Exploratory factor analysis-based co-kriging method for spatial interpolation of multi-layered soil particle-size fractions and texture

Precision mapping of soil texture is critical for hydrological, ecological, environmental, and agricultural modeling and field management. However, the mapping precision is generally restricted by the limited number of soil sampling and insufficient use of available information in spatial interpolation. To map layered soil texture with higher precision, we propose an additive log-ratio (ALR) transformation and exploratory factor analysis (EFA)-based co-kriging (CK) method (ALR-EFA-CK) to study the spatial variability of multi-layered soil particle-size fractions and soil texture. In this method, the ALR transformation is used to reduce the closure effect of soil particle-size fractions as compositional data that are characterized by non-negativity and a constant sum of 100%, and EFA is used to extract common factors from variables related to soil texture that are further used as auxiliary variables of CK. Six interpolation methods, ordinary kriging (OK), traditional CK (CC-CK), and EFA-CK for both the original and ALR transformed data, were evaluated in a case study with data collected at seven soil layers of 108 sampling points in the middle reach of Heihe River basin in Northwest China. CC-CK is superior to OK by including auxiliary data in interpolation, EFA-CK is more effective in improving the interpolation precision by taking full advantages of auxiliary information, and ALR transformation can improve the interpolation precision effectively for soil particle-size fractions as compositional data. Therefore, the proposed ALR-EFA-CK method is beneficial in improving the interpolation precision and recommended to interpolate multi-layered soil texture.