Estimating spatially distributed soil texture using time series of thermal remote sensing &ndash; a case study in central Europe

Müller, Benjamin; Bernhardt, Matthias; Jackisch, Conrad; Schulz, Karsten

doi:https://doi.org/10.5194/hess-20-3765-2016

Articles | Volume 20, issue 9

https://doi.org/10.5194/hess-20-3765-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-20-3765-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 20, issue 9

Research article

|

12 Sep 2016

Research article |

| 12 Sep 2016

Estimating spatially distributed soil texture using time series of thermal remote sensing – a case study in central Europe

Benjamin Müller, Matthias Bernhardt, Conrad Jackisch, and Karsten Schulz

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Final revised paper (published on 12 Sep 2016)
Preprint (discussion started on 04 Apr 2016)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Comments on Muller et al. "Estimating distributed soil texture using time series of thermal remote sensing - a case study in central Europe"', Anonymous Referee #1, 20 Apr 2016
- RC2: 'Reviewer comment: hess-2016-115', Anonymous Referee #2, 07 May 2016
  - AC2: 'Reply to Reviewer 2:', Matthias Bernhardt, 21 Jun 2016
- AC1: 'Reply to Reviewer 1:', Matthias Bernhardt, 21 Jun 2016
- AC3: 'Additional Figures', Matthias Bernhardt, 21 Jun 2016

Peer-review completion

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

ED: Publish subject to minor revisions (Editor review) (14 Jul 2016) by Bob Su

AR by Matthias Bernhardt on behalf of the Authors (02 Aug 2016) Author's response Manuscript

ED: Publish as is (22 Aug 2016) by Bob Su

AR by Matthias Bernhardt on behalf of the Authors (29 Aug 2016)

Short summary

A technology for the spatial derivation of soil texture classes is presented. Information about soil texture is key for predicting the local and regional hydrological cycle. It is needed for the calculation of soil water movement, the share of surface runoff, the evapotranspiration rate and others. Nevertheless, the derivation of soil texture classes is expensive and time-consuming. The presented technique uses soil samples and remotely sensed data for estimating their spatial distribution.