Articles | Volume 16, issue 5
Model description paper
17 Mar 2023
Model description paper |  | 17 Mar 2023

Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats

Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-211', Anonymous Referee #1, 24 Oct 2022
    • AC1: 'Reply on RC1', Chaopeng Shen, 06 Dec 2022
  • RC2: 'Comment on gmd-2022-211', Richard Mills, 09 Jan 2023
    • AC2: 'Reply on RC2', Chaopeng Shen, 09 Jan 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Chaopeng Shen on behalf of the Authors (27 Jan 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (09 Feb 2023) by Christoph Müller
AR by Chaopeng Shen on behalf of the Authors (15 Feb 2023)  Manuscript 
Short summary
Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.