International journal of applied earth observation and geoinformation : ITC journal最新文献

{"title":"Estimating actual maize yield with WOFOST in data-scarce small-scale cropping systems of South Africa: Data assimilation approach","authors":"Luleka Dlamini ,&nbsp;Olivier Crespo ,&nbsp;Jos van Dam ,&nbsp;Deborah V. Gaso ,&nbsp;Allard de Wit","doi":"10.1016/j.jag.2025.104848","DOIUrl":"10.1016/j.jag.2025.104848","url":null,"abstract":"<div><div>Accurate farm-level yield estimation is crucial for informed agricultural decision-making and food security, particularly in data-scarce, small-scale rainfed systems such as those found in South Africa. This study explores the integration of remote sensing-derived leaf area index (LAI) into the WOFOST crop model using a recalibration-based data assimilation (DA) approach. Data from eight farms across two growing seasons (2020–2021) in the Eastern Cape were used. Key phenological parameters were calibrated, and a yield gap factor influencing daily gross assimilation was introduced and optimized alongside the specific leaf area using LAI observations from two farms in 2020. The optimized parameters were validated across additional farms and seasons. Results show that DA significantly improved yield predictions (RMSE = 472 kg.ha⁻¹; NRMSE = 11%) compared to simulations without assimilation (RMSE = 4817 kg.ha⁻¹; NRMSE = 112%). These findings highlight the method’s potential to adapt crop models to data-limited contexts, quantify yield gaps, and support efficient resource management. The approach offers scalable benefits for decision-making in similarly constrained agricultural systems.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104848"},"PeriodicalIF":8.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic fusion of medium-resolution optical and SAR imagery for methane source infrastructure classification","authors":"Yanglangxing He ,&nbsp;Xueliang Zhang ,&nbsp;Pengfeng Xiao ,&nbsp;Zhenshi Li ,&nbsp;Dilxat Muhtar ,&nbsp;Feng Gu ,&nbsp;Binxiao Liu ,&nbsp;Pengming Feng","doi":"10.1016/j.jag.2025.104876","DOIUrl":"10.1016/j.jag.2025.104876","url":null,"abstract":"<div><div>Accurate classification of methane source infrastructure across sectors is critical for building comprehensive emission inventories and tracing emission sources. Existing approaches predominantly rely on high-resolution remote sensing imagery to capture discriminative features, but their scalability is limited by high costs and restricted availability. In contrast, medium-resolution imagery offers scalable alternatives with enhanced spectral signatures, while its lower spatial resolution challenges precise characterization and facility differentiation. To address this issue, we propose a multimodal fusion method on Sentinel-2 and Sentinel-1 data, with the aim of exploiting the complementary characteristics of optical, infrared, and SAR imagery to improve classification accuracy. We present a multimodal dynamic fusion network (DMFNet), which incorporates a gating module and multimodal attention fusion modules (MAFM) to adaptively address sample variability and multimodal heterogeneity. Additionally, DMFNet enables tracking and interpreting the fusion process by analyzing data-driven weights, providing deep insights into modality combinations and fusion strategies for specific facility. Experiments on the METER-ML dataset demonstrate that the proposed model achieves a precision of 0.740 and a recall of 0.757, outperforming existing single-modal and static fusion methods. Transferability experiments further confirm the practical applicability of the proposed method and its complementarity with existing open-source data in improving methane emission inventories.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104876"},"PeriodicalIF":8.6,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can optical water types be used as ecological indicators? Insights from a temperate estuary","authors":"Giulia Sent ,&nbsp;Evangelos Spyrakos ,&nbsp;Thomas Jackson ,&nbsp;Elizabeth C. Atwood ,&nbsp;Vanda Brotas ,&nbsp;Steve Groom ,&nbsp;Ana C. Brito","doi":"10.1016/j.jag.2025.104880","DOIUrl":"10.1016/j.jag.2025.104880","url":null,"abstract":"<div><div>Monitoring water quality and understanding how estuarine ecosystems respond to environmental changes is essential to sustain their ecological integrity and associated ecosystem services. In this study, we demonstrate that Optical Water Type (OWT) classification from Sentinel-2 MSI can be used as a stand-alone product rather than an intermediate tool for algorithm refinement, providing valuable insights for environmental monitoring, using the Tagus Estuary as an example. In-situ measurements (Chl-<em>a</em>, temperature, salinity, pH, and dissolved oxygen) were employed to characterize environmental conditions associated with OWT classes and to analyse the spatio-temporal and tidal variability during 2017–2024. We identified distinct water quality profiles among groups of OWTs, which were associated with marine, transitional and riverine waters with different physico-chemical characteristics that were related to different environmental drivers. Tides significantly influenced the distribution of OWTs, with high and neap tides favouring the occurrence of clearer marine waters. Specific OWT classes emerged as indicators for key environmental processes, including marine water intrusion, freshwater inputs and phytoplankton-rich waters. Time-series analysis revealed a trend of increasing marine waters inside the estuary alongside interannual variability driven by hydro-climatic forcings. Our findings highlight OWT classification as a valuable stand-alone satellite product for water quality monitoring, providing a powerful and scalable tool for estuarine science, policy, and management.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104880"},"PeriodicalIF":8.6,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Volumetric estimation of landslide-induced terrain change using conditional GAN with multi-temporal DEMs and satellite imagery","authors":"Yu-En Yang,&nbsp;Teng-To Yu","doi":"10.1016/j.jag.2025.104864","DOIUrl":"10.1016/j.jag.2025.104864","url":null,"abstract":"<div><div>Estimating landslide volumes remains challenging because of the limited availability of high-resolution terrain data. Traditional methods often rely on empirical area-to-volume conversion formulas, which introduce significant uncertainties due to terrain variability and simplified assumptions. This study proposes a deep learning framework that integrates multitemporal satellite imagery, LiDAR-derived Digital Elevation Models (DEMs), terrain attributes, and a conditional generative adversarial network (cGAN) to simulate the DEM of Difference (DoD) and estimate landslide-induced volumetric changes. The input DEMs were resampled to 20-meter resolution for publicly available coverage exceeding 78,183 km². A total of 2,881 map frames contained two DEM epochs, and 348 frames had three epochs, with time intervals typically ranging from 5 to 8 years. From these, 198 deep-seated landslide cases were extracted for analysis, covering approximately 7.12 km², including 4.89 km² of erosion area and 2.23 km² of deposition area. The proposed model achieved an overall classification accuracy of 0.66, with F1-scores of 0.46 for erosion, 0.30 for deposition, and 0.78 for background. Volumetric estimations revealed a consistent underestimation trend, with median erosion errors of approximately − 50 % and deposition errors approaching − 100 % across the five-fold cross-validation. The framework effectively captures three-dimensional spatial distributions and enables accurate volumetric estimation without the need for post-event DEMs, offering a practical solution for data-scarce regions. Additionally, it enhances sediment volume assessments that are crucial for disaster prevention and sediment management, bridging the gap between empirical estimation and modern deep learning techniques.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104864"},"PeriodicalIF":8.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An insight into POLDER database over Arctic through the angular information","authors":"Jing Guo ,&nbsp;Ziti Jiao ,&nbsp;Anxin Ding ,&nbsp;Siyang Yin ,&nbsp;Yidong Tong ,&nbsp;Chenxia Wang","doi":"10.1016/j.jag.2025.104863","DOIUrl":"10.1016/j.jag.2025.104863","url":null,"abstract":"<div><div>Snow and ice are among the most important components of the cryosphere; therefore, accurate identification of snow and ice is key to improve the understanding of the influence of various studies on global warming, e.g., studies on global water cycling, climate change and the radiation budget balance. The spectral information at single-view, e.g., the normalized difference snow index (NDSI) has been widely used in the recognition of snow cover at both local and global scales. However, special information sources related to the angular pattern of snow reflectance have rarely been considered in such applications. In this study, we proposed an angular index named the snow anisotropic reflectance index (SARI) based on the RossThick–LisparseReciprocal–Snow (RTLSRS) bidirectional reflectance distribution function (BRDF) model to achieve snow identification. The POLarization and Directionality of the Earth’s Reflectances (POLDER) database is one of the most widely-used multiangular database because of the abundant measurements and the pixels marked as “snow” are served as the data foundation in this study. The purpose of this study is to explore the potential of multiangular information to differentiate these pixels over Arctic in various situations. First, we preprocess the POLDER snow database to select the pixels with a good BRDF sampling distribution. Then, the SARI is used to preliminarily classify these POLDER datasets as snow and nonsnow, together with the classification result derived by single-view reflectances as comparison in the red and NIR bands. Next, the classification result is validated by using the MODIS MOD10A2 product. Finally, further analysis of the BRDF variations, regarding the SARI, for the classification results is performed by using the ArcticDEM and the Arctic Vegetation Map, a more detailed snow BRDF database over Arctic is obtained. The main results are as follows: (1) The potential of multiangular information (SARI) in recognizing snow had been confirmed according to MOD10A2 product as indirect validation data, the overall accuracy can reach to 86.9%, higher than the conventional single-view method (81.7%). (2) Within–pixel variations in surface terrain and components have a significant influence on the variability in BRDF shapes in the forward and backward scattering directions; in turn, these variations can be easily captured by using several simple thresholds for subcategorization for this POLDER snow database in Arctic Circle. Finally, this study provides a detailed POLDER database over Arctic and explain BRDF variations in various situations, facilitates the future potential applications of the remote sensing community.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104863"},"PeriodicalIF":8.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global intraday land surface temperature estimation of enhanced coverage by fusion of passive microwave data between different polar orbits","authors":"Peilin Song ,&nbsp;Mengran Wang ,&nbsp;Ronghan Xu ,&nbsp;Lin Chen ,&nbsp;Jie Liao ,&nbsp;Shengli Wu ,&nbsp;Guicai Li ,&nbsp;Xiuqing Hu","doi":"10.1016/j.jag.2025.104873","DOIUrl":"10.1016/j.jag.2025.104873","url":null,"abstract":"<div><div>Land surface temperature (LST) is a critical parameter for understanding land–atmosphere interactions, hydrology, and ecological dynamics. While thermal infrared (TIR) remote sensing has traditionally been used for LST retrieval, its effectiveness is limited by cloud cover and atmospheric interference. Passive microwave (PMW) remote sensing offers a significant advantage by enabling all-weather LST retrieval, as microwave signals can penetrate clouds and precipitation. However, PMW-based LST observations from a single satellite platform suffer from significant orbital gaps, particularly in middle and low latitudes, due to the limited swath width of current sensors. Additionally, the intraday revisit frequency of PMW LST is constrained to a maximum of two times per day (ascending and descending modes), which is insufficient for capturing rapid diurnal temperature variations or supporting high-temporal-resolution applications.</div><div>In this study, therefore, we address these limitations by proposing an innovative framework for intraday LST estimation with enhanced spatial coverage. This is achieved by fusing PMW data from two polar-orbiting satellites, Fengyun-3D (FY-3D) and Fengyun-3F (FY-3F), which operate at different equatorial crossing times. A sophisticated gap-filling algorithm is introduced, leveraging temporally adjacent LST estimates from intraday brightness temperature (TB) observations. Results demonstrate that the gap-filled LST from the optimal data fusion scheme exhibits a minimal positive bias of approximately 0.1–0.2 K compared to the original LST retrievals, while achieving an intraday revisit frequency of up to four times per day in middle latitudes. This represents a significant improvement over the pre-gap-filling frequency of 2.5 times per day. The framework not only enhances the spatial coverage of PMW-based LST but also provides a foundation for future satellite missions to further improve global LST monitoring. By enabling all-weather, high-frequency LST observations, this framework advances our understanding of land–atmosphere interactions, supports climate modeling, and enhances environmental monitoring capabilities.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104873"},"PeriodicalIF":8.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Significant variations in terrestrial water flux in mainland China during 2024 using GRACE-FO: impacts of extreme climate events","authors":"Yulong Zhong ,&nbsp;Jingwen Zhou ,&nbsp;Baoming Tian ,&nbsp;Guodong Xu ,&nbsp;Yunlong Wu","doi":"10.1016/j.jag.2025.104875","DOIUrl":"10.1016/j.jag.2025.104875","url":null,"abstract":"<div><div>In 2024, China experiences frequent and severe hydrological extremes, including record-breaking rainfall and widespread droughts, reflecting the intensifying impacts of climate change. The significant changes in terrestrial water storage (TWS) caused by these extreme precipitation events require more detailed analysis to assess short-term hydrological dynamics. Here, we first analyze precipitation anomalies (PA) and percentage of PA (PPA) across mainland China from April to August 2024. The results reveal that PA and PPA in most regions exhibit extreme values in different months, resulting in severe droughts, floods, and abrupt drought-to-flood transitions. To assess the associated water storage changes, we define and apply the terrestrial water flux (TWF), the differenceof GRACE/GRACE-FO-derived TWS anomalies in two adjacent months, as a diagnostic indicator of short-term hydrological variability. Relative to 2002–2024, the grids with TWF percentiles within the 0-10th and 90-100th ranges respectively account for 36.52%, 46.22%, 44.79%, and 46.48% of the total grids from April to August in China. Additionally, 19.89% of grids have the maximum TWF value in 2024. These extremes closely align with variations in precipitation, suggesting that intensified TWF is primarily driven by meteorological factors rather than GRACE-FO data uncertainties. Overall, this study demonstrates the effectiveness of TWF in capturing rapid hydrological changes under climate extremes. The findings provide critical insights into the impacts of climate change on regional hydrological processes and offer a valuable reference for future climate risk management and adaptation strategies at both national and global scales.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104875"},"PeriodicalIF":8.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A CNN-based statistical method for land cover classification to assess urban vulnerability to explosions: Case study of Paris, France","authors":"N. Regnier ,&nbsp;V. Mungkung ,&nbsp;L. Mezeix","doi":"10.1016/j.jag.2025.104878","DOIUrl":"10.1016/j.jag.2025.104878","url":null,"abstract":"<div><div>Explosions in urban areas pose serious risks to human life and infrastructure, emphasizing the need for accurate vulnerability mapping. While Computational Fluid Dynamics (CFD) is commonly used to model explosions, its complexity and high computational cost limit its use to small areas. This study proposes a novel method combining statistical modeling with land cover data processed by Convolutional Neural Networks (CNNs) to estimate blast and thermal radiation effects. Satellite imagery of Paris is used to classify Buildings, Roads, and Trees, each with a dedicated CNN model achieving up to 95% accuracy. Explosion effects under varying TNT weights are simulated to estimate casualties, structural damage, and costs. The method enables large-scale scenario analysis with minimal computational demand. Applied to Paris, the results demonstrate the model’s value for emergency planning, providing confidence intervals that account for uncertainty. This approach offers a scalable, data-efficient tool to support disaster preparedness and public safety decision-making.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104878"},"PeriodicalIF":8.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-perspective perception of city image in Shanghai via massive short videos","authors":"Minxin Chen ,&nbsp;Zhen Wei ,&nbsp;Kai Cao","doi":"10.1016/j.jag.2025.104844","DOIUrl":"10.1016/j.jag.2025.104844","url":null,"abstract":"<div><div>The concept of city image plays a pivotal role in urban branding, serving as a strategic tool for promoting sustainable urban development. A city’s image not only shapes its identity but also enhances its global competitiveness, attracting tourism, investment, and new residents. Traditional media such as newspapers and television offer limited perspectives on city image, whereas emerging platforms like short video apps provide richer and more diverse insights. Nevertheless, existing studies remain dominated by traditional media analysis, with insufficient examination of how short video platforms represent the spatial, landscape, social, and emotional dimensions of a city’s image. This study proposed a novel approach by leveraging the diverse and extensive short video data from Douyin to thoroughly deconstruct Shanghai’s comprehensive city image. We constructed a geotagged short video database, categorizing landscape and social images based on visual features and assessing emotional tones through audio-text analysis. The findings revealed distinct spatial patterns, with core areas highly concentrated and peripheral areas more dispersed. The analysis further identified nine core image areas, three major development axes, and ten prominent zones that defined Shanghai’s unique geographic and cultural identity. This approach could provide a quantitative characterization of city image, highlighting the potential for improved public space design and enhanced citizen engagement through short videos. Moreover, these results underscored the significant role of social media platforms, especially short video apps, in shaping public perception of cities. Such insights could also offer new perspectives for city image research and valuable guidance for urban planning and branding strategies.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104844"},"PeriodicalIF":8.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated analysis of harvest statistics provided by remote sensing, national forest inventories and administrative survey systems: An example from Italy","authors":"Roberto Pilli ,&nbsp;Alexandra Runge ,&nbsp;Gherardo Chirici ,&nbsp;Elia Vangi ,&nbsp;Alessio Collalti ,&nbsp;Martin Herold","doi":"10.1016/j.jag.2025.104871","DOIUrl":"10.1016/j.jag.2025.104871","url":null,"abstract":"<div><div>Forests are a key element for reaching the EU climate neutrality targets, but natural disturbances, climate change and human activities, make it urgent to set up a continuous monitoring system of the main parameters affecting forest ecosystems. A key parameter is the annual felling: the volume of trees felled during a given reference period.</div><div>We combine National Forest Inventory data, based on direct field measurements, with yearly time series data derived by remote sensing, to assess the amount of fellings carried out in Italy between 2000 and 2023. Italy was selected as a representative case study as harvest statistics are missing or partially inconsistent, which is also the case for other European countries.</div><div>We highlighted that no data source, considered individually, can provide a comprehensive estimate of the harvest level and its evolution in time.</div><div>Between 2000 and 2023, total fellings ranged from a peak of 16.5 million m³ in 2006 to a low of 10.4 million m³ in 2014.</div><div>A near real-time assessment of the harvest level, such as estimated within our study, is increasingly important to quantify the impact of human activities on forest ecosystems. According to our results, the fellings rate, i.e. the ratio between fellings and increment, was about 0.38 within the latest years. Nevertheless, considering the uncertainty of all input data, the total fellings ranged by about ± 50 %, and the corresponding fellings rate could be significantly larger. From this arises the urgent need to set up a continuous monitoring system, integrating National Forest Inventory surveys and remote sensing data reliably, not only in Italy, but across the EU.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104871"},"PeriodicalIF":8.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}