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

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

doi:https://doi.org/10.5194/bg-15-5779-2018

Articles | Volume 15, issue 19

https://doi.org/10.5194/bg-15-5779-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-15-5779-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

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Final revised paper (published on 02 Oct 2018)
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Preprint (discussion started on 22 Jun 2018)
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Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Reviewer Comment 1: Yao Zhang et al.', Anonymous Referee #1, 24 Jul 2018
- AC1: 'response to reviewer 1', Yao Zhang, 24 Aug 2018
RC2: 'Interactive comment on “A global spatially Continuous Solar Induced Fluorescence (CSIF) dataset using neural networks"', Anonymous Referee #2, 09 Aug 2018
- AC2: 'response to reviewer 2', Yao Zhang, 24 Aug 2018

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

AR by Yao Zhang on behalf of the Authors (14 Sep 2018)

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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.