Advances in Space Research最新文献

{"title":"Long-term atmospheric dynamics: air temperature inversion trends at King Abdulaziz Airport (Jeddah, Saudi Arabia), a high-traffic aviation center","authors":"Abdullrahman Maghrabi,&nbsp;Abdulah Al-Dosari,&nbsp;Mohammed Altlasi,&nbsp;Abdulah Alsherhri,&nbsp;Maohammed Almutairi","doi":"10.1016/j.asr.2026.04.064","DOIUrl":"10.1016/j.asr.2026.04.064","url":null,"abstract":"<div><div>Temperature inversions, characterized by a temperature increase with altitude, significantly affect aviation operations, particularly in regions with meteorological conditions like Jeddah, Saudi Arabia. King Abdulaziz International Airport, a critical hub for pilgrims traveling to Mecca and Medina, experiences high traffic throughout the year, supporting Saudi Vision 2030′s goal of accommodating 30 million annual pilgrims by 2030. This study analyzes 15,432 inversion events from 1985 to 2023 using radiosonde data from the Saudi Bureau of Meteorology, evaluating their frequency, characteristics, and impacts on airport operations. Results show a marked increase in inversion frequency, with low-level inversions (≤1000 m) prevailing at night (81.25% of nighttime cases) and peaking in summer (850–866 cases, June–August), driven by radiative cooling and marine influences. High-level inversions (&gt;1000 m) are more common during winter daytime, associated with subsidence and warm air advection. Mann-Kendall trend tests confirm a significant rise in nighttime low-level inversions at 2.48 cases per year (p &lt; 0.05). These inversions are possibly to extend takeoff and landing distances, increase fuel consumption, and elevate risks of wind shear and reduced visibility, leading to flight delays. Jeddah’s coastal-desert environment intensifies these effects through sea-land breezes and dust events. This study addresses a critical research gap by providing Jeddah-specific insights, laying the groundwork for improved forecasting and operational strategies in a globally significant, consistently busy aviation hub.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 2","pages":"Pages 643-659"},"PeriodicalIF":3.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148528785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote sensing-based aboveground carbon estimation to assess edge effects in Atlantic forests landscapes","authors":"Gabriel E. Suárez-Fernández,&nbsp;Pedro Arias,&nbsp;Juan Luis Rodríguez-Somoza,&nbsp;Joaquín Martínez-Sánchez","doi":"10.1016/j.asr.2026.04.099","DOIUrl":"10.1016/j.asr.2026.04.099","url":null,"abstract":"<div><div>Forests and roads are both fundamental to sustainable development, yet their interaction produces complex ecological consequences. Understanding how road infrastructure alters forest structure is critical to mitigating long-term degradation in biodiversity-rich regions. However, the effects of road infrastructure on aboveground carbon (AGC) remain understudied—particularly when considered alongside structural forest attributes such as canopy height or tree density. This study aimed to quantify and characterize edge effects on AGC stocks and forest structure near various types of road infrastructure, employing a combination of remote sensing and machine learning techniques. An ensemble learning model is trained and applied using high-resolution LiDAR and multispectral imagery from satellite and drone platforms to estimate AGC. These predictions are then used to evaluate spatial edge patterns through cumulative diagnostics. The analyses revealed that road-induced edge effects varied by road type and distance, exhibiting non-uniform and non-linear patterns. AGC and canopy structure responded through abrupt, delayed, or irregular changes. The ensemble model outperformed individual learners in predictive accuracy, achieving a high R² of 0.75 during testing. These findings demonstrate that road-induced edge effects are neither uniform nor monotonic, but rather unfold through complex and delayed structural shifts. This nuanced understanding can support more accurate detection of degradation patterns and guide managers in identifying priority edge zones for targeted mitigation and spatial planning.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 2","pages":"Pages 434-451"},"PeriodicalIF":3.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148528877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the changes of vegetation density, urbanization and surface urban heat islands in Prague using Landsat-8 spectral remote sensing indices","authors":"Furkan Yilgan,&nbsp;Tugba Dogan,&nbsp;Mustafa Ustuner,&nbsp;Sona Guliyeva,&nbsp;Sinasi Kaya,&nbsp;Claire Gallacher","doi":"10.1016/j.asr.2026.04.091","DOIUrl":"10.1016/j.asr.2026.04.091","url":null,"abstract":"<div><div>Rapid urbanization and industrialization, coupled with uncontrolled demographic shifts, have led to significant challenges, including increased pressure on energy resources and unsustainable encroachment on natural landscapes, as rural to urban migration continues to pose serious implications. Thus, urban intensification, characterized by an increased impervious surface, has far-reaching consequences on local climate patterns, accentuating surface temperature and thereby influencing broader climatic dynamics. High temperatures in metropolitan cities can trigger health problems in humans. This experimental research aims to assess the relationships among vegetation density, urbanization, drought, land surface temperature, and surface urban heat island (SUHI) in Prague, Czech Republic, from 2015 to 2022. For these reasons, the normalized difference vegetation index (NDVI), vegetation condition index (VCI), the normalized difference built-up index (NDBI), land surface temperature (LST), and SUHI were calculated in Prague, Czech Republic. The dataset was chosen as the Landsat-8 operational land imager (OLI) and thermal infrared sensor (TIRS) satellite data by Google Earth Engine (GEE) (a cloud-based platform) for summer, the hottest season of the year, from 2015 to 2022. The accuracy of Landsat LST data was assessed using the Moderate Resolution Imaging Spectroradiometer (MODIS) daily 1 km spatial resolution LST dataset as well as data from meteorological ground stations. The correlation between Landsat LST and MODIS was r ≥ 0.38 for all years, and root mean square errors (RMSE) were ≤6°C. In addition, a 0.97 correlation was found with 1.21°C RMSE between Landsat LST and ground data. The study reveals a negative correlation between NDVI and LST, while a positive correlation exists between NDBI and LST. The change in average NDBI from 2015 to 2022 shows an increase of 28.5%, while the average NDVI decreased by 13%. The study shows that, despite the different trends in LST variation, SUHI remains persistent. The cooling effect of vegetation and the heat-retaining nature of built-up areas contribute to these changes. The study also highlights the rising urban heat island effect, particularly in outer city areas, due to demographic shifts. Despite its limitations, this research provides valuable insights for sustainable urban planning. Additionally, this study will contribute to landscape and environmental planning.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 2","pages":"Pages 415-433"},"PeriodicalIF":3.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148528878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy evaluation and adaptability analysis of NRLMSIS 2.1 model during different geomagnetic disturbances at different orbital heights","authors":"Peicheng Li,&nbsp;Bingbing Zhang,&nbsp;Yi Shen,&nbsp;Hongrui Li,&nbsp;Mengyang Li,&nbsp;Zijian Liu","doi":"10.1016/j.asr.2026.04.051","DOIUrl":"10.1016/j.asr.2026.04.051","url":null,"abstract":"<div><div>Thermospheric density is a critical parameter for understanding the space environment. Despite extensive research and the development of various atmospheric models, their accuracy remains limited, particularly during geomagnetic storms when model deviations can exceed 100%. This study systematically evaluates the performance of the NRLMSIS 2.1 model during five geomagnetic storm events of varying intensity (weak, moderate, strong, severe, and extreme) from 2018 to 2024. The evaluation utilizes thermospheric density data derived from GRACE-FO (at approximately 500 km altitude) and Swarm-C (at approximately 450 km altitude) satellite accelerometers, focusing on the model’s response characteristics at different altitudes and phases of geomagnetic storms. The results reveal significant systematic biases in the NRLMSIS 2.1 model during storm periods. Specifically, the model generally underestimates density peaks during the main phase and overestimates density during the initial phase, and tends to underestimate during the recovery phase due to thermal inertia. Furthermore, the model inadequately captures the high-latitude density enhancements and rapid diffusion processes of atmospheric density at high latitudes. We also find that the discrepancies between the modeled and observed densities become substantially larger during intense and extreme geomagnetic storm events, indicating that the NRLMSIS 2.1 model has difficulty capturing rapid and highly localized storm-time thermospheric responses. Moreover, altitude-dependent analysis reveals that the larger absolute errors at 450 km result from a more severe systematic background overestimation combined with atmospheric density scaling effects, rather than inherently poorer model applicability. In conclusion, the NRLMSIS 2.1 model exhibits considerable limitations in accurately characterizing the magnitude, spatial structure, and temporal delay of thermospheric responses during geomagnetic storms.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 2","pages":"Pages 1404-1426"},"PeriodicalIF":3.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148528989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aerodynamic optimization of a Martian drone integrating propulsive effects and neural-network viscous corrections","authors":"Andrea Aprovitola,&nbsp;Luigi Iuspa,&nbsp;Giuseppe Pezzella,&nbsp;Antonio Viviani","doi":"10.1016/j.asr.2026.04.092","DOIUrl":"10.1016/j.asr.2026.04.092","url":null,"abstract":"<div><div>This work presents an optimization procedure for the conceptual design of a fixed-wing Martian exploration drone, combining low-fidelity aerodynamic computation with viscous corrections assisted by neural-network. The aircraft geometry is described through a dual aligned parameterization of airfoil sections and wing planform, enabling synchronized updates of airfoil shape and wing planform within the optimization loop. The methodology is assessed for a <span><math><mrow><mn>1</mn><mspace></mspace><mi>km</mi></mrow></math></span> low-altitude mission in the Martian atmosphere and an estimated free-stream Mach number of <span><math><mrow><msub><mrow><mi>M</mi></mrow><mrow><mi>∞</mi></mrow></msub><mo>=</mo><mn>0.2</mn></mrow></math></span>. The optimization maximizes the lift-to-drag ratio under mass and geometric constraints, while enforcing a preliminary longitudinal static-stability requirement. Aerodynamic performance is evaluated through a fast hybrid framework in which a Vortex Lattice method predicts the three-dimensional aerodynamic loads, while a Physics-Informed Neural Network provides viscous and profile-drag corrections guiding the objective-function. The optimized configuration achieves a lift-to-drag ratio of approximately <span><math><mrow><msub><mrow><mo>(</mo><mi>L</mi><mo>/</mo><mi>D</mi><mo>)</mo></mrow><mrow><mi>max</mi></mrow></msub><mo>=</mo><mn>15</mn></mrow></math></span>, with a total mass of <span><math><mrow><msub><mrow><mi>M</mi></mrow><mrow><mi>TOM</mi></mrow></msub><mo>=</mo><mn>26</mn><mspace></mspace><mtext>kg</mtext></mrow></math></span> at the design operating point, showing a 15% efficiency improvement over the baseline configuration, while satisfying the longitudinal static-stability constraint with <span><math><mrow><msub><mrow><mi>C</mi></mrow><mrow><mi>My</mi><mi>α</mi></mrow></msub><mo>=</mo><mo>-</mo><mn>0.35</mn><mspace></mspace><msup><mrow><mi>rad</mi></mrow><mrow><mo>-</mo><mn>1</mn></mrow></msup></mrow></math></span>. A preliminary propulsive assessment of the hovering phase, indicates a power demand of approximately 6 kW. The optimized architecture preserves the baseline mission constraints while achieving an efficiency-oriented configuration, offering promising potential for future Vertical Take-Off and Landing applications.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 2","pages":"Pages 1592-1609"},"PeriodicalIF":3.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148528946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning estimation of surface ozone using Sentinel-5P data and meteorological and ground observations","authors":"Ali Sam-Khaniani,&nbsp;Mohammad Fallah,&nbsp;Arsalan Ghorbanian,&nbsp;Saeid Homayouni,&nbsp;Sogand Rezaei","doi":"10.1016/j.asr.2026.04.089","DOIUrl":"10.1016/j.asr.2026.04.089","url":null,"abstract":"<div><div>Surface ozone is a critical air pollutant with serious implications for human health and ecosystem balance. This study utilized Sentinel-5P satellite data, meteorological parameters, and ground observations from 2018 to 2021 to estimate surface ozone concentrations in Tehran, capital of Iran. Four machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), were employed for this purpose. The RF algorithm outperformed the others, achieving average Mean Bias Error (MBE) of 0.14 (<span><math><mrow><mi>μ</mi><mi>g</mi><mo>/</mo><msup><mrow><mi>m</mi></mrow><mn>3</mn></msup></mrow></math></span>), Root Mean Square Error (RMSE) of 13.54 (<span><math><mrow><mi>μ</mi><mi>g</mi><mo>/</mo><msup><mrow><mi>m</mi></mrow><mn>3</mn></msup></mrow></math></span>), and Normalized RMSE (NRMSE) of 9.48%. Statistical analysis using the Wilcoxon test confirmed that the RF algorithm’s performance was significantly superior. Permutation analysis was then applied to interpret the RF behavior in surface ozone estimation, highlighting the contribution of total column nitrogen dioxide, temperature, relative humidity, and v-component wind speed. Further analysis revealed that incorporating meteorological parameters at various pressure levels. Finally, an Ozone Exposure Risk (OER) index was introduced to relatively vulnerable areas in Tehran by integrating surface ozone estimates with population density data. Based on the results, the southern and eastern parts of the study region exhibited the highest OER compared to the other regions. The results of this study can serve as input for policymakers and urban planners to inform the establishment of legislation that promotes sustainable urban development and improves society’s public health.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 2","pages":"Pages 921-942"},"PeriodicalIF":3.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148529084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Satellite-based precipitation retrieval using cross-temporal attention and multiscale spatial representation","authors":"Hao Li,&nbsp;Xing Wang,&nbsp;Yue Zhang","doi":"10.1016/j.asr.2026.04.109","DOIUrl":"10.1016/j.asr.2026.04.109","url":null,"abstract":"<div><div>Geostationary meteorological satellites provide valuable observations for hourly precipitation monitoring. However, single-time snapshot observations struggle to capture the dynamic evolution of cloud systems. Furthermore, the scale mismatch between point-based rain gauges and grid-based satellite pixels, combined with complex terrain, limits the spatial generalization of current retrieval models. To address these challenges, we propose DTPNet, a deep learning framework designed for station-level hourly precipitation retrieval. Instead of relying on a single snapshot, the model utilizes consecutive multi-channel observations from geostationary satellites (e.g., FY-4A/AGRI) to explicitly track cloud movement and development over time. A cross-temporal attention mechanism is introduced to extract these dynamic cloud evolution patterns, alongside multiscale spatial representations to bridge the spatial gap between satellite pixels and ground stations. To ensure robust performance across diverse landscapes, the model dynamically adapts to local terrain elevation and spatiotemporal context. It simultaneously detects precipitation occurrence and classifies precipitation intensity. Evaluated against nationwide hourly rain gauge data in China for 2021, DTPNet significantly outperforms operational products like GPM IMERG and FY-4 QPE. Specifically, the Critical Success Index (CSI) for precipitation detection improves by approximately 53% and 108% compared to these products, respectively, and the Macro-F1 score for intensity classification reaches 0.4370. These results demonstrate the method’s superior accuracy and robust generalization for satellite-based precipitation monitoring.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 2","pages":"Pages 1132-1150"},"PeriodicalIF":3.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148529091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrothermal alteration mapping and mineralization potential assessment in the Taznakht region (Morocco) through integrated ASTER imagery, airborne radiometric and magnetic data","authors":"Abdelhadi Ifliliss,&nbsp;Younesse El Cheikh,&nbsp;Lahsen Achkouch,&nbsp;Mohammed Boumehdi,&nbsp;Moulay Ahmed Ben Driss,&nbsp;Said Ettazarini,&nbsp;Youssef Hahou","doi":"10.1016/j.asr.2026.04.071","DOIUrl":"10.1016/j.asr.2026.04.071","url":null,"abstract":"<div><div>Mapping hydrothermal alteration zones is a key step in mineral exploration because they often mark subsurface mineralization and the concentration of economically valuable metals. Hydrothermal processes can lead to the concentration of economically valuable metals, making alteration zones important exploration targets. This study focuses on the Taznakht area in the Anti-Atlas region of Morocco, where a combined remote sensing and geophysical approach was used to locate potential mineralized zones. ASTER satellite images and airborne gamma-ray spectrometry data were analyzed to support mineral exploration. Radiometric mapping revealed the distribution of potassium (K), equivalent uranium (eU), and equivalent thorium (eTh), as well as derived indicators like the K/eTh ratio and the F-parameter, which are useful for identifying hydrothermal alterations. In parallel, ASTER band ratio techniques were used to map minerals related to argillic, phyllic, and propylitic alterations, along with iron oxides. Structural data, including geological faults and magnetic lineaments, are interpreted as preferred pathways for hydrothermal fluid circulation. The integration of these datasets using a geometric average model, selected for its ability to balance the influence of heterogeneous datasets, led to the creation of a mineral prospectivity map. This map highlighted four areas of hydrothermal alteration that showed a strong spatial correlation with known mineral occurrences (63.63 %). Field observations confirmed the geological importance of these zones, showing that this approach can be applied to similar metallogenic provinces in the Anti-Atlas and worldwide to effectively guide future mineral exploration.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 2","pages":"Pages 684-702"},"PeriodicalIF":3.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148529116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing GNSS water vapor tomography using machine learning-generated virtual tropospheric delays","authors":"Ning Huang,&nbsp;Biyan Chen,&nbsp;Feiyang Hu,&nbsp;Tiezhu Li","doi":"10.1016/j.asr.2026.04.084","DOIUrl":"10.1016/j.asr.2026.04.084","url":null,"abstract":"<div><div>The accuracy of ground-based Global Navigation Satellite System (GNSS) tomography is constrained by the ill-posed inverse problem, particularly in regions with sparse station coverage. To address this limitation, this study proposes an augmented tomography framework that introduces virtual observations to improve the observation geometry. A LightGBM model, incorporating Digital Elevation Model (DEM) data and ERA5 priors, was developed to predict virtual Slant Wet Delay (SWD) values. These virtual rays are integrated into the tomographic system to provide constraints for voxels unprobed by real signals, thereby reducing reliance on empirical smoothing constraints. Validation against radiosonde profiles and ERA5 data across four station density scenarios (S1–S4) yields two primary results. First, the proposed method improves reconstruction accuracy compared to the traditional approach. The sparse scenario (S4, 40 real stations) achieved an RMSE of 9.08 mm/km, outperforming the traditional benchmark (123 real stations, RMSE 11.51 mm/km) by approximately 21%. Notably, the method reduced the systematic negative bias in the Planetary Boundary Layer (0–2 km) from −11.16 to −5.67 mm/km. Second, a comparison between scenarios reveals that the reduced network (S2) marginally outperformed the dense network (S1). This suggests that once the observation geometry is stabilized by virtual rays, mitigating geometric conflicts from redundant observations contributes more to solution quality than maximizing station density. These findings indicate that the proposed framework is an effective approach for high-resolution water vapor reconstruction in data-sparse regions.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 2","pages":"Pages 850-871"},"PeriodicalIF":3.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148557705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the suitability assessment and geo-precision zoning of geological disasters in Shaanxi Province based on InSAR","authors":"Jingtao Li,&nbsp;Jing Zhang,&nbsp;Shuangcheng Zhang,&nbsp;Ning Liu,&nbsp;Shuai Gao,&nbsp;Zhen Li,&nbsp;Zhijie Feng,&nbsp;Sijiezi Li","doi":"10.1016/j.asr.2026.04.082","DOIUrl":"10.1016/j.asr.2026.04.082","url":null,"abstract":"<div><div>At present, synthetic aperture radar interferometry (InSAR) has become the main means of geological disaster monitoring, but it will produce different degrees of geometric distortion in provincial monitoring. InSAR suitability evaluation is of great significance to its observation quality. Taking Shaanxi Province as an example, this paper proposes a maximum detectable deformation gradient (MDDG) calculation method based on continuous incident angle, carries out InSAR suitability evaluation, and obtains four types of suitability results: high, medium, low, and very low with a resolution of 90 m. At the same time, a geographical boundary method based on elevation variation coefficient, geological lithology, InSAR suitability, and elevation factor is constructed. The results show that: (1) Affected by the surface type, the response degree of different regions in Shaanxi Province to InSAR suitability is significantly different. The proportion of high, medium, low and very low suitability is 27.1%, 13.3%, 13.5% and 46.1% respectively; (2) The three types of geomorphic units in Shaanxi Province are distinct, and the vicinity of the 0.5 isoline is the macro-tectonic-geomorphological boundary between the Guanzhong Basin and the surrounding plateau/mountain. The northern boundary is bounded by the transitional belt area composed of Weibei loess tableland and Beishan mountain system, and the southern boundary is bounded by the northern foot fault zone of Qinling Mountains; (3) The continuous incidence angle interpolation mapping method has higher spatial continuity and significant systematic differences between different incidence angles. In each IW strip, the detection ability gradually decreases from the near end to the far end, and the MDDG system decreases by 13.1%, and the rate of change per degree is about 1.302% / °. This method can provide a certain reference value and paradigm for provincial InSAR suitability assessment and geo-precision zoning.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 2","pages":"Pages 830-849"},"PeriodicalIF":3.2,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148557719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}