Due to the inherent limitations of current remote sensing techniques, the actual occurrence and accurate abundance of water ice in lunar regolith cannot be directly identified, making in-situ verification urgently necessary. The Chang’E-7 mission of China’s Lunar Exploration Program plans to conduct water ice detection at the lunar south pole, where an in-situ micro-sampling device mounted on the rover will perform quantitative collection of lunar regolith for the volatiles in-situ measurement instrument. However, the uncertainty of lunar surface conditions leads to considerable dispersion in sampling mass. Moreover, water ice sublimation loss caused by tool-soil temperature difference and mechanical interactions during sampling will further reduce the detection accuracy. To ensure the reliability of detection data from the volatiles measurement instrument, this paper proposes calibration methods for sampling mass and water ice loss. Icy lunar regolith simulant is prepared via vapor deposition during sampling, and sampling mass calibration experiments are carried out using a self-developed calibration device. Preliminary results show that sampling mass varies significantly with regolith particle size distribution. Further research will be conducted on the physical mechanism of icy lunar regolith sampling under multi-factor coupling conditions. This study aims to provide a high-confidence physical response parameter spectrum for accurate interpretation and scientific analysis of Chang’E-7 in-orbit data.

The Chang’E-7 orbiter is expected to carry the Wide-band InfraRed Imaging Spectrometer (WIRIS), which will acquire high spectral resolution images and thermal emission data of the lunar surface across a broad spectral range from the visible to longwave infrared (0.45～10 µm). These data will support scientific investigations into lunar surface mineral composition, thermal environment, and water/hydroxyl detection. Compared to previous lunar orbital hyperspectral instruments, WIRIS enhances quantitative retrieval capabilities for key spectral features such as the Christiansen Feature (CF) of silicate minerals and molecular water. Building upon the design of the Tianwen-1 Mars Mineralogical Spectrometer, WIRIS extends its spectral coverage into the mid- to long-wave infrared range (3.3～10 μm), and incorporates simultaneous temperature measurements to reduce thermal correction uncertainties in the 3 μm water/hydroxyl absorption region. This study addresses the quantitative calibration requirements of the newly extended spectral range by proposing spectral, radiometric, and geometric calibration methods tailored for the mid- to long-wave infrared bands. Based on calibration experiments, the sources of error and associated uncertainties are analyzed. The results provide essential methodological and technical support for accurate physical parameter retrieval and scientific application of WIRIS mid- to long-wave infrared data.

The lunar surface becomes charged under the influence of solar wind and sunlight, and the potential differences on the surface form an electric field environment of various scales, which is the main mechanism for material transfer on the lunar surface. The electric field probe of Chang’E-7 will, for the first time, conduct in-situ electric field detection on the lunar surface. This paper introduces the design and results of the physical verification test of the probe in plasma environment based on the qualification model of the electric field probe. The basic principle of the electric field probe is the plasma electric probe principle. By clamping the probe current at a specific value, the potential of the probe can be determined according to the plasma V-I characteristic curve. The potential difference between different probes is the potential difference formed by the electric field environment on the plasma environment. Whether the probe can measure the plasma V-I characteristic curve is the key to the success of the probe design. With the help of a ground low-energy plasma simulation device, this test was carried out, and fixed current drive and scanning current measurement were conducted. The test results show that the electric field probe of Chang’E-7 can correctly reflect the plasma environment inside the simulation device and obtain a stable V-I characteristic curve. This proves that the electric field probe can achieve the function of obtaining the plasma potential by driving the probe current when working on the lunar surface, and the physical characteristics of the probe have been fully verified through the test.

As a key indicator of global climate change and an essential freshwater resource, the accurate acquisition of multiple physical parameters of glaciers holds significant importance for global climate change research, ecological conservation, and water resource planning. In China, glaciers are predominantly mountain glaciers distributed in high-altitude regions. Constrained by harsh environments and complex terrain, traditional in-situ detection methods fail to achieve large-scale continuous monitoring of internal glacier parameters. Satellite-borne glacier remote sensing, meanwhile, faces limitations in resolution and interference from complex ground clutter in mountainous glacier regions, and thus has yet to be operationalized. Airborne radar, with its superior spatial resolution and flexible detection capabilities, has become a critical technical tool for glacier monitoring and research. However, airborne detection of mountain glaciers still confronts challenges posed by undulating ice surfaces and complex subglacial topography: scattering clutter from the uneven ice surface interferes with radar signal interpretation and precise inversion of key parameters, while the intricate subglacial structure and scattering losses caused by ice surface topography interact with dielectric losses within the ice, impeding accurate inversion of glacier dielectric constants. To address these challenges, this study integrates airborne ultra-wideband radar detection data from mountain glaciers with the Pseudo-Spectral Time Domain (PSTD) numerical simulation method. A coupled model of ice surface-subglacial dual interface topography and dielectric parameters is established. Through two-dimensional PSTD electromagnetic simulations, the interaction mechanism between topographic scattering and ice dielectric loss is elucidated. Furthermore, an inversion method for the imaginary part of the ice layer dielectric constant in measured regions is proposed based on dynamic range analysis. For the measured data from Laohugou Glacier No. 12, iterative optimization converges the estimated imaginary part value to 6.0×10^–4. The relative error between the estimated imaginary part and the theoretical mean is 21%. Cross-validation between simulation results and theoretical models demonstrates that this method effectively improves the inversion accuracy of glacier dielectric parameters in complex terrain by decoupling the synergistic interference between topographic relief and dielectric parameters, thereby offering a viable solution for studying internal dielectric properties of glaciers.

Currently, there are relatively few spaceborne methods for detecting near-space atmospheric wind fields, and the Fabry-Perot Interferometer (FPI) is one of the more important and widely used detection techniques. To address the gap in China’s space-based FPI wind sensing capabilities, the National Space Science Center developed a spaceborne FPI wind interferometer. This paper mainly introduces this instrument’s optical design, structural design, thermal control design, optical simulation, and result analysis. First, the optical design is discussed based on the wideband detection requirements, and the imaging system’s image quality is evaluated. Then, based on optical simulation data, wind speed inversion and accuracy analysis of the spaceborne FPI instrument are conducted. The wind speed errors at the 557.7 nm and 762.0 nm bands are –1.722 m·s^–1 and –2.3672 m·s^–1, respectively, indicating that the spaceborne instrument design meets the wind measurement requirements. Then, the key points of the instrument’s structural design and the thermal control solution for the imaging part are presented, along with a translational filter switching device driven by a trapezoidal lead screw and a micro gear stepping motor or micro linear motor. The paper also explores the relationship between the temperature control accuracy of the instrument’s core components (the etalon) and wind measurement errors. A combined active and passive design is adopted to minimize the impact of temperature fluctuations on the results, which is verified with simulation results.

Accurate observation of sea surface wind fields is essential for tropical cyclone forecasting and meteorological hazard mitigation. The HY-2 series microwave scatterometer continuously measures Ku-band ocean surface winds. However, its current wind speed retrieval algorithm struggles in high wind conditions and systematically underestimates speeds during extreme events such as typhoons. To address this bias, this study utilized the HY-2 wind speed data of nine tropical cyclones between 2021 and 2022 as the data source. The Stepped Frequency Microwave Radiometer (SFMR) wind speed measurements served as the ground truth. A modeling dataset was constructed by resampling the SFMR reference data to match the 25 km spatial resolution of the HY-2 scatterometer, followed by spatiotemporal matching within a two-hour time window. The matched dataset was then randomly divided into a training set and a testing set at a 7∶3 ratio. Subsequently, the Broad Learning System (BLS) was employed to conduct the regression analysis and develop a high-wind-speed correction model. BLS employs a shallow, flat architecture in which input features are expanded into “enhanced nodes”, avoiding the deep stacks typical of conventional neural networks. This structure reduces computational cost and accelerates convergence while maintaining predictive performance. Validation results demonstrate that the corrected HY-2 wind speeds achieved a Root Mean Square Error (RMSE) of 4.47 m·s^–1, representing a 35% improvement compared to the uncorrected data. For wind speeds exceeding 25 m·s^–1, the corrected RMSE reached 6.76 m·s^–1, marking significant enhancements over the original values of 13.27 m·s^–1. Additionally, a comparative analysis using Typhoon Chanthu (in 2021) as a case study revealed that the corrected HY-2C maximum wind speed increased from 22.09 m·s^–1 to 32.73 m·s^–1, closely matching wind fields retrieved by Synthetic Aperture Radar (SAR). Further validation through wind speed profile comparisons confirmed the effectiveness of the proposed model. These results demonstrate that our correction framework markedly improves extreme-wind retrieval accuracy, yielding bias-corrected HY-2 products that are more reliable for applications, such as storm surge simulation and typhoon track forecasting.

Equatorial Plasma Bubbles (EPBs) are large-scale depletion structures characterized by significantly reduced electron density, which frequently emerge in the low-latitude ionosphere during post-sunset hours. These dynamic plasma irregularities play a crucial role in space weather phenomena, as their evolution can induce severe amplitude and phase scintillations in radio signals, leading to disruptions in satellite communications, global navigation systems, and radar operations. Given their substantial impact on technological systems, accurate prediction of EPB evolution has become a critical challenge in both space physics research and operational space weather forecasting. To address this challenge, this study introduces a novel data-driven approach for EPB evolution prediction by leveraging the SimVP (Simpler yet Better Video Prediction) framework, an advanced deep learning architecture designed for spatiotemporal sequence forecasting. The proposed model learns the complex nonlinear dynamics of EPB structures from historical airglow image sequences, capturing both their morphological transformations and drift patterns. Through extensive experimentation, we systematically evaluate the influence of key parameters—including time resolution, input/output sequence length, and environmental noise—on prediction performance. Our findings demonstrate that an optimal configuration with a 3 min temporal resolution and a 6-frame input/output structure achieves superior predictive accuracy, as evidenced by high Structural Similarity (SSIM=0.989) and Peak Signal-to-Noise Ratio (PSNR=34.704) metrics. Further analysis reveals that the spatial complexity of EPB structures, such as bifurcation events and irregular boundary deformations, significantly affects prediction fidelity, whereas the impact of light pollution—a common issue in ground-based airglow observations—is comparatively minor. The model proposed in this paper demonstrates robust cross-station applicability. Beyond forecasting, the model also exhibits potential for reconstructing corrupted airglow data, offering a computational solution to enhance observational datasets affected by atmospheric or instrumental noise. This work not only establishes a robust, machine learning-based tool for EPB evolution analysis but also contributes to the broader development of Artificial Intelligence (AI) applications in space weather modeling and ionospheric research.

Polar Mesospheric Clouds (PMCs), as ice crystal clouds formed in the middle and upper atmosphere (approximately 83 km high), have a seasonal onset that serves as an important parameter for studying the coupling processes between thermodynamics and dynamics in the polar mesosphere. Based on multi-source observational data from 1979 to 2023, the long-term evolution characteristics of the onset of PMCs in both hemispheres are systematically analyzed, and their correlations with the reversal time of stratospheric zonal mean wind and solar activity are examined. Results show that there are significant differences in the onset of PMCs between the two hemispheres: the interannual variation (with a standard deviation of 22 d) in the southern hemisphere is about twice that in the northern hemisphere (11 d), which may be related to differences in thermal and dynamic processes such as inter-hemispheric circulation modes and the intensity of gravity wave activity. In the southern hemisphere, the onset of PMCs season exhibits a very strong positive correlation with the reversal time of the stratospheric zonal mean wind, while in the northern hemisphere, although a negative correlation is observed, the approximately 60-day difference does not directly indicate a causal relationship between the two. The regulation of the onset by solar activity (Lyman-α radiation) also shows hemispheric asymmetry. In the northern hemisphere, there was a certain negative correlation with solar activity before 2011 that later weakened due to changes in the stratospheric dynamic background, whereas the southern hemisphere exhibited only a weak response. This indicates that both solar radiation effects and dynamic processes may jointly contribute. In addition, the discrepancies among multi-source data suggest that differences in detection systems and data types can introduce uncertainties in studies of the long-term variation characteristics of PMCs.

The Chang’E-7 lunar mission, scheduled for launch in 2026, has the primary scientific objective of detecting water-ice deposits within the Permanently Shadowed Regions (PSRs) at the lunar south pole. Understanding the distribution and concentration of lunar water ice is crucial for both fundamental science and future In-situ Resource Utilization (ISRU). In this study, we developed a high-fidelity model of the Chang’E-7 Lunar Neutron and Gamma-ray Spectrometer (LNGS) payload using the Geant4 toolkit (Version 10.07.p02) and established a quantitative inversion relationship between lunar surface water content and epithermal neutron count rates. The LNGS model, constructed by importing a detailed CAD model into Geant4, was rigorously validated against neutron beam calibration experiments conducted at the China Spallation Neutron Source (CSNS) Back-n facility. The results are as follows. The detector model shows excellent agreement with experimental data across the 0.4 eV to 1000 eV energy range, with a relative error of less than 6%, confirming the accuracy of the mass modeling and simulation setup. LNGS exhibits significant capability in discriminating soils with varying water content, as evidenced by both simulation and ground-based validation experiments using layered soil and water samples. Within the water-ice content range of 0.01% to 20%, simulations of Galactic Cosmic Ray (GCR) bombardment and subsequent neutron transport show that the epithermal neutron (400～700 keV) count rate decreases significantly with increasing hydrogen abundance. This relationship follows a modified Lawrence model with an exceptional coefficient of determination (R² = 0.9993). The slight parameter differences compared to the original Lawrence model are attributed to the different simulation tools, lunar regolith composition models, and distinct detector designs and energy responses between LNGS and the Lunar Prospector neutron spectrometer. This study provides a robust theoretical framework and a specific, validated inversion model for interpreting Chang’E-7 orbital neutron data, directly enabling the mapping of hydrogen concentrations from measured count rates. It establishes fundamental technical support for the development of in-situ resource utilization technologies on the Moon and paves the way for high-precision assessment of water ice resources in the lunar polar regions.

The responses of thermospheric winds at middle latitudes to the moderate geomagnetic storm of 18－19 March 2018, are examined using two ground-based Fabry-Perot Interferometer (FPI) observations from the Xinglong (XLON, 40.2°N, 117.6°E; magnetic latitude 35°N) and the Sutherland Astronomical Observatory (SAAO, 32.2°S, 20.48°E; magnetic latitude 40.7°S), combined with simulations from the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM). The storm reached a maximum Kp index of 6, classifying it as a moderate storm. Ground-based FPI measurements provided high-resolution wind data at both stations, capturing the temporal evolution of zonal (east-west) and meridional (north-south) wind components. Meanwhile, the TIEGCM simulations offered a theoretical framework to interpret the observed disturbances and assess the model’s capability in reproducing storm-induced thermospheric dynamics. The results reveal that the response of thermospheric winds to the geomagnetic storm is more pronounced in the southern hemisphere than that in the northern hemisphere. Significant enhancements in equatorward and westward winds are observed at the SAAO station, with maximum meridional wind speeds reaching 128.4 m·s^–1 (equatorward) and maximum zonal wind speeds reaching –165.6 m·s^–1 (westward). Comparative analysis with TIEGCM simulations indicates that the model can reasonably reproduce the disturbance trends in observations, particularly in the variations of meridional winds at SAAO and zonal winds at XLON. The model successfully captured the transition from quiet-time wind patterns to storm-driven disturbances, including the shift toward westward and equatorward. However, certain quantitative discrepancies remain in the model’s predictions: the model underestimates the eastward zonal winds at SAAO and overestimates the equatorward meridional winds at XLON. Future studies could consider using multiple ground-based stations and a variety of observations, such as temperature, density, chemical composition for the study. Furthermore, investigating the role of seasonal and local time effects in modulating hemispheric asymmetries could provide deeper insights into thermospheric storm responses. Overall, this study contributes to a better understanding of the storm impacts on thermospheric winds and hemispheric differences, as well as their potential physical causes.