Resource
2026 EN
Zhanglin Shen · Qiujie Chen · Yunzhong Shen
+1 more
Temporal gravity field solutions from the Gravity Recovery and Climate Experiment Follow-on (GRACE-FO) mission are inherently constrained by aliasing effects stemming from imperfect background models. Recent advancements in ocean tide modeling (e.g., FES2022) and non-tidal de-aliasing products (e.g., Atmosphere and Ocean De-Aliasing Level-1B (AOD1B) RL07) have the potential to enhance signal retrieval; however, their combined impact on gravity field estimation from the K-band Ranging System (KBR) and the more precise Laser Ranging Interferometer (LRI) remains insufficiently quantified. In this study, we assess the influence of these updated background models using eight sets of monthly GRACE-FO gravity field solutions spanning June 2018 to December 2022. Our analysis demonstrates that LRI-based solutions achieve lower noise levels than KBR-based ones while maintaining consistent temporal signal characteristics. The adoption of FES2022 and AOD1B RL07 effectively reduces noise levels across both oceanic and desert regions and enhances the temporal consistency of mass variation signals. Moreover, LRI-based solutions exhibit more pronounced noise reduction than KBR-based ones, with decreases of 5.3% and 8.7% over oceans after applying P4M6 decorrelation filtering, suggesting the LRI's superior measurement sensitivity. Overall, this study provides quantitative evidence that refining background models is crucial for realizing the potential of LRI observations to improve monthly gravity field solutions. This advancement is expected to hold more significant implications for the design of future satellite gravimetry missions.
Resource
2026 EN
Nereida Rodriguez-Alvarez · Xavier Bosch-Lluis · Kamal Oudrhiri
Global Navigation Satellite System Reflectometry (GNSS-R) signals are frequently used to analyze surface scattering properties at L-band, providing valuable observations of Earth's surface conditions. However, GNSS-based remote sensing is inherently vulnerable to radio-frequency interference (RFI) and signal disruptions, particularly in conflict zones where electronic warfare tactics may be employed. According to a study of the Strategic Studies Institute (SSI) US Army War College, the Ukraine-Russia war, for example, has seen an unprecedented level of GNSS denial operations, with military and tactical jamming systems deployed to disrupt satellite-based navigation and communication. Such interference not only affects positioning, navigation, and timing (PNT) services but also has significant implications for remote sensing systems that rely on GNSS signals. This study examines worldwide detections of RFI through L2c GNSS-R measurements through the Soil Moisture Active Passive Reflectometer (SMAP-R) in the last decade. While the RFI would be detected by any GNSS-R mission with worldwide coverage, employing SMAP-R data enables worldwide observations of the RFI dating back to 2015, and enables measuring the polarimetric imprint of the RFI over time for the first time. RFI is extensively detected over East Europe, arising from regions affected by the Ukraine-Russia war. Additionally, the conflict in Syria, and the civil war in Burma are both very distinctive areas. Besides observing an increased noise in the GPS L2c band, which translates into a dramatically reduced signal-to-noise ratio (SNR), SMAP-R shows anomalous polarimetric responses resulting from the signal distortion, the degraded signal coherence, and the unexpected scattering patterns of interfering signals transmitted from jammers. SMAP-R proves a vast depolarization of the GNSS signal through the Stokes and child Stokes parameters. RFI over those areas makes the retrieval of geophysical parameters unfeasible. A flagging methodology is defined as it is being added to the SMAP-R dataset.
Resource
2026 EN
Aina Garcia-Espriu · Cristina Gonzalez-Haro · Veronica Gonzalez-Gambau
+3 more
Satellite datasets are growing larger due to extended mission durations and improved instrument resolutions, creating challenges in efficiently projecting measurements onto geographical grids. This requires the implementation of big data algorithms and specialized data management techniques, with a particular focus on optimizing interpolations and projections. These processing steps are critical as they propagate measurement errors and significantly increase computational time. This work presents a new interpolation algorithm for satellite missions where individual values for each measurement are retrieved. We conduct this study using the Sea Surface Salinity (SSS) processor of the Soil Moisture and Ocean Salinity (SMOS) mission. However, it can easily be extended to other multi-angular acquisition missions. We suggest keeping the measurements within the instrument coordinate system (antenna coordinates) until the final product is generated. This allows us to avoid multiple projection-related errors during the intermediate interpolations. Additionally, we introduce a novel algorithm to project those measurements, taking into account the actual area of the acquisitions instead of considering them as points. Therefore, measurements are weighted based on the area they cover over the Earth. This method is numerically optimized to transform 2D areas into discrete measurements, increasing its computational efficiency and favoring parallelization. The methodology was successfully tested with the SMOS mission's SSS processor at the Barcelona Expert Center (BEC). Final level 3 SSS maps maintain a high resolution close to the one native on the instrument, enabling the characterization of ocean dynamics at finer scales.
Resource
2026 EN
Guangyuan Tan · Weihua Bai · Xiangguang Meng
+9 more
The bottomside thickness parameter (B0) 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 B0 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 B0 achieves high reliability, yielding correlation coefficients exceeding 0.86 and an RMSE of approximately 20 km in low and mid-latitudes. 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 B0 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 B0 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.
Resource
2026 EN
Yifan Wang · Huan Xie · Xiongfeng Yan
+5 more
Determining the rotation axis of small bodies during the approach phase is essential for both mission operations and scientific investigations. Estimating the axis from the motion trajectories of image features has proven effective, but challenges remain due to limited image availability, weak surface textures, and uncertain observation geometries. In particular, tracking errors, unreliable trajectories, and dependence on accurately known rotation periods reduce the robustness and efficiency of existing methods. To address these challenges, this study proposes a rotation-axis estimation method for small bodies during the approach phase, based on image feature tracking and trajectory selection. The method employs sparse optical flow to extract feature trajectories and removes unstable tracks using image masks and bidirectional flow. An adaptive trajectory selection and shape classification are then performed based on the statistical distribution of fitted parameters using the histogram. Finally, a geometry-based optimization model identifies the correct rotation axis solution via a genetic algorithm, without requiring prior knowledge of the rotation period. The proposed algorithm was tested on over 400 simulated cases considering varying sun phase angles, approach angles, image numbers per rotation period, and small body shapes. The results demonstrate that the proposed method significantly outperforms the existing algorithms. The proposed algorithm achieved estimation errors below 3° in 89% of the cases and below 5° in 92% of the cases, and the running time of all the cases was less than 3 min. Validation using in-orbit data from the OSIRIS-REx mission confirmed that the proposed algorithm can estimate the rotation axis of asteroid Bennu with an error of only 2.69°. The results validate the proposed algorithm's effectiveness and efficiency, proving its potential for small body exploration missions.
Resource
2026 EN
Huanting Huang · Xiaolan Xu · Simon H. Yueh
This paper presents a comprehensive analysis of L-band SAR backscatter (σ) from the Soil Moisture Active Passive (SMAP) mission with respect to soil moisture in terms of the real part of soil dielectric constant, vegetation water content (VWC), and surface topography represented by the standard deviation of slope (STD slope). We examine backscatter response across seven landcover types and four major regions globally, employing a two-dimensional (2D) binned framework to isolate the individual impacts of soil moisture and surface roughness under a range of VWC. Results show that radar backscatter increases substantially with the STD slope and soil moisture, with the strongest response under low VWC conditions. The radar backscatter also shows a general increase with VWC for VWC below 5 kg/m2. A piecewise linear function is used to model the relationship between STD slope and backscatter, as the rate of change is higher at lower STD slope ranges. Under low vegetation conditions (VWC < 0.25 kg/m²), the backscatter sensitivity to STD slope (∂σ/∂(STD slope)) is approximately 1 dB/° for STD slopes less than 8° and about 0.3 dB/° for slopes between 8° and 16°, while the sensitivity becomes negligible when VWC approaches 5 kg/m². We also utilize the semi-empirical water cloud model (WCM) of the vegetated soil surfaces to quantify the attenuation and volume scattering by vegetation. The results reveal an exponential decay of vegetation attenuation with increasing VWC and a nonlinear increase in volume scattering, consistent with prior theoretical and experimental studies.
Resource
2026 EN
Quentin Bonassies · Thanh Huy Nguyen · Ludovic Cassan
+6 more
Floods are one of the most common and devastating natural disasters worldwide. The contribution of remote sensing is important for reducing the impact of flooding both during the event itself and for improving hydrodynamic models by reducing their associated uncertainties. This article presents the innovative capabilities of the SurfaceWater and Ocean Topography (SWOT) mission, especially its river node products, to enhance the accuracy of riverine flood reanalysis, performed on a 50-km stretch of the Garonne River. The challenge addressed here is quantifying how SWOT river observations, alone and in combination with in-situ gauges, can improve hydraulic parameter estimation and river water level prediction in flood reanalysis. The experiments incorporate various data assimilation strategies, based on the ensemble Kalman filter (EnKF), which allows for sequential updates of model parameters based on available observations. The experimental results show that while SWOT data alone offers some improvements, combining it with in-situ water level measurements provides the most accurate representation of flood dynamics, both at gauge stations and along the river. The study also investigates the impact of different SWOT revisit frequencies on the model's performance, revealing that assimilating more frequent SWOT observations leads to more reliable flood reanalyses. In the real event, it was demonstrated that the assimilation of SWOT and in-situ data accurately reproduces the water level dynamics, offering promising prospects for future flood monitoring systems. Results show that in the OSSE framework, assimilation reduced water level errors by an order of magnitude, while in the real 2024 event the errors were reduced to below 17 cm, demonstrating the reliability of the approach. This study underscores the complementary role of Earth Observation data in enhancing flood dynamics representation in the riverbed and the floodplains.
Resource
2026 EN
Philipp Sibler · Francescopaolo Sica · Michael Schmitt
Synthetic simulation of Interferometric Synthetic Aperture Radar (InSAR) data plays a critical role in algorithm development, mission design, and machine learning pretraining. However, most existing approaches rely on handcrafted simulation pipelines or rigid data generation setups that lack adaptability to varying sensor configurations. In this work, we propose a novel deep learning-based framework for generating realistic, complex-valued InSAR data that is explicitly conditioned on acquisition parameters, such as wavelength, baseline, incidence angle, and revisit time. The proposed multitask model jointly synthesizes scene reflectivity, coherence magnitude, and interferometric phase while enabling the simulation of temporal decorrelation and noise characteristics via both analytical and data-driven modules. We further introduce a reparameterizable noise model for speckle and phase noise, whose statistics are tuned using Particle Swarm Optimization to match real sensor characteristics. Extensive experiments on the high-resolution GeoNRW TDX dataset demonstrate the realism and flexibility of our simulation framework, which can generalize to unseen acquisition scenarios and support SAR mission design.
Resource
2026 EN
Yin Jin · Huadong Guo · Hanlin Ye
+3 more
Stray light degrades image quality and may damage the Moon-based multispectral camera. This study focuses on the effects of stray light on the lunar south pole on Earth observation in the Chang'e-8 mission. Compared with space-borne platform, the illumination conditions of Moon-based sensor are more complex, and the extent of its impact on the sensor remains unclear. In this study, we constructed a three-dimensional (3D) lunar illumination model based on the Hapke radiative transfer model and Monte Carlo ray tracing algorithm to accurately simulate illumination conditions and analyze the distribution of stray light on the lunar surface. In addition, the effects of sunlight hitting the camera lens directly and sunlight entering the sensor through diffuse reflection on Moon-based Earth observation were also analyzed. Based on this model, the working time of the Moon-based multispectral camera can be analyzed to avoid both solar intrusion and lunar nights. In addition, the impact of stray light on the sensor's entrance pupil under different work environments can also be evaluated. The results show that in the Chang'e-8 candidate landing area, solar elevation mainly affects the total radiance at the entrance pupil, while solar azimuth governs the spatial distribution of incident light. These findings provide important insights into timing constraints and optical interference, supporting observation scheduling and performance evaluation for the Chang'e-8 Moon-based Earth observation mission.
Resource
2026 EN
Zhipeng Zeng · Jiangjun Ran · Zhengwen Yan
+1 more
This study quantitatively evaluates four candidate 4-satellite constellations-Dual-pair (DP) GRACE-type, Bender-type, DP Cartwheel-type, and DP Pendulum-type-for recovering Sub-Weekly to Weekly Earth time-variable gravity fields (TVGF). Using controlled numerical closed-loop simulations (identical altitude, satellite count, and sub-cycles), we conduct an equitable comparison of these constellations' performances in spectral and spatial domains under identical conditions. Comparative analyses under the optimal spatial resolution for each constellation configuration reveal that the DP Cartwheel-type and DP Pendulum-type configurations outperform the Bender-type configuration in recovering time-variable gravity field models at higher temporal resolutions. Spectral and spatial analyses confirm that the DP Cartwheel and DP Pendulum configurations surpass the Bender-type configuration, reducing the error by approximately 12%. In geoscience applications, the DP Cartwheel-type and DP Pendulum-type configurations slightly outperform the highly advocated Bender constellation design. Specifically, compared to the Bender-type, the DP Cartwheel-type and DP Pendulum-type configurations improve large-basin signal recovery accuracy by 7.4%, Greenland Ice Sheet's southern sector mass balance signals by 3.2%, and co-seismic signals by 13.2%. The simulation results indicate that the DP Cartwheel-type and DP Pendulum-type configurations show superior performance in gravity signal detection. However, due to current limitations in fuel consumption, laser pointing accuracy, and mission budget, maintaining these configurations remains technically and economically challenging. With future reductions in launch costs and advances in relevant technologies, these two configurations may become promising candidates for next-generation gravity missions.