Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality

Lv, Shaoning; Schalge, Bernd; Saavedra Garfias, Pablo; Simmer, Clemens

doi:https://doi.org/10.5194/hess-24-1957-2020

Articles | Volume 24, issue 4

https://doi.org/10.5194/hess-24-1957-2020

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

https://doi.org/10.5194/hess-24-1957-2020

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

Articles | Volume 24, issue 4

Research article

|

17 Apr 2020

Research article |

| 17 Apr 2020

Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality

Shaoning Lv, Bernd Schalge, Pablo Saavedra Garfias, and Clemens Simmer

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Final revised paper (published on 17 Apr 2020)
Preprint (discussion started on 04 Jun 2019)

Interactive discussion

Status: closed

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

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RC1: 'Interesting and well written study', Anonymous Referee #1, 24 Jul 2019
- AC1: 'Replies to comments from Reviewer #1', Shaoning Lv, 22 Oct 2019
RC2: 'Interesting, well executed study, but lacking context', Anonymous Referee #2, 30 Jul 2019
- AC2: 'Replies to comments from Reviewer #2', Shaoning Lv, 22 Oct 2019
RC3: 'Review', Anonymous Referee #3, 06 Aug 2019
- AC3: 'Replies to comments from Reviewer #3', Shaoning Lv, 22 Oct 2019

Peer-review completion

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

ED: Publish subject to revisions (further review by editor and referees) (08 Nov 2019) by Pierre Gentine

AR by Shaoning Lv on behalf of the Authors (15 Dec 2019) Author's response Manuscript

ED: Publish subject to minor revisions (further review by editor) (20 Dec 2019) by Pierre Gentine

AR by Anna Wenzel on behalf of the Authors (28 Feb 2020) Author's response

ED: Publish as is (17 Mar 2020) by Pierre Gentine

Short summary

Passive remote sensing of soil moisture has good potential to improve weather forecasting via data assimilation in theory. We use the virtual reality data set (VR01) to infer the impact of sampling density on soil moisture ground cal/val activity. It shows how the sampling error is growing with an increasing sampling distance for a SMOS–SMAP scale footprint in about 40 km, 9 km, and 3 km. The conclusion will help in understanding the passive remote sensing soil moisture products.