Articles | Volume 17, issue 10
https://doi.org/10.5194/essd-17-5181-2025
https://doi.org/10.5194/essd-17-5181-2025
Data description paper
 | 
07 Oct 2025
Data description paper |  | 07 Oct 2025

A seamless global daily 5 km soil moisture product from 1982 to 2021 using AVHRR satellite data and an attention-based deep learning model

Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Feng Tian, Guodong Zhang, and Jianglei Xu

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC2: 'Comment on essd-2024-553', SHAOBO SUN, 19 Feb 2025
  • RC1: 'Comment on essd-2024-553', Anonymous Referee #1, 27 Feb 2025
  • CC3: 'Comment on essd-2024-553', SHAOBO SUN, 06 May 2025
  • RC2: 'Comment on essd-2024-553', Anonymous Referee #2, 06 May 2025
  • RC3: 'Comment on essd-2024-553', Anonymous Referee #3, 08 May 2025
  • CC4: 'Comment on essd-2024-553', Noemi Vergopolan, 13 May 2025
  • RC4: 'Comment on essd-2024-553', Noemi Vergopolan, 14 May 2025
  • AC1: 'Comment on essd-2024-553', Yufang Zhang, 21 Jun 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Yufang Zhang on behalf of the Authors (21 Jun 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (23 Jun 2025) by Jiafu Mao
RR by Anonymous Referee #1 (24 Jun 2025)
RR by Anonymous Referee #3 (02 Jul 2025)
ED: Reconsider after major revisions (08 Jul 2025) by Jiafu Mao
AR by Yufang Zhang on behalf of the Authors (15 Jul 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (16 Jul 2025) by Jiafu Mao
AR by Yufang Zhang on behalf of the Authors (20 Jul 2025)
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Short summary
Soil moisture (SM) plays a vital role in climate, agriculture, and hydrology, yet reliable long-term, seamless global datasets remain scarce. To fill this gap, we developed a four-decade seamless global daily 5 km SM product using multi-source datasets and deep learning models. This product has long-term coverage, spatial and temporal integrity, and high accuracy, making it a valuable resource for applications like SM trend analysis, drought monitoring, and assessment of vegetation responses.
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