Geostatistical analysis of soil texture fractions on the field scale
M. Delbari, P. Afrasiab, W. Loiskandlhttps://doi.org/10.17221/9/2010-SWRCitation:Delbari M., Afrasiab P., Loiskandl W. (2011): Geostatistical analysis of soil texture fractions on the field scale. Soil & Water Res., 6: 173-189.
Geostatistical estimation methods including ordinary kriging (OK), lognormal ordinary kriging (LOK), cokriging (COK), and indicator kriging (IK) are compared for the purposes of prediction and, in particular, uncertainty assessment of the soil texture fractions, i.e. sand, silt, and clay proportions, in an erosion experimental field in Lower Austria. The soil samples were taken on 136 sites, about 30-m apart. The validation technique was cross-validation, and the comparison criteria were the mean bias error (MBE) and root mean squared error (RMSE). Statistical analysis revealed that the sand content is positively skewed, thus persuading us to use LOK for the estimation. COK was also used due to a good negative correlation seen between the texture fractions. The autocorrelation analysis showed that the soil texture fractions in the study area are strongly to moderately correlated in space. Cross-validation indicated that COK is the most accurate method for estimating the silt and clay contents; RMSE equalling to 3.17% and 1.85%, respectively. For the sand content, IK with RMSE (12%) slightly smaller than COK (RMSE = 14%) was the best estimation method. However, COK maps presented the true variability of the soil texture fractions much better than the other approaches, i.e. they achieved the smallest smoothness. Regarding the local uncertainty, the estimation variance maps produced by OK, LOK, and COK methods similarly indicated that the lowest uncertainty occurred near the data locations, and that the highest uncertainty was seen in the areas of sparse sampling. The uncertainty, however, varied much less across the study area compared to conditional variance for IK. The IK conditional variance maps showed, in contrast, some relations to the data values. The estimation uncertainty needs to be evaluated for the incorporation into the risk analysis in the soil management.Keywords:
estimation uncertainty; kriging; prediction; soil texture fractions; spatial variability