Predicting multiple soil thermal properties using statistical-physical models and data from SoilGrids

A robust assessment of thermal properties is essential for understanding surface energy partitioning and heat transfer in soil. In this study, we proposed a novel approach for predicting thermal conductivity, heat capacity, thermal diffusivity, and thermal inertia using a modified statistical-physical model based on specific heat, energy conservation, and the configurations of mineral, organic, water, and air particles. The input data for this approach, including the contents of the main textural fractions, quartz and other minerals, organic matter, water, bulk density, particle density, and air-filled porosity at three soil water potentials of −10, −33, and −1500 kPa, were obtained from the SoilGrids database. The approach's was tested across soils with varying textures at nine locations in Eastern Poland. The results showed that, irrespective of soil texture and depth (up to 1.5 m), the values of thermal conductivity, heat capacity, thermal diffusivity, and thermal inertia were consistently lower at a soil water potential of −1500 kPa than at the other two potentials. Higher sand/quartz content was associated with lower soil thermal conductivity and greater variations in thermal inertia in the soil profile at a potential of −1500 kPa, as well as greater variations in soil thermal diffusivity at all soil water potentials. The proposed approach is a proof of concept for estimating the four soil thermal properties and confirms that the assumptions regarding the thermal properties were well-founded. It enables rapid and efficient forecasting of soil thermal properties at the regional scale using input data from the global SoilGrids database.