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

Data sets

ASTER Level 1A data EOSDIS - NASA's Earth Observing System Data and Information System http://reverb.echo.nasa.gov/reverb/

CAOS - Catchments as Organised Systems CAOS http://www.caos-project.de

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.