Estimating Wave Direction by Using Terrestrial GNSS Reflectometry

Reinking, Jörg; Roggenbuck, Ole; Even-Tzur, Gilad

doi:10.20944/preprints201904.0081.v1

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…However, this effect is difficult to quantify for river level, in part because smaller roughness that occurs in river surface (typically smaller than 0.3 m in height). The effect of significant wave height is more likely to be notable if the wind blows along the azimuth the antenna is pointing (Reinking et al., 2019). Residuals were reduced after the antenna orientation change.…”

Section: Water Level Results and Discussionmentioning

confidence: 99%

Raspberry Pi Reflector (RPR): A Low‐Cost Water‐Level Monitoring System Based on GNSS Interferometric Reflectometry

Karegar

Kusche

Geremia‐Nievinski

et al. 2022

Water Resources Research

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One of the challenges for hydrologists and environmental scientists is the need to obtain and sustain in situ water level measurements for calibrating and improving forecast models, validating satellite and airborne data products, and developing early-warning flood systems. Ground-based measurements are still scarce in many regions. In particular, stream flow monitoring gauges have been declining sharply since the mid 1980s due to high maintenance cost, funding shortfalls and (geo-) political constraints (Hannah et al., 2011;Reid et al., 2019;Ruhi et al., 2016Ruhi et al., , 2018. While satellite remote sensing techniques have been utilized to monitor oceanic and land surface water with unprecedented global coverage, their measurements are associated with moderate uncertainties and temporal resolution (>7-day return) (Escudier et al., 2017;Jarihani et al., 2013). The upcoming NASA's Surface Water Ocean Topography (SWOT) satellite mission will collect high-accuracy measurements of inland surface water elevation (10 cm error for 1 km 2 areas) at unprecedented scales (10-70 m resolution) using Ka-band interferometric synthetic aperture radar. SWOT will provide global maps of water surface elevation, slope and inundated areas for rivers wider than 100 m (Biancamaria et al., 2016). The SWOT interferometric swath will pass over a given location two or three times every 21-day orbital cycle (Tuozzolo et al., 2019). However, the coarse temporal resolution of satellite altimetry missions such as SWOT and the requirement for monitoring smaller rivers and tributaries underline the significance of in situ monitoring sites. Measurements of sea surface and river water level using ground-based sensors conventionally rely on contact methods, such as traditional float and stilling well gauges (Noye, 1974) and bubbler pressure gauges (Pugh, 1972), or proximal sensing gauges, such as acoustic (

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Section: Water Level Results and Discussionmentioning

confidence: 99%

Raspberry Pi Reflector (RPR): A Low‐Cost Water‐Level Monitoring System Based on GNSS Interferometric Reflectometry

Karegar

Kusche

Geremia‐Nievinski

et al. 2022

Water Resources Research

View full text Add to dashboard Cite

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Estimation of Swell Height Using Spaceborne GNSS-R Data from Eight CYGNSS Satellites

Park

et al. 2022

Remote Sensing

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Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) technology has opened a new window for ocean remote sensing because of its unique advantages, including short revisit period, low observation cost, and high spatial-temporal resolution. In this article, we investigated the potential of estimating swell height from delay-Doppler maps (DDMs) data generated by spaceborne GNSS-R. Three observables extracted from the DDM are introduced for swell height estimation, including delay-Doppler map average (DDMA), the leading edge slope (LES) of the integrated delay waveform (IDW), and trailing edge slope (TES) of the IDW. We propose one modeling scheme for each observable. To improve the swell height estimation performance of a single observable-based method, we present a data fusion approach based on particle swarm optimization (PSO). Furthermore, a simulated annealing aided PSO (SA-PSO) algorithm is proposed to handle the problem of local optimal solution for the PSO algorithm. Extensive testing has been performed and the results show that the swell height estimated by the proposed methods is highly consistent with reference data, i.e., the ERA5 swell height. The correlation coefficient (CC) is 0.86 and the root mean square error (RMSE) is 0.56 m. Particularly, the SA-PSO method achieved the best performance, with RMSE, CC, and mean absolute percentage error (MAPE) being 0.39 m, 0.92, and 18.98%, respectively. Compared with the DDMA, LES, TES, and PSO methods, the RMSE of the SA-PSO method is improved by 23.53%, 26.42%, 30.36%, and 7.14%, respectively.

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Prediction of Significant Wave Heights with Engineered Features from GNSS Reflectometry

Becker

Roggenbuck

2023

Remote Sensing

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Estimating reflector heights at stationary GNSS sites with interferometric reflectometry (IR) is a well-established technique in ocean remote sensing. Additionally, IR offers the possibility to estimate the significant wave height (SWH) with a linear model using the damping coefficient from an inverse modelling applied to GNSS signal-to-noise ratio (SNR) observations. Such a linear model serves as a benchmark in the present study, where an alternative approach for the estimation of both SWH and reflector height is presented that is based on kernel regression and clustering techniques. In this alternative approach, the reflector height is estimated by analyzing local extrema occurring in the interference pattern that is present in GNSS SNR observations. Various predictors are derived from clustering statistics and the estimated reflector heights. These predictors are used for the SWH determination with supervised machine learning. Linear models, bagged regression trees, and artificial neural networks are applied and respective results are compared for various predictor sets. In a second step, damping coefficients obtained from the inverse modelling mentioned above are additionally taken into account as predictors. The usability of the alternative approach is demonstrated. Compared to the benchmark, a significant improvement in terms of accuracy is found for an artificial neural network with predictors from both the alternative and the inverse modelling approach.

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Estimating Wave Direction by Using Terrestrial GNSS Reflectometry

Cited by 4 publications

References 15 publications

Raspberry Pi Reflector (RPR): A Low‐Cost Water‐Level Monitoring System Based on GNSS Interferometric Reflectometry

Raspberry Pi Reflector (RPR): A Low‐Cost Water‐Level Monitoring System Based on GNSS Interferometric Reflectometry

Estimation of Swell Height Using Spaceborne GNSS-R Data from Eight CYGNSS Satellites

Prediction of Significant Wave Heights with Engineered Features from GNSS Reflectometry

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