Global Total Electron Content Maps Research Articles

Operational Forecasting of Global Ionospheric TEC Maps 1-, 2-, and 3-Day in Advance by ConvLSTM Model

In this paper, we propose a global ionospheric total electron content (TEC) maps (GIM) prediction model based on deep learning methods that is both straightforward and practical, meeting the requirements of various applications. The proposed model utilizes an encoder-decoder structure with a Convolution Long Short-Term Memory (ConvLSTM) network and has a spatial resolution of 5° longitude and 2.5° latitude, with a time resolution of 1 h. We utilized the Center for Orbit Determination in Europe (CODE) GIM dataset for 18 years from 2002 to 2019, without requiring any other external input parameters, to train the ConvLSTM models for forecasting GIM 1, 2, and 3 days in advance. Using the CODE GIM data from 1 January 2020 to 31 December 2023 as the test dataset, the performance evaluation results show that the average root mean square errors (RMSE) for 1, 2 and 3 days of forecasts are 2.81 TECU, 3.16 TECU, and 3.41 TECU, respectively. These results show improved performance compared to the IRI-Plas model and CODE’s 1-day forecast product c1pg, and comparable to CODE’s 2-day forecast c2pg. The model’s predictions get worse as the intensity of the storm increases, and the prediction error of the model increases with the lead time.

Different Response of the Ionospheric TEC and EEJ to Ultra‐Fast Kelvin Waves in the Mesosphere and Lower Thermosphere

AbstractWe studied the response of ionospheric total electron content (TEC) and equatorial electrojet (EEJ) to the ultra‐fast Kelvin wave (UFKW) at the equator in the mesosphere using zonal wind data obtained from TIMED Doppler Interferometer (TIDI), EEJ data over the monitoring station Jicamarca (12°S, 77°W) and global TEC maps. The least squares fitting method is utilized to perform a spectral analysis of zonal wind, EEJ and TEC. Our analysis results demonstrate that UFKW events can be divided into four categories: (a) UFKW events with both TEC and EEJ response; (b) UFKW events with TEC response but without EEJ response; (c) UFKW events with EEJ response but without TEC response; (d) UFKW events without neither TEC response nor EEJ response. The first type of UFKW events occur the most often and is generally thought to generate a response in EEJ at approximately 105–110 km through the dynamo effect. The polarization electric field associated with EEJ then produces a response in the ionospheric TEC through the fountain effect. The lack of EEJ response in the second type of UFKWs may be due to the influence of eastward background winds. We found that all UFKW events with EEJ response have a response in TEC. The fourth type of UFKWs have smaller amplitudes, shorter vertical wavelengths and longer periods, which make them more likely to dissipate and cannot propagate to higher altitudes. These UFKWs cannot propagate to the altitude of EEJ and produce a response in EEJ, much less in TEC.

Statistical Characteristics of Total Electron Content Intensifications on Global Ionospheric Maps

AbstractGlobal ionospheric total electron content (TEC) maps exhibit TEC intensifications and depletions of various sizes and shapes. Characterizing key features on TEC maps and understanding their dynamic coupling with external drivers can significantly benefit space weather forecasting. However, comprehensive analysis of ionospheric structuring over decades of TEC maps is currently lacking due to large data volume. We develop feature extraction software based on image processing techniques to extract TEC intensification regions, that is, contiguous regions with sufficiently elevated TEC values than surrounding areas, from global TEC maps. Applying the software to the Jet Propulsion Laboratory Global Ionospheric Map data, we generate a TEC intensification data set for years 2003–2022 and carry out a statistical study on the number and strength of TEC intensifications. We find that the majority of the TEC maps (about 86%) are characterized with one or two intensification(s), while the rest of the TEC maps have three or more intensifications. Both the number and strength of TEC intensifications exhibit semi‐annual variation that peaks near equinoxes and dips near solstices, as well as an annual asymmetry with larger values around December solstice compared to June solstice. The number and strength of intensifications increase with enhanced solar extreme‐violet irradiance. The strength of intensifications also increases with elevated geomagnetic activity, but the number of intensifications does not. In addition, the number of intensifications is not correlated with the strength of intensifications.

A High-Precision and Lightweight Prediction Model for Global Total Electron Content

Precise prediction of the global spatial–temporal distribution of total electron content (TEC) is a challenge in space weather. Existing models are generally able to provide rather good prediction results at the cost of a large amount of computing resources. This limits the application of the method. A lightweight and highly accurate global TEC prediction model was developed in this study. Our model is capable of forecasting the global TEC map up to 12 h in advance with a step of one hour. The predicted results during geomagnetic quiet periods were consistent with measurements, with a maximum and average mean error (ME) of 1.5 TECU and −0.04 TECU under conditions of high solar activity, respectively. Our model also performed well during geomagnetic disturbed periods, with a maximum ME of 4.5 TECU and 2.5 TECU under conditions of high and low solar activities, respectively. Our model significantly reduces the training time (47%) and basic requirement of memory (60%) relative to the model of Liu et al. (2022) with no remarkable loss of model accuracy.

Forecast Global Ionospheric TEC: Apply Modified U‐Net on VISTA TEC Data Set

AbstractThis work developed a modified U‐Net model (a convolutional network architecture) to predict global Total Electron Content (TEC) maps. The input includes the current global TEC map, the current F10.7, the time history of the Interplanetary Magnetic Field Bz and SYM‐H in the previous 4 days, the Hour of Day, and the Day of Year. The output is the global TEC map several hours or several days ahead. The modified U‐Net was trained and validated on a brand new TEC database, the VISTA (Video Imputation with SoftImpute, Temporal smoothing and Auxiliary data) TEC database. The VISTA TEC maps can reveal important large‐scale TEC structures and preserve mesoscale structures simultaneously. Taking advantage of the new neural network and the new database, our model achieves an root of the mean squared error from 1.2 TECU to 2.4 TECU as the prediction horizon increases from 1 hr to 7 days. In addition, the model could reveal multiscale structures in the predicted TEC maps.

Regional ionospheric TEC modeling during geomagnetic storm in August 2021- data fusion using multi-instrument observations

Regional ionospheric Total Electron Content (TEC) models are most significant to investigate the variability of ionosphere during all-weather conditions. Since the ground-based TEC measurements are not available in oceanic and polar regions, space-based observations are incorporated to increase the positional accuracy and reliability of Global Ionospheric TEC Maps (GIMs). Therefore, data fusion techniques are necessary to enhance the prediction capability of regional ionospheric models by considering multi-source data. In view of this, an attempt is made to investigate Equatorial Ionization Anomaly (EIA) structures in low-latitudinal Indian region using multi-instrument data. Data observations from background model CODEGIM, available International GNSS Service (IGS) stations in India, GNSS receivers at KLEF and Tripura stations, FORMOSAT/COSMIC radio occultation and SWARM during geomagnetic storm period August 26–28, 2021 are used for the study. Spherical Harmonics Function (SHF) of order 4 is employed along with Weighted Least Squares analysis (WLS), where separate weight constraint is computed for each data source. TEC maps of spatial resolution 5° x 5° and temporal resolution of 1 h are generated to illustrate the dynamic behavior of ionosphere. The standard deviation for 3 days is obtained as 0.42, 0.61 and 1.43 TECU respectively. TEC depletions and enhancements are observed on August 27 and 28, with mean values of 12.74 and 15.27 TECU respectively, demonstrating negative and positive storm effects.

Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA

Ionospheric total electron content (TEC) derived from multi-frequency Global Navigation Satellite System (GNSS) signals and the relevant products have become one of the most utilized parameters in the space weather and ionospheric research community. However, there are a couple of challenges in using the global TEC map data including large data gaps over oceans and the potential of losing meso-scale ionospheric structures when applying traditional reconstruction and smoothing algorithms. In this paper, we describe and release a global TEC map database, constructed and completed based on the Madrigal TEC database with a novel video imputation algorithm called VISTA (Video Imputation with SoftImpute, Temporal smoothing and Auxiliary data). The complete TEC maps reveal important large-scale TEC structures and preserve the observed meso-scale structures. Basic ideas and the pipeline of the video imputation algorithm are introduced briefly, followed by discussions on the computational costs and fine tuning of the adopted algorithm. Discussions on potential usages of the complete TEC database are given, together with a concrete example of applying this database.

Improvement of international reference ionospheric model total electron content maps: a case study using artificial neural network in Egypt

Abstract An essential ionosphere parameter that can be applied for ionosphere corrections in radio systems is the ionosphere’s total electron content (TEC). TEC is a crucial parameter for ionospheric correction in the Global Navigation Satellite Systems (GNSS) of positioning, navigation, and radio science. This study uses the artificial neural network (ANN) application to improve the International Reference Ionospheric Model (IRI-2016) TEC maps across Egypt. The study period is based on the data that were accessible between 2013 and 2020. The ANN model input parameters are (year, day, hour, latitude, and longitude). The ANN1 and ANN2 estimate TEC values of the enhanced IRI-2020 and IRI-2016 according to the Center for Orbit Determination in Europe (CODE), respectively. ANN3 and ANN4 estimate TEC values of the enhanced IRI-2020 and IRI-2016 regarding IGS stations data analyzed by GNSS Analysis software for the multi-constellation and multi-frequency Precise Positioning (GAMP) model, respectively. The ANN model’s validations were based on the root mean square error (RMSE), correlation coefficient (CC), and T-test. According to the results, the suggested ANN can accurately predict the TEC over Egypt. In comparison to the IRI model, the TEC maps that the ANN models produced are significantly more in accordance with the related CODE and GAMP TEC maps. These results demonstrate that the developed approach can enhance IRI 2016 and IRI-2020s ability to estimate global TEC maps. For the ANN1 model, the mean CC and RMSE are 0.92, and 5.15 TECU for all the global data sets compared by CODE. On the other hand, the CC and RMSE between IRI-2020 and CODE are 0.847 and 7.67 TECU. For the ANN2, the mean CC and RMSE are 0.87, 5.59 TECU compared by CODE, respectively. Although the CC and RMSE between IRI-2016 and CODE are 0.820 and 9.052 TECU respectively. For the ANN3, the CC and RMSE are 0.830 and 4.87 TECU compared with GAMP for all global data, respectively. On the other hand, the CC and RMSE between IRI-2020 and GAMP are 0.644 and 10.41, respectively. For the ANN4 the CC and RMSE are 0.82, and 5.95 TECU compared with GAMP, respectively. Although the CC and RMSE between IRI-2016 and GAMP are 0.665 and 12.347 TECU respectively.

Global TEC Map Fusion Through a Hybrid Deep Learning Model: RFGAN

AbstractTimely, reliable and comprehensive global observation information is essential for space weather research. However, limited observation technology hinders the consecutive global coverage of observation data. For the integrity and continuity of the global observation data, deep learning can obtain a global Ionospheric total electron content (TEC) map by fusing multi‐source TEC maps. Different from the previous methods, in the study, a deep learning hybrid model (RFGAN) based on Dual‐Discriminator Conditional Generative Adversarial Network (DDcGAN) and Free‐Form Image Inpainting with Gated Convolution (Deepfill v2) is proposed to fuse the Massachusetts Institute of Technology (MIT)—TEC, International Global Navigation Satellite System TEC (IGS‐TEC) and altimetry satellite TEC. Throughout the RFGAN structure, we use an autoencoder model with gated convolution to inpaint the missing parts of MIT‐TEC and altimetry satellite TEC. Meanwhile, DDcGAN fuses the inpainted MIT‐TEC (MIT'‐TEC) and IGS‐TEC to get a global TEC map with high accuracy. To a certain extent, we inpainted the ocean area of MIT‐TEC through RFGAN. At the same time, RFGAN keeps the consistency of RFGAN‐TEC and MIT‐TEC in the continent area. Our proposed deep learning hybrid model can be easily extended and widely applied to other fields of space science, especially in addressing observational data loss and multi‐source data fusion.

Statistical analysis of large and extreme global ionospheric total electron content

Ionospheric disturbances, due to photoionization and energetic particle precipitation, occurring both on a regular recurring basis, and heightened due to space weather, impact global positioning, navigation, and timing services provided by Global Navigation Satellite Systems (GNSS). The development of operational services to mitigate the risks to users requires knowledge of how space weather impacts critical systems, and the frequency and magnitude of ionospheric disturbances including extreme events. Analysis of the largest values of total electron content (TEC) is important for the assessment of space weather hazards for systems sensitive to ionospheric effects on radio wave propagation and for the development of risk mitigation strategies. In this study, twenty years of global TEC maps are analyzed to determine the global distribution of the daily maximum TEC. Maximum TEC is shown to reach magnitudes of about 200 TECU in extreme cases, with the largest TEC normally observed at equatorial latitudes. A generalized extreme value distribution has been applied to the daily global maximum TEC and for the high (60°–90°; −90° to −60°), mid (30°–60°; −60° to −30°), and low-latitude (0°–30°; −30°–0°) regions in geographic latitudes to determine the 1-in-44 year (4 solar cycles) and 1-in-100 year maximum TEC with a 95% confidence interval. Additionally, the return period for TEC thresholds defined for safe operation by the International Civil Aviation Organization are discussed. Extreme values provide criteria for the development of operational systems and vulnerability assessment of critical systems dependant on GNSS.

Ionospheric Total Electron Content and Disturbance Observations From Space-Borne Coherent GNSS-R Measurements

In this article, we investigate coherent global navigation satellite system reflectometry (GNSS-R) measurements obtained at the low earth orbits (LEOs) as a potential new data source for ionospheric total electron content (TEC) and ionospheric disturbance observations. Current global ionospheric TEC maps (GIMs) have limited spatial and temporal resolutions and accuracy due to lack of GNSS observations over oceans, polar caps, and inaccessible terrains. Our analysis of Spire Global’s CubeSat data indicates that coherent GNSS signals reflected off sea ice, inland water bodies, and calm ocean surface can be processed to achieve cm-level precision carrier phase estimations. Signal coherency is especially prevalent over sea ice where 41.7% reflections are coherent, compared to 4.3% in the overall dataset. This article presents the methodology to estimate slant TEC along the reflection signal ray path using coherent dual-frequency GNSS-R pseudoranage and carrier phase estimations obtained from low-cost CubeSats. The methodology is applied to Spire Global’s CubeSat data. The results show that the slant TEC retrieved from GNSS-R measurements and from GIM follow a similar trend. Moreover, the GNSS-R-based TEC time series offer a nearly “frozen in time” observation of the ionospheric structures due to the rapid scan velocity of GNSS-R rays across the ionosphere. The study demonstrates the potential of GNSS-R observations to fill the data gaps over the polar regions where space weather activities and TEC disturbances are most frequent and intense. Potential error sources and mitigation techniques are also discussed.

SEASONAL FEATURES OF THE CORRELATION OF THE TOTAL ELECTRON CONTENT AT MAGNETICALLY CONJUGATE POINTS

The paper presents the results of studies of the seasonal variability of statistical relationships between Magnetoconjugated Points (MCP) of the ionosphere. The analysis is based on the calculation of the correlation coefficients between the variations in the Total Electron Content (TEC) at points located on the same field line of the dipole magnetic field on both sides of the geomagnetic equator. Global TEC maps were used as initial data. For the four seasons of 2009 and 2015, the values of the Pearson’s correlation coefficient between the variations in the Total Electron Content in the MCP were calculated. For two levels of solar activity, we examined the seasonal features of statistical relationships between TEC variations at points located on the same field line of the dipole magnetic field on both sides of the geomagnetic equator. Pearson's correlation coefficient was calculated for the mean daily TEC variations. It was shown in the work that during the period of low solar activity, the correlation between the TEC variations in the MCP regions is weak or absent, except for autumn. In 2015, a significant correlation between magnetoconjugated regions is observed during all seasons, while in winter and summer they are localized at low latitudes and in spring and autumn at high and middle latitudes.

An Ionospheric Climate Index Based on GNSS

AbstractThe ionospheric total electron content (TEC) is a significant parameter for scientific studies of the ionosphere and space weather. Ground‐based Global Navigation Satellite System (GNSS) station network provides an opportunity for modeling the ionospheric TEC with high accuracy. Based on global ionospheric TEC maps, by using data from the International GNSS Service (IGS) stations for 21 years (from 1998 to 2018), we establish an ionospheric climate index (ICI) for representing the general state of the ionosphere. In addition, we build a multiple linear regression model for fitting ICI using the SSN, F10.7, Ap, and Dst indices and upper atmospheric wind field. We quantitatively analyze the contribution of these factors to ICI time series. ICI during the past six solar cycles, namely, from 1958 to 2018, are presented, which are assigned as the adjectives (cold, cool, mild, warm, hot, and sizzling) for ionospheric climatology studies. This new index is a very promising representative of day‐to‐day variations of middle‐ and low‐latitude ionosphere.

One‐Day Forecasting of Global TEC Using a Novel Deep Learning Model

AbstractIn this study, we make a global total electron content (TEC) forecasting using a novel deep learning method, which is based on conditional generative adversarial networks. For training, we use the International GNSS Service (IGS) TEC maps from 2003 to 2012 with 2‐h time cadence. Our model has two input images (IGS TEC map and 1‐day difference map between the present day and the previous day) and one output image (1‐day future map). The model is tested with two data sets: solar maximum period (2013–2014) and solar minimum period (2017–2018). Then, we compare the results of our model with those of 1‐day Center for Orbit Determination in Europe (CODE) prediction model. Our major results can be summarized as follows. First, we successfully apply our model to the forecast of global TEC maps. Second, our model well predicts daily TEC maps with 1 day in advance using only previous TEC maps. The averaged root mean square error, bias, and standard deviation between AI‐generated and IGS TEC maps are 2.74 TECU, −0.32 TECU, and 2.59 TECU, respectively. Third, our model generates some peak structures around equatorial regions. Fourth, our model shows better performance than 1‐day CODE prediction model during both solar maximum and minimum periods. Fifth, another model with additional input data Kp index gives a slight improvement of the results. Our study shows that our deep learning model based on an image translation method will be effective for forecasting of future images using previous data.

Probing ionospheric TEC near South Geomagnetic Pole by use of GNSS observations

Total electron content (TEC) is one of the most important parameters of the Earth’s ionosphere. This parameter determines the degree of ionization and the dispersion properties of the ionosphere. Now the most affordable way to determine ionospheric parameters is the use of radio signals from the global navigation satellite systems GLONASS, GPS, GALILEO and BDS. TEC is now being routinely monitored by global GNSS networks almost everywhere on Earth except geomagnetic poles. The observations of TEC at the Vostok antarctic station were carried out jointly by Saint Petersburg State University and Arctic and Antarctic Research Institute. Observations were started in 2016 and continued to this day with small interruptions. In this work we have processed the GNSS observations with RTKLIB and GeNeSiS software packages. As a result of this processing we have estimated the TEC from code and phase observables. The resulting TEC time series were analysed and compared to the global TEC map. A reasonable accordance with previous results has been reached. However, our results are specific to the South Geomagnetic Pole and reflect ionospheric behaviour near it. It can be seen that TEC changes more abruptly near the Geomagnetic Pole than elsewhere. These results may potentially augment our knowledge about ionosphere structure and evolution near the geomagnetic poles.

Forecasting Global Ionospheric TEC Using Deep Learning Approach

AbstractGlobal ionospheric total electron content (TEC) maps are widely utilized in research regarding ionospheric physics and the associated space weather impacts, so there is a great interest in the community in short‐term ionosphere TEC forecasting. In this study, the long short‐term memory (LSTM) neural network (NN) is applied to forecast the 256 spherical harmonic (SH) coefficients that are traditionally used to construct global ionospheric maps (GIM). Multiple input data, including historical time series of the SH coefficients, solar extreme ultraviolet (EUV) flux, disturbance storm time (Dst) index, and hour of the day, are used in the developed LSTM NN model. Different combinations of the above parameters have been used in constructing the LSTM NN model, and it is found that the model using all four parameters performs the best. Then the best performing LSTM model is used to forecast the SH coefficients, and the global hourly TEC maps are reproduced using the 256 predicted SH coefficients. A comprehensive evaluation is carried out with respect to the CODE GIM TEC. Results show that the first/second hour TEC root mean square error (RMSE) is 1.27/2.20 TECU during storm time and 0.86/1.51 TECU during quiet time, so the developed model performs well during both quiet and storm times. Moreover, typical ionospheric structures, such as equatorial ionization anomaly (EIA) and storm‐enhanced density (SED), are well reproduced in the predicted TEC maps during storm time. The developed model also shows competitive performance in predicting global TEC when compared to the persistence model and two empirical models (IRI‐2016 and NeQuick‐2).

Variation on Solar Wind Parameters and Total Electron Content Over Middle‐ to Low‐Latitude Regions During Intense Geomagnetic Storms

AbstractStreams of the particle ejected from the Sun and the extreme space weather conditions like storms, high‐speed streamers (HSSs), interplanetary coronal mass ejections (ICMEs), corotating interaction regions (CIRs), and interplanetary shocks (IS) termed as geomagnetic storms have massive influence in the climate and components of the Earth's upper atmosphere such as total electron content (TEC). The study of TEC helps to understand variations in ionospheric electron density during geomagnetic storms. Global ionospheric maps of TEC are a real‐time mapping of GPS observations produced by ground‐based stations. In this paper, we have analyzed three intense geomagnetic storms of the year 2015: during 16–21 March 2015 (the St. Patrick's Day storm), 21–24 June 2015, and 18–22 December 2015. We present the variations of IMF‐Bz, solar wind parameters (Vsw, Nsw, and Psw), and geomagnetic indices (AE and SYM‐H) and the variations of vertical total electron content (VTEC) using simultaneous VTEC data from 12 GPS‐TEC stations over the Indian, Australian, Brazilian, and South African regions. We describe contrast in TEC throughout the globe using global ionospheric maps at a regular 2 hr interval of UT during the three intense geomagnetic storms. Moreover, we observed that heavily TEC‐influenced areas were found to be transposing through the equatorial plane starting from eastern sectors to the western sectors. The Indian Ocean, Atlantic Ocean, and South Pacific Ocean sectors were affected flowingly. Global ionospheric maps evince that Indian and Brazilian sectors were affected heavily explaining the traveling ionospheric disturbances (TID) and equatorial anomaly as seen in those areas. The equatorial and low‐latitude regions have been mainly affected by geomagnetic storms. All these results suggested that the acute disruption of global winds (surging toward the equator from higher latitudes) and electric fields commenced from magnetosphere‐ionosphere interaction causing the severe modification in the equatorial, low‐latitude region. We also checked the cross correlation of VTEC of LCK3 station and various other stations during the period of high solar and geomagnetic activities; the correlation gradually increased with the nearby stations by latitudes in most of the cases which was another intriguing result. Thus, these results suggested that the storms were affected globally, which is why we believe that variation of TEC over various stations of the globe could turn out to be very helpful in predicting solar wind coupling with the magnetosphere‐ionosphere.

Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks

AbstractIn this study, we make a model, which is called DeepIRI, to generate improved International Reference Ionosphere (IRI) total electron content (TEC) maps by deep learning based on conditional Generative Adversarial Networks. For this we consider 48,901 pairs of IRI TEC maps and International Global Navigation Satellite Systems (GNSS) Service (IGS) TEC maps from 2001 to 2011 for training the model. We evaluate the model by comparing IGS TEC maps and DeepIRI TEC ones from 2013 to 2017. The DeepIRI TEC maps that our model generated are much more consistent with the corresponding IGS TEC maps than the IRI TEC ones. Especially, ionospheric peak structures are successfully generated in DeepIRI TEC maps while they are not in IRI‐2016 ones. From the average differences between IRI and IGS TEC maps, our model greatly improved the IRI TEC at low‐latitude region around the equatorial anomaly. These results show that our model can improve the global TEC prediction ability of the IRI‐2016. Our study suggests a sufficient possibility to generate DeepIRI global TEC maps in near real time if IRI is generated in time. Our approach can be applied to make improved versions of empirical models if more realistic observations are available with a time delay.

Assessing Global Ionosphere TEC Maps with Satellite Altimetry and Ionospheric Radio Occultation Observations.

As global navigation satellite system (GNSS)stations are sparsely distributed in oceanic area, oceanic areas usually have lower precision than continental areas on a global ionosphere maps (GIM). On the other hand, space-borne observations like satellite altimetry (SA) and ionospheric radio occultation (IRO) have substantial dual-frequency observations in oceanic areas, which could be used for total electron content (TEC) retrieval. In this paper, the Jason-2 SA and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) IRO products were used to assess the precision of IGS GIM products. Both the systematic biases and scaling factors between the international GNSS service (IGS) GIM TEC and space-borne TEC were calculated, and the statistical results show that the biases and the scaling factors obviously vary under different temporal-spatial conditions. This analysis shows that these differences are variable with diurnal and latitude factors, that is, the differences in biases during the day time are higher than those during the night time, and larger biases are experienced at lower latitude areas than at high latitude areas. The results also show that in the southern hemisphere middle-high latitude area and some other central oceanic areas, the space-borne TEC values are even higher than GIM TEC values. As the precision of space-borne TEC should be evenly distributed around different areas on Earth, it can be explain that the TEC in these areas is undervalued by the current GIM model, and the space-borne SA and IRO techniques could be used as complementary observations to improve the accuracy and reliability of TEC values in these areas.

Sentinel-1 interferometry with ionospheric correction from global and local TEC maps for land displacement detection in Taiwan

Sentinel-1 satellite launched by the European Space Agency (ESA) offers an opportunity to apply the differential interferometric synthetic aperture radar (DInSAR) in 12-day revisit, a potential solution to effectively measure surface deformation. However, for areas affected by the Equatorial Ionization Anomaly (EIA) in a global ionosphere pattern, the total electron content (TEC) irregularity between snapshots may induce extra fringes and further mislead the unwrapping and displacement results. To test the necessity of ionospheric correction, we utilize global and regional ionospheric vertical TEC maps, namely the Global Ionosphere Map (GIM) released by the International GNSS Service and the Taiwan Regional Ionospheric Map (TRIM) provided by a local GNSS network, to compensate ionospheric phases in the interferograms.Our experiments investigate 47 ascending image pairs in 2016 and 2017. Among them, two pairs of images whose differenced TEC (ΔTEC) stronger than 16.73 TEC unit in azimuthal gradient contain the clearest extra fringes. The results show that TRIM outperforms in reducing extra fringes caused by the ionospheric effect, with a root-mean-square error (RMSE) of the estimated satellite line-of-sight displacement reduced from 105.03 mm to 9.94 mm as compared against ground truth data. We conclude that TRIM TEC map is able to remove long-wavelength background of ionosphere-induced displacement when a differenced TEC gradient larger than an empirical threshold of 15 TECU.