Sequential indicator simulation and indicator kriging estimation of 3-dimensional soil textures
Y. He A B , D. Chen B , B. G. Li A , Y. F. Huang A , K. L. Hu A C , Y. Li B and I. R. Willett BA Department of Soil and Water Sciences, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China.
B School of Resource Management and Geography, Melbourne School of Land and Environment, The University of Melbourne, Vic. 3010, Australia.
C Corresponding author. Email: hukel@cau.edu.cn
Australian Journal of Soil Research 47(6) 622-631 https://doi.org/10.1071/SR08218
Submitted: 29 September 2008 Accepted: 6 May 2009 Published: 30 September 2009
Abstract
The complex distribution characteristics of soil textures at a large or regional scale are difficult to understand with the current state of knowledge and limited soil profile data. In this study, an indicator variogram was used to describe the spatial structural characteristics of soil textures of 139 soil profiles. The profiles were 2 m deep with sampling intervals of 0.05 m, from an area of 15 km2 in the North China Plain. The ratios of nugget-to-sill values (SH) of experimental variograms of the soil profiles in the vertical direction were equal to 0, showing strong spatial auto-correlation. In contrast, SH ratios of 0.48–0.81 in the horizontal direction, with sampling distances of ~300 m, showed weaker spatial auto-correlation. Sequential indicator simulation (SIS) and indicator kriging (IK) methods were then used to simulate and estimate the 3D spatial distribution of soil textures. The outcomes of the 2 methods were evaluated by the reproduction of the histogram and variogram, and by mean absolute error of predictions. Simulated results conducted on dense and sparse datasets showed that when denser sample data are used, complex patterns of soil textures can be captured and simulated realisations can reproduce variograms with reasonable fluctuations. When data are sparse, a general pattern of major soil textures still can be captured, with minor textures being poorly simulated or estimated. The results also showed that when data are sufficient, the reproduction of the histogram and variogram by SIS was significantly better than by the IK method for the predominant texture (clay). However, when data are sparse, there is little difference between the 2 methods.
Additional keywords: soil textures, spatial variability, 3D, SIS and IK.
Acknowledgments
We thank Dr Weidong Li (Kent State University) for providing the data used in this paper. We also thank Dr Jianbing Wu (Stanford University) and Dr Helen Suter for their constructive comments. We appreciate funding support of the National Key Basic Research Special Funds (2009CB118607), ACIAR project (LWR/2003/039), the National Key Technologies R&D Program (2006BDA10A06), and by the Program for New Century Excellent Talents in University (NCET-07-0809).
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