Detection of Soil Moisture Variations Using GPS and GLONASS SNR Data for Elevation Angles Ranging From 2° to 70°

Roussel, Nicolas; Frappart, Frédéric; Ramillien, Guillaume; Darrozes, José; Baup, Frédéric; Lestarquit, Laurent; Ha, Minh Cuong

doi:10.1109/jstars.2016.2537847

Cited by 62 publications

(50 citation statements)

References 24 publications

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“…They were recorded in [21] and are shown in Figure 3. Then, soil moisture can be calculated using Equations (6) and (9).…”

Section: Simulation Results: Reconstruction and Retrievalmentioning

confidence: 99%

“…Additionally, the model validation was based on the simulated SNR. Among these factors, the antenna gain pattern used is shown in Figure 3, which is a measurement of the RHCP component of one actual antenna [21]. An eighth-order Fourier fitting was used to smooth the gain pattern.…”

Section: Simulationsmentioning

confidence: 99%

“…Finally, Hallikainen's well-known empirical dielectric model [17] was used to simulate the soil moisture's effect on reflected signal through relative permittivity ε r . Under the assumption that the soil type is silt clay that contains 18% sand (S) and 41% clay (C) in terms of textural components (measured by weight) [21] and has zero conductivity, the empirical model can be expressed as follows:…”

Section: Simulationsmentioning

confidence: 99%

“…To validate the simulation results, GPS L1 data collected at Lamasquère, France (43 • 29 14.45 N; 1 • 13 44.11 E), were processed, covering about 40 days from 5th of February 2014 to 15th of March 2014 [21]. The site was a soya field equipped with a Leica GR25 receiver and an AR10 antenna.…”

Section: Experimental Data Validationmentioning

confidence: 99%

“…The soil temperature variation was between 5 • C and 16 • C. Additionally, the ground was nearly bare in the entire experiment period. Other site information is thoroughly recorded in [21], so its description is omitted here. Since the proposed SNR model tries to reconstruct the reflected signal, only the low-elevation-angle (2 • −30 • ) SNR with strong reflection was processed.…”

Section: Experimental Data Validationmentioning

confidence: 99%

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A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data

et al. 2018

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Abstract:The Global Navigation Satellite System-Interferometry and Reflectometry (GNSS-IR) technique on soil moisture remote sensing was studied. A semi-empirical Signal-to-Noise Ratio (SNR) model was proposed as a curve-fitting model for SNR data routinely collected by a GNSS receiver. This model aims at reconstructing the direct and reflected signal from SNR data and at the same time extracting frequency and phase information that is affected by soil moisture as proposed by K. M. Larson et al. This is achieved empirically through approximating the direct and reflected signal by a second-order and fourth-order polynomial, respectively, based on the well-established SNR model. Compared with other models (K. M. Larson et al., T. Yang et al.), this model can improve the Quality of Fit (QoF) with little prior knowledge needed and can allow soil permittivity to be estimated from the reconstructed signals. In developing this model, we showed how noise affects the receiver SNR estimation and thus the model performance through simulations under the bare soil assumption. Results showed that the reconstructed signals with a grazing angle of 5 • -15 • were better for soil moisture retrieval. The QoF was improved by around 45%, which resulted in better estimation of the frequency and phase information. However, we found that the improvement on phase estimation could be neglected. Experimental data collected at Lamasquère, France, were also used to validate the proposed model. The results were compared with the simulation and previous works. It was found that the model could ensure good fitting quality even in the case of irregular SNR variation. Additionally, the soil moisture calculated from the reconstructed signals was about 15% closer in relation to the ground truth measurements. A deeper insight into the Larson model and the proposed model was given at this stage, which formed a possible explanation of this fact. Furthermore, frequency and phase information extracted using this model were also studied for their capability to monitor soil moisture variation. Finally, phenomena such as retrieval ambiguity and error sensitivity were stated and discussed.

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“…They were recorded in [21] and are shown in Figure 3. Then, soil moisture can be calculated using Equations (6) and (9).…”

Section: Simulation Results: Reconstruction and Retrievalmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

Section: Experimental Data Validationmentioning

confidence: 99%

Section: Experimental Data Validationmentioning

confidence: 99%

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A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data

et al. 2018

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Using the signal-to-noise ratio of GPS records to detect motion of structures

Peppa

Psimoulis

Meng

2017

Struct Control Health Monit

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SUMMARYAlthough major breakthroughs have been achieved during the last decades in the use of GPS technology on Structural Health Monitoring, the mitigation of the biases and errors impeding its positioning accuracy remains a challenge. This paper tests an alternative approach that can increase the reliability of the GPS system in structural monitoring by using the spectral content of the signal-to-noise ratio (SNR) of GPS signals to detect frequencies of antenna vibrations. This approach suggests the potential of using SNR data analysis as a supplement to low-quality positioning solution or as a near real-time alert of excessive vibration proceeding the position solution calculation. Experiments, involving a GPS antenna subjected to vertical vibrations of 0.4-cm to 4.5-cm amplitude at a range of frequencies between 0.007 Hz and 1 Hz, examine the dynamic multipath induced SNR response corresponding to the antenna motion. Synchronised fluctuations in the SNR time series were observed to reflect the antenna motion and their spectral content to include the frequencies of motion. SNR records from the GPS monitoring of the Wilford suspension bridge were used to validate the SNR sensitivity to controlled vibrations of the bridge deck. The natural frequency of 1.64 Hz was extracted from SNR measurements using spectral analysis on a 6-mm amplitude vibration, and the frequency of the semi-static displacement (∼ 0.02 Hz) was revealed in the SNR records permitting, after appropriate filtering, the estimation of a few millimetre semi-static displacement from the GPS time series without the need for any other sensor.

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Cross-polarization Correction for Soil Moisture Retrieval Using GNSS SNR Data

Han

Yang

Zhang

et al. 2022

International Association of Geodesy Symposia

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The Global Navigation Satellite System – Interferometric Reflectometry (GNSS-IR) technique utilizes the GNSS Signal-to-Noise Ratio (SNR) data to retrieve soil moisture. In this study, the physical SNR model incorporating antenna’s cross-polarization component is re-derived to a more intuitive and straightforward degree, which is based on the work of V. U. Zavorotny et al. 2009. Then this model is used in combination with the signal reconstruction method we proposed before to correct the cross-polarization effect when retrieving soil moisture. Both simulation and experiment data were used for validation purpose. The results showed that the mean retrieval error could be reduced by about 0.16cm3⋅cm−3 after cross-polarization correction.

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Detection of Soil Moisture Variations Using GPS and GLONASS SNR Data for Elevation Angles Ranging From 2° to 70°

Cited by 62 publications

References 24 publications

A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data

A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data

Using the signal-to-noise ratio of GPS records to detect motion of structures

Cross-polarization Correction for Soil Moisture Retrieval Using GNSS SNR Data

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