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Environmental Research with Global Navigation Satellite System (GNSS)

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 29570

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Special Issue Editor

Prof. Martin Vermeer

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Guest Editor

Rakennetun ympäristön laitos (Department of the Built Environment), Insinööritieteiden korkeakoulu (School of Engineering), Aalto-yliopisto (Aalto University), Greater Helsinki, Finland
Interests: crustal deformation; geomatics; observation; parameters; global navigation satellite system; geodesy; tectonics; satellite geodesy; navigation; gravimetry

Special Issue Information

Dear Colleagues,

The signals from the various global navigation satellite systems currently operational or approaching operationality are more and more being used to remotely sense the Earth's surface, oceans, atmosphere, vegetation, hydrosphere and cryosphere. Observables of interest may be the strength of the radio signal, either directly received through a medium or reflected, their propagation delay, their phase or code modulations, their polarization, or the precise geometric acceleration information contained in them. In this special issue, we aim to bring together research contributions related to all these techniques and their applications.

Suggestions for relevant research subjects, not meant to be limiting:

atmospheric studies of ionosphere and troposphere, water vapour, total electon content, tomographic techniques
ocean surface, sea-ice, soil-moisture, vegetation and other studies using reflected GNSS signals
radio-occultation studies of the atmosphere
airborne and orbital gravimetric techniques where GNSS measurements are part of the observable.

However, what is outside the scope of the special issue is the use of GNSS for simply geolocating sensors or georeferencing acquired remote-sensing data.

Contributions may be of many different kinds, ranging from research papers describing development and taking into use of a technique for obtaining valuable remote-sensing data, through more theoretical studies of existing and future techniques and missions and their promise, to application oriented papers describing use cases: how data obtained by any of these techniques is already being practically used in a field of application.

Especially interesting would be also inter- or multidisciplinary studies on the combined or synergistic use of multiple techniques. Also of interest is the combined use of several global navigation satellite systems together, as well as comparative studies between the systems. Finally, the time dimension: how these techniques may be put to use to study the changes over time, including anthropogenic changes, taking place on our planet.

Prof. Martin Vermeer
Guest Editor

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Keywords

GNSS
Remote sensing
GNSS-R
Radio occultation
Atmospheric tomography
Gravimetry
Polarimetry

Published Papers (9 papers)

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Research

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25 pages, 4355 KiB

Open AccessArticle

Kinematic ZTD Estimation from Train-Borne Single-Frequency GNSS: Validation and Assimilation

by Matthias Aichinger-Rosenberger, Robert Weber and Natalia Hanna

Remote Sens. 2021, 13(19), 3793; https://doi.org/10.3390/rs13193793 - 22 Sep 2021

Cited by 1 | Viewed by 1941

Abstract

Water vapour is one of the most important parameters utilized for the description of state and evolution of the Earth’s atmosphere. It is the most effective greenhouse gas and shows high variability, both in space and time. Thus, detailed knowledge of its distribution is of immense importance for weather forecasting, and therefore high resolution observations are crucial for accurate precipitation forecasts, especially for the short-term prediction of severe weather. Although not intentionally built for this purpose, Global Navigation Satellite Systems (GNSS) have proven to meet those requirements. The derivation of water vapour content from GNSS observations is based on the fact that electromagnetic signals are delayed when travelling through the atmosphere. The most prominent parameterization of this delay is the Zenith Total Delay (ZTD), which has been studied extensively as a major error term in GNSS positioning. On the other hand, the ZTD has also been proven to provide substantial benefits for atmospheric research and especially Numerical Weather Prediction (NWP) model performance. Based on these facts, the scientific area of GNSS Meteorology has emerged. The present study goes beyond the current status of GNSS Meteorology, showing how reasonable estimates of ZTD can be derived from highly-kinematic, single-frequency (SF) GNSS data. This data was gathered from trains of the Austrian Federal Railways (ÖBB) and processed using the Precise Point Positioning (PPP) technique. The special nature of the observations yields a number of additional challenges, ranging from appropriate pre-processing and parameter settings in PPP to more sophisticated validation and assimilation methodologies . The treatment of the ionosphere for SF-GNSS data represents one of the major challenges of this study. Two test cases (train travels) were processed using different strategies and validated using ZTD calculated from ERA5 reanalysis data. The validation results indicate a good overall agreement between the GNSS-ZTD solutions and ERA5-derived ZTD, although substantial variability between solutions was still observed for specific sections of the test tracks. The bias and standard deviation values ranged between 1 mm and 8 cm, heavily depending on the utilized processing strategy and investigated train route. Finally, initial experiments for the assimilation of GNSS-ZTD estimates into a NWP model were conducted, and the results showed observation acceptance rates of 30–100% largely depending on the test case and processing strategy. Full article

(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))

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16 pages, 5294 KiB

Open AccessArticle

Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm

by Nan Ding, Xiangrong Yan, Shubi Zhang, Suqin Wu, Xiaoming Wang, Yu Zhang, Yuchen Wang, Xin Liu, Wenyuan Zhang, Lucas Holden and Kefei Zhang

Remote Sens. 2020, 12(17), 2744; https://doi.org/10.3390/rs12172744 - 25 Aug 2020

Cited by 3 | Viewed by 3869

Abstract

Global Navigation Satellite Systems (GNSS) tomography plays an important role in the monitoring and tracking of the tropospheric water vapor. In this study, a new approach for improving the node-based GNSS tomography is proposed, which makes a trade-off between the real observed region and the complexity of the discretization of the tomographic region. To obtain dynamically the approximate observed region, the convex hull algorithm and minimum bounding box algorithm are used at each tomographic epoch. This new approach can dynamically define the tomographic model for all types of study areas based on the GNSS data. The performance of the new approach is tested by comparing it against the common node-based GNSS tomographic approach. Test data in May 2015 are obtained from the Hong Kong GNSS network to build the tomographic models and the radiosonde data as a reference are used for validating the quality of the new approach. The experimental results show that the root-mean-square errors of the new approach, in most cases, have a 38 percent improvement and the values of standard deviation reduce to over 43 percent compared with the common approach. The results indicate that the new approach is applicable to the node-based GNSS tomography. Full article

(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))

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20 pages, 5359 KiB

Open AccessArticle

Extracting Seasonal Signals in GNSS Coordinate Time Series via Weighted Nuclear Norm Minimization

by Baozhou Chen, Jiawen Bian, Kaihua Ding, Haochen Wu and Hongwei Li

Remote Sens. 2020, 12(12), 2027; https://doi.org/10.3390/rs12122027 - 24 Jun 2020

Cited by 10 | Viewed by 2965

Abstract

Global Navigation Satellite System (GNSS) coordinate time series contains obvious seasonal signals, which mainly manifest as a superposition of annual and semi-annual oscillations. Accurate extraction of seasonal signals is of great importance for understanding various geophysical phenomena. In this paper, a Weighted Nuclear Norm Minimization (WNNM) is proposed to extract the seasonal signals from the GNSS coordinate time series. WNNM assigns different weights to different singular values that enable us to estimate an approximate low rank matrix from its noisy matrix. To address this issue, the low rank characteristics of the Hankel matrix induced by GNSS coordinate time series was investigated first, and then the WNNM is applied to extract the seasonal signals in the GNSS coordinate time series. Meanwhile, the residuals have been analyzed, obtaining the estimation of the uncertainty of velocity. To demonstrate the effectiveness of the proposed algorithm, a number of tests have been carried out on both simulated and real GNSS dataset. Experimental results indicate that the proposed scheme offers preferable performances compared with many state-of-the-art methods. Full article

(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))

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23 pages, 13923 KiB

Open AccessArticle

A Tropospheric Tomography Method with a Novel Height Factor Model Including Two Parts: Isotropic and Anisotropic Height Factors

by Wenyuan Zhang, Shubi Zhang, Nan Ding and Qingzhi Zhao

Remote Sens. 2020, 12(11), 1848; https://doi.org/10.3390/rs12111848 - 8 Jun 2020

Cited by 18 | Viewed by 2178

Abstract

Global Navigation Satellite System (GNSS) tomography has developed into an efficient tool for sensing the high spatiotemporal variability of atmospheric water vapor. The integration of GNSS top signals and side rays for tropospheric tomography systems using a novel height factor model (HFM) is proposed and discussed in this paper. Within the HFM, the sectional slant wet delay (SWD) of inside signals (the part of the side signal inside the tomography area), which is considered a key factor for modeling side rays, is separated into isotropic and anisotropic components. Correspondingly, two height factors are defined to calculate the isotropic and anisotropic part of tropospheric delays in the HFM. In addition, the dynamic tomography top boundary is first analyzed and determined based on 30-year radiosonde data to reasonably divide signals into top and side rays. Four special experimental schemes based on different tomography regions of Hong Kong are performed to assess the proposed HFM method, the results of which show increases of 33.42% in the mean utilization of rays, as well as decreases of 0.46 g/m³ in the average root mean square error (RMSE), compared to the traditional approach, revealing the improvement of tomography solutions when the side signals are included in the modeling. Furthermore, compared with the existing correction model for modeling side rays, the water vapor profiles retrieved from the proposed improved model are closer to the radiosonde data, which highlights the advantages of the proposed HFM for optimizing the GNSS tomography model. Full article

(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))

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18 pages, 2670 KiB

Open AccessArticle

Evaluation of Precipitable Water Vapor from Five Reanalysis Products with Ground-Based GNSS Observations

by Shuaimin Wang, Tianhe Xu, Wenfeng Nie, Chunhua Jiang, Yuguo Yang, Zhenlong Fang, Mowen Li and Zhen Zhang

Remote Sens. 2020, 12(11), 1817; https://doi.org/10.3390/rs12111817 - 4 Jun 2020

Cited by 61 | Viewed by 4200

Abstract

At present, the global reliability and accuracy of Precipitable Water Vapor (PWV) from different reanalysis products have not been comprehensively evaluated. In this study, PWV values derived by 268 Global Navigation Satellite Systems (GNSS) stations around the world covering the period from 2016 to 2018 are used to evaluate the accuracies of PWV values from five reanalysis products. The temporal and spatial evolution is not taken into account in this analysis, although the temporal and spatial evolution of atmospheric flows is one of the most important information elements available in numerical weather prediction products. The evaluation results present that five reanalysis products with PWV accuracy from high to low are in the order of the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5), ERA-Interim, Japanese 55-year Reanalysis (JRA-55), National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), and NCEP/DOE (Department of Energy) according to root mean square error (RMSE), bias and correlation coefficient. The ERA5 has the smallest RMSE value of 1.84 mm, while NCEP/NCAR and NCEP/DOE have bigger RMSE values of 3.34 mm and 3.51 mm, respectively. The findings demonstrate that ERA5 and two NCEP reanalysis products have the best and worst performance, respectively, among five reanalysis products. The differences in the accuracy of the five reanalysis products are mainly attributed to the differences in the spatial resolution of reanalysis products. There are some large absolute biases greater than 4 mm between GNSS PWV values and the PWV values of five reanalysis products in the southwest of South America and western China due to the limit of terrains and fewer observations. The accuracies of five reanalysis products are compared in different climatic zones. The results indicate that the absolute accuracies of five reanalysis products are highest in the polar regions and lowest in the tropics. Furthermore, the effects of different seasons on the accuracies of five reanalysis products are also analyzed, which indicates that RMSE values of five reanalysis products in summer and in winter are the largest and the smallest in the temperate regions. Evaluation results from five reanalysis products can help us to learn more about the advantages and disadvantages of the five released water vapor products and promote their applications. Full article

(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))

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18 pages, 4723 KiB

Open AccessArticle

A Causal Long Short-Term Memory Sequence to Sequence Model for TEC Prediction Using GNSS Observations

by Maria Kaselimi, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis and Demitris Delikaraoglou

Remote Sens. 2020, 12(9), 1354; https://doi.org/10.3390/rs12091354 - 25 Apr 2020

Cited by 44 | Viewed by 4209

Abstract

The necessity of predicting the spatio-temporal phenomenon of ionospheric variability is closely related to the requirement of many users to be able to obtain high accuracy positioning with low cost equipment. The Precise Point Positioning (PPP) technique is highly accepted by the scientific community as a means for providing high level of position accuracy from a single receiver. However, its main drawback is the long convergence time to achieve centimeter-level accuracy in positioning. Hereby, we propose a deep learning-based approach for ionospheric modeling. This method exploits the advantages of Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) for timeseries modeling and predicts the total electron content per satellite from a specific station by making use of a causal, supervised deep learning method. The scope of the proposed method is to compare and evaluate the between-satellites ionospheric delay estimation, and to aggregate the Total Electron Content (TEC) outcomes per-satellite into a single solution over the station, thus constructing regional TEC models, in an attempt to replace Global Ionospheric Maps (GIM) data. The evaluation of our proposed recurrent method for the prediction of vertical total electron content (VTEC) values is compared against the traditional Autoregressive (AR) and the Autoregressive Moving Average (ARMA) methods, per satellite. The proposed model achieves error lower than 1.5 TECU which is slightly better than the accuracy of the current GIM products which is currently about 2.0–3.0 TECU. Full article

(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))

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16 pages, 13618 KiB

Open AccessArticle

Signal Extraction from GNSS Position Time Series Using Weighted Wavelet Analysis

by Kunpu Ji, Yunzhong Shen and Fengwei Wang

Remote Sens. 2020, 12(6), 992; https://doi.org/10.3390/rs12060992 - 19 Mar 2020

Cited by 12 | Viewed by 3114

Abstract

The daily position time series derived by Global Navigation Satellite System (GNSS) contain nonlinear signals which are suitably extracted by using wavelet analysis. Considering formal errors are also provided in daily GNSS solutions, a weighted wavelet analysis is proposed in this contribution where the weight factors are constructed via the formal errors. The proposed approach is applied to process the position time series of 27 permanent stations from the Crustal Movement Observation Network of China (CMONOC), compared to traditional wavelet analysis. The results show that the proposed approach can extract more exact signals than traditional wavelet analysis, with the average error reductions are 13.24%, 13.53% and 9.35% in north, east and up coordinate components, respectively. The results from 500 simulations indicate that the signals extracted by proposed approach are closer to true signals than the traditional wavelet analysis. Full article

(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))

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16 pages, 3101 KiB

Open AccessArticle

Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry

by Hui Qiu and Shuanggen Jin

Remote Sens. 2020, 12(3), 356; https://doi.org/10.3390/rs12030356 - 21 Jan 2020

Cited by 13 | Viewed by 3301

Abstract

Mean sea surface height (MSSH) is an important parameter, which plays an important role in the analysis of the geoid gap and the prediction of ocean dynamics. Traditional measurement methods, such as the buoy and ship survey, have a small cover area, sparse data, and high cost. Recently, the Global Navigation Satellite System-Reflectometry (GNSS-R) and the spaceborne Cyclone Global Navigation Satellite System (CYGNSS) mission, which were launched on 15 December 2016, have provided a new opportunity to estimate MSSH with all-weather, global coverage, high spatial-temporal resolution, rich signal sources, and strong concealability. In this paper, the global MSSH was estimated by using the relationship between the waveform characteristics of the delay waveform (DM) obtained by the delay Doppler map (DDM) of CYGNSS data, which was validated by satellite altimetry. Compared with the altimetry CNES_CLS2015 product provided by AVISO, the mean absolute error was 1.33 m, the root mean square error was 2.26 m, and the correlation coefficient was 0.97. Compared with the sea surface height model DTU10, the mean absolute error was 1.20 m, the root mean square error was 2.15 m, and the correlation coefficient was 0.97. Furthermore, the sea surface height obtained from CYGNSS was consistent with Jason-2′s results by the average absolute error of 2.63 m, a root mean square error (

RMSE

) of 3.56 m and, a correlation coefficient (

R

) of 0.95. Full article

(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))

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Review

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29 pages, 67572 KiB

Open AccessReview

A Review of Voxel-Based Computerized Ionospheric Tomography with GNSS Ground Receivers

by Weijun Lu, Guanyi Ma and Qingtao Wan

Remote Sens. 2021, 13(17), 3432; https://doi.org/10.3390/rs13173432 - 29 Aug 2021

Cited by 8 | Viewed by 2739

Abstract

Ionized by solar radiation, the ionosphere causes a phase rotation or time delay to trans-ionospheric radio waves. Reconstruction of ionospheric electron density profiles with global navigation satellite system (GNSS) observations has become an indispensable technique for various purposes ranging from space physics studies to radio applications. This paper conducts a comprehensive review on the development of voxel-based computerized ionospheric tomography (CIT) in the last 30 years. A brief introduction is given in chronological order starting from the first report of CIT with simulation to the newly proposed voxel-based algorithms for ionospheric event analysis. The statement of the tomographic geometry and voxel models are outlined with the ill-posed and ill-conditioned nature of CIT addressed. With the additional information from other instrumental observations or initial models supplemented to make the coefficient matrix less ill-conditioned, equation constructions are categorized into constraints, virtual data assimilation and multi-source observation fusion. Then, the paper classifies and assesses the voxel-based CIT algorithms of the algebraic method, statistical approach and artificial neural networks for equation solving or electron density estimation. The advantages and limitations of the algorithms are also pointed out. Moreover, the paper illustrates the representative height profiles and two-dimensional images of ionospheric electron densities from CIT. Ionospheric disturbances studied with CIT are presented. It also demonstrates how the CIT benefits ionospheric correction and ionospheric monitoring. Finally, some suggestions are provided for further research about voxel-based CIT. Full article

(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))

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