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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 Müller et al.

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AAFC: Agriculture and Agri-Food Canada, The National Soil DataBase (NSDB), http://sis.agr.gc.ca/cansis/nsdb/index.html, last access: 17 July 2015.
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Arlot, S. and Celisse, A.: A survey of cross-validation procedures for model selection, Stat. Surv., 4, 40–79, https://doi.org/10.1214/09-SS054, 2010.
Betts, A. K., Ball, J. H., Beljaars, A. C. M., Miller, M. J., and Viterbo, P. A.: The land surface-atmosphere interaction: A review based on observational and global modeling perspectives, J. Geophys. Res., 101, 7209–7225, 1996.
Box, G. E. P. and Cox, D. R.: An Analysis of Transformations, J. R. Stat. Soc. B, 26, 211–252, 1964.
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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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