Floods are severe natural disasters that are harmful and frequently occur across the world. From May to July 2022, the strongest, broadest, and longest rainfall event in recent years occurred in Guangdong Province, China. The flooding caused by continuous precipitation and a typhoon resulted in severe losses to local people and property. During flood events, there is an urgent need for timely and detailed flood inundation mapping for areas that have been severely affected. However, current satellite missions cannot provide sufficient information at a high enough spatio-temporal resolution for flooding applications. In contrast, spaceborne Global Navigation Satellite System reflectometry technology can be used to observe the Earth’s surface at a high spatio-temporal resolution without being affected by clouds or surface vegetation, providing a feasible scheme for flood disaster research. In this study, Cyclone Global Navigation Satellite System (CYGNSS) L1 science data were processed to obtain the change in the delay-Doppler map and surface reflectivity (SR) during the flood event. Then, a flood inundation map of the extreme precipitation was drawn using the threshold method based on the CYGNSS SR. Additionally, the flooded areas that were calculated based on the soil moisture from the Soil Moisture Active Passive (SMAP) data were used as a reference. Furthermore, the daily Dry Wet Abrupt Alternation Index (DWAAI) was used to identify the occurrence of the flood events. The results showed good agreement between the flood inundation that was derived from the CYGNSS SR and SMAP soil moisture. Moreover, compared with the SMAP results, the CYGNSS SR can provide the daily flood inundation with higher accuracy due to its high spatio-temporal resolution. Furthermore, the DWAAI can identify the transformation from droughts to floods in a relatively short period. Consequently, the distributions of and variations in flood inundation under extreme weather conditions can be identified on a daily scale with good accuracy using the CYGNSS data.

The ionospheric anomalies before an earthquake may be related to earthquake preparation. The study of the ionospheric anomalies before an earthquake provides potential value for earthquake prediction. This paper proposes a method for detecting ionospheric total electron content (TEC) anomalies before an earthquake, taking the M_S 7.8 earthquake in Türkiye on 6 February 2023 as an example. First, the data of four ground-based GNSS stations close to the epicenter were processed by using the sliding interquartile range method and the long short-term memory (LSTM) network. The anomaly dates detected by the two methods were identified as potential pre-earthquake TEC anomaly dates after eliminating solar and geomagnetic interference. Then, by using the sliding interquartile range method to process and analyze the CODE GIM (Center for Orbit Determination in Europe, Global Ionospheric Map) data from a global perspective, we further verified the existence of TEC anomalies before the earthquake on the above TEC anomaly days. Finally, the influence of the equatorial ionospheric anomaly (EIA) on the TEC anomaly disturbance was excluded. The results show that the ionospheric TEC anomalies on January 20, January 27, February 4, and February 5 before the Türkiye earthquake may be correlated with the earthquake.

An accurate model of the Zenith Tropospheric Delay (ZTD) plays a crucial role in Global Navigation Satellite System (GNSS) precise positioning, water vapor retrieval, and meteorological research. Current empirical models (such as the GPT3 model) can only reflect the approximate change trend of ZTD but cannot accurately reflect nonlinear changes such as rapid fluctuations in ZTD. In recent years, the application of machine learning methods in the modeling and prediction of ZTD has gained prominence, yielding commendable results. Utilizing the ZTD products from 53 International GNSS Service (IGS) stations in Europe during the year 2021 as a foundational dataset, a Generalized Regression Neural Network (GRNN) is employed to model IGS ZTD while considering spatiotemporal factors and its association with GPT3 ZTD. This endeavor culminates in the development of a refined GRNN model. To verify the performance of the model, the prediction results are compared with two other ZTD values. One is obtained based on the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data, and the other is obtained by the GPT3 model. The results show that the bias of the GRNN refined model is almost 0 mm, and the average Root-Mean-Square Error (RMSE) and Mean Absolute Error (MAE) are 18.33 mm and 14.08 mm, respectively. Compared with ERA5 ZTD and GPT3 ZTD, the RMSE of GRNN ZTD has decreased by 19.5% and 63.4%, respectively, and the MAE of GRNN ZTD has decreased by 24.8% and 67.1%. Compared with the other two models, the GRNN refined model has better performance in reflecting the rapid fluctuations of ZTD. In addition, also discussed is the impact of spatial factors and time factors on modeling. The findings indicate that modeling accuracy within the central region of the modeling area surpasses that at the periphery by approximately 17.8%. The period from June to October is associated with the lowest accuracy, whereas the optimal accuracy is typically observed from January to April. The most substantial differences in accuracy were observed at station OP71 (Paris, France), with the highest accuracy recorded (9.51 mm) in April and the lowest (24.00 mm) in September.

BDS-3 is now providing global positioning, navigation and timing (PNT) services. BDS-3 has new B1C, B2a and B2b signals compared to BDS-2. This work presents two single-frequency (SF) PPP time transfer models using BDS-3, B1C and B1I observations, and studies the performance of BDS-3 SF PPP time transfer by using 30-day data of 10 globally distributed stations from a multi-GNSS experiment (MGEX). We found that the ionospheric constraint SF PPP (SF1) time transfer model outperforms the method of SF PPP with the receiver clock offset at first epoch as the datum (SF2). Importantly, the statistical uncertainty of SF1 was less than 1 nanosecond, with (0.75, 0.71) ns in the average scheme for all time-links, using both B1I and B1C observations, respectively. The frequency stability of SF1 with B1C observations was improved from 1.73% to 13.04% in the short-term and from 0.88% to 17.49% in the long term, compared to that of B1I for all time-links. Hence, SF1 with B1C observations was recommended for SF PPP time transfer.

The ambiguity resolution (AR) and validation of the global navigation satellite system (GNSS) have been challenging tasks for some decades. Considering the reliability problem of extra-wide-lane (EWL) ambiguity in the triple-carrier ambiguity resolution (TCAR), a method for validating the reliability of the EWL ambiguity using a single epoch was proposed for the BeiDou Navigation Satellite System (BDS). For the initial EWL ambiguity, obtained using a rounding estimator with a geometry-free (GF) model, the double-difference ionospheric delay was first estimated to construct a relative positioning model with an initial fixed ambiguity. Second, based on the theory of gross error detection and the AR characteristics of EWL, the second-best ambiguity candidate was constructed. Finally, among the two sets of ambiguities, the one with the smaller posterior variance was taken as the reliable ambiguity. The study showed that, for a single epoch, when only one or two satellites had incorrect ambiguities, the AR success rate after ambiguity validation and correction could reach 100% for medium baselines. For long baselines, due to the increase of atmospheric error, the validation was affected to some extent. However, the AR success rates for two long baselines increased from 96.82% and 98.44% to 98.80% and 99.67%, respectively.