Statistical Comparison and Combination of GPS, GLONASS, and Multi-GNSS Multipath Reflectometry Applied to Snow Depth Retrieval

Tabibi, Sajad; Geremia‐Nievinski, Felipe; Dam, Tonie van

doi:10.1109/tgrs.2017.2679899

Cited by 85 publications

(56 citation statements)

References 33 publications

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“…For GNSS-R analysis, the key variable is the SNR (usually recorded in dB-Hz) for a given GNSS satellite and signal of interest. For simplicity, our analysis is limited to the use of the GPS L1 C/A signal but we note that the precision and frequency of sea level measurements would likely improve with more satellite constellations or when using different signals [10], [17]. In preparation for retrieving sea level measurements, the SNR data is arranged into time periods during which the elevation angle, θ, and azimuth angle, α, of the satellite relative to the antenna are within pre-defined limits.…”

Section: B Sea Level Retrievalmentioning

confidence: 99%

“…A similar approach was taken in [11], who used global optimization based on interval analysis to fit the SNR data and found an improvement in both the precision and the computation time in their analysis compared to spectral analysis. A rigorous forward model to produce synthetic SNR data has been developed by [15] and used to retrieve snow depth measurements via inverse modelling [16], [17], but this approach has not yet been applied to sea level measurements.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the Uncertainty in Ground-Based GNSS-Reflectometry Sea Level Measurements

Purnell

Gomez

Chan

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Global Navigation Satellite System Reflectometry (GNSS-R) tide gauges are a promising alternative to traditional tide gauges. However, the precision of GNSS-R sea level measurements when compared to measurements from a co-located tide gauge is highly variable, with no clear indication of what causes the variability. Here we present a modelling technique to estimate the precision of GNSS-R sea level measurements that relies on creating and analyzing synthetic Signal-to-Noise-Ratio (SNR) data. The modelled value obtained from the synthetic SNR data is compared to observed RMSE between GNSS-R measurements and a co-located tide gauge at five sites and using two retrieval methods: spectral analysis and inverse modelling. We find that the inverse method is more precise than the spectral analysis method by up to 60% for individual measurements but the two methods perform similarly for daily and monthly means. We quantify the contribution of dominant effects to the variations in precision and find that noise is the dominant source of uncertainty for spectral analysis whereas the effect of the dynamic sea surface is the dominant source of uncertainty for the inverse method. Additionally, we test the sensitivity of sea level measurements to the choice of elevation angle interval and find that the spectral analysis method is more sensitive to the choice of elevation angle interval than the inverse method due to the effect of noise, which is greater at larger elevation angle intervals. Conversely, the effect of tropospheric delay increases for lower elevation angle intervals but is generally a minor contribution.

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Section: B Sea Level Retrievalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying the Uncertainty in Ground-Based GNSS-Reflectometry Sea Level Measurements

Purnell

Gomez

Chan

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…with τ being the time constant of the low-pass filter. For this approach, τ was set to 4 h. Alternatively, the estimatě d s,fitted (t n ) could be obtained also from the combination of the individual snow parameter estimates of each arc using the standard deviations of the snow parameter estimates in the weighting, as proposed in (4) in Tabibi et al [38]. However, (35) and (36), in their current form, have the advantage that they can also be applied very efficiently with large data sets.…”

Section: E Filtering Of Snow Parameter Estimatementioning

confidence: 99%

Snow Water Equivalent of Dry Snow Derived From GNSS Carrier Phases

Henkel

Koch

Appel

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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Snow water equivalent (SWE) is a key variable for various hydrological applications. It is defined as the depth of water that would result upon complete melting of a mass of snow. However, until now, continuous measurements of the SWE are either scarce, expensive, labor-intense, or lack temporal or spatial resolution especially in mountainous and remote regions. We derive the SWE for dry-snow conditions using carrier phase measurements from the Global Navigation Satellite System (GNSS) receivers. Two static GNSS receivers are used, whereby one antenna is placed below the snow and the other antenna is placed above the snow. The carrier phase measurements of both receivers are combined in double differences (DDs) to eliminate clock offsets and phase biases and to mitigate atmospheric errors. Each DD carrier phase measurement depends on the relative position between both antennas, an integer ambiguity due to the periodic nature of the carrier phase signal, and the SWE projected into the direction of incidence. The relative positions of the antennas are determined under snow-free conditions with millimeter accuracy using real-time kinematic positioning. Subsequently, the SWE and carrier phase integer ambiguities are jointly estimated with an integer least-squares estimator. We tested our method at an Alpine test site in Switzerland during the dry-snow season 2015-2016. The SWE derived solely by the GNSS shows very high correlation with conventionally measured snow pillow (root mean square error: 11 mm) and manual snow pit data. This method can be applied to dense low-cost GNSS receiver networks to improve the spatial and temporal information on snow.

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“…Moreover, multi-constellation receivers and antennas could be selected. With large SNR observation set at hand, the definition of specific criteria for selecting and combining LSPs best suited for antenna height retrieval could help to reduce uncertainty of antenna heights and to investigate residual low frequencies in the LSP to asses the quality of the direct SNR removal step [39][40][41][42]. Moreover, suitable statistics tools could be exploited in order to increase precision of combined antenna heights.…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

GNSS-R with Low-Cost Receivers for Retrieval of Antenna Height from Snow Surfaces Using Single-Frequency Observations

Rover

Vitti

2019

Sensors

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Snowpack is an important fresh water storage; the retrieval of snow water equivalents from satellite data permits to estimate potentially available water amounts which is an essential parameter in water management plans running in several application fields (e.g., basic needs, hydroelectric, agriculture, hazard and risk monitoring, climate change studies). The possibility to assess snowpack height from Global Navigation Satellite Systems (GNSS) observations by means of the GNSS reflectometry technique (GNSS-R) has been shown by several studies. However, in general, studies are being conducted using observations collected by continuously operating reference stations (CORS) built for geodetic purposes and equipped with geodetic-grade instruments. Moreover, CORS are located on sites selected according to criteria different from those more suitable for snowpack studies. In this work, beside an overview of key elements of GNSS reflectometry, single-frequency GNSS observations collected by u-blox M8T GNSS receivers and patch antennas from u-blox and Tallysman have been considered for the determination of antenna height from the snowpack surface on a selected test site. Results demonstrate the feasibility of GNSS-R even with non-geodetic-grade instruments, opening the way towards diffuse GNSS-R targeted applications.

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Statistical Comparison and Combination of GPS, GLONASS, and Multi-GNSS Multipath Reflectometry Applied to Snow Depth Retrieval

Cited by 85 publications

References 33 publications

Quantifying the Uncertainty in Ground-Based GNSS-Reflectometry Sea Level Measurements

Quantifying the Uncertainty in Ground-Based GNSS-Reflectometry Sea Level Measurements

Snow Water Equivalent of Dry Snow Derived From GNSS Carrier Phases

GNSS-R with Low-Cost Receivers for Retrieval of Antenna Height from Snow Surfaces Using Single-Frequency Observations

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