Application of multivariate adaptive regression splines (Mars) to simulate soil temperature

A new and flexible regression model, Multivariate Adaptive Regression Splines (MARS), is introduced and applied to simulate soil temperature at three depths. MARS uses a divide-and-conquer approach to automatically classify the training data into several groups. In each group, a regression line or hyperplane is generated. Compared to other intelligent computing technologies, MARS is fast, flexible, and capable of determining the important inputs to the model. The inputs to the model include the day of the year the maximum and minimum air temperatures, rainfall, and potential evapotranspiration. The outputs contain the soil temperatures at depths of 100, 500, and 1500 mm. The performance of MARS was compared to that of artificial neural networks (ANNs). The correlation coefficients of linear regression from both MARS and ANNs were always higher than 0.950. MARS also indicated that the day of the year is the input that is most significant to the output, followed by the minimum air temperature. The results demonstrate the potential of MARS to be used as a regression technology in agricultural applications.