Search Results (240)

Global Navigation Satellite System Reflectometry (GNSS-R) platforms, such as the CYGNSS constellation, provide unprecedented spatial coverage for monitoring ionospheric scintillation via the $S_4$ index. However, the operational utility of GNSS-R for space weather is substantially degraded by surface-induced signal contamination when sharp land–water boundaries (coastlines) trigger massive, false-positive $S_4$ pseudo-scintillations that imitate true ionospheric plasma irregularities. In this study, a robust machine learning (ML) methodology to autonomously distinguish surface-induced reflections from true atmospheric volumetric scattering was proposed. Using 1 Hz Level 1 continuous Signal-to-Noise Ratio (SNR) time-series data, morphologic features (e.g., maximum amplitude, peak prominence, and standard deviation) were extracted to train a Random Forest (RF) classifier. The model achieves 98% accuracy in differentiating coastal boundaries from ionospheric scintillation, evaluated on a global dataset of over ~450,000 anomalous events. Moreover, a multi-sensor case study of the historic May 2024 G5 geomagnetic storm is presented to validate the geophysical fidelity of the filtered data. The ML-isolated CYGNSS anomalies demonstrate strong spatial correlation with COSMIC-2 Radio Occultation (RO) F2-peak electron density (NmF2) variations and ground-based Rate of TEC Index (ROTI) maps. Furthermore, temporal cross-validation with 1 Hz localized ground magnetometer data in Northwest Mexico reveals positive synchronization between CYGNSS scattering events and localized electrodynamic disturbances. Finally, the results demonstrate that ML-de-aliased GNSS-R data can reliably link the oceanic observational gaps inherent to ground-based networks, offering a powerful new tool for global space weather monitoring. Full article

Freeze–thaw (F/T) processes play a critical role in the regulation of soil hydrothermal dynamics, land–atmosphere energy exchange, and ecosystem functioning. The spaceborne global navigation satellite system reflectometry (GNSS-R) has shown great potential for land surface F/T state detection; however, its monitoring capability remains limited by spatial resolution, revisit interval, observation coverage, and complex land surface conditions. In this study, Tianmu-1 (TM-1) multi-GNSS-R observations were used to detect daily land surface F/T states over the mid-to-high latitudes of the Northern Hemisphere. First, surface reflectivity observations from multi-GNSS, including the Global Positioning System (GPS), BeiDou Navigation Satellite System (BDS), Galileo, and GLONASS, were fused using a weighted averaging method based on the number of specular reflection points. Then, TM-1 multi-GNSS-R reflectivity was used as the primary remote-sensing input, while vegetation water content (VWC), surface roughness, and snow cover information were introduced as auxiliary environmental variables. The Soil Moisture Active Passive (SMAP) F/T product was used to provide supervised reference labels for developing Bayesian-optimized extreme gradient boosting (XGBoost) models for F/T state classification. Evaluation against SMAP F/T reference labels showed that the multi-GNSS fusion model achieved an area under the curve (AUC) of 0.853 and an overall accuracy of 77.3% without incorporating snow cover information, outperforming the single-GNSS models. After incorporating snow cover information, the AUC increased to 0.959, and the overall accuracy reached 89.3%. Shapley additive explanations (SHAP) analysis further showed that snow cover made the largest contribution to the final model output, suggesting that its improvement effect may reflect both physical snow-related surface information and seasonal contextual information. An independent point-based comparison with in situ observations from the international soil moisture network (ISMN) showed that the TM-1 F/T classification accuracy reached 85.2% after incorporating snow cover information, which was comparable to that of the SMAP product. These results demonstrate that TM-1 multi-GNSS-R observations have promising potential for detecting land surface F/T states during the autumn–winter freezing development period, and that integrating multi-GNSS-R reflectivity with snow cover information can substantially improve classification performance and spatial consistency within the available observation period. Full article

Global Navigation Satellite System Reflectometry Interferometric Synthetic Aperture Radar (GNSS-R InSAR) offers all-weather, all-day observation capabilities and high temporal resolution, enabling elevation deformation monitoring with a single satellite. However, in hazardous regions, such as tailings dam slopes, measuring the deformation of a greater number of target points is essential for a more accurate assessment of geological hazard risks. Since navigation satellite signals are not originally designed for imaging purposes, their inherent narrow bandwidths result in low spatial resolution and limited target recognition capabilities, rendering them inadequate for such scenarios. To address these limitations, this paper investigates a GNSS-R InSAR deformation measurement architecture utilizing dual-frequency BeiDou-3 (BDS-3) signal fusion. Specifically, a coherent spectrum fusion method is introduced to effectively expand the signal bandwidth, thereby significantly enhancing range resolution and target identification capabilities. Building upon this, deformation measurements are conducted to achieve more refined and detailed monitoring. Full article

Global Navigation Satellite System Reflectometry (GNSS-R), with its all-weather observation capability and low-cost advantage, provides an innovative solution for dynamic sea ice monitoring. In this paper, multi-dimensional features, including the GNSS-R Normalised Integrated Delay Waveform (N-IDW), the scattering coefficient and incidence angle derived from FY-3F satellite data, and the Delay Doppler Map (DDM) bistatic radar cross-section coefficient, are jointly used as model inputs. Experimental results show that this method successfully integrates FY-3F satellite data for sea ice thickness (SIT) retrieval, confirming the viability of employing FY-3F GNSS-R data for this purpose. An assessment of different algorithms in terms of their retrieval performance is conducted—covering RF, DT, KNN, SVM, ET, GBR, XGBR, and LR—and uses these eight models as base learners to construct different stacking models. After comparison, the ensemble stacking model using ET, LR, XGBR, and GBR as base models achieves the best retrieval performance. The MSE of this model for sea ice thickness retrieval reaches 0.0112 m, the RMSE reaches 0.1026 m and the correlation coefficient reaches 0.8876. Full article

The Global Navigation Satellite System Reflectometry (GNSS-R) technique provides global ocean surface wind observations unaffected by rainfall with high spatiotemporal resolution. The Fengyun-3E (FY-3E) mission, as the first operational GNSS-R satellite in China, offers low-latency data suitable for numerical weather prediction (NWP). However, the dense along-track sampling of GNSS-R winds poses challenges for observation error specification in data assimilation. In this study, FY-3E GNSS-R winds are assimilated into the Weather Research and Forecasting (WRF) model to investigate the impacts of different observation error configurations. Both static and dynamic error specifications, with and without data thinning, are evaluated through a sensitivity experiment and subsequent Observing System Experiments (OSEs). The results indicate that using a static observation error of 6 m/s without data thinning achieves the best performance. Under this configuration, GNSS-R winds influence atmospheric analyses from the surface up to approximately 700 hPa in a single assimilation case, while cycling experiments further extend the impact vertically and spatially. These findings highlight the importance of appropriate observation error specification for dense GNSS-R data and provide a practical reference for their assimilation in WRF, with potential applicability to other NWP systems. Full article

DDM is the primary Level-1 observable of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R). Over the past decade, the discretization strategy of Delay-Doppler Map (DDM) systems has been primarily optimized for ocean remote sensing. This study highlights the impact of discretization effects in DDM sampling on land applications. The discretization effect in the Doppler dimension is first evaluated by comparing simulated and observed DDM slices at the Doppler bin corresponding to the DDM peak. The results indicate that the noise in DDM observations can be approximated as additive thermal noise. Based on an ideal autocorrelation function template, a matched filtering analysis is then applied to estimate the optimized specular point delay and reconstruct the peak power. Using multi-constellation observations from Tianmu-1, the results show that the original DDM peak delay exhibits a systematic delay relative to the optimized specular point delay, with biases of approximately 0.02 chips for GPS and GLONASS, and 0.17 chips for BDS (BeiDou) and Galileo. For BOC(1,1) signals in BDS and Galileo, the reflectivity remains underestimated by ~1.4 dB even at a delay sampling interval of 1/8 chip. The results indicate that under coherent scattering conditions over land, direct use of the DDM peak leads to underestimation of reflectivity due to discretization. The correction proposed in this study reduces the relative differences in reflectivity observations among the four GNSS systems. This study suggests that peak under-sampling should be considered in GNSS-R applications, and higher delay sampling resolution is required for land observations. Full article

This study aims to evaluate the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) based on signal-to-noise ratio (SNR) analysis for monitoring crop structure and moisture. Data were collected using a GNSS antenna placed within an experimental meadow located in NW Italy. GNSS-IR exploits the interference between direct and ground-reflected signals to derive physical parameters such as the vegetation phase center height and soil moisture. In this work, by analyzing and modeling the oscillations in SNR time series, the sensitivity to crop growth dynamics was assessed. Vegetation height and dielectric parameters were compared against corresponding ground-surveyed values collected using a ruler and buried soil moisture sensors. Results suggest that GNSS-IR can detect canopy height with a high degree of consistency (Pearson’s r = 0.89, MAPE = 18%). Results also show that changes in the amplitude and phase of the interference pattern are sensitive to biomass density and dielectric properties of the reflecting surface (r = −0.81 and r = 0.86 respectively). GNSS-IR observables were analyzed across four representative measurement campaigns capturing distinct seasonal stages of meadow development. Despite the limited temporal sampling (n = 4), the selected observations correspond to contrasting vegetation and soil moisture conditions, allowing the identification of systematic variations in crop biophysical properties. These findings open promising perspectives for the development of innovative monitoring strategies in precision agriculture, leveraging existing GNSS infrastructure to obtain key biophysical parameters with minimal additional equipment and operational complexity. Full article

The high-precision, continuous monitoring of the surface water level is of great importance for water resource management and the conservation of ecological systems. This study proposes a GNSS-IR-based water-level estimation method using NMEA observations collected from low-cost GNSS receivers. First, the NMEA-recorded satellite elevation angle, azimuth angle, and signal-to-noise ratio (SNR) are processed using time-series characteristics for improving the resolution and applicability of these GNSS observations. Then, the multi-frequency GNSS signal-based reflector height inversion models are developed by making use of the Lomb–Scargle periodogram method. Finally, the Velocity Pausing Particle Swarm Optimization (VPPSO) algorithm is employed to calculate the reflector height estimation and thus the water level. Two experimental data sets collected in two different environments were used to test the proposed method. The experimental results show that the root mean square error (RMSE) of the water-level estimation error is less than 6 cm for the proposed method when the in situ ones are in the range of 196.4 cm to 296.1 cm. This study provides a theoretical and technical foundation for the development of the low-cost GNSS-IR water-level measuring instrument. Full article

Significant Wave Height (SWH) is a critical parameter for ocean observation. SWH retrieval using GNSS-R data faces challenges including difficult feature selection, insufficient temporal dependency modeling, and limitations due to single-modality data. This paper proposes a novel Multimodal Transformer Network (MultTransNet) to enhance the accuracy of GNSS-R SWH retrieval. To optimize the feature set, we designed an XGBoost-based iterative feature selection module that effectively eliminates redundant features. To capture complex temporal dependencies and global context, the model employs a Transformer encoder utilizing its self-attention mechanism. Furthermore, to overcome the constraints of single-modality data, we innovatively fused 2D DDM image data with 1D auxiliary parameters, enabling multi-source information integration. Simulation results show that the Transformer architecture reduces Root Mean Square Error (RMSE) by 8.91% and increases Correlation Coefficient (CC) by 4.05% compared to a conventional Deep Neural Network (DNN) model. More significantly, the proposed multimodal algorithm further improves retrieval accuracy by 27.05% (RMSE reduction) and 7.21% (CC increase) compared to its single-modality Transformer counterpart, demonstrating superior performance, especially in complex sea-state conditions. Full article

Sea ice classification is of fundamental importance for polar monitoring and global climate research. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a frontier technology in polar remote sensing due to its high spatiotemporal resolution and cost-effectiveness. Based on BeiDou System Reflectometry (BDS-R) data acquired from the Fengyun-3E (FY-3E) satellite, this study introduces a classification approach that integrates multi-dimensional sea ice information. A comprehensive feature set was constructed by integrating the Spectral Entropy (SE) of the Normalized Integrated Delay Waveform (NIDW) First-order Differential Curve to characterize the oscillatory complexity of the trailing edge power decay process as a scattering dynamic property, the Root Mean Square height (RMS) to characterize the attenuation magnitude of scattering intensity arising from surface roughness and related factors as a scattering intensity attenuation property, and salinity (S) and L-band brightness temperature (TB) data from SMOS to describe dielectric and radiative properties. These novel features are combined with traditional GNSS-R features. After selecting the optimal feature set via an ablation study, the features were used to train a Random Forest (RF) classifier for sea ice classification. Validated against Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice type products, the proposed method yielded an overall accuracy of 93.86% and a Kappa coefficient of 0.8061. The integration of multi-dimensional features notably improved the identification of Multi-Year Ice (MYI), achieving a Recall of 85.11% and an F1-score of 84.43%. These results indicate that the proposed multi-dimensional feature set provides an effective solution for GNSS-R-based sea ice classification. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising approach for wind speed retrieval over inland waters, with relevance to wind energy assessment and lake–atmosphere exchange studies. Existing GNSS-R wind retrieval methods are well established for open oceans but face major challenges over inland waters, where coherent scattering dominates and traditional ocean models produce large systematic biases. Unlike open oceans, inland waters are dominated by coherent scattering due to limited fetch, resulting in Delay-Doppler Maps (DDM) with highly concentrated energy and minimal spreading. These characteristics render conventional ocean-based retrieval models—built on incoherent scattering assumptions—often inadequate. To overcome this, we develop a lightweight convolutional neural network (CNN) tailored to the coherent regime, using raw CYGNSS DDM as input for end-to-end wind speed regression. Cross-seasonal validation over Lake Victoria and Lake Hongze shows that the model robustly captures wind-driven spatiotemporal patterns aligned with ERA5. Notably, ERA5 reanalysis winds exhibit uncertainties over inland waters, with a root mean square error (RMSE) of 1.5–2.5 m/s against in situ buoys. The model yields a low RMSE (<0.7 m/s) in reconstructing ERA5-resolved wind patterns. This work extends GNSS-R to inland waters, offering a lightweight, deployable remote sensing solution for wind energy and lake–atmosphere research. Full article

In recent years, the Cyclone Global Navigation Satellite System (CYGNSS) of NASA has attracted widespread attention for the retrieval of freeze/thaw (F/T) states through the analysis of reflected signals. F/T variations in high-altitude regions have long been a focal point in this field. However, these areas lack benchmark observational data with high temporal and spatial resolution. A model named Partial Convolution–Time Convolutional Network (PTCN) is proposed in this paper to reconstruct CYGNSS data at a 3 km resolution. This model integrates partial convolution with a time convolutional network (TCN) and does not rely on any auxiliary data. Partial convolution is employed to distinguish valid pixels, with the interference of missing values being removed. TCN is employed to capture temporal features, which results in the reconstruction of observational data. Compared with the original observational data (at a 3 km resolution), the coverage of the reconstructed data is six times that of the original. A simulation of missing data is applied for the first time in the quantitative evaluation of observational data reconstruction. The results show that the value of R for the reconstructed data reaches 0.92, and the value of the root mean square error (RMSE) reaches 2.7. The reconstructed data is used for daily F/T retrieval. At both 36 km and 9 km resolutions, the F/T retrieval accuracy after reconstruction is comparable to that before reconstruction. The temporal resolution is improved by 256%, which successfully fills 92% of the observational gaps in soil moisture passive–active (SMAP) data. Compared with ground-based F/T retrievals, the reconstructed F/T accuracies are 87.71% at 36 km and 82.3% at 9 km.The model successfully reconstructs high-temporal and spatial resolution CYGNSS data while maintaining accuracy. In the future, this method holds significant potential for the application of global GNSS-R high-temporal and spatial resolution remote sensing observations. Full article

Paddy field water management and rice phenology strongly affect crop productivity and environmental processes, requiring continuous and quantitative monitoring. This study combined satellite synthetic aperture radar (SAR) observations and ground-based Global Navigation Satellite System (GNSS) interferometric reflectometry (GNSS-IR) over a paddy field to analyze their sensitivities to water-level variations and phenological dynamics. Sentinel-1 (C-band) and ALOS-2/PALSAR-2 (L-band) SAR time series were compared with continuous GNSS-IR observations acquired using geodetic-grade instrumentation. For GNSS-IR, Lomb–Scargle periodogram (LSP) analysis of SNR data was applied to derive two indicators: (i) the dominant spectral peak (f_water) frequency associated with the effective reflecting surface, and (ii) a normalized spectral integral (GNSS Phenology Indicator, GPI) representing vegetation-induced scattering and attenuation effects. The temporal evolution of LSP spectra exhibited systematic changes with rice phenological progression, including peak broadening and the emergence of multiple peaks as vegetation developed. For water level variations, L-band SAR co-polarized backscatter (VV and HH) and the GNSS-IR spectral peak exhibited comparable relationships with in situ water level, whereas C-band SAR showed weaker sensitivity. For phenological dynamics, GPI showed temporal behavior similar to that of the SAR polarization ratio (VH/VV), with clear responses around key growth stages, such as heading and harvest. These results suggest that SAR polarization-based indicators and GNSS-IR spectral characteristics can be interpreted within a consistent electromagnetic framework: co-polarized L-band SAR responses correspond to the water-surface-related GNSS-IR peak, whereas cross-polarized indicators correspond to GPI. This study demonstrated the potential of GNSS-IR as complementary information for physically interpreting SAR scattering mechanisms, highlighting a pathway toward more integrated microwave-based monitoring of land surface processes. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising approach for global ocean wind monitoring with high temporal resolution. However, accurate wind speed retrieval remains challenging due to the complex scattering mechanisms and the nonlinear coupling between delay–Doppler maps (DDMs) and observation geometries. To address these limitations, a Physics-Aware Hybrid CNN–Transformer Network (PA-HCTN) is proposed accordingly. The model integrates a CNN for local DDM feature extraction, a Transformer encoder for global context modeling, and a cross-attention module to dynamically fuse auxiliary physical parameters. A geophysical model function (GMF)-constrained loss is incorporated to enhance physical consistency. Evaluated on CYGNSS and ERA5 data, the PA-HCTN achieves an RMSE of 1.35 m/s and an R² of 0.75, outperforming existing benchmarks and significantly mitigating high-wind-speed underestimation. In addition, through independent validation using NDBC buoy data from four sites, the results demonstrate the effectiveness of the hybrid architecture and physics-aware design for GNSS-R wind retrieval. Full article

Inland water bodies are vital to the Earth’s ecosystem, global water cycles, and climate regulation. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a powerful tool for water detection, particularly with the deployment of the Fengyun-3 (FY-3) E, F, and G satellites. This study proposes an inland water body detection method by integrating the Z-score algorithm with specular point land surface reflectivity (SR_sp) derived from FY-3 Level-1 GNSS-R data. Using 2024 observations, the method was validated in the Amazon and Congo basins against optical water body products. The results demonstrate high detection performance, achieving overall accuracies of 95.39% and 97.38% in the two regions, respectively. Analysis of reflectivity expressed in decibels (dB) reveals that while dB-units enhance the detection of small tributaries, they are more susceptible to noise-induced misclassification compared to linear units. Furthermore, a comparative assessment of GNSS constellations shows that multi-system combination significantly reduces noise compared to single-system approaches. Notably, the Galileo system exhibited limited sensitivity to small tributaries due to lower observational density. Sensitivity analyses further reveal that interpolation methods and Z-score threshold selection are important factors influencing detection accuracy. As the first systematic evaluation of FY-3 GNSS-R data for inland water detection, this research provides a critical benchmark for future multi-platform and multi-constellation land surface retrieval studies. Full article