4.7 Article

A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF)

Journal

REMOTE SENSING OF ENVIRONMENT
Volume 239, Issue -, Pages -

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2020.111644

Keywords

SIP; Multi-instrument harmonization; Downscaling; GOME-2; SCIAMACHY; Machine learning; CDF matching

Funding

  1. USAID Feed the Future program [7200AA18CA00014]
  2. Earth Sciences Division MEaSUREs program
  3. Earth Science U.S. Participating Investigator [NNX15AH95G]
  4. European Commission Joint Research Centre by the Copernicus-2 - DG-GROW [5054]
  5. OCO-2 Science Team at the Jet Propulsion Laboratory, California Institute of Technology
  6. National Science Foundation [DMS-1712554, TRIPODS 1740882]
  7. 2018 China Scholarship Council (CSC)-IBM Future Data Scientist Scholarship Program
  8. USDA-NIFA postdoctoral fellowship [2018-67012-27985]

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Several decade-long satellite retrievals of solar-induced chlorophyll fluorescence (SIP) 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 degrees ((SIF) over bar (GOME2_005) and (SIF) over bar (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) over bar (GOME2_005) and (SIF) over bar (SCIA_005) inherited from the original instrumental discrepancies to generate a harmonized SIF time series from 2002 to present ((SIF) over bar (005)). Finally, we quantify the uncertainty of (SIF) over bar (005). (SIF) over bar (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 SIFyield derived from (SIF) over bar (005) has high seasonal consistency with the ground measurements (R-2 = 0.93), suggesting that the harmonized product (SIF) over bar (005) carries physiological information beyond the absorbed photosynthetically active radiation. Additionally, (SIF) over bar (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 SIP-capable satellite platforms once issues related to inter-sensor calibration are resolved and SIF physiology is better understood.

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