Title: Multi-scale European Soil Information System (MEUSIS): a multi-scale method to derive soil indicators
Citation: COMPUTATIONAL GEOSCIENCES vol. 15 no. 3 p. 463-475
Publisher: SPRINGER
Publication Year: 2011
JRC N°: JRC62845
ISSN: 1420-0597
URI: http://www.springerlink.com/content/q4nk170757q37v57/
DOI: 10.1007/s10596-010-9216-0
Type: Articles in periodicals and books
Abstract: The Multi-Scale Soil Information System(MEUSIS) can be a suitable framework for building a nested system of soil data that could facilitate interoperability through a common coordinate reference system, a unique grid coding database, a set of detailed and standardized metadata and an open exchangeable format. In the context of INSPIRE Directive, MEUSIS may be implemented as a system facilitating the update of existing soil information and accelerating the harmonization of various soil information systems. In environmental data like the soil one, it is common to generalize accurate data obtained at the field to coarser scales using either the pedotransfer rules or knowledge of experts or even some statistical solutions which combine single values of spatially distributed data. The most common statistical process for generalization is averaging the values within the study area. In this paper, we don¿t present a simple averaging of numerical values without any further processed information. The upscaling process is accompanied with significant statistical analysis in order to demonstrate the method suitability. The coarser resolution nested grids cells (10km x 10km) represent broad regions where the calculated soil property (e.g. Organic Carbon) can be accurately upscaled. Multi-scaled approaches are urgently required to integrate different disciplines (such as Statistics) and provide a meta-model platform to improve current mechanistic modeling frameworks, request new collected data and identify critical research questions. Past papers have described in detail the Upscaling methodology while our present approach is to demonstrate an important application of this methodology accompanied with statistical evidence.
JRC Directorate:Sustainable Resources

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