Estimation of basic soil properties in alpine areas using a Bayesian Regression Model

The understanding of soil characteristics in alpine regions is crucial for the comprehension of infiltration or runoff dynamics. However, the estimation of soil properties in these areas poses significant challenges, which can be attributed to the complexity of the terrain, the variability of microclimates, or the limited accessibility. The existing database on soil properties in these regions is currently insufficient, so there is an urgent need for alternative approaches to reliably predict the basic soil properties.

We employed Bayesian Regression Models (BRMS) to predict basic soil properties, including soil texture and organic carbon content. This method combines environmental covariates derived from remote sensing data and digital elevation models (DEMs) with prior knowledge about the various alpine soil types and their associated properties in order to enhance the accuracy of predictions in these heterogeneous landscapes. This approach accounts for spatial variability and uncertainty, producing robust estimations of key soil properties, even with limited field observations. The results demonstrate significant spatial variability in soil properties, influenced by factors such as altitude, slope, and vegetation cover.

This study combines traditional statistical approaches with domain expertise, thereby facilitating enhanced soil property estimation in challenging environments. The methodology provides a machine learning framework for similar applications in other remote or heterogeneous regions with limited data. It contributes to global initiatives focused on the comprehensive assessment of soil quality and the implementation of environmentally land management practices.