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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 19 Aug 2019

Submitted as: data description paper | 19 Aug 2019

Review status
A revised version of this preprint is currently under review for the journal ESSD.

A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity

Gregory Duveiller1, Federico Filipponi1, Sophia Walther2, Philipp Köhler3, Christian Frankenberg3,4, Luis Guanter5, and Alessandro Cescatti1 Gregory Duveiller et al.
  • 1European Commission Joint Research Centre, Ispra, Italy
  • 2Max Planck Institute for Biogeochemistry, Jena, Germany
  • 3California Institute of Technology, Pasadena, CA, USA
  • 4Jet Propulsion Laboratory, California Institute of Technology, CA, USA
  • 5Universitat Politècnica de València, Valencia, Spain

Abstract. Sun-induced chlorophyll fluorescence (SIF) retrieved from satellite spectrometers can be a highly valuable proxy for photosynthesis. The SIF signal is very small and notoriously difficult to measure, requiring sub-nanometer spectral resolution measurements, which to-date are only available from atmospheric spectrometers sampling at coarse spatial resolution. For example, the widely used SIF dataset derived from the GOME-2 mission is typically provided in 0.5 decimal degree composites. This paper presents a new SIF dataset based on GOME-2 satellite observations with an enhanced spatial resolution of 0.05 decimal degrees and an 8-day time step covering the period 2007–2018. It leverages on a proven methodology that relies on using a light use efficiency (LUE) modelling approach to establishing a semi-empirical relationship between SIF and various explanatory variables derived from remote sensing at finer spatial resolution. An optimal set of explanatory variables is selected based on an independent validation with OCO-2 SIF observations, which are only sparsely available but have a high accuracy and spatial resolution. After a bias-correction, the resulting downscaled SIF data shows high spatio-temporal agreement with the first SIF retrievals from the new TROPOMI mission, opening the path towards establishing a surrogate archive for this promising new dataset. We foresee that this new SIF dataset should be a valuable asset for Earth System Science in general, and for monitoring vegetation productivity in particular. The dataset is available at: (Duveiller et al., 2019).

Gregory Duveiller et al.

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Gregory Duveiller et al.

Data sets

Downscaled-GOME2-SIF G. Duveiller, F. Filipponi, S. Walther, P. Köhler, C. Frankenberg, L. Guanter, and A. Cescatti

Gregory Duveiller et al.


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Short summary
Sun-induced chlorophyll fluorescence is a valuable indicator of vegetation productivity but our capacity to measure it from space using satellite remote techniques has been hampered by a lack of spatial detail. Based on prior knowledge of how ecosystem should respond to growing conditions, on some modelling along with ancillary satellite observations, we here provide a new enhanced dataset with finer spatial resolution that better represents the spatial patterns of vegetation growth over land.
Sun-induced chlorophyll fluorescence is a valuable indicator of vegetation productivity but our...