Title: A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF)
Authors: WEN JIAMINGKOEHLER PHILIPPDUVEILLER BOGDAN GRÉGORY HENRY EPARAZOO NICHOLASMAGNEY T. S.HOOKER GYU LONGLONGCHRISTINE CHANGSUN YING
Citation: REMOTE SENSING OF ENVIRONMENT vol. 239 p. 111644
Publisher: ELSEVIER SCIENCE INC
Publication Year: 2020
JRC N°: JRC115831
ISSN: 0034-4257 (online)
URI: https://www.sciencedirect.com/science/article/pii/S0034425720300134?via%3Dihub
https://publications.jrc.ec.europa.eu/repository/handle/JRC115831
DOI: 10.1016/j.rse.2020.111644
Type: Articles in periodicals and books
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 regionaliza- tion 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, R 2 = 0.73), and 3) ground-based SIF measurements at a subalpine coniferous forest (R 2 = 0.91). The SIF yield derived from SIF 005 has high seasonal consistency with the ground measurements (R 2 = 0.93), suggesting that the harmonized product SIF 005 carries physiological in- formation 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 pro- ducts from all SIF-capable satellite platforms once issues related to inter-sensor calibration are resolved and SIF physiology is better understood.
JRC Directorate:Sustainable Resources

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