The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short‐term memory (bi‐LSTM) method and observations from 257 ground‐based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi‐LSTM‐based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. The bi‐LSTM‐based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi‐LSTM‐based model. Moreover, the TEC maps resulting from the bi‐LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation.

The total electron content (TEC) of ionosphere at low latitudes is significantly influenced by solar-geomagnetic activity and seasonal variations. Traditional ionospheric models often struggle to accurately forecast TEC in low latitudes, which limits the improvement of positioning accuracy for single-frequency GNSS (Global Navigation Satellite System) receivers. This study focuses on the Sichuan and Yunnan areas of China that locate on the northern crest of the equatorial ionization anomaly, utilizing data from 48 stations of the Chinese GNSS network. It employs a Convolutional Long Short-Term Memory network with multi-channel characteristics, combined with the Whale Optimization Algorithm, to construct a WOA-CNN-LSTM model for predicting TEC variations. The results demonstrate that the WOA-CNN-LSTM model outperforms Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU), Bidirectional Long Short-Term Memory (BiLSTM), and Recurrent Neural Network (RNN) models in spatial morphology. In 2015 (a year with geomagnetic storms), the root mean square error (RMSE) values for all four seasons are at or below 1.96TECu, with mean absolute error (MAE) values at or below 1.42TECu, and Pearson correlation coefficients at or above 0.98. In 2019 (a calm year), the RMSE values are all below 0.74TECu, MAE values are all below 0.54TECu, and Pearson correlation coefficients remain at or above 0.95. In terms of temporal variation, the RMSE prediction results for the four observation stations are all at or below 2.75TECu for each of the four seasons in 2015, improving to 1.56TECu in 2019. Therefore, this model significantly enhances ionospheric prediction accuracy in low-latitude regions, benefiting navigation positioning, space environment forecasting, and disaster early warning systems.

Although numerous validations for the ionospheric peak parameters values (IPPVs) obtained from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) have been conducted using ionosonde measurements as a reference, comprehensive evaluations of the quality of the COSMIC-2 data are still undesirable, especially under geomagnetic storm conditions. In this study, the IPPVs measured by ionosondes (Ramey, Boa Vista, Sao Luis, Jicamarca, Cachoeira Paulista, and Santa Maria) during the period 1 October 2019 to 31 August 2021, are used to evaluate the quality of COSMIC-2 data over low-latitude regions of the Americas. The results show that the $N_m{\mathrm{F}}_2$ ($h_m{\mathrm{F}}_2$) from COSMIC-2 agrees well with the ionosonde measurements, and the correlation coefficients for the two sets of data at the above six stations are 0.93 (0.84), 0.91 (0.85), 0.91 (0.88), 0.88 (0.79), 0.96 (0.83), and 0.96 (0.87), respectively. The data quality of COSMIC-2 derived $N_m{\mathrm{F}}_2$ is largely dependent on geomagnetic latitude. It was also found that $N_m{\mathrm{F}}_2$ derived from COSMIC-2 tends to be underestimated over the stations in Boa Vista and Cachoeira Paulista, which are close to the crests of the equatorial ionization anomaly (EIA), whilst that of the other stations is slightly overestimated. A comparison between COSMIC-measured and ionosonde-derived $h_m{\mathrm{F}}_2$ indicates that the former is systematically higher than the latter. In addition, the differences in the two $N_m{\mathrm{F}}_2$ datasets derived from COSMIC-2 and ionosonde measurements at night are generally smaller than those of daytime, when the EIA is well developed, and vice versa for $h_m{\mathrm{F}}_2$, whose RMSE is slightly smaller during daytime (with the exception of Ramey). Furthermore, $N_m{\mathrm{F}}_2$ obtained from COSMIC-2 is shown to perform best in summer at Ramey, Boa Vista, Sao Luis, and Santa Maria, best in winter at Jicamarca and Cachoeira Paulista. Finally, the COSMIC-2 electron densities capture the ionospheric dynamic enhancements under a moderate geomagnetic storm condition very well.

A high-spatial-resolution hyperspectral image (HR-HSI) can be obtained by fusing a hyperspectral image and a multispectral image (HSI-MSI) since it takes the advantage of combing the low-spatial-resolution hyperspectral image (LR-HSI) and the high-spatial-resolution multispectral image (HR-MSI). Currently, most HSI-MSI fusion methods decompose HSIs in both spatial and spectral domains, which destroys the correlation of the two domains and results in poor fusion results. To further explore the correlation of spatial and spectral information, an unsupervised blind spectral-spatial cross-super-resolution network (CSRNet) is proposed for HSI-MSI fusion. The network transforms the fusion into two super-resolution reconstructions for a HR-HSI to avoid the decomposition of HSIs and achieves interaction with the two reconstructions to provide strong constraints for each other. The network uses the following procedure. First, the HSI-MSI fusion is transformed into a spectral-spatial cross-super-resolution model, which consists two branches: one for spatial and the other for spectral super-resolution reconstructions. As the basic blocks of the two branches, spatial super-resolution (SpaSR) and the spectral super-resolution (SpeSR) blocks in the new network are designed via iterations unfolding from half quadratic splitting (HQS). Then their corresponding spatial constraint (SpaC) and spectral constraint (SpeC) modules are established for the mutual constraints and interactions between the two branches. The SpaSR/SpeSR and SpaC/SpeC modules are alternately connected to form the above spatial/spectral super-resolution branches for reconstructing a HR-HSI. Both visual and quantitative results of experiments based on both simulated and real observation datasets showed that the proposed method outperformed seven commonly used methods, suggesting that the new method is effective for preserving the correlation of spatial and spectral information in HR-HSIs.

This paper presents a new model for ionospheric total electron content (TEC) over China. The new model is developed using a hybrid method composed of the particle swarm optimization (PSO) and artificial neural network and long‐term observations from 257 ground‐based global navigation satellite systems (GNSS) stations and space‐borne GNSS radio occultation systems (COSMIC and Fengyun) during the 14‐year period of 2008–2021. The PSO algorithm is used to optimize the traditional back‐propagation neural network (BP‐NN) model by reducing the effects of the local minimum problem. The new model is validated using out‐of‐sample data, and its results are compared to the BP‐NN, IRI‐2016 model, and global ionospheric maps provided by the International GNSS Service. Results show that TEC predicted from the new model agrees better with the reference TEC than the BP‐NN and IRI‐2016 models. The improvements made by the new model over the BP‐NN and IRI‐2016 models in the equinox, summer, and winter seasons of the solar maximum year (2015) are 4%–20%/20%–36%, 9%–21%/26%–42%, and 6%–22%/21%–43%, respectively, and their corresponding results in the solar minimum year (2019) are 12%–24%/41%–59%, 9%–24%/28%–56%, and 10%–26%/53%–72%. Furthermore, the new model well captures the diurnal evolution, seasonal variation, and variations in the ionospheric TEC under different solar activity levels. It also well captures the mid‐latitude summer nighttime anomaly over China, and the diurnal anomaly is more pronounced in the solar minimum year (2019) than in the solar maximum year (2015) in terms of the nighttime‐to‐noontime ratio and the range of months it lasts in a year.