Articles | Volume 22, issue 10
https://doi.org/10.5194/hess-22-5341-2018
https://doi.org/10.5194/hess-22-5341-2018
Research article
 | 
17 Oct 2018
Research article |  | 17 Oct 2018

Global downscaling of remotely sensed soil moisture using neural networks

Seyed Hamed Alemohammad, Jana Kolassa, Catherine Prigent, Filipe Aires, and Pierre Gentine

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (04 Jun 2018) by Shraddhanand Shukla
AR by Hamed Alemohammad on behalf of the Authors (23 Jul 2018)  Author's response   Manuscript 
ED: Reconsider after major revisions (further review by editor and referees) (02 Aug 2018) by Shraddhanand Shukla
AR by Hamed Alemohammad on behalf of the Authors (02 Aug 2018)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (02 Aug 2018) by Shraddhanand Shukla
RR by Anonymous Referee #1 (07 Aug 2018)
RR by Anonymous Referee #2 (31 Aug 2018)
ED: Publish subject to technical corrections (14 Sep 2018) by Shraddhanand Shukla
AR by Hamed Alemohammad on behalf of the Authors (24 Sep 2018)  Manuscript 
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Short summary
A new machine learning algorithm is developed to downscale satellite-based soil moisture estimates from their native spatial scale of 9 km to 2.25 km.