Potential of Cost-Efficient Single Frequency GNSS Receivers for Water Vapor Monitoring
<p>Positions of the SEID reference stations of the DPGA (red triangles) and EUREF (purple stars) constellations, the experimental setup in Delft (green square), and the AWS (orange cross). The sites DLF1, Geo Monitoring Unit (GMU), and u-blox NEO-M8T evaluation toolkit (UBX) are all co-located within a 10 m<sup>2</sup> area.</p> "> Figure 2
<p>ZTD scatter plots of all post-processed reference datasets at DLF1 in 2017. The solid red line depicts the regression line. RMSE, bias, and std values are in millimeter. The r-value depicts the correlation.</p> "> Figure 3
<p>ZTD scatter plots of the IGS reference dataset, the dual-frequency PPP-processed DLF1 dataset (DLF1_DF), and the SEID-processed single-frequency data from UBX, GMU, and DLF1_SF utilizing the DPGA SEID reference network. The plots on the left depict the comparison of the goGPS estimations to the IGS reference dataset. The scatter plots on the right show the SEID and instrument errors. The observation period is from 1 January 2017 (DLF1_DF & DLF1_SF) and 1 September 2017 (GMU & UBX) until 31 December 2017. The solid red line depicts the regression line.</p> "> Figure 4
<p>ZTD scatter plots of the reference dataset IGS, the dual-frequency PPP-processed DLF1 dataset, and the SEID-processed single-frequency data from UBX, GMU, and DLF1_SF utilizing the EUREF SEID constellation. The plots on the left depict the comparison of the goGPS dual-frequency estimation to the IGS reference dataset. The scatter plots on the right show the SEID and instrument errors. The observation period is from 1 September 2017 until 31 December 2017. The solid red line depicts the regression line.</p> "> Figure 5
<p>ZTD differences between the goGPS-processed dual-frequency reference dataset and the SEID-processed single-frequency estimations DLF1_SF, GMU, and UBX. The figures at the top depict the DPGA SEID constellation results, whilst the ones on the bottom are from the EUREF SEID constellation, respectively. DLF1_SF (<b>a</b>) is the synthesized single-frequency dataset obtained from the IGS stations DLF1. GMU (<b>b</b>) and UBX (<b>c</b>) are the two cost-efficient single-frequency receivers utilizing different antenna types but sharing the same receiver type. A comparison period of 1 September–31 December 2017 was selected since all receivers provide observations during this period.</p> "> Figure 6
<p>Boxplots of the SEID-single-frequency ZTD differences to the DLF1_DF reference using the DPGA SEID constellation (blue) and the EUREF reference stations (orange).</p> "> Figure 7
<p>Cumulative error distribution plots of the single-frequency ZTD estimations DLF1_SF, GMU, and UBX compared to the DLF1_DF reference dataset. The figure at the left (<b>a</b>) depicts the DPGA SEID constellation results, while (<b>b</b>) is from the EUREF SEID case, respectively.</p> "> Figure 8
<p>Positions of the Italy-experiment SEID reference stations constellation (red triangles) and experimental setup (green square). The sites GRTR, GRED, and SAPH use the same Trimble Zephyr 2 antenna (TRM55971.00). GRTR is the dual-frequency receiver reference (Trimble BD930 receiver), GRED the cost-efficient single-frequency device (u-blox), and SAPH a low-cost experimental dual-frequency receiver.</p> "> Figure 9
<p>ZTD differences between GRTR_SF and GRTR_DF (<b>a</b>), GRTR_DF and SAPH_DF (<b>b</b>), and GRTR_SF - GRED_SF (<b>c</b>). The GRTR_SF and GRED_SF estimations are based on the SEID algorithm, whilst GRTR_DF and SAPH_DF use PPP-only.</p> ">
Abstract
:1. Introduction
2. Methodology
2.1. Water Vapor from GNSS Measurements
2.2. SEID Ionospheric Delay Modeling
2.3. Experimental Setup and Data Processing
3. Results
3.1. Inter-Comparison of Different ZTD Reference Datasets
3.2. SEID-PPP-Processed ZTD Estimations
3.3. PWV Computation
3.4. Splitting of A Geodetic Antenna to Different Receiver Types (Italy)
4. Discussion
4.1. Inter-Comparison of Reference Datasets and Analysis of the Software-Related Error
4.2. SEID DPGA Experiment and Antenna Impact
4.3. SEID EUREF Experiment
4.4. PWV Estimations
4.5. Antenna Splitting
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
AC | Analysis Center |
AWS | Automatic Weather Station |
BKG | Federal Agency for Cartography and Geodesy |
CDDIS | Crustal Dynamics Data Information System |
COCONet | Continuously Operating Carribbean GPS Observational Network |
DLF1_DF | IGS station DLF1 (dual-frequency) |
DLF1_SF | IGS station DLF1 (single-frequency) |
DPGA | Dutch Permanent GNSS Array |
EGM08 | Earth Gravity Model 2008 |
E-GVAP | EUMETNET GNSS water vapor Programme |
EPN | EUREF Permanent Network |
GFZ | German Research Centre for Geosciences |
GMF | Global Mapping Function |
GMU | GeoGuard Monitoring Unit |
GNSS | Global Navigation Satellite System |
GPS | Global Positioning System |
GPT | Global Pressure/Temperature |
IGS | International GNSS Service |
IP | Ingress Protection |
IPP | Ionospheric Pierce Point |
IWV | Integrated Water Vapor |
KNMI | Royal Netherlands Meteorological Institute |
LPT | Federal Office of Topography |
NGL | Nevada Geodetic Laboratory |
NRT | Near-Real Time |
PPP | Precise Point Positioning |
PWV | Precipitable Water Vapor |
RF | Radio Frequency |
RINEX | Receiver Independent Exchange Format |
RMSE | Root Mean Square Error |
ROB | Royal Observatory of Belgium |
RTK | Real-Time Kinematic |
SEID | Satellite-specific and Epoch-differenced Ionospheric Delay |
STD | Slant Total Delay |
teqc | translation, editing and quality check |
UBX | u-blox NEO-M8T evaluation toolkit |
ZHD | Zenith Hydrostatic Delay |
ZTD | Zenith Tropospheric Delay |
ZWD | Zenith Wet Delay |
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Item | Processing Strategies |
---|---|
Software | goGPS v. 0.4.3 |
Observations | GPS-only |
Sampling interval | 30-second |
Processing mode | SEID-PPP |
Antenna calibration | IGS (if available) |
Troposphere modeling | Saastamoinen (with GPT model) |
Troposphere mapping function | GMF |
Elevation cutoff | 10 |
Ocean loading | FES2004 |
Observation weighting | same weight for all observations |
Clock & orbits | IGS Final |
Kalman filter reset | no (seamless) |
Code observation error threshold | 30 m |
Phase observation error threshold | 0.05 m |
Code least-squares estimation error st. dev. threshold | 40 m |
AC | Processing Engine | Processing Method | Cutoff () | Resolution | Missing Days |
---|---|---|---|---|---|
IGS | Bernese 5.0 & 5.2 | PPP | 7 | 5 min | 10 |
NGL | GIPSY/OASIS II | PPP | 7 | 5 min | 10 |
BKG | Bernese 5.2 | Double-Differences | 3 | 60 min | 1 |
LPT | Bernese 5.3 | Double-Differences | 3 | 60 min | 4 |
ROB | Bernese 5.2 | Double-Differences | 3 | 60 min | 4 |
Case | Site | RMSE [mm] | Bias [mm] | [mm] | Corr | %≥3 | %≥10 mm | %Missing |
---|---|---|---|---|---|---|---|---|
DPGA | DLF1_SF | 3.93 | 0.52 | 3.90 | 0.9967 | 1.64 | 2.61 | 0.46 |
GMU | 5.55 | 0.99 | 5.46 | 0.9938 | 1.44 | 6.77 | 7.10 | |
UBX | 7.10 | −4.96 | 5.08 | 0.9945 | 2.63 | 12.68 | 0.87 | |
EUREF | DLF1_SF | 10.32 | −0.20 | 10.32 | 0.9769 | 0.92 | 29.04 | 0.28 |
GMU | 10.20 | 1.11 | 10.14 | 0.9772 | 0.62 | 29.87 | 6.89 | |
UBX | 12.09 | −4.93 | 11.04 | 0.9731 | 1.38 | 37.12 | 0.86 |
Case | Site | RMSE [mm] | Bias [mm] | [mm] |
---|---|---|---|---|
SEID (DPGA) | DLF1_SF | 0.60 | 0.08 | 0.59 |
GMU | 0.85 | 0.17 | 0.84 | |
UBX | 1.05 | -0.72 | 0.77 | |
SEID (EUREF) | DLF1_SF | 1.61 | -0.03 | 1.61 |
GMU | 1.60 | 0.20 | 1.59 | |
UBX | 1.86 | -0.70 | 1.72 |
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Krietemeyer, A.; Ten Veldhuis, M.-c.; Van der Marel, H.; Realini, E.; Van de Giesen, N. Potential of Cost-Efficient Single Frequency GNSS Receivers for Water Vapor Monitoring. Remote Sens. 2018, 10, 1493. https://doi.org/10.3390/rs10091493
Krietemeyer A, Ten Veldhuis M-c, Van der Marel H, Realini E, Van de Giesen N. Potential of Cost-Efficient Single Frequency GNSS Receivers for Water Vapor Monitoring. Remote Sensing. 2018; 10(9):1493. https://doi.org/10.3390/rs10091493
Chicago/Turabian StyleKrietemeyer, Andreas, Marie-claire Ten Veldhuis, Hans Van der Marel, Eugenio Realini, and Nick Van de Giesen. 2018. "Potential of Cost-Efficient Single Frequency GNSS Receivers for Water Vapor Monitoring" Remote Sensing 10, no. 9: 1493. https://doi.org/10.3390/rs10091493