The presence of seasonal snow cover in the cold season can significantly affect the thermal conditions of the ground. Understanding the change of the snow–soil interface temperature (TSS) and its environmental impact factors is essential for predicting subnivean species changes and carbon balance in future climatic conditions. An improved Snow Thermal Model (SNTHERM) is employed to quantify TSS in farmland of Northeast China (NEC) in a 39-year period (1979–2018) firstly. This study also explored the variation tendency of TSS and its main influencing factors on grid scale. The result shows that annual average TSS and the difference between TSS and air temperature (TDSSA) increased rapidly between 1979 and 2018 in the farmland of NEC, and we used the Mann–Kendall test to further verify the increasing trends of TSS and TDSSA on aggregated farmland of NEC. The correlation analysis showed that mean snow depth (MSD) is the most pivotal control factor in 95% of pixels and TDSSA increases as MSD increases. Snow depth can better predict the change of TSS in deep–snow regions than average winter temperature (TSA). The results of this study are of great significance for understanding the impact of snow cover on the energy exchange between the ground and the atmosphere in the cold climate.
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