Abstract Several decade-long satellite retrievals of solar-induced chlorophyll fluorescence (SIF) have become available during the past few years, but understanding the long-term dynamics of SIF and elucidating its co-variation with historical gross primary production (GPP) remains a challenge. Part of the challenge is due to the lack of direct comparability among these SIF products as they are derived from various satellite platforms with different retrieval methods, instruments characteristics, overpass time, and viewing-illumination geometries. This study presents a framework that circumvents these discrepancies and allows the harmonization of SIF products from multiple instruments to achieve long-term coverage. We demonstrate this framework by fusing SIF retrievals from SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) and Global Ozone Monitoring Experiment 2 (GOME-2) onboard MetOp-A developed at German Research Center for Geosciences (GFZ). We first downscale both original SIF datasets from their native resolutions to 0.05° ( SIF ¯ GOME2_005 and SIF ¯ SCIA_005 respectively) using machine learning (ML) algorithms imposed with regionalization constraints to account for the varying relationships between predictors and SIF in space and time. We then apply the cumulative distribution function (CDF) matching technique to correct the offset between SIF ¯ GOME2_005 and SIF ¯ SCIA_005 inherited from the original instrumental discrepancies to generate a harmonized SIF time series from 2002 to present ( SIF ¯ 005). Finally, we quantify the uncertainty of SIF ¯ 005. SIF ¯ 005 is validated with 1) the original retrievals to ensure the spatial and temporal variabilities are preserved, 2) airborne SIF derived from the Chlorophyll Fluorescence Imaging Spectrometer (CFIS, R2 = 0.73), and 3) ground-based SIF measurements at a subalpine coniferous forest (R2 = 0.91). The SIFyield derived from SIF ¯ 005 has high seasonal consistency with the ground measurements (R2 = 0.93), suggesting that the harmonized product SIF ¯ 005 carries physiological information beyond the absorbed photosynthetically active radiation. Additionally, SIF ¯ 005 has a good capability for large-scale stress monitoring as demonstrated with several major historical drought and heatwave events. The framework developed in this study sets the stage for future development of even more advanced SIF products from all SIF-capable satellite platforms once issues related to inter-sensor calibration are resolved and SIF physiology is better understood.

中文翻译：

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协调多个卫星仪器以产生长期全球高空间分辨率太阳诱导叶绿素荧光 (SIF) 的框架

摘要 在过去几年中，太阳诱导叶绿素荧光 (SIF) 的数十年卫星检索已经可用，但了解 SIF 的长期动态并阐明其与历史总初级生产 (GPP) 的共变仍然是一个难题。挑战。部分挑战是由于这些 SIF 产品之间缺乏直接可比性，因为它们源自具有不同检索方法、仪器特性、立交时间和观察照明几何形状的各种卫星平台。本研究提出了一个框架来规避这些差异，并允许协调来自多种工具的 SIF 产品以实现长期覆盖。我们通过融合来自德国地球科学研究中心 (GFZ) 开发的 MetOp-A 上的大气图谱扫描成像吸收光谱仪 (SCIAMACHY) 和全球臭氧监测实验 2 (GOME-2) 的 SIF 检索来展示该框架。我们首先使用带有区域化约束的机器学习 (ML) 算法将两个原始 SIF 数据集从其原始分辨率缩小到 0.05°（分别为 SIF ¯ GOME2_005 和 SIF ¯ SCIA_005），以解释预测变量和 SIF 在空间和时间上的不同关系。然后，我们应用累积分布函数 (CDF) 匹配技术来纠正从原始仪器差异继承的 SIF_GOME2_005 和 SIF_SCIA_005 之间的偏移，以生成从 2002 年至今 (SIF_005) 的协调 SIF 时间序列。最后，我们量化了 SIF ¯ 005 的不确定性。 SIF ¯ 005 通过 1) 原始检索进行验证，以确保保留空间和时间变化，2) 来自叶绿素荧光成像光谱仪（CFIS，R2 = 0.73）的机载 SIF，以及3) 亚高山针叶林的地面 SIF 测量值 (R2 = 0.91)。源自 SIF ¯ 005 的 SIF 产量与地面测量具有高度的季节性一致性 (R2 = 0.93)，表明协调产物 SIF ¯ 005 携带的生理信息超出了吸收的光合有效辐射。此外，SIF 005 具有良好的大规模应力监测能力，如几次重大的历史干旱和热浪事件所证明的那样。