Estimation of Surface Characteristics Using GNSS LH-Reflected Signals: Land Versus Water

Jia, Yan; Savi, Patrizia; Canone, Davide; Notarpietro, Riccardo

doi:10.1109/jstars.2016.2584092

Cited by 38 publications

(49 citation statements)

References 21 publications

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“…The results illustrate an RMSE equal to 0.06 m 3 /m 3 . This accuracy is in agreement with those retrieved by other studies using GNSS-R airborne measurements [20][21][22][23].…”

Section: Application To the Glori Datasupporting

confidence: 91%

“…Although this technique was initially used for oceanographic applications, various experimental in situ and airborne campaigns have led to a considerably improved understanding of the interaction of these signals with the Earth's surface, and their potential for monitoring land surface conditions, including that of the cryosphere [14]. In recent years, the GNSS-R technique has been increasingly tested and validated over land surfaces [15][16][17][18][19][20][21], using either single GNSS-R physical signal, an essential step to propose operational satellite algorithms for land surface properties mapping.…”

Section: Introductionmentioning

confidence: 99%

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Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign

et al. 2018

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Abstract:The aim of this study is to analyze the sensitivity of airborne Global Navigation Satellite System Reflectometry (GNSS-R) on soil surface and vegetation cover characteristics in agricultural areas. Airborne polarimetric GNSS-R data were acquired in the context of the GLORI'2015 campaign over two study sites in Southwest France in June and July of 2015. Ground measurements of soil surface parameters (moisture content) and vegetation characteristics (leaf area index (LAI), and vegetation height) were recorded for different types of crops (corn, sunflower, wheat, soybean, vegetable) simultaneously with the airborne GNSS-R measurements. Three GNSS-R observables (apparent reflectivity, the reflected signal-to-noise-ratio (SNR), and the polarimetric ratio (PR)) were found to be well correlated with soil moisture and a major vegetation characteristic (LAI). A tau-omega model was used to explain the dependence of the GNSS-R reflectivity on both the soil moisture and vegetation parameters.

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“…The results illustrate an RMSE equal to 0.06 m 3 /m 3 . This accuracy is in agreement with those retrieved by other studies using GNSS-R airborne measurements [20][21][22][23].…”

Section: Application To the Glori Datasupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign

et al. 2018

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“…In this campaign, the direct GPS signals were measured using a right-hand circular polarized (RHCP) antenna, while the reflected signals were measured with a left-hand circular polarized (LHCP) antenna. The complete description of the setup can be found in [21]. In addition to the GNSS-R measurements, Time-Domain Reflectometry (TDR) measurements were carried out to be used as reference.…”

Section: Resultsmentioning

confidence: 99%

Determining Real Permittivity From Fresnel Coefficients in GNSS-R

Savi

Bertoldo

Milani³

2019

PIER M

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Global Navigation Satellite System Reflectometry (GNSS-R) can be used to derive information about the composition or the properties of ground surfaces, by analyzing signals emitted by GNSS satellites and reflected from the ground. If the received power is measured with linearly polarized antennas, under the condition of smooth surface, the reflected signal is proportional to the modulus of the perpendicular and parallel polarization Fresnel coefficients, which depend on the incidence angle θ, and on the dielectric constant ε of the soil. In general, ε is a complex number; for non-dispersive soils, the imaginary part of ε can be neglected, and a real value of ε is sought. We solve the realvalued problem explicitly giving formulas that can be used to determine the dielectric constant ε and we compare the analytical solution with experimental data in the case of sand soil.

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“…In addition, observation of cryosphere and land is also being developed. Applications include snow depth estimation [31][32][33][34][35][36] and soil moisture and vegetation sensing [37][38][39][40], respectively. GNSS-R works in a bistatic configuration, in which the transmitter (Tx) and the receiver (Rx) are at different locations.…”

Section: Introductionmentioning

confidence: 99%

Sea Ice Remote Sensing Using GNSS-R: A Review

Yan

Huang

2019

Remote Sensing

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Knowledge of sea ice is critical for offshore oil and gas exploration, global shipping industries, and climate change studies. During recent decades, Global Navigation Satellite System-Reflectometry (GNSS-R) has evolved as an efficient tool for sea ice remote sensing. In particular, thanks to the availability of the TechDemoSat-1 (TDS-1) data over high-latitude regions, remote sensing of sea ice based on spaceborne GNSS-R has been rapidly growing. The goal of this paper is to provide a review of the state-of-the-art methods for sea ice remote sensing offered by the GNSS-R technique. In this review, the fundamentals of these applications are described, and their performances are evaluated. Specifically, recent progress in sea ice sensing using TDS-1 data is highlighted including sea ice detection, sea ice concentration estimation, sea ice type classification, sea ice thickness retrieval, and sea ice altimetry. In addition, studies of sea ice sensing using airborne and ground-based data are also noted. Lastly, applications based on various platforms along with remaining challenges are summarized and possible future trends are explored. In this review, concepts, research methods, and experimental techniques of GNSS-R-based sea ice sensing are delivered, and this can benefit the scientific community by providing insights into this topic to further advance this field or transfer the relevant knowledge and practice to other studies.

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Estimation of Surface Characteristics Using GNSS LH-Reflected Signals: Land Versus Water

Cited by 38 publications

References 21 publications

Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign

Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign

Determining Real Permittivity From Fresnel Coefficients in GNSS-R

Sea Ice Remote Sensing Using GNSS-R: A Review

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