Soil Salinity Frequency-Dependent Prediction Model Using Electrical Conductivity Spectroscopy Measurement

TL;DR

This paper introduces a novel frequency-dependent model using electrical conductivity spectroscopy to accurately predict soil salinity, considering soil type, moisture, and porosity, with potential applications in agriculture, geology, and hydrology.

Contribution

A comprehensive frequency-dependent soil salinity prediction model based on electrical conductivity spectroscopy, improving accuracy over previous frequency-independent models.

Findings

Model accurately predicts soil salinity from ECS measurements.

Incorporates soil type, moisture, and porosity for enhanced predictions.

Outperforms previous models lacking frequency considerations.

Abstract

Soil salinity is a critical factor influencing agricultural productivity and environmental sustainability, requiring precise monitoring tools. This paper focuses on developing a frequency-dependent model to predict soil salinity based on electrical conductivity (EC) and volumetric water content (VWC). A dataset of 40 soil samples with varying levels of salinity and moisture, consisting of two soil types (sandy and clayey), was experimentally measured for EC in the frequency range of 10 to 295 MHz using electrical conductivity spectroscopy (ECS) measurement with the DAK-VNA (Dielectric Assessment Kit - Vector Network Analyzer) system. A new, more comprehensive frequency-dependent model is proposed, surpassing previous models that lacked frequency considerations. This modelling approach was conducted in stages: initially, a frequency-independent model for electrical conductivity as a function of salinity and moisture was developed. Next, a frequency-dependent model was introduced. Finally, a comparison between pure sandy soil and a sandy-clay mixture led to the final model, which also incorporates effective porosity. The results of the proposed model, comparing measured and predicted values, provide a robust approach to accurately predict soil salinity. Findings demonstrate that the model can enhance salinity prediction accuracy, extending its applicability beyond agriculture to geological and hydrological applications in real-world scenarios.