ORIGINAL PAPER Predicting moisture content of soil from thermal properties using artificial neural network Oluseun Adetola Sanuade 1,2 & Peter Adetokunbo 3 & Michael Adeyinka Oladunjoye 4 & Abayomi Adesola Olaojo 5 Received: 16 November 2016 /Accepted: 11 September 2018 # Saudi Society for Geosciences 2018 Abstract Monitoring of soil moisture contents is an important practice for irrigation water management. The benefit of periodic soil water content data includes improved irrigation scheduling in order to optimize water usage for improved crop productivity. However, the in situ equipment for measuring soil water contents have high maintenance and operation cost and are highly affected by neighboring soil conditions, and some have overwhelming calibration and data interpretation, whereas the common standard laboratory procedure requires much effort and can be time-consuming for large dataset. The objective of this study is to evaluate the applicability of artificial neural network (ANN) to predict moisture content of soil using available or measured thermal properties (thermal conductivity, thermal diffusivity, specific heat, and temperature) of soil. We used both multilayered perception (MLP) and radial basis function (RBF) types of ANN. The study area is a farmland situated within the premises of the University of Ibadan campus. Thermal properties were measured with KD2 Pro at 42 points along seven transects. Soil samples were also collected at these points to determine their moisture contents in the laboratory. ANN analysis carried out effectively predicted the soil moisture content with very low root-mean-square error (RMSE) and high correlation coefficient (R) of approximately 0.9 for the two methods evaluated. The overall results suggest that ANN can be incorporated to predict the moisture content of soil in this area where thermal properties are known. Keywords Thermal properties . Moisture content . Artificial neural network Introduction Sustainable agricultural system requires the knowledge of the moisture content of soil, which is a major determining factor to make informed decision about irrigation scheduling and water management for increased yield, crop quality improve- ment, water conservation, and cost saving (Saxton et al. 1986; Zhang et al. 2002; Farooq et al. 2009). Therefore, there is a need to keep track of available water since plants begin to experience water stress below the wilting point, which is det- rimental for overall soil condition and plant health. Monitoring of soil moisture content is however challenging since the laboratory procedure could be time-consuming with large dataset and may not be cost-effective, while the in situ testing equipment generally have a number of issues, for in- stance, tensiometer has maintenance issues, and capacitance sensor and neuron probe are expensive and highly affected by neighboring soil conditions. The objective of this study is therefore to evaluate the applicability of artificial neural net- work (ANN) as an alternative technique to estimate soil mois- ture content giving its thermal properties, namely, thermal conductivity, thermal diffusivity, specific heat, and tempera- ture, which are easily accessible from remote sensing data and/or can be easily measured with portable equipment. Thermal properties play a significant influence on water retention capacity of soil, and several works have proposed regression relationships between thermal properties and soil moisture content (Salomone et al. 1984; Ghuman and Lal * Oluseun Adetola Sanuade sheunsky@gmail.com 1 Geosciences Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia 2 Department of Geophysics, Federal University Oye-Ekiti, Oye, Ekiti State, Nigeria 3 Department of Geology, State University of New York at Buffalo, New York, USA 4 Department of Geology, University of Ibadan, Ibadan, Nigeria 5 Department of Earth Sciences, Ajayi Crowther University, Oyo, Nigeria Arabian Journal of Geosciences (2018) 11:566 https://doi.org/10.1007/s12517-018-3917-4