Vaporized samarium (Sm) has been released in the ionosphere for artificially creating plasma clouds, which may be tailored as radio wave reflectors for over-the-horizon communications. The model of samarium releases needs to be further refined for filling the gap between experimental observations and theoretical simulations. A sophisticated simulation model, so far to date, for samarium release in the ionosphere has been developed, and the redundancy reaction, inversion and the photochemical reactions are all taken into account. The model results are consistent with experimental observations. Due to the geomagnetic field and neutral wind, the shape of artificial plasma clouds changes significantly. The error of the spherically symmetric reflection model is large. According to the density distribution of plasma clouds, the geometric theory of diffraction (GTD) is introduced here to dealing with the electromagnetic scattering. The GTD-based scattering model has been established and used to study the time-space evolution of the ground received power patterns in the paper.

Hyperspectral image classification (HSIC) has attracted widespread attention due to its important application in environment alterations and geophysical disaster monitoring. However, surface cultivation is not static as time passes, which leads to different hyperspectral images information collected from the same area at different time periods. Therefore, researchers are currently eager to construct a HSIC model that continuously acquires new classes of data. During the continuous learning process, the model is expected to not only effective in extracting unique spatial-spectral features of the hyperspectral image, but also ensures the ability to maintain the old classes knowledge while learning new data. To achieve this purpose, we propose a method which based on geometric spatial-spectral feature integration network with class incremental learning (GS2FIN-CIL) framework in continuous learning to make the model adaptable to new classes data and not overly forgetting the old classes knowledge during the training process. We conduct extensive experiments with the proposed GS2FIN-CIL method on widely-used hyperspectral datasets including Indian Pines, PaviaU and Salinas. The experimental results show that our GS2FIN-CIL method can achieve significantly improved results compared to current state-of-the-art class incremental learning methods, allowing for efficient adaptation and utilization of spatial-spectral features in processing new classes of hyperspectral images and alleviating the problem of catastrophic forgetting of learned old classes knowledge. The GS2FIN-CIL method could be successfully applied to the challenge of adding new classes data in HSIC task.

The GPS-based computerized ionospheric tomography (CIT) has the capacity to reconstruct the three-dimensional ionosphere (i.e., electron density distribution), making it one of the most important techniques for ionospheric observation. However, the CIT technology is unable of high vertical resolution due to the restricted viewing angle. Therefore, the precision of CIT cannot be substantially enhanced with GPS only. Aiming at this issue, this paper proposes a bi-iteration algorithm that integrates GPS, Phased Array L-band Synthetic Aperture Radar on board the Advanced Land Observing Satellite (ALOS PALSAR), and ionosonde data. The key is that the joint retrieval of PALSAR and ionosonde may offer high-precision one-dimensional electron density profile along the whole path, which can effectively enhance the authenticity of the iterative initial value. The corrected initial value is then fused into the process of CIT, which can finally improve the precision of vertical resolution after two iterations. Experimental verification demonstrates that due to the ability to obtain more realistic initial values, the reconstruction accuracy of the algorithm proposed in this paper is 51.4 percent and 45.1 percent higher than the accuracy with GPS alone and with GPS and ionosonde data, respectively. This indicates that the combination of these three kinds of data can effectively improve the precision of CIT.

It is well known that the spaceborne synthetic aperture radar (SAR) at VHF‐UHF band can be seriously affected by the ionosphere. Thus, the geophysical information of the ionosphere will be embedded in the low‐frequency SAR echoes after they transverse the ionosphere. Correspondingly, the total electron content (TEC), a typical ionospheric information parameter, can be retrieved from the spaceborne SAR data. However, the existing dual‐band techniques for TEC retrieval usually do not include consideration of multiple scattering effects caused by turbulent ionosphere, which plays an important role in the total path delay of signal under the strong fluctuation regimes. The result of TEC retrieval is therefore inaccurate and not applicable. Aiming at this issue, first, this paper analyzes the effects of regular background and the irregularity of electron density on SAR at L‐band, and the theoretical formulation is given. Then, a triband path delay technique of TEC retrieval based on the SAR data is proposed. By using three path delays corresponding to three specific frequencies within the signal bandwidth, this technique can remove the errors of multiple scattering due to the irregularity, and a high accuracy resolution of TEC value therefore can be obtained. Meanwhile, the sensitivity of this technique is analyzed. Finally, compared with traditional dual‐band technique, the numerical simulations show that the correction of SAR imaging based on triband technique is improved significantly. In addition, the resolution of reconstruction imaging using computerized ionospheric tomography performs significantly better based on the triband technique.

Signals from spaceborne polarimetric synthetic aperture radar will suffer from Faraday rotations when they propagate through the ionosphere, especially those at L-band or lower frequencies, such as signals from the Phased Array type L-band Synthetic Aperture Radar (PALSAR). For this reason, Faraday rotation compensation should be considered. On the other hand, Faraday rotation could also be retrieved from distorted echoes. Moreover, combining Faraday rotation with the radar parameters and the model of magnetic field, we could derive the total electron content (TEC) along the signal path. Benefiting from the high spatial resolution of the SAR system, TEC obtained from PALSAR could be orders of magnitude higher in spatial resolution than that from GPS. Besides, we demonstrated that the precision of TEC from PALSAR is also much higher than that from GPS. With the precise TEC available, we could fuse it with data from other ionosphere detection devices to improve their performances. In this paper, we adopted it to help modify the empirically modeled topside profile of ionosonde. The results show that the divergence between the modified profile and the referenced incoherent scattering radar profile reduced by about 30 percent when compared to the original ionosonde topside profile.

The Qinghai-Tibet Plateau (QTP) is located at the roof of the world. Since its unique geography, the spatial-temporal characteristics of sporadic E layer (Es) over and around there could be hints of the coupling between upper and lower atmosphere. It also plays an important role on radio propagation at high frequency (HF) and very HF (VHF) band. In this letter, the intensity and spatial distribution of Es over the QTP, and the response to solar activity are studied. With the bonus of the Chinese ionosonde network for more than 60 years, some conclusions are arrived at firstly for the QTP: 1) the diurnal and seasonal variations of Es layer are significant. The intensity and critical frequency are the highest at 11:00 and 12:00 local time (LT) in summer, while the lowest at sunrise in winter; 2) it is found that the Es intensity is negatively correlated with the solar cycle. In 2020, the solar minimum of 200 years, Es intensity is obvious higher than other years; and 3) the Es intensity positively correlates with the terrain. Es intensity in the southeast is higher than northwest of the Plateau significantly.