Remote Sensing Applications-Society and Environment最新文献

{"title":"A comparative analysis of Landsat-8 and MODIS data for forecasting cyanobacterial blooms in small-scale reservoirs","authors":"Yohei Miura ,&nbsp;Yoshiya Touge ,&nbsp;Shoya Tanaka ,&nbsp;Yoshifumi Masago ,&nbsp;Hiroomi Imamoto ,&nbsp;Yasuhiro Asada ,&nbsp;Michihiro Akiba ,&nbsp;Osamu Nishimura ,&nbsp;Daisuke Sano","doi":"10.1016/j.rsase.2025.101672","DOIUrl":"10.1016/j.rsase.2025.101672","url":null,"abstract":"<div><div>Cyanobacterial blooms pose significant risks in freshwater ecosystems and human activities. Short-term prediction technologies of such blooms enhance decision-making processes to mitigate their detrimental impacts. Data from earth observation satellites proves invaluable for monitoring cyanobacterial blooms across diverse aquatic environments due to its consistent and systematic surveillance of water surfaces. Smaller water bodies, crucial for water supply in numerous countries, have been largely overlooked in developing predictive models for cyanobacterial blooms using satellite-derived data in previous research. With its low spatial and high temporal resolution, MODIS has been employed to forecast cyanobacterial blooms across vast water bodies, including large lakes and coastal areas. However, to our knowledge, high spatial resolution satellites such as Landsat-8 have not been previously utilized in developing models for small-scale water bodies. In this study, we constructed models to forecast <em>Dolichospermum</em> spp. concentrations with 7-day lead time, a genus of scum-forming cyanobacteria found in small Japanese reservoirs, using variables related to water quality, hydrology, meteorology, and Landsat-8 and MODIS data, integrated through three machine learning algorithms. We established three distinct temporal intervals for satellite-derived land surface temperature (LST) and normalized difference turbidity index (NDTI) to assess their temporal influence on bloom occurrences. The optimal model, employing MODIS data and the XGBoost algorithm, achieved an R² value of 0.84 and a root mean squared error of 0.44 in log₁₀ (cells/L). Satellite data from 23 to 38 days before a <em>Dolichospermum</em> spp. bloom enabled the most accurate models in two reservoirs, with LST generally showing higher relative importance than NDTI. This investigation emphasizes the potential of satellite-derived LST and NDTI as critical predictors in accurately predicting cyanobacterial blooms in small-scale reservoirs.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101672"},"PeriodicalIF":3.8,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713025","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":"Enhancing U-Net performance for high-resolution land cover classification using a dynamic epoch-centric optimizer (DECO)","authors":"Mahdi Farhangi ,&nbsp;Asghar Milan ,&nbsp;Danesh Shokri ,&nbsp;Saeid Homayouni","doi":"10.1016/j.rsase.2025.101668","DOIUrl":"10.1016/j.rsase.2025.101668","url":null,"abstract":"<div><div>In recent years, deep learning models—particularly U-Net—have garnered significant attention for applications such as high-resolution land cover mapping. A key challenge in improving these models' performance lies in the proper selection and tuning of optimizers: each algorithm (e.g., Adam, Nadam) offers distinct strengths and weaknesses, and reliance on a single optimizer may not yield optimal results across all training stages. Here, we introduce DECO, a novel hybrid optimizer that dynamically switches among multiple optimizers across epochs to enhance overall convergence and stability. U-Net trained with DECO on aerial imagery of buildings, forests, roads, and water in the Minski region of Warsaw, Poland, achieved 96.13 % overall accuracy, a Kappa coefficient of 91.49 %, an F1 score of 96.08 %, and a Jaccard index of 64.53 %. To assess generalizability, the model was further evaluated on a test region in the Malopolskie province, yielding 86.74 % accuracy, 73.75 % Kappa, 87.29 % F1, and 55.02 % Jaccard. Moreover, to demonstrate DECO's broader applicability, we implemented it on the DeepLab v3+ architecture, observing likewise improvements in validation accuracy and training stability. These findings substantiate that dynamic, epoch-centric optimizer switching can substantially boost the precision and robustness of deep learning models for high-resolution land cover classification.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101668"},"PeriodicalIF":3.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711468","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":"Seasionality of Kappaphycus alvarezii production trends in South Sulawesi using UAV monitoring","authors":"Nurjannah Nurdin ,&nbsp;Agus Aris ,&nbsp;M. Akbar AS","doi":"10.1016/j.rsase.2025.101659","DOIUrl":"10.1016/j.rsase.2025.101659","url":null,"abstract":"<div><div>Climate change poses significant challenges to seaweed farming, particularly in South Sulawesi, where seasonal variations influence the production dynamics. This study examined the impact of climate variability on <em>Kappaphycus alvarezii</em> seaweed farming by analyzing production trends across four key seasons: the west monsoon, east monsoon, transition from east to west monsoon, and transition from west to east monsoon. Using satellite and UAV imagery, we assessed environmental parameters, such as temperature, salinity, and eutrophication levels, to understand their effects on seaweed growth. Our findings indicated that seasonal fluctuations significantly affected seaweed productivity, with environmental stressors leading to variable growth rates and production yields. A predictive model integrating geospatial data and climate variability from 2019 to 2024 illustrates production trends and their spatial patterns. These insights contribute to climate adaptation strategies, allowing policymakers and farmers to optimize farming schedules based on changing environmental conditions. In addition to economic benefits, aligning seaweed farming with ecosystem dynamics supports sustainable coastal management by improving nutrient regulation and aiding marine ecosystem recovery. This study underscores the importance of integrating geospatial technology into aquaculture practices, offering a scalable approach to enhance the resilience of coastal communities to climate change.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101659"},"PeriodicalIF":3.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696748","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":"Advancing InSAR analysis: TimeSAPS for linear and nonlinear displacement modeling","authors":"Eugenia Giorgini ,&nbsp;Luca Tavasci ,&nbsp;Enrica Vecchi ,&nbsp;Luca Poluzzi ,&nbsp;Stefano Gandolfi","doi":"10.1016/j.rsase.2025.101656","DOIUrl":"10.1016/j.rsase.2025.101656","url":null,"abstract":"<div><div>The Synthetic Aperture Radar Interferometry (InSAR) technique enables precise monitoring of ground displacements over extensive areas based on radar data. While several open-source software packages have been developed for SAR data processing, most retrieve the average velocity of Persistent Scatterers (PS) clusters under the assumption of linear behavior, limiting their application in complex scenarios.</div><div>To enable more advanced and detailed analysis of InSAR time series, the TimeSAPS software package has been developed. This tool addresses the limitations of existing open-source packages, which primarily focus on linear approximations of displacement time series, by introducing advanced capabilities for analyzing both linear trends and nonlinear components. TimeSAPS performs a comprehensive analysis of PS derived from InSAR processing, characterizing time series in terms of linear trends, periodic signals, and nonlinear movements. Nonlinear components are modeled as a combination of sinusoids, each defined by its phase, amplitude, and frequency power spectrum. TimeSAPS overcomes the limitations of existing tools by providing advanced methods to recognize and model nonlinear surface movements, even when they are not known a priori.</div><div>This paper presents the theoretical foundations of TimeSAPS and demonstrates its capabilities through two case studies based on real InSAR data. These examples showcase the software’s effectiveness in reconstructing nonlinear displacement patterns and identifying periodic trends. The results underline TimeSAPS’s potential to analyze complex ground displacement scenarios, making it a valuable resource for the scientific and engineering communities.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101656"},"PeriodicalIF":3.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711400","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":"Recent global snow cover trends using the MODIS dataset from 2000 to 2021","authors":"Aftab Ahmed Khan PhD ,&nbsp;Xingong Li PhD","doi":"10.1016/j.rsase.2025.101662","DOIUrl":"10.1016/j.rsase.2025.101662","url":null,"abstract":"<div><div>The assessment of snow cover change trends at the global level has not been addressed adequately, regardless of snow's crucial role in the environment. This study uses MODIS daily products from 2000 to 2021 to analyze pixel-based Snow Cover Frequency (SCF) trends. This study examined trends at the daily, monthly, and annual levels. The trend analysis indicates a general decline in monthly mean SCF across both hemispheres, with the Northern Hemisphere experiencing a more pronounced negative trend. The Mann-Kendall test reveals that the Northern Hemisphere's decline is least negative in January, February, March, and December. Globally, SCF trends are negative for most months except November, which exhibits a positive slope trend, driven primarily by the Northern Hemisphere. In contrast, the Southern Hemisphere shows a consistently negative trend throughout the year and in the annual mean SCF.</div><div>Overall, the annual mean SCF is declining globally at a rate of −0.037 (%/year). These changing trends have several implications for millions of populations, biodiversity, agriculture, and commercial activities around the world. The results are computed for the short period (2000–2021), limiting the projections for long-term trends. Our global trends are varied, with several local and regional trends due to many complex regional and local contexts that need further localized assessments.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101662"},"PeriodicalIF":3.8,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656544","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":"Transfer learning-enhanced deep learning for tree crown geometric analysis and crop yield estimation using UAV imagery","authors":"Youness Hnida ,&nbsp;Mohamed Adnane Mahraz ,&nbsp;Jamal Riffi ,&nbsp;Ali Achebour ,&nbsp;Ali Yahyaouy ,&nbsp;Hamid Tairi","doi":"10.1016/j.rsase.2025.101663","DOIUrl":"10.1016/j.rsase.2025.101663","url":null,"abstract":"<div><div>Estimating tree yields is essential for optimizing productivity in precision agriculture, where informed decisions rely on accurate measurements of tree characteristics. Crown parameters such as diameter, radius, depth, base height, ratio, predicted area, shape, and volume play a key role in assessing canopy biovolume, which refers to the above-ground space occupied by the tree canopy and serves as a proxy for biomass. This study proposes an innovative methodology for estimating the biovolume and predicting the productivity of trees from multi-view images captured by drones. Trees are classified into two main geometric categories (oval and round) before being analyzed using advanced segmentation and geometric analysis techniques. A pre-trained segmentation model, FastSAM, was refined through YOLOv11-specific fine-tuning to optimize the detection and segmentation of olive tree crowns, allowing precise crown isolation and extraction of essential geometric parameters. The geometric analysis of aerial view contours relies on the Convex Hull method to derive shape parameters, while tree heights are determined from side views using a 2D triangle projection technique. These characteristics, combined with photogrammetry principles, are then used to estimate volume and predict the weight of each individual tree. The extracted data, along with field measurements, were used in regression models to establish correlations between crown size, height, and volume. The segmentation results showed an accuracy of 97.90 % for top-view images and 97.60 % for front views. Canopy volume estimation achieved 96.45 % accuracy for oval crowns and 95.36 % for round crowns. Productivity prediction using regression models yielded an R² of 95.84 % without interaction terms and 97.78 % with interactions. Additionally, the relative error in crop yield estimation was 6.05 %. By integrating multi-view drone imagery, fine-tuning of pre-trained models, advanced geometric analysis, and 2D projection techniques, this methodology provides a robust, accurate, and scalable solution for enhancing precision agriculture practices, enabling efficient prediction of tree yields and biomass.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101663"},"PeriodicalIF":3.8,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670678","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":"Mapping grain crop sowing date in smallholder systems using optical imagery","authors":"Victor Hugo Rohden Prudente ,&nbsp;Mariana Garcia-Medina ,&nbsp;Vijesh Krishna ,&nbsp;Michael Euler ,&nbsp;Nishan Bhattarai ,&nbsp;Amy M. Lerner ,&nbsp;Andrew James McDonald ,&nbsp;Sonam Sherpa ,&nbsp;Harshit Rajan ,&nbsp;Anton Urfels ,&nbsp;Cleverton Tiago Carneiro de Santana ,&nbsp;Meha Jain","doi":"10.1016/j.rsase.2025.101660","DOIUrl":"10.1016/j.rsase.2025.101660","url":null,"abstract":"<div><div>Sowing date prediction using Earth observation data is challenging in smallholder systems due to small field sizes, heterogeneity in management practices, and a lack of reference data. This study aims to develop a generalizable algorithm that does not require any ground data for calibration to map sowing date using the Normalized Difference Vegetation Index (NDVI) from three optical datasets: MODIS, Harmonized Landsat and Sentinel (HLS), and Sentinel-2. We applied Savitzky-Golay (SG) and spline smoothing algorithms to each dataset and developed a derivative approach to identify the inflection point that represents the Start of Season (SoS), which was then converted to sowing date. We applied our methodology to map the sowing date of winter wheat in Bihar, India and spring-summer maize in the state of Mexico, Mexico. Overall, Sentinel-2 data led to the highest accuracies, but the performance of the smoothing algorithm differed across locations. In India, prediction models using SG achieved an R² of 0.45 and a root mean square deviation (RMSD) of 11.44 days. In Mexico, prediction models using spline performed best, with an R² of 0.19 and an RMSD of 4.24 weeks. The lower accuracy in Mexico was due to more complex cropping patterns as well as noise in the observed sowing date dataset. Our algorithm shows potential to identify SoS, and ultimately sowing date, at scale using Sentinel-2 imagery. However, challenges from low-quality validation datasets, small field sizes, cloud cover, and landscape complexity continue to pose challenges to predict sowing date using Earth observation data products.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101660"},"PeriodicalIF":3.8,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679569","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":"Characteristics of warm clouds under the Köppen climate classification system with contoured frequency by optical depth diagrams","authors":"Panuwong Wongnim ,&nbsp;Minrui Wang ,&nbsp;Takashi Y. Nakajima","doi":"10.1016/j.rsase.2025.101657","DOIUrl":"10.1016/j.rsase.2025.101657","url":null,"abstract":"<div><div>This study investigates the impact of atmospheric factors such as humidity, temperature, and aerosol properties on the growth and internal structure of warm clouds, focusing on variations in cloud optical depth (COD) and cloud droplet effective radius (CDR). Using satellite data from Aqua's Moderate Resolution Imaging Spectroradiometer and CloudSat's Cloud Profiling Radar (2006–2014), the study employs Contoured Frequency by Optical Depth Diagrams (CFODD) to analyze cloud evolution as CDR increases. The Köppen climate classification is applied to assess how atmospheric conditions influence warm cloud characteristics. The study finds that regions with similar climates exhibit comparable cloud behaviors. In tropical regions, cloud droplet mode dominates at CDR values between 6 and 15 μm, transitioning to rain as CDR exceeds 15 μm. In arid regions, limited moisture availability results in thinner warm clouds, a smaller CDR range, and fast cloud dissipation. Temperate and continental regions exhibit slower transitions from cloud droplet mode to drizzle and rain, with CDR exceeding 18 μm, while lower surface temperatures promote more drizzle formation. In polar regions, extremely low temperatures exacerbate these limitations by weakening turbulence and vertical motion, which further slow collision and coalescence processes, requiring a much larger CDR exceeding 21 μm for rain formation, with most precipitation occurring as drizzle.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101657"},"PeriodicalIF":3.8,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663141","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":"Continuous anomaly detection using remote sensing to monitor on-farm restoration in sub-Saharan Africa","authors":"J. Kleinsmann ,&nbsp;M. Ahmad ,&nbsp;L. Kooistra ,&nbsp;T.G. Vagen","doi":"10.1016/j.rsase.2025.101644","DOIUrl":"10.1016/j.rsase.2025.101644","url":null,"abstract":"<div><div>Land degradation poses a significant threat to ecosystem health and food security, particularly in the global South. Given the severity of land degradation globally, land restoration is urgently needed to recover degraded ecosystems through, for example, tree planting and (farmer-managed) natural regeneration (FMNR). In this study we monitor the impacts of farmer-managed land restoration using satellite time series data through a Continuous Anomaly Detection after Intervention (CADI) approach. We also propose ways that this approach can be used to generate insights to help design future land restoration interventions. Data was collected for 127,782 restoration plots in seven sub-Saharan countries through the use of the “Regreening App”, which was designed for citizen science data collection. For each plot, a reference NDVI was modelled based on multiple years prior to restoration interventions which was compared to the actual NDVI to quantify the restoration impact. A comparison between our CADI approach and the residual trend (RESTREND) method was done based on the visual interpretation of 645 validation points. The CADI analysis proved better able to detect greening compared to RESTREND (F-score: 0.84 vs 0.79) and it performed better in arid regions (F-score: 0.88) than in dry sub-humid ecosystems (F-score: 0.75). FMNR was predominantly preferred in arid regions where higher greening was observed, indicating FMNR as a powerful and cost-effective option for future land restoration initiatives. To stimulate further use by policy makers and practitioners, the CADI analysis has been made available as an online tool <span><span>here</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101644"},"PeriodicalIF":3.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589075","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 multi-temporal remote sensing and advanced drought modeling to assess desertification dynamics in semi-arid Andhra Pradesh, India: A framework for sustainable Land management","authors":"Pradeep Kumar Badapalli","doi":"10.1016/j.rsase.2025.101654","DOIUrl":"10.1016/j.rsase.2025.101654","url":null,"abstract":"<div><div>This study aims to develop a robust framework for assessing desertification dynamics in the semi-arid landscapes of Andhra Pradesh, India, by integrating multi-temporal remote sensing data with advanced drought modeling. The primary objective is to evaluate the spatiotemporal progression of land degradation by analyzing vegetation response to drought stress over a 30-year period (1990–2020). The Standardized Precipitation Index (SPI) was calculated using RStudio at 3-, 6-, 9-, and 12 - months intervals based on rainfall data derived from CHIRPS satellite-based precipitation, to characterize drought intensity and frequency. Concurrently, Landsat imagery (TM, ETM+, and OLI/TIRS) was processed to generate Normalized Difference Vegetation Index (NDVI) time series to assess vegetation cover changes. A Desertification Status Map (DSM) was prepared by integrating SPI metrics with NDVI-based land cover classifications for the years 1990, 2000, 2010, and 2020. The DSM classified the landscape into four severity categories: Highly Safe (79.45 km²), Safe (248.54 km²), Degraded (320.39 km²), and Desertified Land (402.57 km²). Results highlight a significant increase in degraded and desertified areas, particularly in the western region and along the Hagari River, driven by prolonged drought, vegetation loss, and aeolian activity. Validation of the DSM using 120 ground truth points and high-resolution overlays achieved an overall accuracy of 87.5 % confirming classification reliability. The proposed framework offers a scalable tool for monitoring desertification and supports data-driven planning for sustainable land management, particularly in vulnerable semi-arid ecosystems affected by climate variability and anthropogenic pressures.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101654"},"PeriodicalIF":3.8,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589077","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}