  Soil temperature is one of the most significant parameters that plays a
crucial role in glacier energy, dynamics of mass balance, processes of surface
hydrological, coaction of glacier-atmosphere, nutrient cycling, ecological
stability, the management of soil, water, and field crop. In this work, we
introduce a novel approach using transformer models for the purpose of
forecasting soil temperature prediction. To the best of our knowledge, the
usage of transformer models in this work is the very first attempt to predict
soil temperature. Experiments are carried out using six different FLUXNET
stations by modeling them with five different transformer models, namely,
Vanilla Transformer, Informer, Autoformer, Reformer, and ETSformer. To
demonstrate the effectiveness of the proposed model, experiment results are
compared with both deep learning approaches and literature studies. Experiment
results show that the utilization of transformer models ensures a significant
contribution to the literature, thence determining the new state-of-the-art.
