Simulation of soil thermal conductivity based on different schemes: An empirical comparison of 13 models

Thermal conductivity is a key soil property widely used for agricultural production, land surface processing research, and geothermal resource development, among others. Although the rapid and accurate determination of soil thermal conductivity (λ) has been a hot topic in recent years, there is still no unified model for the different soil types of soil. Furthermore, the lack of data on thermal conductivity and soil properties leads to errors in parametric models of thermal conductivity. In order to overcome the data shortage, a comprehensive λ dataset of 2972 items was established and 10 influential parameters on thermal conductivity were identified in this study. Based on this, an empirical comparison was made between four classical parametric models and nine machine-learning models with and without an intelligent optimization algorithm was carried out. Of all the methods, the ensemble machine-learning methods perform better in λ simulations. The XGBoost model has better simulation accuracy and generalization capability. Soil moisture properties are the key parameters in performing λ simulations, while the soil texture-related properties such as bulk density and solid thermal conductivity, along with the sand content, also play an important role. The results of this study can provide basic thermal conductivity data and a parameterization scheme for referencing in land surface processing research. •A dataset of 2972 thermal conductivity entries for 270 soils was established and 10 influential factors were identified.•The XGBoost method is optimal for thermal conductivity simulations.•Soil moisture characteristics water saturation level and content are key input parameters when using machine learning for soil thermal conductivity simulations.