Abstract
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 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.
Highlights
The prediction ofsurface water and solute transport processes, from the plot to the basin scale, heavily rely on spatial information of soil hydraulic properties (SHPs)
F tests were performed to evaluate the multilinear regression estimator (MLRE) model performance with regard to the number of different principal components (PCs) considered as regressors, as well as to all possible combinations of PCs
Low p values are taken as an overall indication for the “relevance” of the MLRE
Summary
The prediction of (sub-)surface water and solute transport processes, from the plot to the basin scale, heavily rely on spatial information of soil hydraulic properties (SHPs). From country to global levels, is available from a variety of sources They vary in resolution, method of production and quality. Applied interpolation techniques and landscape evolution models, as well as pattern estimation methods, control the quality of derived spatial soil texture products. B. Müller et al.: Estimating spatially distributed soil texture using time series of thermal remote sensing. This study uses thermal remote sensing (RS) data in combination with plot measurements to generate spatially distributed soil texture maps. Parameters that influence the surface temperature are incoming radiation, land use, albedo and available water content The latter is strongly controlled by soil texture, which subsequently should influence the thermal inertia signature as given by the temporal patterns of surface temperature.
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