Skip to main content
NCBI home page
Wiley - PMC COVID-19 Collection logoLink to Wiley - PMC COVID-19 Collection
. 2020 Sep 29;47(19):e2020GL089647. doi: 10.1029/2020GL089647

Imprints of COVID‐19 Lockdown on GNSS Observations: An Initial Demonstration Using GNSS Interferometric Reflectometry

Makan A Karegar 1,✉, Jürgen Kusche 1
PMCID: PMC7537003  PMID: 33041388

Abstract

The ongoing coronavirus disease 2019 (COVID‐19) pandemic has imposed tight mobility restrictions in urban areas, causing substantial reduction in roadway traffic. Many public parking lots are nearly vacant as people across the world have gone on lockdown since mid‐March. This environmental change may have impacts on Global Navigation Satellite System (GNSS) sensors installed on roof of buildings. Here, we use a monitoring site in Boston to exemplify a likely sensitivity of precise GNSS sensors to their nearby dynamic environments including parked vehicles in parking lots. We show that reduced number of parked vehicles since 23 March has decreased the reflector roughness, resulting in an increase in the reflected signal power whose amplitude is quantified by GNSS interferometric reflectometry technique. The uncertainty of retrieved GNSS antenna height drops with beginning of lockdown, allowing more accurate estimate of reflector height, which could have a general implication for better understanding of the fundamental limitations of the technique.

Keywords: Global Navigation Satellite System, COVID‐19, interferometric reflectometry, multipath signal

Key Points

  • The reflected signal (RS) from a parking lot next to a precise GNSS sensor is analyzed using GNSS interferometric reflectometry technique

  • The amplitude of RS increases since 23 March 2020 with the reflector surface becoming more planer due to absence of cars in the parking lot

  • The uncertainty of retrieved antenna height from RS drops with beginning of lockdown, allowing more accurate estimate of antenna height

1. Introduction

The current global pandemic of the coronavirus disease 2019 (COVID‐19) is causing substantial mobility restrictions within urban and rural areas. Scientists and researchers in a variety of fields are deploying resources to understand the economic, social, psychological, and environmental effects of the COVID‐19 pandemic. However, this crisis provides a unique opportunity for researchers in Earth Science to study the environmental impacts of global lockdown through the Earth system monitoring technologies and, vice versa, to understand the nature of ambient cultural noise that in normal times limits the sensitivity of sensors. In the first effort, geoscientists compared satellite images from National Aeronautics and Space Administration (NASA) and European Space Agency (ESA) missions prior to and following the shutdown and showed a massive decline in pollution level (e.g., nitrogen dioxide concentrations) associated with reduced fossil fuel productions in most cities across the world (ESA, 2020; NASA, 2020). The next effort came with connections between human‐induced ground vibrations—mostly caused by moving vehicles and industrial machinery—and dramatic decline in transport networks and urban life resulting from the COVID‐19 outbreak (Gibney, 2020; Lecocq et al., 2020; Poli et al., 2020). Seismologists who study Earth's vibrations using seismometers—highly sensitive detectors—greatly benefit these days from receiving cleaner and less noisier vibrations coming from crashing ocean waves, volcanic activity, and natural earthquakes. This will allow seismologists to detect smaller earthquakes, better probe Earth's interior structure, and better separate anthropogenic and natural microseismicity and genuine instrument noise. Motivated by these two efforts, we as geodesists who study Earth's shape and size seek to contribute to the Earth science community through our new findings related to COVID‐19 shutdown.

Precise positioning using Global Navigation Satellite System (GNSS) is a critical tool for geodesy. For Earth sciences, high‐precision GNSS sensors have been instrumental by enabling a breakthrough in measuring Earth's land motion through global and regional monitoring networks. Figure 1 shows the present spatial distribution of permanent GNSS sites that is publicly available to the scientific community (Blewitt et al., 2018). GNSS data at these sites provide robust coverage over most of the Europe, Japan, the United States, and Eastern Australia. The availability of continuous GNSS observations at an ever‐increasing number of regional and global permanent stations, improved processing and analysis techniques (e.g., Herring et al., 2016), longer time spans of data (e.g., Mao et al., 1999; Santamaría‐Gómez et al., 2011), and better realization of reference frames (e.g., Altamimi et al., 2016; Rebischung et al., 2016) provide a means to better study Earth's surface deformation.

Figure 1.

Figure 1

Open in a new tab

Global distribution of precise continuously operating reference GNSS sites (red dots) as of June 2020. The locations of GNSS sites are available from Blewitt et al. (2018). The blue dots are GNSS sites within urban areas. The urban extent data are from Schneider et al. (2009). The plate tectonic boundaries are shown with black lines. Inset plot is cumulative number of GNSS sites around the globe and in urban areas.

To achieve the highest precision for positions (millimeter level) and for long‐term velocities (submillimeter per year level), identifying and mitigating sources of noise is important, especially for real‐time applications (Blewitt et al., 2009). Among others, local environmental factors, describing the structures and terrain around an antenna, can significantly affect the estimates of site position and its motion (Bock & Melgar, 2016). This is important for many GNSS instruments that are installed on roof of buildings (for easier access to power supply and long‐term maintenance) because the environments surrounding buildings can be “kinematic,” regularly being changed over time. Daily variations in the number of parked motor vehicles is the most abrupt and apparent change. Under the current strict quarantine, the use of private mobility has dramatically reduced. Many public and shared parking lots have now been left deserted as billions of people have gone on lockdown due to closure of nonessential businesses and governmental offices. The absence of motor vehicles in parking lots during this unusual time can give us some insights into impact of nonstationary environmental noise on GNSS observations. We make use of an innovative and emerging GNSS technique, GNSS interferometric reflectometry (GNSS‐IR), at a monitoring sensor in Boston (United States) to demonstrate the potential influence of motor vehicles in surrounding terrains on the strength of reflecting signals.

2. Method

For geodetic studies of Earth processes that require high‐precision positioning, the ground‐reflected signal is a source of noise, requiring attempt to suppress (Bilich & Larson, 2007; Georgiadou & Kleusberg, 1988; Ragheb et al., 2007). However, the analysis of reflected signal and its interference with direct signal has recently been used to infer useful information about reflecting surface in the vicinity of the GNSS antenna (e.g., Gutman et al., 2009; Karegar et al., 2020; Larson et al., 2008; Larson, Braun, et al., 2009; Larson, Gutmann, et al., 2009; Larson, Lofgren, et al., 2013; Larson, Ray, et al., 2013; Siegfried et al., 2017).

2.1. GNSS Interferometric Reflectometry

A GNSS sensor receives electromagnetic signals directly from a network of satellites orbiting around the Earth. After collecting thousands of observations at different orientations and applying a series of precise corrections to raw observations at postprocessing level, the precise position of the GNSS antenna is estimated (Herring et al., 2016; Karegar et al., 2018). Beside direct signals, a GNSS sensor receives reflected signals from objects nearby the antenna such as ground surface, buildings, vehicles, trees and vegetations, water, and any nearby infrastructure. These signals, often called indirect or multipath signals, travel a longer path than the direct signals, reaching the antenna later and interfering with the direct signals. The interferometric signals are recorded as signal‐to‐noise ratio (SNR) measurements (Figure 2). For a horizontal and planar ground reflection (flat terrains), the daily SNR measurements show oscillating patterns that can be modeled using a sinusoidal motion with the so‐called multipath frequency, 4πH r/λ depending on the wavelength of the direct signals (λ, GNSS L‐band frequency) and antenna height (H r):

SNR=Asin4πHrλsine+φ (1)

where A and φ are amplitude and phase of SNR data, respectively, and e is satellite elevation angle with respect to local horizon. The antenna height (H r) is the vertical distance between the phase center of GNSS antenna and top of the ground surface (Figure 2a). To isolate multipath oscillation cycles in the daily SNR data, contribution of direct signal is removed by fitting a low‐order polynomial to SNR measurements (Bilich & Larson, 2007). A number of different power spectral density (PSD) analysis methods can be used to analyze periodic oscillations in SNR data and to identify the dominant frequency. Once the multipath frequency (f) is found, the antenna height is computed as H r = λf/2.

Figure 2.

Figure 2

Open in a new tab

(a) GNSS interferometric reflectometry geometry for a horizontal planar reflector. A GNSS antenna measures the interference between the direct (blue) and reflected (red) signals (modified from Larson, 2016). (b) Signal‐to‐noise ratio (SNR) data on L1 C/A and L2 C/A frequencies as a function of satellite elevation angle for a GPS satellite track with Pseudo‐Random Noise (PRN) Code 28 and mean azimuth 166.47° at NOAA's CORS site MAMI with choke ring antenna (Figure 3) for one single day. The direct signals show long‐term trends (red lines) and the reflected signals represent oscillation patterns (at low elevation angles) in SNR data.

2.2. Site Descriptions and Data Acquisition

To illustrate the kinematic variations in reflected signal, we present an example of a geodetic GNSS sensor in an urban environment. The precise GNSS sensor MAMI is operated by Massachusetts Department of Transportation (DoT) and located on roof of a one‐story brick building belonging to DoT in Boston, the United States (latitude: 42.272°, longitude: −71.049°). This GNSS sensor collects data with 30‐s rate from U.S. GPS and Russian GLONASS satellites since March 2014 and monitors the movement of Earth's land with an uncertainty better than 0.7 mm/year (Blewitt et al., 2018). The GNSS equipment consists of two components: (i) LEICA GRX1200 + GNSS receiver installed on March 2014 and replaced with LEICA GR50 receiver on August 2017 and (ii) Leica AR20 three‐dimentioal choke ring antenna (Bedford et al., 2009; Walford, 2009) with a radome for protection (Figure 3). The raw GNSS data are archived by National Oceanic and Atmospheric Administration (NOAA)'s Continuously Operating Reference Station (CORS). The GNSS RINEX Version 2.11 daily data (up to mid‐July 2020) were obtained from the CORS's public ftp server (https://www.ngs.noaa.gov/CORS/data.shtml). The choke ring model is a standard geodetic antenna for permanent reference station and high‐precision monitoring applications (Tranquilla et al., 1994). The central antenna is surrounded by a few concentric ring structures, which suppress multipath signals from low elevations and improve the quality of the GNSS observables. The choke ring design and the gain pattern of the antenna cause the magnitude of reflected signal to be smaller than direct signal, but the multipath signals are not completely rejected and interfere with direct signal at low elevation angles (Figure 2b).

Figure 3.

Figure 3

Open in a new tab

(a, b) Site photos of GNSS site MAMI and choke ring antenna provided by NOAA's National Geodetic Survey and Massachusetts Department of Transportation. (c) Google Earth street view of GNSS sites and the parking lot.

The approximate 7.45‐m antenna height from the ground allows sensing of the first Fresnel zone with maximum dimensions of 8 m by 95 m for each satellite ground tracks where satellite signals are first bounced off from these areas and then reach to the GNSS antenna (Figure 4). We imposed azimuth and elevation angle masks to limit the reflection data to the parking lot next to the GNSS antenna. The parking lot is 60 m by 120 m flat with asphalt surface and the reflector surface can be regarded as a horizontal plane (Figure 3). However, available historical images back to 2019 and 2018 from Google Earth show that the parking lot is completely filled with vehicles during the week days (see supporting information Figure S1). The parking lot is a free Park and Ride facility located in the suburb of Greater Boston with public transport connections that allow public commuters to leave their vehicles overnight for travel to city centers (https://www.mass.gov/locations/milton-park-and-ride). Thus, the parking lot may be occupied during day, night and weekends.

Figure 4.

Figure 4

Open in a new tab

Overview of MAMI GNSS site and footprints of the reflected GPS and GLONASS L1, L2 signals projected on a Google Earth image taken on 23 June 2019. Yellow ellipses are first Fresnel zones for all available satellites, and cyan ellipses are masked zones for elevation angles between 5° and 16° and azimuths 145°–180°. Azimuth and elevation angle masks were imposed to isolate reflections data from the parking lot from additional sources of reflection.

2.3. SNR Data Processing

We processed the SNR data in 24‐hr batches in the following steps:

  1. The GNSS receiver records the ratio of signal power to noise power spectral density (C/N 0) per unit bandwidth in decibel‐hertz (dB‐Hz) in RINEX file format. Assuming a 1‐Hz bandwidth, SNR quantity is related to C/N 0 using the noise bandwidth (B) as SNR = (C/N 0)/B (Larson, Ray, et al., 2013). The SNR data are extracted from daily RINEX observation files using gnssSNR.f code (Roesler & Larson, 2018) and IGS Standard Product 3 (SP3) GNSS orbits. The SNR‐ready files contain elevation and azimuth angles of GNSS satellites with respect to the station horizon and their SNR values for civilian Coarse Acquisition code (C/A) on the L1 and L2 frequencies from GPS and GLONASS satellites. Note that we limited extracting SNR data to minimum and maximum elevation cutoffs of 5° and 25°. Below elevation angles of 5° the geodetic receivers typically do not track the satellites due to low accuracy for positioning applications, while above 25° the multipath oscillation cycles become weak (Figure 2b).

  2. We imposed satellite azimuth and elevation angle masks to limit the reflections data to the parking lot and to avoid reflections from additional sources such as buildings, trees, and any nearby infrastructure (Figure 4).

  3. We detrended daily SNR data using a third‐order polynomial to remove the contribution of direct signal and to isolate the multipath oscillations (Figure 2b).

  4. We determined the oscillation frequency of each satellite track's SNR data using the spectral analysis. Although daily SNR data are uniformly recorded time series, they are represented unevenly as a function of satellite elevation; thus, we used the Lomb‐Scargle Periodogram (LSP) to identify the dominant frequency (Larson, Gutmann, et al., 2009; Lomb, 1976; Scargle, 1982).

  5. To ensure a significant peak amplitude, we discarded SNR data with a peak‐to‐noise level ratio smaller than 2.7 (Roesler & Larson, 2018). To calculate the noise level, the LSP amplitudes corresponding to a range of scaled frequencies were averaged from 0 to 10 m. SNR data with peak amplitudes smaller than 8 (volt/volt) were discarded.

  6. The LSP frequency (f) was then scaled to the unit of reflector height (meter) as H r = λf/2.

  7. We calculated a quantity called “daily resolution,” total number of quality‐controlled satellite tracks in which the peak‐to‐noise ratio for a reflected signal gets greater than 2.7 threshold.

  8. We then averaged reflector heights and peak amplitudes from all quality‐controlled satellite tracks over 24 hr to produce daily time series.

3. Results and Discussion

A rapid rise is evident in resolution of reflected signals since the beginning of strict lockdown of Boston in 23 March (https://www.mass.gov/doc/reopening-massachusetts-may-18-2020/download) when the parking lot started to become empty (Figure 5). Before the lockdown, the satellite signals were reflected from parked vehicles and scattered around rather than coherently reflecting from the ground surface. On 18 May, the first phase of reopening was announced by the Massachusetts government. Essential business and outdoor services were encouraged to reopen, and since 25 May, many outdoor activities have been back. The resolution of reflected signals gradually drops off since mid‐May to the level prior to the lockdown as the parking lot gets filled by vehicles.

Figure 5.

Figure 5

Open in a new tab

Daily resolution of reflected signal defined as number of satellite tracks with peak‐to‐noise greater than 2.7. On 23 March, a lockdown order was issued in most of counties in Boston. The first phase of reopening was started on 18 May.

The daily averaged powers of reflected signals from all quality‐controlled satellite tracks is used to represent the effect of surface roughness (the parking lot) on the reflected signal. A minimum number of 15 satellite tracks (Figure 5) are required to produce a reliable daily average. The averaged amplitude of indirect signal increases since 23 March with the reflector surface becoming more planer due to fewer cars (Figure 6a), thereby resulting in coherent scattering regime. Comparing Figures 5 and 6a, we find that during the lockdown, there are more satellite tracks for which we are able to retrieve the GNSS antenna height from oscillation in the SNR data.

Figure 6.

Figure 6

Open in a new tab

(a) Daily mean amplitude of reflected signals. Days with number of satellite track smaller than 15 were disregarded (b) (upper panel) Daily reflector height changes. (lower panel) Noise (standard deviation of mean) in reflector height. The noise level in retrieved reflector height drops with beginning of lockdown. The brown horizontal lines represent averaged noise for time spans before the lockdown and during the lockdown.

Figure 6b shows daily average of antenna height from all valid satellite tracks since 2019. We calculated the standard deviation of the mean as the uncertainty of average reflector height. A minimum number of three satellite tracks are used to produce the daily average and its standard deviation. The uncertainty of GNSS antenna height drops with beginning of lockdown, allowing more accurate estimate of reflector height. After the lockdown, the uncertainty reduced around 50%, from average of 4 cm to about 2 cm. In general, the more useful satellite tracks (daily resolution), the more precise the GNSS antenna height tends to be. For example, Figure S2 shows how the relationship between daily resolution and uncertainty of the reflector height changes during the lockdown. As daily resolution increases, the uncertainty tends to decreases.

A rise in amplitude of reflected signals due to demobilization exemplifies likely sensitivity of precise GNSS sensors to their nearby dynamic environments that could arise several implications in geodesy. For instance, precise GNSS is becoming a routine technique for detecting coseismic surface displacements due to earthquakes (e.g., Melbourne et al., 2019). Environmental noises such as vehicles in parking lots may suppress earthquake deformation observing by GNSS instruments in urban areas. This may provide a potential research opportunity and it is worth keeping an eye on. However, stronger and cleaner amplitude in reflected signals can be viewed as a positive alter for applications in GNSS‐IR. For instance, Karegar et al. (2020) have recently made use of GNSS‐IR approach to suggest a new technique in which the reflected signals are analyzed in order to monitor the reflector height changes in coastal areas, allowing for quantification of shallow sediment compaction. Compaction of shallow sediments can potentially play a major role in increasing flooding risk for regions where the sediment is deposited during the Holocene (around 11,500 years before present). It is noteworthy that majority of existing GNSS sites in low elevation coastal zones (e.g., see Karegar et al., 2016, 2020) are installed on the top of buildings in built environments. Thus, obtaining desirable reflections for shallow subsidence monitoring could be challenging.

To ensure the robustness of the results, we repeated the analyses for observations collected during the day time (6 a.m. to 4 p.m. local time) the night time (10 p.m. to 6 a.m.). The subdaily analyses showed similar results. During the night, when the parking lot is effectively empty, flat and slight surface roughness results in larger amplitude of SNR oscillations, higher resolution (Figure S3) and lower noise in retrieved reflector height (Figure S4), relative to the daytime. In contrast, during the day when the parking lot is occupied, as surface roughness increases, the amount of signal scattering increases, which reduces the resolution and causes additional uncertainty in reflector height.

4. Future Prospects

GNSS monitoring networks are essential components for Earth system monitoring technologies. Currently, more than 17,000 real‐time GNSS stations are operating worldwide for monitoring Earth's surface changes and natural hazard assessment. GNSS‐based systems are also used to support applications for the public good, through early warning systems for mitigating the effects of natural geohazards including earthquakes, tsunamis, volcanoes, and extreme weather, and for a better understanding of anthropogenic hazards (Bock & Melgar, 2016). Although it is desired to install GNSS sites far from urban areas to avoid environmental noise, growing number of cities are being equipped with precise and real‐time GNSS sensors to support positioning services for survey engineering purposes (Figure 1), and eventually for crustal deformation studies and the natural hazards assessment (e.g., Karegar et al., 2017; Kreemer et al., 2020; Snay et al., 2016). The number of building‐based reference GNSS sensors are increasing in urban areas because of lower installation and operational costs and convenient installation, although not generally recommended for geodetic purposes due to instabilities (IGS, 2019). For instance, HoustonNet is a permanent GPS network consisting of over 230 stations designed for subsidence and fault monitoring in Houston, United States (Agudelo et al., 2020; Wang, 2019). Majority of these stations are installed on one or two‐story buildings next to parking lots, leaving imprint on time series of antenna height changes, which is recently suggested for shallow subsidence monitoring.

The example shown here demonstrates a clear evidence of environmental impact of parking lots and streets surrounding a monitoring site on GNSS measurements. Such kinematic environments will perturb the amplitude of reflected signals to GNSS sensors and thus leave time‐variable imprints on GNSS observations. For GNSS‐IR applications such as monitoring shallow vertical land motion in low elevation coastal zones, installation of GNSS sensors next to parking lots should be avoided. Quantifying these effects on precise position more challenging but such research could lead us to implications toward better and more precise measure of Earth's surface changes resulting from natural processes.

Supporting information

Supporting Information S1

Click here for additional data file. (4.7MB, pdf)

Acknowledgments

M. A. K. and J. K acknowledge financial support from the German Federal Ministry of Education and Research (BMBF) and the Ministry of Culture and Science of the State of North Rhine‐Westphalia (MKW) as part of the Excellence Strategy of the federal and state governments. We thank Kristine M. Larson for very useful comments on the earlier draft of this manuscript. We thank Manoochehr Shirzaei and an anonymous reviewer for their constructive comments and suggestions.

Karegar, M. A. , & Kusche, J. (2020). Imprints of COVID‐19 lockdown on GNSS observations: An initial demonstration using GNSS interferometric reflectometry. Geophysical Research Letters, 47, e2020GL089647 10.1029/2020GL089647

Data Availability Statement

GNSS raw data processed in this study are archived at National Oceanic and Atmospheric Administration's Continuously Operating Reference Station (http://www.ngs.noaa.gov/CORS/).

References

  1. Agudelo, G. , Wang, G. , Liu, Y. , Bao, Y. , & Turco, M. J. (2020). GPS geodetic infrastructure for subsidence and fault monitoring in Houston, Texas, USA. Proceedings of the International Association of Hydrological Sciences, 382, 11–18. 10.5194/piahs-382-11-2020 [DOI] [Google Scholar]
  2. Altamimi, Z. , Rebischung, P. , Métivier, L. , & Collilieux, X. (2016). ITRF2014: A new release of the International Terrestrial Reference Frame modeling nonlinear station motions. Journal of Geophysical Research: Solid Earth, 121, 6109–6131. 10.1002/2016JB013098 [DOI] [Google Scholar]
  3. Bedford, L. , Brown, N. , & Walford, J. (2009). New 3D Choke ring antenna, proceedings of ION 2009. January 26th‐28th, CAL
  4. Bilich, A. , & Larson, K. M. (2007). Correction published 29 March 2008: Mapping the GPS multipath environment using the signal‐to‐noise ratio (SNR). Radio Science, 42, RS6003 10.1029/2007RS003652 [DOI] [Google Scholar]
  5. Blewitt, G. , Hammond, W. C. , & Kreemer, C. (2018). Harnessing the GPS data explosion for interdisciplinary science. Eos, 99, 1–2. 10.1029/2018EO104623 [DOI] [Google Scholar]
  6. Blewitt, G. , Hammond, W. C. , Kreemer, C. , Plag, H. P. , Stein, S. , & Okal, E. (2009). GPS for real‐time earthquake source determination and tsunami warning systems. Journal of Geodesy, 83, 335–343. 10.1007/s00190-008-0262-5 [DOI] [Google Scholar]
  7. Bock, Y. , & Melgar, D. (2016). Physical applications of GPS geodesy: A review. Reports on Progress in Physics, 79, 106801 10.1088/0034-4885/79/10/106801 [DOI] [PubMed] [Google Scholar]
  8. ESA (2020). http://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-5P/Coronavirus_lockdown_leading_to_drop_in_pollution_across_Europe#.XvmZSIILjJI.link, accessed on 29 June 2020.
  9. Georgiadou, Y. , & Kleusberg, A. (1988). On carrier signal multipath effects in relative GPS positioning. Manuscripta Geodaetica, 13(3), 172–179. [Google Scholar]
  10. Gibney, E. (2020). Nature news, Coronavirus lockdowns have changed the way Earth moves, https://www.nature.com/articles/d41586-020-00965-x [DOI] [PubMed]
  11. Herring, T. A. , Melbourne, T. I. , Murray, M. H. , Floyd, M. A. , Szeliga, W. M. , King, R. W. , Phillips, D. A. , Puskas, C. M. , Santillan, M. , & Wang, L. (2016). Plate boundary observatory and related networks: GPS data analysis methods and geodetic products. Reviews of Geophysics, 54, 759–808. 10.1002/2016RG000529 [DOI] [Google Scholar]
  12. IGS (International GNSS Service) . (2019). Monumentation recommendations. https://kb.igs.org/hc/en-us/articles/202094816-Monumentation-Recommendations. Accessed on 29 June 2020.
  13. Karegar, M. A. , Dixon, T. H. , & Engelhart, S. E. (2016). Subsidence along the Atlantic Coast of North America: Insights from GPS and late Holocene relative sea level data. Geophysical Research Letters, 43, 3126–3133. 10.1002/2016GL068015 [DOI] [Google Scholar]
  14. Karegar, M. A. , Dixon, T. H. , Kusche, J. , & Chambers, D. P. (2018). A new hybrid method for estimating hydrologically induced vertical deformation from GRACE and a hydrological model: an example from Central North America. Journal of Advances in Modeling Earth Systems, 10, 1196–1217. 10.1029/2017MS001181 [DOI] [Google Scholar]
  15. Karegar, M. A. , Dixon, T. H. , Malservisi, R. , Kusche, J. , & Engelhart, S. E. (2017). Nuisance flooding and relative sea‐level rise: The importance of present‐day land motion. Scientific Reports, 7, 1–9. 10.1038/s41598-017-11544-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Karegar, M. A. , Larson, K. M. , Kusche, J. , & Dixon, T. H. (2020). Novel quantification of shallow sediment compaction by GPS interferometric reflectometry and implications for flood susceptibility. Geophysical Research Letters, 47, e2020GL087807 10.1029/2020GL087807 [DOI] [Google Scholar]
  17. Kreemer, C. , Blewitt, G. , & Davis, P. M. (2020). Geodetic evidence for a buoyant mantle plume beneath the Eifel volcanic area, NW Europe. Geophysical Journal International, 222, 1316–1332. 10.1093/gji/ggaa227 [DOI] [Google Scholar]
  18. Larson, K. M. (2016). GPS interferometric reflectometry: Applications to surface soil moisture, snow depth, and vegetation water content in the western United States. Wiley Interdisciplinary Reviews Water, 3, 775–787. 10.1002/wat2.1167 [DOI] [Google Scholar]
  19. Larson, K. M. , Braun, J. J. , Small, E. E. , Zavorotny, V. U. , Gutmann, E. D. , & Bilich, A. L. (2009). GPS multipath and its relation to near‐surface soil moisture content. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3, 91–99. 10.1109/jstars.2009.2033612 [DOI] [Google Scholar]
  20. Larson, K. M. , Gutmann, E. D. , Zavorotny, V. U. , Braun, J. J. , Williams, M. W. , & Nievinski, F. G. (2009). Can we measure snow depth with GPS receivers? Geophysical Research Letters, 36, L17502 10.1029/2009GL039430 [DOI] [Google Scholar]
  21. Larson, K. M. , Löfgren, J. S. , & Haas, R. (2013). Coastal sea level measurements using a single geodetic GPS receiver. Advances in Space Research, 51, 1301–1310. 10.1016/j.asr.2012.04.017 [DOI] [Google Scholar]
  22. Larson, K. M. , Ray, R. D. , Nievinski, F. G. , & Freymueller, J. T. (2013). The accidental tide gauge: A GPS reflection case study from Kachemak Bay, Alaska. IEEE Geoscience and Remote Sensing Letters, 10, 1200–1204. 10.1109/LGRS.2012.2236075 [DOI] [Google Scholar]
  23. Larson, K. M. , Small, E. E. , Gutmann, E. D. , Bilich, A. L. , Braun, J. J. , & Zavorotny, V. U. (2008). Use of GPS receivers as a soil moisture network for water cycle studies. Geophysical Research Letters, 35, L24405 10.1029/2008GL036013 [DOI] [Google Scholar]
  24. Lecocq, T. , Hicks, S. P. , Van Noten, K. , van Wijk, K. , Koelemeijer, P. , De Plaen, R. S. , Massin, F. , Hillers, G. , Anthony, R. E. , Apoloner, M. T. , & Arroyo‐Solórzano, M. (2020). Global quieting of high‐frequency seismic noise due to COVID‐19 pandemic lockdown measures. Science, 369(6509), 1338–1343. 10.1126/science.abd2438 [DOI] [PubMed] [Google Scholar]
  25. Lomb, N. R. (1976). Least‐squares frequency analysis of unequally spaced data. Astrophysics and Space Science, 39(2), 447–462. 10.1007/BF00648343 [DOI] [Google Scholar]
  26. Mao, A. , Harrison, C. G. , & Dixon, T. H. (1999). Noise in GPS coordinate time series. Journal of Geophysical Research, 104(B2), 2797–2816. 10.1029/1998JB900033 [DOI] [Google Scholar]
  27. Melbourne, T. I. , Melgar, D. , Crowell, B. W. , & Szeliga, W. M. (2019). Seismic sensors in orbit, Eos, 90, 10.1029/2019EO138001. Published on 26 December 2019. [DOI]
  28. NASA (2020). https://www.earthobservatory.nasa.gov/images/146362/airborne-nitrogen-dioxide-plummets-over-china, accessed on 29 June 2020.
  29. Poli, P. , Boaga, J. , Molinari, I. , Cascone, V. , & Boschi, L. (2020). The 2020 coronavirus lockdown and seismic monitoring of anthropic activities in Northern Italy. Scientific Reports, 10, 1–8. 10.1038/s41598-020-66368-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Ragheb, A. E. , Clarke, P. J. , & Edwards, S. J. (2007). GPS sidereal filtering: Coordinate‐and carrier‐phase‐level strategies. Journal of Geodesy, 81(5), 325–335. 10.1007/s00190-006-0113-1 [DOI] [Google Scholar]
  31. Rebischung, P. , Altamimi, Z. , Ray, J. , & Garayt, B. (2016). The IGS contribution to ITRF2014. Journal of Geodesy, 90, 611–630. 10.1007/s00190-016-0897-6 [DOI] [Google Scholar]
  32. Roesler, C. , & Larson, K. M. (2018). Software tools for GNSS interferometric reflectometry (GNSS‐IR). GPS Solutions, 22, 80 10.1007/s10291-018-0744-8 [DOI] [Google Scholar]
  33. Santamaría‐Gómez, A. , Bouin, M. N. , Collilieux, X. , & Wöppelmann, G. (2011). Correlated errors in GPS position time series: Implications for velocity estimates. Journal of Geophysical Research, 116, B01405 10.1029/2010JB007701 [DOI] [Google Scholar]
  34. Scargle, J. D. (1982). Studies in astronomical time series analysis. II—Statistical aspects of spectral analysis of unevenly spaced data. The Astrophysical Journal, 263, 835–853. 10.1086/160554 [DOI] [Google Scholar]
  35. Schneider, A. , Friedl, M. A. , & Potere, D. (2009). A new map of global urban extent from MODIS satellite data. Environmental Research Letters, 4, 044003 10.1088/1748-9326/4/4/044003 [DOI] [Google Scholar]
  36. Siegfried, M. R. , Medley, B. , Larson, K. M. , Fricker, H. A. , & Tulaczyk, S. (2017). Snow accumulation variability on a West Antarctic ice stream observed with GPS reflectometry, 2007–2017. Geophysical Research Letters, 44, 7808–7816. 10.1002/2017GL074039 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Snay, R. A. , Freymueller, J. T. , Craymer, M. R. , Pearson, C. F. , & Saleh, J. (2016). Modeling 3‐D crustal velocities in the United States and Canada. Journal of Geophysical Research: Solid Earth, 121, 5365–5388. 10.1002/2016JB012884 [DOI] [Google Scholar]
  38. Tranquilla, J. M. , Carr, J. P. , & Al‐Rizzo, H. M. (1994). Analysis of a choke ring groundplane for multipath control in global positioning system (GPS) applications. IEEE Transactions on Antennas and Propagation, 42(7), 905–911. 10.1109/8.299591 [DOI] [Google Scholar]
  39. Walford, J. (2009). State of the Art, Leading Edge Geodetic Antennas from Leica Geosystems, Leica GNSS Reference Antennas White Paper (Leica Geosystems).
  40. Wang, G. (2019). HoustonNet GPS network—UHKD‐University of Houston Katy deep P.S., the GAGE facility operated by UNAVCO, Inc., GPS/GNSS observations dataset, 10.7283/TPMK-9682 [DOI]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information S1

Click here for additional data file. (4.7MB, pdf)

Data Availability Statement

GNSS raw data processed in this study are archived at National Oceanic and Atmospheric Administration's Continuously Operating Reference Station (http://www.ngs.noaa.gov/CORS/).

Articles from Geophysical Research Letters are provided here courtesy of Wiley

RESOURCES