Mapping Soil Particle Size Fractions Using Compositional Kriging, Cokriging and Additive Log-ratio Cokriging in Two Case
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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 Zhang
Received: 4 August 2013 / Accepted: 23 November 2013 © International Association for Mathematical Geosciences 2014
Abstract 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
X.-L. Sun · Y.-G. Zhao · G.-L. Zhang (B) State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, 71 Beijing East Road, Nanjing 210008, China e-mail: [email protected] X.-L. Sun School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China Y.-J. Wu Nanjing Institute of Environment Sciences, Ministry of Environment Protection, Nanjing 210042, China H.-L. Wang Institute of Forest Soil and Fertilizer, Guangxi Academy of Forestry Sciences, Nanning 530002, China
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Math Geosci
log-ratio transformed data. Mahalanobis distance did not prove to be a good indicator for selecting an optimal method to map soil psf. Keywords
Compositional data · Geostatistics · Data transformation · Soil mapping
1 Introduction Gridded information on the spatial distribution of soil particle-size fractions (psf) is a common survey product. For example, the GlobalSoilMap.net (2012) specifications require prediction of coarse fragments, sand, silt and clay fractions at a series of depths. The spatial distribution of soil psf affects many physical, chemical, biological and hydrological soil properties, such as structure, cation exchange capacity, aeration and water holding capacity. Thus, this information is widely needed as a key input for many applications, especially for soil physical and chemical process mode
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