Python software tools for GNSS interferometric reflectometry (GNSS-IR)

Martín, Angel; Luján, Raquel; Julián, Ana Belén Anquela

doi:10.1007/s10291-020-01010-0

Cited by 9 publications

(2 citation statements)

References 20 publications

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“…If the wave range is chosen as a criterion for monitoring characteristics, then this gives the following systematics: radar monitoring (Kozlov et al, 2020), optical monitoring (Huang et al, 2018), thermal monitoring (Martín et al, 2020), laser monitoring (Anthony et al, 2010.…”

Section: Analysis Of the Content Of Satellite Monitoringmentioning

confidence: 99%

Satellite Monitoring

2023

Russian Journal of Astrophysical Research. Series A

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The article explores satellite monitoring. This technology is one of the space research technologies. The difference between space monitoring and satellite monitoring is shown. Two implementations of satellite monitoring are described. The systematics of satellite monitoring is given. The influence of Earth sciences on the development of satellite monitoring is described. The importance of the concept of the information field and information space on the concept of satellite monitoring is noted. The key concepts of satellite monitoring are described. The content of satellite monitoring is revealed through key concepts. The monitoring field, monitoring situation, monitoring methods, monitoring result models are described. The value of the information situation model for satellite monitoring is revealed. The difference between the information situation and the monitoring field is shown. Methods and models of monitoring are described. Object monitoring and process monitoring are described. The difference between active and passive satellite monitoring is shown. The difference between indicator and analytical monitoring is shown. Detective and interpretive monitoring is described. Prospective satellite monitoring is described, which, based on time series, makes it possible to make forecasts. It is shown that, unlike space monitoring, in which angular measurements predominate, satellite monitoring makes it possible to obtain the linear dimensions of objects. In relation to space monitoring, satellite monitoring is internal. Satellite monitoring is applicable in the study of other planets. In this case, it should be called orbital monitoring.

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Section: Analysis Of the Content Of Satellite Monitoringmentioning

confidence: 99%

Satellite Monitoring

2023

Russian Journal of Astrophysical Research. Series A

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“…In Martín et al (2020b), a software package is presented to transform GPS SNR observations to indirect SNRadjusted waves, where the amplitude and phase of the waves for the good satellite tracks are the final output (https:// geode sy. noaa.…”

Section: Introductionmentioning

confidence: 99%

Python software to transform GPS SNR wave phases to volumetric water content

et al. 2021

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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.

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Multi-constellation GNSS interferometric reflectometry for tidal analysis: mitigations for K1 and K2 biases due to GPS geometrical errors

Peng,

Lin,

Lee

et al. 2024

J Geod

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It has been observed that when using sea levels derived from GPS (Global Positioning System) signal-to-noise ratio (SNR) data to perform tidal analysis, the luni-solar semidiurnal (K2) and the luni-solar diurnal (K1) constituents are biased due to geometrical errors in the reflection data, which result from their periods coinciding with the GPS orbital period and revisit period. In this work, we use 18 months of GNSS SNR data from multiple frequencies and multiple constellations at three sites to further investigate the biases and how to mitigate them. We first estimate sea levels using SNR data from the GPS, GLONASS, and Galileo signals, both individually and by combination. Secondly, we conduct tidal harmonic analysis using these sea-level estimates. By comparing the eight major tidal constituents estimated from SNR data with those estimated from the co-located tide-gauge records, we find that the biases in the K1 and K2 amplitudes from GPS S1C, S2X and S5X SNR data can reach 5 cm, and they can be mitigated by supplementing GLONASS- and Galileo-based sea-level estimates. With a proper combination of sea-level estimates from GPS, GLONASS, and Galileo, SNR-based tidal constituents can reach agreement at the millimeter level with those from tide gauges.

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Python software tools for GNSS interferometric reflectometry (GNSS-IR)

Cited by 9 publications

References 20 publications

Satellite Monitoring

Satellite Monitoring

Python software to transform GPS SNR wave phases to volumetric water content

Multi-constellation GNSS interferometric reflectometry for tidal analysis: mitigations for K1 and K2 biases due to GPS geometrical errors

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