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

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

doi:https://doi.org/10.5194/hess-25-2739-2021

Articles | Volume 25, issue 5

https://doi.org/10.5194/hess-25-2739-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-2739-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 5

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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Final revised paper (published on 25 May 2021)
Supplement to the final revised paper
Preprint (discussion started on 18 Aug 2020)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Referee report: Advances in Soil Moisture Retrieval from Multispectral Remote Sensing Using Unmanned Aircraft Systems and Machine Learning Techniques', Anonymous Referee #1, 22 Sep 2020
- AC1: 'Author Response to Referee Comments', Samuel Negusse Araya, 13 Dec 2020
RC2: 'Review', Salvatore Manfreda, 13 Oct 2020
- AC1: 'Author Response to Referee Comments', Samuel Negusse Araya, 13 Dec 2020

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

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.