Sea state monitoring using coastal GNSS‐R

Soulat, F.; Caparrini, M.; Germain, Olivier; López-Dekker, Paco; Taani, M.; Ruffini, Giulio

doi:10.1029/2004gl020680

Cited by 118 publications

(72 citation statements)

References 4 publications

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“…The modification to the current algorithm was made according to the true values collected by UWG. The results in CORE are better than Francois et al's work in accuracy [17] . The mean difference and standard deviation of SWH retrievals are 2.88 cm and 3.28 cm respectively.…”

Section: It Is a New Way For Oceanographic Remote Sensing Using The Gcontrasting

confidence: 58%

“…In 2004, a method that relates SWH to the coherence time F τ ′ of the Interferometric Field (IF) and deduces SWH from IF with Level0 data was proposed by Francois et al [17] . Based on this point, we derived an empirical expression by fitting with the Oceanpal data and UWG measurements in situ during September 2-12, and then obtained a quadric polynomial as follows:…”

Section: It Is a New Way For Oceanographic Remote Sensing Using The Gmentioning

confidence: 99%

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First China ocean reflection experiment using coastal GNSS-R

et al. 2008

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Section: It Is a New Way For Oceanographic Remote Sensing Using The Gcontrasting

confidence: 58%

Section: It Is a New Way For Oceanographic Remote Sensing Using The Gmentioning

confidence: 99%

First China ocean reflection experiment using coastal GNSS-R

et al. 2008

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“…[23] Analysis of the DDM has revealed a geophysical signature associated with the mean sea surface scatterer velocity: specula appear to travel on longer waves with velocities that can reach 5 -10 m/s, impacting significantly the Doppler bandwidth observed on slow-moving receivers (e.g., airborne or ground-based, Soulat et al [2004]). This geophysical signature opens new opportunities for GNSS-R speculometry: to infer either DMSS l or a combination of DMSS l and scatterer velocity, depending on the aircraft speed.…”

Section: Resultsmentioning

confidence: 99%

The Eddy Experiment: GNSS‐R speculometry for directional sea‐roughness retrieval from low altitude aircraft

Germain

Ruffini

Soulat

et al. 2004

Geophysical Research Letters

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We report on the retrieval of directional sea surface roughness, in terms of its full directional mean square slope (including direction and isotropy), from Global Navigation Satellite System Reflections (GNSS-R) Delay-Doppler-Map (DDM) data collected during an experimental flight at 1 km altitude. This study emphasizes the utilization of the entire DDM to more precisely infer ocean roughness directional parameters. In particular, we argue that the DDM exhibits the impact of both roughness and scatterer velocity. Obtained estimates are analyzed and compared to co-located Jason-1 measurements, ECMWF numerical weather model outputs and optical data.

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“…Surface roughness can be estimated from the analysis of the delay Doppler maps (DDM) derived from the waveforms of the reflected signals. They can be related to parameters such as soil moisture (Katzberg et al, 2006;RodriguezAlvarez et al, 2009RodriguezAlvarez et al, , 2011 over land, wave heights and wind speed (Komjathy et al, 2000;Zavorotny and Voronovich, 2000;Rius et al, 2002;Soulat et al, 2004) over the ocean, or ice properties (Gleason, 2006;Cardellach et al, 2012). The GNSS-R technique presents two main advantages: (1) a dense spatial and temporal coverage, not only limited to a single measurement point or a non-repetitive transect as with using classical GNSS buoys, and (2) a guarantee of service for the next decades (because of the strategic role played by these systems).…”

Section: Introductionmentioning

confidence: 99%

Simulations of direct and reflected wave trajectories for ground-based GNSS-R experiments

et al. 2014

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Abstract. The detection of Global Navigation Satellite System (GNSS) signals that are reflected off the surface, along with the reception of direct GNSS signals, offers a unique opportunity to monitor water level variations over land and ocean. The time delay between the reception of the direct and reflected signals gives access to the altitude of the receiver over the reflecting surface. The field of view of the receiver is highly dependent on both the orbits of the GNSS satellites and the configuration of the study site geometries. A simulator has been developed to determine the location of the reflection points on the surface accurately by modeling the trajectories of GNSS electromagnetic waves that are reflected by the surface of the Earth. Only the geometric problem was considered using a specular reflection assumption. The orbit of the GNSS constellation satellites (mainly GPS, GLONASS and Galileo), and the position of a fixed receiver, are used as inputs. Four different simulation modes are proposed, depending on the choice of the Earth surface model (local plane, osculating sphere or ellipsoid) and the consideration of topography likely to cause masking effects. Angular refraction effects derived from adaptive mapping functions are also taken into account. This simulator was developed to determine where the GNSS-R receivers should be located to monitor a given study area efficiently. In this study, two test sites were considered: the first one at the top of the 65 m Cordouan lighthouse in the Gironde estuary, France, and the second one on the shore of Lake Geneva (50 m above the reflecting surface), at the border between France and Switzerland. This site is hidden by mountains in the south (orthometric altitude up to 2000 m), and overlooking the lake in the north (orthometric altitude of 370 m). For this second test site configuration, reflections occur until 560 m from the receiver. The planimetric (arc length) differences (or altimetric difference as WGS84 ellipsoid height) between the positions of the specular reflection points obtained considering the Earth's surface as an osculating sphere or as an ellipsoid were found to be on average 9 cm (or less than 1 mm) for satellite elevation angles greater than 10 • , and 13.9 cm (or less than 1 mm) for satellite elevation angles between 5 and 10 • . The altimetric and planimetric differences between the plane and sphere approximations are on average below 1.4 cm (or less than 1 mm) for satellite elevation angles greater than 10 • and below 6.2 cm (or 2.4 mm) for satellite elevation angles between 5 and 10 • . These results are the means of the differences obtained during a 24 h simulation with a complete GPS and GLONASS constellation, and thus depend on how the satellite elevation angle is sampled over the day of simulation. The simulations highlight the importance of the digital elevation model (DEM) integration: average planimetric differences (or altimetric) with and without integrating the DEM (with respect to the ellipsoid approximation) were found to...

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Sea state monitoring using coastal GNSS‐R

Cited by 118 publications

References 4 publications

First China ocean reflection experiment using coastal GNSS-R

First China ocean reflection experiment using coastal GNSS-R

The Eddy Experiment: GNSS‐R speculometry for directional sea‐roughness retrieval from low altitude aircraft

Simulations of direct and reflected wave trajectories for ground-based GNSS-R experiments

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