Low-cost strapdown inertial sensors have been increasingly applied to navigation and positioning in the Internet of Things (IoT) devices in recent years. The initial alignment accuracy of low-cost inertial sensors is critical for subsequent navigation performance. However, they face significant alignment challenges, particularly in complex environments. This article proposes a robust in-motion alignment method, which takes the bias of the gyroscope, the bias of the accelerometer, and the lever arm into account, thus avoiding the continuous accumulation of inertial measurement unit (IMU) bias errors during alignment. The accuracy of vector construction is improved by developing a novel state-space model for online calibration and compensation of unknown IMU errors. The reconstructed observation vectors achieved through the residual term enable the detection and isolation of outliers within the aided velocity information. The proposed dual-input interval type-2 (IT2) fuzzy inference system enables accurate adjustment of the measurement noise covariance matrix, even when the system is subject to outlier interference. Simulation and experimental results show that the proposed alignment method significantly enhances alignment accuracy with an improvement of over 63% when compared to the HIMCA method and more than 37% when compared to the IICA method. In particular, for the heading angle, the proposed method demonstrates outstanding performance with errors stabilizing within 1.28° after 350 s, while the other four methods struggle to achieve convergence. The proposed alignment method exhibits stronger anti-interference capability and can significantly improve the alignment accuracy.

This study introduces a method for filling the data gaps in the International Soil Moisture Network (ISMN) by soil moisture (SM) estimated using data from the Cyclone Global Navigation Satellite System (CYGNSS). The estimation process leverages the random forest (RF) algorithm, incorporating CYGNSS-derived products along with soil and surface parameters as input features. This research was conducted based on the daily SM data from the ISMN for the entire years of 2019 and 2020, which served as training and test datasets. Comparison experiments were performed to highlight the limitations of existing methods and SM products for gap filling in ISMN SM data. Subsequently, the optimal retrieval model was deployed to estimate SM for the duration of the study, thereby filling the gaps within the ISMN dataset. The SM results after gap filling showed strong consistency with measured SM, achieving an R-squared ($R^{2}$) of 0.7930 and a root-mean-square error (RMSE) of 0.0492 cm3/cm3. These results indicate that CYGNSS-based SM inversion is a promising approach to enhance the completeness of the ISMN dataset.

Recent advances in Cyclone Global Navigation Satellite System (CYGNSS) data have significantly improved the extraction of monthly surface water fraction (SWF), with neural networks being widely used for large-scale water body mapping based on GNSS-R signals. However, inherent noise in CYGNSS signals, such as multipath effects and interference, presents substantial challenges to the accuracy of SWF estimation. Diffusion models, an emerging class of generative deep learning techniques, have shown remarkable capabilities in capturing complex data distributions. By leveraging an iterative process of noise addition and removal, these models demonstrate significant advantages in processing low signal-to-noise ratio data, offering a novel methodology for precise SWF estimation from CYGNSS data. This study introduces DiffWater, a framework designed to address the unique characteristics of CYGNSS data and systematically explore the applicability of conditional diffusion models for remote sensing tasks. Utilizing a composite reference dataset, which includes the Global Surface Water (GSW) dataset and the Global Surface Water Dynamics (GLAD) dataset as training targets, DiffWater enhances the objectives of conditional diffusion models by integrating advanced conditional feature extractors and implementing multi-level fusion of conditional and temporal features, thereby achieving significant improvements in SWF estimation performance. Comprehensive experimental evaluations on the reference dataset demonstrate that DiffWater achieved the best performance, with a root mean square error (RMSE) of 4.987% and a correlation coefficient (R) of 0.946. Compared to state-of-the-art SWF estimation methods, the proposed approach demonstrated significant improvements in both quantitative and qualitative results.

Water level measurement is essential for managing and conserving water resources. The Yangtze River has a significant impact on agriculture, transportation, and local ecosystems, while traditional water level measurement still has some limitations, e.g. high cost and low coverage. Recently, Global Navigation Satellite System-Interferometric Reflectometry (GNSS-IR) has become a new means for water level monitoring. This paper integrates the reflected signals from GPS, GLONASS, Galileo and BDS constellations for real time high-frequency water level estimation at Yangtze River stations (BADO and DATO). By integrating a sliding window with Variational Mode Decomposition (VMD), Iteratively Reweighted Least Squares (IRLS), and Savitzky-Golay (S-G) filtering, water level measurements at a 5-minute temporal resolution are obtained and evaluated. Our individual and combined results show that the VMD method efficiently provides more accurate and stable inversion results using signal-to-noise ratio (SNR) data in each window. The combined and filtered results increased the accuracy by over 45.88% when compared to single-system measurements. The root mean square error (RMSE) was 4.86 cm at BADO and 2.28 cm at DATO with coefficient of determination (R2) 0.99 at both stations. Our results demonstrated that the combined method enabled precise and continuous water level monitoring, which provides new pathways for hydrological monitoring and real-time water resource management.

This study explores the use of CYGNSS satellite data for detecting cyanobacterial harmful algal blooms (HABs). CYGNSS's unique capabilities in measuring surface reflectivity are harnessed to identify the telltale signs of algal blooms, offering a significant improvement over the traditional observation method. Preliminary results demonstrate that machine learning algorithms applied to CYGNSS data provide effective and timely detection of HABs. This method is promising for enhancing global water quality monitoring and management.

Orthogonal frequency division multiplexing (OFDM) signals have been widely studied as a potential waveform used in integrated sensing and communication (ISAC) systems. High computational effort, however, is required to estimate the azimuth of the target and suppress the interference from the non-target directions. Moreover, along the non-target directions, the transmitted confidential information can be easily intercepted by the eavesdroppers. In this paper, directional modulation (DM) technology combined with OFDM waveforms namely, OFDM-DM, is proposed for ISAC systems. From the sensing perspective, the interference from the non-target direction can be suppressed, and three-dimensional (3-D) radar images can be calculated without consuming extra computational resources. From a communication perspective, the OFDM-DM signals provide a secured physical-layer wireless transmission link and thus the confidential information can be securely delivered to the target. The efficacy of the proposed OFDM-DM ISAC waveforms is validated via numerical results for both sensing and communication functionalities by comparison with the traditional OFDM ISAC waveforms.

Traditional ionospheric models were mostly constructed based on a single layer assumption from Global Navigation Satellite System (GNSS) observations, while it cannot capture vertical information of the ionosphere. This study proposes a new method to construct a double-layer ionospheric model based on constraints from a three-dimensional ionospheric model, whereby the bottom and topside ionospheric TEC can be represented by two spherical harmonic (SH) functions. The new improved model allows two SH functions to capture the spatiotemporal TEC variations across the vertical range of the ionosphere. The determination of the two thin layer heights (TLHs) in the double-layer model is achieved through minimum mapping function error. Moreover, the performance of the new model is validated using GPS, BDS, and Galileo data from the International GNSS Server (IGS) Network, and compared with the global ionospheric map (GIM). During the experiment period, the results indicate that (1) the TLHs of the bottom and topside ionosphere exhibit distinct spatiotemporal trends with the optimal global heights as 350 km and 650 km, respectively; (2) the average relative accuracies of the bottom and topside ionospheric models are up to 86.80 % and 85.33 %, respectively; (3) the new model demonstrates an improvement of approximately 20–27 % in terms of TEC when compared to the GIM model, with the RMS better than 4.64 TECU, 2.99 TECU, and 3.61 TECU in the low, middle, and high latitudes, respectively; and (4) with the increase of geomagnetic activity, the performance of the double-layer model shows a slight decline, but its relative accuracy can still reach over 84.8%.

The precise 3-D ionospheric electron density (IED) modeling is in general difficult due to costly and insufficient observations in the ionosphere. The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission has provided a significant opportunity for estimating the IED profile during the past 15 years. In this article, a precise empirical 3-D global electron density model (GEDM) from COSMIC GNSS radio occultation (RO) data is developed based on Fourier expansion and principal component analysis (PCA) and evaluated with independent datasets. Each profile is described by five essential parameters: the mean scale height Hm, bottom and topside slopes of the scale height (a1 and a2), peak density of the F2 layer (NmF2), and height of the F2 layer peak density (hmF2). The GEDM model can provide IED at any local time (LT) (0–24 h), F10.7 (70–150 sfu), Kp (0–9), latitude (90°S–90°N), longitude (180°W–180°E), and altitude, especially in the F region. The model is validated by the incoherent scatter radar (ISR), International Reference Ionosphere (IRI)-2020, K-Band Ranging system (KBR) of the Gravity Recovery and Climate Experiment (GRACE), and the Global Ionospheric Map (GIM) TEC in 2010 and 2014. The results show that the GEDM model has preferable reliability and consistency with respect to the independent observations and the IRI-2020 model. The statistical error of the GEDM model is lower than that of the IRI-2020, particularly in the mid-latitude region. Furthermore, the GEDM model is capable of precisely capturing typical ionospheric features, such as the equatorial ionization anomaly (EIA), winter anomaly, and the annual anomaly.

A new method, which integrates multi-variable consisting of Soil Moisture (SM) Active Passive (SMAP)-derived SM and vegetation optical depth, the water seasonality, geolocation, digital elevation model, slope, and biomass as inputs and adopts the technique of Bootstrap Aggregation of Regression Trees (BARTs) is proposed for retrieving monthly surface water fraction (SWF) at a spatial resolution of 0.025° from Cyclone Global Navigation Satellite System (CYGNSS) data. The model is trained using Surface Water Microwave Product Series (SWAMPS) data with a coarser resolution of 25 km and then applied to CYGNSS data with an enhanced resolution of 0.025° to generate high-resolution water maps. The resulting CYGNSS SWF (CSWF) maps are evaluated by comparing them with other water data sources, namely SWAMPS, Global Surface Water (GSW), and Global surface water dynamics (GLAD), as well as ground measurements. A quadruple collocation analysis indicates that the CSWF results exhibit the lowest error variance among the four SWF datasets. Furthermore, additional testing with water level measurements demonstrates a strong correlation with station data and clear seasonal patterns. Notably, the CSWF estimates significantly improve spatial coverage compared to both optical data (GSW and GLAD) with enhanced spatial resolution and the coarser SWAMPS data. This study underscores the effectiveness and efficiency of CSWF estimates, highlighting their potential as a valuable complement to existing microwave- and optical-based surface water products.

The use of the Cyclone Global Navigation Satellite System (CYGNSS) for soil moisture (SM) estimation is of interest. However, the advantage of the variable resolution of CYGNSS was not fully utilized, leading to the loss of detailed information. Geographically Weighted Regression (GWR) permits the co-existence of diverse spatial relationships across different geographic regions, with the regression coefficient varying spatially rather than being globally constant, thus enabling coefficient adjustments within specific spatial boundaries. Advanced GWR-based SM estimation offers a significant improvement over other competing estimation models. This study demonstrated that the CYGNSS with high temporal and spatial resolution has the potential for high-resolution independent SM retrieval.

The sensitivity of Cyclone Global Navigation Satellite System (CyGNSS) data to inland water bodies was well documented, however, its advantage over other sensors has seldom been reported. In this work, a semantic segmentation method is adopted for detecting inland water bodies using the CyGNSS data. The widely used LinkNet with the global attention mechanism (GAM) and atrous spatial pyramid pooling (ASPP), namely GA-LinkNet, is equipped to better extract water distributions. The performance comparison with an existing method and other deep networks proved the accuracy and effectiveness of this approach. Satisfactory agreement between the derived and referenced water masks was achieved, with the overall accuracy being 0.959 and 0.976, the mean intersection over union being 0.785 and 0.641, and the F1 scores being 0.879 and 0.781 for the Amazon and Congo regions, respectively. Furthermore, underestimation of water by the reference data was shown during evaluation, which proves the usefulness of the CyGNSS-derived water mask for improving the existing water mask products.

Above-ground biomass (AGB) is an important indicator for studying and understanding the ecological environment. However, the traditional AGB estimation methods using terrestrial LiDAR data still suffer from biases for different tree species or forest sites as well as low accuracy using the tree metrics. To overcome these challenges, this paper developed a novel model based on fractal geometry. Firstly, a theory was built to reveal the relationship between fractal geometry and AGB estimation. To realize this, three different theories were involved, including fractal theory, traditional AGB estimation theory and stem form factor theory. The allometric AGB estimation equation was then developed based on fractal geometry parameters (i.e., fractal dimension and intercept). To test the proposed model, 101 individual trees located at different forest sites with corresponding harvested reference AGBs were adopted. Experimental results show that the proposed model can achieve better AGB results when compared with traditional allometric equations built upon tree metrics. All the utilized accuracy indicators revealed that the proposed method was the best. Relative root mean square error (RMSE) was improved by 53%, 22% and 18% when compared with traditional allometric models built upon diameter at breast height (DBH), tree height and the combined two variables (DBH and tree height). Furthermore, the performance of the developed model was also analyzed towards different tree species and different leaves on or off conditions. Results indicated that the developed model can produce satisfactory performance.

The El Niño-Southern Oscillation (ENSO) event often causes natural disasters in mainland China. Existing quantitative analysis of ENSO event’s effects on climate change in mainland China is insufficient. The monthly scale prediction effectiveness of ENSO events is still low. Global Navigation Satellite System (GNSS) can estimate zenith tropospheric delay (ZTD) with high accuracy, which can study ZTD responses to ENSO and improve the prediction accuracy of ENSO events. This study quantitatively analyzed the response patterns of GNSS ZTD time–frequency variation to ENSO events in mainland China. The monthly multivariate ENSO index (MEI) thresholds for GNSS ZTD anomaly response to ENSO events are (−1.12, 1.92) for the tropical monsoon zone (TPMZ), (−1.12, 1.61) for the subtropical monsoon zone (SMZ), (−1.19, 1.62) for the temperate monsoon zone (TMZ), (−1.26, 1.64) for the temperate continental zone (TCZ), and (−1.22, 1.72) for the mountain plateau zone (MPZ). The ENSO event causes the amplitude of the nine-month variation period to decrease and the amplitude of the 0.8–3-month period to increase for the GNSS ZTD in mainland China. Furthermore, a forecasting model is proposed by integrating fast Fourier transform and long short-term memory extended (FFT-LSTME). The model uses monthly MEI as the primary input and the GNSS ZTD reconstruction sequence that responds to ENSO as the auxiliary input. It can predict ENSO events in the next 24 months with an index of agreement (IA) of 91.56% and a root mean square error (RMSE) of 0.25. The RMSE is optimized by 70.48%, 43.95%, and 11.6% when compared with radial basis function (RBF), LSTM, and FFT-LSTM.

Global Navigation Satellite System-Reflectometry (GNSS-R) is a remote sensing technique and can be regarded as a bistatic radar system. GNSS-R uses GNSS signals as signal sources and obtains the Earth's surface environmental parameters, such as soil moisture (SM), by receiving the L-band microwave signal reflected from the Earth's surface. However, the surface vegetation could be one of the main factors influencing the accuracy of GNSS-R land applications since the plants, including branches and leaves, attenuate the GNSS signal. Also, the evaluation of signal attenuations caused by plant canopy is quite difficult. In this paper, we present a sensitivity study of received GPS signals (L1 and L2 bands) to the vegetation leaf area index (LAI) over different types of plants. The relationship of GPS signal Signal-to-noise ratio (SNR) attenuations (above-canopy and below-canopy) versus LAIs is established through field experiments. The results show that the SNR received at the L2 band is with a larger standard deviation (SD) than at the L1 band for each satellite. The sensitivity of L1 and L2 bands signal to LAI is revealed, which shows a larger sensitivity and a relatively good Person correlation coefficient (R) for lower vegetation biomass. In addition, the sensitivity of the L2 band signal to LAI is lower than the L1 band signal, and with a lower R. This study is significant for improving the quantitative representation of error estimations in GNSS-R SM retrieval.

The Moon-based Earth Radiation Observatory (MERO) has the potential to complement current Earth Radiation Budget (ERB) missions by providing higher temporal resolution data, especially for the Earth polar regions. Regarding the MERO mission design, quantifying its mixed-pixel-induced uncertainty is crucial, which occupies an important part in the MERO inherent systematic errors. However, current knowledge about this MERO mixed-pixel-induced uncertainty is still limited. In this study, we proposed a MERO 2-endmember-mixed-pixel error quantification method and explored such errors in the land-snow, land-waterbody and waterbody-snow mixing scenarios. Results indicate that the land-snow mixing leads to the biggest measurement errors, which are as large as 4.02% and 7.98 % for the Earth top of the atmosphere (TOA) outgoing solar-reflected shortwave radiation (OSR) and outgoing thermally-emitted longwave radiation (OLR) fluxes respectively. The waterbody-snow mixing caused the secondly largest measurement error with TOA OSR maximum of 3.01% and TOA OLR maximum of 7.08 %. The land-waterbody mixing results in the least measurement error with TOA OSR maximum of 0.79% and TOA OLR maximum of 0.41 %.

This work presents a novel scheme to retrieve soil moisture (SM) from the Cyclone Global Navigation Satellite System (CyGNSS) data, which is accomplished by using a bagged regression trees (BRT) algorithm with the inputs being the CyGNSS-derived products, the corresponding geolocation, and associated climate type. This algorithm is validated with thein situhourly SM data acquired by China’s automatic SM observation stations throughout the year 2018. High consistency between the retrieved SM results and the measured SM is achieved, with a correlation coefficient of 0.86 and a root-mean-square error of 0.05 cm3/cm3. The results obtained in this work indicate that the proposed BRT-based method can effectively estimate SM from CyGNSS data in different scenarios of various station locations and climate types in a near real-time manner.

As the significant error sources influencing the Global Navigation Satellite System (GNSS) positioning, navigation, and timing (PNT) services, ionospheric delay and satellite hardware delay should be properly calibrated. Particularly, the pseudorange observable-specific signal bias (OSB) is convenient and can be directly corrected in the raw pseudorange measurement. In this work, we present a novel single-frequency ionospheric-free-half precise point positioning (PPP) (SFPPP2) approach for ionospheric studies, in which the ionospheric vertical total electron content (VTEC) and satellite OSB are isolated from the slant ionospheric observables by means of the ionospheric multilayer mapping function (MF). The computation and parameterization methods of the ionospheric VTEC and satellite pseudorange OSB are present. To validate the effectiveness and reliability of the novel method, we investigate and compare the performance of the single-frequency ionospheric-float PPP (SFPPP1), SFPPP2, and dual-frequency ionospheric-float PPP (DFPPP1) solutions for ionosphere sensing. The analytical results indicate that the novel approach can extract the slant ionospheric observables with the accuracy of submeters. The accuracy of the estimated ionospheric VTEC by the single-frequency PPP approaches is in the submeter level, which exhibits a slightly worse accuracy than that from the dual-frequency PPP solution. The estimated ionospheric VTEC accuracy is improved with the multilayer MF compared with the single-layer MF. The estimated BeiDou Navigation Satellite System (BDS) pseudorange OSB with the proposed SFPPP2 approach is stable, reliable, and of the high accuracy, and the rms’s with respect to Chinese Academy of Sciences (CAS) product for C2I and C6I signals with single-layer and multilayer MFs are 0.40, 0.41, 0.60, and 0.63 ns, respectively. The proposed PPP approach can retrieve the VTEC and satellite pseudorange OSB by mass-market receivers for the GNSS users.

Accurate and high-frequency sea level monitoring is of great importance in ocean environments and global climatic studies, but traditional techniques have their respective limitations. In the last decades, the application of Global Navigation Satellite System Multipath Reflectometry (GNSS-MR) in sea level monitoring has developed rapidly. Recently, more available GNSS signals are expected to bring new opportunities to improve its performance and achieve high spatial-temporal resolution. In this paper, a new algorithm is developed to optimize the method of multi-GNSS multipath reflectometry and improve the precision and sampling rate for GNSS-MR sea level monitoring. In order to make full use of the short-term multipath oscillation information, a sliding window is used to collect the SNR sequences. A weighted iterative least-square method is introduced to combine the selected SNR observations of GPS, GLONASS, Galileo, and BDS systems, and retrieve sea level with 10-minute intervals at BRST station for one year. A novel index called Local Kurtosis (LK) is proposed, which can be used to evaluate the quality of the Lomb-Scargle periodogram (LSP) and design the weight matrix in the least-square combinatorial process. Compared to using individual signals, the optimized combination algorithm decreased the root mean square error (RMSE) by 78%, from 0.610 m to 0.134 m, and increased the correlation coefficient R2from 0.851 to 0.992. In addition, the tidal constituents monitored by multi-GNSS-MR and tide gauge are highly consistent, demonstrating that the multi-GNSS-MR can accurately retrieve daily and subdaily tidal constituents of periods longer than 10 min.

Global Navigation Satellite System-Reflectometry (GNSS-R) can retrieve Earth's surface parameters, such as soil moisture (SM) using the reflected signals transmitted from GNSS constellations. GNSS-R has advantages of non-contact, large coverage area, real-time, and continuity. The CYclone GNSS (CYGNSS) data used for SM retrieval have generated considerable interests. In this paper, estimating SM on a global scale is performed using machine learning (ML) regression. The the optimal XGBoost predicted model with root mean square error (RMSE) of 0.064 cm3/cm3is adopted. In addition, satisfactory daily SM estimation outcome with an overall correlation coefficient value of 0.86 is achieved at a global scale.

In this paper, a new scheme is proposed for simultaneously retrieving soil moisture (SM) and vegetation optical depth ($\tau$), solely from the Cyclone Global Navigation Satellite System (CyGNSS) data. This work is accomplished by employing two pre-trained neural networks as well as a brute-force searching. By adopting the proposed method, the posterior$\text{SM}/\tau$can be estimated merely using the CyGNSS data, free from other auxiliary data. Satisfactory agreements between the retrieved and referred$\text{SM}/\tau$illustrates the capability of CyGNSS as a new independent source for estimating pantropical SM and$\tau$.

Elastic least-squares reverse-time migration (ELSRTM) yields subsurface high-resolution elastic images from multicomponent seismic data. For ocean bottom seismic (OBS) acquisitions, both pressure and displacement components are measured using four-component (4C) modern receivers. However, these 4C data cannot be effectively simulated and migrated using conventional ELSRTM approaches due to the limitation of the standard elastic wave equation. For this reason, we propose a new ELSRTM method based on a modified acoustic-elastic coupled (AEC) equation. Owing to this equation, either the prediction or the migration of OBS 4C data can be implemented using a one-time wavefield simulation. Incorporation with a perturbation imaging condition, the proposed method can invert for P- and S-wave velocity and density and generate amplitude-preserving images. Besides, it can provide subsurface impedance reflectivity models by stacking the obtained multi-parameter images. In the least-squares migration, a preconditioned conjugate gradient algorithm is implemented based on a multi-parameter diagonal Hessian. Numerical experiments on an uncorrelated layer model and a portion of the Southern Yellow China sea model can demonstrate the effectiveness of this method on improving imaging resolution and accelerating convergence rate.

In this paper, the sensitivity of Cyclone Global Navigation Satellite System (CYGNSS) data to precipitation is investigated. First, the soil moisture (SM) is estimated from the CYGNSS and Soil Moisture Active Passive (SMAP) data based on a three-layer model. Next, the correlation between the CYGNSS-derived global SM and the precipitation rate is analyzed. The CYGNSS data collected over the land surfaces within ±37° (latitude) during the whole year of 2018 are employed and the CPC Merged Analysis of Precipitation (CMAP) data are adopted. Experimental evaluation proves the sensitivity of CYGNSS-derived SM to precipitation, indicating possible applications of CYGNSS data for detecting rainfall events and estimating precipitation.

In this paper, an approach for estimating soil moisture (SM) from Cyclone Global Navigation Satellite System (CYGNSS) data is developed. Here, a three-layer model of air, vegetation cover, and soil is proposed. In application, the surface reflectivity along with its statistics are derived from the CYGNSS data and the ancillary vegetation opacity data are obtained from Soil Moisture Active Passive (SMAP). These variables are adopted for estimating SM using the devised linear regression function. Through comparing with the reference SM data obtained from SMAP, CYGNSS-derived SM demonstrates its satisfactory accuracy and plausible global coverage. The achieved results prove CYGNSS as an efficient complementary tool for global daily SM sensing.

This study tested and compared the suitability of SPOT-5 image and LiDAR data both separately and combined for the classification of the urban area using Rotation Forest (ROF) classifier. Experimental results revealed that the integration of the SPOT-5 image and LiDAR data classification scheme gave better classification accuracies, when compared to the classification schedules using one of the two solely. Furthermore, RoF classifier produced better classification results than that of other two classifiers (i.e., SVMs (Support Vector Machines) and RF (Random Forests)). Finally, it should be noted that RoF classifier provided an effective way of combining SPOT-5 image and LiDAR data for classification, which is robust to the important parameter M (i.e. the number of features in each subset).

Mare Moscoviense (148°E, 27°N) is one of the few large maria on the lunar farside. In this paper, the China Chang'E-2 Microwave Sounder (CELMS) data were employed to study the microwave thermal emission features of Moscoviense basin. The findings are as follows. (1) The four basaltic units present distinctly different TBperformances at noon and midnight; (2) A new understanding about the basaltic units is made according to the classification results using the maximum likelihood method; (3) The substrate temperature of Moscoviense basin is likely much higher than what we know. The results will be of great significances to understand the mare volcanism on the lunar farside.

In the past three decades, GNSS-R/IR has emerged as a new and promising remote sensing technique. Its applications have extended from ocean surface to land scenario. However, to our knowledge, few works focus on the soil salinity detection by GNSS-R/IR. For the first time, this paper studies the feasibility of soil salinity detection by using the microwave scattering models. As the reflected signals or interferometric signals of the GNSS constellations are collected from the first Fresnel zone, Fresnel reflectivities at both linear and circular polarizations are used to simulate the coherent scattering of soil salinity. In order to consider the azimuth angle effects, i.e. to consider the scattering coming from the specular cone, the advance integral equation model is employed. Our scattering simulations at both linear and circular polarizations indicate that soil salinity is detectable at some angles. The polarizations and angles should be carefully considered to find the best combinations for detection.

GNSS-R (Global Navigation Satellite System-Reflectomery) has emerged as a new and promising remote sensing techniques. In the recent two years, several airborne experimental campaigns have been carried out to demonstrate the possibility of wetland monitoring by GNSS-R. However, to our knowledge, there are no theoretical models for this new application. As for GNSS-R, whose working mode is an essential bistatic radar, the received power is DDM (Delay Doppler Map). A wetland GNSS-reflections simulator is presented. This model is based on the integral bistatic radar equation by taking into consideration the wetland, system impulse responses and geometry observation system. The wetland parameters affect the DDM waveforms through the BRCS (bistatic radar cross sections), which is a function of soil (surface roughness, soil moisture, soil freeze/thaw state) and vegetation parameters (vegetation moisture, biomass and growth). The simulator contains the inundated wetlands including two kinds of Aspen, whose effects on the final GNSS reflected power (DDM) can be predicted by the simulator. The developed simulator can assist as mechanism tools for wetland monitoring with GNSS-R technique, and this model can provide theoretical backup for wetland mapping, data analysis and experiment design.

In the past three decades, GNSS-R (reflectometry) /IR (interferometric reflectometry) has emerged as a new and promising remote sensing technique. Many researchers have paid much attention and dedicated on its prosperity and development. As for land surface, its applications have extended from soil moisture to snow depth (snow water equivalence) and vegetation parameters retrievals. To our knowledge, we have reviewed the progress on GNSS-R/IR land surface scattering models and presented our recent research on this subject. The developments of our bistatic scattering models, DDM simulators and forward GNSS multipath simulators are presented. The developed land surface models are oriented towards bare or vegetation- covered soil moisture, vegetation biomass/growth. Also the new application of GNSSR/IR on soil freeze/thaw process detection is also included in our models.

The global positioning system (GPS) can navigate and position at any time and weather conditions, but subject to a variety of error sources. The most important one of these errors is the ionospheric delay when the GPS signals propagate through the ionosphere. The first order ionospheric effects can be removed using dual frequency GPS receivers, while the higher order ionospheric (HOI) effects are much smaller than the first order effects, which are generally neglected. However, under highly active solar activities condition, high order ionospheric effects may be big and should be eliminated. In this study, the high order ionospheric effects on 3D GPS coordinate components was investigated with 30 days (June 2011) of GPS data at 8 GPS stations in Turkey. Firstly, the high order ionospheric effects were corrected in the RINEX observation file, and then raw and corrected observations were processed in GAMIT software to obtain precise coordinate components. Results show that average 10, 7 and 24 millimeters changes occurred in the north, east and up components by high-order ionospheric effects, respectively. During the peak solar activity days in 2011, it is observed that the high-order ionospheric effects in the north, east and up components can reach 18, 11 and 43 millimeters change, respectively.

This study uses high-resolution blended anomaly data from The United States (US) National Oceanic and Atmospheric Administration (NOAA) in order to analyze Sea Surface Temperature (SST) variability in the Black Sea over the 1981-2015 period. The linear regression indicates that there has been a slight increase in SST with a rate of 0.04±0.005 °C/yr during this all period. However, the results show that there was a surface cooling in the 1980s followed by the strong warming after this period. Since 1990s, especially for summer sea water temperatures have been consistently higher than those of previous years. On the other hand, the observed sea level rise during 1993-1999 and 2008-2014 is highly correlated with sea surface temperature. Furthermore, monthly averages of the SST anomalies reveal that the maximum SST in the Black Sea occurs in August, while its minimum occurs in January-March. The amplitudes of the annual and the semi-annual cycles of SST anomalies are about 0.8 °C and 0.2 °C, respectively.

Monitoring and understanding geophysical processes in the thermosphere is primordial for space physics, low Earth orbiters, and ground-based technologies. In the last half century, thermospheric variations, anomalies, and climatology have been investigated and reported, but were limited due to lack of observations and large uncertainty in the models. Today, Global Navigation Satellite Systems (GNSS) and accelerometers on small satellites can sense neutraldensity and wind variations with unprecedented accuracy, which contribute to understand thermospheric variations and improve the current empirical and physical models. In this paper, an overview of past and present developments and efforts in sensing and modeling thermospheric density and wind variations is presented, as well as the future challenges and perspectives for GNSS and accelerometers on small satellites.

The precipitable water vapor (PWV) can be estimated from dual-frequency GPS observations, which has been widely used for atmospheric study and weather forecasting. However, high-order ionospheric delays are normally ignored in GPS applications. In this paper, high-order ionospheric effects on GPS-estimated precipitable water vapor are investigated from 30 days of GPS data in June 2011 at 8 GPS stations in Turkey. Firstly, the second- and third-order effects on GPS data are corrected in RINEX using IGRF11 model, and then the precipitable water vapor (PWV) is obtained from raw and corrected RINEX data with GAMIT software. Results show that high-order ionospheric effects are up to 1 millimeter on PWV during this period. Solar activity is an important effect on ionospheric activities. When data during the peak solar activity days are used for same stations in 2011, high-order ionospheric effects on PWV is increasing, while low solar activities, high-order ionospheric effects are much smaller.

The Mw=8.8 Maule earthquake occurred on 27 February 2010 near the coast of central Chile, which caused large economic and lives losses. Meanwhile, the earthquake triggered a tsunami with devastating several coastal towns in south-central Chile and damaging the port at Talcahuano. Several geodetic observations data and methods were used to investigate seismic ionospheric disturbances and coseismic crustal deformations on this earthquake, such as InSAR and GPS measurements. However, the precursor and detailed rupture properties of this earthquake are still not clear. In this study, tropospheric anomalies following the Mw=8.8 Maule earthquake are investigated using zenith tropospheric delay (ZTD) computed from Global Positioning System (GPS) observations. Results show zenith tropospheric delay anomalies are found during and after the main shock at CONZ that is the closest GPS station to the epicenter and VALP station that is close to the fault rupture. The possible mechanism of seismic-tropospheric disturbances is discussed, which will be further studied in near future with more data.

Up to now, it is still difficult to obtain reliable precursors and to understand the detail and nature of earthquakes from traditional technique observations. Nowadays, the ionospheric total electron content (TEC) from ground-based global navigation satellite systems (GNSS) and space-borne GNSS Radio Occultation can be used to investigate the seismo-ionospheric disturbances, which may provide insights on the earthquake. In this paper, seismo-ionopsheric anomalies from GNSS observations are investigated, particularly for recent bigger earthquake of the 2011 Mw 9.1 Tohoku Earthquakes and global earthquakes with Mw≥5.0 from 1998 to 2014. Obvious disturbances in TEC are found about 4 hours before the main shock of 2011 Tohoku earthquake. Comparing to pre-seismic effects, post-seismic is more apparent. In addition, statistical analysis of 10-day TEC data before global Mw≥5.0 earthquakes shows significant enhancement 5 days before an earthquake of Mw≥6.0 at a 95% confidence level. Sharpness in cumulative percentages is evident in seismo-ionospheric disturbance prior to Mw≥6.0 earthquakes. The relative values reveal high ratios (up to 2) and low ratios (up to -0.5) within 5 days prior to global earthquakes for positive and negative anomalies. The anomalous patterns in TEC related to earthquakes are possibly due to the coupling of high amounts of energy from earthquake breeding zones of higher magnitude and shallower focal depth.

Global mean sea level change (GMSL) is one of the most significant effects of climate change. However the contribution of land water and glaciers to GMSL is difficult to quantify due to lack of in situ measurements and complex dynamics of galcier melting. In this study, we use the GRACE Release 5 (RL05) data from 2003.01 to 2012.12 to quantify the land water and glaciers contributions to the sea level rise as well as their uncertainties. The contribution of land water to the sea level change is 0.15±0.25 mm/yr, and the contribution of glaciers is 1.94±0.29 mm/yr. The impact of the degree 1 and degree 2 Stokes gravity field coefficients computed from different GRACE analysis centers (the Jet Propulsion Laboratory (JPL), the University of Texas, Center for Space Research (UTCSR) and the GeoForschungsZentrum (GFZ)) on the sea level change are investigated and discussed. The impact of first-order coefficient to the mass-induced sea level variations is 0.10±0.08 mm/yr, and the second-order coefficient to the mass-induced sea level variations is 0.16±0.04 mm/yr. The results from CSR are consistent with GFZ results, while the JPL's results are slightly smaller.

In this study, lake level changes in East Africa (i.e. Lakes Victoria, Tanganyika, and Malawi) are investigated using satellite gravimetry and altimetry as well as hydrological models and Landsat data from January 2003 to January 2013. The fitted trend of the time series of altimetric lake height shows a significant decline in the average lake level for all of the three lakes between 2003 and 2006 with different rates. The main reason of this significant declination is the drought happened in much of East Africa during this period of time. After 2006, the lake level started to increase rapidly until 2009 for Lakes Victoria and Malawi and until 2010 for Lake Tanganyika. After which it shows a slight increase for Lakes Victoria and Tanganyika while it shows a significant decrease between 2010 and 2012 with -215 mm/yr for Lake Malawi. For each lake, the STL decomposition plot of the monthly GRACE TWS shows the same pattern as the time series of altimetric lake height. It shows a significant decrease in the TWS between 2003 and 2006 with the max rate in Lake Victoria, followed by a significant increase until 2009. After which it shows a slight increase for Lake Victoria while Lakes Tanganyika and Malawi experienced a reduction between 2010 and 2013. Water discharge in East Africa is also similar with results from Landsat-7 ETM+ data.

The Mw= 7.2 earthquake occurred in northern California on June 15, 2005 as results of strike-slip fault. Dramatic ionospheric disturbances are detected by GNSS measurements collected by UNAVCO in the southeast of epicenter just about 10 minutes after the onset, while no obvious seismic ionospheric disturbance is observed in the northwest. Seismic ionospheric disturbances with two speed modes, i.e. 1.24km/s and 1.97 km/s are extracted from GPS TEC with different satellite elevation angles. Based on their occurrence epochs and propagation speeds, it is believed that the slow mode perturbation is the acoustic wave propagating horizontally at ionospheric height induced by the fault rupture in focal regions. Due to the relatively smaller earthquake for co-seismic ionospheric effects and good GPS ionospheric observation geometry, seismic ionospheric disturbances from acoustic wave at the ionospheric height and Rayleigh wave are distinguished clearly in near-field.

Snow plays a key role in water cycle, while snow monitoring has some limitations from traditional ground technique, e.g., low resolution and high costs. Recently it has been demonstrated to monitor snow depth by GPS multipath reflectometry (GPS-MR). However, most previous studies mainly used new L2C Signal-Noise-Ratio (SNR) data that are available after 2005, while some older GNSS receivers cannot track this signal and only L1C/A signals before 2005. This paper aims to overcome above limitations and old L1C/A SNR data are used to retrieve snow depth at three IGS stations with in situ measurements. Snow depth estimations from L1C/A SNR data are evaluated and validated with in situ snow depth data, showing a good correlation. The result indicates geodetic GPS observations with L1C/A SNR records can also estimate snow depth well. In addition, the effect of elevation angles is discussed and results of the different range of elevation angles are further compared.

The Tropopause in Tibet has complex and variable characteristics due to its harsh natural conditions, while it is hard to monitor the tropopause in Tibet from traditional techniques. Nowadays, the high resolution data from GPS Radio Occultation provide a unique opportunity to monitor the tropopause variation, particularly over Tibet. In this paper, the high resolution cold-point tropopause (CPT) in Tibet are investigated using temperature profiles derived from Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) GPS radio occultation measurements from June 2006 to Feb 2015. The structure of upper troposphere and lower stratosphere (UTLS) is analyzed, which provides observations of stratosphere and troposphere exchange (STE) over the Tibetan Plateau. The multiple tropopauses occur in winter with a high frequency over the Tibetan Plateau. The reasons for the multiple tropopauses and temperature variations are the variations of zonal winds or atmospheric gravity waves. Multiple tropopause events during winter season are associated with tropopause folds near the subtropical westerly jet. The multiple tropopauses consistently varied with the movement of the jet. The multiple tropopauses become a single tropopause with the development of the monsoon, which helps to forbid the atmosphere constituents to defuse in stratosphere and upper atmosphere. In addition, the temperature of CPT increases from the last decade. These zonal winds not only affect the temperature but also affect the heights. Meanwhile Gravity waves activity increases in the Tibetan Plateau. Atmospheric Research reanalysis and MERRA/AIRS winds shows that the waves are primarily forced by strong flow across the topography. The variation of tropopause in the Tibet plateau is mostly associated with the wind components.

The GPS ionospheric delay was considered as an error source, and now it can be determined as the useful ionospheric parameter, e.g., total electron content (TEC) and electron density, particularly from high spatial resolution GPS Radio Occultation (RO). However, second-order ionospheric delay was normally ignored in ionospheric electron density estimation from GPS RO. In this paper, the second-order ionospheric effect on ionospheric electron density estimation is investigated from COSMIC GPS Radio Occultation. Firstly, we use the ionospheric Nequick2 model and IGRF11 geomagnetic field model to calculate the second-order ionospheric delay and then analyze the characteristics of this error. Results show that the second-ionospheric delay has large effect with up to 800 electron/cm-3, which should be corrected in high-precision ionospheric electron density estimation from GPS radio occultation. The second-order ionospheric error affects the electron density at 250km-300km obviously. In addition, the second-order ionospheric effects are investigated at different RO azimuths, which show the maximum negative value at around 0° and the maximum positive value at around ±150°. Some possible errors and mechanism are further discussed.

Nowadays, GPS multipath can be used to estimate snow depth, soil moisture and vegetation growth using signal-noise to radio (SNR) data. Particularly with the modernization of GPS, one new signal, called L5, has been broadcasted by Block IIF satellites, the combination of three-frequency (L1, L2 and L5) observations may help remove the ionospheric delay errors and obtain better multipath signals. In this paper, the pseudorange and phase multipath are extracted from three-frequency GPS pseudorange and phase combinations at GANP station in Slovakia with available co-located snow data, which are used to estimate snow depth variations based on the GPS Multipath Refletometry (GPS-MR) theory. Snow depth estimations from three-frequency GPS pseudorange combinations are compared with in-situ observations, which shows a correlation of 0.83 and RMSE of 0.11m, while results from three-frequency GPS phase combinations have a little better correlation of 0.86 and RMSE of 0.08m. However, the results from dual-frequency (L1 and L2) combinations have a much lower correlation of 0.64 and larger RMSE of 0.16m. Therefore, it is better to estimate snow depth using three-frequency combinations than double-frequency measurements.

The geodetic-quality GPS receivers can be used for geophysical parameters retrieval, i.e. soil moisture, vegetation growth and snow depth, and it is called GPS-Interferometric Reflectometry (GPS-IR) remote sensing. In this paper, to detect the near surface soil freeze-thaw process using GPS-IR is evaluated for the first time. When the soil changes from frozen state to thawn state, the corresponding permittivities and reflectivities at RR, LR, VR and HR pol are changed. Triple-frequency effects are evaluated, for different frequencies, the modulations for SNR data are not the same, but within L-band, the observed surface has almost the same reflected properties (e.g. GPS L1 band, L2band and L5band). A GPS site in the EarthScope Plate Boundary Observatory (PBO) and a corresponding soil climate site in SCAN (Soil Climate Analysis Network) is used to examine the potential for near surface soil freeze-thaw process detection. Two representative days and a time series of SNR data show strong correlation with near surface soil temperature. Our initial results demonstrate that GPS-IR has the potential for bare soil freeze-thaw detection. Therefore, the new low-cost bare soil freeze-thaw monitoring networks are expected from geodetic GNSS network.

The Gravity Recovery and Climate Experiment (GRACE) and the Ice, Cloud, and land Elevation Satellite (ICESat) missions can estimate the ice mass changes in Greenland. The GRACE monthly time-varying gravity filed solution can be used to infer the redistribution of temporal redistribution with a limited spatial resolution of 300-500 km, while the ICESat measurements can estimate the elevation variations of ice-sheet with high-precision but have a limited temporal coverage and the assumption of snow/ice density when used to monitor the mass change of Greenland ice sheet. The combination of these two independent measurements will overcome each shortcoming, improving not only the resolution capability but also the reliability of estimated results of ice mass changes. In this paper, glacier variations in Greenland are investigated by joint GRACE and ICESat measurements. The ice mass change rate in Greenland is - 142.14 Gt/yr, and the acceleration is -16.28 Gt/yr2from GRACE during January 2003 to June 2007, while the total ice mass loss is at 173.03Gt/yr at the same period from ICESat measurements. The combined estimation of Greenland ice sheet loss rate is -157.59 Gt/yr fromGRACE and ICESat with more smooth and high resolution.

Evapotranspiration (ET) is one of the key variables in water cycle and ecological systems, whereas it is difficult to quantify ET variations from traditional observations in large river basins, e.g., Mississippi River basin (MRB). In this paper, a new geodetic tool, i.e., Global Positioning System (GPS), is used for the first time to estimate monthly ET variations at a regional scale. Based on the water balance equation, the monthly ET variation is estimated using the GPS-derived terrestrial water storage (TWS) from January 2006 to July 2015 in MRB. The annual amplitude of GPS-inferred TWS in MRB agrees well with the results of Gravity Recovery and Climate Experiment. The ET variations from the water balance approach agree well with the land surface modeling and remote sensing data. The correlation of GPS-inferred ET with other ET products is higher than 0.8, which indicates that the GPS-estimated ET well characterizes the ET variations in MRB. The annual amplitude of GPS-inferred ET variations is 47.9 mm/month, which is close to that from land surface modeling of North American Land Data Assimilation System, and a little larger than MODerate Resolution Imaging Spectroradiometer. The mean monthly ET reaches its maximum in June-July and its minimum in December, which is consistent with the periodic pattern of radiative energy in a year. Furthermore, the ET variations are mainly dominated by the temperature change in MRB.

Water vapor is one of the most variable atmospheric constituents. Knowledge of both the spatial and temporal variations of atmospheric water vapor is very important in forecasting regional weather and understanding the global climate system. The Moderate Resolution Imaging Spectroradiometer (MODIS) is the first space instrument to obtain precipitable water vapor (PWV) with near-infrared (nIR) bands and the traditional IR bands, which provides an opportunity to monitor PWV with wide coverage during both daytime and nighttime. However, the accuracy of PWV measurements obtained with IR bands is much lower than that with nIR bands. Moreover, seldom have studies been devoted to the calibrations of MODIS IR PWV. In this paper, the accuracy of MODIS IR water vapor product during the nighttime is assessed by ERA-Interim data, Global Positioning System, and radiosonde observations. Results reveal that the performance of MODIS IR water vapor product is much poorer than that from the other observations, and the MODIS IR PWV needs to be calibrated. As such, we propose a differential linear calibration model (DLCM) to calibrate the MODIS IR water vapor product during the nighttime. Case studies under both dry and moist atmosphere in midlatitude and equatorial regions are used to test and assess the performance of the DLCM. Results show that the DLCM can effectively enhance the accuracy of MODIS IR retrievals at nighttime. Furthermore, while the traditional least square model may over calibrate the MODIS IR PWV measurements occasionally, the DLCM can avoid that defect successfully.

Water vapor variations affect the interferometric synthetic aperture radar (InSAR) signal transmission and the accuracy of the InSAR measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) near infrared (nIR) water vapor product can correct InSAR atmospheric effects effectively, but it only works for the synthetic aperture radar (SAR) images acquired during the daytime. Although the MODIS infrared (IR) water vapor product owns poorer accuracy and spatial resolution than the nIR product, it is available for daytime as well as nighttime. In order to improve the accuracy of water vapor measurements from the MODIS IR product, a differential linear calibration model (DLCM) has been developed in this paper. The calibrated water vapor measurements from the IR product are then used for wet delay map production and nighttime overpass SAR interferogram atmospheric correction. Results show that the accuracy of the MODIS IR product can be improved effectively after calibration with the DLCM, and the derived wet delays are more suitable for InSAR atmospheric correction than original measurements from the IR product. Furthermore, a MODIS altitude-correlated turbulence model (MATM) is incorporated to correct the atmospheric effects from another descending ASAR interferogram. Results show that the MATM can reduce altitude-dependent water vapor artifacts more effectively than the traditional correction method without the need to incorporate the altitude information.

Over the last two decades, the repeated Interferometric Synthetic Aperture Radar (InSAR) has proven to be useful for accurate topographic mapping and ground-surface motion monitoring. However, atmospheric delay variations are one of main errors in SAR interferograms. Since the GPS stations are sparse and the water vapor product of multispectral sensors is sensitive to the presence of clouds in the Tibetan Plateau (TP), the water vapor delay distribution maps are estimated by integrating GPS Radio occultation (RO) observations and National Centers for Environmental Prediction (NCEP) data, which are used to mitigate the atmospheric phase delays in SAR interferograms. Results shows that GPS RO inferred wet delay together with NCEP model can generate more detailed wet delay distribution maps, especially in remote regions, which would be helpful to mitigate the InSAR tropospheric delay effectively.

Terrestrial water storage (TWS) is an important parameter in water resource manages and research of land-surface processes and hydrological cycle. However, the traditional instruments are very difficult to monitor global high temporal-spatial terrestrial water storage and its variability without a comprehensive global monitoring network of hydrological parameters due to high cost and high labor intensity. The recent Gravity Recovery and Climate Experiment (GRACE) mission provides a unique opportunity to directly measure the global TWS and its change at multi-scales from August 2002 to February 2011. In this paper, the global terrestrial water storages with monthly resolution are derived from approximate 10 years of monthly GRACE measurements (2002 August-2011 February), and their changes at seasonal and long-term scales are investigated and compared with GLDAS (Global Land Data Assimilation System) model. Results show that significant annual variations of TWS are found at the globe, agreeing well with GLDAS model estimates. The secular trends are also observed at specific areas, reflecting extreme climate events, e.g., ice melting in Antarctica, Greenland, Canadian Islands, Alaska, Himalayan and Patagonia glaciers, La Plata drought in South American as well as floods in North Amazon, while the GLDAS model cannot capture these signals well.

Robust seismic signals around the globe could estimate the gross nature of earthquakes, but the details are usually unclear due to the lack of near-field observations. Although ground measurements, e.g., GNSS/InSAR and strong motion measurements, provide unique insights on the kinematic rupture and nature of the earthquake, but the temporal-spatial resolutions are still limited. In this paper, GNSS atmospheric seismology is proposed and a case study of the 2008 Wenchuan earthquake is performed using ground GNSS measurements. Significant ionospheric disturbances are found at continuous GNSS sites near the epicenter with an intensive N-shape shock-acoustic wave propagating south-eastward, almost consisting with seismometer, indicating that the co-seismic ionospheric TEC disturbances were mainly derived from the main shock. Furthermore, the co-seismic tropospheric anomalies during the mainshock are also found, mainly in the zenith hydrostatic delay component (ZHD), which is supported by the same pattern of surface observed atmospheric pressure changes at co-located GNSS site that are driven by the ground-coupled air waves from ground vertical motion of seismic waves propagating. Therefore, the co-seismic atmospheric disturbances indicate again the acoustic coupling effect of the atmosphere and solid-Earth with air wave propagation from the ground to the top atmosphere.

The ionosphere has been normally detected by traditional instruments, such as ionosonde, scatter radars, topside sounders onboard satellites and in situ rocket. However, most instruments are expensive and also restricted to either the bottomside ionosphere or the lower part of the topside ionosphere (usually lower than 800 km), such as ground based radar measurements. Nowadays, GPS satellites in high altitude orbits (~20,200 km) are capable of providing details on the structure of the entire ionosphere, even the plasmasphere. In this paper, a Regional Ionospheric Mapping and Tomography (RIMT) tool was developed, which can be used to retrieve 2-D TEC and 3-D ionospheric electron density profiles using ground-based or space-borne GPS measurements. Some results are presented from the RIMT tool using regional GPS networks in South Korea and validated using the independent ionosonde. GPS can provide time-varying ionospheric profiles and information at any specified grid related to ionospheric activities and states, including the electron density response at the F2-layer peak (the NmF2) during geomagnetic storms.

Water vapor plays the key role in the global hydrologic cycle and climate change. However, the distribution and variability of water vapor in the troposphere is not understood well in the globe, particularly the high-resolution variation. In this paper, 13-year 2-h precipitable water vapors (PWV) are derived from globally distributed 155 Global Positioning System sites observations and global three-hourly surface weather data and six-hourly National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis products, which are the first used to investigate multiscale water-vapor variability on a global scale. It has been found that the distinct seasonal cycles are in summer with a maximum water vapor and in winter with a minimum water vapor. The higher amplitudes of annual PWV variations are located in midlatitudes with about 10–20$\pm$0.5 mm, and the lower amplitudes are found in high latitudes and equatorial areas with about 5$\pm$0.5 mm. The larger differences of mean PWV between in summer and winter are located in midlatitudes with about 10–30 mm, particularly in the Northern Hemisphere. The semiannual variation amplitudes are relatively weaker with about 0.5$\pm$0.2 mm. In addition, significant diurnal variations of PWV are found over most International Global Navigation Satellite Systems Service stations. The diurnal (24 h) cycle has amplitude of 0.2–1.2$\pm$0.1 mm, and the peak time is from the noon to midnight. The semidiurnal (12 h) cycle is weaker, with amplitude of less than 0.3 mm.