Articles | Volume 24, issue 4
Hydrol. Earth Syst. Sci., 24, 1957–1973, 2020
https://doi.org/10.5194/hess-24-1957-2020
Hydrol. Earth Syst. Sci., 24, 1957–1973, 2020
https://doi.org/10.5194/hess-24-1957-2020

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 et al.

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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) (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
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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.