Articles | Volume 15, issue 6
The Cryosphere, 15, 2835–2856, 2021
https://doi.org/10.5194/tc-15-2835-2021
The Cryosphere, 15, 2835–2856, 2021
https://doi.org/10.5194/tc-15-2835-2021

Research article 24 Jun 2021

Research article | 24 Jun 2021

Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning

Zhixiang Yin et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2020-363', Anonymous Referee #1, 07 Feb 2021
  • RC2: 'Comment on tc-2020-363', Anonymous Referee #2, 10 Mar 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (11 May 2021) by Stef Lhermitte
AR by Feng Ling on behalf of the Authors (12 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (12 May 2021) by Stef Lhermitte
RR by Anonymous Referee #1 (24 May 2021)
RR by Anonymous Referee #2 (24 May 2021)
ED: Publish subject to minor revisions (review by editor) (25 May 2021) by Stef Lhermitte
AR by Feng Ling on behalf of the Authors (26 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (28 May 2021) by Stef Lhermitte
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Short summary
MODIS thermal infrared (TIR) imagery provides promising data to study the rapid variations in the Arctic turbulent heat flux (THF). The accuracy of estimated THF, however, is low (especially for small leads) due to the coarse resolution of the MODIS TIR data. We train a deep neural network to enhance the spatial resolution of estimated THF over leads from MODIS TIR imagery. The method is found to be effective and can generate a result which is close to that derived from Landsat-8 TIR imagery.