Title: Spatial prediction of soil properties at European scale using the LUCAS database as an harmonization layer
Authors: BALLABIO CRISTIANOPANAGOS PanagiotisMONTANARELLA Luca
Publisher: CRC Press
Publication Year: 2013
JRC N°: JRC89540
ISBN: 978-1-138-00119-0
URI: http://www.crcpress.com/product/isbn/9781138001190
http://publications.jrc.ec.europa.eu/repository/handle/JRC89540
Type: Articles in periodicals and books
Abstract: The Land Use and Cover Area frame Statistical survey (LUCAS) is a project, initiated by Eurostat, aimed at the collection of harmonized data about the state of land use/ land cover over the extent of European Union (EU). The survey, initiated in 2006, started with the classification, through photo-interpretation, of 106 georeferenced points placed at the nodes of a 2km grid covering EU. Among these 2105 were selected for validation and 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) DEM and its derived slope, aspect and curvature. In this study we will discuss how the LUCAS database can be used to map soil properties at continental scale over the geographical extent of Europe. Several soil properties (namely: soil texture, pH, carbon and nitrogen content) were predicted using hybrid approaches like regression kriging. 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. Cross validation of the fitted models proved that the LUCAS dataset constitutes a good sample for mapping puropses leading to regression R2 between 0.4 and 0.7 for different soil properties and normalised errors between 4 and 10%. Finally a strategy about how to use LUCAS as an harmonization layer to attune heterogeneous soil information sources is presented and discussed.
JRC Directorate:Sustainable Resources

Files in This Item:
There are no files associated with this item.


Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.