**About the Editor**

## **Fabio Giannattasio**

Fabio Giannattasio (Ph.D., II Level Master in Space Science and Technology) is a Researcher at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) in Rome, Italy. His research activity is focused on the study of Sun–Earth interactions and space weather. He has a particular interest magnetosphere-ionosphere coupling, the complex nature of physical processes in the ionosphere, and some aspects inherent to solar physics.

## *Editorial* **Ionosphere Monitoring with Remote Sensing**

**Fabio Giannattasio**

Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata 605, 00143 Rome, Italy; fabio.giannattasio@ingv.it

#### **1. Introduction**

Characterising the physical properties of the Earth's ionosphere is fundamental to shed light on the dynamic processes occurring therein on a wide range of both spatial and temporal scales and to understand several phenomena relevant to Space Weather.

In fact, due to the presence of ions and electrons, the ionosphere reacts to the onset, amplification and evolution of magnetic and electric fields.

This response may substantially change the physical properties of the ionosphere and its energetic budget and may be reflected, for example, in the modification of the propagation properties of electromagnetic signals traveling through the ionospheric medium.

Due to the conspicuous amount of high-quality data, these features can be reliably investigated at different scales taking advantage of remote sensing and in situ facilities such as ionosondes, radars, satellites and Global Navigation Satellite Systems (GNSS) receivers.

#### **2. Overview of Contribution and Future Perspectives**

In this context, the Special Issue "Ionosphere Monitoring with Remote Sensing" aims at promoting significant advances in our knowledge of the ionosphere through the use of different data from different facilities as well as currently recognized ionospheric models. In fact, the Special Issue focuses on: (1) the investigation of the impact of sunlit, solar and geomagnetic activity on the ionosphere at all latitudes; (2) the investigation of the impact of ionospheric variations on contemporary technology; (3) the improvement of ionospheric models through new instrumental observations, analyses and data-handling techniques; (4) the investigation of magnetosphere–ionosphere coupling through multi-instrumental approaches; and (5) the promotion of new instruments, missions and tools to monitor the ionosphere.

The Special Issue provides 15 original research papers describing results obtained with a wide range of tools, data and analysis techniques and focused on the characterisation of several properties of the ionosphere.

As mentioned above, great attention has been paid to the development of new facilities and analysis techniques to increase our knowledge of the ionosphere. Shindin et al. [1] presented a prototype of a low-cost and good-quality fast ionosonde capable of performing with the unprecedented speed of one second cadence, which allows recording fast quasiperiodic and moving ionospheric disturbances in the F, E and Es layers. An additional strength is that the ionosonde is equipped with cheap, publicly available components, which favours the multi-position registration of ionograms and, as a consequence, the investigation of ionospheric disturbances in a three-dimensional region of space and the possibility to create a network of observation points. A layer of critical importance for ionospheric studies is the transition region between the lower and upper atmosphere, namely, the sporadic E (Es) layer, which consists of a region of enhanced ion plasma at altitudes between 90 and 120 km with a vertical extent of several kilometres and a horizontal extension of tens of kilometres [2]. The existence of this region can be explained by the wind shear theory and the convergence of metal ions and can be influenced by shear instabilities, tidal, planetary or gravity waves, meteors and thunderstorms [3–8]. The vertical structure of the Es layer is

**Citation:** Giannattasio, F. Ionosphere Monitoring with Remote Sensing. *Remote Sens.* **2022**, *14*, 5325. https:// doi.org/10.3390/rs14215325

Received: 13 October 2022 Accepted: 21 October 2022 Published: 25 October 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

still poorly understood due to its transient and complex nature together with limitations in observation techniques [9–13]. Liu et al. [14] applied the frequency domain interferometry (FDI) technique by using the Es layer measurements near Wuhan, China, on 8 June 2021. They pointed out that this technique allowed them to obtain increased spatial resolution of ionosonde imaging capable of identifying different types of Es and to capture its internal fine structure. Unlike the "classic" vertical sounding mode, for the oblique sounding mode, the transmitter and the receiver are located at stations that can be hundreds or thousands of kilometres apart [15–18]. The resulting oblique ionograms can capture ionospheric properties at the reflection point, which is usually located at the middle point between the transmitter and receiver. However, the problem of how to automatically scale oblique ionograms is still open, and various solutions have been identified over the years [19–23]. Jiang et al. [24] developed a method to carry out the automatic inversion of oblique ionograms to extract the parameters of the ionosphere together with the electron density profile. Their results show that the accuracy of the inferred autoscaled maximum observable frequency and minimum group path of the ordinary trace of the F2 layer is about 91.98% and 86.41%, respectively. Kim et al. [25] used Vertical Incidence Pulsed Ionospheric Radar (VIPIR) to observe the polar ionosphere with Dynasonde analysis software at Jang Bogo Station (74.6◦S, 164.2◦E), Antarctica, which is located in the polar cap, cusp or auroral oval depending on the local time and the geomagnetic activity conditions. The resulting F2-layer peak electron density (NmF2) and bottomside total electron content (TEC) exhibit an overall good correlation with GPS TEC measurements during quiet conditions. During the daytime and in summer, the bottomside TEC is less correlated with the GPS TEC due to particle precipitation and the onset of large density irregularities in the polar ionosphere. However, the Dynasonde analysis show some limitations and needs to be improved in order to provide accurate density profiles, especially during disturbed geomagnetic conditions. The use of radar imaging and interferometry techniques also provides important information on the physical properties of the ionosphere. In a horizontally structured atmosphere, radar echoes are strongest near the zenith and decrease with the angle off the zenith. In the presence of ionospheric field-aligned plasma irregularities (FAIs), radar echoes are strongest at the beam direction perpendicular to the geomagnetic field, with a fast decrease in the angle off the perpendicular direction. The aspect angle, which is a measure of the aspect sensitivity, i.e., the half width of half power or the standard deviation of Gaussian fitting in the angular power distribution, is of the order of degrees [26,27]. On the contrary, it can be of order 0.1 degrees or less in FAI echoes (see, e.g., Kudeki and Farley [28]). A way to effectively measure an FAI's aspect angle lies in the radar interferometry technique [28,29]. Chen et al. [30] applied the coherent radar imaging (CRI) technique to estimate the aspect angle of mid-latitude E region FAIs. CRI requires the use of separate antennas as independent receiving channels to collect radar echoes [27,31]. The echoes received allow one to retrieve the in-beam angular power distribution. By using the multireceiver and multifrequency capabilities of the 46.5 MHz middle and upper atmosphere radar in Japan, Chen et al. [30] showed that, among the three methods (namely, Fourier, Capon and norm-constrained Capon) used to recover the brightness distribution, the norm-constrained Capon method produces more reliable results and more trustworthy aspect angle values consistent with those obtained with the RI technique. Their results may help to shed light on the spatial and temporal properties of plasma irregularities in the ionosphere.

Karpachev [32] separated and classified ionospheric troughs (regions of anomalously decreased electron density) in the winter ionosphere of the Southern hemisphere by using CHAMP satellite data during high solar activity (between 2000 and 2002). In particular, the authors identified two kinds of high-latitude troughs: (1) a wide trough associated with a region of particle precipitation on the poleward edge of the auroral oval; (2) a narrow trough of ionisation presumably associated with an electric field. Moreover, the main ionospheric trough (MIT) was separated from the ring ionospheric trough (RIT), the latter being formed by the decay of the magnetospheric ring current.

A relevant aspect at the centre of ionospheric investigation concerns plasma density irregularities, which play a key role in the propagation of electromagnetic signals, being a cause of disturbance for the GNSS. In fact, irregularities are responsible for degradation and, eventually, interruptions in the signals received by the system. In the equatorial F region, irregularities are also known as plasma bubbles and develop on the nightside [33] at magnetic latitudes up to 20◦ in both hemispheres [34], at heights up to 1000–1550 km [35] and on a wide range of spatial scales, from hundreds of kilometres down to a few decametres [36]. Their spatial and temporal distribution depends on solar and geomagnetic activity and exhibits a diurnal and seasonal variation [33,37,38]. The origin of plasma bubbles is recognised to be due to the establishment of density gradients sufficient to trigger a Rayleigh–Taylor instability growth mechanism [34,39]. The irregularities generated in this way expand vertically and then follow the geomagnetic field lines in both directions above and below the magnetic equator. This dynamic is typically overlaid by an eastward drift motion due to polarising electric fields generated by neutral zonal winds. The instability of these structures can, in turn, generate secondary irregularities and trigger a cascading process. Several studies have pointed out the turbulent nature of plasma bubbles [40–45]. In this context, De Michelis et al. [46] focused on the relationship between the spectral features of electron density and magnetic field strength inside plasma bubbles in order to understand whether it is possible to study the dynamical features of plasma bubbles by using either the magnetic field or the electron density measurements. This is motivated by the fact that, in the past, important plasma bubble features have been detected by analysing their magnetic signatures using the diamagnetic effect [47]. However, studying plasma bubbles by using only magnetic field data may not be the correct way, as it implies that the scaling properties of electron density and magnetic fields are equal. To address this point, De Michelis et al. [46] studied the scaling properties of both electron density and magnetic fields associated with plasma bubbles using about two years of Swarm measurements at 1 Hz. Specifically, they applied the local detrended structure function analysis [48] and found that a complex relation may exist between the spectral features of electron density and magnetic field that depends on local time and latitude due to the evolution and turbulent nature of plasma bubbles. A more in-depth study of diamagnetic currents at high latitudes obtained by Swarm measurements has been performed by Lovati et al. [49]. Such weak currents are driven by pressure gradients and produce a magnetic field that is directed opposite to the background geomagnetic field and causes its reduction. The authors used 4 years of electron density, electron temperature and magnetic field data at 1 Hz to investigate the dependence of diamagnetic currents on local time, season, solar and geomagnetic activity and sunlit conditions. They confirmed the enhancement of diamagnetic currents at high latitudes, around the cleft region, during disturbed periods due to the increase in plasma pressure gradients. In the polar cap, currents flow regardless of the geomagnetic activity due to plasma instabilities driving irregularities and pressure gradients. Moreover, during disturbed periods, features in the correspondence of the auroral oval move to lower latitudes. These findings may help to improve current geomagnetic field models and understand the impact of ionospheric irregularities on dynamics at spatial scales of tens of kilometres.

New insights into dynamic processes in the ionosphere are obtained by studying its turbulent nature, which underlies, for example, chaotic plasma behaviour. The turbulent dynamics of ionospheric plasma has long been established, especially at high latitudes, by investigating, for example, fluctuations in magnetic and electric fields and electron density. Such fluctuations are characterised by power-law spectral densities, scaling features and non-Gaussian statistics of increments at all scales (see, e.g., [50–53]) and can affect plasma dynamics via the ExB drift term. At both high and low latitudes, variations in vertical plasma velocity drift plays a key role in the generation of irregularities [54–56]. In light of this, Consolini et al. [57] used electric and magnetic field measurements provided by the Chinese Seismo-Electromagnetic Satellite (CSES-01) to investigate the properties of the plasma ExB drift velocity during a crossing of the Southern auroral F region. Specifically, they analysed the spectral and scaling features of velocity fluctuations and pointed out the turbulent nature of the drift. In more detail, the authors provided evidence of 2D intermittent turbulence at scales from tens of meters to tens of kilometres. This is consistent with filamentary or thin-tube-like features.

One of the most important application issues is the risk assessment of the impact that ionospheric variations may have on technology. A proper risk assessment allows the development of effective mitigation strategies. For example, ionospheric anomalies may result in potential threats for the ground-based augmentation system (GBAS), which is an airport-based augmentation of the GNSS capable of providing advanced civil-aviation services. When GNSS signals travel through ionospheric regions with enhanced gradients, severe errors may be observed and compromise the reliability of the GBAS. Thus, it is fundamental to quickly detect anomalies. In this context, Gao et al. [58] developed a monitor to clearly detect anomalies with an average detection speed improved by more than 16% when dealing with real data instead of simulations. Valdés-Abreu et al. [59] studied the effects of an annular solar eclipse on GNSS position estimation accuracy based on TEC measurements performed by over 2000 stations worldwide, which were validated with measurements by the Swarm satellite mission and four digisondes in Central and South America. In particular, TEC maps pointed out a TEC depletion under the moon's shadow and important variations in both crests of the Equatorial Ionization Anomaly (EIA). Variations typically affect the amplitude of the signal and its delay (see Bravo et al. [60] and references therein) and can affect regions outside the umbra and penumbra of the eclipse [61–63]. With this global coverage, the work of Valdés-Abreu et al. [59] allowed them to find other locations in the world that could be affected by perturbations in the North Pole and infer how that perturbations propagate to those potential locations.

A fundamental physical parameter for studying the impact of sunlit, solar and geomagnetic activity on the upper ionosphere and its coupling with the magnetosphere is the electron temperature. This quantity exhibits distinct features with spatial, diurnal, seasonal and activity variability [64–71]. Pignalberi et al. [72] performed a statistical and global study of the electron temperature in the topside ionosphere derived from seven years of in situ data acquired by the Swarm mission at 1 s cadence. The results obtained with this unprecedented data set were compared to data modelled by the International Reference Ionosphere (IRI) model, as well as data obtained from incoherent scatter radars (ISRs). This also allowed an understanding of the deviation between the IRI model and the measurements and testing the reliability of including Swarm data in the empirical data set layer of the IRI itself. Finally, the authors showed that adding the Lomidze calibration to Swarm data [73] improved their agreement with ISR data and the IRI model, especially at mid-latitudes and during the daytime. Another significant parameter representative of the ionosphere is the equivalent slab thickness (EST), i.e., the ration of the TEC to the NmF2. By definition, this parameter represents an imaginary equivalent depth of the ionosphere and includes information on both the topside and bottomside ionosphere, thus being useful in the study of variations in the upper atmosphere (see, e.g., [74–76]). EST exhibits diurnal, seasonal solar and geomagnetic activity variations with a dependence on the location of the observing station. The greatest variability is observed during periods of geomagnetic storms. Zhang et al. [77] analysed the EST in Guam, at equatorial latitudes, confirming and discussing previous results in the literature. In addition, they obtained some new results pointing out diurnal and seasonal changes and the effect of geomagnetic storms on EST at the magnetic equator. In particular, they found that during positive storms, the penetration electric field increases plasma uplift, causing an increase in TEC accompanied by small increases in NmF2. Moreover, equatorward winds drive plasma into the topside ionosphere at the equator resulting in TEC that does not undergo severe depletion like NmF2 does during negative storms. Thus, geomagnetic storms enhance EST both during positive and negative storms.

The monitoring of the physical properties of the ionosphere and their perturbation also has applications in the study of phenomena that can be considered as precursors of major seismic events. Since the early work of Moore [78] and Davies & Baker [79], the idea was proposed that the processes of earthquake preparation and occurrence could be linked to ionospheric disturbances due to lithosphere–atmosphere–ionosphere coupling. With the increase in available data, this idea has become more and more widespread, and in the last decades, new satellite missions have been conceived to monitor natural disaster activities (QuakeSat, SICH-1M, COMPASS-2, DEMETER, CSES). Satellites with other declared purposes, such as the European Space Agency's Swarm constellation, have also provided important information for ionospheric disturbances. Recently, several works investigated magnetic field anomalies observed by both ground and space facilities to study the lithosphere–atmosphere–ionosphere coupling effects of earthquakes [80–82]. In this context, deep learning techniques are used to carry out statistical studies based on the analysis of large numbers of earthquakes. Xiong et al. [83] proposed a deep learning framework for pre-earthquake ionospheric perturbation identification model called SafeNet, which performs better in identifying possible pre-earthquake ionospheric anomalies the more intense the earthquakes are. Ionospheric scintillations are also used for correlations with the occurrence of earthquakes. Some studies in the literature pointed out that thermal expansion of the atmosphere derived from land surface temperature increase before earthquakes can generate small gravity waves altering the electron density profile and causing changes in the TEC, and, on the other hand, ionospheric perturbations can be detected in the hours after large earthquakes [84,85] (Tsugawa et al. 2011, Pavilidou et al. 2019). Few works in the literature investigated the correlation between the occurrence of earthquakes and ionospheric scintillation (see, e.g., [86]). These studies take advantage of GPS data from ground stations or ionosondes to measure the scintillation index S4 and study its correlation with earthquakes in the same region. By using statistical tools, Molina et al. [87] for the first time used the GNSS reflectometry [88] technique to obtain global oceanic maps of ionospheric scintillation and correlate them to earthquake precursors. Their results point out a small positive correlation for earthquakes with magnitudes above 4, with better results for increasing magnitudes. Correlation was better when positive increments in the S4 index were observed between 6 and 3 days before the earthquakes than the ones observed after them. In the best case, the correct prediction probability is about 32% and the false alarm probability is 16%; however, the probability of detection is small overall. The authors also recognise that the signature of ionospheric scintillation increments as precursors of earthquakes is still small and should not be regarded as an early warning system for earthquakes.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Technical Note* **The Prototype of a Fast Vertical Ionosonde Based on Modern Software-Defined Radio Devices**

**Alexei V. Shindin 1,2,\*, Sergey P. Moiseev 1,2, Fedor I. Vybornov 1,3, Kseniya K. Grechneva 2, Viktoriya A. Pavlova <sup>1</sup> and Vladimir R. Khashev <sup>2</sup>**


**Abstract:** The description and test results of the prototype of a fast ionosonde for the vertical sounding of the ionosphere, which makes it possible to record ionograms once a second, are presented. Such a high rate of registration of ionograms is required to study the fast processes of redistribution of electron concentration during heating experiments, for registration of fast quasiperiodic and moving ionospheric disturbances in the F, E, and Es layers. The key feature of the presented development is the usage of publicly available radio-electronic components. This provided a significant reduction in the cost of creating the prototype. In the current version, the prototype is based on the softwaredefined radio (SDR) devices Red Pitaya SDRlab 122-16 and LimeSDR. The test results showed that the quality of the ionograms recorded using the prototype is not worse than the quality of ionograms recorded using the professional CADI ionosonde. The low cost of the components allows providing multi-position registration of ionograms for determination the dynamics of natural and artificial ionospheric disturbances in 3D region of space at a lower expenses rate, as well as to create a network of ionospheric observation points with an increased number of ionosondes.

**Keywords:** ionosphere; vertical pulse sounding; ionosonde; ionogram; software-defined radio

#### **1. Introduction**

Over the past 20 years, software-defined radio (SDR) devices have expanded its applicability from professional to amateur radio (see, for example, [1]). This was facilitated by the following factors: cheaper hardware components and the development of fieldprogrammable gate array (FPGA) technology. The latter factor allowed extreme simplification of the structure of SDR devices for the RF range, factually excluding application-specific integrated circuits (ASIC) chips from it. Moreover, a substantial role was played by the fact that development environments for FPGAs from major vendors became free for individual use. Modern ADC/DAC with a sampling rate of more than 80 MHz allow the entire RF range (3–30 MHz) to be transmitted to the FPGA for processing. All of the above makes such devices extremely convenient for monitoring the passage of HF radio waves through the ionosphere and, in particular, for the implementation of the chirp ionosonde [2,3]. To monitor the current ionospheric situation and structure of the ionosphere, the reconstruction of the electron density profile, the most common technique is the vertical sounding of the ionosphere with short coded pulses with a filling frequency varying within 1–20 MHz. This paper presents the description and test results of the vertical sounding ionosonde prototype based on currently available SDR devices. The developed prototype can be used by scientific groups to create their own devices for monitoring ionospheric conditions.

**Citation:** Shindin, A.V.; Moiseev, S.P.; Vybornov, F.I.; Grechneva, K.K.; Pavlova, V.A.; Khashev, V.R. The Prototype of a Fast Vertical Ionosonde Based on Modern Software-Defined Radio Devices. *Remote Sens.* **2022**, *14*, 547. https:// doi.org/10.3390/rs14030547

Academic Editor: Fabio Giannattasio

Received: 21 December 2021 Accepted: 22 January 2022 Published: 24 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **2. Materials and Methods**

#### *2.1. Hardware Part*

The vertical sounding ionosonde is an HF radar station (for more details, see [4]). During operation, the ionosonde emits short radio pulses with a filling frequency ranging from 1 to 20 MHz. As a rule, various types of manipulations are applied to the emitted pulses (usually amplitude or phase). The pulses reflected from the ionosphere are recorded and processed using the receiving part. As a result of signal processing a height-frequency characteristic is obtained, so that it is possible to restore the electron concentration profile in the altitude range of 80–700 km.

While developing the ionosonde prototype for vertical sounding, we focused on the technical characteristics of the CADI ionosonde [5] at our disposal: the radiation power is 600 W, the type of pulse encoding is phase shift keying with a 13-bit Barker code with a duration of one bit of 40 μs, the pulse repetition rate is from 25 μs, the range of probing frequencies is from 1 to 20 MHz with a step of 50 kHz in standard mode. In [6], you can find examples of ionograms obtained using the CADI ionosonde. It should be noted that in order to increase the signal-to-noise ratio when registering a signal reflected from the ionosphere, our CADI ionosonde uses averaging over 4 pulses emitted with the same filling frequency. In general, this requires approximately 40 s to emit all the pulses, and then one minute to process the signal and record the ionogram data into the file. The height resolution of ionograms obtained using the CADI ionosonde is 3 km.

The block diagram of the developed prototype of the fast vertical-sounding ionosonde is shown in Figure 1. In the current version, the transmitting and receiving parts of the ionosonde are separate devices. The transmitting part includes (1) DIY HF linear amplifier on LDMOS transistors [7] with a maximum power of 600 W with an operating range of 1.8–72 MHz; (2) 5 W pre-amplifier with an operating range of 100 kHz–40 MHz; (3) programmable attenuator with an operating range of up to 6 GHz and a maximum attenuation of 30 dB; (4) SDR device Red Pitaya SDRlab 122-16 [8]. Figure 2 shows a photo of the transmitting part of the ionosonde mockup without housing on a laboratory bench.

**Figure 1.** Block diagram of the transmitting (**bottom**) and receiving (**top**) parts of the ionosonde.

The Red Pitaya SDRlab 122-16 is the development board based on a Xilinx Zynq 7020 system that combines FPGAs and a general-purpose dual-core ARM processor. The board is equipped with two-channel 14-bit DAC and 16-bit ADC with a sampling rate of 122.88 MS/s, as well as Ethernet and USB2.0 interfaces for data transfer, communication with other devices (PC), and for connecting additional devices to the board (e.g., WI-FI dongle). This board is used as a master oscillator (probe pulse generator). We plan to use this device as a basis for the receiving part of the ionosonde in the future.

**Figure 2.** The photo of the assembled model of the transmitting part of the ionosonde without a housing. The numbers indicate 1—amplifier power supply, 2—linear amplifier A600, 3—5 W preamplifier, 4—SDRlab 122-16 in a 3d-printed case as a master oscillator, 5—matched load.

The receiving part of the developed layout (see Figure 3) is based on a two-channel SDR device LimeSDR [9] with a declared operating frequency range from 100 kHz to 3.8 GHz, which is capable of recording a signal in the 61.44 MHz bands. To be able to register the HF signal in the 10 MHz bands modified upconverters were used filtering the HF signal and moving it to the frequency range 120–130 MHz, where the LimeSDR can work more efficiently. Since the operation of the ionosonde assumes precise frequency matching between the transmitting and receiving parts, we used the Leo Bodnar precision GPS reference clock to generate the reference signal for the LimeSDR and upconverters. Two channels of the device allow, in presence of appropriate antennas and a polarizer, to register two polarizations of the reflected signal (O and X modes). A low noise amplifier (+20 dB) was used to amplify the received signal. To record a signal in the 10 MHz bands, the LimeSDR was connected to a PC or laptop via the USB3.0 interface.

**Figure 3.** Photo of the assembled model of the ionosonde receiving part without a control PC. The numbers indicate: 1—USB hub, 2—precision GPS reference clock, 3—upconverter, 4—LimeSDR.

#### *2.2. Software Part*

The SDR concept assumes that the functionality and even the purpose of the device is determined by software components that can be easily changed or upgraded. In our case, the function and operation speed of the vertical sounding ionosonde is provided by the development of firmware for the SDRlab 122-16 FPGA board and software for signal processing and obtaining ionograms.

To achieve the ionogram recording time of the order of 1 s, we: (1) used a pulse repetition period of 5 ms instead of 25 ms (as in the CADI ionosonde); (2) abandoned the averaging of signals over four pulses at one frequency; (3) reduced the frequency range of sounding to 10 MHz; (4) developed software that allows recording ionograms in real-time, i.e., with a delay less than the time required for the emission of all probing pulses. All these measures ensured a sounding time of 0.9 s.

The functional block diagram of the master oscillator firmware on the SDRlab 122-16 board is shown in Figure 4. The master oscillator firmware was implemented by means of the Xilinx Vivado development environment in the Verilog hardware description language using embedded Xilinx IP cores. The project consists of separate modules that have a functional connection with one another: the input clock frequency of 122.88 MHz, coming from the crystal oscillator of the Red Pitaya board, is fed to the input of the clk\_wizard module, where it is converted to the frequency of 100 MHz. The clock pulse is fed to the input of the clk\_divider module and is divided into two clock frequencies: 25 kHz is the symbol frequency (1 symbol corresponds to 1 bit of a 13-bit Barker code with a duration of 40 μs) and 200 Hz is the frequency of the probing pulses (200 Hz corresponds 5 ms sounding pulse period). These two frequencies provide AM and FM control. In the FM\_Modulator module, the phase increment of the harmonic signal changes every 5 ms with a frequency step of 50 kHz (to ensure the ionogram recording time—0.9 s), after which the increment is fed to the input of the DDS digital computational synthesizer module, where the cosine values are generated. The received harmonic signal enters the amplitude modulation module and the already modulated pulse is fed to the input of the digitalto-analog converter control module. Phase shift keying was implemented with a slight upgrade of the AMK firmware, taking advantage of the fact that changing the phase by *π* is equivalent to multiplying the signal by −1. Note that the selected firmware parameters implement the radiation scheme of the CADI ionosonde, which we were guided by.

**Figure 4.** Functional block diagram of the master oscillator firmware on the SDRlab 122-16 board.

The software for recording ionograms was created on the basis of the GNU radio framework [10]. In addition, a trigger module was developed to compensate for lacking synchronization between the transmitting and receiving parts of the model at this stage, as well as possible missing samples. The used flow graph of the GNU Radio Companion tool is shown in Figure 5. An additional program written in python using the libraries numpy [11], scipy [12], and matplotlib [13] was used for autocorrelation analysis and ionograms. The developed software makes it possible to register ionograms with a height resolution of 1.5 km. The computing power of a laptop equipped with a quad-core central processor is sufficient to obtain an ionogram in less than 1 s, which in fact provides real-time

monitoring of the ionosphere. A separate application (for example, OBS Studio [14]) can be used to quickly publish ionograms on the Internet video services such as YouTube.

**Figure 5.** GNU Radio Companion flowgraph used in ionosonde's receiving part.

#### **3. Test Results**

Several series of tests were carried out for the developed prototype of the fast ionosonde. The first series of experiments included continuous operation of the ionosonde for 24 h at the maximum ionogram recording rate. Tests have shown that the thermal regime and performance of the components are not disturbed under prolonged loads. We have to mention that the computing capabilities of the hardware used are sufficient to obtain ionograms and their automatic publication on the Internet. The second series of experiments consisted in using several types of receiving antennas as part of the ionosonde. Among them: (1) a large diagnostic transmitting/receiving antenna (in-phase horizontal antenna array 126 × 126 m in size, suspended on 12 masts 16 m high; each of the two linear polarizations has 12 emitters; each emitter consists of three dipoles of different length connected in parallel, due to which the antenna has three resonant frequencies of 2.95, 4.6 and 5.7 MHz), that used to register artificial radio emission of the ionosphere, multifrequency Doppler sounding, diagnostics of the lower layers of the ionosphere and mesosphere, etc. (see Figure 6); standard receiving (two broadband crossed dipoles on four 12 m masts with an operating range of 2–10 MHz) antenna of the CADI ionosonde available at the Vasilsursk experimental base (see Figure 8); the receiving-transmitting Delta type antenna of the chirp ionosonde on a 15 m mast with the northern directional pattern and with an effective operating range of 4–15 MHz. Note that in all the tests, the standard transmitting antenna of the CADI ionosonde was used as the transmitting antenna (vertical delta antenna on a 40 m mast with an operating range of 2–30 MHz). All transceiver equipment was located at a distance of no more than 1 km from each other. It is planned to use the T2FD antenna for the mobile version of the ionosonde. The developed ionosonde prototype can be used together with any broadband HF antenna operating in the range of 1.8–10 MHz and vertical antenna pattern.

The receiving part of the developed ionosonde prototype in various test sessions included either an Acer Predator Helios 500 laptop with the Manjaro Linux 21 operating system installed or an ECS Liva SF110-A320 platform with an AMD Ryzen 5 PRO 2400GE quad-core processor.

**Figure 6.** (**Left panel**): an example of an ionogram recorded with the developed ionosonde prototype using a diagnostic antenna (O mode output). Red square corresponds to the ionograms's subarray which presented in detail in Figure 7. (**Right panel**): an example of an ionogram registered with a CADI ionosonde. Ionogram's registration times are shown in UTC at the top of the panels.

**Figure 7.** An example of 9 consecutive ionograms recorded at a rate of 1 ionogram per second, illustrating the effect of a rapid decrease in the effective reflection height in the F region of the ionosphere.

**Figure 8.** Examples of ionograms recorded using the developed prototype ionosonde using CADI ionosonde antennas. (**Left panel**): Barker code amplitude manipulation. (**Right panel**): Barker code phase shift keying.

Comparative analysis of the obtained ionograms showed that the diagnostic antenna, due to its good parameters, provides ionograms with the highest signal-to-noise ratio. Figure 7 shows 9 consecutive ionograms registered by the developed ionosonde prototype using a diagnostic antenna with a time resolution of 1 s. The figure shows the process of an abrupt change in the reflection height by a value of about 15 km in the ionosphere F layer, which could not be detected at the standard ionogram registration rate. Video files demonstrating the operation of the developed ionosonde prototype can be found in the Supplementary Materials. The standard receiving antenna of the CADI ionosonde required the use of a low-noise amplifier (we used 20 dB 1–30 MHz amplifier) to obtain ionograms of comparable quality. When using an inclined antenna of the chirp ionosonde, it was impossible to obtain ionograms with distinguishable traces of ionospheric layers at this stage of testing. In this case, oblique-sounding ionograms for this antenna are successfully recorded. In the third series of tests, two modes of operation of the ionosonde were compared: with amplitude and phase manipulations of the probe pulses. In both cases, the encoding was carried out with a 13-bit Barker code. Tests have shown that phase shift keying ionograms have a significantly higher signal-to-noise ratio. Apparently, this is due to the higher average sounding power in this mode.

#### **4. Discussion**

As can be seen from Figures 6–8, the developed prototype of the vertical ionosonde is capable of registering ionograms comparable in quality to ionograms obtained using the CADI ionosonde. At the same time, the developed prototype uses practically the same temporal radiation pattern and the same transmitter power. The difference in the representation of ionograms by the two instruments is due to many factors. Among the main ones are different characteristics of the receiving parts (the CADI ionosonde has an 8-bit ADC in the receiver) and different approaches to obtaining ionograms (the CADI ionosonde uses fast Fourier transform). The ionograms obtained by the developed ionosonde are quite suitable for further analysis (scaling) in order to reconstruct the electron density profile. The high rate of ionogram registration makes it possible to determine the parameters of fast movements in the ionosphere.

The fast ionosonde can be used to study fast natural variations of electron density profiles in the F-layer of the ionosphere. For example, the use of the fast ionosonde with an ionogram recording time of 2 s allowed detecting local disturbances moving vertically at speeds up to 50 m/s [15]. The horizontal stratification found in this case is capable of performing cyclic vertical movements with an amplitude of up to 5 km and a period of about 90 s.

It is known that traveling ionospheric disturbances (TIDs) with a characteristic spatial scale of up to 100 km are often observed at mid-latitudes in the daytime. Ionosondes operating according to the standard 15-min ionosphere sounding program record them on single ionograms (see, for example, [16]). The rare territorial location of ionosondes does not allow to determine TIDs parameters unambiguously. The relevance of the problem is explained by the difficulty of predicting TIDs and the strong influence on the HF communication channels. The use of linear frequency modulation (LFM) ionosondes and a one-minute sounding cycle made it possible to determine the spatial and dynamic characteristics of the TID from several (usually 5–15) ionograms [17]. As a rule, in this case, a weekly inclined mode of sounding the ionosphere is used by a system of synchronously operating chirp stations [18,19]. Reducing the sounding time to a few seconds would enable tracing the dynamics of the TID propagation processes in detail, especially if several closely located (at a distance of 50–100 km) automatic synchronously operating ionospheric stations were used. The use of several low-cost fast vertical-sounding ionosondes makes it possible to create such promising automatic systems for recording the TID parameters.

The fast vertical-sounding ionosonde can be useful in the development of studies of the processes occurring in the E and Es layers of the ionosphere. It is known that the processes occurring in these layers are characterized by fast dynamics [20].

Experiments on modifying the Earth's ionosphere with powerful short-wavelength radiation [21] have shown the need to develop and use fast vertical-sounding ionosonde. Firstly, the ionosonde in such experiments is used for diagnostic purposes (determination of the height of reflection of a powerful wave) and control of the operation of the heating facility (selection of the frequency of powerful transmitters). As a source of impulse noise, the ionosonde should work periodically but for a short time without interfering with the operation of the diagnostic equipment. Secondly, a high-speed ionosonde is required for research purposes. The interaction of high-power HF radiation of ordinary polarization is accompanied by the excitation of artificial ionospheric turbulence (AIT). There are several stages of its development with characteristic times (the development of striction parametric instability—5–20 ms, the stage of restoration of the level of the reflected signal of the pump wave—0.5–3 s, anomalous attenuation—0.5–10 s, the development of self-focusing nonstability—10–30 s) [21]. Typically, AIT surveys are conducted at fixed frequencies using a probe wave transmitter. The use of a new fast vertical-sounding ionosonde can be useful in investigating the properties of AIT.

#### **5. Conclusions**

In this paper, we showed that it is possible now to assemble the prototype of vertical sounding ionosonde using publicly available radio-electronic components with the total cost of approximately 1600 EUR (the prices are of December 2021), which has an ionogram recording rate of 1 ionogram per second.

This cost does not contain the cost of a PC for recording ionograms, as well as transmitting and receiving antennas. Moreover, the transmitting part of the prototype costs 1050 EUR. We plan to implement the receiving part on the SDRlab 122-16 board in the future. It will reduce the total cost by 550 EUR. Unfortunately, the CADI ionosonde is currently not available for order. However, at the time of purchase to equip the Vasilsursk base in 2007, it cost about 50,000 USD.

As we can see from Figure 6 (as well as from the demonstrations in the paper Supplementary File and data set), the developed ionosonde prototype allows obtaining ionograms at an unprecedented speed, comparable in quality to CADI ionosonde ionograms, at a significantly lower cost. The authors are not aware of any cases of demonstration of continuous long-term operation of other ionosondes at a similar speed.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/rs14030547/s1, Video S1: AM-Large\_antenna, Video S2: AM-CADI\_antenna, Video S3: PSK\_CADI\_antenna.

**Author Contributions:** Conceptualization, A.V.S. and S.P.M.; methodology, A.V.S., S.P.M.; software, A.V.S., S.P.M., K.K.G., V.A.P. and V.R.K.; set up and conducted the experiments, A.V.S., S.P.M., K.K.G. and V.A.P., data processing, A.V.S. and V.A.P.; theoretical analysis, F.I.V.; writing—original draft preparation, A.V.S.; writing—review and editing, S.P.M. and F.I.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work is supported by a Russian Science Foundation grants #21-72-10131 (A.V.S., S.P.M. and V.A.P., Sections 1, 2 and 5), #20-12-00197 (F.I.V., K.K.G. and V.R.K., Sections 3 and 4).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** SDRlab 122-16 firmwares for AM and PSK fast ionosonde modes, code for receiving part of the fast ionosonde including the GNU RADIO Out-of-Tree module source code, fast ionogram receiving video demonstrations, fast ionogram examples in npz data and image file format can be found in Shindin, Alexey. (2021). The Prototype of a Fast Vertical Ionosonde Based on Modern SDR Devices-paper dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5795786, accessed on 17 December 2021.

**Acknowledgments:** Fedor I. Vybornov is grateful to the project No. 0729-2020-0057 within the framework of the basic part of the State assignment of the Ministry of Science and Higher Education of the Russian Federation for the technical feasibility of using CADI stations in Vasilsursk.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


## *Technical Note* **A Method for Automatic Inversion of Oblique Ionograms**

**Chunhua Jiang 1,\*, Cong Zhao 1, Xuhui Zhang 1, Tongxin Liu 1, Ziwei Chen 2, Guobin Yang <sup>1</sup> and Zhengyu Zhao 1,3**


**Abstract:** In this study, a method is proposed to carry out automatic inversion of oblique ionograms to extract the parameters and electron density profile of the ionosphere. The proposed method adopts the quasi-parabolic segments (QPS) model to represent the ionosphere. Firstly, numerous candidate electron density profiles and corresponding vertical traces were, respectively, calculated and synthesized by adjusting the parameters of the QPS model. Then, the candidate vertical traces were transformed to oblique traces by the secant theorem and Martyn's equivalent path theorem. On the other hand, image processing technology and characteristics of oblique echoes were adopted to automatically scale the key parameters (the maximum observable frequency and minimum group path, etc.) from oblique ionograms. The synthesized oblique traces, whose parameters were close to autoscaled parameters, were selected as the candidate traces to produce a correlation with measured oblique ionograms. Lastly, the proposed algorithm searched the best-fit synthesized oblique trace by comparing the synthesized traces with oblique ionograms. To test its feasibility, oblique ionograms were automatically scaled by the proposed method and these autoscaled parameters were compared with manual scaling results. The preliminary results show that the accuracy of autoscaled maximum observable frequency and minimum group path of the ordinary trace of the F2 layer is, respectively, about 91.98% and 86.41%, which might be accurate enough for space weather specifications. It inspires us to improve the proposed method in future studies.

**Keywords:** oblique ionogram; automatic inversion; electron density profile; quasi-parabolic segments

## **1. Introduction**

There is a long history of remotely sensing the ionosphere through radio waves as a vertical sounding mode. In this sounding mode, the transmitter and receiver are collocated at the same station. The ionosonde, as the vertical sounding mode, is a widely used tool for monitoring the ionosphere and plays a significant role for studying ionosphere characteristics in the near real-time method. With the development of the modern advanced ionospheric sounders, many notable ionosondes, such as DPS-4D (Digisonde Portable Sounder) [1], Dynasonde [2], CADI (Canadian Advanced Digital Ionosonde) [3], AIS-INGV (Advanced Ionospheric Sounder-Istituto Nazionale di Geofi sica e Vulcanologia) [4], WISS (Wuhan Ionospheric Sounding System) [5], etc., have been developed to carry out the vertical sounding of the ionosphere. Subsequently, many well-established software tools, including ARTIST (Automatic Real-Time Ionogram Scaling True-height) [6], NeXtYZ (pronounced "next wise") [7], UDIDA (Univap Digital Ionosonde Data Analysis) [8], Autoscala [9], and ionoScaler [10], have been equipped with ionosondes to automatically extract parameters and electron density profiles from vertical ionograms.

Unlike the vertical sounding mode, for the oblique sounding mode, the transmitter and the receiver are located at different stations, and can be hundreds or thousands of

**Citation:** Jiang, C.; Zhao, C.; Zhang, X.; Liu, T.; Chen, Z.; Yang, G.; Zhao, Z. A Method for Automatic Inversion of Oblique Ionograms. *Remote Sens.* **2022**, *14*, 1671. https://doi.org/ 10.3390/rs14071671

Academic Editor: Fabio Giannattasio

Received: 8 March 2022 Accepted: 29 March 2022 Published: 30 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

kilometers apart. It is possible to implement an oblique sounding function with the improvement and additional development of ionosondes. Oblique ionograms recorded by the oblique sounding receiver can represent characteristics of the ionosphere at the reflection point, which mostly is located at the middle point between the transmitter and receiver. We know that the idea of obtaining electron density profiles from oblique ionograms is not new [11–14]; however, how to automatically scale of oblique ionograms is still a challenging task compared with automatic scaling of vertical ionograms for space weather specifications. Similar to autoscaled techniques of vertical ionograms, algorithms are required to be developed to automatically extract parameters and electron density profiles from oblique ionograms. Redding [15] adopted image processing algorithms to extract the traces from oblique ionograms. Fan et al. [16] utilized image processing algorithms and characteristics of oblique ionograms to obtain parameters of the ionosphere. Settimi et al. [17] calculated a 3D ray-tracing algorithm to synthesize oblique ionograms and compared them with measured oblique ionograms to obtain parameters. Ippolito et al. [18] applied the same technique as the Autoscala programs to oblique ionograms for determination of the maximum usable frequency between the transmitter and receiver. Heitmann et al. [19] propose a robust feature extraction and parameterized fitting algorithm for automatic scaling of oblique and vertical ionograms by analytic ray-tracing.

In our previous work, Song et al. [20] proposed a method for obtaining the trace and parameters of the F2 layer from oblique ionograms using the quasi-parabolic model. However, the method proposed by Song et al. [20] is not accurate enough for inversion of oblique ionograms due to only the F2 layer being considered in the model. In this study, the proposed method aims to improve and extend the algorithm developed by Song et al. [20], and further implement automatic inversion of oblique ionograms. The quasi-parabolic segments (QPS) model (nine parameters) was used to represent the ionosphere in this study. Therefore, the proposed method can obtain the parameters of the oblique propagation in the E, F1 and F2 layers and the corresponding electron density profile of the ionosphere; thus, it can improve the accuracy of inversion of oblique ionograms.

#### **2. Methods**

The quasi-parabolic segments model [21,22], widely used for inversion of vertical ionograms, was adopted to represent the ionosphere in this study. Then, vertical ionograms could be synthesized by the integral of group refraction index along the propagation path in the ionosphere. Furthermore, synthesized vertical ionograms could be transformed to oblique ionogram by the secant theorem and Martyn's equivalent path theorem [23,24]. The synthesized oblique ionograms were further fitted to the measured oblique ionogram. Similar to automatic inversion of vertical ionograms by Jiang et al. [25,26], the initial nine parameters of QPS were determined from the IRI [27] and NeQuick model [28], and then adjusted to obtain a large amount of candidate oblique traces. Last, the best-fitted trace and parameters could be selected as the best-fitted output of automatic inversion of the oblique ionogram. Figure 1 shows the flowchart of the automatic inversion of oblique ionograms.

#### *2.1. Automatic Scaling Key Parameters from Measured Oblique Ionograms*

In the practical application, autoscaled parameters from measured oblique ionograms could be used to reduce the size of candidate traces. Therefore, the proposed method first automatically scaled the key parameters (the maximum observable frequency and minimum group path) from measured oblique ionograms. Similar to automatic scaling of vertical ionograms [10,25,26], the searching window and image projection techniques proposed by Jiang et al. [25,26] could also be utilized to scale oblique ionograms. The image projection technique is similar to the histogram technique by Lynn [29]. At first, the same technique as the ionoScaler software [10] was utilized to automatically extract the maximum observable frequency (MOF) and minimum group path of the E/Es (Sporadic E layer), F1, and F2 layer from oblique ionograms. Figure 2 shows a typical oblique ionogram recorded at 13:07 LT on 3 April 2013 between Beijing (40.3◦ N, 116.25◦ E) and Wuhan (30.5◦ N, 114.37◦ E). The black lines (on the horizontal and vertical axes) in Figure 2, respectively, represent the projection values at the Frequency and group path. As shown in Figure 2, the measured oblique ionogram could be divided into three regions (please see the back line on the vertical axis in Figure 2) according to the characteristics of the image projection at the group path. The maximum observable frequency could be identified by the projection values on frequency (please see the back line on the horizontal axis in Figure 2). Therefore, the searching window could be used to scale the maximum observable frequency and minimum group path for the E/Es, F1 and F2 layer. Therefore, we first defined the size of the searching window on measured oblique ionograms. Generally, the transmitter and receiver stations are known in the oblique sounding mode of the ionosphere. Then, the ground distance between the transmitter and receiver stations was adopted to determine the searching window in this study.

**Figure 1.** Flowchart of the automatic inversion of oblique ionograms.

**Figure 2.** A typical oblique ionogram with projections at the group path and frequency recorded at 13:07 LT on 3 April 2013, between Beijing and Wuhan. The back lines on the vertical and horizontal axes, respectively, represent the projection values at the group path and frequency.

The searching window as W[*M*, *N*], proposed by Jiang et al. [10,25,26], was represented by Equation (1):

$$\begin{cases} \begin{aligned} M &= \text{int}[(\Lambda f\_w)/\Lambda f] + 1\\ N &= \text{int}[(P\_{\text{max}} - P\_{\text{min}})/\Lambda P] + 1 \end{aligned} \tag{1} \end{aligned} \tag{1}$$

where Δ*fw* is the horizontal size of the searching window, Δ*f* is the resolution of the frequency in oblique ionograms, *P*max is the maximum height of the searching window, *P*min is the minimum height of the searching window, and Δ*P* is the resolution of the group path in oblique ionograms.

The present method defined Δ*fw* as the width of the working frequency (2–15 MHz). The values of *P*max and *P*min varies depending on the E and F layers.

For the E/Es and *F*2 layer, the size of the searching window was represented by Equation (2).

$$\begin{cases} P\_{\text{max}}E = 2 \cdot \sqrt{\left(D/2\right)^2 + \left(h\_{\text{m}}E\right)^2} - \delta P\_E\\\ P\_{\text{min}}E = 2 \cdot \sqrt{\left(D/2\right)^2 + \left(h\_{\text{m}}E\right)^2} + \delta P\_E \end{cases}, for \to layer$$

$$\begin{cases}\ P\_{\text{max}}F = P\_{\text{max}}E + \delta P\_F\\\ P\_{\text{min}}F = P\_{\text{max}}E \end{cases}, for \text{ F2 layer}$$

where *D* is the ground distance between the transmitter and receiver stations, *hmE* is the peak altitude of the E layer from the IRI model, and *δPE* and *δPF*, respectively, are the deviation values of the group path of the E layer and F layer in oblique ionograms.

Once the searching window was defined, the image projection values of the searching window [26] were used to calculate the frequency and group path parameters of the E/Es and F layer from measured oblique ionograms. The detail procedure is similar to the methods proposed by Jiang et al. [26] and Song et al. [20]. In this study, we mainly introduce additional routines for identification of the Es and E layers in the present method.

For the Es layer, the proposed method first scaled the maximum observable frequency of the E/Es layer (fMOF\_E\_Es) from measured ionograms. Then, the fMOF\_Emodel was calculated by the secant theorem and Martyn's equivalent path theorem, where the parameters of E layer were estimated from the IRI model. The scaled parameter fMOF\_E\_Es was further compared with the fMOF\_Emodel. If the fMOF\_E\_Es was larger than the fMOF\_Emodel, we

suggest that the Es layer existed in measured oblique ionograms. Otherwise, the proposed method suggested that no Es layer occurred in oblique ionograms. Since the altitude of the Es layer was close to the E layer, if the Es layer existed, the group path of the Es layer was suggested to be equal to the E layer in this study. Figure 3 shows the flowchart for estimating the parameters of the E and Es layer from measured oblique ionograms.

**Figure 3.** Flowchart of estimating the parameters of E and Es layer from measured oblique ionograms.

In measured oblique ionograms, the echoes of the F1 layer usually do not develop well compared with the E and F2 layers. Thus, the parameters of the F1 layer are estimated from the IRI and NeQuick models, but not from measured ionograms in this study. As a result, the frequency and group path parameters of E, Es, F1 and F2 layers could be estimated from measured oblique ionograms.

#### *2.2. Synthesizing Oblique Ionogram through the QPS Model*

The secant theorem and Martyn's equivalent theorem could be used to study the relationship between the vertical ionogram and oblique ionograms. Many studies [20,30] used these theorems to convert oblique ionograms into vertical ionograms. On the contrary, vertical ionograms were required to be converted into oblique ionograms by the secant

theorem and Martyn's equivalent theorem in this study. Equation (3) was adopted to convert vertical traces into oblique traces.

$$\begin{cases} \quad f\_{ob} = f\_{vi} \cdot \sqrt{\left(D/2\right)^2 + \left(h'\right)^2}/h' \\ \quad P\_{ob} = 2 \cdot \sqrt{\left(D/2\right)^2 + \left(h'\right)^2} \end{cases} \tag{3}$$

where *fob* and *Pob*, respectively, represent the frequency and group path in synthesized oblique traces, *D* is the ground distance between the transmitter and receiver stations, *h* is the virtual height of vertical traces, and *fvi* is the frequency of vertical traces.

Figure 4 shows a typical synthesized vertical trace (left) and the converted oblique trace (right). The red line in the left panel of Figure 4 is the electron density profile represented by the QPS model with the parameters by Equation (4). The ground distance between transmitter and receiver stations was set to 1000 km.

$$\begin{cases} foE = 3.5(MHz), y\_mE = 20(km), h\_mE = 110(km) \\ \int foF1 = 5.0(MHz), y\_mF1 = 80(km), h\_mF1 = 180(km) \\ \int foF2 = 12.0(MHz), y\_mF2 = 100(km), h\_mF2 = 300(km) \end{cases} \tag{4}$$

**Figure 4.** A typical synthesized vertical trace (**left**) and the converted oblique trace (**right**); the red line in the left pane represents the corresponding electron density profile.

#### *2.3. Matching Measured Oblique Ionograms with Synthesized Oblique Traces*

Similar to studies of vertical ionograms [26], a large amount of candidate vertical traces could be synthesized by the QPS model in this study. Reasonably, the range of the parameters of the QPS model in this study is similar to the reconstruction of vertical traces [26]. Furthermore, these candidate vertical traces could be transformed to oblique traces. Then, the synthesized oblique traces, whose parameters are close to autoscaled parameters, would be selected as the candidate traces to carry out correlation with measured oblique ionograms. It can reduce the running time of the proposed method to meet the near real-time application associated with inversion of oblique ionograms. Then, the correlation values between synthesized traces and the measured oblique ionogram were compared with the threshold value *Cth*. If there were some correlation values larger than the threshold value *Cth*, the oblique trace with the maximum correlation value and the corresponding parameters would be selected as the best-fitted one. Otherwise, the proposed method would output the autoscaled parameters mentioned above in Section 2.1. Figure 5 shows the flowchart of matching measured oblique ionograms with synthesized oblique traces.

**Figure 5.** Flowchart of matching measured oblique ionograms with synthesized oblique traces.

#### **3. Results**

Figure 6 shows a best-fit synthesized trace (a black line) with the best-fit parameters on a measured oblique ionogram. This typical oblique ionogram includes the echoes of the Es layer. Result shows that the proposed method performed well for automatically scaling the parameters of the E, Es, F1 and F2 layers from oblique ionograms. Because the parameters of the F1 layer were estimated from the model, the synthesized trace did not match well for the echoes of the F1 layer. However, results suggested that it could not affect the performance of the matching trace of the E, Es and F2 layer. The proposed method is inspiring for automatic inversion of measured oblique ionograms, especially for the E/Es layer and F2 layer.

To test the feasibility of this method on different kinds of oblique ionograms, measured oblique ionograms were divided into three categories. The first category is ionograms with the Es layer and F1 layer, the second is without the F1 layer and Es layer, the third is with the F1 layer but without the Es layer. Figure 6 shows the first case. In this section, the second and third cases were tested. Figure 7 shows the best-fitted traces on measured oblique ionograms without the F1 layer and Es layer. Figure 8 shows the third case with the F1 layer but without the Es layer. It can be seen in Figures 7 and 8 that the synthesized oblique traces (black lines) matched well on the echoes of oblique ionograms. The autoscaled parameters were also plotted in Figures 7 and 8. Results matched well on different kinds of measured oblique ionograms, which inspired us to carry out a statistical study of autoscaled parameters on a large amount of oblique ionograms.

**Figure 6.** Oblique ionogram measured at 12:52 BJT on 3 April 2013 with the best-fit synthesized trace (a black line) and best-fit parameters.

**Figure 7.** Similar to Figure 6, but the oblique ionogram was measured at 00:07 BJT on 2 April 2013 without the F1 layer.

Because there are no ionosonde installed at the middle point between Wuhan and Beijing stations, the electron density profile inversed from measured oblique ionograms was not used to test the accuracy of the proposed method. It is well known that the parameters of the F2 layer are of great significance for the propagation of radio waves in the ionosphere. Therefore, the maximum observable frequency (maximum frequency of the observed trace) and the minimum group path of the F2 layer were utilized to verify the performance of this method. In this test, the parameters of fxMOFF2, foMOFF2, and PminF2 were adopted. In the presented method, the Es layer was identified by comparison with the E layer from the IRI model. It is difficult to directly identify the Es layer from measured oblique ionograms by operators when the most observed frequency is not large enough. Thus, the maximum observable frequency and the minimum group path of the E or Es layer were used to test the accuracy of autoscaled data. In the case of the E or Es layer, the operator would scale

the maximum observable frequency and minimum group path of the echoes of the E or Es layer, but cannot identify that it is the E or Es layer. For autoscaled parameters, if the Es layer was identified, its parameters would be used to compare with manual scaled values. Otherwise, the autoscaled parameters of the E layer would be adopted. As a result, there are five parameters (fxMOFF2, foMOFF2, PminF2, fMOFE, PminE) from oblique ionograms. For measured oblique ionograms, the resolutions of working frequency and the group path are, respectively, 0.05 MHz and 5 km. Thus, an autoscaled value is considered to be acceptable if its error is within ±0.5 MHz for the frequency and ±25 km for the group path, which is in line with the URSI limits of ±5Δ (Δ is the reading accuracy).

**Figure 8.** Similar to Figure 6, but oblique ionogram was measured at 13:22 BJT on 2 April 2013 without the Es layer. Oblique ionograms measured during 8–16 April 2013 by the ionosondes between Wuhan and Beijing stations were used to carry out statistical analysis of the performance of the proposed method. The ground distance between Wuhan and Beijing stations is approximately 1040 km. The number of measured oblique ionograms is about 795.

Figures 9 and 10 report the differences between the autoscaled values of the method described here and the standard manual values (Figure 9 for the parameters of the F2 layer, and Figure 10 for the E/Es layer). Table 1 illustrates the percentages of error statistical distributions of parameters for these differences. The accuracy of autoscaled frequency of the F2 layer is above 90%. However, the accuracy of the autoscaled group path is relatively lower (about 86.41%) compared with the maximum observable frequency. Due to the existence of the F1 layer, it is difficult to specify the minimum group path of the F2 layer. On the contrary, the accuracy of the autoscaled minimum group path of the E layer is higher (96.75%) than the autoscaled maximum observable frequency (60.05%). It is noted that the strength of the echoes of the E layer in these measured oblique ionograms are mostly lower, sometimes it is hard to accurately specify the maximum observable frequency of the E layer by the operator (see Figure 8). This will greatly affect the accuracy of autoscaled frequency of the E layer. Therefore, that is the reason that why the accuracy of the maximum observable frequency of the E layer is much less than the F2 layer. For the minimum group path, the lower strength of the echoes of the E layer would not affect the accuracy due to the projection values of the echoes of the E layer at the group path. Thus, we can see that the accuracy of the autoscaled group path for the E layer is high enough. On the contrary, the exact definition of the minimum group path plays a significant role on the accuracy of the autoscaled group path of the F2 layer. In this aspect, the E layer has a greater advantage compared with characteristics of the echoes of the F2 layer. That is the reason why the accuracy of the autoscaled group path of the E layer is greater than the F1 layer. For the lower accuracy of the maximum observable frequency of the E layer, the

accuracy of the F2 layer inspires us to believe that it will be improved greatly if the echoes of the E layer are strong enough.

**Figure 9.** Error distributions of fxMOFF2, foMOFF2, and PminF2 for oblique ionograms between the manual and autoscaled values.

**Figure 10.** Similar to Figure 9, but for fMOFE and PminE.

**Table 1.** The percentages of error statistical distributions of autoscaled parameters from oblique ionograms.


#### **4. Conclusions**

This study describes a method for automatic inversion of oblique ionograms. The proposed method first determined the initial autoscaled parameters using similar technologies of vertical ionograms proposed by Jiang et al. [25,26]. Then, a large number of candidate electron density profiles were constructed by the QPS model, based on the IRI model and the Nequick2 model. Furthermore, the candidate vertical traces, synthesized from electron density profiles, have been converted into oblique traces by the secant theorem and

Martyn's equivalent theorem. Lastly, these candidate oblique traces were used to obtain the best-fitted trace and parameters through matching measured oblique ionograms. Results show that the accuracy of the autoscaled frequency of the F2 layer is above 90% (91.98% for ordinary traces, 96.34% for extraordinary traces). The accuracy of the autoscaled group path is about 86.41%. For the E layer, the accuracy of the autoscaled minimum group path of the E layer can reach up to 96.75%. However, the accuracy of the autoscaled maximum observable frequency is relatively lower (about 60.05%) due to the lower strength of the echoes of the E layer. This indicates that the proposed method might be accurate enough for automatic inversion of oblique ionograms, especially for oblique ionograms with strong echoes (the F2 layer in this study). Results inspire us to develop and improve the performance. The proposed method still requires some adjustments to improve its accuracy and performance, and future studies will focus on the application of the proposed method at different geographic locations.

**Author Contributions:** Data curation, Z.C. and X.Z.; methodology, C.J.; software, C.J.; validation, C.J., G.Y. and T.L.; investigation, C.Z. and C.J.; writing—original draft preparation, C.J.; writing—review and editing, C.J. and Z.Z.; project administration, C.J. and Z.Z.; funding acquisition, T.L. and C.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China (NSFC), grant number 42074184, 41727804, 42104151, 41604133; Youth Foundation of Hubei Provincial Natural (No. 2021CFB134); and the Special Fund for Fundamental Scientific Research Expenses of Central Universities (No. 2042021kf0023).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Oblique ionograms data are available from Chunhua Jiang upon request (chuajiang@whu.edu.cn).

**Acknowledgments:** We are grateful to the Editor and anonymous reviewers for their assistance in evaluating this paper.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Technical Note* **Improved Ionosonde Monitoring of the Sporadic E Layer Using the Frequency Domain Interferometry Technique**

**Tongxin Liu 1, Guobin Yang 1,\*, Chen Zhou 1, Chunhua Jiang 1, Wei Xu 1, Binbin Ni <sup>1</sup> and Zhengyu Zhao 1,2**


**\*** Correspondence: gbyang@whu.edu.cn; Tel.: +86-27-6877-8049

**Abstract:** The sporadic E (Es) layer is a thin layer of ion plasma enhancement in the E-region ionosphere, typically at altitudes of 90–120 km with vertical and horizontal extent of several or several tens of kilometers. As the transition region between the lower and upper atmosphere, this layer is of critical importance for ionospheric studies. The most economical but effective method to observe this layer is using ionosonde, which, however, is incapable of capturing the finer structure or the internal inhomogeneity of the Es layer as the range resolution is on the order of kilometers. To overcome this limitation, we employ the frequency domain interferometry (FDI) technique, a technique that has been successfully applied to the analysis of some radar and sonar measurements. Here, we use the Es layer measurements near Wuhan, China (114◦22 E, 30◦30 N) on 8 June 2021 as examples to showcase the capability of this technique. Our results show that the spatial resolution of ionosonde imaging is remarkably increased: the complexity of the internal fine structure in the Es layer can be well observed in the FDI-processed ionograms, whereas the intrinsic range resolution is several kilometers. Moreover, by comparing the ionograms obtained with and without the FDI technique, it is found that the FDI-processed ionogram is particularly suitable for the observation of evolutional processes in the Es layer, as well as the identification of different types of Es layer. With this level of spatial resolution, ionosonde, in combination with the FDI technique, opens the possibility for more refined observations of the Es layer.

**Keywords:** the sporadic E layer; internal fine structure; high-resolution ionosphere imaging; frequency domain interferometry technique

#### **1. Introduction**

The sporadic E (Es) layer is a relatively thinner (compared to other layers of the ionosphere) layer of enhanced ion plasma in the E-region ionosphere, typically at altitudes of 90–120 km with a vertical extent of several kilometers and a horizontal extension of several tens of kilometers [1].

The formation of the Es layer can be well explained by the wind shear theory and the convergence of metal ions [2–4]. Other than these, shear instabilities, tidal, planetary, or gravity waves, meteors, and thunderstorms could somewhat influence the electron density distribution in the Es layer as well [5–7]. For example, the Kelvin–Helmholtz instability (KHI) can lead to a billow structure and a polarization electric field in the Es layer [8,9]. Tidal and planetary waves can give rise to periodic vertical fluctuations in the Es layer [10,11]. Gravity waves can modulate the Es layer and cause distortions along both horizontal and vertical directions, which are ultimately recorded as quasiperiodic backscatter echoes in the very-high-frequency (VHF) range [12,13]. The seasonality of the Es layer is well related with the occurrence of meteors [14], and, as has been reported numerously, the Es formation in the mid-latitudes is also closely related to the sporadic

**Citation:** Liu, T.; Yang, G.; Zhou, C.; Jiang, C.; Xu, W.; Ni, B.; Zhao, Z. Improved Ionosonde Monitoring of the Sporadic E Layer Using the Frequency Domain Interferometry Technique. *Remote Sens.* **2022**, *14*, 1915. https://doi.org/10.3390/ rs14081915

Academic Editor: Fabio Giannattasio

Received: 30 March 2022 Accepted: 13 April 2022 Published: 15 April 2022

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metal layer [15]. The correlation between the spread F-region ionosphere, thunderstorms, and Es layer has implications for the coupling between the upper and lower layers of the Earth's atmosphere as well [16]. Considering these, as the transition region between the lower and upper atmosphere, the morphological structure, composition, and temporal evolution of the Es layer has been the main focus of various theoretical and observational studies, and improving the spatial resolution of Es layer measurements is critical for better understanding the formation and evolution mechanism of the Es layer, as well as the above-mentioned atmospheric processes.

Up to now, although extensive observational efforts have been made, the vertical structure of the Es layer still remains insufficiently investigated due to its transient nature and limitations in observation techniques. By analyzing the in situ data measured during the campaign of Sporadic E Experiment over Kyush (SEEK), Mori and Oyama found that the Es layer can exhibit a complicated multiple layer structure at altitudes with the separation of 10–12 km [17–19]. This multilayer structure was later confirmed by Damtie et al. using the radio sounding data collected by the European Incoherent Scatter (EISCAT) radar [20]. The authors have specifically found that the electrons in the Es layer were distributed at multiple fine layers with spatial intervals of 1–2 km, and during the downward drifting phase, these fine layers were merged into a single layer. Using the EISCAT data, Turunen et al. have further investigated the undulating movement of the Es layer along the vertical direction and revealed that this layer could be compressed by plasma streams and degenerated owing to the fluctuations in the neutral atmosphere [21].

Along the horizontal direction, the Es layer is also found to exhibit many variations, and a "blanketing" or "patchy" structure is found in most cases [22]. The Es layer can be reconstructed via ionosonde using the critical reflection frequency and the direction of arriving waves, which can be utilized to image the embedded structure of field-aligned irregularities (FAIs) in the Es layer [23,24]. The incoherent scatter radar (ISR) images recorded by Hysell et al. have shown that the Es layer at middle latitudes could exhibit both cloud-like and wave-like structures [25]. As for the Es layer at higher latitudes, for example, in Alaska, Hysell et al. have revealed a two-dimensional "patchy" and "stripe" structure [26]. In addition to radar imaging, the Es layer has been extensively studied using measurements from space, for example, the Global Positioning System (GPS) [27], using the total electron content (TEC) anomalies [28].

Compared to other types of Es layer measurements, for example, ISR and GPS, ionosonde has the advantage of providing persistent observation at a relatively low cost. In particular, compared with ISR, large antenna array and great transmission power are not required, and unlike GPS, the bottom-up sounding of the ionosonde is not affected by the F layer, and even weak Es layers can be observed. However, the inhomogeneity of the fine structure of this layer cannot be well captured due to the poor range resolution of the ionosonde, and its applicability in studies of Es layer formation and evolution is limited. To overcome this limitation represents the main goal of this study and, for this purpose, we utilize the frequency domain interferometry (FDI) technique. We show that this technique, which was originally developed for the analysis of radar and sonar measurements [29], is also applicable to ionosonde measurements. In the following, we explain how this technique is utilized to process ionosonde data, and we use several examples of Es layer measurement to showcase the resolution improvement obtained using this technique.

#### **2. Experiment Setup and Methods**

#### *2.1. Instruments and Experimental Setup*

The ionosonde used in this study is a miniature version of the Wuhan Ionospheric Sounding System (WISS), as developed by the Ionospheric Laboratory of Wuhan University [30]. It specifically uses a 16-bit complementary coded sequence to modulate the high-frequency (HF) signals with a peak power of 500 watts and performs vertical incidence sounding. The transmission duration for any coded chip is 25.6 microseconds (μs), corresponding to an intrinsic range resolution of 3.84 km. In regular mode, each frequencysweep sounding was performed for a total duration of ~3 min at frequencies from 2 to 20 megahertz (MHz) with steps of 0.05 MHz. The number of accumulations was set to be 32. After a 12 min interruption, the typical sounding period of the frequency-sweep detection was 15 min, which is sufficient for general ionospheric observation missions. The WISS uses an inverted-V-shaped antenna for signal transmission and another 30 m three-wire antenna for signal reception. Therefore, during conventional detection, ordinary (O) and extraordinary (X) modes of ionogram echo traces can be distinguished using image processing [31]. It is worth mentioning that WISS has been widely deployed in China and provides reliable data for various ionosphere studies [32].

To ensure good coherence, i.e., reflection of adjacent frequencies at the same height, the frequency step of the ionosonde was set to be 10 kilohertz (kHz) during this experiment. The number of accumulations at each frequency point was 256 since this number provides sufficient samples for the next-step FDI processing. The resident time of each frequency point is about 4.19 s. We emphasize that the time consumption caused by smaller frequency intervals would not significantly affect the Es detection since the Es layer at low and middle latitudes, in general, can last for at least tens of minutes [14,26]. The sounding frequency range in this experiment was set to be 2–4 MHz, as typically needed for good resolution of the Es layer, also taking into account the acceptability of the time consumption. Thus, the ionosonde sounding period was still approximately 15 min, which is consistent with the conventional ionospheric vertical sounding period. Similarly, also in accordance with the conventional ionospheric vertical measurements, short-term changes in the internal structure of the Es layer during the detection period (15 min) are temporarily ignored. Therefore, this paper is more inclined to reflect the relatively static inhomogeneity of the fine structure of Es layer and the evolution process of a 15 min level. The experiments reported in this study were performed near Wuhan, China (114◦22 E, 30◦30 N, geomagnetic dip angle: 45◦) on 8–9 June 2021. During this period, the Kp indexes are less than 2 [33], which means a geomagnetically quiet day.

#### *2.2. Es Layer Imaging Based on the FDI Technique*

The FDI technique has been successfully applied to the analysis of atmospheric radar measurements of ionospheric turbulence and FAIs [34,35]. Good performance has been obtained in general, although with the following drawback: while using a limited number of frequency points to image a small vertical extent, the observation results can only reflect the target response characteristics at a certain frequency band. To remedy this drawback, different from previous radar studies, we first process the frequency-sweep-detected echoes of the Es layer in a wide frequency range. The FDI technique of ionosonde data processing is then applied in the following procedure.

The Es layer is assumed as a target with a slow movement and a narrow height distribution; therefore, when a series of adjacent frequencies is used for sounding and the changes in the Es characteristics in a short duration are ignored, the echo signal *Sp*(*t*) of frequency *fp* is expressed as

$$S\_{\mathcal{P}}(t, r) = A\_{\mathcal{P}}s(t - 2r/c) \cdot e^{-j2\pi f\_p(2r/c) + \varphi\_p} \tag{1}$$

where *c* represents the speed of light, *Ap* represents the echo amplitude, *s*(·) expresses the echo envelope, *r* is the radial distance, and *ϕ<sup>p</sup>* is the initial phase. For a coherent radar system such as the WISS, the initial phase of the transmission signal in a small frequency band can be assumed to be the same; thus, for similar working frequencies, the echo phase difference of the same target must meet the following condition:

$$
\Delta\phi = 2\pi \cdot \frac{2r}{c} \cdot (\Delta f) \tag{2}
$$

where Δ*f* represents the frequency interval. The phase difference is only related to the distance of the target and the frequency interval. The unambiguity range of the signal phase is 2*π*, and subsequent processing is performed based on the order of the range gate; thus, it is necessary to ensure that no phase ambiguity occurs within a range resolution unit. The frequency step Δ*f*<sup>0</sup> during sounding should satisfy the following condition:

$$
\Delta\phi = 2\pi \cdot \frac{2r\_0}{c} \cdot (\Delta f\_0) \le 2\pi \Rightarrow \Delta f\_0 \le \frac{c}{2r\_0} \tag{3}
$$

where *r*<sup>0</sup> represents the initial range resolution of the ionosonde radial distance. This resolution is 3.84 km for the WISS, indicating that Δ*f*<sup>0</sup> must be smaller than 39.0625 kHz. The frequency step of 10 kHz selected in our experiment clearly meets the requirement.

The signal matrix for the echo signals of a certain frequency *fp* and the following *k* adjacent frequencies *fp* ∼ *fp*<sup>+</sup>*k*, (*fp* ≤ 4 MHz − *k*·Δ*f*) is constructed as follows:

$$S(t,r) = \begin{bmatrix} S\_p(t,r), S\_{p+1}(t,r), \dots, S\_{p+k}(t,r) \end{bmatrix}^T \tag{4}$$

where [·] *<sup>T</sup>* represents the matrix transpose. For each range gate, the echo data of each frequency are extracted, and the covariance matrix *RS* is calculated as Equation (5):

$$R\_S = S S^H / n \tag{5}$$

where *n* = 256 is the accumulation number in our experiment, and [·] *<sup>H</sup>* represents the conjugate transpose of the matrix. A range-dimensional steering vector is determined based on the required resolution:

$$\begin{cases} \begin{aligned} a(f\_p, r\_1) &= \left[ \mathfrak{e}^{-j2\pi f\_p(2r\_1/\varepsilon)}, \mathfrak{e}^{-j2\pi f\_{p+1}(2r\_1/\varepsilon)}, \dots, \mathfrak{e}^{-j2\pi f\_{p+k}(2r\_1/\varepsilon)} \right]\_{+}^{H} \\ a(f\_{p'}, r\_2) &= \left[ \mathfrak{e}^{-j2\pi f\_p(2r\_2/\varepsilon)}, \mathfrak{e}^{-j2\pi f\_{p+1}(2r\_2/\varepsilon)}, \dots, \mathfrak{e}^{-j2\pi f\_{p+k}(2r\_2/\varepsilon)} \right]\_{+}^{H} \\ &\vdots \\ a(f\_p, r\_m) &= \left[ \mathfrak{e}^{-j2\pi f\_p(2r\_m/\varepsilon)}, \mathfrak{e}^{-j2\pi f\_{p+1}(2r\_m/\varepsilon)}, \dots, \mathfrak{e}^{-j2\pi f\_{p+k}(2r\_m/\varepsilon)} \right]\_{+}^{H} \end{aligned} \end{cases} \tag{6}$$

where *rs* is defined as the expected resolution, and *m* = *r*0/*rs* represents the refined factor.

Therefore, based on the data of each range gate with the initial resolution, the spectrum of the *j*th refined range unit can be estimated using the Capon method:

$$b(f\_{p\_{\prime}}, r\_{\rangle}) = \frac{1}{a^{\rm H}(f\_{p\_{\prime}}, r\_{\rangle}) \mathcal{R}\_{s}^{-1} a(f\_{p\_{\prime}}, r\_{\rangle})}, j = 1, 2, \cdots, m \tag{7}$$

The sounding range resolution can be increased by *m* times by scanning the range spectrum. Essentially, it is a type of spectrum estimation method in the range dimension that uses the coherence between the echo signals of the same target in the frequency domain.

In this study, we performed the frequency-sweep sounding of the Es layer using the FDI technique in a frequency steeping mode. Considering the observational frequency range of 2–4 MHz, the electromagnetic environments of each frequency were differed slightly, possibly inducing severe interference during the spectral estimation. For enhanced frequency scanning imaging, we normalized the spectral estimation results and used a Gaussian window to perform range smoothing. Eleven adjacent frequencies were considered in one run of the spectral estimation (*k* = 11). The scanning step of the range spectrum was set to 38.4 m (*m* = 100). Note that because the maximum frequency interval of the signals used in one imaging process was only 100 kHz, the difference in the phase-frequency response of the system between the adjacent frequencies was not considered to induce severe adverse effects to the range spectral estimation.

Inevitably, for a single-channel ionosonde, using a wide beam antenna, it is difficult to have the capabilities of direction-estimating and beam-pointing. Naturally, it is impossible to accurately locate the target position. In spite of this, it should be feasible to use this method to monitor the inhomogeneity and complexity of the internal structure of the Es layer. If the Es layer is dense and uniform, the imaging result should also be a narrow thin line. This is because when the signal is not vertically incident, it will be reflected to other directions and will not return to the ionosonde. On the contrary, if the Es layer is inhomogeneous, due to the unsmooth lower boundary of the Es layer or the presence of embedded irregularities, the diffuse range spectrum should be obtained.

#### **3. Results**

#### *3.1. The Inhomogeneous Es Layer*

Figure 1a shows the height–intensity ionogram of the Es layer measured near Wuhan, China (114◦22 E, 30◦30 N) at 17:46 LT (UTC+8) on 8 June 2021. Figure 1b shows the normalized energy at different altitudes and frequencies, while Figure 1c shows the FDIprocessed ionogram with a range resolution of 38.4 m. The FDI technique needs more than one frequency point to ensure good coherence, and thus the frequency points close to the upper boundary of present ionogram cannot be well imaged. As such, in this paper, only the part of frequencies below 3.8 MHz were FDI-processed, which is shown in Figure 1b,c for comparison.

**Figure 1.** *Cont.*

**Figure 1.** Height–intensity ionogram of the Es layer measured near Wuhan, China at 17:46 LT (UTC+8) on 8 June 2021. (**a**) Conventional ionogram with an intrinsic range resolution of 3.84 km. (**b**) Ionogram showing the normalized energy at different frequencies and altitudes. (**c**) Ionogram with a range resolution of 38.4 m as obtained using the FDI technique.

According to International Reference Ionosphere (IRI) 2016 [36], at this time, the peak height of E layer is 110 km and the critical frequency is 1.7 MHz; therefore, Figure 1 shows a diffuse Es layer, for which imaging results do not focus on a certain range. The reflection height corresponding to the strongest energy was approximately 107 km with the smaller echoes distributed at altitudes of 103–123 km. It is clear from Figure 1a,b that, with the intrinsic resolution of 3.84 km, it is almost impossible to recognize the fine structure and diffusion features. In contrast, the echoes due to smaller-scale electron density irregularities (as circled by the dotted blue line) are clearly shown in Figure 1c. The virtual height of the sounding echoes dramatically changes at varying frequencies, as shown in Figure 1c. A direct comparison between Figure 1a–c shows the improvement of spatial resolution of the FDI technique. At the same time, it can also be observed from Figure 1c that this is a highly inhomogeneous Es layer.

#### *3.2. Quiet Es Layer*

In addition to the diffuse Es layer shown in Figure 1, we have also examined the FDI method during quiet Es conditions, as shown in Figure 2. Figure 2a shows an example of quiet Es condition measured by WISS near Wuhan, China at 22:34 LT (UTC+8) on 8 June 2021. The echo trace of the Es layer is a clear and uniform straight line, indicating that the Es layer is dense and stable at this time. In this sense, it can be regarded as quiet. Figure 2a,b show the ionograms obtained without and with applying the FDI technique. The corresponding range resolution is 3.84 km and 38.4 m, respectively. From the comparison between these two panels, it is clear that the sounding echoes are almost flat at ~110 km altitude as in the conventional ionogram (Figure 2a), while smaller-scale fluctuations are resolved in the FDI-processed ionogram (Figure 2b).

#### *3.3. Short-Term Evolution of the Es Layer*

Figure 3a shows the ionograms measured between 22:06 and 23:18 LT (UTC+8) on 8 June 2021. Figure 3b shows similar results, but obtained using the FDI technique with a range resolution of 38.4 m. During this time interval, the echo trace was first compressed and then expanded, implying that the Es layers are evolving from a thin layer to inhomogeneously distributed irregularities. This example somewhat resembles the event recorded by Hysell et al. using ISR [29]. The evolutional process is hardly recognizable in the conventional ionograms, whereas how it was compressed and expanded is clearly resolved in Figure 3b.

**Figure 3.** Drastic short-term evolution process of the Es layer within ~1 h. (**a**) Ionograms of the 3.84 km range resolution at ~22:06–23:18 LT (UTC+8) on 8 June 2021, with an interval of 15 min. Although obvious changes are observed in the Es layer, the details can hardly be observed. (**b**) Ionograms of the 38.4 m range resolution. The evolution details are clearly observed. The compression process of the Es layer proceeds at the same speed at each frequency; however, rediffusion starts at the high-frequency band.

#### *3.4. Different Types of Es Layer*

The proposed FDI technique is also particularly suitable for the identification of different types of Es layer, especially in a relatively narrow frequency band.

Figure 4a shows the conventional ionogram measured at 19:56 LT on 8 June 2021. Figure 4b shows the same event but obtained using the FDI technique. The echo trace at frequencies of 2.4–2.7 MHz shown in Figure 4a is indicative of a multilayer structure, but it is difficult to determine which type of Es layer was recorded. On the other hand, after applying the FDI technique, it is found that the traces of the Es layers were connected and the traces at 2.4–2.6 MHz exhibited a continuous "spike" shape; both features suggest a c(cusp)-type Es layer according to the manual of ionogram scaling [37].

**Figure 4.** *Cont.*

**Figure 4.** Different types of the Es layer. Ionograms of a c-type Es layer measured near Wuhan, China at ~19:56 LT (UTC+8) on 8 June 2021, with a range resolution of (**a**) 3.84 km and (**b**) 38.4 m. The bottom two panels, (**c**) 3.84 km and (**d**) 38.4 m, show similar results, but for an h-type Es layer measured at 01:12 LT (UTC+8) on 9 June 2021.

Another example is shown in the bottom two panels of Figure 4. These two panels show the Es layer measured at 01:12 LT on 9 June 2021. Based on the conventional ionogram (Figure 4c), the Es layer was likely l (low)-type. Nevertheless, after being processed using the FDI method, this Es layer is actually found to be the h (high)-type (Figure 4d) with no "symmetrical spikes". The upper and lower traces are likely to represent the Xmode and O-mode.

#### **4. Discussion**

In Figure 1c, at frequencies lower than 3.2 MHz, diffused range spectra of the echoes were patchily distributed at altitudes of 100–115 km. This reflects the complexity of the internal structure of the Es layer at this time, and the electron density distribution is obviously inhomogeneous. As the spectral peaks are obvious and separated from each other, there may be discontinuous and drastic changes in the spatial distribution of the internal electron density. A reasonable explanation for this phenomenon is, as Whitehead suggested [23], electron density irregularities that are embedded in the Es layer and can considerably scatter the sounding signals. The spectral intensity of the scattered echoes can even suppress the reflected signals at certain frequencies. The inconsistency in terms of the scatterers' position and scale could thus extend the intensity spectra in both range and frequencies. The echo trace between 3.2 and 3.8 MHz is typical of the ionosonde reflection mode and the signal echo height was divided into two layers, indicating an internal multilayer structure with intervals of ~5 km which are likely semi-shielded by each other. Note that the higher layer should not be suspected as caused by the interference due to the reflected waves from nearby objects within the irradiation range of the antenna beam. If this were the case, continuous multilayer echoes would show up in the lowfrequency band as well. This feature is more in line with the partial reflection theory and hole structures of the Es layer along the horizontal direction [38,39]. However, this hypothesis needs to be further examined by using a two-dimensional imaging technique or measuring the incoming direction of the signals for positioning with an antenna array.

As for Figure 2b, these fluctuations are reflective of a weak inhomogeneity distribution of electrons in the Es layer. Of special interest is the virtual heights at 2.6 and 3.5 MHz, which are either higher or lower than the overall trend, and not captured by the conventional ionogram (Figure 2a). In other studies, using the ISR data, such phenomena had also been confirmed to exist in the Es layer [29]. This example also demonstrates that the spatial resolution of the ionosonde is largely increased and the FDI technique can be well used to study the physical processes involved in the formation and evolution of the Es layer. Note that this level of spatial resolution can also be achieved using ISR, but with the cost of much higher power consumption and larger antenna array.

Based on Figure 3b, it is worth noting that the compression speed of the echo trace is faster than that of rediffusion. It can be observed that the diffusion range of the spectral virtual height was compressed from 20 km to a thread with slight jitters, continuous echo traces in <15 min, probably indicting that the Es layer evolved from an uneven structure to a dense and uniform layer. For the echoes of different frequencies, the compression process on the imaging ionogram proceeded at basically the same speed. However, rediffusion started from the high-frequency band. At 3.2–3.8 MHz, the traces first showed folds and then extended to the low-frequency band. A reasonable explanation for this could be that the uniform and dense Es layer first yielded small fragments due to the instability or modulation of atmospheric gravity waves and then broke into patches and drifted. This observation indicates an Es variation from "blanketing" to "patchy", and vice versa, which can be temporally dynamic and unstable. These findings obtained within ~1 h indicated a relatively drastic short-term disturbance in the Es layer. Although it remains unclear whether KHI caused by the strong shear of the background neutral wind led to the formation of the unstable patchy Es and FAIs [8] or the wind field of the gravity waves [29] or the internal instability caused by the polarization electric field [9,40–42] is the dominant mechanism, the FDI technique based on ionosondes can provide a new, convenient, and promising way of investigating this open question.

The effect of Figure 4 reveals that there may be some uncertainties in the ionosonde measurements, as caused by the coarse resolution of conventional ionograms. The FDI technique is thus more suitable for Es layer identification. Note also that the virtual heights of the echo traces in Figure 4b at 2.2–2.4 MHz and in Figure 4d at 2.4–2.6 MHz are higher than their corresponding positions in Figure 4a,c, respectively. This is because although the energy of the echoes at the lower position is strong, they mainly originate from different scatters, and the coherence between the signals at different frequencies is not prominent. Alternatively, the higher echoes in Figure 4b,d are due to the reflected signals, suggesting that good coherence leads to an enhanced-range spectral estimation.

#### **5. Conclusions**

In this study, we employ the FDI technique to improve the spatial resolution of ionosonde measurements. Using the Es layer measurements near Wuhan as examples, our results show that the spatial resolution of height–intensity ionograms is remarkably increased: the complexity and inhomogeneity of the internal fine structures can be well monitored in the FDI-processed ionograms, compared to the intrinsic range resolution of several kilometers. Moreover, it is found that the detailed evolutional processes and different types of Es layer can be better resolved due to this resolution improvement. Given this level of spatial resolution, ionosonde, in combination with the FDI technique, represents a promising means for more refined observation of the Es layer, as well as the physical processes involved in the formation and evolution of this layer.

Based on the current results, two follow-up studies will be carried out. One is using the antenna array to scan and observe the Es layer with a narrow beam and determine its spatial structure. Another is reducing the sounding period and investigating the evolution process of the Es layer at the second level.

**Author Contributions:** Conceptualization, C.Z. and Z.Z.; methodology, T.L.; investigation, T.L. and Z.Z.; data curation, G.Y.; writing—original draft preparation, T.L.; writing—review and editing, C.Z., C.J., B.N. and W.X. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China (NSFC No. 42104151, 41774162, 42074187 and 42074184), the Excellent Youth Foundation of Hubei Provincial Natural Science Foundation (No. 2019CFA054), the Youth Foundation of Hubei Provincial Natural

(No. 2021CFB134), and the Special Fund for Fundamental Scientific Research Expenses of Central Universities (No. 2042021kf0023).

**Data Availability Statement:** Data sharing is not applicable to this article.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

