Articles | Volume 19, issue 6
Research article
31 Mar 2022
Research article |  | 31 Mar 2022

A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet)

Johannes Gensheimer, Alexander J. Turner, Philipp Köhler, Christian Frankenberg, and Jia Chen


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • AC1: 'Link to Data refreshed', Johannes Gensheimer, 06 Jan 2022
  • RC1: 'Comment on bg-2021-348', Anonymous Referee #1, 20 Jan 2022
    • AC2: 'Reply on RC1', Johannes Gensheimer, 22 Feb 2022
  • RC2: 'Comment on bg-2021-348', Anonymous Referee #2, 25 Jan 2022
    • AC3: 'Reply on RC2', Johannes Gensheimer, 22 Feb 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (24 Feb 2022) by Martin De Kauwe
AR by Johannes Gensheimer on behalf of the Authors (01 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (01 Mar 2022) by Martin De Kauwe
Short summary
We develop a convolutional neural network, named SIFnet, that increases the spatial resolution of SIF from TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations, together with a broad range of high-resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis.
Final-revised paper