A novel dowscaling procedure for compositional data in the Aitchison geometry with application to soil texture data

TL;DR

This paper introduces a new downscaling method for compositional data using Aitchison geometry, effectively handling constraints and validated through idealized and real soil map case studies.

Contribution

The novel downscaling procedure respects compositional constraints within Aitchison geometry and integrates with Gaussian simulation for variability assessment.

Findings

Method successfully downscales soil compositional data.

Validated on idealized and real-world soil maps.

Compatible with digital soil mapping systems like SoilGrids.

Abstract

In this work, we present a novel downscaling procedure for compositional quantities based on the Aitchison geometry. The method is able to naturally consider compositional constraints, i.e. unit-sum and positivity. We show that the method can be used in a block sequential Gaussian simulation framework in order to assess the variability of downscaled quantities. Finally, to validate the method, we test it first in an idealized scenario and then apply it for the downscaling of digital soil maps on a more realistic case study. The digital soil maps for the realistic case study are obtained from SoilGrids, a system for automated soil mapping based on state-of-the-art spatial predictions methods.