High-precision underwater navigation is a prerequisite for underwater vehicles to explore the marine environment and resources. Inertial navigation system (INS), Doppler velocity logger (DVL) and Pressure sensor (PS) onboard the underwater vehicles are favored for integrate due to their effectiveness and user-friendly configuration. Specifically, the velocity measurement from the DVL is important to reduce the accumulated error of the INS. Different from the traditional filter-based fusion solution, we firstly introduce a Factor graph optimization (FGO) framework to integrate the INS/DVL/PS navigation system. Since the DVL observations often experience outliers and outage due to the harsh sea condition and range limitation, it can lead to rapid position drift and error accumulation in the navigation system. Thus, we develop an outlier detection scheme based on the improved interquartile range method, which combines sliding window and dynamic threshold adjustment of the propeller revolutions per minute (RPM). Then, we propose a DVL velocity prediction method based on the nonlinear least squares (NLS)-Transformer-LSTM model. We refine the RPM by NLS and use it as one of the key features in the prediction model. Finally, the constructed system is compared with several classical Kalman filters through both simulated and measured experiments. The performance of the NLS-Transformer-LSTM model is comprehensively evaluated. The results indicate that the system can provide more accurate pseudo-DVL velocity estimates. This capability ensures more stable and reliable underwater navigation accuracy during DVL outages. Consequently, it offers strong support for autonomous underwater vehicle applications in complex marine environments.

Seafloor anchor nodes are a crucial component of Internet of Underwater Things (IoUT), which is designed to provide surface and underwater users with positioning, navigation, timing and communication (PNTC) services. Traditional anchor node positioning typically uses cross or circular shaped deployment. However, accurate positioning of underwater anchor nodes becomes a challenge task due to the nonuniform distribution of underwater sound speed, which is seldom considered in the literature. Due to the complexity of distance measurement caused by curved sound lines, the optimization of reference node deployment based on curved sound lines is worthy of further research. This article focuses on the optimal reference node deployment strategies for time-of-arrival (TOA) localization of anchor nodes in IoUT networks in 3-D underwater space. We adopt the criterion that minimizing the trace of the inverse Fisher information matrix (FIM) to facilitate the optimal reference nodes deployment with Gaussian measurement noise, the magnitude of which is positive related to the real signal propagation path. It is proved that the optimal reference node deployment position exhibits central symmetry with respect to the target, which greatly simplifies the analysis of the optimal node deployment position. Then, a semi-closed form of the optimal pitch angle is derived to determine the optimal geometries. To demonstrate the findings in this article, we conducted both simulations and real-world experiments on underwater anchor node positioning. Both the simulation and experiment results demonstrate that the proposed deployment scheme significantly improve the positioning accuracy.

Tidal information is a valuable parameter for scientific studies and navigational safety. Despite traditional tide stations and satellite altimeters, shipborne Global Navigation Satellite System (GNSS) altimeters can provide an alternative method for instantaneous measurements. However, due to ship hydrodynamics and draft variations, especially for large vessels, the baseline between the GNSS positioning antenna and the sea surface is always unavailable or less accurate. This case study presents a novel ship-based altimetry method using GNSS-R phase altimetry, which is capable of accurately monitoring tidal information on a moving ship platform. The delay difference between the direct and reflected GNSS paths is obtained from the signal phase difference generated by open-loop tracking through a software-defined receiver. Spectral analysis was used to further solve the integer ambiguity problem of phase measurements, and then, accurate tidal information was obtained based on high-precision GNSS positioning. To evaluate the performance of the system, a case study of a shipborne experiment was conducted. The results show that the ship-based GNSS-R altimetry system can accurately measure the sea surface height variation. The root-mean-squared error (RMSE) is within 3.0 cm compared to the in situ value. This case study demonstrates the potential of ship-borne GNSS-R phase altimetry as an effective and accurate method for tidal monitoring in dynamic maritime environments.

Global navigation satellite system (GNSS) refractometry is a new technique that utilizes two pairs of GNSS receivers and antennas to collect the refracted signal in the medium and the direct signal in the air, respectively. In the previous study, a linear model that uses wheat height, air temperature, and the amplitude ratio (AR) as inputs was utilized to estimate the volumetric water content (VWC) of wheat. In this study, a second-order nonlinear function is utilized to describe the relationship among GNSS AR, wheat VWC, wheat height, and air temperature, leading to an improved model for estimating wheat VWC. The function coefficients are determined by exploiting the least-squares method to the field measurements collected from April 5, 2023, to June 5, 2023. Once the function coefficients and inputs are obtained, the model can be easily used to calculate the wheat VWC. The model was validated using an independent dataset of field measurements collected from April 5, 2024, to June 5, 2024. The results show that the improved model performs significantly better than the previous linear model, and the root-mean-square (rms) error of the improved model-based GNSS wheat VWC estimation is 0.224 kg/m3 when the in situ wheat VWC ranges from 1.404 to 6.521 kg/m3. This model can help precisely control the timing and water usage of agricultural irrigation, thereby optimizing water management for crops.

This paper firstly reports the combined technique of GNSS positioning and GNSS-IR (GNSS+IR) for imaging the underground coal mining induced ground subsidence deformation over an area of ~10000m2based on a single GNSS station collected observations. The GNSS positioning is utilized to measure the movements of the GNSS antenna; and phase of the reflected GNSS SNR series is utilized to calculate vertical and horizontal distance from the ground specular reflection point to the antenna. The ground subsidence and plane coordinate of the ground specular reflected point can be obtained based on the GNSS antenna movements and the vertical and horizontal distance. An analytical function is developed to describe ground subsidence around the GNSS station; the function coefficients can be estimated by using the least-squares-method to the estimations of the ground subsidence and plane coordinate at the ground reflection points. After obtaining the coefficients, subsidence deformation around the GNSS station can be imaged based on the analytical function. The preliminary results show that there is a good agreement between the proposed method based results and the reference data sets, with the RMSE less than 5cm when the in-situ ground subsidence is in the range from 0cm to 300cm.

Currently, soil moisture (SM) is mostly measured by conventional contact methods, such as dry weighing and time domain reflectometry (TDR). Global navigation satellite system reflectometry (GNSS-R) is a promising and powerful technique for measuring SM at higher resolutions and lower costs than conventional methods. It can be employed in several observation geometries, including ground-based, airborne, and space-borne instruments. To investigate the potential of retrieving SM by drone-based GNSS-R, we selected the L5-band signal of the geostationary component of the Quasi-Zenith Satellite System (QZSS) as a source. We equipped a drone with two antennas connected to a raw IF data collector and conducted two flights over bare soil sparsely covered with low wheat sprouts at an experimental farm in China. Approximately 5 h of data were collected and processed using a software-defined receiver to calculate the L5-band signal power ratio (reflected/direct). From this power ratio, SM values were subsequently derived every second then smoothed within 5-min intervals. The GNSS-R retrieval accuracy analysis indicated its dependence on both SM and drone flight height. The retrieved data were more stable at lower heights under dry soil conditions and at higher heights under wet conditions, achieving RMSE retrieval errors of 0.022 (SM =0.1805 m3/m3) and 0.056 (SM =0.3675 m3/m3) under dry and wet conditions, respectively.

Accurate monitoring of water vapor content is of great significance to analyzing global warming. Presently, the amount of available atmospheric precipitable water vapor (PWV) data has exploded, mainly including all-weather Global Navigation Satellite System (GNSS) PWV, multiband spaceborne remote sensing-PWV, global coverage reanalysis, etc. A 3-D water vapor density field has advantages in reflecting the vertical motion of water vapor. Therefore, based on the constraints built by historical reanalysis, we combined high-precision GNSS data and three types of remote-sensing data [near-infrared: MODIS, long wave infrared: FengYun-4A (FY-4A), microwave: morphed integrated microwave imagery at CIMSS (MIMIC)] to build a tomography model. The retrieved 3-D water vapor density field from the model is validated by water vapor density from reanalysis (ERA5_WVD) and radiosonde data (ROS_WVD), respectively. The tomographic results are more consistent with ERA5_WVD. From the ERA5_WVD validation results, the prior-variance weighting strategy has slight advantages in solving the fused tomographic model over that of the equal weighting strategy, especially for the top layers. Using the weighting strategy, the root mean square errors (RMSEs) of the three tightly coupled tomographic models are less than 2 g/m3 in most epochs, and their average RMSEs are 1.59, 1.69, and 1.65 g/m3, respectively. As the height rises, the RMSE value decreases gradually, but the relative RMSE value increases at first and then decreases. Compared with the single-GNSS tomographic model, the tightly coupled tomography model based on microwave remote-sensing data (MIMIC_PWV) and GNSS data has the highest improvement in accuracy, followed by fusing with long-wave infrared FY4A_PWV and near-infrared MODIS_NIR_PWV. The comparisons of fusing different remote-sensing data contribute to selecting appropriate remote-sensing data to improve the tomographic water vapor density. Furthermore, it cannot be negatable that the tomographic model fusing the remote-sensing data has a significant advantage in tomographic water vapor density near the ground, and the improvement ranges from 5% to 20%. Therefore, adding the remote-sensing data to perform tightly coupled tomography is significant for monitoring water vapor motion in the bottom atmosphere, which is essential to understanding and coping with climate change, water resources management, and ecological environment protection.

The global navigation satellite system (GNSS) refraction pattern technique makes use of two pairs of GNSS receivers and antennas to collect the refracted signal in the medium and the direct signal in the air, respectively. Due to the sensitive response of the refracted signal to variation of the dielectric constant, the technique is suitable for measuring medium dielectric constant-related parameters such as snowpack density, vegetation water content, and soil moisture. In this article, a forward model related to the power ratio of the refracted signal to the direct one is developed to elucidate the mechanism of soil moisture-induced refracted signal strength attenuation. By making use of the simulating results derived from the model, a second-order polynomial function is established to describe the relationship between the power ratio, soil temperature, and soil moisture. Based on the function, an inversion algorithm, which takes the GNSS carrier-to-noise (C/N0) observations under high elevation angles (50°–60°) and soil temperature as the inputs, is proposed for the near-surface soil moisture estimation. The proposed algorithm is validated through a dataset collected in an experimental campaign over two years. The results demonstrate that there exists a good agreement between the proposed method-derived soil moisture estimations and ground-truth ones; and the root-mean-square error (RMSE) of the proposed algorithm-derived soil moisture estimation is 0.009 cm3cm−3 when the ground-truth soil moisture is in the range from 0.150 to 0.550 cm3cm−3. Because the observations were collected by using consumer-grade GNSS chips and antennas, this study also provides a basis for the design and development of the low-cost GNSS soil moisture sensor in the future.

This paper aims to explore improving the accuracy of water vapor retrieval in all-weather conditions by combining the recordings of the near-infrared (NIR) and thermal infrared (TIR) bands of the Moderate-resolution Imaging Spectroradiometer (MODIS). When analyzing different input parameters, we found that the NIR methods are more strongly influenced by a high cloud density than the IR methods. This motivated us to developed an improved model, named Resilient Combination Model of NIR and TIR bands (RCMNT), that is based on machine learning algorithms and optimizes the accuracy of water vapor retrieval depending on varying cloud cover densities. The RCMNT uses high-precision Precipitable Water Vapor (PWV) retrieved by the Global Navigation Satellite System (GNSS) as reference for model training. Compared with GNSS PWV, the results show that the RCMNT is superior to the MOD05 in terms of spatio-temporal forecast accuracy. The RCMNT reveals a significant improvement in accuracy of 69.06% and a reduction in the root mean square error (RMSE) by 6.9335 mm under all-weather conditions compared to MOD05. The RCMNT also shows a substantial enhancement under cloudy conditions, with an increase in temporal prediction accuracy of 69.47% and an improvement in spatial prediction accuracy of 69.84%. The RMSE of the RCMNT decreased by 7.0251 mm, resulting in a 69.50% improvement in accuracy compared to MOD05 under cloudy conditions. Overall, the RCMNT shows its potential through a combined analysis of NIR and TIR data, achieving more accurate results for PWV retrieval across various spatio-temporal scales and weather conditions.

Global Navigation Satellite System (GNSS) displacements are widely used to recover regional terrestrial water storage (TWS) variations. Most previous studies focused on using GNSS vertical displacements (GNSS-VDs) and ignored the horizontal components. Given the important information from GNSS horizontal displacements (GNSS-HDs), we integrate both GNSS vertical and horizontal displacements (GNSS-VHDs) to infer daily TWS variations in southern Europe from January 2011 to December 2022. Our results show that the variations of TWS derived from GNSS are consistent with Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GFO) in the spatiotemporal domain. The root-mean-square error (RMSE) of the TWS time series between GNSS inversion and GRACE/GFO is reduced from 36.5 to 34.3 mm by incorporating the horizontal displacements, revealing an improvement in TWS estimation. We explore the propagation time from meteorological drought to hydrological drought in southern Europe and its driving factors. Results suggest that the propagation time varies from 0 to 7 months, with an average time of 1.6 months. Further analysis shows that the drought propagation time (DPT) is significantly affected by precipitation. It is also found that evapotranspiration dominates the TWS cyclic processes in southern Europe. An approximately 1.7-year interannual periodic TWS signal is detected, which is attributed to the Arctic Oscillation (AO). This study provides an enhanced inversion of TWS variation with additional constraints of GNSS-HDs for the in-depth investigation of drought propagation in southern Europe.

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.

Global navigation satellite system reflectometry (GNSS-R) altimetry has great potential to provide high spatial–temporal resolution sea surface heights (SSHs) at low cost. Interferometric phase measurements between direct and reflected signals can be used for altimetry retrieval to achieve high-precision solutions. The motions of the medium Earth orbit (MEO) satellites cause interferometric phase change rapidly, which would increase the probability of occurrence of the phase unwrapping errors than the case of the geosynchronous Earth orbit (GEO) satellite. In order to overcome this problem, we propose a coastal GNSS-R altimetry algorithm using the signals from Quasi-Zenith Satellite System (QZSS) GEO satellite. Precise SSH variations can be achieved using the interferometric phase measurements without ambiguity fixed. We also perform coastal experiments on a trestle using a specialized GNSS-R setup to verify our algorithm. It is composed of an intermediate frequency (IF) data collector and two antennas. The up-looking antenna is used to receive direct signals, while the down-looking antenna receives the signals reflected from the sea surface. Raw IF data sampled at 62 MHz are collected and processed to derive interferometric carrier phase delay measurements using a self-developed software-defined receiver. Approximately 7 h of reflector heights are retrieved at 1-min intervals and the solutions are evaluated via comparison with measurements provided by a 26-GHz altimetry radar located near the GNSS-R setups. The results show that the root mean square error (RMSE) of sea level estimation is about 1.4 cm by using the QZSS GEO data.

Global Navigation Satellite System Reflectometry (GNSS-R) is a passive technique for remote sensing of soil moisture, which has continuous all-day and all-weather applicability on different platforms. New GNSS signals with advanced modulation and higher power are expected to improve the performance of GNSS-R. In this study, we performed a ground-based dual-antenna GNSS-R experiment on farmland and collected 15-min raw intermediate frequency data with central frequency of 1175.42 MHz hourly over two different 24-h periods. The power ratio between the direct and reflected signals from QZSS satellites were computed using a self-developed software-defined receiver with 1-ms coherent integration and 200-ms incoherent adds. Then, soil moisture was resolved using a semiempirical model based on the power ratios. Solutions were evaluated using measurements obtained using a time-domain reflectometry probe. Results demonstrated that signal power-ratio-based QZSS signals can be used to retrieve soil moisture under bare soil conditions. Moreover, for signal power-ratio-based case, results from geostationary orbit (GEO) satellite signals (STD: 0.013 m3/m3 and 0.007 m3/m3) performed better than those from inclined geosynchronous orbit (IGSO) satellite signals (STD: 0.033–0.071 m3/m3).

Precipitable water vapor (PWV) is an important parameter reflecting atmospheric water vapor, which plays an important role in the global hydrological cycle. At present, brightness temperatures (Tbs) at frequencies of 18 and 23 GHz from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are commonly applied in PWV data retrieval. Two tools are usually employed to perform the retrieval: physical models and neural networks. In this article, we creatively deduced a more universal physical equation and verified the consistency between theoretical and experimental equations both theoretically and numerically. Then, we fully considered the defects of these two tools and proposed a new integrated method that combines the physical model with a neural network. The Tbs in horizontal and vertical polarizations from AMSR2 and GNSS-derived PWV (GNSS-PWV) data from the SuomiNet global network were used to build the physical model. In the construction of the neural network, we also introduced the related surface parameters from GNSS stations as inputs. Validation results obtained with GNSS-PWV data showed that the accuracy of the test set can reach 2.38 and 2.37 mm in the ascending and descending orbit cases, respectively. Compared with the traditional physical model and neural network model, the improvements of the test set were 24.4% and 17.4% in the ascending orbit and 26.4% and 19.4% in the descending orbit. Radiosonde observation (RAOB) data were applied to carry out another external independent verification, and the accuracy of the test set reached 2.70 and 3.54 mm based on the RAOB data.

In precipitable water vapor (PWV) retrieval, results with high spatial coverage but low temporal resolution can be achieved through satellite-borne sensors, such as the Advanced Microwave Scanning Radiometer 2 (AMSR2). Conversely, the ground-based global navigation satellite system (GNSS) can provide PWV with high temporal resolution and high precision but low spatial coverage. To combine the advantages of these two technologies, we introduce a backpropagation neural network (BPNN) to realize PWV retrieval from AMSR2 with ground-based GNSS data. We first detect the optimal configuration for the BPNN. Then, based on the results of the retrieval accuracy from different types of orbits, we find that the descending (De) orbit has the highest retrieval accuracy, with a root-mean-square error (RMSE) of 3.25 mm. Afterward, the influence of brightness temperature (Tb) data at different frequencies on PWV retrieval is analyzed. The results of GNSS- PWV verification indicate that the 18 +23-GHz frequency combination has the highest PWV retrieval accuracy, and the mean RMSE of all 82 test stations distributed globally can reach 3.53 mm. We also analyze the influence of differently located stations on retrieval accuracy, and the results show that the accuracy of high-latitude and polar regions is remarkably higher than that of other areas but with a lower relative error. Finally, we use radiosonde data as another external verification method to assess PWV retrieval accuracy. The results reveal that RMSE can reach 3.87 mm. Through a BPNN approach, we have creatively realized PWV retrieval from AMSR2 using ground-based GNSS data on a global scale.

Compared with tide gauges, Global Navigation Satellite System Multipath Reflectometry (GNSS-MR) can provide low-cost, long-term sea-level data that are not susceptible to crustal loading. Signal-to-noise ratio (SNR) observables in GNSS files are commonly used for GNSS-MR; however, these observables are not always present, especially in early GNSS files. Several different combinations of codes and carrier-phases for GNSS-MR as substitutes to extract sea level have been proposed; however, the requirement of these methods for application of cycle slip detection or multi-frequency observations to isolate multipath signals reduces their applicability. Here, we propose a new method for sea-level estimation using Signal Strength Indicator (SSI) data in GNSS observation files, which is an alternative to existing methods because SSI data always exist. To verify the proposed method, we used four multi-GNSS data from three stations to monitor sea level. Sea-level estimations with root-mean-square errors of 7–8, 5–9, 12–15 and 9–13 cm relative to in-situ data were retrieved, and the correlation coefficients for these stations were bigger than 0.98, 0.98, 0.93 and 0.96, respectively. Moreover, the proposed method measures sea levels with precision similar with the traditional SNR method. In addition, sea-level results derived from the proposed method at these stations were further applied to estimate ocean tides. Ocean-tide coefficients for several main tides determined by different data were in good agreement.

Total precipitable water (TPW) values derived from Constellation Observing System for Meteorology, Ionosphere and Climate-2 (COSMIC-2) are compared with those derived from Special Sensor Microwave Imager Sounder (SSMIS), Global Precipitation Measurement (GPM) Microwave Imager (GMI), and Advanced Microwave Scanning Radiometer-2 (AMSR-2) over the ocean from October 1, 2019 to February 16, 2020. The overall comparison results indicate that TPW values derived from SSMIS, AMSR-2, and GMI have a good correlation and agreement with COSMIC-2 TPW values with the correlation coefficients greater than 0.99 and root mean square (rms) no greater than 2.7 mm. We compare TPW derived from three different microwave radiometers with COSMIC-2 TPW over the subtropical and tropical oceans. The differences illustrate that TPW values derived from three different microwave radiometers are more consistent with COSMIC-2 TPW values over the subtropical ocean than those over the tropical ocean. In addition, we also analyze the relationship between the TPW retrieval accuracy derived from three different microwave radiometers and environmental factors, including cloud, rain rate, wind speed, and surface temperature. The results indicate that four environmental factors have an important influence on the TPW retrieval from three different microwave radiometers.

BACKGROUND: DNA-binding proteins perform important functions in a great number of biological activities. DNA-binding proteins can interact with ssDNA (single-stranded DNA) or dsDNA (double-stranded DNA), and DNA-binding proteins can be categorized as single-stranded DNA-binding proteins (SSBs) and double-stranded DNA-binding proteins (DSBs). The identification of DNA-binding proteins from amino acid sequences can help to annotate protein functions and understand the binding specificity. In this study, we systematically consider a variety of schemes to represent protein sequences: OAAC (overall amino acid composition) features, dipeptide compositions, PSSM (position-specific scoring matrix profiles) and split amino acid composition (SAA), and then we adopt SVM (support vector machine) and RF (random forest) classification model to distinguish SSBs from DSBs. RESULTS: Our results suggest that some sequence features can significantly differentiate DSBs and SSBs. Evaluated by 10 fold cross-validation on the benchmark datasets, our prediction method can achieve the accuracy of 88.7% and AUC (area under the curve) of 0.919. Moreover, our method has good performance in independent testing. CONCLUSIONS: Using various sequence-derived features, a novel method is proposed to distinguish DSBs and SSBs accurately. The method also explores novel features, which could be helpful to discover the binding specificity of DNA-binding proteins.

The iterated version of a family of non‐linear Kalman filters, named the unscented transform (UT) based unscented Kalman filters (UKF), are revisited. Two existing frameworks of the iterated UKF are analysed and some shortcomings of them are pointed out. A new framework is proposed based on the statistical linear regression (SLR) perspective of the UT and the framework of the iterated extended Kalman filter (IEKF). The virtue of the proposed framework is twofold: first, the observation equation is linearised strictly following the SLR perspective implying that the regression error is also considered; second, it strictly follows the framework of the IEKF implying that in each iteration, the linearised equation is used to correct the a priori estimate rather than the latest estimate. A simple but illustrative benchmark example is simulated to check the feasibility of the proposed framework, and the results demonstrate the efficacy of the proposed framework.