High-precision positioning is critical for autonomous vehicles in urban scenarios. The performance of GNSS/INS integration degrades during GNSS signal occlusion, which can be effectively alleviated by the rich visual features existing in the GNSS-denied scenarios. Besides, the strong structural regularity inherent in urban environments can be utilized to provide direction constraints, further enhancing positioning performance. However, the Atlanta world assumption is hard to guarantee in complex environments, which will lead to a degradation in positioning accuracy. To address these issues, we proposed a GNSS/INS/vision integration system based on the lane-driven Atlanta world assumption in man-made environments, where the relationship in domain directions between the lane and the Atlanta world assumption is exploited to provide a more flexible and accurate direction constraint. The Atlanta world employed in the proposed system is activated by the detected straight lanes, thereby enhancing the adaptability in varying urban environments. To improve the quality of the line feature by utilizing the structural property of man-made environment, the gravity and lane are used simultaneously to determine the dominant direction of Atlanta world, which can effectively avoid the pose error caused by the incorrect detection of the line feature. The performances of the proposed integration system are evaluated by both self-collected and public datasets. On two self-collected datasets, the proposed lane-based Atlanta world assumption reduces 3D positioning RMSE by 15.44% and 20.54%, respectively. Furthermore, on public datasets KAIST Urban38, the proposed structure-constrained system outperforms existing open-source frameworks VINS-Fusion and IC-GVINS, achieving RMSE reductions of 74.84% and 60.99%, respectively.

Understanding low-latitude F-layer ionospheric electron density (Ne) under severe geomagnetic conditions is crucial for various GNSS applications. Existing ionospheric models utilizing machine learning (ML) have struggled to accurately capture the complex dynamics of Ne, particularly under extreme geomagnetic conditions. In this study, we propose the attention-based recurrent ResNet18 (ABRR-18) model to predict ionospheric Ne using radio occultation (RO) data obtained from multiple satellite missions between 2002 and 2023. The proposed model integrates ResNet18 and bidirectional-long short-term memory (Bi-LSTM) with a spatial attention mechanism (SAM). Besides, it incorporates various space weather indicators such as solar flux, sunspot number, disturbance storm time, and interplanetary magnetic field (IMF). Experimental results revealed that ABRR-18 outperformed other applied models, such as artificial neural network (ANN)-international reference ionosphere (IRI), ANN-TDD, least-squares boosting (LSBoost), Bi-LSTM, and AlexNet-Bi-LSTM-SAM, achieving a correlation of 0.9674 and a root-mean-square error (RMSE) of$1.0295 \times 10^{5}$ele/cm3. ABRR-18 showed superior performance under severe geomagnetic conditions and during high solar activity years over the IRI-2016 model. In addition, the ABRR-18 model outperforms the IRI-2016 and IRI-2020 models, with predictions closely aligning with incoherent scatter radar (ISR) observations, particularly during extreme conditions. Compared to the IRI model (IRI-2016 and IRI-2020), ABRR-18 demonstrated superior accuracy in characterizing global ionospheric spatial–temporal properties. This study underscores the potential of deep learning (DL) techniques in ionospheric modeling by exhibiting superior performance. The ABRR-18 model introduces an innovative approach, offering notable advancements in comprehending and predicting ionospheric Ne in challenging conditions.

Medium-scale traveling ionospheric disturbances (MSTIDs) are periodic wave-like structures in the ionosphere that can significantly alter the local ionospheric conditions leading to the performance degradation of the radio wave communication and satellite navigation. Due to their complex origins and evolving dynamics, traditional monitoring methods often fail to effectively extract the characteristic features of MSTIDs. Leveraging advances in deep learning, this study proposes the state-of-the-art MSTID intelligent recognition and propagation parameter inversion method based on the “You Only Look Once” (YOLO) series models. The method consists of three main stages: MSTID target detection, MSTID blob instance segmentation, and inter-frame matching of segmented blobs. A dataset comprising 3,422 annotated images for target detection and 236 images for instance segmentation was constructed using Differential Total Electron Content (DTEC) maps from Japan’s GEONET network under the different solar activity conditions. The method automatically extracts key propagation parameters from consecutive image pairs including velocity, azimuth, wavelength, period, and coverage area. Experimental results show that YOLO v9m achieves the highest detection accuracy (78.34%) for MSTID targets, while YOLO v8m-seg excels in instance segmentation (95.58%). All of the models satisfy the real-time processing requirements. Blob features including color, centroid, area, Hu moments and topology, are extracted to compute feature difference scores across consecutive frames for the optimal matches determined by the Hungarian algorithm. Ellipse fitting and pixel-to-geographic coordinate conversion are then employed to calculate propagation parameters. Comparative validation with the traditional three-station cross-spectral and keogram methods demonstrates good consistency, with errors in parameters such as propagation velocity and azimuth within 20%. This AI-based approach offers a promising solution for the advancing intelligent, accurate, and real-time ionospheric disturbance detection.

Global Navigation Satellite System (GNSS) radio occultation (RO), owing to its capability to provide high vertical resolution, high accuracy, calibration-free, and all-weather atmospheric observations, has been widely used in numerical weather prediction (NWP) and climate studies. As China’s first commercial GNSS RO constellation supporting all major GNSS systems, Tianmu-1 (TM-1) offers promising observations. However, its data quality and assimilation performance in NWP remain underexplored. This study first evaluates the TM-1 neutral atmospheric refractivity and bending angle profiles collected in January 2024. Compared with the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ECMWF-ERA5), refractivity fractional differences at 5–30 km have mean and standard deviation within ±0.15% and 1.31%, while bending angle differences are within ±0.55% and 1.99%. Radiosonde comparisons over 0–20 km show refractivity differences within ±0.19% and 1.93%, and bending angle differences within ±0.12% and 5.06%. Larger errors are mainly confined to the lower troposphere and low latitudes, with only minor variations across GNSS constellations. After validating data quality, TM-1 refractivity observations are assimilated using the Weather Research and Forecasting (WRF) model and WRFDA 3DVAR system to assess their impact on regional analyses and short-range forecasts over China. Model outputs are validated against ERA5 reanalysis and radiosonde observations. The results show that assimilating TM-1 refractivity data leads to root mean squared error (RMSE) reductions of ~5–10% for temperature analyses and forecasts in the mid-to-upper troposphere and near the surface, and ~5% in specific humidity in the lower troposphere. Wind impacts are mixed, with RMSE improvements ~2–5% above 600 hPa and degradation in the lower troposphere. Overall, this preliminary study confirms the high quality of TM-1 GNSS RO refractivity data and demonstrates its promising contribution in complementing current operational RO assimilation for regional NWP.

Nowcasting plays a critical role in disaster warning systems, and recent advancements in deep learning have shown great potential in improving the accuracy and timeliness of such predictions. This study proposes a novel deep learning-based model for precipitation nowcasting, which integrates global navigation satellite system (GNSS)-derived precipitable water vapor (PWV) data with radar observations. The model introduces two key innovations: multi-source data fusion and time-dimension attention mechanism. These advancements enhance the model’s capability to accurately forecast precipitation events, particularly under challenging conditions with high rainfall intensity. In comparative experiments conducted using radar and GNSS data from Hong Kong, the model, incorporating both data fusion and the attention mechanism, demonstrated the best overall performance, with critical success index (CSI) scores increasing by 26% and Heidke skill score (HSS) scores by 23% at the 30 mm/h threshold. Moreover, it effectively simulates rainfall regions and their changing trends, demonstrating the complementary value of GNSS PWV data to radar observations.

This study reports the observations of super equatorial plasma bubbles (EPBs) over Asian-Pacific region during the intense May 2024 geomagnetic storm. Utilizing the combined measurements from dense ground-based global navigation satellite system (GNSS) stations and in situ Swarm satellite mission, the generation and evolution process of the super EPBs are clearly captured. Compared to previous events, this case presents following new characteristics in terms of lifetime and morphology: (1) The EPBs lasted up to 18 hours from 8:00 UT on 11 May to 2:00 UT on 12 May. (2) The EPBs exhibited a large-scale inverted C-shape at the early stage and transformed into a large-scale forward C-shape latterly. The storm-time eastward prompt penetration electric filed (PPEF) is believed to play an import role on the formation and development of the super EPBs. These new results provide a more comprehensive evolution characteristic for EPBs, which can help to better know the storm-time ionospheric variations.

Ocean surface wind is vital to the Earth’s meteorological system, and their properties can be detected by spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) measurements. With the growing number of GNSS-R signal sources, machine learning technology exhibits prominent advantages in wind speed estimation. Currently, the deep-learning techniques that establish relationships between GNSS-R measurements and ocean surface wind speeds generally apply grids and sequence structures and lack flexibility and robustness. Additionally, constructing models with individual GNSS-R observations results in the loss of valuable temporal correlation within Delay-Doppler Maps (DDMs). Therefore, this study proposes a novel spatiotemporal graph-based deep neural network (STG-DNN) for retrieving wind speed, which incorporates a graph module with a transformer module to fully exploit the spatial-temporal dependencies of DDMs. Results demonstrate that the graph module significantly improves both the accuracy and reliability in wind speed retrieval. Meanwhile, the transformer module effectively captures temporal features from various DDMs. Validations with Cyclone GNSS (CYGNSS) test data support the superior accuracy of STG-DNN, revealing a correlation coefficient of 0.92 for the wind speeds. The results indicate that the root mean square error (RMSE) of STG-DNN for wind speed is 1.27 m/s, representing improvements of approximately 33.2%, 20.6%, and 13.6% over the minimum variance estimator (MVE), convolutional neural network (CNN), and Vision Graph Neural Networks (VIG), respectively. Additionally, a promising agreement is observed between STG-DNN and ERA5 in the spatial distributions of wind speed retrieval, indicating a robust spatial performance in STG-DNN. As for the temporal scale, the daily variations in retrieval accuracy of STG-DNN exhibit smaller fluctuations compared to both CNN and VIG wind data in the test dataset.

Global soil moisture (SM) mapping at high spatial and temporal resolution contributes significantly to various hydrologic and meteorological researches. This work presents an algorithm for combining fine-resolution Global Navigation Satellite System Reflectometry (GNSS-R) observations from the Cyclone Global Navigation Satellite System (CYGNSS) and coarse-resolution SM estimates from the SM active passive (SMAP) mission to estimate SM at 3 km resolution. In practice, the expression for downscaled 3 km SM is derived from the mathematical double-scale SM equations based on the linear assumption between SM and reflectivity, and the CYGNSS signal-to-noise ratio (SNR) data are leveraged to compensate the differences in heterogeneity between the two scales. Experimental validation over 150 in situ sites shows strong consistency between the SMAP/CYGNSS 3 km SM estimates and the in situ measurements, with a median correlation coefficient of 0.806 and a median unbiased root-mean-square error of 0.038 cm3/cm3. The contributions of this work are twofold: 1) introducing the normalized signal-to-noise ratio (NSNR) to account for the deviations in double-scale coefficients, which depend on the variations in vegetation and surface roughness between 36 and 3 km scales, without relying on any ongoing knowledge of ancillary data; and 2) achieving daily 3 km SM estimations at a quasi-global scale, and providing a new way for enhancing the temporal and spatial resolution of SMAP SM.

A reasonable stochastic model, e.g., the pre-assigned a priori variance information determines the weight of measurements in the Kalman filter of GNSS/SINS and significantly affects the integrated navigation results. However, due to the nonlinear model, non-Gaussian noise, unmodeled error, and gross errors, GNSS may produce over-optimism estimation which cannot reflect the actual error and significantly affect the performance of GNSS/SINS solution. Hence, this work utilizes crowdsourcing datasets and data-driven learning approaches to resolve the inconsistency between estimated variance and actual errors in GNSS. A CNN-LSTM-Attention (CLA) model is constructed to enable long-time step propagation and resilient fusion of features. Furthermore, by leveraging the properties of IMU and the RTS algorithm, the label data can be self-generated, eliminating the reliance on the ground truth system. The CLA model is trained using GNSS features, enabling self-learning of position variance. Lastly, an adaptive fusion of GNSS/SINS is achieved through the semi-tightly coupled integration scheme, and crowdsourced data is employed for the continuous optimization of the CLA model. The test results showed that the consistency of GNSS position variance is improved and the CEP95 of position errors is improved by 64.33% compared to the conventional method.

Pursuing accurate positioning, especially in the lateral direction is a challenging issue in autonomous driving services. However, the commonly used GNSS/INS is often subjected to signal blockages, resulting in rapid divergence for the inertial system in complex urban environments. With the advent and advancement of high-definition (HD) maps, the absolute coordinate information they store can serve as excellent positioning resources. In this case, we propose a real-time and continuous positioning method that fuses multiple information of vehicle-mounted sensors (GNSS, INS, vision, DMI) and HD maps, along with a complete workflow for utilizing commercial HD maps for high-precision positioning. The lane lines are automatically detected from images using a deep learning module, and the corresponding map data is extracted based on the vehicle’s approximate position. Road shape registration is performed to construct measurement equations by iteratively matching the projected and detected lane lines. Finally, the system refines its states with a tightly-coupled INS/DMI/LAN fusion model to acquire reliable positioning results when the GNSS signal is unavailable. Additionally, to address the challenges of encryption offset and missing elevation data in HD maps, an automatic map offset calibration and online height estimation method is proposed using open environment data. The field experiments demonstrate that in a GNSS-denied tunnel environment, the system can maintain centimeter-level accuracy in the lateral direction during a 200-second GNSS signal outage.

To fully leverage the potential of GNSS-R technology for high spatiotemporal resolution SM and F/T retrieval, this study first proposed an observation reconstruction method to address the mutual constraints of spatiotemporal resolution on GNSS-R gridded observations. The reconstructed CYGNSS observations were then utilized for SM and F/T retrievals over the Qinghai-Tibet Plateau region. The RMSE and R of the SM retrieval are 0.063 cm3/cm3and 0.52, respectively, which are comparable to the accuracy of the SM retrieval performed before reconstruction. Similarly, the accuracy of the F/T retrieval was 84.1%. This is also comparable to the accuracy of the F/T retrieval performed before reconstruction. Independent evaluation of the local in situ sites also showed consistent performance with the SM and F/T results from the original CYGNSS observations. Notably, the temporal resolution of the CYGNSS reconstructed observations was improved by 315% over the original CYGNSS 9 km observations.

This study introduces a method that combines Soil Moisture Active Passive (SMAP) radiometer and Cyclone Global Navigation Satellite System (CYGNSS) reflectometer to derive a 9 km quasi-global microwave land surface emissivity product. This algorithm aims to utilize CYGNSS reflectivity to capture spatial heterogeneity at 9 km scale, so as to create an emissivity product with a spatial resolution of 9 km and a temporal resolution of 2 days in the area of interest. The global results demonstrate the satisfactory performance of the SMAP/CYGNSS 9 km emissivity, with a median repeat period of 2.5 days for most mid-latitude regions and 4.7 days for all regions. Compared to the SMAP emissivity that has a native 36 km spatial resolution and a revisit time of 2-3 days, the daily SMAP/CYGNSS 9 km emissivity covers approximately 40% of the United States, increasing to 75% for a 3-day average emissivity.

Visual-inertial simultaneous localization and mapping (VISLAM), which is widely used in autonomous driving, can estimate the ego-motion of vehicles and reconstruct the static environment. Limited by the static-environment assumption, the performance of VISLAM in complex dynamic environments degrades significantly due to the outliers introduced by dynamic objects. To address this issue, a novel monocular visual-inertial simultaneous localization and mapping system called DAP-VINS is proposed to improve the pose accuracy in dynamic environments. In the proposed system, an instance association feature-based module (IAFM) is proposed based on the improved Kuhn-Munkres (KM) algorithm to establish temporal correlations between instance objects in consecutive frames. Based on the probabilistic superposition strategy, an instance ID probability map (IDPM) is constructed to associate instances and landmarks. Dynamic feature detection is achieved with a moving probability propagation model based on a binary Bayes filter (BF-MPP), which can effectively combine geometric constraints and instance information from deep learning. The proposed algorithm is evaluated with public data sets and real-world scenarios. Experimental results demonstrate that DAP-VINS outperforms existing state-of-the-art VISLAM implementations on highly dynamic data sets. Specifically, compared to the results of VINS-Mono, the root mean square error of the absolute trajectory error is reduced by 30%–90% in dynamic scenarios.

The emergence of seamless mobile navigation systems integrating various Internet of Things (IoT) devices has sparked interest in context awareness enhancement technology. In the concept of advanced adaptive integrated navigation technology, the context comprises two key elements: environment characteristics and carrier behaviors, which are not entirely independent, especially in certain scenarios. Leveraging the abundant sensors in smartphones, a model combining context connectivity and behavior association is developed to detect environment scenes accurately with low energy consumption across outdoor, semi-outdoor, and indoor spaces. The model comprises three main parts: sensor-based SVM (Support Vector Machine), behavior-aided HMM (Hidden Markov Model), and classifier combination. The parameters of a behavior-aided HMM are adjusted by behavioral probabilities and a specified EMA method. Four classifier combination techniques, including SA, EWA, EBWA, and stacking, are used to integrate the environment detecting strengths of multiple smartphone sensors. The proposed model is evaluated on a dataset collected from a complex building at Wuhan University and achieves a best environment detection accuracy of 94.22% with stacking ensemble technique. The multisensor model outperforms the other three classifier combination techniques, improving detection accuracy by 6.93% compared to a GNSS-supported model. The proposed model has certain advantages over high recognition accuracy, low model consumption compared to the main existing environment detection models.

As a serious marine environmental disaster, harmful algal blooms (HABs) exhibit characteristics such as high frequency, large impact area, and increasing damage. There is an urgent need for an all-weather, high revisit rate, wide-range, and large-scale monitoring method to address these changes. In this article, we propose a method to detect the distribution and density of HABs using the mean square slope (MSS) observations from Global Navigation Satellite System-Reflectometry (GNSS-R), based on the physical explanation that the coverage of HABs leads to a reduction in wind-driven sea surface roughness. By comparing the Cyclone Global Navigation Satellite System (CYGNSS) MSS data with actual observations, the applicable threshold for HABs monitoring based on MSS is clarified, and a HABs density inversion model is constructed. The classification of two severe HABs areas (Jiaozhou Bay and the Gulf of Mexico) has been achieved with a 59% probability of detection and 1% false alarm rate. The introduction of this method enables daily scale observations of HABs, which can better reveal the spatiotemporal distribution characteristics, migration patterns, and influencing factors of HABs, providing a new means for the monitoring and integrated management of HABs.

Accurate and timely forecasting of ionospheric irregularities is of great significance for the reliable and stable operation of global high-precision communication and navigation systems at low latitudes. In this study, we implement a hybrid ensemble model (HEM) that combines multiple machine learning models for forecasting the occurrence and intensity of ionospheric irregularities instead of considering ionospheric irregularity forecasting as classifications. Meanwhile, this model is trained with the GNSS-derived rate of total electron content index (ROTI) maps from approximately 147 ground-based global navigation satellite systems (GNSS) receivers in Brazil sector (35° S–5° N, 30° W–75° W). Meanwhile, a diverse set of input features, including interplanetary magnetic field (IMF) components, F2 layer critical frequency (foF2), peak height F2-layer (hmF2), F10.7, flow pressure, and SYM-H indices, are carefully selected during January 1, 2022 to 15 October 31, 2022. Regarding the relative importance of various input features, results demonstrate that the performance of the HEM model trained by the ROTI and hmF2 observations for predicting ionospheric irregularities is superior to that of other input features. Furthermore, the deviations of forecasting ionospheric irregularities from the HEM model occur mainly in the southern equatorial ionization anomaly (EIA) regions, and the accuracy of the HEM model with daily standard deviation (STD) and root mean square (rms) is less than 0.1 TECU/min. Hence, the HEM model is more stable and greater than other predicted models. Additionally, the HEM algorithm can forecast the ionospheric irregularity structures and intensity for 30 min over Brazilian territory. It is expected to improve the accuracy of short-term ionospheric irregularities forecasting at low latitudes.

High-precision GNSS positioning plays a crucial role in intelligent transportation systems, and leveraging artificial intelligence can enable the development of reliable context-aware models that enhance context-adaptive GNSS algorithms. However, the complexity of contexts and the variability of features limit the performance of GNSS-based context-aware models. Therefore, this study aims to construct a context-aware model in kinematic vehicle-mounted environments. Firstly, due to the lack of publicly available datasets, a kinematic vehicle-mounted dataset comprising over 40,000 data samples is constructed. This dataset includes five contexts including open area, urban canyon, boulevard, under viaduct, and tunnel. Meanwhile, efficient feature selection methods are applied, namely SHAP (Shapley Additive exPlanations) and SFFS (Sequential Forward Floating Selection), to obtain effective data features. Secondly, considering the temporal correlation of GNSS signals, eight alternative models (SVM, MLP, CNN-b, CNN-r, CNN-i, ML-LSTM, ML-RNN, ML-GRU) are designed and trained. Context probability is introduced to indicate context transition area. Comparative studies show that the ML-LSTM model exhibits the best performance, with an an accuracy of 92.72% and a mean average precision (mAP) of 92.94% in context-separated areas, and with an accuracy of 87.49% and a mAP of 89.21% in context-continuous areas. Furthermore, to address the performance degradation when deploying the model on different receiver types, this study explores model-based transfer learning. Four transfer approaches, namely direct transfer, fully connected layer transfer, partial layer transfer, and all layer transfer, are applied. The all layer transfer approach demonstrates the best performance, exhibiting a 18.34% improvement compared to the non-transfer approach.

The demand for high-precision navigation and positioning is growing rapidly with the advancements in autonomous driving and intelligent robotics. Nowadays, many studies have been conducted on the fusion of the global navigation satellite system (GNSS), inertial navigation systems (INS), and light detection and ranging (LiDAR) because of their complementary characteristics. Notably, in multi-source fusion, it is crucial to precisely model the uncertainty (covariance) of each sensor. While GNSS and IMU covariance modeling are mature, LiDAR covariance modeling remains unsophisticated, resulting in a suboptimal fusion of GNSS/INS/LiDAR. In this work, the LiDAR covariance modeling method is proposed, including the original point cloud covariance, the localmap covariance, and the observation covariance modeling. A point cloud selecting approach based on the eigenvectors and centroids is also introduced to maintain valid surface and edge feature points, thereby reducing repeated observations. These above methods are applied to the tightly coupled RTK/INS/LiDAR_PPL (G+I+PPL) system based on the Multi-State Constraint Kalman Filter (MSCKF) to further improve positioning accuracy and covariance consistency. The experimental results show that the proposed method has better covariance consistency in comparison to LIOSAM and FASTLIO. Meanwhile, G+I+PPL with fused point-to-plane/line (PPL) observations outperforms compared to RTK/INS (G+I) and RTK/INS/LiDAR_CP (G+I+CP) using closest point (CP) observations, in the right, front, and up (RFU) directions, demonstrating a positioning improvement in performance by (50.7%, 58.6%, 54.3%) and (46.2%, 55.0%, 58.8%), respectively.

GNSS (Global Navigation Satellite System) observation insufficiency limits the development of the voxel-based computerized ionospheric tomography (CIT) technique. Electron densities of voxels without observation cannot be accurately estimated by the commonly used algebraic reconstruction techniques. In this study, we proposed a compressed sensing technique (CST) based on dictionary learning for ionospheric tomography. Specifically, the K-SVD (singular value decomposition) algorithm was used for dictionary learning based on training sets that are derived from the NeQuick model, hereafter referred to as the CST_NeQuick algorithm. K-SVD uses the orthogonal matching pursuit (OMP) for sparse coding and the SVD approach for dictionary updating. Both simulations and real experiments demonstrated the feasibility and superiority of the CST algorithm when compared to the widely used multiplicative algebraic reconstruction technique (MART). It was found that the CST_NeQuick algorithm’s tomographic performances were mostly superior to those of the MART algorithm in comparison with the independent slant total electron content (STEC) references. Another CST-based tomographic experiment was performed by using the MART-based solutions for dictionary learning, hereafter referred to as the CST_MART algorithm. It showed that the CST_MART algorithm can reduce the average root mean square (RMS) of the CIT-derived STEC by 37.3 % and 20.2 %, respectively, when compared to the MART and CST_NeQuick algorithms. Besides, the CST_MART algorithm’s electron density profiles also showed more agreement with the electron density profiles that were derived from radio occultation data.

The freeze-thaw (F/T) process plays a significant role in climate change and ecological systems. The soil F/T state can now be determined using microwave remote sensing. However, its monitoring capacity is constrained by its low spatial resolution or long revisit intervals. In this study, spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data with high temporal and spatial resolutions were used to detect daily soil F/T cycles, including completely frozen, completely thawed, and F/T transition states. Firstly, the calibrated Cyclone GNSS (CYGNSS) reflectivity was used for soil F/T classification. Compared with those of Soil Moisture Active and Passive F/T data and in-situ data, the detection accuracies of CYGNSS reach 75.1 and 81.4%, respectively. Subsequently, the changes in spatial characteristics were quantified, including the monthly occurrence days of the soil F/T state. It is found that the completely frozen and completely thawed states have opposite spatial distributions, and the F/T transition states distribute from the east to the west and then back to the east of the Qinghai-Tibet Plateau, which may be due to varying diurnal temperatures in different seasons. Finally, the first day of thawing, last day of thawing, and thawing period of the F/T year were analyzed in terms of the changes in temporal characteristics. The temporal variation of thawing is mainly different between the western and eastern parts of the Tibetan Plateau, which is in agreement with the spatial variation characteristics. The results demonstrate that the CYGNSS can accurately detect the F/T state of near-surface soil in the daily scale. Moreover, it can complement traditional remote sensing missions to improve the F/T detection capability. It can also expand the applications of GNSS-R technology and provide new avenues for cryosphere research.

The global soil moisture (SM) retrievals by the spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) are significantly influenced by the presence of water bodies. The traditional method is to build a grid based on the location of satellite sampling points and determine the presence or absence of water bodies. In this paper, we propose a water body removal method for global spaceborne GNSS-R SM retrievals that combines water bodies and buffers derived from the marginal areas around water bodies as mask data, thus achieving accurate removal of the water body and avoiding margin effects. To verify the effectiveness of the proposed method, the Cyclone GNSS (CYGNSS) data with two different spatial resolutions (36 km and 3 km) were used for SM retrieval, and the Soil Moisture Active and Passive (SMAP) Radiometer SM as well as the International Soil Moisture Network (ISMN) were used as references. Results show that the correlation coefficient (R) and root mean square error (RMSE) of the 36 km grid are 0.50 and 0.057 cm3/cm3, respectively, while the R and RMSE of the 3 km grid are 0.68 and 0.041 cm3/cm3, respectively. Such performances are better than the traditional method. Moreover, the method proposed in this paper preserves more grids. Take the 3 km spatial resolution for example, it preserves 2.2 fold grids more than the traditional water body removal method. In the comparison with SMAP SM, the overall improvement of RMSE by using the water body removal method proposed in this paper is 16.3% (8.2% for the traditional method). In the in-situ validation, the overall improvement of RMSE is 19.4% (-1.2% for the traditional method). Therefore, in the future high spatial resolution SM retrieval, the water body removal method proposed in this paper can preserve the maximum area and effectively eliminate the influence of water bodies on SM retrieval.

Previous studies have reported that signal penetration will introduce an underestimation of snow depth, referred to as the snow depth difference. So far, however, there have been few detailed investigations into the relationship between snow depth difference and signal-to-noise ratio (SNR) metrics. In this study, we briefly describe the snow depth difference and provide a physical explanation of the systematic negative error. The baseline- and short-term variations of snow depth difference and SNR metrics were identified, and their relationships during various snow periods were investigated. The results indicated that the systematic negative errors and SNR metrics during the stable and melting periods are dominated by the layered structures and liquid water content of snowfall, respectively. Meanwhile, compared with the baseline terms, the short-term variations of snow depth difference and SNR metrics were more sensitive to fresh, low-density snowfall over the old snow surface. Additionally, an improved method is proposed to compensate for systematic differences using 2- and 5-parameter multiple linear regression (MLR) models with SNR metrics as independent variables. The results showed that the compensation values conformed with the measured values with correlation coefficients exceeding 0.85. In terms of accuracy, once the MLR models were applied, the root mean squared errors (RMSEs) decreased from 22.05 cm to 3.89 cm and 3.40 cm, respectively. Moreover, the corrected estimates agree well with the meteorological records, with regression slope deviations of less than 2% and correlation coefficients of over 0.97, suggesting no systematic errors between the estimates and reference.

Ionospheric monitoring and modeling have been difficult for a long time over the data-void or data-sparse oceans. As an emerging remote sensing technique, GNSS reflectometry (GNSS-R) has presented great potential in ionosphere sounding over these regions. However, the conventional approach to generate delay-Doppler-map (DDM) involved in the GNSS-R total electron content (TEC) retrieval process ignores the effects of tropospheric delay and the topside ionospheric delay above the GNSS-R receiver. This would cause certain errors in retrieved TEC results. In this contribution, an improved method to estimate ionospheric TEC over oceans using the GNSS-R technique is proposed, which considers the influence of the tropospheric delays and the topside ionospheric delays above the spaceborne GNSS-R receiver. To achieve the best matching between measured and simulated DDM, this paper employs the least squares (LS) fitting method for elastic matching. Additionally, the assessment was performed in May 2015 and 2017 at different solar activities, by comparing the ionospheric TEC derived from our proposed method with that from two ionospheric empirical models (NeQuick2 and IRI-2016), the Global Ionospheric Maps (GIMs) final products, as well as the measured GNSS TEC. The results show good consistency between these models. Meanwhile, when considering the topside ionospheric delay and tropospheric delay in DDM, the TEC accuracy has significantly improved. Especially, the improvements of root mean square error (RMS) can reach 5.3% and 23.5% during high and low solar activities, respectively, versus GNSS TEC. It is expected to benefit the application of GNSS-R in ionospheric modeling and application over the ocean area.

Global ionosphere model with high accuracy and resolution is of great importance for ionospheric research and global navigation satellite system (GNSS) precise positioning applications. In the last 20 years, the accuracy and reliability of global ionospheric model benefitted from the development of GNSS technology have been improved significantly, but its performance is still not good over many regions (e.g., oceans and polar region) due to the lack of ground GNSS stations. Fortunately, the rapid development of low earth orbit (LEO) satellite constellations provides a potential opportunity to address this issue. However, there is still no optimal approach to make use of the LEO-based ionospheric observations because of the different observation ranges compared with that of ground-based GNSS ionospheric observations. In this article, two approaches are proposed to combine GNSS and LEO observation data for ionosphere modeling, which are single-layer normalization (SLN) method and dual-layer superposition (DLS) method, respectively. The results exhibit a significant improvement of ionospheric model accuracy by combining LEO and GNSS observation data based on our proposed methods compared with that using GNSS data only, with a reduction in root mean square (rms) error of about 25% and 21% for SLN method and DLS method, respectively. The article also highlights the relations between the performance of ionospheric model estimated by the SLN method and LEO ionospheric observations with different observation accuracy and different satellite cut-off elevations. The results indicate that ionospheric model estimated by GNSS/LEO using SLN method improves at least 25% compared with that by GNSS only. The improvement of ionospheric model estimated with the cut-off elevation of 50° is the best, followed by 70°, and then 20°.

Precise extraction of ionospheric total electron content (TEC) observations with high precision is the precondition for establishing high-precision ionospheric TEC models. Nowadays, there are several ways to extract TEC observations, e.g., raw-code method (Raw-C), phase-leveled code method (PL-C), and undifferenced and uncombined precise point positioning method (UD-PPP); however, their accuracy is affected by multipath and noise. Considering the limitations of the three traditional methods, we try directly to use the phase observation based on zero-difference integer ambiguity to extract ionospheric observations, namely, PPP-Fixed method. The main goal of this work is to: 1) deduce the expression of ionospheric observables of these four extraction methods in a mathematical formula, especially the satellite and receiver hardware delays; 2) investigate the performance and precision of ionospheric observables extracted from different algorithms using two validation methods, i.e., the co-location experiment by calculating the single difference for each satellite, and the single-frequency PPP (SF-PPP) test by two co-location stations; and 3) use the short arc experiment to demonstrate the advantages of the PPP-Fixed method. The results show that single-difference mean errors of TEC extracted by PL-C, UD-PPP, and PPP-Fixed are 1.81, 0.59, and 0.15 TEC unit (TECU), respectively, and their corresponding maximum single-difference values are 5.12, 1.68, and 0.43 TECU, respectively. Compared with PL-C, the precision of the TEC observations extracted by the PPP-Fixed method is improved by 91.7%, while it is 67.3% for UD-PPP. The SF-PPP experiment shows that PPP-Fixed is the best among these methods in terms of convergence speed, correction accuracy, and reliability of positioning performance. Moreover, the PPP-Fixed method can achieve high accuracy even when the observed arc is short, e.g., within 40 min.

As a cost-effective remote sensing technique, global navigation satellite system reflectometry (GNSS-R) has recently drawn significant attention from both academia and industry. However, research on GNSS-R has mainly been focused on the global positioning system which consists of only medium earth orbit satellites, while the use of geostationary earth orbit (GEO) satellites, such as those in BeiDou navigation satellite system, has received little attention. This paper investigates the GEO-satellite-based GNSS-R with a focus on the application of soil moisture retrieval. Because GEO satellites remain static with the earth, the models of the reflected GNSS signals can be considerably simplified and their signals can be used to estimate soil moisture with a high update rate such as once per hour. Two new soil moisture estimation approaches using GEO signals are proposed, which are termed GEO interferometric reflectometry (GEO-IR) and GEO reflectometry (GEO-R). Two theoretical models (linear and second order) are developed for signal-to-noise ratio (SNR)-based GEO-IR as well as for phase-based GEO-IR. Meanwhile, two empirical models (linear and second order) are developed for signal amplitude-based GEO-R as well as for SNR ratio-based GEO-R. Experimental data sets collected from three different geographical regions were used to evaluate the proposed methods. The results demonstrate that the proposed GEO-IR and GEO-R are able to monitor soil moisture reliably under bare soil condition, augmenting GNSS-R through significantly reduced processing complexity and increased temporal coverage.

This paper discusses the variabilities and trends in the global tropopause based on the gridded monthly mean Global Positioning System radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission during July 2006–February 2014. We find that the tropopause height can reflect El Niño–Southern Oscillation (ENSO) events. The correlation coefficient between global tropopause height anomalies and the Niño 3.4 sea surface temperature index is 0.53, with a maximum correlation coefficient of 0.8 at a lag of three months. We present first the detailed investigations about the spatial distribution of trends in tropopause parameters in each 10°$\times$5° longitude–latitude grid cell over the globe and find that the rates of change in the tropopause parameters during this time period are high in some particular regions such as the Southern Indian Ocean, Antarctica, Western Europe, North Pacific, and the east coast of North America. An analysis of global monthly means of the tropopause parameters indicates a global tropopause height increase of 0.03$\pm$2.36 m/year during 2006–2014, with a corresponding temperature increase of 0.020$\pm$0.008 °C/year, and a pressure increase of 0.11$\pm$0.059 hPa/year. The upward trend of tropopause height is significantly weaker than that in the past years, which might be attributed to the expected stratospheric ozone recovery associated with the Montreal Protocol, the global warming slowdown, and the abnormal global climate change in recent years. The trends of the tropopause parameters are the most significant over the Southern Indian Ocean and Antarctica during September/October/November, which could be due to the stratospheric ozone recovery.

Snow is important to the ecological and climate systems; however, current snowfall and snow depth in situ observations are only available sparsely on the globe. By making use of the networks of Global Positioning System (GPS) stations established for geodetic applications, it is possible to monitor snow distribution on a global scale in an inexpensive way. In this paper, we propose a new snow depth estimation approach using a geodetic GPS station, multipath reflectometry and a linear combination of phase measurements of GPS triple-frequency (L1, L2, and L5) signals. This phase combination is geometry free and is not affected by ionospheric delays. Analytical linear models are first established to describe the relationship between antenna height and spectral peak frequency of combined phase time series, which are calculated based on theoretical formulas. When estimating snow depth in real time, the spectral peak frequency of the phase measurements is obtained, and then the model is used to determine snow depth. Two experimental data sets recorded in two different environments were used to test the proposed method. The results demonstrate that the proposed method shows an improvement with respect to existing methods on average.

The tropopause is a transition layer between the troposphere and the stratosphere. The exchange of air, water vapor, trace gases, and energy between the troposphere and the stratosphere occurs in this layer. Accurate and continuous observations of the tropopause on a global scale are crucial for monitoring stratosphere-troposphere exchange and understanding the properties of atmosphere in the upper troposphere and lower stratosphere. In this paper, the tropopause heights are identified from Global Positioning System radio occultation (RO) bending angle profiles using the covariance transform method. Temporal variations of the tropopause parameters, including the tropopause height, temperature, and pressure at different latitude bands are investigated using the RO observations from the Constellation Observing System for Meteorology, Ionosphere and Climate mission during the period from January 2007 to December 2011. We divide the Earth into 18 nonoverlapping latitude bands 10°wide. Monthly averages of the tropopause parameters weighted by area are calculated at each latitude band and the temporal variations of these tropopause parameters are analyzed. The results indicate that the latitudinal variation patterns of the tropopause parameters in the Northern Hemisphere are different than those in the Southern Hemisphere. The relationship between the variations of different tropopause parameters is studied. The results show that the variation of the tropopause temperature and pressure is negatively correlated with that of the tropopause height in most of the latitude bands. In addition, the trend of the variation of the tropopause height in each latitude band is calculated with the median of pairwise slopes regression method. We find that the overall trend in the tropopause height varies in different latitude bands. The global average tropopause height decreases ~ 7 m/a during the period from 2007 to 2011.