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Detecting, correcting and interpreting the biases of measured soil profile data: A case study in the Cap Bon Region (Tunisia)

Ciampalini, Rossano, Lagacherie, Philippe, Gomez, Cecile, Grünberger, Olivier, Hamrouni, Mohamed Hédi, Mekki, Insaf and Richard, Antoine 2013. Detecting, correcting and interpreting the biases of measured soil profile data: A case study in the Cap Bon Region (Tunisia). Geoderma 192 , pp. 68-76. 10.1016/j.geoderma.2012.07.022

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Abstract

The spatial sets of soil profiles that have been collected for these past 70 years over the world constitute a major source of soil information that are indispensable for operational applications of Digital Soil Mapping. However, significant biases between soil profile datasets issued from different soil surveys could occur because of differences in survey methods (field data collection, laboratory analysis, etc.) or in sampling dates. A pre-processing is therefore needed to detect and remove these biases and then obtain adequate inputs for digital soil-mapping models. Such a pre-processing of legacy soil profile datasets is proposed in this study. The procedure is applied to different sets of geo-referenced legacy soil profiles available in the Cap Bon Region (Northern Tunisia) and use a “reference” spatial sampling of soil surface data that fits with modern standards of soil analysis and was recently collected. The general approach includes three steps: i) define the comparison area (i.e. the intersection of the spatial samplings), ii) compare the distributions of soil profiles properties with the references using a conditional stochastic simulation algorithm and decide whether they are different iii) if needed, apply a correction algorithm to remove the detected biases. Various implementations of this approach were undertaken and tested on theoretical and real soil sampling.

Item Type: Article
Status: Published
Schools: Earth and Ocean Sciences
Publisher: Elsevier
ISSN: 0016-7061
Last Modified: 19 Mar 2016 23:50
URI: http://orca.cf.ac.uk/id/eprint/67777

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