Data are available for the following Physical properties:
- Clay content (%) in topsoil (0-20cm) modelled by Multivariate Additive Regression Splines
- Silt content (%) in topsoil modelled by Multivariate Additive Regression Splines
- Sand content (%) in topsoil modelled by Multivariate Additive Regression Splines
- Coarse fragements (%) content in topsoil modelled by Multivariate Additive Regression Splines
- Bulk density derived from soil texture datasets (obtained from the packing density and themapped clay content following the equation of Jones et al. 2003)
- USDA soil textural classes derived from clay, silt and sand maps
- Available Water Capacity (AWC) for the topsoil fine earth fraction
Note that these data are based on the LUCAS topsoil data for ca 20,000 samples across EU.
Geographical Coverage: European Union
Input data: LUCAS 2009 Topsoil 20,000 sample point data
Model: Multivariate Additive Regression Splines (MARS)
The Land Use and Cover Area frame Statistical survey (LUCAS) aimed at the collecting harmonised data about the state of land use/cover over the extent of European Union (EU).Among these 2 · 105 land use/cover observations selected for validation, a topsoil survey was conducted at about 10% of these sites. Topsoil sampling locations were selected as to be representative of European landscape using a Latin hypercube stratified random sampling, taking into account CORINE land cover 2000, the Shuttle Radar Topography Mission (SRTM) DEMand its derived slope, aspect and curvature.
The LUCAS topsoil database was used to map soil properties at continental scale over the geographical extent of Europe. Several soil properties were predicted using hybrid approaches like regression kriging. For those datasets, we predicted topsoil texture and related derived physical properties. Regression models were fitted using, along other variables, remotely sensed data coming from the MODIS sensor. The high temporal resolution of MODIS allowed detecting changes in the vegetative response due to soil properties, which can then be used to map soil features distribution.We will also discuss the prediction of intrinsically collinear variables like soil texture which required the use of models capable of dealing with multivariate constrained dependent variables like Multivariate Adaptive Regression Splines (MARS). Cross validation of the fitted models proved that the LUCAS dataset constitutes a good sample for mapping purposes leading to cross-validation R2 between 0.47 and 0.50 for soil texture and normalized errors between 4 and 10%.
Ballabio C., Panagos P., Montanarella L. Mapping topsoil physical properties at European scale using the LUCAS database (2016) Geoderma, 261 , pp. 110-123.