Abstract

The global navigation satellite system interferometric reflectometry is often used to extract information about the environment surrounding the antenna. One of the most important applications is soil moisture monitoring. This manuscript presents the main ideas and implementation decisions needed to write the Python code to transform the derived phase of the interferometric GPS waves, obtained from signal-to-noise ratio data continuously observed during a period of several weeks (or months), to volumetric water content. The main goal of the manuscript is to share the software with the scientific community to help users in the GPS-IR computation.

Highlights

  • Using the global navigation satellite system (GNSS) signalto-noise (SNR) observables to monitor soil moisture, called GNSS interferometric reflectometry (GNSS-IR), isThe primary technique is to use continuous Signal-to-noise ratio (SNR) observables for satellites tracks between 5 and 30 elevation angles and convert the SNR observed in dB-Hz units to a linear scale in volts

  • GPS Solutions (2022) 26:7 interferogram pattern can differ for rising and setting tracks of the same satellite; (2) Lomb–Scargle periodogram should be computed from the reflected SNR of each satellite track in order to check that only a clear primary wave is observed, that is, tracks with multiple peaks or low maximum average power should be discarded; (3) a good adjustment between the SNR indirect signal and the sinusoidal wave is needed to consider a track to be valid for a posteriori transformation to volumetric water content (VWC)

  • The data we provided with the software are part of an experiment performed in the installations of the Cajamar Center of Experiences, Paiporta, Valencia, Spain, by Martín et al (2020a)

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Summary

Introduction

Using the global navigation satellite system (GNSS) signalto-noise (SNR) observables to monitor soil moisture, called GNSS interferometric reflectometry (GNSS-IR), is. The primary technique is to use continuous SNR observables for satellites tracks between 5 and 30 elevation angles and convert the SNR observed in dB-Hz units to a linear scale in volts. The reflected signal is isolated by fitting a second-order polynomial to the SNR (in volts) to eliminate the direct satellite signal, and the reflected signal is modeled assuming a sinusoidal behavior, using the following expression: SNRreflected = Acos 4 h sin e +. Where A and φ are the amplitude and phase of the wave, λ is the GNSS signal wavelength, e is the satellite elevation. H is the reflector height, which is the vertical distance between the GNSS antenna phase center and the horizontal reflecting surface, which is assumed to be the distance between the antenna and the floor due to the low signal penetration on the ground. Some precautions are needed to ensure good results: (1) rising or setting satellite tracks should be separated (or tagged for the post-processing), since the

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Outlier detection and elimination
Findings
Conclusions and final remarks

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