Articles | Volume 14, issue 5
Earth Syst. Sci. Data, 14, 2315–2341, 2022
https://doi.org/10.5194/essd-14-2315-2022
Earth Syst. Sci. Data, 14, 2315–2341, 2022
https://doi.org/10.5194/essd-14-2315-2022
Data description paper
13 May 2022
Data description paper | 13 May 2022

A global long-term (1981–2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network

Jianglei Xu 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
  • EC1: 'Comment on essd-2021-250', Christof Lorenz, 17 Sep 2021
    • AC1: 'Reply on EC1', Xu Jianglei, 10 Oct 2021
  • RC1: 'Comment on essd-2021-250', Anonymous Referee #1, 15 Nov 2021
    • AC2: 'Reply on RC1', Xu Jianglei, 29 Nov 2021
  • RC2: 'Comment on essd-2021-250', Anonymous Referee #2, 01 Dec 2021
    • AC3: 'Reply on RC2', Xu Jianglei, 06 Dec 2021
  • EC2: 'Comment on essd-2021-250', Christof Lorenz, 13 Dec 2021
    • AC4: 'Reply on EC2', Xu Jianglei, 14 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Xu Jianglei on behalf of the Authors (14 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Reconsider after major revisions (18 Dec 2021) by Christof Lorenz
AR by Xu Jianglei on behalf of the Authors (25 Jan 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (25 Jan 2022) by Christof Lorenz
RR by Anonymous Referee #1 (28 Jan 2022)
RR by Anonymous Referee #2 (12 Feb 2022)
ED: Publish subject to minor revisions (review by editor) (28 Feb 2022) by Christof Lorenz
AR by Xu Jianglei on behalf of the Authors (09 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (24 Mar 2022) by Christof Lorenz
AR by Xu Jianglei on behalf of the Authors (24 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (12 Apr 2022) by Christof Lorenz
AR by Xu Jianglei on behalf of the Authors (13 Apr 2022)  Author's response    Manuscript
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Short summary
Land surface all-wave net radiation (Rn) is a key parameter in many land processes. Current products have drawbacks of coarse resolutions, large uncertainty, and short time spans. A deep learning method was used to obtain global surface Rn. A long-term Rn product was generated from 1981 to 2019 using AVHRR data. The product has the highest accuracy and a reasonable spatiotemporal variation compared to three other products. Our product will play an important role in long-term climate change.