A spatial-homogeneity-based interpolation algorithm for soil properties

Estimation of soil properties in an un-sampled location is generally calculated with observation values of a number of neighbors around the un-sampled location to reduce the computational complexity. The more similarity there is between the neighbors, the better prediction at the un-sampled location. In this paper, we proposed a spatial homogeneity-based interpolation algorithm, which finds more similar neighbors close to an unmeasured location to improve estimation performance. The algorithm imposes a constraint on interpolation process, which the estimation of soil properties at unknown locations is processed in the context of spatial homogeneity. The experimental results on real data of 196 soil Cu illustrates that the performance of the algorithm is more reliable than OK.

[1]  Alex B. McBratney,et al.  Spatial prediction of soil properties from landform attributes derived from a digital elevation model , 1994 .

[2]  Pierre Goovaerts,et al.  Geostatistical modelling of uncertainty in soil science , 2001 .

[3]  A-Xing Zhu,et al.  Soil Mapping Using GIS, Expert Knowledge, and Fuzzy Logic , 2001 .

[4]  P. Goovaerts.  Geostatistics in soil science: state-of-the-art and perspectives , 1999 .

[5]  B. Minasny,et al.  Kriging method evaluation for assessing the spatial distribution of urban soil lead contamination. , 2002, Journal of environmental quality.

[6]  Wenjiang Huang,et al.  Grouping objects in multi-band images using an improved eigenvector-based algorithm , 2010, Math. Comput. Model..

[7]  Budiman Minasny,et al.  Analysis and prediction of soil properties using local regression-kriging , 2012 .

[8]  B. Hudson,et al.  The Soil Survey as Paradigm-based Science , 1992 .

[9]  J. Franklin,et al.  The elements of statistical learning: data mining, inference and prediction , 2005 .

[10]  A. Stewart Fotheringham,et al.  Trends in quantitative methods I: stressing the local , 1997 .

[11]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[12]  A-Xing Zhu,et al.  A case-based reasoning approach to fuzzy soil mapping , 2002 .

[13]  Budiman Minasny,et al.  On digital soil mapping , 2003 .

[14]  Eamonn Grennan,et al.  Statistical analyses of geochemical variables in soils of Ireland , 2008 .

[15]  Haibo Zhang,et al.  Statistical and geoestatistical characterization of heavy metal concentrations in a contaminated area taking into account soil map units , 2008 .

[16]  A. Bellanca,et al.  Influence of inheritance and pedogenesis on heavy metal distribution in soils of Sicily, Italy , 2000 .

[17]  Gerard B. M. Heuvelink,et al.  About regression-kriging: From equations to case studies , 2007, Comput. Geosci..

[18]  Dennis L. Corwin,et al.  Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests , 2008 .

[19]  A. Zhu,et al.  Derivation of Soil Properties Using a Soil Land Inference Model (SoLIM) , 1997 .

[20]  Yu-Pin Lin,et al.  Simulated annealing and kriging method for identifying the spatial patterns and variability of soil heavy metal , 2000 .

[21]  Pierre Goovaerts,et al.  Fine-resolution mapping of soil organic carbon based on multivariate secondary data , 2006 .

[22]  Luca Montanarella,et al.  Digital soil assessments: Beyond DSM , 2007 .