Articles | Volume 13, issue 8
Earth Syst. Sci. Data, 13, 4241–4261, 2021
https://doi.org/10.5194/essd-13-4241-2021
Earth Syst. Sci. Data, 13, 4241–4261, 2021
https://doi.org/10.5194/essd-13-4241-2021

Data description paper 30 Aug 2021

Data description paper | 30 Aug 2021

An all-sky 1 km daily land surface air temperature product over mainland China for 2003–2019 from MODIS and ancillary data

Yan Chen 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
  • CC1: 'Comment on essd-2021-31', Joshua Fisher, 12 Mar 2021
    • AC1: 'Reply on CC1', Yan Chen, 24 Mar 2021
  • RC1: 'Comment on essd-2021-31', Anonymous Referee #1, 02 Apr 2021
    • AC2: 'Reply on RC1', Yan Chen, 26 Apr 2021
      • RC2: 'Reply on AC2', Anonymous Referee #1, 26 Apr 2021
  • RC3: 'Comment on essd-2021-31', Anonymous Referee #2, 15 Jun 2021
    • AC3: 'Reply on RC3', Yan Chen, 11 Jul 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Yan Chen on behalf of the Authors (11 Jul 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (13 Jul 2021) by David Carlson
AR by Yan Chen on behalf of the Authors (22 Jul 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (27 Jul 2021) by David Carlson
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Short summary
This study used remotely sensed and assimilated data to estimate all-sky land surface air temperature (Ta) using a machine learning method, and developed an all-sky 1 km daily mean land Ta product for 2003–2019 over mainland China. Validation results demonstrated that this dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in studies of climate change and the hydrological cycle.