- Research
- Open Access

# Sea level measurements using multi-frequency GPS and GLONASS observations

- Johan S Löfgren
^{1}Email author and - Rüdiger Haas
^{1}

**2014**:50

https://doi.org/10.1186/1687-6180-2014-50

© Löfgren and Haas; licensee Springer. 2014

**Received:**2 December 2013**Accepted:**21 March 2014**Published:**11 April 2014

## Abstract

Global Positioning System (GPS) tide gauges have been realized in different configurations, e.g., with one zenith-looking antenna, using the multipath interference pattern for signal-to-noise ratio (SNR) analysis, or with one zenith- and one nadir-looking antenna, analyzing the difference in phase delay, to estimate the sea level height. In this study, for the first time, we use a true Global Navigation Satellite System (GNSS) tide gauge, installed at the Onsala Space Observatory. This GNSS tide gauge is recording both GPS and Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) signals and makes it possible to use both the one- and two-antenna analysis approach. Both the SNR analysis and the phase delay analysis were evaluated using dual-frequency GPS and GLONASS signals, i.e., frequencies in the L-band, during a 1-month-long campaign. The GNSS-derived sea level results were compared to independent sea level observations from a co-located pressure tide gauge and show a high correlation for both systems and frequency bands, with correlation coefficients of 0.86 to 0.97. The phase delay results show a better agreement with the tide gauge sea level than the SNR results, with root-mean-square differences of 3.5 cm (GPS L_{1} and L_{2}) and 3.3/3.2 cm (GLONASS L_{1}/L_{2} bands) compared to 4.0/9.0 cm (GPS L_{1}/L_{2}) and 4.7/8.9 cm (GLONASS L_{1}/L_{2} bands). GPS and GLONASS show similar performance in the comparison, and the results prove that for the phase delay analysis, it is possible to use both frequencies, whereas for the SNR analysis, the L_{2} band should be avoided if other signals are available. Note that standard geodetic receivers using code-based tracking, i.e., tracking the un-encrypted C/A-code on L_{1} and using the manufacturers’ proprietary tracking method for L_{2}, were used. Signals with the new C/A-code on L_{2}, the so-called L_{2C}, were not tracked. Using wind speed as an indicator for sea surface roughness, we find that the SNR analysis performs better in rough sea surface conditions than the phase delay analysis. The SNR analysis is possible even during the highest wind speed observed during this campaign (17.5 m/s), while the phase delay analysis becomes difficult for wind speeds above 6 m/s.

## Keywords

- Sea level
- GNSS
- GNSS-R
- GPS
- GLONASS
- Signal-to-noise ratio
- Geodetic analysis
- Phase delay
- Multi-frequency
- Tide gauge

## 1 Introduction

The technique of observing sea level and its changes with the freely available Global Navigation Satellite System (GNSS) signals was first suggested by [1]. Since then, several studies of ocean remote sensing with Global Positioning System (GPS) signals involving land-based, airborne, and spaceborne systems have been carried out, e.g., [2, 3], and [4], respectively. The advantage of using reflected GNSS signals for remote sensing of the sea surface, compared to measurements by traditional tide gauges, is the possibility of measuring absolute sea level, i.e., sea level with respect to the International Terrestrial Reference Frame (ITRF). Sea level measurements from traditional tide gauges are relative to the land where they are established, and they are therefore affected by land surface changes. However, for applications related to changes in the global ocean volume, e.g., the global sea level budget, and for sea level measurements in tectonically active regions, absolute sea level measurements are necessary [5]. Since GNSS can be used to measure land surface changes, e.g., [6], one possibility to achieve absolute sea level is to combine tide gauge measurements of sea level with GNSS measurements of land motion (see e.g., [7, 8]). The assumption made in this case is that the GNSS station is close enough to the tide gauge to be affected by the same land motion. Another possibility, involving only one technique, is to use GNSS signals for both measuring the sea level and the land motion, i.e., to use a so-called GNSS tide gauge.

GNSS tide gauges have been realized in different configurations, e.g., using one or two antennas and with different types of receivers. The one-antenna configuration builds upon using the signal interference pattern, originating from the otherwise unwanted multipath signals interfering with the direct GNSS signals (see, e.g., [9, 10]), and was proposed by [11–13]. In later studies, GPS stations with zenith-looking antennas, installed primarily for geodetic measurements, have been used to measure the sea level of the nearby ocean (see [14–16]). For these types of studies, the interference pattern of the signal-to-noise ratio (SNR) is usually recorded with a geodetic receiver and analyzed with an interferometric approach. In addition, other studies exist, where the SNR of the direct and reflected signals was recorded by a customized receiver connected to an antenna directed towards the horizon (see e.g., [17]).

In the two-antenna configuration, one antenna is right-handed circularly polarized (RHCP) and directed upwards, receiving the direct satellite signals, and one antenna is left-handed circularly polarized (LHCP) and directed downwards, receiving the satellite signals that are reflected off the sea surface. GNSS carrier-phase measurements are recorded either by customized receivers and analyzed using interferometric techniques, e.g., [18, 19], or by geodetic receivers and analyzed with a standard geodetic approach [20–22].

At the Onsala Space Observatory (OSO) in Sweden, a GNSS tide gauge installation was established dedicated to measuring reflected GNSS signals from the ocean. This installation supports both the one- and two-antenna approach and records both GPS and Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) signals, making it a true GNSS tide gauge.

In the present study, we use the GNSS tide gauge at OSO in order to evaluate both the SNR analysis technique (one antenna) and the geodetic analysis technique (two antennas) and to investigate their performances and restrictions. For this evaluation, we utilize the full potential of the GNSS tide gauge, i.e., recording and analyzing signals from multiple systems with multiple frequencies. For the first time, in addition to using reflected GPS signals, also reflected GLONASS signals are used for measuring the local sea level. Furthermore, for both systems, signals from the frequency bands L_{1} and L_{2} are recorded and analyzed, and the results are compared with independent measurements from a co-located pressure sensor gauge.

Section 2 gives a description of the GNSS tide gauge at OSO together with details on the GNSS and the pressure tide gauge dataset used for this study. The two techniques and the respective data analysis are described in Sections 3 and 4, with the sea level results presented in Section 5. This is followed by a discussion in Section 6 and, finally, the conclusions and outlook in Section 7.

## 2 The GNSS tide gauge at the Onsala Space Observatory

_{1}and L

_{2}, that were collected during 1 month from September 29, day-of-year (doy) 273, to October, doy 303, 2012. The receivers used code-based tracking to track and record the carrier phase and SNR data, i.e., tracking the un-encrypted C/A-code on L

_{1}and using the manufacturers’ proprietary tracking method for L

_{2}. Signals with the new C/A-code on L

_{2}, the so-called L

_{2C}, were not tracked. The reasons were that we wanted to maximize the number of GPS observations (there are only a few GPS satellites with L

_{2C}capability so far) and to have a consistent L

_{2}dataset.

The GNSS tide gauge installation is directed towards the open water surface to the south to maximize the number of reflected satellite observations (see Figure 1). To the east, the water surface is limited by bedrock, and to the west, the water surface is limited by a beach and a few smaller islands. Nonetheless, the open water surface has a radius of more than 100 m and extends from azimuth angles 60° to 300°. The mean vertical position of the upward-looking antenna during the campaign was approximately 4.3 m above the sea surface.

To the east of the GNSS tide gauge installation (approximately 10 m), three hydrostatic level transmitters (HLT), i.e., pressure sensors, are installed. The sensors are mounted together on a submerged pole and measure the pressure from the overlying column of water (see e.g., [23]). The sensor type is Mobrey series 9710 HLT (Emerson Process Management Mobrey Ltd., Slough, Berkshire, UK), and the temporal resolution is set to 1 sample per second. The output value consist of the mean over 60 s, so the actual temporal resolution is 1 sample per minute.

## 3 Analysis methods

Both the SNR and the phase delay analysis have been described before, e.g., the SNR analysis in [14, 16] and the phase delay analysis in [22, 24]. However, for the sake of completeness, both analysis methods are summarized below.

### 3.1 The SNR analysis method

GNSS antennas are by design sensitive to the direct satellite signals, suppressing the unwanted signals that are reflected in the surrounding environment before reaching the antenna. Nevertheless, some of the reflected signals interfere with the direct signals which affect the GNSS observables recorded by the receiver (see Figure 2 (left) for an illustration of a zenith-pointing antenna). This effect, known as multipath, is one of the major error sources in high-accuracy positioning, and there are several studies on how to mitigate the effect, e.g., [9, 10, 25]. In addition, there are studies on how to use the reflected signals to measure properties of the reflecting surface, e.g., sea level [13, 14, 16], soil moisture [26], and snow depth [27].

There are two main characteristics of the SNR data in Figure 3 (left): the SNR multipath oscillations and the overall trend. The multipath effect is decreasing with increasing satellite elevation angle, i.e., the amplitude of the interference pattern decreases with increasing satellite elevation angle. This decrease depends on the amplitude (signal strength) of the reflected signal and the antenna gain pattern. For low satellite elevation angles, the reflected signal is mostly RHCP due to the higher amplitude of the RHCP Fresnel reflection coefficient compared to the LHCP coefficient (see e.g., [22, 25]). However, the RHCP reflection coefficient decreases with increasing elevation angle.

*δ*SNR), which consists of the multipath oscillations, can be described by

where *A* is the amplitude, *h*_{
r
} is the distance between the reflecting surface and the antenna phase center (also called reflector height; see Figure 2, left), *λ*_{
i
} is the carrier wavelength of the GNSS signal, *ε* is the satellite elevation angle, and *φ* is a phase offset.

Assuming that the reflector height (e.g., the sea level) is not changing during the satellite arc and that the reflector (e.g., the sea surface) is horizontal, the frequency of the multipath oscillations (2*h*_{
r
}/*λ*_{
i
}) is constant with respect to the sine of the satellite elevation angle. It is also possible to model a reflector height that is changing during a satellite arc [15]. However, this is not necessary for stations with a sub-diurnal tidal range of less than about 2.7 m [16].

By spectral analysis of the *δ* SNR data as a function of sine of elevation angle, it is possible to derive the dominant multipath frequency. This is usually performed using the Lomb-Scargle periodogram (LSP), since it can handle unevenly spaced samples (the SNR data are evenly sampled in time, but not as a function of sine of elevation). As an example, Figure 3 (right) depicts the LSP results after analysis of the SNR data in Figure 3 (left) (note that the SNR data were first detrended). The peaks of the LSP in Figure 3 (right) correspond to the dominant multipath frequencies and thus the reflector heights. The reflector height is negatively correlated with sea level, e.g., a large reflector height, or high multipath frequency, corresponds to a large distance between the antenna and the sea surface, which means a low sea level.

### 3.2 The phase delay analysis method

To achieve high-accuracy positioning with GNSS, analysis of phase delay data is necessary. For the GNSS tide gauge, the setup consists of one zenith-looking antenna, recording the direct satellite signals which are RHCP, and one nadir-looking antenna, recording the satellite signals that are reflected off the sea surface which are mostly LHCP (see Figure 2 (right) for an illustration). As previously described, the RHCP satellite signal is still mostly RHCP for low satellite elevations after reflection. However, the amplitude of the LHCP reflection coefficient is increasing with increasing satellite elevation angle, and for elevation angles over about 8°, the amplitude of the LHCP reflection coefficient is larger than the amplitude of the RHCP reflection coefficient (see e.g., [22, 25]).

Because of the additional travel path of the reflected signals, as compared to the directly received signals, the nadir-looking antenna will in the analysis appear to be a virtual antenna located below the sea surface. The distance between the virtual antenna and the sea surface, *h*_{
a
}, is the same as the distance from the actual LHCP antenna to the sea surface (see Figure 2, right). This means that when there is a change in sea surface, the additional travel path of the reflected signal changes, and the LHCP antenna appears to change its vertical position. The height of the nadir-looking antenna over the sea surface (*h*_{
a
}) can from the geometry presented in Figure 2 (right) be related to the vertical baseline between the two antennas as 2*h*_{
a
}+*d*, where *d* is the vertical separation between the phase centers of the two antennas. Furthermore, the height of the nadir-looking antenna over the sea surface is directly proportional to the sea surface height.

*A*and

*j*, respectively,

where *λ*_{
i
} is the carrier wavelength of the GNSS signal, ${\Phi}_{A}^{j}$ is the observed carrier phase in units of cycles, ${\varrho}_{A}^{j}$ is the geometric range to the satellite, *c* is the speed of light in vacuum, *τ*_{
A
} is the receiver clock bias, *τ*^{
j
} is the satellite clock bias, ${Z}_{A}^{j}$ is the delay caused by the neutral atmosphere (tropospheric delay), ${I}_{A}^{j}$ is the ionospheric delay, and ${N}_{A}^{j}$ is the phase ambiguity (including an integer number of wavelengths and unknown instrumental phase offsets from the satellite and the receiver).

*A*and

*B*, and forming the difference result in the single difference equation for each epoch

where $\Delta {\Phi}_{\mathit{\text{AB}}}^{j}$ is the difference between the measured phases expressed in cycles, $\Delta {\varrho}_{\mathit{\text{AB}}}^{j}$ is the difference in geometry, *Δ* *τ*_{
A
B
} is the difference in receiver clock bias, $\Delta {Z}_{\mathit{\text{AB}}}^{j}$ is the difference in tropospheric delay, $\Delta {I}_{\mathit{\text{AB}}}^{j}$ is the difference in ionospheric delay, and $\Delta {N}_{\mathit{\text{AB}}}^{j}$ is the phase ambiguity difference in cycles. Note that since the difference is taken with respect to the same satellite, the differential satellite clock bias term is not present in Equation 3. Additionally, for short baselines, the tropospheric and ionospheric effects can be assumed to cancel.

*α*, and elevation,

*ε*, angle for each satellite as

where *Δ* *e*, *Δ* *n*, and *Δ* *v* are the east, north, and vertical components of the baseline between the two receivers, respectively. For a known horizontal baseline (the horizontal baseline for our GNSS tide gauge is zero, see Figure 1), the east and north components can be used to adjust the left side of Equation 3.

It is also possible to form additional differences to Equation 3, e.g., double differences (between two receivers and two satellites) and triple differences (between double differences at different epochs). The double difference equation is especially advantageous for GPS observations for which the double differenced phase ambiguity parameters become integers (the receiver clock bias terms also cancel out). However, this is not the case for GLONASS observations, since the visible satellites have different carrier frequencies. Additionally, one extra observation is needed to form double differences as compared to single differences, and for our configuration, the number of (reflected) observations is limited. For these two reasons, we thus focused on single difference analysis.

## 4 Data analysis

The data analyses were made with in-house developed software packages in Matlab. For both the SNR analysis and the phase delay analysis, final orbits provided by the International GNSS Service (IGS) were used [31]. GPS and GLONASS data from frequency bands L_{1} and L_{2} were analyzed independently with both methods.

### 4.1 SNR analysis

From Figure 4 it is apparent that the size of the reflective surface is decreasing for increasing satellite elevation angle. This means that for the SNR analysis, which uses observations from low satellite elevation angles, the reflective surfaces are rather large and extend from about 9 to 90 m from the antenna.

Taking into account that the reflective surfaces should be over the sea, it was found that satellite observations from azimuth angles of 70° to 260° and with elevation angles of 1° to 14.5° were the most affected by multipath from the ocean.

After identifying the SNR data that were strongly affected by multipath, the data were converted from dB-Hz in logarithmic scale to watt per watt in linear scale, assuming a 1-Hz bandwith. For each satellite arc, a second-order polynomial was fitted and removed to create *δ* SNR arcs. Each arc observed for longer than 10 min was chosen for the analysis. The reason was to ensure that several periods of the multipath oscillation were observed for each arc. These *δ* SNR arcs were then downsampled with a factor of 4, to speed up the analysis, and analyzed using the LSP with an oversampling factor of 40 (corresponding to a reflector height precision of about 4 mm). The output from the LSP was analyzed to find the highest spectral power and the corresponding multipath frequency, i.e., the reflector height (when scaled with *λ*_{
i
}/2). In order to prevent the algorithm from picking up unrealistic reflector heights, two restrictions were applied. First, the spectral power was analyzed in a window around an average frequency derived from the mean reflector height. The window was at least two times wider than the actual sea level range at the site. Second, the highest spectral power for each arc had to be at least two times larger than the mean power in the window. When these two requirements were fulfilled, the reflector heights were converted into sea level heights and time tags for each height were calculated from the mean time of the corresponding satellite arc.

In order to remove outliers, a moving average filter with a window size of five time steps (corresponding to approximately 2.5 to 4 h) was applied to the four sea level time series (GPS and GLONASS for frequency bands L_{1} and L_{2}). From the difference between the original time series and the filtered time series, observations outside of 3 *σ* were considered as outliers and removed. For each of the SNR time series, less than 2% of the points were removed.

### 4.2 Phase delay analysis

Similar to the SNR data, the phase delay data were screened to find suitable satellite observations. In particular, the observations from the LHCP nadir-looking antenna were investigated, since these observations originate from signals reflected off the sea surface. In contrast to the SNR method, which is focused on analysis of observations from lower elevations that are highly affected by multipath, the phase delay method takes advantage of observations from higher elevations, which are less affected by multipath. The receiver connected to the zenith-looking RHCP antenna records the direct RHCP satellite signals, whereas the receiver connected to the nadir-looking LHCP antenna records the satellite signals after reflection off the sea surface (see Figure 2). After reflection, the satellite signal is dominantly LHCP for elevation angles over 8°.

Investigating the reflection surface, using the first Fresnel zone as in Section 4.1, for signals from higher satellite elevations, it is clear that these reflections occur close to the antenna. As an example, it is shown in Figure 4 that for an antenna at the height of 4.3 m above the reflector, the specular point of the reflection area is located at 7.5- and 3.6-m distances from the antenna for observations from elevation angles of 30° and 50°, respectively. Taking into account that the reflective surfaces should be over the sea and that the observations from the lower elevation angles should be avoided due to the strong multipath effect, the azimuth and elevation mask were determined to be 60° to 300° and 15° to 90°, respectively.

An equation system equivalent to Equation 3 was solved with a least-squares analysis for data intervals of 20 min at a time. Solutions were calculated for every 10 min using overlapping intervals and assuming that the horizontal baseline components were zero (see Equation 4). Each solution consisted of the vertical baseline component for the full interval, i.e., estimating one constant sea level component for the whole 20-min interval, the phase ambiguity differences for each satellite pair for the current interval, and the receiver clock bias differences for each epoch. For each interval, the conditions were that both receivers had continuous track of the same satellites for at least 10 min and that there were at least two satellites visible at each epoch. Furthermore, satellite observations had to be continuous for at least 10 min to be used in the processing.

The analysis resulted in time series of the vertical baseline component for the four datasets (GPS and GLONASS for frequency bands L_{1} and L_{2}). In order to remove erroneous solutions and outliers, two inspections were made. First, all solutions which had a formal error in the least-squares minimization process of larger than 4 cm were removed. Second, a moving average filter with a window size of 21 time steps (corresponding to approximately 3.5 h) was applied to each time series. From the difference between the original time series and the filtered time series, observations outside of 3 *σ* were considered as outliers and removed. For each of the phase delay time series, less than 12% of the points were removed.

The vertical baselines (the solutions) for each dataset were converted into sea level heights relative to the nadir-looking antenna. Note that the vertical distance between the phase centers of the two antennas was not accounted for, causing a bias.

## 5 Results

In the SNR analysis, only the geometrical component of the signal was considered and not any effects originating from the surface composition and antenna response, which can change the phase of the reflected signal (see [32]). Similarly, for the phase delay analysis, signal phase change from the reflection, e.g., carrier phase wind-up [33], and antenna phase center variations were not taken into account. Therefore, the results derived from the two analysis methods were not compared in an absolute sense. Instead, relative GNSS sea level time series were compared to the independent sea level observations from the co-located tide gauge. The tide gauge consisted of three pressure sensors (see Section 2). In a comparison with a pneumatic bubbler gauge, installed after this campaign, it was found that all three pressure sensors showed systematic errors (see [34]). However, one of the sensors showed only minor errors, and therefore, that sensor was used as the reference tide gauge in this study.

Since the GNSS-derived sea level measurements are relative to the phase center of the antenna and the tide gauge sea level observations were relative to the sensor installation, a mean was removed from each time series before comparison. This means that only the relative variations in sea level were compared and not the absolute sea level values.

_{1}, GLONASS L

_{1}, GPS L

_{2}, and GLONASS L

_{2}. In addition, the tide gauge time series is presented together with each GNSS-derived time series.

In Figure 5, the SNR sea level derived with L_{1} data from both GPS and GLONASS (top two time series) shows good agreement with the tide gauge sea level. The sea level solutions derived from L_{2} data (bottom two time series) are more noisy than those derived from L_{1} data, but they do appear to follow the tide gauge sea level rather well. Since each sea level value is derived from the corresponding multipath oscillation frequency, it is not trivial to assess the accuracy of each estimation. As an approximation of the formal error, the fraction between the mean spectral power (noise power) of the LSP and the peak spectral power (corresponding to the reflector height measurement) of the LSP is taken and multiplied with a factor of 0.2 (see the grey error bars in Figure 5).

The phase delay sea level results in Figure 6 resemble the tide gauge sea level well for GPS and GLONASS at both frequencies. The error bars consist of the standard deviation (the formal error in the least-squares minimization process) multiplied by a factor of 10.

Comparing the SNR and the phase delay results from Figures 5 and 6, which are shown in the same scale, it is clear that the phase delay sea level results are in general consistently in better agreement with the tide gauge sea level than the SNR sea level results are. In order to evaluate the GNSS-derived sea level in a more qualitative way, the Van de Casteele test was used (see e.g., [35]). The Van de Casteele test is a simple diagram based on simultaneous sea level height measurements from a reference tide gauge (here the pressure tide gauge) and from a tide gauge to be tested (here the GNSS tide gauge). Sea level height is presented along the *y*-axis of the diagram, and the tide gauge error, i.e., the difference in sea level height between the two sea level records, is presented along the *x*-axis of the diagram. From the shape of the Van de Casteele diagram, it is possible to easily understand whether systematic errors exist.

_{1}(left) and for GLONASS L

_{1}(right). The results are shown for sea level heights from both the SNR analysis (blue) and the phase delay analysis (green).

From Figure 7, it is again apparent that the phase delay sea level better resembles the tide gauge sea level, i.e., the tide gauge error is smaller, than the SNR sea level. The SNR sea level appears to be a bit more noisy than the phase delay sea level. However, there seems to be a few large outliers in the phase delay results. The shapes of the four Van de Casteele diagrams, centered around zero tide gauge error, indicate that there are no systematic errors for the GNSS-derived sea level. The corresponding four Van de Casteele diagrams for the L_{2} frequency band gave similar results and did not show any systematic errors for the GNSS-derived sea level either. In addition, no major differences were found between GPS and GLONASS results for the phase delay analysis or for the SNR analysis.

**Comparison between the GNSS-derived sea level time series and the reference tide gauge record**

GPS | GLONASS | ||||
---|---|---|---|---|---|

L | L | L | L | ||

Points (nr) | 1,516 | 1,229 | 1,254 | 882 | |

SNR | Corr. coeff. | 0.97 | 0.86 | 0.96 | 0.87 |

RMS | 4.0 | 9.0 | 4.7 | 8.9 | |

Mean diff. | 3.2 | 7.5 | 3.6 | 7.0 | |

Points (nr) | 1,534 | 1,495 | 1,408 | 1,286 | |

Phase delay | Corr. coeff. | 0.95 | 0.95 | 0.96 | 0.96 |

RMS | 3.5 | 3.5 | 3.3 | 3.2 | |

Mean diff. | 2.3 | 2.4 | 2.2 | 2.3 |

First of all, in Table 1, the number of points for each GNSS solution used in the comparison was similar, except for GLONASS L_{2} which had a lower number of solutions. For the SNR method, the number of solutions depends on the number of satellites that rise or set over the ocean everyday. Since GLONASS has about 30% fewer orbiting satellites than GPS, it is also reasonable that there are correspondingly less GLONASS than GPS SNR solutions. The number of SNR L_{1}/L_{2} solutions per day was on average 49/40 and 40/28 for GPS and GLONASS, respectively, i.e., about 20% to 40% less for GLONASS compared to GPS. In addition, the signals from frequency band L_{2} are weaker in signal strength than the signals from frequency band L_{1}. This means that the SNR is lower and thus the reflector height determination is more difficult using the L_{2} signals.

For the phase delay method, the number of solutions can be up to the sampling rate of the receiver. In this study, solutions were produced every 10 min (144 solutions per day). However, as reported before by [22], the geodetic receiver connected to the nadir-looking antenna has problems with keeping track of the satellite signals when the sea surface is rough, which usually corresponds to wind speeds of about 7 to 9 m/s. This means that for these conditions, there are no phase delay solutions.

Both analysis methods show high correlation with the independent tide gauge record, resulting in correlation coefficients of 0.95 to 0.97. The exception is the SNR technique for frequency, with correlation coefficients of 0.86 to 0.87 (see Table 1). The phase delay analysis performs similarly for both frequencies and for both systems with RMS differences of 3.5 cm for GPS and 3.2 to 3.3 cm for GLONASS. As previously mentioned, the results from phase delay analyses show an overall better agreement with the tide gauge sea level than the results from SNR analyses. The SNR analysis achieves an RMS difference of 4.0 to 4.7 cm for frequency L_{1}, whereas the RMS difference is around 9.0 cm for frequency L_{2}.

## 6 Discussion

The two GNSS, GPS and GLONASS, have a similar performance for both analysis methods. However, there are some built-in differences for the two systems: the number of orbiting satellites, which is higher for GPS, and the multiple access technique used. The former impacts, for the SNR analysis, the number of possible sea level solutions per day and, for the phase delay analysis, the robustness of each sea level solution (number of observations in the least-squares solution). The two multiple access techniques used for GPS and GLONASS are code division multiple access (CDMA) and frequency division multiple access (FDMA), respectively. This means that all GPS satellites use the same L_{1} and L_{2} carrier frequencies, whereas GLONASS satellites have slightly different carrier frequencies, separated by multiples of 562.5 and 437.5 kHz for L_{1} and L_{2}, respectively. Furthermore, the GPS carrier frequencies are lower than the GLONASS carrier frequencies.

The different frequencies imply that the size of the reflection area, or footprint, is different for different satellites. However, the difference is very small, e.g., approximating the reflection surface with the first Fresnel zone shows that the maximum difference in footprint, at the same frequency band, for an installation of height 4.3 m over the sea surface, and at elevation angles of 5°, 10°, and 15°, is 7.3, 1.8, and 0.8 m^{2}, respectively. This is much smaller than the actual size of the footprint and the difference decrease with increasing elevation angle.

An additional difference regarding satellite footprints is the repetition frequency of each footprint. For the GPS, the satellite constellation repeats approximately every 12 sidereal hours, which means that the footprint repeats with the same period [24] and each satellite will illuminate the same area every orbit. However, for GLONASS, the satellite constellation repeats approximately every 8 sidereal days. Thus, the combined use of GPS and GLONASS gives both a better temporal resolution (more sea level observations per time unit) and spatial resolution (better sea surface coverage per time unit) than each system alone.

_{1}compared to wind speed, both for SNR analysis and phase delay analysis, is presented in Figure 8.

In Figure 8, the tide gauge errors of the SNR solution are distributed around zero for wind speeds between 0.5 and 17.5 m/s, which were the lowest and highest wind speeds observed during the campaign, respectively. It does not appear that there are significantly less SNR-derived results at higher wind speeds than at lower wind speeds, and there is no visible correlation between the tide gauge error and increasing wind speed. However, the number of phase delay solutions decreases around wind speeds of 6 m/s, and no phase delay results are available for wind speeds of over 11 m/s, which is consistent with [22]. The corresponding graphs for the GNSS sea level results derived from frequency L_{2} observations show similar results.

The reason that the SNR analysis results do not appear to be affected by an increase in sea surface roughness, or wind speed, is most probably due to the receiver, which is locking on to the direct satellite signal (or actually the composite signal comprised by the direct and the multipath signals). The amplitude of the SNR oscillations depends on both the direct and reflected signal power (see e.g., [28]) and will therefore decrease with a decreasing signal power from the reflected signal, i.e., with increasing sea surface roughness. However, even if the reflected signal is weaker, it will still interfere with the direct signal, creating oscillations in the SNR data. In addition, the sea surface roughness effect on the reflected signal is not linearly dependent on the wind speed.

## 7 Conclusions

The aim of this study was to evaluate and compare sea level solutions from two analysis methods (SNR analysis and phase delay analysis), two GNSS (GPS and GLONASS), and two carrier frequency bands (L_{1} and L_{2}). The SNR analysis uses multipath signals observed with an upward-looking antenna, and the phase delay analysis uses the phase delay for both an upward- and a downward-looking antenna. This study has been made possible with the GNSS tide gauge installation at OSO, which is equipped with both a zenith-looking RHCP antenna and a nadir-looking LHCP antenna. The two antennas are both connected to a geodetic GNSS receiver each. Both GPS and GLONASS L_{1} and L_{2} signals were recorded during 1 month and analyzed with both analysis methods, and the results were compared in a relative sense to independent measurements of sea level from a co-located pressure tide gauge.

The GNSS-derived sea level shows a high correlation with the tide gauge sea level for both analysis methods. Correlation coefficients for the phase delay analysis and for the SNR analysis using frequency L_{1} are 0.95 to 0.97, whereas the correlation coefficients for the SNR analysis using frequency L_{2} are 0.86 to 0.87.

Comparing the results from the SNR analysis and the phase delay analysis for frequency L_{1}, it is clear that the sea level from phase delay analysis shows a better agreement with the independent tide gauge sea level than the sea level from SNR analysis. Expressed as RMS differences, the phase delay analysis achieves values of 3.5 cm (GPS) and 3.3 cm (GLONASS), whereas the SNR analysis achieves higher values of 4.0 cm (GPS) and 4.7 cm (GLONASS). The lower RMS difference for the phase delay analysis as compared to the SNR analysis is consistent with [14], where the GPS L_{1} results were compared to a synthetic tide gauge. In this study, we additionally show that the phase delay analysis results for frequency L_{2} are on the same level of precision as those of frequency L_{1} with RMS differences of 3.5 cm (GPS) and 3.2 cm (GLONASS). However, the results derived from the SNR analysis at frequency L_{2} give larger RMS differences than for L_{1} with values of 9.0 cm (GPS) and 8.9 cm (GLONASS).

The two GNSS show a similar performance when compared to the tide gauge record. From the results, our conclusions are that, for the phase delay analysis, it is possible to use both frequency bands and, for the SNR analysis, frequency band L_{2} should be avoided if other signals are available. Note that standard geodetic receivers using code-based tracking, i.e., tracking the un-encrypted C/A-code on L_{1} and using the manufacturers’ proprietary tracking method for L_{2}, were used. Signals with the new C/A-code on L_{2}, the so-called L_{2C}, have proven useful for SNR analysis in previous studies (see e.g., [14, 27]), but these signals were not used in this study.

It has previously been shown in [22] that the phase delay method for GPS frequency L_{1} has difficulties during rough sea conditions, as indicated by a decreasing number of solutions for wind speeds between 7 and 9 m/s. In this study, this result is confirmed by investigating the difference between the GNSS L_{1}/L_{2} phase delay sea level and the tide gauge sea level for increasing wind speeds. In addition, the same investigation was made with the SNR method for GNSS L_{1}/L_{2}. The latter results showed that no visible effects were found for wind speeds up to 17.5 m/s (which was the maximum wind speed observed during the campaign).

For the future, we anticipate multi-GNSS solutions, i.e., combining several GNSS. For example, using both GPS and GLONASS signals together will increase the number of observations in a combined phase delay analysis, providing more accurate sea level estimates. The combination of GPS and GLONASS for the SNR analysis will increase the temporal resolution of the corresponding sea level results. Additionally, an improved handling of antenna phase center variations and signal reflection effects will allow time series of absolute sea level.

The next level of combination will be to use multi-GNSS, multi-frequency, phase delay, and SNR analysis in a filter approach, in order to benefit from the individual advantages. Doing so, we expect that it will be possible to derive continuous and accurate absolute GNSS sea level time series in a wide range of wind speeds.

## Declarations

### Acknowledgements

The authors would like to thank Mattias Bermell Rudfelt, Daniel Hermansson, Annika Johansson, and Otto Torgnyson for their Bachelor thesis on analysis of SNR observations from GPS and GLONASS.

## Authors’ Affiliations

## References

- Martin-Neira M: A Passive Reflectometry and Interferometry System (PARIS): application to ocean altimetry.
*ESA J*1993, 17: 331-355.Google Scholar - Caparrini M, Egido A, Soulat F, Germain O, Farres E, Dunne S, Ruffini G: Oceanpal®;: monitoring sea state with a GNSS-R coastal instrument.
*Paper presented at the International Geoscience and Remote Sensing Symposium, IEEE, Barcelona, Spain,*23–28 July 2007, doi:10.1109/IGARSS.2007.4424004Google Scholar - Lowe ST, Zuffada C, Chao Y, Kroger P, Young LE: 5-cm-Precision aircraft ocean altimetry using GPS reflections.
*Geophys. Res. Lett*2002, 29(10):1375. doi:10.1029/2002GL014759View ArticleGoogle Scholar - Gleason S, Hodgart S, Sun Y, Gommenginger C, Mackin S, Adjrad M, Unwin M: Detection and processing of bistatically reflected GPS signals from low Earth orbit for the purpose of ocean remote sensing.Trans.
*Geosci. Remote Sensing*2005, 43(6):1229-1241. doi:10.1109/TGRS.2005.8456432View ArticleGoogle Scholar - Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (Eds):
*Observations: Oceanic Climate Change and Sea Level, in Climate Change 2007: The Physical Science Basis*. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2007.Google Scholar - Lidberg M, Johansson JM, Scherneck H-G, Milne GA: Recent results based on continuous GPS observations of the GIA process in Fennoscandia from BIFROST.
*J. Geod.*2010, 50(1):8-18. doi:10.1016/j.jog.2009.11.010 10.1016/j.jog.2009.11.010View ArticleGoogle Scholar - Wöppelmann G, Letetrel C, Santamaria A, Bouin M-N, Collilieux X, Altamimi Z, Williams SDP, Martin Miguez B: Rates of sea-level change over the past century in a geocentric reference frame.
*Geophys. Re. Lett*2009, 36: L12607. doi:10.1029/2009GL038720View ArticleGoogle Scholar - Schöne T, Schön N, Thaller D: IGS Tide Gauge benchmark monitoring pilot project (TIGA): scientific benefits.
*J. Geod*2009, 83: 249-261. doi:10.1007/s00190-008-0269-y 10.1007/s00190-008-0269-yView ArticleGoogle Scholar - Georgiadou Y, Kleusberg A: On carrier signal multipath effects in relative GPS positioning.
*Manusc. Geod*1988, 13: 172-179.Google Scholar - Elósegui P, Davis JL, Jaldehag RTK, Johansson JM, Niell AE, Shapiro II: Geodesy using the Global Positioning System: the effects of signal scattering on estimates of site position.
*J. Geophys. Res. 100*1995, B6: 2156-2202. doi:10.1029/95JB00868Google Scholar - Anderson KD: A GPS tide gauge.
*GPS World Showcase*1995, 6: 44-44.Google Scholar - Anderson KD:
*Remote Sensing: A Valuable Source of Information*, Paper presented at the AGARD SPP Symposium. Toulouse, France, 22–25 April 1996Google Scholar - Anderson KD: Determination of water level and tides using interferometric observations of GPS Signals.
*J. Atmos. Oceanic Technol*2000, 17(8):1118-1127. doi:10.1175/1520-0426(2000)017<1118:DOWLAT>2.0.CO;2 10.1175/1520-0426(2000)017<1118:DOWLAT>2.0.CO;2View ArticleGoogle Scholar - Larson KM, Löfgren JS, Haas R: Coastal sea level measurements using a single geodetic GPS receiver.
*Adv. Space Res*2013, 51(8):1301-1310. doi:10.1016/j.asr.2012.04.017 10.1016/j.asr.2012.04.017View ArticleGoogle Scholar - Larson KM, Ray RD, Nievinski FG, Freymueller JT: The accidental tide gauge: a GPS reflection case study from Kachemak Bay Alaska.
*IEEE Geosci. Remote Sens. Lett*2013, 10(5):1200-1204. doi:10.1109/LGRS.2012.2236075View ArticleGoogle Scholar - Löfgren JS, Haas R, Scherneck H-G: Sea level time series and ocean tide analysis from multipath signals at five GPS sites in different parts of the world.
*J. Geodyn., in press*2014. doi:10.1016/j.jog.2014.02.012Google Scholar - Rodriguez-Alvarez N, Bosch-Lluis X, Camps A, Ramos-Perez I, Valencia E, Park H, Vall-llossera M: Water level monitoring using the interference pattern GNSS-R technique.
*IEEE Geoscience and Remote Sensing Symposium (IGARSS)*Vancouver, 24–29 July 2011, pp. 2334–2337. doi:10.1109/IGARSS.2011.6049677Google Scholar - Dunne S, Soulat F, Caparrini M, Germain O, Farres E, Barroso X, Ruffini G: Oceanpal®;, a GPS-reflection coastal instrument to monitor tide and sea-state. Paper presented at Oceans 2005 - Europe 2 . Brest, France, 20–23 June 2005. doi:10.1109/OCEANSE.2005.1513257Google Scholar
- Belmonte Rivas M, Martin-Neira M: Coherent GPS reflections from the sea surface.
*IEEE Geosci. Remote Sens. Lett*2006, 3: 28-31. doi:10.1109/LGRS.2005.855617 10.1109/LGRS.2005.855617View ArticleGoogle Scholar - Martin-Neira M, Colmenarejo P, Ruffini G, Serra C: Altimetry precision of 1 cm over a pond using the wide-lane carrier phase of GPS reflected signals. Can.
*J. Rem. Sens*2002, 28(3):394-403. doi:10.5589/m02-039 10.5589/m02-039View ArticleGoogle Scholar - Löfgren JS, Haas R, Johansson JM: Monitoring coastal sea level using reflected GNSS signals.
*J. Adv. Space Res*2011, 47(2):213-220. doi:10.1016/j.asr.2010.08.015 10.1016/j.asr.2010.08.015View ArticleGoogle Scholar - Löfgren JS, Haas R, Scherneck H-G, Bos MS: Three months of local sea level derived from reflected GNSS signals.
*Radio Sci.*2011, 46: RS0C05. doi:10.1029/2011RS004693View ArticleGoogle Scholar - Intergovernmental Oceanographic Commission (IOC) of UNESCO:
*Manual on Sea-level Measurements and Interpretation, Volume IV: An upyear to 2006, IOC Manuals and Guides No.14, vol. IV*. (JCOMM Technical Report No.31; WMO/TD. No.1339, 78 pp, Paris, 2006)Google Scholar - Hofmann-Wellenhof B, Lichtenegger H, Collins J:
*GPS Theory and Practice, 5th edn.*. (Springer, Wien, New York, 2001), ISBN 3-211-83534-2Google Scholar - Hannah BM: Modelling and simulation of GPS multipath propagation. Dissertation, Queensland University of Technology, 2001Google Scholar
- Larson KM, Small EE, Gutmann ED, Bilich AL, Braun JJ, Zavorotny VU: Use of GPS receivers as a soil moisture network for water cycle studies.
*Geophys. Res. Lett*2008, 35: L24405. doi:10.1029/2008GL036013View ArticleGoogle Scholar - Larson KM, Gutmann ED, Zavorotny VU, Braun JJ, Williams MW, Nievinski FG: Can we measure snow depth with GPS receivers?
*Geophys. Res. Lett*2009, 36: L17502. doi:10.1029/2009GL039430View ArticleGoogle Scholar - Bilich A, Larson KM: Mapping the GPS multipath environment using the signal-to-noise ratio (SNR).
*Radio Sci*2007, 42: RS6003. doi:10.1029/2007RS003652View ArticleGoogle Scholar - Benton CJ, Mitchell CN: Isolating the multipath component in GNSS signal-to-noise data and locating reflecting objects.
*Radio Sci*2011, 46: RS6002. doi:10.1029/2011RS004767View ArticleGoogle Scholar - Blewitt G: Basics of the GPS technique: observation equations.
*Geodetic Applications of GPS*ed. by B Johnson, (Nordic Geodetic Commission, Sweden, 1997). ISSN 0280-5731, 10–54Google Scholar - Dow JM, Neilan RE, Rizos C: The International GNSS Service in a changing landscape of Global Navigation Satellite Systems.
*J. Geod*2009, 83: 191-198. doi:10.1007/s00190-008-0300-3 10.1007/s00190-008-0300-3View ArticleGoogle Scholar - Nievinski FG: Forward and inverse modeling of GPS multipath for snow monitoring. Dissertation, Department of Aerospace Engineering Sciences, University of Colorado, 2013Google Scholar
- Beyerle G: Carrier phase wind-up in GPS reflectometry.
*GPS Solut.*2009, 13: 191-198. doi:10.1007/s10291-008-0112-1 10.1007/s10291-008-0112-1View ArticleGoogle Scholar - Löfgren JS: Local sea level observations using reflected GNSS signals, Dissertation, Department of Earth and Space Sciences. Chalmers University of Technology, 2014Google Scholar
- Miguez BM, Testut L, Wöppelmann G: The Van de Casteele test revisited: an efficient approach to tide gauge error characterization.
*J. Atmos. Oceanic Technol*2008, 25: 1238-1244. doi:10.1175/2007JTECHO554.1 10.1175/2007JTECHO554.1View ArticleGoogle Scholar

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