Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2739-2021
https://doi.org/10.5194/hess-25-2739-2021
Research article
 | 
25 May 2021
Research article |  | 25 May 2021

Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques

Samuel N. Araya, Anna Fryjoff-Hung, Andreas Anderson, Joshua H. Viers, and Teamrat A. Ghezzehei

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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) (21 Dec 2020) by Giuliano Di Baldassarre
AR by Samuel Negusse Araya on behalf of the Authors (02 Feb 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (17 Feb 2021) by Giuliano Di Baldassarre
RR by Salvatore Manfreda (04 Mar 2021)
RR by Anonymous Referee #1 (11 Mar 2021)
ED: Publish subject to technical corrections (29 Mar 2021) by Giuliano Di Baldassarre
AR by Samuel Negusse Araya on behalf of the Authors (09 Apr 2021)  Author's response    Manuscript
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Short summary
We took aerial photos of a grassland area using an unoccupied aerial vehicle and used the images to estimate soil moisture via machine learning. We were able to estimate soil moisture with high accuracy. Furthermore, by analyzing the machine learning models we developed, we learned how different factors drive the distribution of moisture across the landscape. Among the factors, rainfall, evapotranspiration, and topography were most important in controlling surface soil moisture distribution.