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ISPRS Int. J. Geo-Inf. 2017, 6(9), 283; doi:10.3390/ijgi6090283

Spatial Modelling and Prediction Assessment of Soil Iron Using Kriging Interpolation with pH as Auxiliary Information

1
Soil and Water Resources Institute, Hellenic Agricultural Organization (H.A.O.)—“DEMETER”, 570 01 Thessaloniki, Greece
2
Department of Cadastre, Photogrammetry and Cartography, Faculty of Engineering, Aristotle University of Thessaloniki (AUTH), 541 24 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Received: 14 July 2017 / Revised: 28 August 2017 / Accepted: 4 September 2017 / Published: 7 September 2017
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Abstract

In this study, different interpolation techniques are presented, assessed, and compared for the estimation of soil iron (Fe) contents in locations where observations were not available. Initially, 400 soil samples from the Kozani area, which is near Polifitou Lake in northern Greece, were randomly collected from 2013 to 2015 and were analysed in the laboratory to determine the soil Fe concentrations and pH. The soil Fe concentrations were examined for spatial autocorrelation, and semivariograms were used to determine whether pH and Fe exhibited spatial cross correlation. Three interpolation methods, including Ordinary Kriging, Universal Kriging, and Co-Kriging, were applied, and their results were compared with the use of two different cross-validation methods. In the current study, there was evidence of spatial cross correlation of soil Fe and pH for each year, which was subsequently used to improve the interpolation results in locations where there were no measurements. In nearly all cases, Co-Kriging, which takes advantage of the covariance between the two regionalized variables (Fe and pH), outperformed the other interpolation techniques each year. View Full-Text
Keywords: geostatistics; kriging interpolation; soil iron; pH; semivariograms geostatistics; kriging interpolation; soil iron; pH; semivariograms
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Tziachris, P.; Metaxa, E.; Papadopoulos, F.; Papadopoulou, M. Spatial Modelling and Prediction Assessment of Soil Iron Using Kriging Interpolation with pH as Auxiliary Information. ISPRS Int. J. Geo-Inf. 2017, 6, 283.

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