Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools

  • László Pásztor Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Budapest, Hungary
  • Annamária Laborczi Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Budapest, Hungary
  • Katalin Takács Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Budapest, Hungary
  • Gábor Szatmári Department of Physical Geography and Geoinformatics, University of Szeged, Hungary
  • Endre Dobos Department of Physical Geography and Environmental Sciences, University of Miskolc, Hungary
  • Gábor Illés Forest Research Institute, National Agricultural Research and Innovation Centre, Sárvár, Hungary
  • Zsófia Bakacsi Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Budapest, Hungary
  • József Szabó Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Budapest, Hungary
Keywords: classification and regression trees, digital soil mapping, regression kriging, spatial soil information


Due to former soil surveys and mapping activities significant amount of soil information has accumulated in Hungary. Present soil data requirements are mainly fulfilled with these available datasets either by their direct usage or after certain specific and generally fortuitous, thematic and/or spatial inference. Due to the more and more frequently emerging discrepancies between the available and the expected data, there might be notable imperfection as for the accuracy and reliability of the delivered products. With a recently started project we would like to significantly extend the potential, how soil information requirements could be satisfied in Hungary. We started to compile digital soil maps, which fulfil optimally the national and international demands from points of view of thematic, spatial and temporal accuracy. In addition to the auxiliary, spatial data themes related to soil forming factors and/or to indicative environmental elements we heavily lean on the various national soil databases. The set of the applied digital soil mapping techniques is gradually broadened incorporating and eventually integrating geostatistical, data mining and GIS tools. Regression kriging has been used for the spatial inference of certain quantitative data, like particle size distribution components, rootable depth and organic matter content. Classification and regression trees were applied for the understanding of the soil-landscape models involved in existing soil maps, and for the post-formalization of survey/compilation rules. The relationships identified and expressed in decision rules made the compilation of spatially refined category-type soil maps (like genetic soil type and soil productivity maps) possible with the aid of high resolution environmental auxiliary variables. In our paper, we give a short introduction to soil mapping and information management concentrating on the driving forces for the renewal of soil spatial data infrastructure provided by the framework of Digital Soil Mapping. The first results of (Digital, Optimized, Soil Related Maps and Information in Hungary) project are presented in the form of brand new national and regional soil maps.


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How to Cite
PásztorL., LaborcziA., TakácsK., SzatmáriG., DobosE., IllésG., BakacsiZ., & SzabóJ. (2015). Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools. Hungarian Geographical Bulletin, 64(1), 49-64.