Improving GNSS‐R sea level determination through inverse modeling of SNR data

Strandberg, Joakim; Hobiger, Thomas; Haas, Rüdiger

doi:10.1002/2016rs006057

Cited by 83 publications

(82 citation statements)

References 18 publications

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“…Inverse Modelling method provides the better results in terms of accuracy and correlation. The results are in line with those obtained in [4].…”

Section: Resultssupporting

confidence: 92%

“…Regarding local sea level height monitoring by means of GNSS-R, the usual methodology is based on the spectral analysis of SNR using Lomb-Scargle Periodogram method (in the following, LSP method). Recently, Strandberg [4] proposed a new algorithm known as Inverse Modelling method (in the following, IM method) which models sea surface heights as a continuous function using B-splines. In this work both methods are compared in terms of accuracy and correlation with respect to tide gauge records for a co-located GNSS permanent station.…”

Section: Introductionmentioning

confidence: 99%

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Sea Level Determination in the Spanish Coast Using GNSS-R

Puente

Valdes

2019

The II Geomatics Engineering Conference

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GNSS reflectometry (GNSS-R) a technique for the study of variations in the height of sea level and inland waters, and for the study of soil moisture, based on the comparison of the direct and reflected noise signal. The main advantage of this method is that allows the connection between sea level measurements and the global reference frame. The purpose of this contribution is to present the GNSS-R analysis service at the Geodesy department of the National Geographic Institute of Spain and to compare the sea level time series determined by means of GNSS-R with tide gauge measurements. Two different GNSS-R methods using multi-constellation and multi-frequency data are compared: Lomb-Scargle Periodogram and Inverse Modelling. This analysis is performed for 3 GNSS antennas collocated to tide gauges along the Spanish coast. Eventually, advantages and disadvantages of this method are discussed.

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“…Inverse Modelling method provides the better results in terms of accuracy and correlation. The results are in line with those obtained in [4].…”

Section: Resultssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Sea Level Determination in the Spanish Coast Using GNSS-R

Puente

Valdes

2019

The II Geomatics Engineering Conference

View full text Add to dashboard Cite

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“…More advanced SNR analysis techniques have also recently been proposed. These methods have been tested at two sites (in Sweden and Australia) and are significantly more precise than using the Lomb-Scargle periodogram alone (Strandberg et al 2016). Another limitation for the GPS reflection method is the roughness of the surface.…”

Section: Discussionmentioning

confidence: 99%

“…3b), it is necessarily limited by the number of satellite overflights, the precision of the L1 SNR data, and the geometry of the site. The simplest way to increase the number of overflights is to use signals from non-GPS satellite constellations (GLONASS, Galileo, BeiDou) and more frequencies, such as L2C and L5 (Löfgren and Haas 2014;Strandberg et al 2016). More advanced SNR analysis techniques have also recently been proposed.…”

Section: Discussionmentioning

confidence: 99%

A 10-Year Comparison of Water Levels Measured with a Geodetic GPS Receiver versus a Conventional Tide Gauge

Larson

Ray

Williams

2017

Journal of Atmospheric and Oceanic Technology

132

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A standard geodetic GPS receiver and a conventional Aquatrak tide gauge, collocated at Friday Harbor, Washington, are used to assess the quality of 10 years of water levels estimated from GPS sea surface reflections. The GPS results are improved by accounting for (tidal) motion of the reflecting sea surface and for signal propagation delay by the troposphere. The RMS error of individual GPS water level estimates is about 12 cm. Lower water levels are measured slightly more accurately than higher water levels. Forming daily mean sea levels reduces the RMS difference with the tide gauge data to approximately 2 cm. For monthly means, the RMS difference is 1.3 cm. The GPS elevations, of course, can be automatically placed into a well-defined terrestrial reference frame. Ocean tide coefficients, determined from both the GPS and tide gauge data, are in good agreement, with absolute differences below 1 cm for all constituents save K 1 and S 1 . The latter constituent is especially anomalous, probably owing to daily temperature-induced errors in the Aquatrak tide gauge.

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“…As an alternative approach for measuring on dynamic surfaces, we have previously developed an inversion-based method (Strandberg et al 2016) which is also using SNR from off-the-shelf equipment and existing GNSS installations on the ground. This method builds on the idea of fitting an inverse model that represents the recorded SNR data.…”

Section: Introductionmentioning

confidence: 99%

Real-time sea-level monitoring using Kalman filtering of GNSS-R data

2019

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Current GNSS-R (GNSS reflectometry) techniques for sea surface measurements require data collection over longer periods, limiting their usability for real-time applications. In this work, we present a new, alternative GNSS-R approach based on the unscented Kalman filter and the so-called inverse modeling approach. The new method makes use of a mathematical description that relates SNR (signal-to-noise ratio) variations to multipath effects and uses a B-spline formalism to obtain time series of reflector height. The presented algorithm can provide results in real time with a precision that is significantly better than spectral inversion methods and almost comparable to results from inverse modeling in post-processing mode. To verify the performance, the method has been tested at station GTGU at the Onsala Space Observatory, Sweden, and at the station SPBY in Spring Bay, Australia. The RMS (root mean square) error with respect to nearby tide gauge data was found to be 2.0 cm at GTGU and 4.8 cm at SPBY when evaluating the output corresponding to real-time analysis. The method can also be applied in post-processing, resulting in RMS errors of 1.5 cm and 3.3 cm for GTGU and SPBY, respectively. Finally, based on SNR data from GTGU, it is also shown that the Kalman filter approach is able to detect the presence of sea ice with a higher temporal resolution than the previous methods and traditional remote sensing techniques which monitor ice in coastal regions.

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Improving GNSS‐R sea level determination through inverse modeling of SNR data

Cited by 83 publications

References 18 publications

Sea Level Determination in the Spanish Coast Using GNSS-R

Sea Level Determination in the Spanish Coast Using GNSS-R

A 10-Year Comparison of Water Levels Measured with a Geodetic GPS Receiver versus a Conventional Tide Gauge

Real-time sea-level monitoring using Kalman filtering of GNSS-R data

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