气候变化领域集成服务门户(CCPortal): A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF)

CCPortal

DOI	10.1016/j.rse.2020.111644
	A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF)
	Wen J.; Köhler P.; Duveiller G.; Parazoo N.C.; Magney T.S.; Hooker G.; Yu L.; Chang C.Y.; Sun Y.
发表日期	2020
ISSN	00344257
卷号	239
英文摘要	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. © 2020 Elsevier Inc.
英文关键词	CDF matching; Downscaling; GOME-2; Machine learning; Multi-instrument harmonization; SCIAMACHY; SIF
语种	英语
scopus关键词	Chlorophyll; Distribution functions; Forestry; Learning systems; Machine learning; Physiology; Satellites; Spectrometers; CDF matching; Down-scaling; GOME-2; Multi-instrument harmonization; SCIAMACHY; Fluorescence imaging; algorithm; chlorophyll; coniferous forest; downscaling; drought; fluorescence; GOME; heat wave; instrumentation; machine learning; net primary production; SCIAMACHY; spatial resolution
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179449
作者单位	School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, NY, United States; Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, United States; European Commission, Joint Research Centre (JRC), via E. Fermi 2749, Ispra, VA I-21027, Italy; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States; Department of Plant Sciences, University of California Davis, Davis, CA, United States; Department of Statistics and Data Science, Cornell University, Ithaca, NY, United States; Computer Network Information Center, Chinese Academy of Sciences, Beijing, China; School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, China
推荐引用方式 GB/T 7714	Wen J.,Köhler P.,Duveiller G.,et al. A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF)[J],2020,239.
APA	Wen J..,Köhler P..,Duveiller G..,Parazoo N.C..,Magney T.S..,...&Sun Y..(2020).A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF).Remote Sensing of Environment,239.
MLA	Wen J.,et al."A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF)".Remote Sensing of Environment 239(2020).