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• First time study on the NWA using in situ measurements. • Discovery of the larger longitude extension of the NWA in mid-latitudes. • Discovery of the NWA in the equatorial regions. The nighttime winter anomaly (NWA) phenomenon is studied using the in situ electron density measurements from the CSES (China Seismo-Electronmagnetic Satellite) satellite orbiting above the F2 layer peak height (hmF2) region. Observations in the present study indicates that: (1) The NWA phenomenon can appear at mid-latitudes in large longitude ranges above the hmF2 region during low solar activity period, with the most obvious part being located at the American longitude sector during northern winter time and at the Asian longitude sector during the southern winter time as mention in previous studies. (2) The NWA phenomenon can also appear in the equatorial region. The equatorial NWA is located at the American to Eurasian longitude sectors during northern winter time and at the eastern Pacific longitude sector during southern winter time, and its occurrence and distribution vary with solar activity, which is similar to that of the mid-latitude NWA. (3) The formation mechanism for the mid-latitude NWA can be explained by the ionosphere-plasmasphere coupling process, and the formation mechanism for the equatorial NWA can be explained by the wind-driven plasma transporting process. These results are helpful for the in-depth understanding of the topside ionosphere, and the in situ electron density measurements from the CSES satellite provide us good opportunities to study the ionospheric climatological phenomena due to its special and unified observation local times. [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]

• Systematic validation of the CSES RO measurements using ground-based observations. • Discovery of different error variation patterns at and near EIA crest regions. • First statistical validation results for RO EDP measurements using ISR observations. The radio occultation (RO) measurements obtained by GOR payload on board the CSES (the first China Seismo-Electromagnetic Satellite) satellite are validated using the observations from five ionosonde stations located at different latitudes and one incoherent scatter radar (ISR) observatory located at mid-latitude. CSES RO peak values (NmF2) and peak heights (hmF2) are compared with the corresponding measurements from the five ionosonde stations, and RO electron density profiles (EDP) are compared with the corresponding ISR measurements. The results show that: (1) CSES RO NmF2 measurements are generally very consistent with that from ionosondes with a correlation coefficient of 0.9448. Obvious underestimation and overestimation are shown for the daytime NmF2 measurements obtained at and near EIA (Equatorial Ionization Anomaly) crest regions, and different error variation patterns are shown: the error at the EIA peak region increases with increase of the observations, while the error at the region near the EIA crest is almost a constant. Attention should be paid to these daytime measurements obtained at and near EIA region in applications. (2) CSES RO hmF2 measurements are in good agreement with that from ionosondes with a correlation coefficient of 0.8357. There is a tendency the daytime hmF2s are overestimated and the nighttime hmF2s are underestimated. (3) CSES RO EDP measurements generally show good consistency with that from ISR measurements. The RO measurements obtained within the peak height range (200–400 km) have the best performance, but errors increase out of this altitudes range. The error for the altitude of the E layer is relatively large, therefore RO measurements are less reliable for this altitude. [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 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 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]

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]

• High correlation between data of CSES and Swarm on global scale. • High correlation on intersecting ground track probe data. • High correlation on relative relationship between nighttime and daytime data. • In situ electron density is underestimated by CSES LAP. • LAP data are available for application on relative variation studies. A preliminary validation of the in situ electron density data obtained by CSES, a newly launched electromagnetic satellite in China on Feb. 2, 2018, using the Swarm satellites LP measurements, is carried out in this study to assess the consistency and reliability of the LAP observations onboard CSES. Three ways are implemented to achieve the goal: (1) comparison of the global distribution patterns for both the daytime and nighttime data, (2) comparison of the data between CSES and Swarm at intersecting ground tracks with similar local time, and (3) comparison of the relative relationship between nighttime and daytime data using the nighttime enhancement space climatology phenomenon. The results show that the in situ electron densities estimated by LAP onboard CSES are in good agreement with data from the Swarm satellites in terms of global distributions, local details and relationship between nighttime and daytime data. A systematic bias between them is also found: LAP density estimates are much lower than that of Swarm satellites. However, the highly consistent relative variations between the two datasets proves the electron density dataset of LAP onboard CSES for now is applicable for the relative data variation applications on ionosphere-related studies. Further calibration of the LAP dataset is recommended using electron density data obtained by different measurements. [ABSTRACT FROM AUTHOR]

The China Seismo-Electromagnetic Satellite (CSES) was successfully launched on February 2, 2018, with a plasma analyzer package (PAP) onboard to in-situ detect the ionospheric ion parameters. In this paper, the availability of PAP data is investigated for the first time through analyzing three typical ionospheric disturbance events. Firstly, the ionospheric radio and plasma disturbances caused by two high-power terrestrial very low frequency (VLF) transmitters over American NAA station and Australian NWC station are investigated. Furthermore, several possible anomaly observations before the 22 August 2018 Ms7.3 Venezuela earthquake are analyzed. In the southwest of the epicenter, the oxygen ion density increased significantly, and the ion drift velocity in vertical direction reversed synchronously from space-to-ground direction to ground-to-space direction. Finally, during a global intense geomagnetic storm event which started from August 25, 2018, the oxygen ion density well reflected the geomagnetic storm event's temporal evolution process. The results show that the PAP onboard CSES has ability to discern the relative variations and features of ionospheric disturbances. [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]