Comparative Analysis of Multiple Retrieval Algorithms for Tower-based Solar-induced Chlorophyll Fluorescence

CHEN Jinghua; DENG Zhuoying; RAO Yuanyi; HU Zhuoran; WANG Shaoqiang; CHEN Xuan; GU Peng; WANG Jing

doi:10.11834/jrs.20255046

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Comparative Analysis of Multiple Retrieval Algorithms for Tower-based Solar-induced Chlorophyll Fluorescence
Pages: 1-19(2025)
Received：28 January 2025，

Published Online：28 October 2025，
DOI： 10.11834/jrs.20255046
稿件说明：

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陈敬华，邓濯缨，饶圆艺，胡卓然，王绍强，陈璇，谷鹏，王静.XXXX.塔基日光诱导叶绿素荧光多反演算法对比分析.遥感学报，XX（XX）： 1-19 DOI： 10.11834/jrs.20255046.

CHEN Jinghua，DENG Zhuoying，RAO Yuanyi，HU Zhuoran，WANG Shaoqiang，CHEN Xuan，GU Peng，WANG Jing. XXXX. Comparative Analysis of Multiple Retrieval Algorithms for Tower-based Solar-induced Chlorophyll Fluorescence. National Remote Sensing Bulletin， XX（XX）：1-19 DOI： 10.11834/jrs.20255046.

摘要

日光诱导叶绿素荧光（Solar-induced Chlorophyll Fluorescence，SIF）是植被在自然光照下进行光合作用时发射的一种微弱的光学信号。作为指代植被光合作用的无损探针，SIF近年来被广泛应用于植被生产力估算、胁迫监测和物候监测等多个领域。开展近地面塔基SIF观测有助于在精细化尺度上理解植被的生理生化过程，但由于SIF信号十分微弱且与植被反射光混杂，常规手段难以实现SIF的直接观测。当前已有一系列基于植被光谱的SIF反演算法，但是，现有研究对这些算法的差异以及在不同条件下的适应性仍缺乏认识与讨论。因此，本研究基于山地森林站点塔基观测获取的高光谱数据，对六种SIF反演算法（sFLD、3FLD、iFLD、SFM、SVD和BSF）展开了对比分析与评价。结果表明：（1）传统FLD系列算法（sFLD、3FLD与iFLD）反演结果较为相近（R

0.86），SVD算法反演结果相较于其他算法明显偏高，SFM和BSF算法与其余各算法均保持较高相关性；（2）SFM、SVD和BSF算法相较于传统的FLD系列算法呈现出更高的准确性与稳定性（与NIRvR相比：平均ΔR

=0.29，与GPP相比：平均ΔR

=0.10），在云量较多或天气不稳定的情境下，SFM算法仍能保持与GPP较高的相关性，因此最适用于复杂环境下的SIF反演，而SVD算法反演获得的SIF能够较好地表征植被在日内尺度受太阳辐射影响的变化；（3）BSF算法对观测高度与先验权重的设置较为敏感，但对温度参数不敏感，在使用BSF算法反演SIF时需根据使用情境和数据特点进行调整。本研究阐明了六种典型SIF反演算法间的差异，为今后不同使用情境下SIF反演算法的选择提供了参考。

Abstract

Objective Solar-induced chlorophyll fluorescence (SIF)

a weak optical signal emitted by vegetation during photosynthesis under natural sunlight

serves as a non-destructive probe for vegetation photosynthesis and has been widely applied in estimating pr

oductivity

monitoring stress

and tracking phenology. While tower-based near-surface SIF observations enable fine-scale understanding of vegetation physiology

direct SIF measurement remains challenging due to its weak signal and interference from reflected light. Although various spectral-based SIF retrieval algorithms exist

differences in their performance and adaptability under varying conditions are poorly understood. This study aimed to compare and evaluate six SIF retrieval algorithms using tower-based hyperspectral data.Methods With high-resolution hyperspectral data acquired from tower-based observations at a mountainous forest site

six SIF retrieval algorithms—sFLD

3FLD

iFLD

SFM

SVD

and BSF—were systematically applied and compared. Algorithm performance was assessed based on the retrieved SIF signals and their relationships with key vegetation indicators (Near-infrared Radiance of Vegetation

NIRvR; Gross Primary Production

GPP).Results (1) Traditional FLD algorithms (sFLD

3FLD

iFLD) yielded highly consistent results (R

0.86). SVD retrievals were significantly higher than other algorithms

while SFM and BSF maintained strong correlations with all other algorithms. (2) SFM

SVD

and BSF demonstrated superior accuracy and stability compared to traditional FLD algorithms (ΔR

of 0.29 for NIRvR and 0.10 for GPP). SFM retrievals consistently maintained a high correlation with GPP even under cloudy or unstable weather

making SFM most suitable for complex environments. SVD-retrieved SIF effectively captured diurnal variations in vegetation response to solar radiation changes. (3) BSF algorithm showed high sensitivity to observation height and prior weight settings

but low sensitivity to temperature parameters. Optimal BSF application thus requires parameter adjustment based on specific scenarios and data characteristics.Conclusions This study clarifies the differences among six representative SIF retrieval algorithms. SFM is recommended for complex environments like cloudy

conditions

and SVD excels in tracking diurnal radiation-driven variations

while BSF requires careful parameterization. The results can provide critical guidance for selecting appropriate SIF retrieval algorithms based on specific research objectives and environmental conditions.

关键词

Keywords

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