年來，隨著遙感技術的發展，太陽誘導葉綠素熒光（SIF）成為一種具有潛力的新型作物監測指標。

那么，SIF是如何反映作物生長狀況的？它在產量預測中有哪些優勢？又受到哪些因素影響？南京農業大學農學院智慧農業團隊對這些問題展開了系統分析。

「實驗設計與方法」

研究人員在中國進行了連續兩年的小麥田間試驗，設置了不同氮肥施用水平、種植密度和品種的小麥小區。在不同時間尺度（包括關鍵生育期、日變化和全生長季時間序列）采集冠層光譜數據，并同步測定葉面積指數（LAI）、葉綠素含量（Cab）等參數，最終結合收獲實測產量進行分析。

圖：小麥農學參數（LAI和Cab）、日均VIs、日均SIF參數和PPFD的季節變化

「主要發現」

1. 時間尺度很關鍵

研究發現，在較大的時間尺度上，累積的SIF數據通常在小麥產量估計方面表現更好，多個生長時期的累積SIF值顯示出更強的相關性。

2. 非線性模型更優

非線性模型通常能更準確地描述SIF與小麥產量的關系。尤其在瞬時測量尺度下，非線性模型比線性模型擬合效果更好。但隨著時間尺度增大，兩者差異逐漸縮小。

3. optimal觀測時期是開花期

在開花期測量的總近紅外SIF（SIFNIR_tot）與產量相關性zui高，是該時期optimal產量預測指標。

圖：關鍵生育期下SIF參數和植被指數與產量的相關性

4. 冠層結構影響顯著

研究應用主成分分析法和偏最小二乘法的變量投影重要性分析，發現葉面積指數（LAI）和葉綠素含量（Cab）對SIF–產量關系有重要影響。其中，LAI的影響更大。兩者都存在一個“optimal范圍"。

表：偏最小二乘回歸（PLSR）模型中影響因變量（yield/SIFypNIR或yield/SIFypNIR_tot的PC1）的因素（自變量的PC1：LAI、Cab和PAR）的變量投影重要性（VIP）得分

5. NDFI敏感但不強

歸一化差異熒光指數（NDFI）對LAI和Cab的變化十分敏感，但在直接預測產量方面表現不如SIFNIR_tot。

「對農業的啟示」

這項研究不僅明確了SIF在產量預測中的實用性，也指出了其受時間尺度、冠層結構和環境條件的綜合影響。未來，通過多源數據融合與模型優化，SIF有望成為區域乃至global尺度作物產量監測的核心手段。

拓展閱讀：如何獲取高質量SIF數據？

想要開展SIF相關研究或應用，高精度、可定制的地面或無人機載監測設備是關鍵。愛博能研發生產的日光誘導葉綠素熒光監測系統（ABN-SIF-2），配備雙波段光譜儀，可同步獲取SIF信號與多種植被指數，支持在線監測與無人機載監測。相比衛星遙感，該系統具備更高的空間分辨率，適合田間尺度精準監測，為農情研判與作物模型驗證提供可靠數據支持。

案例來源：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.