Awais et al. (2026) Coupling dielectric physics with calibration models for soil moisture sensing: progress and perspectives

Identification

• Journal: Ain Shams Engineering Journal
• Year: 2026
• Date: 2026-01-04
• Authors: Muhammad Awais, Linze Li, Syed Muhammad Zaigham Abbas Naqvi, Wei Zhang, Junfeng Wu, Iskander Tlili, Jiandong Hu
• DOI: 10.1016/j.asej.2025.103968

Research Groups

• Department of Electrical Engineering, Henan Agricultural University, Zhengzhou, China
• Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou, China
• State Key Laboratory of Wheat and Maize Crop Science, Zhengzhou, China
• Department of Mechanical Engineering, Faculty of Engineering, Islamic University of Madinah, Madinah, Saudi Arabia

Short Summary

This review paper systematically outlines the critical necessity of soil-specific calibration for accurate soil water content (SWC) quantification using dielectric permittivity measurements, synthesizing current calibration strategies and proposing future directions involving advanced technologies. It details various calibration methodologies and dielectric models, emphasizing their advantages, limitations, and applicability across diverse soil types and environmental conditions.

Objective

• To describe the dependencies and discrepancies associated with using the dielectric constant for soil water content (SWC) measurement.
• To explain previous methodologies developed to mitigate these discrepancies, evaluating the advantages, types, and trade-offs of different dielectric models.
• To present future research directions, thereby establishing the significance of this review in enhancing the accuracy and reliability of soil moisture monitoring.

Study Configuration

• Spatial Scale: Ranges from small sample volumes (e.g., < 50 cubic centimeters for TDR) to field-scale investigations (e.g., Ground Penetrating Radar) and broader agricultural landscapes and ecosystems (e.g., remote sensing with hyperspectral imaging).
• Temporal Scale: Covers continuous monitoring capabilities (e.g., Capacitance and Resistance Method, automated TDR), dynamic processes (e.g., soil freezing/thawing), and real-time spatio-temporal monitoring facilitated by Internet of Things (IoT) and Wireless Sensor Networks (WSNs).

Methodology and Data

• Models used:
  • Empirical Calibration Models: Topp's Equation, Dobson's Model (polynomial fit), Partial Least Squares (PLS) regression.
  • Physically-based Dielectric Mixing Models: Birchak's Model, Polder-van Santen, Lichtenecker-Rother, Bruggeman, Complex Refractive Index Model (CRIM), Rayleigh Mixing Model.
  • Dielectric Relaxation Models: Debye Model, Cole-Cole Model, Two-pole Debye Model.
  • GPR Calibration Models: Non-linear inversion algorithms, Extrinsic calibration using metal balls, Velocity calibration (hyperbola fitting, migration).
• Data sources:
  • Measurement Techniques: Dielectric Spectroscopy (DES), Time Domain Reflectometry (TDR), Capacitance and Resistance Method (CRM), Frequency Domain Reflectometry (FDR), Ground Penetrating Radar (GPR).
  • Physical Measurements: Gravimetric soil sampling (for calibration), soil properties (texture, bulk density, organic matter content, salinity, temperature, pore-water electrical conductivity).
  • Emerging Technologies: Hyperspectral imaging (drones, satellites).
  • Conceptual: Electromagnetic wave propagation theory, dielectric permittivity measurements.

Main Results

• The accurate quantification of soil water content (SWC) is significantly challenged by the non-universal relationship between soil dielectric permittivity and SWC, which is highly influenced by site-specific factors like soil texture, composition, and salinity.
• Soil-specific calibration is indispensable for all dielectric-based SWC sensors to mitigate substantial errors arising from generic factory calibrations, with various methodologies available for different soil types.
• A comprehensive review of key dielectric measurement techniques (DES, TDR, CRM, FDR, GPR) highlights their distinct operational principles, typical frequency ranges (e.g., TDR: 0.5–1.5 GHz; FDR: 5 MHz–1 GHz; GPR: 10 MHz–2 GHz), advantages, limitations, and specific calibration requirements.
• Calibration models vary from simple empirical equations (e.g., Topp's equation, which may underestimate SWC in clay-rich soils) to complex physically-based dielectric mixing models (e.g., Dobson's, Birchak's, Lichtenecker-Rother, CRIM, Rayleigh, Debye, Cole-Cole), offering varying levels of accuracy and adaptability.
• Future prospects for soil moisture sensing involve the integration of artificial intelligence (AI) and machine learning (ML) for self-adjusting calibration, the deployment of dense Internet of Things (IoT) and Wireless Sensor Networks (WSNs) for real-time spatio-temporal monitoring, and the fusion of in-situ dielectric sensors with remote sensing techniques like hyperspectral imaging for a comprehensive, multi-scale understanding of soil moisture dynamics.

Contributions

• Systematically synthesizes the necessity of soil-specific calibration for accurate soil water content (SWC) measurement, addressing a critical challenge in environmental and agricultural applications.
• Provides a detailed comparative overview of various dielectric-based SWC measurement techniques (DES, TDR, CRM, FDR, GPR), outlining their principles, frequency ranges, advantages, limitations, and calibration needs.
• Reviews and categorizes diverse calibration models, from empirical to physically-based and spectroscopic approaches, explaining their underlying physics and practical applications for different soil conditions.
• Identifies and elaborates on key future research directions, emphasizing the transformative potential of integrating AI/ML, IoT/WSN, and hyperspectral imaging to enhance the accuracy, reliability, and scalability of soil moisture monitoring.
• Offers a decision framework to guide the selection of appropriate soil moisture measurement techniques and calibration approaches based on specific application scenarios and soil conditions.

Funding

• National Key Technologies R&D Program of China during the 14th Five-Year Plan period (2024YFD1700802)
• Key R&D Program Project of Henan Province (221111320700)

Citation

@article{Awais2026Coupling,
  author = {Awais, Muhammad and Li, Linze and Naqvi, Syed Muhammad Zaigham Abbas and Zhang, Wei and Wu, Junfeng and Tlili, Iskander and Hu, Jiandong},
  title = {Coupling dielectric physics with calibration models for soil moisture sensing: progress and perspectives},
  journal = {Ain Shams Engineering Journal},
  year = {2026},
  doi = {10.1016/j.asej.2025.103968},
  url = {https://doi.org/10.1016/j.asej.2025.103968}
}