Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 8
Articles | Volume 16, issue 8
https://doi.org/10.5194/amt-16-2197-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-2197-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 8
Research article
 | 
25 Apr 2023
Research article |  | 25 Apr 2023

Gap filling of turbulent heat fluxes over rice–wheat rotation croplands using the random forest model

Jianbin Zhang, Zexia Duan, Shaohui Zhou, Yubin Li, and Zhiqiu Gao

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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 amt-2022-296', Anonymous Referee #2, 04 Jan 2023
    • AC1: 'Reply on RC1', Yubin Li, 01 Feb 2023
  • RC2: 'Comment on amt-2022-296', Anonymous Referee #1, 01 Feb 2023
    • AC2: 'Reply on RC2', Yubin Li, 01 Feb 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Yubin Li on behalf of the Authors (01 Feb 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (01 Feb 2023) by Simone Lolli
RR by Anonymous Referee #3 (15 Feb 2023)
RR by Anonymous Referee #2 (23 Feb 2023)
ED: Publish subject to minor revisions (review by editor) (03 Mar 2023) by Simone Lolli
AR by Yubin Li on behalf of the Authors (10 Mar 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (22 Mar 2023) by Simone Lolli
AR by Yubin Li on behalf of the Authors (24 Mar 2023)  Manuscript 
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Short summary
In this paper, we used a random forest model to fill the observation gaps of the fluxes measured during 2015–2019. We found that the net radiation was the most important input variable. And we justified the reliability of the model. Further, it was revealed that the model performed better after relative humidity was removed from the input. Lastly, we compared the results of the model with those of three other machine learning models, and we found that the model outperformed all of them.
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