Dielectric database of organic Arctic soils (DDOAS)

Savin, Igor; Mironov, Valery; Muzalevskiy, Konstantin; Fomin, Sergey; Karavayskiy, Andrey; Ruzicka, Zdenek; Lukin, Yuriy

doi:https://doi.org/10.5194/essd-12-3481-2020

Articles | Volume 12, issue 4

https://doi.org/10.5194/essd-12-3481-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-12-3481-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 12, issue 4

Data description paper

|

17 Dec 2020

Data description paper |

| 17 Dec 2020

Dielectric database of organic Arctic soils (DDOAS)

Igor Savin, Valery Mironov, Konstantin Muzalevskiy, Sergey Fomin, Andrey Karavayskiy, Zdenek Ruzicka, and Yuriy Lukin

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Final revised paper (published on 17 Dec 2020)
Preprint (discussion started on 16 Jul 2020)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Referee Comment', Christopher Simpson, 19 Aug 2020
- AC1: 'Response to referee comment, Christopher Simpson', Igor Savin, 01 Sep 2020
RC2: 'Manuscript review', Anonymous Referee #2, 25 Aug 2020
- AC2: 'Response to referee comment, referee #2', Igor Savin, 01 Sep 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Igor Savin on behalf of the Authors (13 Sep 2020) Author's response Manuscript

ED: Publish as is (04 Nov 2020) by Prasad Gogineni

AR by Igor Savin on behalf of the Authors (09 Nov 2020)

Short summary

This article presents a dielectric database of organic Arctic soils. This database was created based on dielectric measurements of seven samples of organic soils collected in various parts of the Arctic tundra. The created database can serve not only as a source of experimental data for the development of new soil dielectric models for the Arctic tundra but also as a source of training data for artificial intelligence satellite algorithms of soil moisture retrievals based on neural networks.