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.
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.
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
CORRESPONDING AUTHOR
Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
Department of Geography, Ludwig-Maximilians-Universität, Munich, Germany
Matthias Bernhardt
CORRESPONDING AUTHOR
Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
Conrad Jackisch
Institute of Water and River Basin Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
Karsten Schulz
Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
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Cited
13 citations as recorded by crossref.
- Event controls on intermittent streamflow in a temperate climate N. Kaplan et al. 10.5194/hess-26-2671-2022
- A novel hyperspectral remote sensing estimation model for surface soil texture using AHSI/ZY1-02D satellite image Q. Shen et al. 10.1016/j.jag.2025.104453
- Digital Mapping of Soil Particle Size Fractions in the Loess Plateau, China, Using Environmental Variables and Multivariate Random Forest W. He et al. 10.3390/rs16050785
- Predicting Lead and Nickel Contamination in Soil using Spectroradiometer B. Pawar & R. Deshmukh 10.35940/ijrte.A5758.0510121
- Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta T. Tran et al. 10.3390/rs11232742
- Emissivity of agricultural soil attributes in southeastern Brazil via terrestrial and satellite sensors D. Salazar et al. 10.1016/j.geoderma.2019.114038
- Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images J. Demattê et al. 10.1016/j.rse.2018.04.047
- Terrain Characterization via Machine vs. Deep Learning Using Remote Sensing J. Ewing et al. 10.3390/s23125505
- Soil texture and organic carbon mapping using surface temperature and reflectance spectra in Southeast Brazil V. Sayão & J. Demattê 10.1016/j.geodrs.2018.e00174
- Predicting probabilities of streamflow intermittency across a temperate mesoscale catchment N. Kaplan et al. 10.5194/hess-24-5453-2020
- Satellite land surface temperature and reflectance related with soil attributes V. Sayão et al. 10.1016/j.geoderma.2018.03.026
- Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics A. Lausch et al. 10.3390/rs11202356
- Multi-temporal bare surface image associated with transfer functions to support soil classification and mapping in southeastern Brazil R. Rizzo et al. 10.1016/j.geoderma.2019.114018
13 citations as recorded by crossref.
- Event controls on intermittent streamflow in a temperate climate N. Kaplan et al. 10.5194/hess-26-2671-2022
- A novel hyperspectral remote sensing estimation model for surface soil texture using AHSI/ZY1-02D satellite image Q. Shen et al. 10.1016/j.jag.2025.104453
- Digital Mapping of Soil Particle Size Fractions in the Loess Plateau, China, Using Environmental Variables and Multivariate Random Forest W. He et al. 10.3390/rs16050785
- Predicting Lead and Nickel Contamination in Soil using Spectroradiometer B. Pawar & R. Deshmukh 10.35940/ijrte.A5758.0510121
- Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta T. Tran et al. 10.3390/rs11232742
- Emissivity of agricultural soil attributes in southeastern Brazil via terrestrial and satellite sensors D. Salazar et al. 10.1016/j.geoderma.2019.114038
- Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images J. Demattê et al. 10.1016/j.rse.2018.04.047
- Terrain Characterization via Machine vs. Deep Learning Using Remote Sensing J. Ewing et al. 10.3390/s23125505
- Soil texture and organic carbon mapping using surface temperature and reflectance spectra in Southeast Brazil V. Sayão & J. Demattê 10.1016/j.geodrs.2018.e00174
- Predicting probabilities of streamflow intermittency across a temperate mesoscale catchment N. Kaplan et al. 10.5194/hess-24-5453-2020
- Satellite land surface temperature and reflectance related with soil attributes V. Sayão et al. 10.1016/j.geoderma.2018.03.026
- Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics A. Lausch et al. 10.3390/rs11202356
- Multi-temporal bare surface image associated with transfer functions to support soil classification and mapping in southeastern Brazil R. Rizzo et al. 10.1016/j.geoderma.2019.114018
Discussed (final revised paper)
Latest update: 24 Apr 2025
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...
