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Related Experiment Video

Updated: Sep 16, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Study on soil moisture estimation using a three-frequency combination of observations integrated with robust

Yintao Liu¹, Chao Ren², Hongjuan Shao³

¹College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, China.

Scientific Reports

|July 5, 2025

Summary

This study presents new methods for soil moisture estimation using Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR). These techniques improve accuracy by utilizing multipath errors and correcting data, leading to better soil moisture predictions.

Keywords:

GNSS-IR Machine learning Robust estimate Soil moisture Three-frequency carrier phase combination Three-frequency pseudorange combination

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Area of Science:

Geodesy and Geophysics
Remote Sensing
Environmental Science

Background:

Soil moisture is a critical variable for agriculture, hydrology, and climate.
Traditional GNSS-IR methods for soil moisture estimation face challenges with signal separation and accuracy.
Advanced signal processing techniques are needed to overcome limitations in current GNSS-IR soil moisture retrieval.

Purpose of the Study:

To introduce and validate two novel methods, Three-frequency pseudorange combination (TFPC) and Three-frequency carrier phase combination (TFCPC), for enhanced soil moisture estimation using GNSS-IR.
To improve the accuracy of soil moisture estimation by leveraging carrier phase and pseudorange multipath errors.
To eliminate the influence of geometrical factors and atmospheric delays on soil moisture estimates.

Main Methods:

Developed TFPC and TFCPC methods to utilize GNSS signal multipath errors for soil moisture estimation.
Applied Minimum Covariance Determinant (MCD) and Moving Average Filter (MAF) for outlier detection and correction in delay phases.
Utilized data from the Plate Boundary Observatory (PBO) H2O project for validation.

Main Results:

TFPC and TFCPC methods effectively eliminated geometrical and atmospheric impacts.
Outlier detection and correction using MCD and MAF significantly improved data quality.
Combined corrected delay phases from multiple satellites showed improved correlations with actual soil moisture.
TFPC achieved correlation coefficients of 0.82 (MLR) and 0.87 (RBFNN).
TFCPC achieved higher correlation coefficients of 0.85 (MLR) and 0.91 (RBFNN).

Conclusions:

The developed TFPC and TFCPC methods offer a significant advancement in high-precision soil moisture estimation.
These methods provide a more accurate and reliable approach to soil moisture monitoring using GNSS-IR.
The findings have substantial implications for precision agriculture, weather forecasting, and environmental monitoring.