Articles | Volume 15, issue 19
Research article
02 Oct 2018
Research article |  | 02 Oct 2018

A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks

Yao Zhang, Joanna Joiner, Seyed Hamed Alemohammad, Sha Zhou, and Pierre Gentine


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (06 Sep 2018) by Trevor Keenan
AR by Yao Zhang on behalf of the Authors (08 Sep 2018)  Author's response    Manuscript
ED: Publish as is (14 Sep 2018) by Trevor Keenan
Short summary
Using satellite reflectance measurements and a machine learning algorithm, we generated a new solar-induced chlorophyll fluorescence (SIF) dataset that is closely linked to plant photosynthesis. This new dataset has higher spatial and temporal resolutions, and lower uncertainty compared to the existing satellite retrievals. We also demonstrated its application in monitoring drought and improving the understanding of the SIF–photosynthesis relationship.
Final-revised paper