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Application of the BME approach to soil texture mapping

  • D. D'Or
  • P. Bogaert
  • G. Christakos

Abstract.

In order to derive accurate space/time maps of soil properties, soil scientists need tools that combine the usually scarce hard data sets with the more easily accessible soft data sets. In the field of modern geostatistics, the Bayesian maximum entropy (BME) approach provides new and powerful means for incorporating various forms of physical knowledge (including hard and soft data, soil classification charts, land cover data from satellite pictures, and digital elevation models) into the space/time mapping process. BME produces the complete probability distribution at each estimation point, thus allowing the calculation of elaborate statistics (even when the distribution is not Gaussian). It also offers a more rigorous and systematic method than kriging for integrating uncertain information into space/time mapping. In this work, BME is used to estimate the three textural fractions involved in a texture map. The first case study focuses on the estimation of the clay fraction, whereas the second one considers the three textural fractions (sand, silt and clay) simultaneously. The BME maps obtained are informative (important soil characteristics are identified, natural variations are well reproduced, etc.). Furthermore, in both case studies, the estimates obtained by BME were more accurate than the simple kriging (SK) estimates, thus offering a better picture of soil reality. In the multivariate case, classification error rate analysis in terms of BME performs considerably better than in terms of kriging. Analysis in terms of BME can offer valuable information to be used in sampling design, in optimizing the hard to soft data ratio, etc.

Key words: Bayesian maximum entropy geostatistics kriging soil science soil texture. 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • D. D'Or
    • 1
  • P. Bogaert
    • 1
  • G. Christakos
    • 2
  1. 1.Unité de Biométrie, Université Catholique de Louvain, Place Croix du Sud, 2 bte 16, B-1348 Louvain-la-Neuve, BelgiumBE
  2. 2.Environmental Modelling Program, Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, 111 Rosenau Hall, CB#7400, NC, USAUS

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