Abstract
Soil temperature (ST) plays a key role in the processes and functions of almost all ecosystems, and is also an essential parameter for various applications such as agricultural production, geothermal development, and their utilization. Although numerous machine learning models have been used in the prediction of ST, and good results have been obtained, most of the current studies have focused on daily or monthly ST predictions, while hourly ST predictions are scarce. This paper presents a novel scheme for forecasting the hourly ST using weather forecast data. The method considers the hourly ST prediction to be the superposition of two parts, namely, the daily average ST prediction and the ST amplitude (the difference between the hourly ST and the daily average ST) prediction. According to the results of correlation analysis, we selected nine meteorological parameters and combined two temporal parameters as the input vectors for predicting the daily average ST. For the task of predicting the ST amplitude, seven meteorological parameters and one temporal parameter were selected as the inputs. Two submodels were constructed using a deep bidirectional long short-term memory network (BiLSTM). For the task of hourly ST prediction at five different soil depths at 30 sites, which are located in 5 common climates in the United States, the results showed the method proposed in this paper performs best at all depths for 30 stations (100% of all) for the root mean square error (RMSE), 27 stations (90% of all) for the mean absolute error (MAE), and 30 stations (100% of all) for the coefficient of determination (R2), respectively. Moreover, the method adopted in this study displays a stronger ST prediction ability than the traditional methods under all climate types involved in the experiment, the hourly ST produced by it can be used as a driving parameter for high-resolution biogeochemical models, land surface models and hydrological models and can provide ideas for an analysis of other time series data.
Highlights
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]
The random forest (RF) model is not as good as the integrated bidirectional long short-term memory network (BiLSTM), long short-term memory network (LSTM), and BiLSTM with respect to the three indicators, root mean square error (RMSE), mean absolute error (MAE), and R2, it can be clearly seen from Figures 6–8 that favorable agreements exist between the results of the RF model and these three deep learning models, and the results at multiple sites are better than deep nerual network (DNN), which the of potential of squared using the RF
The linear regression (LR) was the worst model, producing the minimum R2, and the maximum RMSE and MAE, under all climate types involved in the experiment
Summary
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]. 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 results show that the selected machine learning method performs well for predicting the half-hourly ST at all depths. We treat the hourly ST prediction as the sum of the daily average ST prediction and ST amplitude prediction, and verify them at 30 sites located under different climates. The data-collection times and elevations of these stations are given, and are classified based on the Koppen climate classification method.
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