Remote sensing of solar-induced vegetation chlorophyll fluorescence (SIF) has a rich history of more than 50 years of research covering active and passive techniques from leaf, canopy, and satellite scale. Current satellite-derived SIF products primarily focus on the far-red spectral range, with variations in techniques dependent on sensor capabilities. However, these retrieval methods often rely on parametric spectral models and are constrained to narrow absorption regions. In this paper, we introduce a novel Bayesian retrieval technique, referred to as SIFFI (Siffi Is For Fluorescence Inference), designed for the flexible and robust estimation of spectrally resolved fluorescence spectra. SIFFI utilizes spectral representations for both fluorescence and surface reflectance, enabling its application to distinct spectral ranges, e.g., red, far-red, and full spectral range. Also, its applicability extends to top-of-canopy (TOC) and top-of-atmosphere (TOA) measurements, with the latter being possible when auxiliary information about the atmospheric state is available. For the assessment of SIFFI, we performed an extensive proof-of-concept simulation exercise involving diverse scenarios that integrated measured leaf-level fluorescence and reflectance signals, propagated them to the TOC and TOA levels, and perturbed the resultant signal with instrument Gaussian noise to simulate realistic conditions. Additionally, we extend our assessment exercise to TOC measurements acquired by a fluorescence box (FloX) instrument during two diurnal cycles on sunlit and cloudy conditions. In all the TOC cases, simulations- and measured-based scenarios, we compared our SIF estimates with the results from two well-established methods: the improved Fraunhofer line discrimination method (iFLD) and the Spectral Fitting (SpecFit) method covering the full fluorescence spectra. Notably, our results highlight the versatility and accuracy of SIFFI in estimating spectrally resolved fluorescence, achieving Mean Absolute Error (MAE) values of 0.07 (0.09) [mW/(m2srnm)] in the TOC (TOA) simulation scenarios, improving the SpecFit method estimates, and being aligned with the iFLD method results at the oxygen bands. SIFFI represents a significant advancement in SIF retrieval, providing a robust approach that exploits the full spectral information from the red to the near-infrared regions.

中文翻译：

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SIFFI： 用于植被遥感的贝叶斯太阳诱导荧光反演算法


太阳诱导植被叶绿素荧光 （SIF） 遥感拥有 50 多年的悠久研究历史，涵盖叶、树冠和卫星尺度的主动和被动技术。目前源自卫星的 SIF 产品主要关注远红光谱范围，技术的变化取决于传感器的功能。然而，这些检索方法通常依赖于参数光谱模型，并且仅限于狭窄的吸收区域。在本文中，我们介绍了一种新颖的贝叶斯检索技术，称为 SIFFI （Siffi Is For Fluorescence Inference），旨在灵活、稳健地估计光谱分辨荧光光谱。SIFFI 利用荧光和表面反射的光谱表示，使其能够应用于不同的光谱范围，例如红光、远红光和全光谱范围。此外，它的适用性还扩展到冠层顶部 （TOC） 和大气层顶部 （TOA） 测量，当有关大气状态的辅助信息可用时，后者是可能的。为了评估 SIFFI，我们进行了广泛的概念验证仿真练习，涉及各种场景，这些场景集成了测量的叶级荧光和反射信号，将它们传播到 TOC 和 TOA 水平，并用仪器高斯噪声扰动所得信号以模拟真实条件。此外，我们将评估工作扩展到荧光箱 （FloX） 仪器在阳光明媚和多云条件下的两个昼夜周期中获取的 TOC 测量值。 在所有 TOC 案例、基于模拟和测量的场景中，我们将 SIF 估计值与两种成熟方法的结果进行了比较：改进的弗劳恩霍夫线鉴别法 （iFLD） 和光谱拟合 （SpecFit） 方法覆盖整个荧光光谱。值得注意的是，我们的结果突出了 SIFFI 在估计光谱分辨荧光方面的多功能性和准确性，在 TOC （TOA） 模拟场景中实现了 0.07 （0.09） [mW/（m2srnm）] 的平均绝对误差 （MAE） 值，改进了 SpecFit 方法估计，并与氧带的 iFLD 方法结果保持一致。SIFFI 代表了 SIF 检索的重大进步，提供了一种强大的方法，可以利用从红色区域到近红外区域的完整光谱信息。