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As an emerging remote sensing technology, GNSS reflectometry (GNSS-R) has been widely investigated for retrieving ocean parameters including ocean significant wave height (SWH). Ocean SWH consists of contribution from swell and wind waves, which are commonly modeled separately in the field of marine science and engineering to facilitate practical application. In this study, we present a deep convolutional neural network (DCNN) model for retrieving swell and wind wave SWHs. The DCNN model makes use of auxiliary data and effective DDM features extracted in the convolution layer, and it is trained by using the ERA5 data and CYGNSS observations. The proposed DCNN model and seven existing models [i.e., random forest, extremely randomized trees, bagging tree (BT), decision tree, support vector machine (SVM), artificial neural network, and convolutional neural network] were extensively tested using the ERA5 and WaveWatch III (WW3) data. The results show that when ERA5 is used as reference data, the proposed DCNN model performs best among the eight models, with the root mean square errors (RMSEs) of retrieving swell and wind wave SWH being better than 0.394 m and 0.397 m, respectively, and the correlation coefficient (R) being 0.90. Compared with the SVM model, RMSEs are improved by 28.82% and 31.92%, respectively. When WW3 is employed as reference, the RMSEs of retrieving swell and wind wave SWH are better than 0.497 m and 0.502 m, respectively, with R of 0.89 and 0.90. Compared with the BT model, RMSEs are improved by 26.74% and 27.41%, respectively. The research also found that the auxiliary variables are important for swell and wind wave SWH retrieval. Furthermore, the retrieval of SWH for swells and wind waves using spaceborne GNSS-R technology is affected by rainfall, resulting in about 6% increase in RMSE. This method provides a new idea for studying global ocean swell and wind waves using CYGNSS data. [ABSTRACT FROM AUTHOR]

In this paper, a method for joint sea surface rainfall intensity (RI), wind speed, and wave height retrieval based on spaceborne global navigation satellite system reflectometry (GNSS-R) data is proposed, which especially considers the effects between these two parameters. A method of rainfall detection (RD) according to different wind speed ranges is also proposed by mitigating the impact of swell and wind speed. The results, with data collected over the oceans near Southeast Asia, show that the RD method has a detection accuracy of up to 81.74%. The RI retrieval accuracy can reach about 2 mm/h by simultaneously correcting the effects of wind speed and swell. The accuracy of wind speed retrieval is improved by about 5% after removing rainfall interference through RD in advance. After considering the influence of wind speed and eliminating rainfall interference, the retrieval accuracy of significant wave height (SWH) is improved by about 18%. Finally, the deep convolutional neural network (DCNN) model is built to estimate the SWH of the swell. The results show that the retrieval accuracy of the swell height is better than 0.20 m after excluding rainfall interference. The proposed joint retrieval method provides an important reference for the future acquisition of multiple high-precision marine geophysical parameters by spaceborne GNSS-R technology. [ABSTRACT FROM AUTHOR]

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology that uses GNSS signals reflected from the Earth's surface to estimate geophysical parameters. Because of its unique advantages such as high temporal and spatial resolutions, low observation cost, wide coverage and all-weather operation, it has been widely used in land and ocean remote sensing fields. Ocean wind monitoring is the main objective of the recently launched Cyclone GNSS (CYGNSS). In previous studies, wind speed was usually retrieved using features extracted from delay-Doppler maps (DDMs) and empirical geophysical model functions (GMFs). However, it is a challenge to employ the GMF method if using multiple sea state parameters as model input. Therefore, in this article, we propose an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net. GloWS-Net considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model. In order to verify the improvement of the proposed model, ERA5 and Cross-Calibrated Multi-Platform (CCMP) wind data were used as reference for extensive testing to evaluate the wind speed retrieval performance of the GloWS-Net model and previous models (i.e., GMF, fully connected network (FCN) and convolutional neural network (CNN)). The results show that, when using ERA5 winds as ground truth, the root mean square error (RMSE) of the proposed GloWS-Net model is 23.98% better than that of the MVE method. Although the GloWS-Net model and the FCN model have similar RMSE (1.92 m/s), the mean absolute percentage error (MAPE) of the former is improved by 16.56%; when using CCMP winds as ground truth, the RMSE of the proposed GloWS-Net model is 2.16 m/s, which is 20.27% better than the MVE method. Compared with the FCN model, the MAPE is improved by 17.75%. Meanwhile, the GloWS-Net outperforms the FCN, traditional CNN, modified CNN (MCNN) and CyGNSSnet models in global wind speed retrieval especially at high wind speeds. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) technology has opened a new window for ocean remote sensing because of its unique advantages, including short revisit period, low observation cost, and high spatial-temporal resolution. In this article, we investigated the potential of estimating swell height from delay-Doppler maps (DDMs) data generated by spaceborne GNSS-R. Three observables extracted from the DDM are introduced for swell height estimation, including delay-Doppler map average (DDMA), the leading edge slope (LES) of the integrated delay waveform (IDW), and trailing edge slope (TES) of the IDW. We propose one modeling scheme for each observable. To improve the swell height estimation performance of a single observable-based method, we present a data fusion approach based on particle swarm optimization (PSO). Furthermore, a simulated annealing aided PSO (SA-PSO) algorithm is proposed to handle the problem of local optimal solution for the PSO algorithm. Extensive testing has been performed and the results show that the swell height estimated by the proposed methods is highly consistent with reference data, i.e., the ERA5 swell height. The correlation coefficient (CC) is 0.86 and the root mean square error (RMSE) is 0.56 m. Particularly, the SA-PSO method achieved the best performance, with RMSE, CC, and mean absolute percentage error (MAPE) being 0.39 m, 0.92, and 18.98%, respectively. Compared with the DDMA, LES, TES, and PSO methods, the RMSE of the SA-PSO method is improved by 23.53%, 26.42%, 30.36%, and 7.14%, respectively. [ABSTRACT FROM AUTHOR]

Reliable rainfall information is very important for hydro climatology research, industrial and agricultural production, construction, and drought and flood prediction. As a new remote sensing technology, Global Navigation Satellite System-reflectometry (GNSS-R) can be used to retrieve geophysical parameters due to its unique advantages of high spatial and temporal resolution, low cost, large coverage, and all-weather operation. Existing studies have confirmed that GNSS-R technology can detect rainfall on the ocean surface, but there is no further study on the magnitude of rainfall intensity (RI). Therefore, this article aims to study the utilization of GNSS-R in the retrieval of sea surface RI based on the observables derived from GNSS-R delay-Doppler maps (DDMs). First, a wavelet denoising method is proposed for DDM denoising to improve the quality of DDM data. Then, three models were developed based on the four GNSS-R observables, which were divided into three categories according to the number of observables. The performance of RI retrieval based on the three models was evaluated against reference data IMERG-F extensively. The experimental results show that under very low wind speed (WS) (< 5 m/s), the following holds. First, the retrieval performance of the single observable model based on leading-edge slope of the normalized integrated delay waveform (LES-NIDW) proposed in the first category of model is the worst [root mean square error (RMSE) $=3.69$ mm/h and correlation coefficient (CC) $=0.71$ ]. However, right edge waveform summation of NIDW (REWS-NIDW) is the best, and RMSE and CC are better than 3.17 mm/h and 0.79, respectively. Second, compared with LES-NIDW observable, the model retrieval accuracy based on DDM Average (DDMA), leading-edge waveform summations of NIDW (LEWS-NIDW), and REWS-NIDW observables is improved by 7.05%, 7.96%, and 12.29%, respectively. Third, when using the combined model, compared with LES-NIDW single observable model, the retrieval accuracy of the model based on two observables (i.e., LES-NIDW and REWS-NIDW) combination and three observables (i.e., LES-NIDW, LEWS-NIDW, and REWS-NIDW) combination is improved by 10.34% and 13.60%, respectively. Therefore, considerable performance gain is obtained. [ABSTRACT FROM AUTHOR]

Focused on the Beidou satellite system (BDS), dual satellite system (GPS/GLONASS) and three satellite system (BDS/GPS/GLONASS), the parameters influencing the quality of static and real-time dynamic positioning date of the GNSS systems, such as the multipath effect, ionospheric delay, data utilization rate and signal-to-noise ratio, number of satellites and accuracy factor PDOP value, have been comparatively analyzed in this paper. The results of tests have shown that compared to the single satellite system BDS the average multipath errors (mp1 and mp2) of multi satellite system fusion decreased by 18 and 8% respectively, due to the increase in the number of visible satellites in multi-satellite fusion, it is possible to compensate each other to a certain extent, thus weakening the influence of the multipath effect on the positioning results. When the cut-off angle is greater than 40°, the single system cannot meet the need of navigation and positioning, however having more visible satellites than the single system it is possible for multi satellite system fusion to maintain lower and more stable PDOP value for high precision positioning services. [ABSTRACT FROM AUTHOR]

Abstract By using the observation data and products of precise obit and clock offset from Multi-GNSS Experiment (MGEX) of the International GNSS Service (IGS) and GNSS Research Centre, Curtin University in this paper, the positioning performance of BDS/QZSS satellite navigation system has been analyzed and evaluated in aspects of the quantity of visible satellites, DOP value, multipath effect, signal-to-noise ratio, static PPP and kinematic PPP. The analysis results show that compared to BDS single system when the cutoff angle are 30°and 40°, the DOP value of BDS/QZSS combined system has decreased above 20%, and the quantity of visible satellites increased about 16–30% respectively, because of the improved spatial geometric configuration. The magnitude of satellite multipath effect of BDS system shows the trend of MEO > IGSO > GEO, which is consistent with that of QZSS satellite system, as the constellation structure of the two systems is similar. The variation tendencies of signal-to-noise ratio with respect to elevation angle of the two systems are almost the same at all frequencies, showing that at the same elevation angle the signal-to-noise ratio of MEO satellites is higher than that of IGSO satellites, as the higher obit is the lower transmitting power is obtained. For having a specially designed obit, the variation of signal-to-noise ratio of BDS system is more stable. However, the magnitude of signal-to-noise ratio of QZSS system appears the trend of frequency 3 > frequency 2 > frequency 1. The static PPP performance of the BDS/QZSS combination system has been improved more significantly than the BDS single system in E, N and U directions. When the cutoff angle are at 7°, 15° and 30°, the PPP accuracy is increased about 25–34% in U direction, 10–13% and 23–34% in E and N directions respectively. When the elevation angle is large (40°), compared to BDS single system at lower elevation angles (7° and 15°) the PPP accuracy of the BDS/QZSS combination system is improved above 30% in U direction. In kinematic PPP performance, compared to BDS single system, the accuracy, availability and reliability of the BDS/QZSS combination system has been improved too, especially at large elevation angles (30° and 40°), the kinematic PPP accuracy in E and U directions has been improved about 10–50%, and above 50% in U direction. It can be concluded that the combination with QZSS system can improve the positioning accuracy, reliability and stability of BDS system. In the future, with the improvement of the satellite construction of Japan's QZSS system and the global networking of China's BDS satellites, the QZSS satellites will contribute greatly to improve the positioning accuracy, reliability, availability and stability of GNSS systems in areas such as cities, mountains, densely-packed buildings and severely covered areas in Asian-Pacific region. [ABSTRACT FROM AUTHOR]

Due to the strong penetration ability and high transmission rate of ultra-wideband (UWB) signals, UWB technology plays a very significant role in the field of precise indoor positioning. However, the harsh and volatile indoor environment leads to non-line-of-sight (NLOS) propagation and severe attenuation of UWB signals, which may generate significant ranging and positioning errors. To mitigate NLOS effect and improve positioning accuracy, existing methods use ranging information and channel impulse response (CIR) to identify UWB signal propagation channels. However, these NLOS identification methods often require a priori knowledge and suitable thresholds, and most of them only perform a binary classification between LOS and NLOS in a particular scenario. To address these disadvantages, this paper proposes a novel multi-classification method to classify UWB signal propagation channels based on one-dimensional wavelet packet analysis (ODWPA) and convolutional neural network (CNN). This method first decomposes the CIR of known categories into colored coefficients images using a one-dimensional wavelet packet function, then uses these images to train CNNs with different structures, and finally selects CNN model with the best performance to identify the unknown category of UWB signal propagation channels. The experimental results show that the performance of the proposed method is always the best, regardless of the type of experimental scenario, and the identification accuracy is always higher than 90% and up to 100%. [ABSTRACT FROM AUTHOR]

SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods—LSM, R-Index, and P-NG—against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings. [ABSTRACT FROM AUTHOR]

This paper focuses on sea surface wind speed estimation based on cyclone global navigation satellite system reflectometry (GNSS-R) data. In order to extract useful information from delay-Doppler map (DDM) data, three delay waveforms are presented for wind speed estimation. The delay waveform without Doppler shift is defined as central delay waveform (CDW), and the integral of the delay waveforms with different Doppler shift values is defined as integral delay waveform (IDW), while the difference between normalized IDW (NIDW) and normalized CDW (NCDW) is defined as differential delay waveform (DDW). We first propose a data filtering method based on threshold setting for data quality control. This method can select good-quality DDM data by adjusting the root mean square (RMS) threshold of cleaned DDW. Then, the normalized bistatic radar scattering cross section (NBRCS) and the leading edge slope (LES) of IDW are calculated using clean DDM data. Wind speed estimation models based on NBRCS and LES observations are then developed, respectively, and on this basis, a combination wind speed estimation model based on determination coefficient is further proposed. The CYGNSS data and ECMWF reanalysis data collected from 12 May 2020 to 12 August 2020 are used, excluding data collected on land, to evaluate the proposed models. The evaluation results show that the wind speed estimation accuracy of the piecewise function model based on NBRCS is 2.3 m/s in terms of root mean square error (RMSE), while that of the double-parameter and triple-parameter models is 2.6 and 2.7 m/s, respectively. The wind speed estimation accuracy of the double-parameter and triple-parameter models based on LES is 3.3 and 2.5 m/s. The results also demonstrate that the RMSE of the combination method is 2.1 m/s, and the coefficient of determination is 0.906, achieving a considerable performance gain compared with the individual NBRCS- and LES-based methods. [ABSTRACT FROM AUTHOR]

This article presents a review on spaceborne Global Navigation Satellite System Reflectometry (GNSS-R), which is an important part of GNSS-R technology and has attracted great attention from academia, industry and government agencies in recent years. Compared with ground-based and airborne GNSS-R approaches, spaceborne GNSS-R has a number of advantages, including wide coverage and the ability to sense medium- and large-scale phenomena such as ocean eddies, hurricanes and tsunamis. Since 2014, about seven satellite missions have been successfully conducted and a large number of spaceborne data were recorded. Accordingly, the data have been widely used to carry out a variety of studies for a range of useful applications, and significant research outcomes have been generated. This article provides an overview of these studies with a focus on the basic methods and techniques in the retrieval of a number of geophysical parameters and the detection of several objects. The challenges and future prospects of spaceborne GNSS-R are also addressed. [ABSTRACT FROM AUTHOR]

Snow depth is an essential climate variable (ECV) in hydrologic cycle and is widely used for policy-making in agriculture and water resource management. Regional and global snow depth data also provide useful information for the study of climate change. In this paper, two new methods for snow depth estimation are proposed, which are based on combination of pseudorange measurements recorded by GNSS (Global Navigation Satellite System) geodetic receivers. The first one is the linear combination of pseudorange measurements of GNSS dual-frequency signals, which is geometry-free i.e. free of the effect of geometry between satellite and receiver. The second one is the linear combination of pseudorange measurements of GNSS triple-frequency signals, which is free of effects of both geometry and ionospheric delays. Both pseudorange combination methods do not rely on cycle slip detection and repair, which are required by carrier phase combination methods. Theoretical models are developed to describe the relationship between the spectral peak frequency of the combined multipath series and the antenna height. Two experimental datasets collected by UNAVCO community are employed to validate the proposed methods. The results demonstrate that there exists good agreement between the proposed methods and field observations of snow depth and the RMSE of the proposed methods are less than 3.2 cm at first experimental station. In addition, there is good consistency and similarity in snow depth estimation among the proposed method, SNR method and dual-frequency carrier phase and pseudorange combination method. [ABSTRACT FROM AUTHOR]

Tropospheric delay error must be reduced during interferometric synthetic aperture radar (InSAR) measurement. Depending on different geographical environments, an appropriate correction method should be selected to improve the accuracy of InSAR deformation monitoring. In this study, surface deformation monitoring was conducted in a high mountain gorge region in Yunnan Province, China, using Sentinel-1A images of ascending and descending tracks. The tropospheric delay in the InSAR interferogram was corrected using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 meteorological reanalysis data (ERA5) methods. The correction effect was evaluated by combining phase standard deviation, semi-variance function, elevation correlation, and global navigation satellite system (GNSS) deformation monitoring results. The mean value of the phase standard deviation (Aver) of the linear correction interferogram and the threshold value (sill) of the semi-variogram were reduced by –20.98% and –41%, respectively, while the accuracy of the InSAR deformation points near the GNSS site was increased by 58%. The results showed that the three methods reduced the tropospheric delay error of InSAR deformation monitoring by different degrees in low-latitude mountains and valleys. Linear correction was the best at alleviating the tropospheric delay, followed by GACOS, while ERA5 had poor correction stability. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) technology is capable of detecting large areas of potentially unstable slopes. However, traditional time-series InSAR methods yield fewer valid measurement points (MPs) in alpine canyon regions. Distributed Scatterer (DS) Interferometry (DSI) technology serves as a potent tool for monitoring surface deformation in complex land cover areas; nonetheless, it grapples with high computational demands and low efficiency when interpreting deformation across extended time series. This study proposes an image compression–based DSI (ICDSI) method, which, building upon the DSI method, utilizes principal component analysis (PCA) to compress multi-temporal SAR images in the time dimension. It develops a module for compressing long-time sequence SAR images, acquires the compressed image (referred to as a virtual image), and integrates the developed image compression module into the DSI data processing flow to facilitate the inversion of long-time sequence InSAR land surface deformation information. To validate and assess the credibility of the ICDSI method, we processed a total of 78 ascending and 81 descending scenes of Sentinel-1A images spanning the period 2019–2021 using Small Baseline Subset (SBAS), DSI, and the ICDSI method proposed in this paper. Subsequently, these methods were applied to detect landscape displacements on both coasts of the Jinsha River Basin. The investigation reveals that the ICDSI method outperforms SBAS and DSI significantly in monitoring landslide displacements, enabling the detection of more measurement points (MPs) while utilizing less raw data. The accomplishments of this research program carry crucial theoretical implications and practical application value for the detection of surface deformation using long-time series InSAR. [ABSTRACT FROM AUTHOR]

This paper focuses on sea surface wind speed estimation using L1B level v3.1 data of reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission and European Centre for Medium-range Weather Forecast Reanalysis (ECMWF) wind speed data. Seven machine learning methods are applied for wind speed retrieval, i.e., Regression trees (Binary Tree (BT), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM)), ANN (Artificial neural network), Stepwise Linear Regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made. The wind speed is divided into two different ranges to study the suitability of the different algorithms. A total of 10 observation variables are considered as input parameters to study the importance of individual variables or combinations thereof. The results show that the LGBM model performs the best with an RMSE of 1.419 and a correlation coefficient of 0.849 in the low wind speed interval (0–15 m/s), while the ET model performs the best with an RMSE of 1.100 and a correlation coefficient of 0.767 in the high wind speed interval (15–30 m/s). The effects of the variables used in wind speed retrieval models are investigated using the XGBoost importance metric, showing that a number of variables play a very significant role in wind speed retrieval. It is expected that these results will provide a useful reference for the development of advanced wind speed retrieval algorithms in the future. [ABSTRACT FROM AUTHOR]

This paper focuses on improving indoor Wi-Fi localization by mitigating the effect of fluctuation of received signal strength indication (RSSI). The RSSI data collected at each reference point is first transformed through translation and scaling. The BP (Back Propagation) neural network is then used to construct the ranging model using the transformed RSSI to determine the distances between the target point and each reference point. A genetic algorithm (GA) is developed to optimize the initial values of weights and biases of the BP neural network. For convenience, our proposed ranging model is denoted as GTBPD. A new localization algorithm is then proposed, which uses the GTBPD model and the sequential quadratic programming (SQP, an iterative nonlinear optimization algorithm), and the algorithm is denoted as GTBPD-LSQP for simplicity. Experiments were conducted in three areas of two different teaching buildings with complex environments. The performance of the proposed GTBPD-LSQP algorithm is evaluated and compared with four existing algorithms. The experimental results show that our proposed GTBPD-LSQP algorithm achieves significantly higher location accuracy than the four existing algorithms. [ABSTRACT FROM AUTHOR]