Catchments
; Image texture
; Linear regression
; Mean square error
; Principal component analysis
; Soils
; Solute transport
; Spatial distribution
; Uncertainty analysis
; Advanced spaceborne thermal emission and reflection radiometer
; Hydraulic parameters
; Hydraulic properties
; Multiple linear regressions
; Pedo-transfer functions
; Principle component analysis
; Root mean squared errors
; Thermal remote sensing
; Remote sensing
; ASTER
; catchment
; estimation method
; hydrological modeling
; image analysis
; pedotransfer function
; remote sensing
; satellite imagery
; soil texture
; solute transport
; spatial distribution
; time series
; Belgium
; Central Europe
; Luxembourg [Belgium]
英文摘要:
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 1000 m. Here, a method is presented to derive high-resolution (15 m) spatial topsoil texture patterns for the meso-scale Attert catchment (Luxembourg, 288 km2) 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. � Author(s) 2016.
Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Life Sciences, Vienna, Austria; Department of Geography, Ludwig-Maximilians-Universit�t, Munich, Germany; Institute of Water and River Basin Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
Recommended Citation:
M�ller B,, Bernhardt M,, Jackisch C,et al. Estimating spatially distributed soil texture using time series of thermal remote sensing - A case study in central Europe[J]. Hydrology and Earth System Sciences,2016-01-01,20(9)