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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 9
Hydrol. Earth Syst. Sci., 20, 3765-3775, 2016
https://doi.org/10.5194/hess-20-3765-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 20, 3765-3775, 2016
https://doi.org/10.5194/hess-20-3765-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

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 Müller1,2, Matthias Bernhardt1, Conrad Jackisch3, and Karsten Schulz1 Benjamin Müller et al.
  • 1Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
  • 2Department of Geography, Ludwig-Maximilians-Universität, Munich, Germany
  • 3Institute of Water and River Basin Management, Karlsruhe Institute of Technology, Karlsruhe, Germany

Abstract. For understanding water and solute transport processes, knowledge about the respective hydraulic properties is necessary. Commonly, hydraulic parameters are estimated via pedo-transfer functions using soil texture data to avoid cost-intensive measurements of hydraulic parameters in the laboratory. Therefore, current soil texture information is only available at a coarse spatial resolution of 250 to 1000m.

Here, a method is presented to derive high-resolution (15m) spatial topsoil texture patterns for the meso-scale Attert catchment (Luxembourg, 288km2) from 28 images of ASTER (advanced spaceborne thermal emission and reflection radiometer) thermal remote sensing. A principle component analysis of the images reveals the most dominant thermal patterns (principle components, PCs) that are related to 212 fractional soil texture samples. Within a multiple linear regression framework, distributed soil texture information is estimated and related uncertainties are assessed. An overall root mean squared error (RMSE) of 12.7 percentage points (pp) lies well within and even below the range of recent studies on soil texture estimation, while requiring sparser sample setups and a less diverse set of basic spatial input.

This approach will improve the generation of spatially distributed topsoil maps, particularly for hydrologic modeling purposes, and will expand the usage of thermal remote sensing products.

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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.
A technology for the spatial derivation of soil texture classes is presented. Information about...
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