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14 pages, 17655 KiB

Open AccessEditor’s ChoiceArticle

Wide-Area GNSS Corrections for Precise Positioning and Navigation in Agriculture

by Manuel Hernández-Pajares, Germán Olivares-Pulido, Victoria Graffigna, Alberto García-Rigo, Haixia Lyu, David Roma-Dollase, M. Clara de Lacy, Carles Fernández-Prades, Javier Arribas, Marc Majoral, Zizis Tisropoulos, Panagiotis Stamatelopoulos, Machi Symeonidou, Michael Schmidt, Andreas Goss, Eren Erdogan, Frits K. van Evert, Pieter M. Blok, Juan Grosso, Emiliano Spaltro, Jacobo Domínguez, Esther López and Alina Hriscu Show full author list Hide full author list

Remote Sens. 2022, 14(16), 3845; https://doi.org/10.3390/rs14163845 - 9 Aug 2022

Cited by 2 | Viewed by 4669

Abstract

This paper characterizes, with static and roving GNSS receivers in the context of precision agriculture research, the hybrid ionospheric-geodetic GNSS model Wide-Area Real-Time Kinematics (WARTK), which computes and broadcasts real-time corrections for high-precision GNSS positioning and navigation within sparse GNSS receiver networks. This [...] Read more.

This paper characterizes, with static and roving GNSS receivers in the context of precision agriculture research, the hybrid ionospheric-geodetic GNSS model Wide-Area Real-Time Kinematics (WARTK), which computes and broadcasts real-time corrections for high-precision GNSS positioning and navigation within sparse GNSS receiver networks. This research is motivated by the potential benefits of the low-cost precise WARTK technique on mass-market applications such as precision agriculture. The results from two experiments summarized in this work, the second one involving a working spraying tractor, show, firstly, that the corrections from the model are in good agreement with the corrections provided by IGS (International GNSS Services) analysis centers computed in post-processing from global GNSS data. Moreover, secondly and most importantly, we have shown that WARTK provides navigation solutions at decimeter-level accuracy, and the ionospheric corrections significantly reduce the computational time for ambiguity estimation: up to convergence times for the 50%, 75% and 95% of cases equal or below 30 s (single-epoch), 150 s and 600 s approximately, vs. 1000 s, 2750 s and 4850 s without ionospheric corrections, everything for a roving receiver at more than 100 km far away from the nearest permanent receiver. The real-time horizontal position errors reach up to 3 cm, 5 cm and 12 cm for 50%, 75% and 95% of cases, respectively, by constraining and continuously updating the ambiguities without updating the permanent receiver coordinates, vs. the 6 cm, 12 cm and 32 cm, respectively, in the same conditions but without WARTK ionospheric corrections. Full article

(This article belongs to the Special Issue GNSS Atmospheric Modelling)

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15 pages, 6631 KiB

Open AccessArticle

Ionospheric Assimilation of GNSS TEC into IRI Model Using a Local Ensemble Kalman Filter

by Jun Tang, Shimeng Zhang, Xingliang Huo and Xuequn Wu

Remote Sens. 2022, 14(14), 3267; https://doi.org/10.3390/rs14143267 - 7 Jul 2022

Cited by 7 | Viewed by 2446

Abstract

Ionospheric total electron content (TEC) is important data for ionospheric morphology, and also an important parameter for ionospheric correction in Global Navigation Satellite System (GNSS) precise positioning, navigation, and radio science. In this study, we present a data assimilation model for regional ionosphere [...] Read more.

Ionospheric total electron content (TEC) is important data for ionospheric morphology, and also an important parameter for ionospheric correction in Global Navigation Satellite System (GNSS) precise positioning, navigation, and radio science. In this study, we present a data assimilation model for regional ionosphere based on a local ensemble Kalman filter (LEnKF) with the International Reference Ionosphere 2016 (IRI-2016) model as the background, to assimilate ionospheric TEC observations from GNSS. To demonstrate the results, the TEC estimates from the Crustal Movement Observation Network of China (CMONOC), which is about 260 stations in China, are applied as observation. The assessments are performed against the TEC estimates from BeiDou Navigation Satellite System (BDS) geostationary earth orbit (GEO) and against the final products from the Center for Orbit Determination in Europe (CODE). The assimilation results are in good agreement with BDS GEO TEC and the CODE TEC on a quiet or disturbed day. The correlation coefficient after assimilation is increased by about 17% compared with that before assimilation, and the RMSE after assimilation is decreased by about 42% compared with that before assimilation. Furthermore, the assimilated method is also evaluated in the single-frequency precise point positioning (PPP). The experimental results indicate that the PPP/Assimilated method can improve the GNSS positioning accuracy more effectively in comparison to the PPP/CODE. These results reveal that the LEnKF method can be considered as a useful tool for ionospheric assimilation. Full article

(This article belongs to the Special Issue GNSS Atmospheric Modelling)

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27 pages, 9353 KiB

Open AccessArticle

A Spatiotemporal Network Model for Global Ionospheric TEC Forecasting

by Xu Lin, Hongyue Wang, Qingqing Zhang, Chaolong Yao, Changxin Chen, Lin Cheng and Zhaoxiong Li

Remote Sens. 2022, 14(7), 1717; https://doi.org/10.3390/rs14071717 - 2 Apr 2022

Cited by 18 | Viewed by 2766

Abstract

In the Global Navigation Satellite System, ionospheric delay is a significant source of error. The magnitude of the ionosphere total electron content (TEC) directly impacts the magnitude of the ionospheric delay. Correcting the ionospheric delay and improving the accuracy of satellite navigation positioning [...] Read more.

In the Global Navigation Satellite System, ionospheric delay is a significant source of error. The magnitude of the ionosphere total electron content (TEC) directly impacts the magnitude of the ionospheric delay. Correcting the ionospheric delay and improving the accuracy of satellite navigation positioning can both benefit from the accurate modeling and forecasting of ionospheric TEC. The majority of current ionospheric TEC forecasting research only considers the temporal or spatial dimensions, ignoring the ionospheric TEC’s spatial and temporal autocorrelation. Therefore, we constructed a spatiotemporal network model with two modules: (i) global spatiotemporal characteristics extraction via forwarding spatiotemporal characteristics transfer and (ii) regional spatiotemporal characteristics correction via reverse spatiotemporal characteristics transfer. This model can realize the complementarity of TEC global spatiotemporal characteristics and regional spatiotemporal characteristics. It also ensures that the global spatiotemporal characteristics of the global ionospheric TEC are transferred to each other in both temporal and spatial domains at the same time. The spatiotemporal network model thus achieves a spatiotemporal prediction of global ionospheric TEC. The Huber loss function is also used to suppress the gross error and noise in the ionospheric TEC data to improve the forecasting accuracy of global ionospheric TEC. We compare the results of the spatiotemporal network model with the Center for Orbit Determination in Europe (CODE), the convolutional Long Short-Term Memory (convLSTM) model and the Predictive Recurrent Neural Network (PredRNN) model for one-day forecasts of global ionospheric TEC under different conditions of time and solar activity, respectively. With internal data validation, the average root mean square error (RMSE) of our proposed algorithm increased by 21.19, 15.75, and 9.67%, respectively, during the maximum solar activity period. During the minimum solar activity period, the RMSE improved by 38.69, 38.02, and 13.54%, respectively. This algorithm can effectively be applied to ionospheric delay error correction and can improve the accuracy of satellite navigation and positioning. Full article

(This article belongs to the Special Issue GNSS Atmospheric Modelling)

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20 pages, 3885 KiB

Open AccessArticle

Evaluation of foF2 and hmF2 Parameters of IRI-2016 Model in Different Latitudes over China under High and Low Solar Activity Years

by Bingbing Zhang, Zhengtao Wang, Yi Shen, Wang Li, Feng Xu and Xiaoxiao Li

Remote Sens. 2022, 14(4), 860; https://doi.org/10.3390/rs14040860 - 11 Feb 2022

Cited by 13 | Viewed by 3236

Abstract

The height of the peak electron density (hmF2) and the critical frequency of the F2 layer (foF2) are very important in the research of ionospheric electrodynamics and high frequency (HF) wireless communication. In the article, we validated the hmF2/foF2 model values of the [...] Read more.

The height of the peak electron density (hmF2) and the critical frequency of the F2 layer (foF2) are very important in the research of ionospheric electrodynamics and high frequency (HF) wireless communication. In the article, we validated the hmF2/foF2 model values of the latest version of the International Reference Ionosphere (IRI-2016) with observations from three ionosonde stations which belong to low, middle, and high latitudes (i.e., Sanya, Beijing and Mohe) over China during a high solar activity year (2014, F10.7 = 145.9 sfu) and a low solar activity year (2016, F10.7 = 88.7 sfu). Among them, foF2 model values can be obtained through the International Radio Consulting Committee (CCIR) model or the International Union of Radio Science (URSI) model, both of which have the “F-peak storm model” on or ‘off’ options; hmF2 model values can be obtained through Bilitza-Sheikh-Eyfrig (BSE-1979), Altadill-Magdaleno-Torta-Blanch (AMTB-2013), or SHUbin (SHU-2015) model. The IRI-2016 hmF2/foF2 model values were evaluated by root mean square (RMS) values and mean absolute relative error (MARE). The results show that for the foF2 parameter, the performance of IRI-2016 can be improved by choosing “F-peak storm model” on option in geomagnetic-disturbed days. Whether in high or low solar activity years, for foF2, the IRI-2016 options of CCIR have better prediction ability than IRI-2016 options of URSI in low and high latitudes over China, and the IRI-2016 options of URSI have better prediction ability than IRI-2016 options of URSI in middle latitudes. For hmF2, the IRI-2016 option of SHU-2015 has better prediction ability than the IRI-2016 options of AMTB-2013 and BSE-1949 in high latitudes over China, the IRI-2016 options of SHU-2015 and BSE-1979 have better prediction ability than IRI-2016 options of AMTB-2013 in mid and low latitudes over China. Full article

(This article belongs to the Special Issue GNSS Atmospheric Modelling)

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19 pages, 48266 KiB

Open AccessArticle

Algorithm Research Using GNSS-TEC Data to Calibrate TEC Calculated by the IRI-2016 Model over China

by Wen Zhang, Xingliang Huo, Yunbin Yuan, Zishen Li and Ningbo Wang

Remote Sens. 2021, 13(19), 4002; https://doi.org/10.3390/rs13194002 - 6 Oct 2021

Cited by 7 | Viewed by 2617

Abstract

The International Reference Ionosphere (IRI) is an empirical model widely used to describe ionospheric characteristics. In the previous research, high-precision total ionospheric electron content (TEC) data derived from global navigation satellite system (GNSS) data were used to adjust the ionospheric global index IG [...] Read more.

The International Reference Ionosphere (IRI) is an empirical model widely used to describe ionospheric characteristics. In the previous research, high-precision total ionospheric electron content (TEC) data derived from global navigation satellite system (GNSS) data were used to adjust the ionospheric global index IG₁₂ used as a driving parameter in the standard IRI model; thus, the errors between IRI-TEC and GNSS-TEC were minimized, and IRI-TEC was calibrated by modifying IRI with the updated IG₁₂ index (IG-up). This paper investigates various interpolation strategies for IG-up values calculated from GNSS reference stations and the calibrated TEC accuracy achieved using the modified IRI-2016 model with the interpolated IG-up values as driving parameters. Experimental results from 2015 and 2019 show that interpolating IG-up with a 2.5° × 5° spatial grid and a 1-h time resolution drives IRI-2016 to generate ionospheric TEC values consistent with GNSS-TEC. For 2015 and 2019, the mean absolute error (MAE) of the modified IRI-TEC is improved by 78.57% and 77.42%, respectively, and the root mean square error (RMSE) is improved by 78.79% and 77.14%, respectively. The corresponding correlations of the linear regression between GNSS-TEC and the modified IRI-TEC are 0.986 and 0.966, more than 0.2 higher than with the standard IRI-TEC. Full article

(This article belongs to the Special Issue GNSS Atmospheric Modelling)

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21 pages, 40230 KiB

Open AccessArticle

An Adaptive Non-Uniform Vertical Stratification Method for Troposphere Water Vapor Tomography

by Hao Wang, Nan Ding and Wenyuan Zhang

Remote Sens. 2021, 13(19), 3818; https://doi.org/10.3390/rs13193818 - 24 Sep 2021

Cited by 2 | Viewed by 1998

Abstract

Global Navigation Satellite System (GNSS) water vapor tomography provides a four-dimensional (4-D) distribution of water vapor in the atmosphere for weather monitoring. It has developed into a widely used technique in numerical weather prediction (NWP). Vertical stratification is essential in discretizing the tomographic [...] Read more.

Global Navigation Satellite System (GNSS) water vapor tomography provides a four-dimensional (4-D) distribution of water vapor in the atmosphere for weather monitoring. It has developed into a widely used technique in numerical weather prediction (NWP). Vertical stratification is essential in discretizing the tomographic region. Traditional discretization methods divide the tomographic area into regular voxels with an equal height interval, which ignores the dynamic exponential distribution of water vapor. In recent years, non-uniform stratification methods have been widely validated by tomographic experiments. However, such experiments have not proposed a specific calculation method for stratification thickness. Therefore, in this paper, we introduced an adaptive non-uniform stratification method that follows the exponential distribution of water vapor in the tomographic region and presented the process of iterative calculation to acquire the optimal stratification interval. The proposed approach was applied based on the exponential decreasing trend in water vapor with increasing altitude. Moreover, it could adaptively calculate the interval of stratification height according to water vapor content. The tomographic experiments were performed using Global Positioning System (GPS) data from 19 ground-based stations in the Hong Kong Satellite Positioning Reference Station Network (SatRef) from 1 to 31 August 2019. The results indicated that, compared to the traditional stratification method, the root mean square error derived from the proposed approach was reduced by 0.26 g/m³. Additionally, severe weather can negatively affect the accuracy of the tomographic results. The results also showed that the accuracy of the tomographic results was reduced with increasing altitude. Moreover, the performance of the tomographic water vapor fields below 3000 m was improved by the proposed approach. Full article

(This article belongs to the Special Issue GNSS Atmospheric Modelling)

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13 pages, 20192 KiB

Open AccessTechnical Note

Climatology and Long-Term Trends in the Stratospheric Temperature and Wind Using ERA5

by Michal Kozubek, Jan Laštovička and Radek Zajicek

Remote Sens. 2021, 13(23), 4923; https://doi.org/10.3390/rs13234923 - 3 Dec 2021

Cited by 4 | Viewed by 2988

Abstract

This study analyses long-term trends in temperature and wind climatology based on ERA5 data. We study climatology and trends separately for every decade from 1980 to 2020 and their changes during this period. This study is focused on the pressure levels between 100–1 [...] Read more.

This study analyses long-term trends in temperature and wind climatology based on ERA5 data. We study climatology and trends separately for every decade from 1980 to 2020 and their changes during this period. This study is focused on the pressure levels between 100–1 hPa, which essentially covers the whole stratosphere. We also analyze the impact of the sudden stratospheric warmings (SSW), North Atlantic Oscillation (NAO), El Nino Southern Oscillation (ENSO) and Quasi-biennial oscillation (QBO). This helps us to find details of climatology and trend behavior in the stratosphere in connection to these phenomena. ERA5 is one of the newest reanalysis, which is widely used for the middle atmosphere. We identify the largest differences which occur between 1990–2000 and 2000–2010 in both temperature climatology and trends. We suggest that these differences could relate to the different occurrence frequency of SSWs in 1990–2000 versus 2000–2010. Full article

(This article belongs to the Special Issue GNSS Atmospheric Modelling)

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12 pages, 4157 KiB

Open AccessTechnical Note

Accuracy of Global Ionosphere Maps in Relation to Their Time Interval

by Beata Milanowska, Paweł Wielgosz, Anna Krypiak-Gregorczyk and Wojciech Jarmołowski

Remote Sens. 2021, 13(18), 3552; https://doi.org/10.3390/rs13183552 - 7 Sep 2021

Cited by 8 | Viewed by 2321

Abstract

Global ionosphere maps (GIMs) representing ionospheric total electron content (TEC) are applicable in many scientific and engineering applications. However, the GIMs provided by seven Ionosphere Associated Analysis Centers (IAACs) are generated with different temporal resolutions and using different modeling techniques. In this study, [...] Read more.

Global ionosphere maps (GIMs) representing ionospheric total electron content (TEC) are applicable in many scientific and engineering applications. However, the GIMs provided by seven Ionosphere Associated Analysis Centers (IAACs) are generated with different temporal resolutions and using different modeling techniques. In this study, we focused on the influence of map time interval on the empirical accuracy of these ionospheric products. We investigated performance of the high-resolution GIMs during high (2014) and low (2018) solar activity periods as well as under geomagnetic storms (19 February 2014 and 17 March 2015). In each of the analyzed periods, GIMs were also assessed over different geomagnetic latitudes. For the evaluation, we used direct comparison of GIM-derived slant TEC (STEC) with dual-frequency GNSS observations obtained from 18 globally distributed stations. In order to perform a comprehensive study, we also evaluated GIMs with respect to altimetry-derived vertical TEC (VTEC) obtained from the Jason-2 and Jason-3 satellites. The study confirmed the influence of GIMs time interval on the provided TEC accuracy, which was particularly evident during high solar activity, geomagnetic storms, and also at low latitudes. The results show that 120-min interval contributes significantly to the accuracy degradation, whereas 60-min one is sufficient to maintain TEC accuracy. Full article

(This article belongs to the Special Issue GNSS Atmospheric Modelling)

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