Some practical aspects of predicting texture data in digital soil mapping

Abstract

Soil texture is the most well-known composition in soil science. When separate components of the texture (sand, silt, and clay) are predicted independently in digital soil mapping (DSM), there is no guarantee that the separate estimates will sum to 100%. Log-ratio transformations before DSM modelling are alternatives to guarantee a constant sum of the estimates. Little is known about the effect of non-summing to 100% and transformations of particle-size fractions (PSFs) when DSM products were used to predict soil functional properties using pedotransfer functions (PTFs). Therefore, this study was conducted to investigate the effect of different soil texture modelling methods on the estimation of available soil water capacity (AWC) and the total amount of irrigation water (TIW) required for wheat production on a 4600 ha area in Khuzestan province, southwestern Iran. Specifically, this study aimed i) to assess the performance of random forest models (RF) to predict untransformed (UT) and transformed PSFs using environmental covariates; ii) to study the effects of three widely used log-ratio transformations including additive, centroid and isometric log-ratio transformations (alr, clr, and ilr respectively) on the estimations of AWC and TIW. A total of 150 soil samples were collected from the surface layers (0–30 cm) based on the conditioned Latin hypercube sampling (cLHS) procedure. Results indicated that, in terms of root mean square error (RMSE), RF provided similar accuracies in predicting PSFs for both untransformed and transformed data. However, transformation resulted in biased estimates. In addition, RF prediction based on untransformed data resulted in more correctly soil texture classes allocation when compared to transformed data. The spatial distribution of the sum of the predicted untransformed fractions indicated only small parts of the area conformed to the 100% sum. Almost the same accuracies for estimates of AWC were obtained when both untransformed and transformed predicted texture components were used as the inputs to PTFs. Data transformation can result in biased estimates of AWC. The findings indicated no significant difference between transformation methods in predicting AWC and TIW. The general patterns of the spatial distribution of the predicted AWCs across the whole area were the same for transformed and untransformed data (except for clr transformation).