The global navigation satellite system (GNSS)-based applications are highly vulnerable to radio frequency interference in GNSS bands (RFI-GB). Unfortunately, such interference currently occurs frequently worldwide. As a GNSS-based passive remote sensor, spaceborne GNSS reflectometry (GNSS-R) receivers inevitably suffer from terrestrial RFI-GBs (T-RFI-GBs). To investigate the anti-jamming performance differences between Global Positioning System (GPS) reflectometry (GPS-R) and Galileo reflectometry (GAL-R) on a global scale, this study first establishes a comparative model using FY-3F/GNOS-II delay-Doppler map (DDM) data and then validates it through a ground-based experimental platform. The main findings are as follows: 1) Europe, the Middle East, the Red Sea, and West Asia experience a high incidence of intense T-RFI-GBs, which significantly elevate the DDM noise floor, degrade the DDM peak signal-to-noise ratio (SNR), and consequently reduce DDM availability rates for both systems through quality control (QC) filtering. 2) Globally, most intentional and unintentional L1-band T-RFI-GBs concentrate their power near the L1 center frequency. Owing to the split-spectrum characteristic of the binary offset carrier (BOC) modulation employed by GAL E1B signals, GAL-R demonstrates superior anti-jamming performance compared to GPS-R, generally resulting in higher DDM availability rates. In addition, GAL-R exhibits marginally better thermal noise resistance than GPS-R. 3) With the increasing worldwide prevalence of T-RFI-GBs, future implementations should consider adopting new-generation BOC-modulated signals such as L1C and B1C for GPS-R and BeiDou Satellite Navigation System (BDS) reflectometry (BDS-R) to improve their anti-jamming performance. 4) The different patterns in the noise part of affected DDMs, combined with their noise floor values, show potential for identifying T-RFI-GBs types and estimating their intensity.

Abstract. This study uses observations from COSMIC-2, FY-3, GRACE-C/D, KOMPSAT-5, MetOp-A/C, PAZ, PlanetiQ, Sentinel-6A, Spire, TerraSAR-X/TanDEM-X, TM-1, and YY-1 during 1–7 January 2026 to compare their spatial distributions, grid coverage, local-time sampling, and retrieval accuracy; it also assesses the benefits of multi-mission integration and the potential effects of COSMIC-2 retirement on the integrated observing capability. The results show that orbital configuration determines latitudinal and local-time sampling, whereas event abundance and constellation size primarily control grid coverage. Large constellations such as YY-1 and TM-1 provide the strongest global continuity, while missions with different orbital inclinations offer complementary sampling across latitude bands. Combining all missions substantially reduces spatial gaps and provides near-global coverage at 1.0° and 2.0° resolutions. Within 6 h windows, joint coverage reaches 70.98 % at 2.0° resolution. The combined observations also broaden local-time sampling, whereas COSMIC-2 remains particularly important for equatorial coverage. Therefore, the retirement of COSMIC-2 would reduce equatorial sampling capability and should be considered in the development of future multi-mission GNSS-RO observing systems. Comparisons with ERA5 indicate broadly consistent retrieval accuracy among most missions between 10 and 35 km, with larger differences in the lower troposphere. These findings demonstrate the value of integrating missions with diverse orbital configurations and highlight the need to sustain complementary sampling after the retirement of key missions.

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.

In this study, we analyze the impact of the May 2024 geomagnetic storm on the thermospheric mass density by using TianMu-1 constellation satellite (TM02, TM06, TM07, TM11, TM15) observations. These observations reveal intense large-scale traveling atmospheric disturbances (TADs) originating at high latitudes and propagating equatorward. Observations by TM02 captured the evolution of a TAD structure: An initial amplitude of similar to 3.89 x 10(-12) kg/m(3) at hundred-kilometer scale subsequently intensified to 4.78 x 10(-12) kg/m(3), with the spatial extent expanding to the thousand-kilometer level. Significant hemispheric asymmetry was observed: the absolute density was higher predominantly in the northern hemisphere (TM02, TM06, TM07, TM11), whereas the difference in the relative density consistently showed greater enhancements in the southern hemisphere across all satellites, with the maximum north-south density differences exceeding 195%-640% above 60 degrees latitude. In conjunction with SuperDARN (Super Dual Auroral Radar Network) observations, this striking hemispheric asymmetry can likely be attributed to disparities in plasma convection patterns between the two hemispheres. Furthermore, density perturbation characteristics exhibited strong local time (LT) dependence: Near noon (similar to 10.7 LT, TM02 descending), the northern hemisphere onset preceded the southern onset. Conversely, near dusk (similar to 17.6 LT, TM15 descending), the southern onset led the northern onset by approximately 3 hours. Ascending orbits (TM02, TM06, TM07, TM15) typically yielded larger global density enhancements compared with smaller southern-confined enhancements during descending orbits. Satellite TM11 showed comparable perturbations in both ascending and descending orbits. By leveraging its unique orbital architecture, the TianMu-1 constellation enables global near-simultaneous multi-LT sampling, providing a robust data foundation for both scientific research and engineering applications.

To achieve low hemispherical axial ratio (AR) over a wide operating bandwidth for spaceborne antennas, this letter proposes two mechanisms. First, modal analysis reveals that the in-band efficiency zero (EZ) in the stacked antenna originates from a non-radiating energy-storage mode (NESM) excited in the patch continuous-boundary region; short-circuiting its equivalent capacitance eliminates the EZ and extends the bandwidth. Second, a choke-ring-free isophase-front distortion suppression strategy is introduced. Orthogonally stacked patches reshape the cross-polarized field distribution, while transverse serrations (TS) and longitudinal serrations (LS) on the metal wall suppress surface-wave propagation in different directions, thereby mitigating the phase center variation (PCV) degradation caused by the nonuniform hemispherical AR distribution of circularly polarized (CP) antennas. Based on these mechanisms, a stacked global navigation satellite systems (GNSS) antenna is designed. Measurements show AR < 5 dB over a 180° field of view across 1.15–1.65 GHz and PCV within ±0.9 mm, demonstrating its potential for high-precision spaceborne navigation.

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.

Currently, GNSS radio occultation (RO) detection technology has been successfully implemented in satellite missions, including CHAMP, GRACE, COSMIC, Metop, the FengYun-3 series, et al., and has been operationally integrated into numerical weather prediction systems. Despite its technical maturity, the lack of standardized observation-independent exchange format remains a significant challenge for effective data exchange and unified processing of occultation data. To address this limitation, the National Space Science Center (NSSC) of the Chinese Academy of Sciences has proposed and developed the GNSS Radio Occultation Observation Independent Exchange Format (ROEX), aiming to improve data sharing and enhance the application of RO technology in atmospheric sounding and weather forecasting. Based on this, this paper develops a python-based open-source software tool for ROEX file preprocessing, named PyROEX. The software is a graphical user interface (GUI) tool which provides functions for file observation monitoring, observation scientific combination, data integrity checking, and file cutting. Users can freely select the observation they wish to view or combine, and analyze the file quality through plotted graphs. This paper introduces the ROEX format and the functional modules of PyROEX, as well as the analysis results of its operation. It is expected that this software will contribute to the widespread adoption of the ROEX format, thereby reducing the economic costs, time costs, and personnel costs associated with data exchange, communication, and software development caused by inconsistent data formats, and accelerate the operational application of GNSS occultation products.

FengYun meteorological satellites, as the major components of the world’s earth observation constellations, play a crucial role in global meteorological monitoring, yet they are confronted with increasing demands driven by technological progress and higher user expectations. Meanwhile, small satellite constellations have demonstrated remarkable potential in enhancing meteorological observation capabilities through rapid deployment, cost efficiency, and high-density observations. This study introduces FengYun virtual constellation, namely ‘FengYun+’, an integrated system that consists existing FengYun backbone satellites with complementary small satellites. The small satellite framework is designed to fulfill three main goals: new technology verification, thematic earth system observation, and networked collaborative observation. These small satellites encounter key technological challenges related to miniaturization, systematization, rapid processing, and standardization across satellite platforms, payloads, inter-satellite interconnection, and data processing. The collaborative architecture of the virtual constellation, enabled by inter-satellite connectivity, dynamic task allocation, and AI-enhanced processing, supports multi-element, high-frequency, and near real-time global meteorological services. Collaboration with international programs ensures alignment with global meteorological constellation. FengYun+ will advance the Sustainable Development Goals (SDGs) and Early Warnings Initiatives of United Nations (UN) by delivering critical data for weather prediction, climate research, and disaster mitigation.

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.

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.

Abstract In situ observations of thermospheric composition have long been scarce, limiting our understanding of the upper atmosphere and its response to space weather. This study focuses on the Neutral Gas Mass Spectrometer (NGMS) onboard the TM19 and TM20 satellites of China's TianMu‐1 constellation, presenting the first systematic elaboration of its core technical characteristics and novel in situ observational results of O and N 2 number densities. During the 10–12 October 2024 geomagnetic storm, the ratio of O to N 2 number densities (O/N 2 ) decreased significantly at mid‐high latitudes, owing to the stronger enhancement of N 2 compared with O. N 2 perturbations were confined to latitudes above ∼±30° in both hemispheres, whereas O perturbations exhibited a global latitudinal distribution, likely reflecting that heavy N 2 is locally enhanced by upwelling and decays equatorward via diffusion, while O is transported globally by storm‐time circulation and waves. Additionally, the O/N 2 observations from TM19 and TM20 showed high cross‐satellite response consistency with a correlation coefficient of 0.89, while the MSIS 2.0 model systematically overestimated the O/N 2 under geomagnetically quiet conditions. The NGMS onboard TianMu‐1 provides crucial data support for optimizing atmospheric models.

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.

The Fengyun-3 G (FY-3 G) satellite equipped with a Global Navigation Occultation Sounder II (GNOS-II) is capable of tracking BeiDou Navigation Satellite System (BDS) and Global Positioning System (GPS) dual-frequency signals by its zenith precise orbit determination antenna. Currently, most global navigation satellite system (GNSS)-related studies derived the topside total electron content (TEC) only by GPS signals, while there are relatively few studies using BDS signals. In addition, as FY-3 G satellite operates at a lower orbit with an altitude of 410 km, it is closer to the pivotal regions of the ionosphere, particularly the F2 layer. This grants it the capability of capturing dynamic changes and intricate details of the ionosphere with enhanced resolution and sensitivity. In this article, we derive the topside TEC from the six months of FY-3 G GNOS-II data and analyze the performance of differential code bias (DCB). The final retrieved topside BDS and GPS TEC has also been evaluated. The internal assessment of the FY-3 G receiver DCB is conducted by evaluating the long-term stability, which shows that the standard deviation (STD) of the FY-3 G receiver DCB reaches 0.86 TECU for the GPS C1C-C2W observation type and 0.83 TECU for BDS B2I-B6I type. The FY-3 G topside slant TEC (STEC) shows a high agreement with the space weather and atmospheric response mission TEC product, with a mean bias of 0.836 TECU and STD of 2.044 TECU. A comparative analysis in FY-3 G STEC between BDS and GPS satellites reveals no significant systematic bias, and the TEC retrieval performance of BDS-3 is superior to that of BDS-2. Moreover, the ionospheric characteristics of the monthly average FY-3 G topside vertical TEC map is consistent with the International GNSS service Global Ionospheric Map product in latitude, season, and local time. Above all, this research shows that FY-3 G GNOS-II can provide high-quality topside TEC observations, which can serve as a valuable dataset for research in ionospheric and plasma sciences.

The FY-4 series represents China's second generation of geostationary meteorological satellites. FY-4B was launched in June 2021 as the successor to FY-4A, carrying improved payloads including an enhanced Space Environment Package. This paper describes the design and capabilities of the Space Environment In-Situ Suite (SEISS) onboard FY-4B, which comprises seven sensors to detect charged particles, radiation dose, electric potential, and magnetic fields across a wide spectrum of energies. The suite provides real-time monitoring of space weather conditions from geostationary orbit, benefiting activities such as satellite operations, research, and forecasting. Preliminary on-orbit performance results demonstrate the SEISS is successfully measuring various space weather phenomena. Compared to its predecessor, the FY-4B SEISS features expanded detection capabilities with finer energy resolution and faster sampling. The system represents a significant advancement in China's space environment monitoring capabilities from geostationary orbit. (c) 2025 COSPAR. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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.