Chapter 17 Advances in the Determination of Soil Moisture Content

Author First name, Last name, Institution

Abdel-Mohsen Onsy Mohamed
Evan K. Paleologos

Document Type

Book Chapter

Source of Publication

Fundamentals of Geoenvironmental Engineering

Publication Date



This chapter discusses some advanced methods that are used to extract information from electrical signals, and how this could be used to predict soil moisture content. A brief explanation of what is meant by signal and various signal processing techniques, either by summation of elemental signals (synthesis) or by decomposition into elemental signals (analysis), is discussed. To demonstrate the synthesis methods, pulse and sinusoid signals are applied; whereas, for decomposition analysis, both time domain and frequency domain analyses are discussed. In frequency domain analysis of signals the use of the Time Domain Reflectometry (TDR) and Fourier spectral analysis to predict soil moisture content and salt concentration is demonstrated. Unlike Fourier decomposition, which partitions signals based on harmonic frequencies by using parametric sines and cosines, eigen-decomposition analysis, separates signal components by differences in their power. These methods are applied in several case studies for the determination of soil moisture content, soil density, clay content, salt concentration, and organic fluid content. In addition, a Neuro-Fuzzy Logic method of analysis, which simply uses both neural networks and fuzzy logic, is discussed. To demonstrate the use of this method to predict soil moisture content, the changes in spectral magnitude and phase angle of the tested soil systems were considered as specific signatures for different soil conditions and were used to train Neuro-Fuzzy Logic models. This method of analysis provided a powerful design technique that combines the ability to learn from data sets, the transparent representation of knowledge acquired, and the ability to cope with uncertainties.




Transp. Res. Part C Emerg. Technol. 15 2 2007


Computer Sciences

Indexed in Scopus


Open Access