Mathematical Geosciences

, Volume 46, Issue 4, pp 429–443 | Cite as

Mapping Soil Particle Size Fractions Using Compositional Kriging, Cokriging and Additive Log-ratio Cokriging in Two Case Studies

  • Xiao-Lin Sun
  • Yun-Jin Wu
  • Hui-Li Wang
  • Yu-Guo Zhao
  • Gan-Lin ZhangEmail author


Information on the spatial distribution of soil particle-size fractions (psf) is required for a wide range of applications. Geostatistics is often used to map spatial distribution from point observations; however, for compositional data such as soil psf, conventional multivariate geostatistics are not optimal. Several solutions have been proposed, including compositional kriging and transformation to a composition followed by cokriging. These have been shown to perform differently in different situations, so that there is no procedure to choose an optimal method. To address this, two case studies of soil psf mapping were carried out using compositional kriging, log-ratio cokriging, cokriging, and additive log-ratio cokriging; and the performance of Mahalanobis distance as a criterion for choosing an optimal mapping method was tested. All methods generated very similar results. However, the compositional kriging and cokriging results were slightly more similar to each other than to the other pair, as were log-ratio cokriging and additive log-ratio cokriging. The similar results of the two methods within each pair were due to similarities of the methods themselves, for example, the same variogram models and prediction techniques, and the similar results between the two pairs were due to the mathematical relationship between original and log-ratio transformed data. Mahalanobis distance did not prove to be a good indicator for selecting an optimal method to map soil psf.


Compositional data Geostatistics Data transformation Soil mapping 



This study was financially supported by the International Science and Technology Cooperation Project of China (Grant No. 2010DFB24140), Natural Science Foundation of China (41201216) and State Key Laboratory of Soil and Sustainable Agriculture (Y212000004). The authors are grateful to Dr. Raimon Tolosana-Delgado for his patient explains on and contributive reason to results of this study, and an anonymous US English editor.


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Copyright information

© International Association for Mathematical Geosciences 2014

Authors and Affiliations

  • Xiao-Lin Sun
    • 1
    • 2
  • Yun-Jin Wu
    • 3
  • Hui-Li Wang
    • 4
  • Yu-Guo Zhao
    • 1
  • Gan-Lin Zhang
    • 1
    Email author
  1. 1.State Key Laboratory of Soil and Sustainable AgricultureInstitute of Soil Science, Chinese Academy of SciencesNanjingChina
  2. 2.School of Geography and PlanningSun Yat-sen UniversityGuangzhouChina
  3. 3.Nanjing Institute of Environment SciencesMinistry of Environment ProtectionNanjingChina
  4. 4.Institute of Forest Soil and FertilizerGuangxi Academy of Forestry SciencesNanningChina

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