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

{"title":"Mapping and monitoring spatiotemporal desertification patterns in the arid transition zone of Algeria over the period 2002–2022","authors":"Amel Faiza Tandjaoui ,&nbsp;Mejdi Kaddour ,&nbsp;Lucas De Oto ,&nbsp;Hachem Guerid ,&nbsp;Mahdi Khodadadzadeh","doi":"10.1016/j.jag.2025.104799","DOIUrl":"10.1016/j.jag.2025.104799","url":null,"abstract":"<div><div>Desertification represents a critical threat to ecosystems, agriculture, and livelihoods in arid and semi-arid regions. This study provides a comprehensive analysis of the spatio-temporal evolution of desertification across Algeria’s steppic and northern Sahara regions from 2002 to 2022. Using Earth observation data and cloud-based processing platforms, we developed an analytical workflow<span><span>¹</span></span> that combines multitemporal vegetation sparsity mapping and desertification trend analysis. Our approach employed a derived composite index, the Vegetation Sparsity Index (VSI), along with intensity analysis, gravity center change, and regionalization-based clustering. The results showed that desertification dynamics in the study area are not uniform, but characterized by distinct phases of degradation and revegetation. Specifically, desertified areas expanded northward in the western and central regions from 2010, with the central western region, which is vital for agriculture and pastoralism, degraded beyond what rainfall trends alone would predict. In contrast, the eastern regions showed significant greening. These results underscore the heterogeneous nature of desertification in the region and provide critical insights for targeted mitigation efforts.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104799"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048973","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":"Water transparency and color in large rivers observed by Sentinel-2 MSI and its implications for SDG 6.3.2 monitoring","authors":"Xuezhu Jiang ,&nbsp;Shenglei Wang ,&nbsp;Junsheng Li ,&nbsp;Evangelos Spyrakos ,&nbsp;Huaxin Yao ,&nbsp;Fangfang Zhang ,&nbsp;Andrew N. Tyler ,&nbsp;Bing Zhang","doi":"10.1016/j.jag.2025.104826","DOIUrl":"10.1016/j.jag.2025.104826","url":null,"abstract":"<div><div>Rivers are vital to Earth’s water cycle and human societies, yet their water quality is increasingly threatened by climate change and human activities. While satellite remote sensing has emerged as a powerful tool for large-scale water quality monitoring across diverse aquatic ecosystems, a systematic analysis of water optical properties in rivers remains limited, restricting its use in supporting Sustainable Development Goal (SDG) monitoring. This study presents the first comprehensive analysis of water transparency (Secchi disk depth, Z_SD) and color (Forel-Ule Index) in the five large rivers (Yangtze, Danube, Mississippi, Nile, and Amazon) using Sentinel-2 MSI data (2019–2021). Results reveal significant spatial-seasonal variations: Danube had the highest transparency (Z_SD) and bluest color (FUI), followed by Nile, Yangtze, Mississippi, and Amazon. These differences were primarily driven by basin-specific soil erodibility and precipitation. Spatially, the Yangtze, Mississippi, and Amazon exhibited decreasing Z_SD and increasing FUI from their upper to lower reaches, contrasting with different trends in Danube and Nile, highlighting the influence of large dams. Seasonally, two different patterns were observed in the five rivers, underscoring the hydrological influences on water optical properties. Furthermore, as two key optical water quality parameters, Z_SD and FUI were analyzed for their complementary roles in characterizing river turbidity across varying water conditions. By quantifying spatiotemporal patterns, this study establishes a global baseline for river optical properties and supports SDG 6.3.2 monitoring. Our findings offer new insights into large-scale river ecosystem dynamics under environmental change.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104826"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007499","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 general algorithm to retrieve cloud top properties by incorporating spectral characteristics and lidar measurements","authors":"Chuanye Shi ,&nbsp;Tianxing Wang ,&nbsp;Zheng Li ,&nbsp;Xuewei Yan ,&nbsp;Husi Letu","doi":"10.1016/j.jag.2025.104842","DOIUrl":"10.1016/j.jag.2025.104842","url":null,"abstract":"<div><div>Although clouds and their properties are critical to radiation budget and weather change, the cloud products derived from passive radiometers remain significant uncertainties due to the complex variations of clouds and the limited spectral characterization of existing algorithms. In this study, a general algorithm is proposed to retrieve cloud top height (CTH), cloud top temperature (CTT) and cloud top pressure (CTP) simultaneously by establishing a look-up table (LUT) between lidar measurements and the cloud-sensitive spectral characteristics. Validated by an independent year, the algorithm has achieved accurate retrievals under both daytime and nighttime conditions, with an averaged Root Mean Square Error (RMSE) of 1.70 km, 9.0 K and 118 hPa for CTH, CTT and CTP, respectively. The above RMSEs are much lower than those reported for other algorithms proposed in recent years, and have decreased by about 40 % compared to the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) products, which indicates the better performance of the proposed algorithm. The algorithm’s superior performance and independence from auxiliary data make it a promising approach for characterizing the spatio-temporal patterns of global cloud layers.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104842"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048970","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 LiDAR-based framework for visualizing and quantifying forest void space: an intrinsic component of forest ecosystems","authors":"Guoran Huang ,&nbsp;Wangfei Zhang ,&nbsp;Haitao Yang ,&nbsp;Yuling Chen ,&nbsp;Hongcan Guan ,&nbsp;Changfeng Lu ,&nbsp;Jianhui Zhao ,&nbsp;Zhiyong Qi ,&nbsp;Tianyu Xiang ,&nbsp;Shun Li ,&nbsp;Shiyu Yan ,&nbsp;Guangcai Xu ,&nbsp;Qinghua Guo","doi":"10.1016/j.jag.2025.104845","DOIUrl":"10.1016/j.jag.2025.104845","url":null,"abstract":"<div><div>Forest voids—three-dimensional (3D), unoccupied spaces within forest ecosystems—form a critical yet under-described component of stand structure. Shaped by vegetation, microclimate, and disturbance regimes, these voids govern light penetration, airflow, and habitat connectivity. We propose a LiDAR-based framework that identifies, visualizes, and quantifies forest voids directly from terrestrial and mobile laser-scanning (TLS/MLS) point clouds. By treating voids as the 3D regions between the digital elevation model (DEM) or digital surface model (DSM) where no returns are detected, our method bypasses canopy-centric metrics and simplified radiative assumptions, yielding a scalable, assumption-light representation of forest architecture. Across sites, void configurations reflect underlying stand architecture. Forests with high structural heterogeneity—multi-layered canopies and irregular stem distributions—exhibit diffuse, vertically extensive voids. In contrast, structurally uniform stands contain more confined voids, largely restricted to lower strata because of diminished understory development. These patterns demonstrate that forest voids integrate overstory and understory attributes, providing a structural lens on spatial openness under diverse conditions. Although challenges in scalability and independent validation remain, extending this framework to multi-platform LiDAR will enable broader applications in biodiversity monitoring, habitat suitability, and climate-adaptation research. By formalizing forest-void quantification, our study advances structural complexity assessment and offers fresh insights into ecosystem dynamics and function.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104845"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048974","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 spatiotemporal variation analysis and AI prediction of terrestrial high-temperature droughts","authors":"Xiangxiang Ma ,&nbsp;Kebiao Mao ,&nbsp;Zijin Yuan ,&nbsp;Zhonghua Guo ,&nbsp;Xuehong Sun ,&nbsp;Sayed M. Bateni","doi":"10.1016/j.jag.2025.104835","DOIUrl":"10.1016/j.jag.2025.104835","url":null,"abstract":"<div><div>In recent years, the frequency and intensity of compound high-temperature drought events have significantly increased on a global scale, posing severe challenges to agricultural production and ecological environments. To elucidate the spatiotemporal variation patterns of such extreme events and enhance prediction accuracy, this study systematically analyzed the distribution patterns and evolutionary trends of terrestrial high-temperature drought events based on the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI), utilizing global multi-source observational data from 1980 to 2022. The results indicate that regions such as Brazil, West Africa, the Arabian Desert, South Asia, and Mexico exhibit particularly prominent high-temperature trends, while precipitation significantly decreases in parts of South America, South Asia, Libya, western United States, eastern Canada, and southwestern China. Additionally, the recurrence intervals of high-temperature droughts in Venezuela, Brazil, northern Russia, Iran, and southwestern China have markedly shortened. To further improve prediction accuracy, this study employed wavelet transforms in combination with three deep learning methods—Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory Network (LSTM)—to develop multi-scale predictive models for SPI and STI. The results demonstrate that all three models achieved coefficients of determination (R²) exceeding 0.98 for SPI and STI predictions, with mean absolute errors (MAE) below 0.036 and 0.07, root mean squared errors (RMSE) below 0.09 and 0.05, respectively, indicating high reliability in extreme event prediction. Forecasts for 2019–2026 suggest that the frequency and intensity of compound high-temperature drought events will generally continue to rise, providing critical references for subsequent climate risk assessments and agricultural disaster prevention and control.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104835"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048968","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":"Evaluation of bathymetry retrieval using China’s new-generation ocean color satellite HY-1E","authors":"Ningning Li ,&nbsp;Mi Wang ,&nbsp;Bo Yang ,&nbsp;Jun Pan ,&nbsp;Zhongli Fan","doi":"10.1016/j.jag.2025.104841","DOIUrl":"10.1016/j.jag.2025.104841","url":null,"abstract":"<div><div>Satellite-derived bathymetry (SDB) has been widely applied in shallow-water mapping using multispectral sensors such as Sentinel-2, Landsat-8/9, and HY-1D. However, limitations in revisit frequency, spatial resolution, and spectral coverage constrain their performance in dynamic and optically complex coastal environments. HY-1E, the new-generation ocean color satellite following HY-1D, offers improved spatial resolution, an expanded spectral range, and a reduced revisit time. These improvements are expected to advance remote sensing applications in SDB. Prior to its operational application, a systematic assessment of HY-1E’s bathymetric retrieval capability is essential. In this study, we evaluate the performance of HY-1E imagery for SDB using an ICESat-2-assisted approach across multiple study areas, acquisition times, and image types. We also discuss the influence of key spatial, temporal, and spectral parameters on bathymetry retrieval. Across 25 study areas, HY-1E consistently achieved an RMSE below 1 m and a coefficient of determination (R²) exceeding 0.86. On average, HY-1E outperformed HY-1D by 0.13 m, Sentinel-2 10 m by 0.09 m, and Sentinel-2 20 m by 0.01 m in terms of RMSE. In waters shallower than 10 m, most HY-1E-derived bathymetric models met the ZOC C standard. In terms of terrain characterization, HY-1E demonstrated significant improvements in detail resolution over HY-1D and produced DBMs with lower noise levels compared to Sentinel-2. In addition, this study proposes a data source selection strategy tailored to different types of coastal and reef environments, considering factors such as spectral bands, spatial resolution, revisit cycle, and swath coverage, in order to optimize the balance between inversion efficiency and accuracy.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104841"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048972","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":"Contrasting features of cloud and radiative heating from multi-satellite observations over Oklahoma and Korea","authors":"Jihoon Ryu ,&nbsp;Minjin Choi ,&nbsp;Hwan-Jin Song ,&nbsp;Won-Jun Choi","doi":"10.1016/j.jag.2025.104832","DOIUrl":"10.1016/j.jag.2025.104832","url":null,"abstract":"<div><div>This study investigated regional contrasts in the vertical structure and radiative characteristics of clouds between two mid-latitude continental regions—Oklahoma in the central United States and the Korean Peninsula in East Asia—using 12 years of satellite observations (2006–2017) from CloudSat and CALIPSO. Despite being located within a similar latitudinal band, the two regions exhibited contrasting cloud regimes due to variations in meteorological conditions, boundary-layer structure, and convective forcing. Satellite observation analyses showed that vertical development of clouds was generally deeper and more vertically continuous over Oklahoma, while clouds over Korea were typically shallower and more seasonally variable, especially confined to lower altitudes during winter. The analysis of radiative heating indicates that relatively stronger shortwave heating and longwave cooling frequently occur in the upper troposphere over Oklahoma, consistent with deep convective systems. In contrast, Korea exhibited heating rates that were dominant below the mid troposphere, associated with relatively weak vertical development of clouds. Thermodynamic analysis using equivalent potential temperature and relative humidity profiles showed that the atmospheric conditions over Korea were relatively more stable and humid, and exhibited greater seasonal variability compared to those over Oklahoma. Our findings showed that cloud radiative characteristics between Oklahoma and Korea regions were notably different based on long-term satellite observations. This study can contribute to providing a conceptual reference for conducting observational analysis of mid-latitudinal climate regions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104832"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996773","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":"Integrating phenology knowledge graph into parcel-scale crop classification using multi-period deep time series modelling","authors":"Qianhui Shen,&nbsp;Da He,&nbsp;Xiaoping Liu,&nbsp;Qian Shi","doi":"10.1016/j.jag.2025.104809","DOIUrl":"10.1016/j.jag.2025.104809","url":null,"abstract":"<div><div>Parcels are the fundamental units of agricultural management; accurate crop classification of cropland parcels is crucial for the implementation of precision agriculture. Despite extensive knowledge of crop growth processes, the difficulty in acquiring this knowledge and its modal differences with remote sensing data hinder its application in crop classification research. Moreover, the highly complex and variable growth patterns of crops present significant challenges for time-series crop classification. We propose a novel crop classification framework that extracts intricate multi-period features of crop growth from remote sensing time-series signals. Additionally, we introduce an automatic construction process for crop remote sensing knowledge graphs based on a decision tree structure, capturing the association between crops and remote sensing time-series data. Through graph convolution, knowledge graph serves as a global guide to improve crop classification. By combining field survey samples with visible, near-infrared, and radar signals, we constructed a parcel-scale dataset of rice and wheat crops across four cities in the middle and lower reaches of the Yangtze River using zonal feature aggregation methods for evaluation. The results indicate that the proposed framework achieves accuracies ranging from 89.45 % to 94.43 % across the four datasets. We conducted inferences in the four cities and compared the results with county-level statistical data, achieving R² values of 0.89 and 0.97 for wheat and rice planting areas, respectively. Our proposed framework can automatically generate crop knowledge graphs based on samples from different regions, overcoming the modal barriers between the knowledge space and the remote sensing feature space, thus enhancing crop recognition accuracy.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104809"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932445","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":"Flood mapping using Sentinel-1 imagery with topographical and hydrological contextualization: Case study from Ribe, Denmark","authors":"Mark Hansen ,&nbsp;Jacob Vejby ,&nbsp;Julian Koch","doi":"10.1016/j.jag.2025.104816","DOIUrl":"10.1016/j.jag.2025.104816","url":null,"abstract":"<div><div>Advancements in Synthetic Aperture Radar (SAR) imagery have made it the standard datasource for large-scale operational flood mapping. SAR’s applicability under all-weather conditions and at night is a major advantage. However, challenges remain in mapping low-contrast surface water due to emergent vegetation and heterogenous flood extent variability. To address these issues, we propose a framework applicable for fully automatic flood mapping. The proposed framework was tested using Sentinel-1 SAR imagery in Ribe, Denmark, a site with frequent inundation with highly variable magnitudes. The framework features several novel methods for refining surface water extents with topographical and hydrological contextualization. A bimodal mask is generated from quadtree decomposition and gaussian mixture modelling, in combination with a bimodality test, which enables straightforward determination of local thresholds separating water and background. Mapped flood extents are contextually refined with ancillary topographical and hydrological datasets, using region-growing and linear regression. A nuanced surface water likelihood output is created from a fuzzy logic procedure using image specific backscatter coefficient statistics, topographic position index and height above nearest drainage. Results were verified through comprehensive spatial- and temporal validation, using Sentinel-2 optical imagery, a permanent water dataset, and timeseries of gauged stream water elevation. A satisfying result was achieved with an average overall accuracy of 98.5 %, a temporal correlation with gauged stream elevations of 0.92, and a total of 82.4 % of permanent water surfaces mapped correctly during peak flooding.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104816"},"PeriodicalIF":8.6,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918026","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":"Landsat-based fire maps reveal higher fire emissions from larger area of low-severity burnings than coarse resolution data in Southeast Asia","authors":"Mingjian Xiahou ,&nbsp;Zehao Shen","doi":"10.1016/j.jag.2025.104815","DOIUrl":"10.1016/j.jag.2025.104815","url":null,"abstract":"<div><div>Accurately describing the distribution of burned area and fire emissions is essential for fire management and sustainable development. Medium-resolution datasets, such as MCD64A1 Version 6.1 and the Global Fire Emissions Database (GFED), indicate a decrease in tropical and subtropical fire activity over the past 20 years. However, these datasets often miss small burned patches, leading to underestimations in heterogeneous landscapes. Southeast Asia (SEA) is a major hotspot for fire activity and biodiversity. This study provides a high-resolution (30 m) dataset of burned area (1990–2023) and fire-related carbon emissions (2001–2023) across SEA, derived from Landsat data. We curated training data from MODIS, VIIRS, and Landsat imagery, used a Random Forest algorithm to predict burned areas, and created monthly burned area maps with 30-meter resolution. Additionally, we simulated fire-related carbon emissions using the GFED framework and high-resolution maps. Our findings reveal an average annual burned area of 125,212 km² and emissions of 270 Tg C, both significantly exceeding previous estimates from coarse-resolution datasets by 11,771 km² and 83.67 Tg C, respectively. Both burned area and carbon emissions have remained stable over time, with agricultural and forest fires contributing significantly to the total burned areas and emissions. Forest burning accounts for 49 % of the total burned area, leading to substantial forest loss (12 % of total forest loss) and carbon emissions (76 % of total fire emissions), with an increase since 2001. Agricultural burning contributes 28 % of the burned area and 12 % of carbon emissions in SEA. These results emphasize the importance of high-resolution monitoring for improving fire prediction and management in fragmented landscapes.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104815"},"PeriodicalIF":8.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918024","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}