Accurate acquisition of uncrewed aerial vehicle (UAV) altitude relative to the ground is essential for applications such as aerial mapping and precision agriculture. Although global navigation satellite system reflectometry (GNSS-R) has demonstrated potential for airborne altimetry, its deployment on UAV platforms is limited by payload and power constraints. To the best of our knowledge, this study presents the first miniaturized GNSS-R receiver specifically developed for UAV platforms that supports real-time altimetry. The system enables 5 Hz height inversion with low power consumption and rapid response. A real-time correction framework is implemented to reduce motion-related errors. System performance was evaluated through static and dynamic experiments. The static reservoir water level (WL) test achieved a root-mean-square error (RMSE) of 16.67 cm, while dynamic UAV experiments over a land-based bare soil surface yielded an RMSE of 1.825 m, with retrieval precision improving at higher flight altitudes and degrading at lower altitudes due to waveform aliasing effects. These results demonstrate the reliability and feasibility of the proposed system for UAV-based real-time airborne altimetry, particularly in applications requiring rapid deployment and operational flexibility, such as emergency surveying and environmental monitoring.

Sea surface oil spill detection is of great significance to marine environmental protection and resource development. As an emerging remote sensing technology, Global Navigation Satellite System Reflectometry (GNSS-R) provides a supplementary approach for oil spill monitoring. However, due to the rare and unpredictable occurrence of oil spill events, the development and application of GNSS-R technology for sea surface oil detection has been relatively limited. This study therefore conducted a ground-based GNSS-R experiment for marine oil spill detection, consisting of two key parts: oil spill detection and oil type identification. By leveraging the differential responses of GNSS reflected signals to oil and seawater surfaces, this study integrated reflectivity models, oil-seawater reflectivity calibration models, and emulsified oil moisture content models to simulate and analyze experimental data. The results indicate that the calibrated reflectivity of oil is 2-6 dB lower than that of seawater, allowing qualitative discrimination between oil and seawater based on reflectivity levels. In addition, the calibrated oil reflectivity is 6-8 dB higher than the theoretical reflectivity of pure oil. This difference arises because oil emulsification increases the effective dielectric constant, thereby elevating reflectivity. The moisture content derived from the reflectivity data aligns well with the spatial distribution of oil and seawater. Combined with moisture content-time model analysis, the moisture contents of palm oil, crude oil, and heavy oil, all fall within the theoretical range, validating the feasibility of quantitatively interpreting reflectivity using emulsified oil moisture content. This study confirms the viability of GNSS-R for oil spill detection and offers ground-based validation data and theoretical support for the development of spaceborne GNSS-R algorithms aimed at oil spill monitoring.

High-resolution topographic data are critical for geomorphic studies and hazard monitoring. Although LiDAR and InSAR provide high accuracy, their high cost and weather dependence limit large-scale deployment. Airborne GNSS reflectometry (GNSS-R) offers a low-cost, all-weather alternative, but its vertical accuracy in complex terrain often degrades to the 10-m level, restricting fine-scale applications. To address this limitation, this study proposes a deep learning-based architecture, GNSS-R adaptive topography estimation network (GATENet), for GNSS-R elevation inversion. GATENet extracts spatial features from delay-Doppler maps (DDMs), integrates flight geometry information, and incorporates surface-type-dependent characteristics to adapt to different terrain conditions. Experiments on the airborne RONGOWAI dataset show that the proposed framework outperforms traditional geometric inversion methods. In ocean environments used as a baseline, the GATENet-based retrieval achieves an RMSE of 3.298 m, representing a 50.7% reduction relative to the baseline. In complex land scenarios, elevation errors generally remain within 5 m. By further incorporating a specular point-based topographic reconstruction strategy, the resulting vertical accuracy reaches up to 2.5 m. These results demonstrate the potential of the proposed framework for practical airborne GNSS-R-based topographic mapping.

Recognizing the critical role of polar Sea Ice Concentration (SIC) in climate feedback mechanisms, this study presents the first comprehensive investigation of China’s Fengyun-3E(FY-3E) GNOS-II Global Navigation Satellite System Reflectometry (GNSS-R) for bipolar SIC retrieval. Specifically, reflected signals from multiple Global Navigation Satellite Systems (GNSS) are utilized to extract characteristic parameters from Delay Doppler Maps (DDMs). By integrating regional partitioning and dynamic thresholding for sea ice detection, a Random Forest Regression (RFR) model incorporating a rolling-window training strategy is developed to estimate SIC. The retrieved SIC products are generated at the native GNSS-R observation resolution of approximately 1 × 6 km, with each SIC estimate corresponding to an individual GNSS-R observation time. Owing to the limited daily spatial coverage of GNSS-R measurements, the retrieved SIC results are further aggregated into monthly composites for spatial distribution analysis. The model is trained and validated across both polar regions, including targeted ice–water boundary zones. Retrieved SIC estimates are compared with reference data from the OSI SAF Special Sensor Microwave Imager Sounder (SSMIS), demonstrating strong agreement. Based on an extensive dataset, the average correlation coefficient (R) reaches 0.9450 in the Arctic and 0.9602 in the Antarctic for the testing set, with corresponding Root Mean Squared Error (RMSE) of 0.1262 and 0.0818, respectively. Even in the more challenging ice–water transition zones, RMSE values remain within acceptable ranges, reaching 0.1486 in the Arctic and 0.1404 in the Antarctic. This study demonstrates the feasibility and accuracy of GNSS-R-based SIC retrieval, offering a robust and effective approach for cryospheric monitoring at high latitudes in both polar regions.

Calibration is one of the foundations of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) geophysical parameter retrievals. This article highlights that, besides power calibration, accurate time delay calibration is also crucial to GNSS-R ocean wind retrievals as the computation of normalized bistatic radar cross section (NBRCS) requires the location of the specular point (SP) in the delay-Doppler map (DDM). First, the specular delay tracking algorithm of Fengyun-3 (FY-3) GNSS-R missions is described, which particularly considers the geoid undulation and land topography. The sources and impacts of the specular delay error are discussed. Then, three methods that evaluate and calibrate the specular delay in the FY-3E and FY-3F in-orbit data are proposed: by sea water observations, sea ice observations, and waveform retracking. Results from the three methods agree with each other, finding a difference in the specular delay tracking between FY-3E and FY-3F, which also differs for different GNSS systems. The calibration by sea ice observations was found to be the optimal method. Finally, the impacts of delay errors on the ocean wind retrieval are quantified by actual observations and simulations. A bias in the specular delay could cause several issues, such as NBRCS inconsistency and a decrease in wind retrieval accuracy, depending on the magnitude and sign of the bias.

The bottomside thickness parameter (B-0) is a critical component for accurately representing electron density profiles in the International Reference Ionosphere (IRI) model. This study presents a comprehensive validation and global analysis of B-0 derived from the Fengyun-3 (FY-3) GNSS radio occultation (RO) mission during the high solar activity period of 2022-2024. By integrating observations from the FY-3 constellation, this work effectively complements the data coverage of FORMOSAT-7/COSMIC-2 (F7/C2) by extending analysis to mid- and high-latitude regions. Validation against global digisonde measurements demonstrates that FY-3 derived B-0 achieves high reliability, yielding correlation coefficients exceeding 0.86 and an RMSE of approximately 20 km in low and midlatitudes. Comparisons with three IRI-2020 model options reveal that the ABT-2009 option offers the best overall agreement with observations, particularly in reproducing hemispheric asymmetries, whereas the Gul-1987 and Bil-2000 options exhibit notable deficiencies in capturing geomagnetic modulation and spatial variability. Global morphological analysis identifies a synchronized but inversely correlated relationship between B-0 and peak electron density (NmF2) in the equatorial ionization anomaly region. Furthermore, distinct longitudinal structures are observed, characterized by wavenumber-4 patterns during equinoxes, and wavenumber-2 and -3 patterns during summer and winter solstices, respectively. In high latitudes, observations suggest a B-0 enhancement near the South Magnetic Pole during the Southern Hemisphere winter, which likely reflects the influence of geomagnetic control. These findings confirm the utility of FY-3 RO data for characterizing the global ionosphere and offer valuable constraints for future refinements of the IRI model.

Soil freeze-thaw (F/T) states are a key indicator of the Arctic climate, highlighting the need for their accurate retrieval. Global navigation satellite system-reflectometry (GNSS-R) offers a promising approach for retrieving soil F/T states, as F/T states significantly affect soil dielectric properties and reflectivity. Traditional reflectivity-based retrieval algorithms, such as the seasonal threshold algorithm (STA), are highly susceptible to surface interference, including vegetation attenuation and terrain effects, which limits their retrieval accuracy when using a fixed classification threshold. This study proposes an STA-Bayesian algorithm that formulates soil F/T retrieval as a probabilistic inference task. By combining seasonal prior information with GNSS-R observation likelihoods under frozen and thawed states, the algorithm derives posterior probabilities for Bayesian-optimal F/T state decisions, replacing fixed-threshold retrieval with a probabilistic approach that is more robust to surface heterogeneity. The algorithm is applied to GNSS-R data from the Fengyun-3E (FY-3E) satellite and Tianmu-1 (TM-1) constellation. In validation against ERA5-Land soil temperature-derived F/T states, the STA-Bayesian F/T product attains an average accuracy of 92.97%, outperforming both the STA (75.35%), and the soil moisture active passive (SMAP) (85.22%). The STA-Bayesian algorithm shows substantial improvements over both the STA and SMAP F/T products in densely vegetated regions (e.g., forests and shrublands) and demonstrates greater robustness to terrain roughness effects. The results demonstrate the STA-Bayesian algorithm's promise for soil F/T monitoring over heterogeneous surfaces and its adaptability to retrieval in the complex environmental conditions of the Arctic.

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.

This paper proposes a passive in-orbit calibration method for phased array antennas using GNSS carrier-phase measurements. By performing synchronous observation and exploiting the short-baseline property between the positioning antenna and array elements, the first differencing operation eliminates space propagation errors and clock biases. By further utilizing receiver channel consistency, the second differencing operation cancels out the receiver channel errors, thereby extracting the relative receive-chain phase error of the element under test. Under typical operating conditions, the calibration accuracy can reach an RMS error of approximately 3.02mm, corresponding to a phase accuracy of 5.72 degrees in the GPS L1 band. This accuracy is close to the 5.625 degrees minimum phase step of a 6-bit digital phase shifter, and can be further improved under higher C/N0 and well-controlled residual error conditions. Without requiring a dedicated GNSS band excitation signal, this method avoids co-frequency self-interference with the positioning antenna, which provides an auxiliary approach for in-orbit calibration of phased array receive chains.

Highlights What are the main findings? A GNSS-R altimetry algorithm based on signal separation is proposed to address direct and reflected signal mixing in low-altitude UAV observations. The direct-signal-priority strategy suppresses the dominant direct signal and retrieves the geometric delay, enabling stable real-time height estimation. What are the implications of the main findings? The proposed method improves the robustness of GNSS-R altimetry under strong signal coupling conditions typical of low-altitude platforms. The results support the application of UAV-based GNSS-R for rapid surface elevation monitoring and inland water observation.Highlights What are the main findings? A GNSS-R altimetry algorithm based on signal separation is proposed to address direct and reflected signal mixing in low-altitude UAV observations. The direct-signal-priority strategy suppresses the dominant direct signal and retrieves the geometric delay, enabling stable real-time height estimation. What are the implications of the main findings? The proposed method improves the robustness of GNSS-R altimetry under strong signal coupling conditions typical of low-altitude platforms. The results support the application of UAV-based GNSS-R for rapid surface elevation monitoring and inland water observation.Abstract GNSS reflectometry (GNSS-R) altimetry has been widely used for retrieving surface elevation over oceans, cryosphere, and land. Recently, UAV-borne GNSS-R systems have gained attention due to their flexibility for low-altitude and localized observations. However, lightweight UAV platforms impose strict payload and real-time processing constraints. At low altitudes, the small geometric delay between direct and reflected signals often leads to waveform overlap, degrading conventional altimetry algorithms. In this study, a lightweight UAV-borne GNSS-R receiver and a signal-separation-based altimetry method are proposed. Direct and reflected signals are separated using waveform characteristics without relying on external height information, mitigating the impact of waveform overlap. Simulations and experiments using a SPIRENT 9000 GNSS simulator demonstrate stable height retrieval under dynamic low-altitude conditions while maintaining real-time capability, confirming the feasibility of lightweight UAV GNSS-R altimetry for rapid elevation monitoring.

This article introduces the Chinese Tianmu-1 Global Navigation Satellite System Reflectometry (GNSS-R) constellation of 22 small satellites launched in 2023-2024 and comprehensively evaluates the latest version of the in-orbit data. First, the mission design and instrument technology are described, which largely builds on the FengYun-3/GNOS-II missions. Notable innovations include full GNSS compatibility and dual-polarization antenna. Then, the spatiotemporal characteristics of the constellation are analyzed-specifically, coverage percentage (CP) and mean revisit time at different latitudes. Next, the accuracy of its science products including ocean surface winds and land soil moisture has been assessed, with two application cases demonstrating the mission's utility for monitoring tropical cyclones (TCs) and flooding. Finally, this article, for the first time, evaluates Tianmu's polarized observations including horizontal (H), vertical (V), left-hand circularly polarized (LHCP), and right-hand circularly polarized (RHCP). Analysis of the signal-to-noise ratio (SNR) and reflectivity shows that the dual-polarimetric observations follow the trend of theoretical models and hold promise for advancing land remote sensing.

Surface soil moisture (SM) is a critical factor in hydrological modeling, agricultural management, and numerical weather forecasting. This paper presents a highly effective soil moisture retrieval algorithm developed for the FY-3E (FengYun-3E) GNOS-R (GNSS Occultation Sounder II-Reflectometry) instrument. The algorithm incorporates a first-order vegetation model that considers vegetation density and volume scattering. Utilizing multi-angle GNOS-R observations, the algorithm derives surface reflectivity, which is combined with ancillary data on opacity, vegetation water content, and soil moisture from SMAP (Soil Moisture Active Passive) to optimize the retrieval process. The algorithm has been specifically tailored for different surface conditions, including bare soil, areas with low vegetation, and densely vegetated regions. The algorithm directly incorporates the angle-dependence of observations, leading to enhanced retrieval accuracy. Additionally, a new approach parameterizes surface roughness as a function of angle, allowing for refined corrections in reflectivity measurements. For vegetated areas, the algorithm effectively isolates the soil surface signal by eliminating volume scattering and vegetation effects, enabling the accurate estimation of soil moisture. By leveraging multi-angle data, the algorithm achieves significantly improved retrieval accuracy, with root mean square errors of 0.0235, 0.0264, and 0.0191 (g/cm3) for bare, low-vegetation, and dense-vegetation areas, respectively. This innovative methodology offers robust global soil moisture estimation capabilities using the GNOS-R instrument, surpassing the accuracy of previous techniques.

High-precision sea surface significant wave height (SWH) data are crucial for advancing oceanic shipping and marine resource development. In this study, we conducted a comparative analysis of parameter sensitivity and accuracy in SWH retrieval between BDS-R and GPS-R based on multi-global navigation satellite system reflectometry (GNSS-R) system data from the Fengyun-3E satellite. Initially, a sensitivity analysis of delay-Doppler map observations, including leading edge slope (LES), normalized bistatic radar cross section, and normalized signal-to-noise ratio, was performed against the ERA5 SWH using the multiple linear regression approach. The results highlighted LES as the most sensitive parameter influencing SWH, with BDS-R LES demonstrating superior performance compared with GPS-R LES, achieving a lower root-mean-square error, potentially due to its broader bandwidth. Subsequently, based on LES observations and the double exponential function, we developed a GNSS-R SWH retrieval model and validated its accuracy using ERA5 SWH data and ocean buoy data. Results demonstrate that BDS-R slightly outperforms GPS-R in SWH retrieval, with an accuracy of up to 0.52 m (0.55 m for GPS-R), and both exhibit high correlations of 0.90 in the retrieved results. Moreover, an evaluation using NDBC buoy data indicates that BDS-R surpasses GPS-R in terms of accuracy, correlation, and bias, aligning with the results from the sensitivity analysis. This study provides theoretical and technical guidance for achieving higher precision SWH retrieval in the future using broader bandwidth global navigation satellite system signals.

Global Navigation Satellite System (GNSS) Radio Occultation (RO) and GNSS Reflectometry (GNSS-R) are the two major spaceborne GNSS remote sensing (GNSS-RS) techniques, providing observations of atmospheric profiles and the Earth’s surface. With the rapid development of GNSS-RS techniques and spaceborne missions, many experiments and studies were conducted to assimilate those observational data into numerical weather-prediction models for tropical cyclone (TC) forecasts. GNSS RO data, known for its high precision and all-weather observation capability, is particularly effective in forecasting mid-to-upper atmospheric levels. GNSS-R, on the other hand, plays a significant role in improving TC track and intensity predictions by observing ocean surface winds under high precipitation in the inner core of TCs. Different methods were developed to assimilate these remote sensing data. This review summarizes the results of assimilation studies using GNSS-RS data for TC forecasting. It concludes that assimilating GNSS RO data mainly enhances the prediction of precipitation and humidity, while assimilating GNSS-R data improves forecasts of the TC track and intensity. In the future, it is promising to combine GNSS RO and GNSS-R data for joint retrieval and assimilation, exploring better effects for TC forecasting.

An evaporation duct is a kind of atmospheric event with a refractive index exceeding the curvature of the Earth, which mostly exists on the ocean surface. Evaporation ducts have a great influence on radar, such as causing blind zones or achieving over-the-horizon detection. However, there is a lack of effective technology for evaporation duct detection, especially for passive methods. Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated potential in various remote sensing applications. However, its utilization for evaporation duct retrieval has not yet been successfully achieved. This study investigates the impact of evaporation ducts on GNSS-R delay maps (DMs), demonstrating that they elevate the non-specular point region, with the extent of this rising zone correlating with the evaporation duct height (EDH). Through semi-physical simulation, the rise signal is modeled. During a four-day experiment, GPS-R DMs with obvious features of evaporation ducts were repeatedly observed. Additionally, this study attempts to find the maximum code delay in the experimental data. The EDH is retrieved using the maximum code delay and GPS elevation angle, exhibiting a 4 m error relative to the reference model under the condition that all effective waveforms are successfully received. The results demonstrate that the GNSS-R offers a promising passive method for evaporation duct detection.

Global Navigation Satellite System (GNSS) Radio Occultation measurements can remote sensing Earth’s neutral atmosphere and ionosphere [1]. Meanwhile, another GNSS remote sensing technique named GNSS Reflectometry (GNSS-R) has been developed rapidly, which can retrieve sea surface wind speed [2] and soil moisture. The GNSS Occultation Sounder (GNOS) II has been designed to integrate these two GNSS remote sensing techniques in one instrument which is one payload of the third batch FengYun-3 (FY-3) satellites. At present, three GNOS II instruments are on orbit which were manufactured by National Space Science Center (NSSC), Chinese Academy of Sciences. This paper presents the on-orbit performance of GNOS II instrument and the accuracy of retrieved products including atmospheric refractivity, ionosphere electron density, sea wind speeds and soil moisture. We show that the atmosphere refractivity standard deviation of GPS and BDS(Beidou) occultation data is better than 1%, the sea wind speed errors for GPS-R and BDS-R are less than 1.5m/s, compared with the SMAP satellite microwave radiometer, the accuracy of soil moisture is better than 0.05 0.05cm3/cm3.

The Fengyun-3 (FY-3) GNSS-R constellation launched in 2021-2023 has three satellites (FY-3E/F/G) and the Tianmu-1 (TM-1) GNSS-R constellation launched in 2023-2024 has 22 satellites till now. The Level 2 ocean surface wind products of those satellites have been continuously generated operationally. In order to maximize their applications, the Level 3 merged wind product needs to be developed. This paper describes the development of the Level 3 wind product of FY-3 and TM-1 and presents preliminary results of two applications. The first application of the merged wind products is monitoring of tropical cyclones in every 6 hours. The second is improving the initial state and forecasts of AI-based weather models.

Global marine vertical deflection is essential for understanding Earth's internal mass and density distribution and improving the long-term inertial navigation accuracy of underwater vehicles. Spaceborne global navigation satellite system reflectometry (GNSS-R) interferometric altimetry, with its advantages of low-cost, rapid global coverage, and revisiting capability, shows promise as new data for compensating the radar altimeters. In this study, we present the first simulation of spaceborne GNSS-R interferometric altimetry data based on FY-3E GNSS-R trajectory and evaluate its potential for inverting global marine vertical deflection. Our results showed that, in the operational scenario (with a spatial resolution of 10 km and an altimetry accuracy of 14.68 cm), the total accuracy of marine vertical deflection for global 2.5 ', 5 ', and 20 ' grids, obtained from ten years of simulation data, was 4.998 '', 1.268 '', and 0.1 '', respectively. Furthermore, we observed that, when the average global revisit times reached approximately 90 times and 6 times, the total accuracy of marine vertical deflection for global 5 ' and 20 ' grids was below 1 ''. As the GNSS-R detection on the FY-3E satellite covers 90% of the global sea area in 20 ' grids within 23 days, with an average revisit time of 7.3, spaceborne GNSS-R interferometric altimetry has the potential to invert the high-precision time-variable gravity field with a 20 ' resolution, thus compensating for the lower resolution provided by satellite gravimetry.

The Earth’s time-variable gravity field holds significant research and application value. However, satellite gravimetry missions such as GRACE and GRACE-FO face limitations in spatial resolution when detecting monthly gravity fields, while traditional radar altimeters lack the observational efficiency needed for monthly gravity anomaly inversion. These limitations hinder further exploration and application of the Earth’s time-variable gravity field. Leveraging its advantages, such as rapid global coverage, high revisit frequency, and low cost for constellation formation, spaceborne GNSS-R technology holds the potential to address the observational efficiency gaps of traditional radar altimeters. This study presents the first assessment of the capability of spaceborne GNSS-R interferometric altimetry for high spatial resolution monthly marine gravity anomaly inversion through simulations. The results indicate that under the PARIS Operational scenario of a single GNSS-R satellite (a spaceborne GNSS-R interferometric altimetry scenario proposed by Martin-Neira), a 30′ grid resolution marine gravity anomaly can be inverted with an accuracy of 4.93 mGal using one month of simulated data. For a dual-satellite constellation, the grid resolution improves to 20′, achieving an accuracy of 4.82 mGal. These findings underscore the promise of spaceborne GNSS-R interferometric altimetry technology for high spatial resolution monthly marine gravity anomaly inversion.