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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Cited articles

AAFC: Agriculture and Agri-Food Canada, The National Soil DataBase (NSDB),, last access: 17 July 2015.
Ahmed, Z. and Iqbal, J.: Evaluation of Landsat TM5 Multispectral Data for Automated Mapping of Surface Soil Texture and Organic Matter in GIS, Eur. J. Remote Sens., 47, 557–573, 2014.
Arlot, S. and Celisse, A.: A survey of cross-validation procedures for model selection, Stat. Surv., 4, 40–79,, 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.
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.