Robust variogram estimation combined with isometric log-ratio transformation for improved accuracy of soil particle-size fraction mapping

Mapping soil particle-size fractions (psf) plays an important role in regional hydrological, ecological, geological, agricultural and environmental studies. To map soil compositional data like soil psf, interpolators such as compositional kriging and the combination of log-ratio transformations with ordinary kriging or cokriging were developed. In addition, robust estimators were proposed for these interpolators to improve the variogram models. However, few studies have focused on how to choose log-ratio transformation, kriging, cokriging, or robust variogram estimation methods based on data characteristics to achieve optimal performance when mapping soil psf by comprehensive comparative analysis. Here, we selected different compositional kriging, log-ratio kriging, log-ratio cokriging and log-ratio cokriging methods combined with a robust variogram estimator to improve the accuracy of spatial predictions of soil psf when using 262 soil samples from the upper reaches of the Heihe River in China. In this study, a comprehensive comparative analysis of soil psf maps generated by using different interpolators is presented, and appropriate methods for mapping psf based on the characteristics of the available data are explored. The results show that using isometric log-ratio (ILR) transformation with different interpolators can achieve relatively better performance than the other log-ratio transformation methods. In addition, combining the interpolators with robust variogram estimators significantly improve the prediction accuracy compared with using standard estimators, which presented reasonable and smooth transitions when mapping soil psf. Combining ILR cokriging with a robust variogram estimator had the best accuracy, with the lowest root mean squared error (sand, 10.50%; silt, 11.24%; clay, 7.32%), an Aitchison's distance of 0.76, a standardized residual sum of squares of 0.70 and a relatively higher rate of correctly predicting soil texture types 90.04%. In the future, guideline for using log-ratio transformation methods with linear regression, a generalized linear model or random forest should be developed and combined with ancillary variables to improve the interpolators.