利用全发射光谱验证冠层叶绿素荧光辐射传递模型的现场方法

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Remote Sensing of Environment Pub Date : 2024-11-06 DOI:10.1016/j.rse.2024.114490
Weiwei Liu , Matti Mõttus , Zbyněk Malenovský , Shengwei Shi , Luis Alonso , Jon Atherton , Albert Porcar-Castell
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引用次数: 0

摘要

太阳诱导叶绿素荧光(SIF)的强度和光谱特性可提供有关植物光合作用和生产力的宝贵信息,但同时也受到叶片和冠层结构的影响。基于物理的模型为研究 SIF 强度和光谱如何从光合系统传播和扩展到叶片和冠层提供了定量方法。然而,由于缺乏对叶片和冠层的全荧光光谱进行直接、独立和互补测量的方法,冠层 SIF 模型的验证受到了限制。在此,我们提出了一种结合叶片和冠层荧光光谱原位测量的新型验证方法。该方法通过对一种水稻作物冠层发育两个不同阶段的测量进行了演示。我们原位测量了叶片反射率、透射率和荧光光谱,随后利用 Fluspect-Cx 模型反演了叶片结构和生化参数并确定了叶片荧光量子效率(FQE)。对两种 FQE 反演方法(反演-IIA 和反演-IIB)进行了测试,以正向模拟叶片荧光光谱。然后使用一维、二维和三维辐射传递方案(SCOPE、mSCOPE 和 DART)将叶片荧光光谱放大到冠层水平,并与在红、绿、蓝和白光照明下直接测量的冠层荧光光谱观测结果进行比较。验证结果表明,考虑三维冠层结构(如 DART 模型)对于成功地将荧光光谱从叶片扩展到冠层至关重要,而一维 SCOPE 甚至二维 mSCOPE 都无法完全再现冠层荧光光谱。结果还表明,反演-IIB 方法与测量结果的匹配度相对较高,DART、mSCOPE 和 SCOPE 的平均相对绝对误差 (MRAE) 分别为 20%、37% 和 43%,而反演-IIA 的平均相对绝对误差 (MRAE) 分别为 62%、100% 和 108%。我们认为,我们的验证方法可应用于其他植物物种和冠层几何形状,从而提供一种方法来标准化和评估冠层 SIF 模型的性能,并提高我们对冠层 SIF 观测结果的理解。
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An in situ approach for validation of canopy chlorophyll fluorescence radiative transfer models using the full emission spectrum
The intensity and spectral properties of solar-induced chlorophyll fluorescence (SIF) carry valuable information on plant photosynthesis and productivity, but are also influenced by leaf and canopy structure. Physically based models provide a quantitative means to investigate how SIF intensity and spectra propagate and scale from the photosystem to the leaf and to the canopy levels. However, the validation of canopy SIF models is limited by the lack of methods that combine direct, independent, and complementary measurements of the full fluorescence spectrum at the leaf and canopy levels. Here, we propose a novel validation approach that combines in situ measurements of leaf and canopy fluorescence spectra. The approach is demonstrated with measurements in a rice crop at two contrasting stages of canopy development. We measured leaf reflectance, transmittance, and fluorescence spectra in situ, and subsequently inverted leaf structural and biochemical parameters and determined the leaf fluorescence quantum efficiency (FQE) using the Fluspect-Cx model. Two FQE inversion methods (Inversion-IIA and Inversion-IIB) were tested for the forward simulation of leaf fluorescence spectra. Leaf fluorescence spectra were then scaled up to the canopy level using 1D, 2D, and 3D radiative transfer schemes (SCOPE, mSCOPE, and DART), and compared with the direct canopy fluorescence spectral observations measured under red, green, blue, and white illumination. The validation results demonstrate that accounting for 3D canopy structure, as in the DART model, is critical to successfully scale the fluorescence spectrum from the leaf to the canopy level, whereas 1D SCOPE or even 2D mSCOPE were unable to fully reproduce the canopy fluorescence spectra. The results also demonstrate that the Inversion-IIB method matches relatively well the measurements with mean relative absolute errors (MRAE) of 20 %, 37 %, and 43 % versus Inversion-IIA with mean relative absolute errors (MRAE) of 62 %, 100 %, and 108 % for DART, mSCOPE, and SCOPE, respectively. We suggest that our validation approach is transferable to other plant species and canopy geometries, providing a means to standardize and evaluate the performance of canopy SIF models and improve our understanding of canopy SIF observations.
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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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