日光诱导叶绿素荧光不同组分在作物总初级生产力估算中的贡献比例: 多时间尺度分析

doi:10.17521/cjpe.2024.0283

摘要/Abstract

摘要：

在全球碳循环和气候变化研究中, 准确地监测陆地生态系统的总初级生产力(GPP)至关重要。日光诱导叶绿素荧光(SIF)与GPP之间的近线性关系提供了一个从地面样点尺度到全球尺度估算植被碳吸收的新途径。然而, 在不同时间尺度下, SIF的辐射、结构和生理信息对GPP估算的贡献比例仍不明晰。该研究使用位于河南商丘的野外定点的冠层光谱和涡度协方差通量观测数据, 对小麦(Triticum aestivum)和玉米(Zea mays)这两种典型的C₃和C₄作物进行研究。通过对比基于近红外植被反射率指数(NIRv)和荧光校正植被指数(FCVI)拆分SIF的辐射、结构和生理组分的方法, 改进留一法量化了这些组分对GPP估算的贡献, 并分析了作物类型和时间分辨率对SIF与GPP关系的影响。研究表明, 作物类型差异及时间分辨率的降低显著影响SIF与GPP之间的关系; 在较短的时间尺度如0.5 h和1 d内, 辐射组分是SIF与GPP关系的主要驱动力。然而, 随着时间尺度延长到一周或更长, 结构和生理组分的影响逐渐显著; 基于NIRv和FCVI的方法在拆分SIF的辐射、结构和生理组分时表现出高度的一致性。通过深入理解并精确量化SIF与GPP的关系, 该研究有助于优化全球植被监测及碳循环研究中的遥感技术和模型。

关键词: 日光诱导叶绿素荧光, 总初级生产力, 时间分辨率, 作物, 生理信息, 冠层结构

Abstract:

Aims Accurate monitoring of gross primary production (GPP) in terrestrial ecosystems is crucial for global carbon cycle and climate change studies. The near-linear relationship between solar-induced chlorophyll fluorescence (SIF) and GPP offers a new avenue for estimating vegetation carbon uptake from local to global scales. However, the contributions of radiative, structural, and physiological information of SIF to GPP estimation at different temporal scales remain unclear.

Methods This study utilizes canopy spectroscopy and eddy covariance flux data from a field site in Shangqiu, Henan, focusing on wheat (Triticum aestivum) and maize (Zea mays) as representative C₃ and C₄ crops, respectively. By comparing methods based on the near-infrared reflectance of terrestrial vegetation (NIRv) and the fluorescence correction vegetation index (FCVI) to decompose SIF components, we refined the leave-one-out method to quantify their contributions to GPP estimation and analyzed the impact of crop type and temporal resolution on the SIF-GPP relationship.

Important findings The study reveals that the SIF-GPP relationship is influenced by differences in crop type and significantly strengthens as temporal resolution decreases. At shorter temporal scales, such as half-hour and one-day, the radiative component predominantly drives the SIF-GPP relationship. However, as the time scale extends to a week or longer, the influences of structural and physiological components become increasingly significant. The methods based on NIRv and FCVI showed high consistency in decomposing the radiative, structural, and physiological components of SIF. Through a deeper understanding and precise quantification of the SIF-GPP relationship, this research enhances the application of remote sensing technologies and models in global vegetation monitoring and carbon cycle studies.

Key words: solar-induced chlorophyll fluorescence (SIF), gross primary production (GPP), temporal resolution, crop, physiological information, canopy structure

王贝贝, 吴苏, 王苗苗, 胡锦涛. 日光诱导叶绿素荧光不同组分在作物总初级生产力估算中的贡献比例: 多时间尺度分析. 植物生态学报, 2025, 49(4): 562-572. DOI: 10.17521/cjpe.2024.0283

WANG Bei-Bei, WU Su, WANG Miao-Miao, HU Jin-Tao. Contributions of radiative, structural, and physiological information of solar-induced chlorophyll fluorescence on predicting crop gross primary production across temporal scales. Chinese Journal of Plant Ecology, 2025, 49(4): 562-572. DOI: 10.17521/cjpe.2024.0283

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2024.0283

https://www.plant-ecology.com/CN/Y2025/V49/I4/562

图/表 5

表1 冠层光谱观测系统FluoSpec 2光谱仪参数

Table 1 Configuration parameters of the spectrometers used in FluoSpec 2 system

光谱仪 Spectrometer	波段范围 Band range (nm)	光谱分辨率 Spectral resolution (nm)	信噪比 Signal-to-noise ratio	计算参数 Calculational parameter
QEpro	730-785	0.17	1 000	Solar-induced chlorophyll fluorescence
HR2000+	350-1 100	1.10	250	Reflectance vegetation index

图1 2019年商丘小麦(蓝色圆圈)和玉米(红色圆圈)的总初级生产力(GPP) (A)、日光诱导叶绿素荧光(SIF) (B)、光合有效辐射(PAR) (C)、植被近红外反射率指数(NIRv) (D)、荧光校正植被指数(FCVI) (E)、基于NIRv方法的荧光量子效率(ΦF_NIRv) (F)和基于FCVI方法的荧光量子效率(ΦF_FCVI)的季节变化(G)。

Fig. 1 Seasonal variations of gross primary productivity (GPP) (A), solar-induced chlorophyll fluorescence (SIF) (B), photosynthetically active radiation (PAR) (C), near-infrared reflectance of vegetation (NIRv) (D), fluorescence corrected vegetation index (FCVI) (E), fluorescence quantum efficiency based on NIRv method (ΦF_FCVI) (F), and fluorescence quantum efficiency based on FCVI method (ΦF_FCVI) for Triticum aestivum (blue circle) and Zea mays (red circle) of Shangqiu in 2019 (G).

图2 不同作物类型和时间分辨率下的日光诱导叶绿素荧光(SIF)与总初级生产力(GPP)之间的关系及线性关系的决定系数(R2)。A, 0.5 h尺度。B, 1 d尺度。C, 1周尺度。D, 2周尺度。E, 1个月尺度。F, 不同时间分辨率下R2的柱状图。

Fig. 2 Relationships between solar-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) at different temporal resolutions and crops and the determination coefficient (R2) of the linear relationships. A, 0.5 h scale. B, One-day scale. C, One-week scale. D, Two-week scale. E, One-month scale. F, Histogram of R2 at different temporal resolutions.

图3 日光诱导叶绿素荧光(SIF)的生理、结构和辐射组分在SIF与总初级生产力的线性关系的相对贡献百分比。FCVI, 荧光校正植被指数; NIRv, 植被近红外反射率指数。

Fig. 3 Relative contribution percentage of the physiological, structural and radiative components of solar-induced chlorophyll fluorescence (SIF) to the linear relationship between SIF and gross primary productivity. FCVI, fluorescence corrected vegetation index; NIRv, near-infrared reflectance of vegetation.

图4 基于植被近红外反射率指数(NIRv)方法的小麦(A)和玉米(B)的日光诱导叶绿素荧光(SIF)的生理、结构和辐射组分在SIF与总初级生产力的线性关系的相对贡献百分比。

Fig. 4 Relative contribution percentage of the physiological, structural and radiative components of the solar-induced chlorophyll fluorescence (SIF) of Triticum aestivum (A) and Zea mays (B) based on the near-infrared reflectance of vegetation (NIRv) method to the linear relationship between SIF and gross primary productivity.

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