# Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network
## 1 Introduction  
Among the many soil factors, soil temperature (ST) is the most important, affecting the processes and functions of almost all ecosystems [1], such as soil infiltration rates [2], soil respiration [3], soil organic matter accumulation and degradation [4], soil chemical and physical reactions [5], land surface hydrological processes, and land atmosphere interactions [6]. ST is extremely important to the growth of crops [7], affecting their germination, root growth, and the absorption of nutrients [8]. [PERSON] [9] reported that an increase in ST will reduce the growth cycle of crops and significantly increase the yield. The ST is also an essential important parameter in a geothermal exploration [10], groundheat exchanger applications [11], thermal energy storage [12], ground source heat pump (GSHP) systems [13], and other geothermal development and utilization applications. The ST measurement system represents one of the most accurate and direct methods of ST acquisition in the world. However, ST measurements are costly and time-consuming [14], and it is not easy to obtain different large-scale depths of ST in real-time. A machine learning method is an effective way to estimate the ST. This type of method predicts the ST at different depths by establishing a nonlinear relationship between the input and output data. The input parameters are usually easily obtained meteorological parameters involving a simple calculation process and offering a high prediction accuracy. [PERSON] [15] adopted four machine learning algorithms for predicting the half-hourly ST at four different depths. The results show that the selected machine learning method performs well for predicting the half-hourly ST at all depths. [PERSON] [16] designed a daily ST prediction model using a support vector machine (SVM) algorithm, which performs well in the daily ST prediction tasks of 16 observation stations distributed throughout the United States. Artificial neural network (ANN) are frequently used in ST prediction and have achieved good results. [PERSON] [17] developed models for 1 day ahead ST predictions based on an ANN. Its performance is in terms of the Nash-Sutcliffe co-efficient of efficiency values of \(>\)0.94 and a correlation co-efficient of \(>\)0.96. [PERSON] [18] compared the capability of linear repression, nonlinear repression, and an ANN at different depths, and found that the accuracy of an ANN is better than that of the other two methods. [PERSON] [19] used an ANN to predict the monthly average ST at a depth of 10 cm. Compared with multiple linear regression, the developed ANN achieves a good performance and robustness. In [20], gene expression programming (GEP), an ANN, and an adaptive neuro-fuzzy inference system (ANFIS) were used to estimate the monthly mean ST. The comparison shows that the ANN and ANFIS achieve a better performance than the GEP model. [PERSON] [21] estimated the monthly ST using a multi-layer perceptron (MLP), radial basis neural network (RBNN), generated regression neural network (GRNN), and multiple linear regression (MLR) at different depths. The results show that the RBNN algorithm achieves the highest prediction accuracy at depths of 5 and 10 cm, and an MLR and a GRNN are better at ST prediction at depths of 50 and 100 cm. Although many machine learning models have been used in the prediction of ST and good results have been obtained, there are still some inherent problems. First, in a large number of studies, the input parameters of the model were selected through a correlation analysis, and the ST was considered as the output of the model. However, the correlation between meteorological parameters and the ST is not the same owing to the difference in climate at different sites. As a result, a model that considers a single correlation between meteorological parameters and the ST across different sites will suffer from obvious limitations.