Spectral analysis of multi-year GNSS code multipath time-series

In the presented study multi-year time series of changes in the L1 pseudo-range multipath are analysed. Data from 8 stations of the EUREF Permanent Network (EPN) were used in the study. Periodic components present in the signal and their stability over time were analysed. Also, the type of background noise was determined, based on the spectral index. In some cases, the presence of weak components with a 1/2 and 1/3 of the Chandler period has also been found. Time-frequency analysis shows that periodic signals are not stationary in most of the examined cases, and particular signal components occur only temporarily. The analysed signals were characterised by pink noise in the lower frequency range and by white noise for higher frequencies, which is also characteristic for time series of coordinates obtained from GNSS measurements

GNSS-IR Model of Sea Level Height Estimation Combining Variational Mode Decomposition

The Global Navigation Satellite System-Reflections (GNSS-R) signal has been confirmed to be used to retrieve sea level height. At present, the GNSS-Interferometric Reflectometry (GNSS-IR) technology based on the least square method to process signal-to-noise ratio (SNR) data is restricted by the satellite elevation angle in terms of accuracy and stability. This paper proposes a new GNSS-IR model combining variational mode decomposition (VMD) for sea level height estimation. VMD is used to decompose the SNR data into intrinsic mode functions (IMF) of layers with different frequencies, remove the IMF representing the trend item of the SNR data, and reconstruct the remaining IMF components to obtain the SNR oscillation item. In order to verify the validity of the new GNSS-IR model, the measurement data provided by the Onsala Space Observatory in Sweden is used to evaluate the performance of the algorithm and its stability in high elevation range. The experimental results show that the VMD method has good results in terms of accuracy and stability, and has advantages compared to other methods. For the half-year GNSS SNR data, the root mean square error (RMSE) and correlation coefficient of the new model based on the VMD method are 4.86 cm and 0.97, respectively

SBAS DFMC service for road transport: positioning and integrity monitoring with a new weighting model

In 2017, the new generation satellite-based augmentation system (SBAS) test-bed was initiated by Australia and New Zealand, which supports the dual-frequency multi-constellation (DFMC) positioning with both GPS and Galileo signals. This new SBAS DFMC service allows the elimination of the first-order term of the ionospheric delays, and extends the service area to the entire footprint of the geostationary satellite. In addition to the satellite clock and orbital corrections, the integrity information is also broadcast by the SBAS satellite to users, so that protection levels can be computed to bound the positioning errors with a pre-defined probability of hazardous misleading information. Different from the aeronautical applications, the ground-based applications for road transport may suffer from new problems in different measurement environments, e.g. complicated multipath behaviours and frequent filter re-initialisations during positioning in urban areas. A new weighting model allowing different impacts of the elevation angles, the signal-to-noise-ratios and the smoothing time after re-initialisations is proposed and compared with the traditional elevation-dependent weighting model. The model is applied to the carrier-smoothed code measurements in different environments, i.e., the open-sky scenario, the suburban scenario and the urban scenario. It is found that the new weighting model effectively de-weights the large residuals in the suburban and the urban scenarios, where the mean values and the standard deviations of the overbounding excess-mass cumulative density function can be significantly reduced for the combined weighted noise and multipath. Using 1 Hz GNSS observations measured in these three measurement environments, the horizontal positioning errors (HPEs) and the horizontal protection levels (HPLs) are computed for different filter smoothing windows. Applying the new weighting model, significant reduction can be observed in the mean HPLs in the suburban and urban scenarios. Among them, the reduction in the HPLs have reached about 35–40% in the suburban scenario. The mean absolute HPEs are also reduced by about 10% in the urban scenario. However, when under the open-sky scenario, the traditional elevation-dependent weighting model is sufficient for the positioning and integrity monitoring using the SBAS DFMC service

New GPS Time Series Analysis and a Simplified Model to Compute an Accurate Seasonal Amplitude of Tropospheric Delay

Horizontal and vertical deformation of the Earth’s crust is due to a variety of different geophysical processes that take place on various spatiotemporal scales. The quality of the observations from spaced-based geodesy instruments such as Global Positioning System (GPS) and differential interferometric synthetic aperture radar (DInSAR) data for monitoring these deformations are dependent on numerous error sources. Therefore, accurately identifying and eliminating the dominant sources of the error, such as troposphere error in GPS signals, is fundamental to obtain high quality, sub-centimeter accuracy levels in positioning results. In this work, I present the results of double-differenced processing of five years of GPS data, between 2008 and 2012, for sparsely distributed GPS stations in southeastern Ontario and western Québec. I employ Bernese GPS Software Version 5.0 (BSW5.0) and found two optimal sub-networks which can provide high accuracy estimation of the position changes. I demonstrate good agreement between the resulted coordinate time series and the estimates of the crustal motions obtained from a global solution. In addition, I analyzed the GPS position time series by using a complex noise model, a combination of white and power-law noises. The estimated spectral index of the noise model demonstrates that the flicker noise is the dominant noise in most GPS stations in our study area. The interpretation of the observed velocities suggests that they provide an accurate constraint on glacial isostatic adjustment (GIA) prediction models. Based on a deeper analysis of these same GPS stations, I propose a model that accurately estimates the seasonal amplitude of zenith tropospheric delay (ZTD) error in the GPS data on local and regional spatial scales. I process the data for the period 2008 through 2012 from eight GPS stations in eastern Ontario and western Québec using precise point positioning (PPP) online analysis available from Natural Resource Canada (NRCan) (https://webapp.geod.nrcan.gc.ca/geod/tools-outils/ppp.php). The model is an elevation-dependent model and is a function of the decay parameter of refractivity with altitude and the seasonal amplitude of refractivity computed from atmospheric data (pressure, temperature, and water vapor pressure) at a given reference station. I demonstrate that it can accurately estimate the seasonal amplitude of ZTD signals for the GPS stations at any altitude relative to that reference station. Based on the comparison of the observed seasonal amplitudes of the differenced ZTD at each station and the estimates from the proposed model, it can provide an accurate estimation for the stations under normal atmospheric conditions. The differenced ZTD is defined as the differences of ZTD derived from PPP at each station and ZTD at the reference station. Moreover, I successfully compute a five-year precipitable water vapor (PWV) at each GPS site, based on the ZTD derived from meteorological data and GPS processing. The results provide an accurate platform to monitor long-term climate changes and inform future weather predictions. In an extension of this research, I analyze DInSAR data between 2014 and 2017 with high temporal and spatial resolution, from Kilauea volcano in Hawaii in order to derive the spatial and temporal pattern of the seasonal amplitude of ZTD. I propose an elevation-dependent model by the data from a radiosonde station and observations at a surface weather station for modeling the seasonal amplitudes of ZTD at any arbitrary elevation. The results obtained from this model fit the vertical profile of the observed seasonal amplitude of ZTD in DInSAR data, increasing systematically from the elevation of the DInSAR reference point. I demonstrate that the proposed model could be used to estimate the seasonal amplitude of the differenced ZTD at each GPS station within a local network with high accuracy. The results of this study concluded that, employing this model in GPS processing applications eliminates the need for the meteorological observations at each GPS site

BDS GNSS for Earth Observation

For millennia, human communities have wondered about the possibility of observing phenomena in their surroundings, and in particular those affecting the Earth on which they live. More generally, it can be conceptually defined as Earth observation (EO) and is the collection of information about the biological, chemical and physical systems of planet Earth. It can be undertaken through sensors in direct contact with the ground or airborne platforms (such as weather balloons and stations) or remote-sensing technologies. However, the definition of EO has only become significant in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit. Referring strictly to civil applications, satellites of this type were initially designed to provide satellite images; later, their purpose expanded to include the study of information on land characteristics, growing vegetation, crops, and environmental pollution. The data collected are used for several purposes, including the identification of natural resources and the production of accurate cartography. Satellite observations can cover the land, the atmosphere, and the oceans. Remote-sensing satellites may be equipped with passive instrumentation such as infrared or cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly called ’temporal resolution’), i.e., in a certain number of orbits around the Earth. The first remote-sensing satellites were the American NASA/USGS Landsat Program; subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the Chinese BuFeng-1 and Fengyun-3 series. Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers worldwide for a multitude of Earth monitoring and exploration applications. On the other hand, over the past 40 years, GNSSs have become an essential part of many human activities. As is widely noted, there are currently four fully operational GNSSs; two of these were developed for military purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation Satellite System (IRNSS/NavIC), will become available in the next few years, which will have enormous potential for scientific applications and geomatics professionals. In addition to their traditional role of providing global positioning, navigation, and timing (PNT) information, GNSS navigation signals are now being used in new and innovative ways. Across the globe, new fields of scientific study are opening up to examine how signals can provide information about the characteristics of the atmosphere and even the surfaces from which they are reflected before being collected by a receiver. EO researchers monitor global environmental systems using in situ and remote monitoring tools. Their findings provide tools to support decision makers in various areas of interest, from security to the natural environment. GNSS signals are considered an important new source of information because they are a free, real-time, and globally available resource for the EO community

The estimation of precipitable water vapour from GPS measurements in South Africa

Includes bibliographical references (leaves 110-115).The propagation of the Global Positioning System (GPS) signal from the satellite to the receiver is affected by, among other factors, the atmosphere through which it passes and, whereas the affects of the ionosphere can be eliminated by the differencing of two transmitted frequencies, the affects of the troposphere remain one of the major sources of noise in traditional geodetic and positioning applications of GPS. This noise can, however, be turned into a signal for the meteorologist and, by applying suitable constraints and processing strategies, it is possible to estimate the amount of precipitable water vapour (PWV) in the atmosphere. The application of the GPS data for the estimation of PWV in the atmosphere is not a new concept and has been described in numerous publications and reports since the early 1990's (Bevis et al., 1992, Rocken et al., 1993). This project is, however, an attempt to test the technique using the South African network of permanent GPS base stations. This thesis sets out to answer four fundamental questions: i. In theory, can GPS observations be used to estimate the amount of precipitable water vapour (PWV) in the atmosphere? ii. What permanent GPS networks are being used in other countries around the world for similar applications and how successful are these applications? iii. Can data derived from the South African network of permanent GPS base stations, TrigNet, be used to estimate PWV with sufficient accuracy to be able to supplement the radiosonde upper air measurements of the South African Weather Service (SAWS)? iv. Is the estimation of PWV as derived from the GPS observations a true reflection of reality using the radiosonde ascent measurements and numerical weather model (NWM) data as a method of independent verification? The primary data sets used to estimate atmospheric PWV at hourly intervals for March 2004 were; i. GPS data derived from the South African network of permanent GPS base stations provided by the Chief Directorate of Surveys and Mapping (CDSM); and ii. Surface meteorological measurements supplied by the South African Weather Service (SAWS). The two independent data sets used to verify and test the technique were; i. Upper air measurements derived from radiosonde ascents provided by the SAWS. These measurements were used to compute Integrated Water Vapour (IWV) and then converted to PWV; and ii. PWV estimates derived from a Numerical Weather Model provided by the Department of Environmental and Geographical Sciences of UCT. By the comparing the estimates of PWV from the three techniques, viz. GPS, radiosonde and NWM, it was found that GPS will meet the accuracy requirements of the meteorologist and could be used to supplement radiosonde measurements for use in numerical weather models

Satellite Positioning

Satellite positioning techniques, particularly global navigation satellite systems (GNSS), are capable of measuring small changes of the Earths shape and atmosphere, as well as surface characteristics with an unprecedented accuracy. This book is devoted to presenting recent results and development in satellite positioning technique and applications, including GNSS positioning methods, models, atmospheric sounding, and reflectometry as well their applications in the atmosphere, land, oceans and cryosphere. This book provides a good reference for satellite positioning techniques, engineers, scientists as well as user community

Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop

This work aims to estimate soil moisture and vegetation height from Global Navigation Satellite System (GNSS) Signal to Noise Ratio (SNR) data using direct and reflected signals by the land surface surrounding a ground-based antenna. Observations are collected from a rainfed wheat field in southwestern France. Surface soil moisture is retrieved based on SNR phases estimated by the Least Square Estimation method, assuming the relative antenna height is constant. It is found that vegetation growth breaks up the constant relative antenna height assumption. A vegetation-height retrieval algorithm is proposed using the SNR-dominant period (the peak period in the average power spectrum derived from a wavelet analysis of SNR). Soil moisture and vegetation height are retrieved at different time periods (before and after vegetation's significant growth in March). The retrievals are compared with two independent reference data sets: in situ observations of soil moisture and vegetation height, and numerical simulations of soil moisture, vegetation height and above-ground dry biomass from the ISBA (interactions between soil, biosphere and atmosphere) land surface model. Results show that changes in soil moisture mainly affect the multipath phase of the SNR data (assuming the relative antenna height is constant) with little change in the dominant period of the SNR data, whereas changes in vegetation height are more likely to modulate the SNR-dominant period. Surface volumetric soil moisture can be estimated (R2  =  0.74, RMSE  =  0.009 m3 m−3) when the wheat is smaller than one wavelength (∼ 19 cm). The quality of the estimates markedly decreases when the vegetation height increases. This is because the reflected GNSS signal is less affected by the soil. When vegetation replaces soil as the dominant reflecting surface, a wavelet analysis provides an accurate estimation of the wheat crop height (R2  =  0.98, RMSE  =  6.2 cm). The latter correlates with modeled above-ground dry biomass of the wheat from stem elongation to ripening. It is found that the vegetation height retrievals are sensitive to changes in plant height of at least one wavelength. A simple smoothing of the retrieved plant height allows an excellent matching to in situ observations, and to modeled above-ground dry biomass

Information retrieval from spaceborne GNSS Reflectometry observations using physics- and learning-based techniques

This dissertation proposes a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The proposed methodology has been built upon the literature review, analyses, and findings from a number of published studies throughout the dissertation research. Namely, a Sig- nals of Opportunity Coherent Bistatic scattering model (SCoBi) has been first developed at MSU and then its simulator has been open-sourced. Simulated GNSS-Reflectometry (GNSS-R) analyses have been conducted by using SCoBi. Significant findings have been noted such that (1) Although the dominance of either the coherent reflections or incoher- ent scattering over land is a debate, we demonstrated that coherent reflections are stronger for flat and smooth surfaces covered by low-to-moderate vegetation canopy; (2) The influ- ence of several land geophysical parameters such as SM, vegetation water content (VWC), and surface roughness on the bistatic reflectivity was quantified, the dynamic ranges of reflectivity changes due to SM and VWC are much higher than the changes due to the surface roughness. Such findings of these analyses, combined with a comprehensive lit- erature survey, have led to the present inversion algorithm: Physics- and learning-based retrieval of soil moisture information from space-borne GNSS-R measurements that are taken by NASA’s CYGNSS mission. The study is the first work that proposes a machine learning-based, non-parametric, and non-linear regression algorithm for CYGNSS-based soil moisture estimation. The results over point-scale soil moisture observations demon- strate promising performance for applicability to large scales. Potential future work will be extension of the methodology to global scales by training the model with larger and diverse data sets