陆地叶绿素荧光光谱的全球检索:TROPOMI的首次结果

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Feng Zhao , Weiwei Ma , Jun Zhao , Yiqing Guo , Mateen Tariq , Juan Li
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引用次数: 0

摘要

太阳诱导叶绿素荧光(SIF)可以作为光合状态的指标,因为它与光合装置之间有着密切的关系。陆地SIF在整个红色和近红外光谱中发射,其特征在于分别以685nm和740nm为中心的两个峰值。在这项研究中,我们提出了一种数据驱动的方法,通过Sentinel-5前体任务上的对流层监测仪器(TROPOMI)的测量重建陆地SIF光谱。这种方法利用在没有强大气吸收特征的组合光谱窗口中的太阳夫琅和费线来从由表面和大气系统反射的太阳辐射中检索SIF信号。信息内容主要来自靠近红色和远红色SIF峰的两个窗口,663–686 nm和743–758 nm。提出了一种线性正演模型,该模型表示为SIF自由辐射光谱和SIF分量的相加,并对其参数设置进行了适当的选择。SIF分量被模拟为2个基本SIF光谱的线性组合。通过反演线性正演模型,从地表和大气系统反射的太阳辐射中反演出SIF谱。检索结果的评估是通过与其他SIF数据集的相互比较来执行的。比较显示,2019年6月和12月以及2021年7月和12日的前两周,每周全球SIF复合物的空间分布相似。特别是将远红色SIF数据集与其他专用的远红色SIV检索进行比较,显示出密切的一致性,表明检索方法之间的一致性。重建的TROPOMI红色SIF显示出改进的、更合理的时空分布。每周全局合成的检索不确定性分别约为红色和远红色SIF峰值的12%和2%,这可以被视为SIF观测的全局合成的令人满意的误差阈值。根据重建的SIF光谱,几个典型生物群落的不同光谱特征表明,红色和远红色SIF可能携带植物光合功能和生物物理特性的互补信息。首次实现了星载测量SIF谱的重建,有可能为更好地理解生态系统功能开辟新的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Global retrieval of the spectrum of terrestrial chlorophyll fluorescence: First results with TROPOMI

Solar-Induced chlorophyll Fluorescence (SIF) could be used as an indicator of photosynthetic status due to the close relationship between SIF and the photosynthetic apparatus. Terrestrial SIF is emitted throughout the red and near-infrared spectrum and is characterized by two peaks centered around 685 nm and 740 nm, respectively. In this study, we present a data-driven approach to reconstruct the terrestrial SIF spectrum from measurements by TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 precursor mission. This approach makes use of solar Fraunhofer lines in the combined spectral windows devoid of strong atmospheric absorption features to retrieve SIF signal from the solar radiation reflected by the surface and atmosphere system. Information contents are mainly from the two windows close to the red and far-red SIF peaks, 663–686 nm and 743–758 nm. A linear forward model represented as an addition of the SIF-free radiance spectrum and the SIF component is proposed with a proper selection of its parameter settings. The SIF component was simulated as linear combinations of 2 basis SIF spectra. Through inverting the linear forward model, the SIF spectrum was retrieved from the solar radiation reflected by the surface and atmosphere system. The evaluation of the retrieval results is performed by inter-comparison with other SIF datasets. The comparisons display similar spatial distributions for the weekly global SIF composites for the first two weeks in June and December of 2019 and July and December of 2021. Especially the comparison of the far-red SIF datasets with other dedicated far-red SIF retrievals demonstrates close agreement, indicating consistency among the retrieval approaches. The reconstructed TROPOMI red SIF shows improved and more reasonable spatiotemporal distributions. The retrieval uncertainty for the weekly global composite is about 12% and 2% of the peak red and far-red SIF values, respectively, which can be considered as satisfactory error thresholds for global composites of SIF observations. Different spectral features for several typical biomes from reconstructed SIF spectra suggest that red and far-red SIF may carry complementary information on photosynthetic function and biophysical properties of the plant. For the first time, the reconstruction of the SIF spectrum is achieved for spaceborne measurements with the potential to open new applications for better understanding of the ecosystem function.

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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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