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, accounting for the scale invariance and relative scale of these data. 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.

在这项工作中，我们提出了一种基于Aitchison几何结构的新颖的降尺度程序。该方法能够自然地考虑组成约束，即单位和和正数，从而说明这些数据的尺度不变性和相对尺度。我们表明，该方法可用于块顺序高斯仿真框架中，以评估缩减量的可变性。最后，为了验证该方法，我们首先在理想的情况下对其进行测试，然后将其应用于更现实的案例研究中对数字土壤图的缩小比例。从SoilGrids获得了用于实际案例研究的数字土壤图，SoilGrids是一种基于最新空间预测方法的自动土壤映射系统。