Remote Sensing Applications-Society and Environment最新文献

{"title":"Coastal salinity and agricultural land use suitability: A RS-GIS-based multi-criteria assessment in the Indian Sundarbans","authors":"Roni Paul,&nbsp;Natarajan Mathimaran","doi":"10.1016/j.rsase.2026.102193","DOIUrl":"10.1016/j.rsase.2026.102193","url":null,"abstract":"<div><div>Sustainable agricultural land use is now gaining importance as an urgent adaptation measure to address the impacts of salinity in coastal deltas, ensuring food security and maintaining coastal management. Accelerating sea-level rise is intensifying saltwater intrusion into tropical coasts, eroding soil fertility and imperilling food security. We combined high-resolution multispectral imagery, GIS-based criteria, and a multi-criteria decision-making ensemble (AHP–VIKOR) to delineate salt-resilient farmland in India's Sundarbans. The analysis reveals 1474.5 km² biophysically suited to salt-tolerant agriculture, yet only 244.8 km² (16.6%) is currently farmed. A further 537 km² could be sustainably cultivated without encroaching on vegetation or water bodies, thereby maintaining critical coastal buffers. Integrating projected salinity trajectories with land use zoning offers a cost-effective adaptation pathway that complements but does not displace aquaculture. The framework is readily transferable to low-lying coasts worldwide, unlocking under-utilized land, conserving soil fertility and reinforcing livelihood resilience under rising seas.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"43 ","pages":"Article 102193"},"PeriodicalIF":4.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148731703","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":"Disentangling dominant near-shore Sentinel-2 reflectance contributions from water, bottom, adjacency, and surface glint","authors":"Tuuli Soomets,&nbsp;Martin Ligi,&nbsp;Laura Argus,&nbsp;Kaire Toming,&nbsp;Tiit Kutser","doi":"10.1016/j.rsase.2026.102206","DOIUrl":"10.1016/j.rsase.2026.102206","url":null,"abstract":"<div><div>Remote sensing of inland and near-coastal waters is strongly affected by signal contamination from nearby land, shallow-water bottom influence, and surface glint. These effects frequently co-occur within near-shore pixels, leading many studies to mask optically complex areas despite their ecological importance. This limitation is particularly critical for inland waters, where a large fraction of biogeochemical processes occurs close to the shoreline.</div><div>In this study, we present a multi-index workflow that partitions near-shore Sentinel-2 MultiSpectral Instrument (MSI) reflectance into dominant contributions from the water column, bottom influence, adjacency effects, and surface glint. The approach operates on top-of-atmosphere reflectance and exploits characteristic spectral behaviour associated with different disturbance types. The workflow integrates improved land–water separation with indices that identify optically deep water and discriminate among bottom-, adjacency-, and glint-affected pixels.</div><div>The method was developed and evaluated using Sentinel-2 MSI imagery acquired between April and October 2024–2025, based on 17 cloud-free scenes across five lakes in Estonia and Switzerland. Across the analysed Sentinel-2 scenes, the workflow consistently distinguished the dominant signal contributions associated with optically deep water, bottom influence, adjacency effects, and surface glint. The workflow is intended as an interpretation framework rather than a quantitatively validated predictive classifier.</div><div>By moving beyond binary masking approaches that treat optically complex near-shore pixels as unusable, the proposed workflow provides a transferable framework for classifying dominant signal contamination and improves the interpretability and usability of Sentinel-2 data in inland and near-coastal waters.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"43 ","pages":"Article 102206"},"PeriodicalIF":4.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148731805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emission factors dominate the rice methane gap with EDGAR in Southeast Asia: A 10 m harvested-area assessment","authors":"Gianmarco Goycochea Casas,&nbsp;Zool Hilmi Ismail","doi":"10.1016/j.rsase.2026.102205","DOIUrl":"10.1016/j.rsase.2026.102205","url":null,"abstract":"<div><div>Bottom-up inventories disagree on the magnitude and spatial distribution of rice methane in Southeast Asia. We combine a 10<!--> <!-->m harvested-area product, the IPCC 2019 Refinement Tier-2 methodology, a fractional peatland mask and a 2000-member Monte Carlo ensemble to build a 0.1° CH₄ inventory for nine Southeast Asian countries (SEA-9). The deterministic estimate is 6.61<!--> <!-->Tg/yr (5%–95% interval 3.9<!--> <!-->Tg/yr to 12.3<!--> <!-->Tg/yr), about one third lower than EDGAR v8.0 and directionally consistent with the most recent EDGAR Community GHG 2025 release. An EDGAR-anchored sequential decomposition and an internal Shapley decomposition concur that the gap is driven primarily by the emission-factor structure — cropping-cycle stratification, organic amendment and the country water regime — and only secondarily by the area prior. The bottom-up estimate matches Indonesia’s UNFCCC submission to within 10<!--> <!-->% and falls inside the Philippines and Thailand reported ranges; its spatial pattern correlates more strongly with TROPOMI/Sentinel-5P XCH₄ than EDGAR v8.0 does (Pearson <span><math><mrow><mi>r</mi><mo>=</mo><mo>+</mo><mn>0.28</mn></mrow></math></span> vs ＋0.11 at the cell scale). The negative residual concentrates over the irrigated mainland deltas, where alternate wetting and drying (AWD) offers the largest mitigation gradient; a positive counter-signal appears over the peat-rich rice fringes of Sumatra and Kalimantan. The estimate is robust to alternative peat masks and to fractional cell–country intersection remapping, and is provided as a re-usable 0.1° netCDF prior for regional monitoring, reporting and verification (MRV) applications.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"43 ","pages":"Article 102205"},"PeriodicalIF":4.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148731806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A time adaptive weighting framework for enhanced vegetation health index based drought monitoring","authors":"Muhammad Waseem,&nbsp;Ali Hasan Jaffry,&nbsp;Mudassar Iqbal,&nbsp;Muhammad Ajmal,&nbsp;Jiaqing Xiao,&nbsp;Tao Yang,&nbsp;Pengfei Shi,&nbsp;Awais Naeem Sarwar","doi":"10.1016/j.rsase.2026.102155","DOIUrl":"10.1016/j.rsase.2026.102155","url":null,"abstract":"<div><div>Agricultural Drought monitoring, particularly through indices such as the Vegetation Health Index (VHI), plays a vital role in mitigating the impacts of climate variability on agriculture and ecosystems. However, the traditional VHI index relies on constant weighting for its components, i.e., the Temperature Condition Index (TCI) and the Vegetation Condition Index (VCI), which fail to capture temporal shifts in vegetation and climate interactions. To address these limitations, this study proposes an enhanced VHI (EVHI) framework based on time-adaptive weighting in which the contributions of VCI and TCI are independently adjusted at each time step through learned weight sequences, calibrated through 3-month Standardized Precipitation Evapotranspiration Index (SPEI-3) across 141 districts of Pakistan over 2002-2024 rather than applying static or regionally fixed weights. This framework was evaluated on key performance metrics, namely False Alarm Rate (FAR), Critical Success Index (CSI), Miss Rate, and Probability of Detection (POD), using Standardized Soil Moisture Index (SSI) for independent validation. Analysis of the time-varying weight sequences (α_t and β_t) reveals that temperature stress consistently dominates during pre-monsoon and drought onset periods (β_t &gt; α_t). On average across all 141 districts and all-time steps, β_t = 0.531 and α_average = 0.468, further confirming the overall dominance of thermal stress in Pakistan's drought dynamics. Moreover, the overall results show that EVHI consistently reduces false alarms and increases the probability of detection and critical success index compared to the traditional VHI during both calibration (first 70% of data) and validation (30% of data). These findings demonstrate that time-adaptive weighting significantly improves drought detection by better representing the evolving dominance of temperature and vegetation stress conditions across Pakistan's diverse climate zones.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"43 ","pages":"Article 102155"},"PeriodicalIF":4.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer-based semantic segmentation for informal settlement mapping using Sentinel-2 MSI imagery","authors":"Dadirai Matarira,&nbsp;Onisimo Mutanga,&nbsp;Pride Mafuratidze","doi":"10.1016/j.rsase.2026.102169","DOIUrl":"10.1016/j.rsase.2026.102169","url":null,"abstract":"<div><div>Informal settlements house over one billion people globally, yet they remain chronically undermapped in official datasets, undermining progress towards Sustainable Development Goal 11. While deep learning has advanced automated mapping, convolutional neural networks (CNN) are constrained by local receptive fields that cannot adequately capture the long-range spatial dependencies required to distinguish fragmented settlement clusters from spectrally similar urban backgrounds. This study introduces and validates SegFormer, a Transformer-based semantic segmentation framework, for operational informal settlement mapping using freely available 10m Sentinel-2 MSI imagery. Using a 32-channel feature stack, combining spectral, built-up, textural and morphological features, SegFormer outperforms U-Net and DeepLabV3+ (Dice: 0.9934; mIoU: 0.9914), with only 3.6 million parameters, less than half the size of benchmarked CNNs. The framework reduces systematic boundary underestimation and spectral confusion with non-settlement surfaces, addressing persistent limitations of both traditional machine learning and CNN-based approaches. Thus, this scalable approach has the potential to support monitoring of informal settlement growth, flood risk exposure, and targetted infrastructure upgrading across data-scarce urban environments in the Global South.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"43 ","pages":"Article 102169"},"PeriodicalIF":4.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148651555","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":"Improved PCNN with transfer learning for IoT based rice leaf disease detection with Modified Trans U-net segmentation","authors":"Govindarajan Subburaman,&nbsp;Mary Vennila Selvadurai","doi":"10.1016/j.rsase.2026.102154","DOIUrl":"10.1016/j.rsase.2026.102154","url":null,"abstract":"<div><div>Agriculture plays a vital role in driving economic prosperity, and early detection of plant diseases is essential to improving crop yields and ensuring sustainability for a growing population. Traditional diagnostic methods rely on expert inspectors to identify phenotypic symptoms, but these approaches are costly, time-intensive, and dependent on laboratory facilities and specialized expertise, limiting accessibility for farmers. To address these challenges, this paper introduces a novel Transfer Learning-based Rice Leaf Disease Detection (TL-RLDD) framework. The system begins by capturing rice leaf images through IoT-enabled devices deployed in agricultural fields. Data augmentation is applied to overcome dataset limitations and enhance model robustness. In the preprocessing stage, Gaussian Filtering (GF) reduces noise and improves image clarity, followed by segmentation using a Modified Trans-UNet (MT-UNet) model to isolate diseased regions from healthy tissue. Feature extraction is then performed using a hybrid approach that integrates Multi-Texton (MT), Median Binary Pattern (MBP), and Improved Local Monotonic Pattern (ILMP) descriptors, effectively capturing texture and pattern information. Finally, disease classification is achieved through an Enhanced Parallel Convolutional Neural Network (EPCNet) augmented with Transfer Learning, leveraging pre-trained knowledge to boost prediction accuracy. Experimental results demonstrate that the TL-RLDD framework provides a scalable, efficient, and accurate solution for rice leaf disease detection, offering significant potential to support sustainable agriculture and empower farmers with accessible technology. Using 80% of the data for training, TL-RLDD outperformed better than conventional techniques, obtaining an accuracy of 0.963 and 0.961, precision of 0.943 and 0.921, and negative predictive value of 0.973 and 0.974 for datasets 1 and 2, respectively. These outcomes demonstrate the framework's effectiveness in accurate and efficient rice leaf disease detection.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"43 ","pages":"Article 102154"},"PeriodicalIF":4.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635663","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":"Optimizing urban park site selection for enhanced SUHI mitigation: Integrating thermal effects of land cover types via game theory","authors":"Shuo Dang,&nbsp;Zhiwei Xie,&nbsp;Lei Fang","doi":"10.1016/j.rsase.2026.102160","DOIUrl":"10.1016/j.rsase.2026.102160","url":null,"abstract":"<div><div>Urban parks are an important nature-based solution for mitigating the effects of the surface urban heat island (SUHI). Yet conventional park siting methods usually treat SUHI as a single indicator and overlook thermal differences associated with land-cover transitions. To address this gap, this study developed an integrated framework combining a Land Cover Thermal Transition Matrix (LCTTM) and Game Theory Comprehensive Weighting (GTCW) for urban park suitability assessment, and applied it to Shenyang, Xi'an, and Wuhan, China. The LCTTM was constructed from bi-temporal Landsat 8 imagery through targeted pixel sampling to quantify directional temperature changes associated with land-cover conversion, and its outputs were incorporated as land-cover-specific thermal sub-weights within a GTCW-based multi-criteria assessment. Results show that dominant cooling pathways differed across cities, with the strongest cooling effect observed in the transition from bare ground to green space in Shenyang (6.63°C), while built-up land to water conversion showed the largest cooling in Xi'an and Wuhan (up to 8.77°C). The incorporation of LCTTM altered suitability patterns and improved the consistency between suitability assessment and observed thermal conditions. These findings indicate that integrating land-cover thermal transition effects can enhance the spatial precision and city-specificity of SUHI-oriented park siting. The proposed framework provides a transferable basis for climate-adaptive urban green infrastructure planning across diverse urban contexts.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"43 ","pages":"Article 102160"},"PeriodicalIF":4.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635723","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 and environmental control analysis of susceptibility to retrogressive thaw slumps in the central Qinghai Tibet Plateau","authors":"Ruikai Hong,&nbsp;Xu He,&nbsp;Jichao Lv,&nbsp;Bin Xiao,&nbsp;Age Shama,&nbsp;Zhihan Yang,&nbsp;Yunjie Yang,&nbsp;Bo Zhang,&nbsp;Dujuan Zhang,&nbsp;Rui Zhang","doi":"10.1016/j.rsase.2026.102187","DOIUrl":"10.1016/j.rsase.2026.102187","url":null,"abstract":"<div><div>The Qinghai–Tibet Plateau represents the world's core region of high-altitude permafrost. In recent years, climate warming has accelerated permafrost degradation, leading to an increasing frequency of retrogressive thaw slumps (RTSs), which pose severe threats to infrastructure safety and ecosystem stability. Existing RTS susceptibility assessments commonly overlook slope-related dynamic processes and terrain continuity, thereby limiting both predictive accuracy and result interpretability. This study proposes an RTS susceptibility mapping framework that adopts slope units as the fundamental evaluation units, enabling the estimation of potential unstable areas within a spatial structure that is more consistent with geomorphic processes. During sample construction, negative samples are optimized by jointly considering environmental similarity and geographic proximity. Susceptibility prediction is conducted using multiple machine-learning models, and the Shapley value method is applied to the optimal model to quantify the contributions of environmental control factors to RTS susceptibility. The results show that tree-based ensemble models achieved stronger predictive performance than SVM, with all AUC values exceeding 0.98. More importantly, when comparing the SHAP-based interpretation results under different sampling strategies, the slope-unit-based framework produced more consistent factor attribution patterns than the point-based strategy, indicating greater stability in model interpretation. Within this more stable interpretive framework, temperature, slope gradient, and NDVI were identified as the dominant predictors associated with modelled RTS susceptibility. A marked interaction between mean temperature and temperature variability was also observed, suggesting the presence of a thermally sensitive susceptibility range for RTS development. This study provides a more robust assessment framework for future RTS susceptibility evaluations based on static environmental factors.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"43 ","pages":"Article 102187"},"PeriodicalIF":4.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148730276","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":"BART-Disaster-X: an effective model to extract information from disaster-related tweets on X","authors":"Su Yicheng,&nbsp;Stephanus Albertus Louw,&nbsp;Yin Min Thant,&nbsp;Abhinandan Arya,&nbsp;Rakesh Teja Konduru,&nbsp;Ram Avtar","doi":"10.1016/j.rsase.2026.102174","DOIUrl":"10.1016/j.rsase.2026.102174","url":null,"abstract":"<div><div>Micro-blogging presents a wealth of rapidly updating crowd-sourced information valuable during natural disasters. However, the data volume necessitates the help of tools from Natural Language Processing to filter and identify relevant information needed by disaster response teams. We propose and compare transformer-based architectures for disaster tweet classification. The best-performing framework in our experiments uses BART-based text representations with an LSTM classification head and classifies tweets into disaster vs. non-disaster, informative vs. non-informative, and distinguishes eight disaster types. The resultant 12-class model achieves a micro-F1 score of 0.8871 and a macro-F1 score of 0.8764 – on par with similar studies considering fewer classes. The proposed framework can be implemented to support disaster response teams or other systems to quickly filter tweets to flag tweets containing informative and actionable information specific to a particular disaster or disaster type.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"43 ","pages":"Article 102174"},"PeriodicalIF":4.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635659","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":"Long-term changes in spatio-temporal heterogeneities of vegetation greenness and land surface temperature in China","authors":"Rui Yao,&nbsp;Yongjie Li,&nbsp;Tingting Ma,&nbsp;Minfeng Yuan,&nbsp;Zhihao Zhang,&nbsp;Siyuan Chen,&nbsp;Chuan Wang,&nbsp;Binghan Wang,&nbsp;Zhenyu Fu,&nbsp;Hengwei Li","doi":"10.1016/j.rsase.2026.102224","DOIUrl":"10.1016/j.rsase.2026.102224","url":null,"abstract":"<div><div>While long-term trends in vegetation greenness and land surface temperature (LST) have been extensively researched, long-term trends in their spatiotemporal heterogeneities remain underexplored. In this study, the changes in the spatiotemporal heterogeneities of vegetation greenness and LST were analyzed in China from 2001 to 2024. Results showed that the spatial heterogeneities of vegetation greenness and nighttime LST increased (decreased) significantly (p &lt; 0.05) in 52.0%–65.2% (3.1%–6.4%) and 26.1%–29.8% (5.9%–13.3%) of China, respectively. In contrast, trends in spatial heterogeneity of daytime LST showed regionally varying patterns. The increase in spatial heterogeneities of vegetation greenness and LST can primarily be attributed to the growing differences in vegetation greenness and LST between urban and rural areas, as well as between vegetated and desert areas. Regarding seasonal heterogeneity, areas with an increasing trend significantly outnumbered those with a decrease for both vegetation greenness and nighttime LST, while daytime LST showed no significant trend in most regions. Additionally, the diurnal LST range decreased significantly (p &lt; 0.05) in over half of China but increased in only 1.24% of the area. This research is of great significance for deepening our understanding of the responses of vegetation greenness and LST to global warming and greening.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"43 ","pages":"Article 102224"},"PeriodicalIF":4.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148770893","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}