Soil Salinity Frequency-Dependent Prediction Model Using Permittivity Spectroscopy

Soil salinity is a critical factor impacting agriculture and the environment. This study develops a frequency-dependent model to predict soil salinity based on the dielectric permittivity and volumetric water content. A targeted soil database was created, incorporating varied salinity and moisture levels from two soil types: sandy and clay. Real and imaginary parts of permittivity were measured across a frequency range of 10–295 MHz using the dielectric assessment kit vector network analyzer system, as permittivity in this band shows the highest variation and sensitivity to moisture and salinity. After analyzing the behavior of permittivity changes and minimizing prediction errors, two separate models were proposed for the real and imaginary parts. These models are significantly more sophisticated and flexible than previous ones, designed to accept inputs that are easily measurable by commercial sensors. The comparison between model outputs and measured data demonstrates a high level of alignment, indicating that the model can improve salinity prediction accuracy and extend its applications beyond agriculture to practical uses in geology and hydrology. The average prediction error was less than 8.2% for the real part and 9.3% for the imaginary part.