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The ionospheric quasi-periodic wave is a type of typical and common electromagnetic wave phenomenon occurring in extremely low-frequency (ELF) and very low-frequency ranges (VLF). These emissions propagate in a distinct whistler-wave mode, with varying periodic modulations of the wave intensity over time scales from several seconds to a few minutes. We developed an automatic detection model for the QP waves in the ELF band recorded by the China Seismo-Electromagnetic Satellite. Based on the 827 QP wave events, which were collected through visual screening from the electromagnetic field observations, an automatic detection model based on the Transformer architecture was built. This model, comprising 34.27 million parameters, was trained and evaluated. It achieved mean average precision of 92.3% on the validation dataset, operating at a frame rate of 39.3 frames per second. Notably, after incorporating the proton cyclotron frequency constraint, the model displayed promising performance. Its lightweight design facilitates easy deployment on satellite equipment, significantly enhancing the feasibility of on-board detection. [ABSTRACT FROM AUTHOR]

Driven by the scientific objective of geophysical field detection and natural hazard monitoring from space, China launched an electromagnetic satellite, which is known as the China Seismo-Electromagnetic Satellite (CSES-01), on 2 February 2018, into a circular sun-synchronous orbit with an altitude of about 507 km in the ionosphere. The CSES-01 has been in orbit for over 6 years, successfully exceeding its designed 5-year lifespan, and will continually operate as long as possible. A second identical successor (CSES-02) will be launched in December 2024 in the same orbit space. The ionosphere is a highly dynamic and complicated system, and it is necessary to comprehensively understand the electromagnetic environment and the physical effects caused by various disturbance sources. The motivation of this report is to introduce the typical electromagnetic waves, mainly in the ELF/VLF band (i.e., ~100 Hz to 25 kHz), recorded by the CSES-01 in order to call the international community for deep research on EM wave activities and geophysical sphere coupling mechanisms. The wave spectral properties and the wave propagation parameters of those typical EM wave activities in the upper ionosphere are demonstrated in this study based on wave vector analysis using the singular value decomposition (SVD) method. The analysis shows that those typical and common natural EM waves in the upper ionosphere mainly include the ionospheric hiss and proton whistlers in the ELF band (below 1 kHz), the quasiperiodic (QP) emissions, magnetospheric line radiations (MLR), the falling-tone lightning whistlers, and V-shaped streaks in the ELF/VLF band (below 20 kHz). The typical artificial EM waves in the ELF/VLF band, such as power line harmonic radiation (PLHR) and radio waves in the VLF band, are also well recorded in the ionosphere. [ABSTRACT FROM AUTHOR]

The post-midnight irregularities in equator and its adjcent regions, which have remained least explored so far, are investigated using the in situ electron density (Ne) measurements from 2019 to 2021 obtained by the China Seismo-Electromagnetic Satellite (CSES) in the topside ionosphere. The followings are salient points of our findings: 1) Equatorial and low latitude post-midnight irregularities are distributed along the dip equator, exhibiting a wavenumber 4 longitude variation pattern and a seasonal variation pattern characterized by peaks in summer and winter and valleys in spring and autumn during low solar activity (LSA) period in the topside ionosphere, 2) The longitude variation pattern of post-midnight irregularities aligns with that of the background Ne, with irregularities concentrated at the peaks of background Ne, 3) The occurrence rate of post-midnight irregularities increases significantly with higher solar flux, which can be attributed to the rapid increase in the background Ne under LSA conditions. Additionally, the growth rate of occurrence rate varies across different longitudes, with the Pacific and the Atlantic longitudes standing out as prominent regions, and 4) Post-midnight irregularities in the topside ionosphere may have two possible origins. One is irregularities generated at the lower altitudes that drift upward into the topside ionosphere, while the other is the irregularities generated directly in the topside ionosphere. The latter is supported by the presence of conditions conducive to irregularity generation in the topside ionosphere during midnight hours in LSA period. Given the scarcity of research on post-midnight irregularity compared to post-sunset irregularity, further investigations are essential to fully comprehend their generation mechanisms. The abundance of post-midnight Ne measurements from the CSES satellite offers a valuable opportunity for this research, which can enhance our understanding of post-midnight ionospheric dynamics, and their variations with solar activity. [ABSTRACT FROM AUTHOR]

From August to October 2018, a series of strong earthquake (EQ) events occurred in southeast Asia and northern Oceania (22°S to 0°N, 115°E to 170°E) within 50 days. In this paper, we analyze the features of ionospheric plasma perturbations, recorded by the Plasma Analyzer Package (PAP) and Langmuir probe (LAP) onboard the China Seismo-Electromagnetic Satellite (CSES-01), before four EQs with magnitudes of Ms 6.9 to Ms 7.4. The ion parameters such as the oxygen ion density (No+), the ion drift velocity in the vertical direction (Vz) under the conditions of geomagnetic storms, and strong EQs are compared. The results show that within 1 to 15 days before the strong EQs, the No+ and the electron density (Ne) increased while the electron temperature (Te) decreased synchronously. Meanwhile, the Vz significantly increased along the ground-to-space direction. The relative variation of No+ and Vz before the strong EQs is more prominent, and the Vz is not easily influenced by the geomagnetic storm but is susceptible to the seismic activities. Our results suggest that the anomaly of ionospheric plasma perturbations occurring in this area is possibly related to the pre-EQ signatures. [ABSTRACT FROM AUTHOR]

Based on the observations by the sun‐synchronous circular orbit China Seismo‐Electromagnetic Satellite(CSES), a typical case of the rising‐tone quasiperiodic (QP) emissions with a large period of around 2 min was reported to appear on the dayside on 23 October 2021. The frequency of QP waves ranges from 2,000 ∼ 3,000 Hz and the wave spectral structure consists of four elements with right‐handed polarization. Three elements of them are located at extremely low L‐shells from 2 ∼ 3.5. The periodic precipitating fluxes of 100 ∼ 400 keV electrons were found in the conjugate region also observed by CSES, which appeared in the same magnetic field lines with the elements of QP waves, respectively. By numerical simulations of quasi‐linear diffusion theory, we confirm that QP emissions can effectively precipitate energetic electrons near the loss cone into the atmosphere in the inner radiation belt. To our best knowledge, this is the first evidence that the rising‐tone QP whistler waves scatter electrons in the inner radiation belt. This new finding will help to deepen our understanding of the electron precipitation dynamics in radiation belt physics. Plain Language Summary: The whistler‐mode quasiperiodic (QP) emissions with a repetition period from several seconds to several minutes have been reported by a large amount of satellites and ground‐based detector stations observations, and their generation mechanisms have been studied a lot. However, the reports about diffusion or acceleration effects on the relativistic electrons induced by QP waves are rare and only a little focused on the outer radiation belts. CSES satellite with the sun‐synchronous polar orbits at 507 km altitude provides an advantage to study the wave‐particle interaction in the extremely low L‐shell regions including the inner radiation belts. In this work, we present the rising‐tone QP whistler waves observed by CSES satellite in the inner radiation belt. Simultaneously, the periodic precipitating fluxes of 100–400 keV electrons were found in the same magnetic field lines with the elements of QP waves, respectively. To our knowledge, this provides the first evidence that QP emissions can effectively precipitate energetic electrons into the atmosphere in the inner radiation belt. This scenario is confirmed by our numerical simulations of quasi‐linear diffusion theory. This new finding will help to deeply understand the electron precipitation dynamics in radiation belt physics. Key Points: The rising‐tone quasi‐periodic (QP) whistler waves are observed by CSES satellite in the inner radiation belt100 ∼ 400 keV electrons within 30° equatorial pitch angles in the conjugate region was periodically precipitatedQP whistler waves can efficiently diffuse relativistic electrons near the bounce loss cone in extremely low L−shells (L = 2 ∼ 3.5) [ABSTRACT FROM AUTHOR]

This paper reports a large‐scale magnetospheric line radiation (MLR) event during a moderate geomagnetic storm on 11 September 2018, which was well recorded by the China‐Seismo‐Electromagnetic Satellite (CSES) in the upper ionosphere. The event shows a symmetrical propagation feature at the conjugated locations between the two hemispheres, exhibiting a large spatial extension roughly from the latitudes 54°N to 53°S. The parallel structures are visible both in the electric and magnetic fields at a frequency band ranging from the local proton cyclotron frequency to ∼1.6 kHz. The wave intensity of parallel spectral lines was primarily enhanced in high latitude regions, gradually weakening at mid‐low latitudes, and then got absorbed in the equatorial region, presenting a distinct V‐shaped structure. The frequency spacings between neighboring spectral lines roughly vary from ∼80 to 110 Hz at the high latitudes and ∼80–130 Hz at the low latitudes, suggesting a slight variation feature with latitude. The parallel spectral structures of MLR drift between ∼0.39 and 0.57 Hz/s at high latitudes and ∼0.18–0.19 Hz/s at low latitudes. The wave vector analysis shows that the MLR waves are right‐hand polarized, obliquely propagating toward the Earth and in the azimuthal direction, where the Poynting flux is primarily oriented perpendicular to the ambient magnetic field. The other large‐scale MLR events all exhibit similar parallel structures and polarization characteristics, suggesting the universality of such a phenomenon. However, the azimuthal angles differ among different events, showing complex features. Plain Language Summary: Magnetospheric line radiation (MLR) is a unique electromagnetic wave distinguished by parallel spectral lines. This study reports a large‐scale MLR event that occurred in the dayside ionosphere. The event shows a symmetrical propagation feature, with a large spatial extension between latitudes 54°N and 53°S. The parallel structures are visible both in the electric and magnetic spectrogram, ranging from the local proton cyclotron frequency to ∼1.6 kHz. The MLR structures were primarily enhanced in high latitude regions, gradually weakening at mid‐low latitudes, and then got absorbed in the equatorial region, presenting a distinct V‐shaped structure. The frequency spacings of MLR roughly vary from ∼80 to 110 Hz in the high latitudes and from ∼80 to 130 Hz in the mid‐low latitude region, slightly varying with latitude. The MLR structures drift between ∼0.39 and 0.57 Hz/s at high latitudes and ∼0.18–0.19 Hz/s at low latitudes. This MLR event is right‐hand polarized, obliquely propagating toward the Earth and in the azimuthal direction, and the Poynting flux is primarily oriented perpendicular to the ambient magnetic field. However, the azimuthal angles differ among different events, indicating the complexity of the wave propagation feature. Key Points: A large‐scale magnetospheric line radiation (MLR) event shows a symmetrical propagation feature in two hemispheres, presenting a distinct V‐shaped structureBoth the frequencies of the parallel spectral lines and their frequency spacings slightly drift with latitudesThe MLR waves are right‐hand polarized, obliquely propagating toward the Earth and azimuthal direction [ABSTRACT FROM AUTHOR]

This study reports the temporal and spatial distributions of the extremely/very low frequency (ELF/VLF) wave activities and the energetic electron fluxes in the ionosphere during an intense storm (geomagnetic activity index Dst of approximately −174 nT) that occurred on 26 August 2018, based on the observations by a set of detectors onboard the China Seismo-Electromagnetic Satellite (CSES). A good correlation of the ionospheric ELF/VLF wave activities with energetic electron precipitations during the various storm evolution phases was revealed. The strongest ELF/VLF emissions at a broad frequency band extending up to 20 kHz occurred from the near-end main phase to the early recovery phase of the storm, while the wave activities mainly appeared at the frequency range below 6 kHz during other phases. Variations in the precipitating fluxes were also spotted in correspondence with changing geomagnetic activity, with the max values primarily appearing outside of the plasmapause during active conditions. The energetic electrons at energies below 1.5 MeV got strong enhancements during the whole storm time on both the day and night side. Examinations of the half-orbit data showed that under the quiet condition, the CSES was able to depict the outer/inner radiation belt as well as the slot region well, whereas under disturbed conditions, such regions became less sharply defined. The regions poleward from geomagnetic latitudes over 50° were found to host the most robust electron precipitation regardless of the quiet or active conditions, and in the equatorward regions below 30°, flux enhancements were mainly observed during storm time and only occasionally in quiet time. The nightside ionosphere also showed remarkable temporal variability along with the storm evolution process but with relatively weaker wave activities and similar level of fluxes enhancement compared to the ones in the dayside ionosphere. The ELF/VLF whistler-mode waves recorded by the CSES mainly included structure-less VLF waves, structured VLF quasi-periodic emissions, and structure-less ELF hiss waves. A wave vector analysis showed that during storm time, these ELF/VLF whistler-mode waves obliquely propagated, mostly likely from the radiation belt toward the Earth direction. We suggest that energetic electrons in the high latitude ionosphere are most likely transported from the outer radiation belt as a consequence of their interactions with ELF/VLF waves. [ABSTRACT FROM AUTHOR]

For prediction of urban remote sensing surface temperature, cloud, cloud shadow and snow contamination lead to the failure of surface temperature inversion and vegetation‐related index calculation. A time series prediction framework of urban surface temperature under cloud interference is proposed in this paper. This is helpful to solve the problem of the impact of data loss on surface temperature prediction. Spatial and temporal variation trends of surface temperature and vegetation index are analyzed using Landsat 7/8 remote sensing data of 2010 to 2019 from Beijing. The geographically weighed regression (GWR) method is used to realize the simulation of surface temperature based on the current date. The deep learning prediction network based on convolution and long short‐term memory (LSTM) networks was constructed to predict the spatial distribution of surface temperature on the next observation date. The time series analysis shows that the NDBI is less than −0.2, which indicates that there may be cloud contamination. The land surface temperature (LST) modeling results show that the precision of estimation using GWR method on impervious surface and water bodies is superior compared to the vegetation area. For LST prediction using deep learning methods, the result of the prediction on surface temperature space distribution was relatively good. The purpose of this study is to make up for the missing data affected by cloud, snow, and other interference factors, and to be applied to the prediction of the spatial and temporal distributions of LST. This article is categorized under:Technologies > Machine Learning [ABSTRACT FROM AUTHOR]

The stratification phenomenon is investigated using the simultaneous in situ plasma density measurements obtained by the Swarm satellites orbiting at different altitudes above the F2 peak. For the first time, the continuous distribution morphology and the exact locations are obtained for the nighttime stratification, which show that the stratification events are centered at the EIA (equatorial ionization anomaly) trough and extend towards the two EIA crests, with the most significant part being located at the EIA trough. Another new discovery is the stratification in southern mid-latitudes; stratification events in this region are located on a local plasma peak sandwiched by two lower density strips covering all the longitudes. The formation mechanism of the stratification for the two latitudinal regions is discussed, but the stratification mechanism in southern mid-latitudes remains an unsolved problem. Highlights. This paper addresses the following: first application of in situ plasma densities for the direct analysis of the stratification in F2 layer, refined features of the exact location and continuous morphology for the stratification phenomenon, a new discovery of stratification covering all longitudes in southern mid-latitudes. [ABSTRACT FROM AUTHOR]

CSES (China Seismo-Electromagnetic Satellite) is a newly launched electric-magnetic satellite in China. A GNSS occultation receiver (GOR) is installed on the satellite to retrieve electron density related parameters. In order to validate the radio occultation (RO) data from the GOR on board CSES, a comparison between CSES RO and the co-located COSMIC RO data is conducted to check the consistency and reliability of the CSES RO data using measurements from 12 February 2018 to 31 March 2019. CSES RO peak values (Nm F2), peak heights (hm F2), and electron density profiles (EPDs) are compared with corresponding COSMIC measurements in this study. The results show that (1) Nm F2 between CSES and COSMIC is in extremely good agreement, with a correlation coefficient of 0.9898. The near-zero bias between the two sets is 0.005363×105 cm -3 with a RMSE of 0.3638×105 cm -3 , and the relative bias is 1.97 % with a relative RMSE of 16.17 %, which are in accordance with previous studies according to error propagation rules. (2) hm F2 between the two missions is also in very good agreement with a correlation coefficient of 0.9385; the mean difference between the two sets is 0.59 km with a RMSE of 12.28 km, which is within the error limits of previous studies. (3) Co-located EDPs between the two sets are generally in good agreement, but with a better agreement for data above 200 km than those below this altitude. Data at the peak height ranges show the best agreement, and then data above the peak regions; data below the peak regions, especially at the altitude of about the E layer, show relatively large fluctuations. It is concluded that CSES RO data are in good agreement with COSMIC measurements, and the CSES RO data are applicable for most ionosphere-related studies considering the wide acceptance and application of COSMIC RO measurements. However, particular attention should be paid to EDP data below peak regions in application as data at the bottom side of the profiles are less reliable than that at the peak and topside regions. [ABSTRACT FROM AUTHOR]

Feature selection of very high-resolution (VHR) images is a key prerequisite for supervised classification. However, it is always difficult to acquire the features which have the highest correlation to the type of land cover for improving classification accuracy. To address this problem, this paper proposed a methodology of feature selection using the results of multiple segmentation via genetic algorithm (GA) and correlation feature selection (CFS) integrating sparse auto-encoder (SAE). Firstly, 61 features, including spectral features and spatial features, are extracted from the results of multi-scale segmentation over a WorldView-2 image in Xicheng District, Beijing. Then, 40-dimensional features and 30-dimensional features are derived from the selection with GA+CFS and the optimization with SAE, respectively. Thirdly, the final classification is achieved by logistic regression (LR) based on different subsets of features extracted from the WorldView-2 image. It is found that the result of feature selection could contribute to increase in the intra-species separation and reduction in the inner-species variability. Adding extra lower-ranked features appeared to reduce the accuracy of classification. The results indicate that the overall classification accuracy with 30-dimensional features reached 87.56%, and increased 5.61% compared to the results with 61-dimensional features. For the two kinds of optimized features, the Z-test values are all greater than 1.96, which implied that feature dimensionality reduction and feature space optimization could significantly improve the accuracy of image land cover classification. The texture features in the wavelet domain are the most important features for the study area in the WorldView-2 image classification. Adding wavelet and the grey-level co-occurrence matrix (GLCM) information, especially for GLCM features in wavelet, appeared not to improve classification accuracy. The SAE-based method can produce feature subsets for improving mapping accuracy more efficiently. [ABSTRACT FROM AUTHOR]

Many studies have revealed the stratification phenomenon of the topside ionospheric F2 layer using ground-based or satellite-based ionograms, which can show direct signs of this phenomenon. However, it is difficult to identify this phenomenon using the satellite-based in situ electron density data. Therefore, a statistical method, using the shuffle resampling skill, is adopted in this paper. For the first time, in situ electron density data, recorded by the same Langmuir probe aboard the DEMETER (Detection of Electro-Magnetic Emission Transmitted from Earthquake Regions) satellite at different altitudes, are analyzed, and a possible stratification phenomenon is identified using the proposed method. Our results show that the nighttime stratification, possibly a permanent phenomenon, can cover most longitudes near the geomagnetic equator, which is not found from the daytime data. The arch-like nighttime stratification decreases slowly on the summer hemisphere and thus extends a larger latitudinal distance from the geomagnetic equator. All results, obtained by the proposed method, indicate that the stratification phenomenon is more complex than what has previously been found. The proposed method is thus an effective one, which can also be used in similar studies of comparing fluctuated data. [ABSTRACT FROM AUTHOR]

The Search Coil Magnetometer(SCM) on China Seismo-Electromagnetism Satellite(CSES), on the basis of Faraday's law of electromagnetic induction, can obtain ULF/ELF/VLF information from variable magnetic field in the ionosphere. The information is used to study anomaly phenomena such as seismic activities, magnetic storm and unknown signals etc.This study is aimed at detecting abnormal phenomena from VLF power spectrum data in SCM ranging from 12.5hz to 25.6khz. VLF signals radiated by artificial VLF transmitters and magnetic storm are defined as abnormal phenomena. Constructing a detection model contributes to search abnormal phenomena in big data quickly and accurately.Firstly, in specific frequency, building training and testing samples, based on the acquired data from both the areas above artificial VLF transmitters (termed as positive samples) and other observed areas (termed as negative samples) respectively, needs to use fixed time window in each orbit to create two-dimensional images through Mel Frequency Cepstrum Coefficient (MFCC). Secondly, deep learning methods used as a detection model, such as CNN, is to detect abnormal phenomena from those samples. Finally, we can use this method to detect each time window in all of the VLF power spectrum data in order to fast find some interesting phenomena. In our experiments, we collect 440 samples including positive samples and negative samples. And we use 110 samples for each class as training data, and the others as testing data. The results show that testing precision of our method reaches 86%. If more data is added to CNN, our method can detect abnormal phenomena more efficiently. [ABSTRACT FROM AUTHOR]