A GNSS station, located on the shore of sea and inland waters, and equipped with standard geodetic receivers and antennas, can be used to measure water levels using a technique called GNSS Interferometric Reflectometry (GNSS-IR). The classical GNSS-IR method is based on SNR data and LSP spectrum analysis method. In order to promote the application of GNSS-IR, the accuracy of the results needs to be further improved, and quality control needs to be achieved better. Classical quality control methods include denoising filtering based on data source SNR; post-processing filtering based on results; morphological analysis based on parameters, such as the ratio of the maximum peak value to the background noise mean, the ratio of the maximum peak to the sub-peak, and the amplitude of the maximum peak. All three methods have the problem of correct frequency extraction under multiple approximate peak conditions. This paper focuses on the performance analysis of three methods of quality control for two situations with real examples, summarizes the advantages and disadvantages of each method, and discusses the measures in applications. Considering the limitations in the threshold setting for the third method, a new quality control method combining multiple parameters and external constraints is proposed. This method is more flexible, especially in dealing with a periodogram with multiple similar peaks, breaking through the premise that the frequency corresponding to the maximum peak is the correct frequency, and validated in two different environments. The experimental results show that the proposed method can improve the accuracy of the measured water level while ensuring the amount of the results. It eliminates the gross errors effectively and uses the data efficiently.

Deep learning-based semantic segmentation algorithms have proven effective in landslide detection. For the past decade, convolutional neural networks (CNNs) have been the prevailing approach for semantic segmentation. Nevertheless, the intrinsic limitations of convolutional operations hinder the acquisition of global contextual information. Recently, Transformers have garnered attention for their exceptional global modeling capabilities. This study proposes a dual-branch semantic aggregation network (DBSANet) by integrating ResNet and a Swin Transformer. A Feature Fusion Module (FFM) is designed to effectively integrate semantic information extracted from the ResNet and Swin Transformer branches. Considering the significant semantic gap between the encoder and decoder, a Spatial Gate Attention Module (SGAM) is used to suppress the noise from the decoder feature maps during decoding and guides the encoder feature maps based on its output, thereby reducing the semantic gap during the fusion of low-level and high-level semantic information. The DBSANet model demonstrated superior performance compared to existing models such as UNet, Deeplabv3+, ResUNet, SwinUNet, TransUNet, TransFuse, and UNetFormer on the Bijie and Luding datasets, achieving IoU values of 77.12% and 75.23%, respectively, with average improvements of 4.91% and 2.96%. This study introduces a novel perspective for landslide detection based on remote sensing images, focusing on how to effectively integrate the strengths of CNNs and Transformers for their application in landslide detection. Furthermore, it offers technical support for the application of hybrid models in landslide detection.

It is of great significance for the global navigation satellite system (GNSS) service to detect the polar ionospheric total electron content (TEC) and its variations, particularly under disturbed ionosphere conditions, including different phases of solar activity, the polar day and night alternation, the Weddell Sea anomaly (WSA) as well as geomagnetic storms. In this paper, four different models are utilized to map the ionospheric TEC over the Arctic and Antarctic for about one solar cycle: the polynomial (POLY) model, the generalized trigonometric series function (GTSF) model, the spherical harmonic (SH) model, and the spherical cap harmonic (SCH) model. Compared to other models, the SCH model has the best performance with ±0.8 TECU of residual mean value and 1.5–3.5 TECU of root mean square error. The spatiotemporal distributions and variations of the polar ionospheric TEC are investigated and compared under different ionosphere conditions in the Arctic and Antarctic. The results show that the solar activity significantly affects the TEC variations. During polar days, the ionospheric TEC is more active than it is during polar nights. In polar days over the Antarctic, the maximum value of TEC always appears at night in the Antarctic Peninsula and Weddell Sea area affected by the WSA. In the same year, the ionospheric TEC of the Antarctic has a larger amplitude of annual variation than that of the TEC in the Arctic. In addition, the evolution of the ionization patch during a geomagnetic storm over the Antarctic can be clearly tracked employing the SCH model, which appears to be adequate for mapping the polar TEC, and provides a sound basis for further automatic identification of ionization patches.

The BeiDou Navigation Satellite System (BDS) has completed third phase construction and currently provides global services, with a mixed constellation of BDS-2 and BDS-3. The newly launched BDS-3 satellites are equipped with rubidium and passive hydrogen maser (PHM) atomic clocks. The performance of atomic clocks is one of the cores of satellite navigation system, which will affect the performance of positioning, navigation and timing (PNT). In this paper, we systematically analyze the characteristics of BDS-2 and BDS-3 atomic clocks, based on more than one year of precise satellite clock products and broadcast ephemeris. Firstly, the results of overlapping Allan variations demonstrate that BDS-3 Rb and PHM clocks improve better in stability than BDS-2 Rb clock and are comparable to GPS IIF Rb and Galileo PHM clocks. Accordingly, the STDs of BDS-3 broadcast satellite clock are better than GPS and BDS-2, which are at the same level with that of Galileo. Secondly, the inter-system bias (ISB) between BDS-2 and BDS-3 is analyzed by satellite clock datum comparison and precise point positioning (PPP). Surprisingly, the discrepancy between BDS-2 and BDS-3 satellite clock datum has a great difference between products that could reach up to about 10 ns for WHU satellite clock products and broadcast ephemeris. Moreover, the ISBs between BDS-2 and BDS-3 satellite clocks are quite stable over one-year periods. Thirdly, due to the improved stability of BDS-3 atomic clock, the 68% positioning accuracy is better than 0.65 m at 10 min for BDS-3 PPP, based on broadcast ephemeris. Besides, the non-negligible bias between BDS-2 and BDS-3 will greatly affect the BDS precise data processing. The accuracy of positioning is greatly improved when considering the ISB.

The conversion between the line-of-sight slant total electron content (STEC) and the vertical total electron content (VTEC) depends on the mapping function (MF) under the widely used thin layer ionospheric model. The thin layer ionospheric height (TLIH) is an essential parameter of the MF, which affects the accuracy of the conversion between the STEC and VTEC. Due to the influence of temporal and spatial variations of the ionosphere, the optimal TLIH is not constant over the globe, particularly in the polar regions. In this paper, a new method for determining the optimal TLIH is proposed, which compares the mapping function values (MFVs) from the MF at different given TLIHs with the “truth” mapping values from the UQRG global ionospheric maps (GIMs) and the differential TEC (dSTEC) method, namely the dSTEC- and GIM-based thin layer ionospheric height (dG-TLIH) techniques. The optimal TLIH is determined using the dG-TLIH method based on GNSS data over the Antarctic and Arctic. Furthermore, we analyze the relationship between the optimal TLIH derived from the dG-TLIH method and the height of maximum density of the F2 layer (hmF2) based on COSMIC data in the polar regions. According to the dG-TLIH method, the optimal TLIH is mainly distributed between 370 and 500 km over the Arctic and between 400 and 500 km over the Antarctic in a solar cycle. In the Arctic, the correlation coefficient between the hmF2 and optimal TLIH is 0.7, and the deviation between them is 162 km. Meanwhile, in the Antarctic, the correlation coefficient is 0.60, with a phase lag of ~3 months, with the hmF2 leading the optimal TLIH, and the deviation between them is 177 km.