Articles | Volume 12, issue 2
Earth Syst. Sci. Data, 12, 1101–1116, 2020
https://doi.org/10.5194/essd-12-1101-2020
Earth Syst. Sci. Data, 12, 1101–1116, 2020
https://doi.org/10.5194/essd-12-1101-2020
Data description paper
13 May 2020
Data description paper | 13 May 2020

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

Gregory Duveiller et al.

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Cited articles

Badgley, G., Field, C. B., and Berry, J. A.: Canopy near-infrared reflectance and terrestrial photosynthesis, Science Advances, 3, e1602244, https://doi.org/10.1126/sciadv.1602244, 2017. a, b
Baker, N. R.: Chlorophyll fluorescence: a probe of photosynthesis in vivo., Annu. Rev. Plant Biol., 59, 89–113, https://doi.org/10.1146/annurev.arplant.59.032607.092759, 2008. a
Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C.: A Limited Memory Algorithm for Bound Constrained Optimization, SIAM J. Sci. Comput., 16, 1190–1208, https://doi.org/10.1137/0916069, 1995. a
Duveiller, G. and Cescatti, A.: Spatially downscaling sun-induced chlorophyll fluorescence leads to an improved temporal correlation with gross primary productivity, Remote Sens. Environ., 182, 72–89, https://doi.org/10.1016/j.rse.2016.04.027, 2016. a, b, c, d, e, f, g, h, i, j, k, l, m
Duveiller, G. and Filipponi, F.: GregDuveiller/sif-downscaling-essd: code associated with the paper Duveiller et al. 2020 ESSD (Version v1.0), Zenodo, https://doi.org/10.5281/zenodo.3753521, 2020. a
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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 ecosystems should respond to growing conditions in some modelling along with ancillary satellite observations, we provide here a new enhanced dataset with higher spatial resolution that better represents the spatial patterns of vegetation growth over land.