[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2678":3,"related-2678":108},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":87,"slug":90,"view_count":22,"doi":91,"paper":92,"created_at":107},2678,"Simulating the Impact of Land Use and Land Cover Change on Surface Air Temperature Trends in South-Central Vietnam","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faea745","Abstract The decline of vegetation, especially due to deforestation and urbanization, has significantly increased the temperature at local and regional scales in many places. In contrast, the development of water resources, increased investment in fertilizers for agricultural production, and afforestation to cover bare land have contributed to reducing temperature increases in many areas. This study aims to quantify the impact of land use and land cover change (LUCC) on temperature trends in South Central Vietnam. The data used are monthly averages over the past 24 years, including the Normalized Difference Vegetation Index (NDVI) and air temperature at 2 meters above the earth's surface. The methods used in the study include statistical analysis and ANN simulation. The main influencing variables included in the analysis are NDVI and its slope in buffer zones around weather stations with diameters ranging from 1 to 20 km. The results of the study show that the closer NDVI changes to the weather station, especially within a range of less than 2 km, the greater the impact on temperature trends. The variables in the buffer zone around the weather station where NDVI changes the most also have the best relationship with temperature trends. Using NDVI-derived variables, the ANN model reliably reproduced the temperature trend and clarified the contribution of LUCC. The NDVI trend variables contribute 46% to the simulation accuracy and 50% to the temperature trend differences between weather stations. This research direction can be used to assess the environmental impacts of LUCC, and to separate temperature trends due to global climate change from local causes.","植被退化，尤其是森林砍伐和城市化导致的植被减少，在许多地区显著加剧了局地和区域尺度的升温。与之相对，水资源开发、农业生产中肥料投入的增加以及裸露土地造林等措施，在许多地区有助于减缓温度上升。本研究旨在量化土地利用与土地覆被变化（LUCC）对越南中南部温度趋势的影响。所用数据为过去24年的月平均值，包括归一化植被指数（NDVI）和距地表2米高度的气温。研究方法包括统计分析和人工神经网络（ANN）模拟。分析中纳入的主要影响变量为气象站周围缓冲区内（直径1至20公里）的NDVI及其变化斜率。研究结果表明，NDVI变化距气象站越近，尤其是在2公里以内的范围内，对温度趋势的影响越大。气象站周围缓冲区内NDVI变化最大的变量与温度趋势的关系也最为密切。利用NDVI衍生的变量，ANN模型可靠地再现了温度趋势，并阐明了LUCC的贡献。NDVI趋势变量对模拟精度的贡献率为46%，对气象站间温度趋势差异的贡献率为50%。这一研究方向可用于评估LUCC的环境影响，并将全球气候变化引起的温度趋势与局地成因区分开来。",null,"Environmental Research Communications","2026-09-14T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,17,13,8,1,"以NDVI与ANN量化越南中南部落LUCC对气温趋势的影响，方法新颖、结论可靠，对农业遥感与气候适应研究有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业人工智能","气候变化","遥感","土地利用","植被覆盖",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,59,65,71,77,82],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},7,true,[],[28,50],"climate change",[28],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"F3",3,[],[57],"肥料",[],{"code":60,"score":54,"supported":47,"title":61,"abstract":62,"tags":64},"H3",[],[63],"水资源",[],{"code":66,"score":54,"supported":47,"title":67,"abstract":68,"tags":70},"I7",[],[69],"研究方法",[],{"code":72,"score":73,"supported":14,"title":74,"abstract":75,"tags":76},"I3",2,[],[],[31,29],{"code":78,"score":22,"supported":14,"title":79,"abstract":80,"tags":81},"H6",[],[],[30],{"code":83,"score":22,"supported":14,"title":84,"abstract":85,"tags":86},"I2",[],[],[27],[88,89],"农业人工智能 土地利用 植被覆盖 气候变化","农业人工智能 土地利用","农业人工智能土地利用植被覆盖气候变化-2678","10.1088\u002F2515-7620\u002Faea745",{"doi":91,"openalex_id":93,"authors":94,"venue":10,"cited_by_count":98,"oa_url":99,"card":100,"direction":104,"ingested_from":106},"W7212835908",[95],{"name":96,"orcid":97},"Luong Van Viet","https:\u002F\u002Forcid.org\u002F0000-0003-4416-7200",0,"https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faea745\u002Fpdf",{"tldr":101,"method":102,"finding":103,"direction":104,"opportunity":105},"量化越南中南部24年土地利用\u002F覆盖变化对气温趋势的影响。","用NDVI与2米气温月均数据，结合统计分析和ANN模拟。","站点2公里内NDVI变化对气温趋势影响最大，NDVI趋势变量贡献46%模拟精度。","农业遥感与作物表型","可结合多源遥感与深度学习，分离全球变暖与局地LUCC对气温的贡献。","openalex","2026-09-16T23:30:32.318792Z",{"total":109,"page":22,"page_size":109,"items":110},6,[111,182,266,321,415,475],{"id":112,"title":113,"url":114,"summary":115,"summary_zh":116,"content":9,"source_name":117,"source_url":114,"published_at":118,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":119,"score_detail":120,"sources":124,"tags":126,"primary_topic":130,"secondary_topics":135,"classification":137,"search_phrases":158,"slug":161,"view_count":98,"doi":162,"paper":163,"created_at":181},5320,"Machine Learning for Drought Analysis and Forecasting: A Critical Review of Data Fusion, Extreme-Event Sensitivity, Explainability and Generalization","https:\u002F\u002Fdoi.org\u002F10.17485\u002Fijst\u002Fv19i36.1263","Background: Machine learning is increasingly applied to drought monitoring and forecasting, but studies differ widely in how drought is defined, what data are used, and how models are validated, which makes reported performance difficult to compare directly. Objectives: To explain why reported machine-learning drought-model performance varies across definitions, data sources and validation schemes, and to assess whether recent studies adequately address rare severe events and cross-domain transferability. Method: Journal articles published January 2018 to August 2026 were reviewed, starting from a 70-record high-relevance subset of a 359-record discovery export and extended through citation chaining. Publisher and DOI verification produced a final evidence base of 55 articles: 48 primary studies and 7 reviews. Target definition, input data, model family, validation design, metrics, class-imbalance treatment, explainability and external testing were compared across studies. Findings: Reported performance varied with the modelling task rather than with any single algorithm: Random Forest reached 97.7% AUC-ROC for agricultural-drought mapping in Queensland, SVM reached average R²=0.90 for hydrological drought in Algeria, and a CatBoost stacking model reached R²=0.9065 for remote-sensing-based drought monitoring. These scores are not directly comparable because targets, data and validation protocols differ. Recent studies increasingly test cross-product and cross-region transfer, use SHAP\u002FLIME explainability, and adopt extreme-aware losses, yet class-wise reporting of severe and extreme drought, ordinal-aware evaluation, and leakage-resistant temporal validation remain inconsistently applied across the 48 primary studies. Novelty: The review jointly evaluates predictive accuracy alongside rare-event sensitivity, ordinal drought severity, leakage-resistant validation, explainability, uncertainty and cross-domain generalization, criteria largely absent from earlier machine-learning drought reviews. Keywords: Drought Forecasting, Machine Learning, Remote Sensing, Explainable AI, Extreme Drought","背景：机器学习正日益应用于干旱监测与预报，但各研究在干旱定义、所用数据和模型验证方式上差异很大，导致所报告的模型性能难以直接比较。目标：解释为何已报告的机器学习干旱模型性能会因定义、数据来源和验证方案的不同而变化，并评估近期研究是否充分处理了罕见的严重事件和跨域可迁移性问题。方法：综述了2018年1月至2026年8月发表的期刊论文，起始于359条发现导出记录中70条高相关性记录的子集，并通过引文追溯加以扩展。经出版商和DOI核验，最终证据基础为55篇文章：48篇原始研究和7篇综述。比较了各研究的目标定义、输入数据、模型族、验证设计、指标、类别不平衡处理、可解释性和外部测试。发现：所报告的性能随建模任务而非任何单一算法而变化：随机森林在昆士兰农业干旱制图中达到97.7%的AUC-ROC，SVM在阿尔及利亚水文干旱中达到平均R²=0.90，CatBoost堆叠模型在基于遥感的干旱监测中达到R²=0.9065。这些分数不能直接比较，因为目标、数据和验证方案不同。近期研究越来越多地测试跨产品和跨区域迁移，使用SHAP\u002FLIME可解释性，并采用极端事件感知损失函数，然而在48篇原始研究中，严重和极端干旱的分类别报告、序数感知评估以及防泄漏的时间验证仍应用不一致。新颖性：本综述将预测准确性与罕见事件敏感性、序数干旱严重程度、防泄漏验证、可解释性、不确定性和跨域泛化等标准共同评估，这些标准在以往的机器学习干旱综述中大多缺失。关键词：干旱预报，机器学习，遥感，可解释人工智能，极端干旱","Indian Journal of Science and Technology","2026-10-18T00:00:00Z",71,{"impact":121,"substance":18,"depth":19,"authority":121,"freshness":122,"relevant":22,"comment":123},12,9,"系统综述机器学习干旱分析与预测，指出评估口径不一、极端事件敏感性与可解释性不足，对农业旱情监测信息化有参考价值，但属学术综述、影响面偏细分领域。",[125],{"name":117,"url":114},[27,127,128,29,129],"可解释AI","极端天气","干旱监测",{"code":83,"slug":131,"name":132,"path":133,"parent_code":134},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[136],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":138},{"candidates":139,"agricultural_evidence":47},[140,146,153],{"code":83,"score":17,"supported":47,"title":141,"abstract":143,"tags":145},[142],"machine learning",[144,142],"机器学习",[27,127],{"code":33,"score":147,"supported":47,"title":148,"abstract":150,"tags":152},15,[149],"drought",[151,149],"干旱",[129],{"code":72,"score":46,"supported":47,"title":154,"abstract":155,"tags":157},[],[29,156],"remote sensing",[29],[159,160],"机器学习 干旱预测","遥感 干旱监测","机器学习干旱预测-5320","10.17485\u002Fijst\u002Fv19i36.1263",{"doi":162,"openalex_id":164,"authors":165,"venue":117,"cited_by_count":98,"oa_url":114,"card":175,"direction":104,"ingested_from":106},"W7222882764",[166,169,172],{"name":167,"orcid":168},"Ritik Mallik","https:\u002F\u002Forcid.org\u002F0009-0000-2785-0265",{"name":170,"orcid":171},"Dayal Kumar Behera","https:\u002F\u002Forcid.org\u002F0000-0002-9740-8066",{"name":173,"orcid":174},"Satarupa Mohanty","https:\u002F\u002Forcid.org\u002F0000-0002-1434-7158",{"tldr":176,"method":177,"finding":178,"direction":179,"opportunity":180},"综述55篇文献，比较机器学习干旱监测预报在定义、数据与验证上的差异。","系统综述2018-2026年55篇文献，比较目标定义、数据、模型、验证与可解释性","模型性能差异主要源于任务与验证设计而非算法，极端事件与泄漏防护评估仍不足。","农业人工智能与决策模型","可构建面向极端干旱的序数感知评估与抗泄漏时序验证框架，提升跨区域泛化。","2026-10-19T23:30:22.840943Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":9,"source_name":188,"source_url":185,"published_at":189,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":190,"score_detail":191,"sources":194,"tags":196,"primary_topic":200,"secondary_topics":201,"classification":203,"search_phrases":248,"slug":251,"view_count":22,"doi":252,"paper":253,"created_at":265},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z",70,{"impact":192,"substance":17,"depth":147,"authority":121,"freshness":122,"relevant":22,"comment":193},18,"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[195],{"name":188,"url":185},[197,27,198,28,199],"智慧农业","可持续发展","遥感监测",{"code":83,"slug":131,"name":132,"path":133,"parent_code":134},[202],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":204},{"candidates":205,"agricultural_evidence":47},[206,213,217,224,228,235,241],{"code":83,"score":192,"supported":47,"title":207,"abstract":209,"tags":212},[208],"artificial intelligence",[210,144,142,211,208],"深度学习","deep learning",[27],{"code":33,"score":147,"supported":47,"title":214,"abstract":215,"tags":216},[50],[28,50],[28],{"code":218,"score":46,"supported":47,"title":219,"abstract":220,"tags":223},"I1",[],[221,222],"精准农业","precision agriculture",[197],{"code":72,"score":46,"supported":47,"title":225,"abstract":226,"tags":227},[],[29,156],[199],{"code":229,"score":109,"supported":47,"title":230,"abstract":231,"tags":234},"H4",[],[232,233],"碳排放","carbon emission",[],{"code":78,"score":109,"supported":47,"title":236,"abstract":237,"tags":240},[],[238,239],"生物多样性","biodiversity",[],{"code":242,"score":109,"supported":47,"title":243,"abstract":244,"tags":247},"I5",[],[245,246],"物联网","internet of things",[],[249,250],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":252,"openalex_id":254,"authors":255,"venue":188,"cited_by_count":98,"oa_url":185,"card":258,"direction":264,"ingested_from":106},"W7222092855",[256],{"name":257,"orcid":9},"Deepmala Suryavanshi",{"tldr":259,"method":260,"finding":261,"direction":262,"opportunity":263},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","智慧农业 \u002F 农业物联网","2026-10-16T23:30:15.059867Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":271,"content":9,"source_name":272,"source_url":269,"published_at":273,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":190,"score_detail":274,"sources":277,"tags":279,"primary_topic":282,"secondary_topics":283,"classification":284,"search_phrases":300,"slug":303,"view_count":22,"doi":304,"paper":305,"created_at":320},4756,"ديناميكيات وحساسية المناخ والغطاء النباتي في العراق باستخدام بيانات رصد الأرض طويلة الأمد","https:\u002F\u002Fdoi.org\u002F10.66026\u002Fy7543b35","تؤدي تغيرات المناخ إلى تكثيف الإجهاد الحراري والمائي عبر المناطق الجافة، مما يزيد من الحاجة إلى رصد متسق مكانياً وطويل الأمد لاستجابات الغطاء النباتي. العراق معرض بشكل خاص لهذه التغيرات لأن التدرجات المناخية القوية، والجفاف المتكرر، ومحدودية توافر المياه تتزامن مع مساحات شاسعة من الأراضي المطرية والمروية. قامت هذه الدراسة بتقييم ديناميكيات المناخ والغطاء النباتي في العراق من عام 2001 إلى 2025 باستخدام مؤشر الاختلاف الغطائي النباتي الطبيعي (MODIS NDVI)، وبيانات هطول الأمطار (CHIRPS)، وبيانات درجة حرارة الهواء على ارتفاع مترين (ERA5-Land). تم تحليل متوسط هطول الأمطار للفترة من أكتوبر إلى مايو، واتجاهات مؤشر (NDVI) للفترة من يناير إلى مايو، وارتباطات المناخ مع مؤشر (NDVI) بعد إزالة الاتجاه (detrended) على شبكة مشتركة بدقة 0.1 درجة.تم بعد ذلك تصنيف البكسلات النباتية على أنها: حساسة لهطول الأمطار، أو حساسة للحرارة، أو حساسة لكليهما، أو لا تظهر علاقة متوقعة قوية، أو تظهر اتجاهاً قوياً آخر. أظهر هطول الأمطار في الموسم الرطب تدرجاً واضحاً من إجماليات عالية جداً في شمال وشمال شرق العراق إلى إجماليات منخفضة ومنخفضة جداً عبر الغرب والجنوب الغربي والجنوب. كان الاخضرار ذو الدلالة الإحصائية أكثر انتشاراً بشكل ملحوظ من الاسمرار، حيث تركزت أقوى اتجاهات مؤشر (NDVI) الإيجابية في المناطق الزراعية في الشمال والشمال الشرقي والوسط. بعد إزالة الاتجاهات الخطية، كانت ارتباطات هطول الأمطار مع مؤشر (NDVI) إيجابية في الغالب وواسعة النطاق مكانياً، في حين كانت ارتباطات درجة الحرارة مع مؤشر (NDVI) ذات دلالة سلبية بشكل رئيسي ومركّزة في شمال وشرق وجنوب شرق العراق. هيمنت حساسية هطول الأمطار على التصنيف المتكامل، بينما شكلت الحساسية المزدوجة لهطول الأمطار والحرارة مجموعات متميزة في شمال-وسط وجنوب شرق البلاد. توفر هذه النتائج خط أساس مكانياً وطنياً لتحديد مناطق الغطاء النباتي المرتبطة بتقلبات المياه والحرارة بين السنوات. وتمثل فئات الحساسية ارتباطات إحصائية وليست عزوًا سببيًا.","气候变化正在加剧干旱地区的高温与水分胁迫，从而增加了对植被响应进行空间一致、长期监测的需求。伊拉克尤其容易受到这些变化的影响，因为强烈的气候梯度、频繁的干旱以及有限的水资源可用性，与大片雨养和灌溉土地并存。本研究利用MODIS归一化植被指数（NDVI）、降雨数据（CHIRPS）以及2米高度气温数据（ERA5-Land），评估了2001年至2025年伊拉克的气候与植被动态。研究在0.1度分辨率的统一网格上，分析了10月至5月的平均降雨量、1月至5月的NDVI趋势，以及去趋势（detrended）后气候与NDVI的相关性。随后将植被像元分类为：对降雨敏感、对温度敏感、对两者均敏感、未表现出强预期关系，或表现出其他强趋势。湿季降雨呈现出从伊拉克北部和东北部极高总量向西、西南和南部低及极低总量的明显梯度。具有统计显著性的绿化现象明显比褐化更为广泛，其中最强的NDVI正趋势集中在北部、东北部和中部的农业区。去除线性趋势后，降雨与NDVI的相关性大多为正且在空间上广泛分布，而温度与NDVI的相关性主要为显著负相关，集中在伊拉克北部、东部和东南部。降雨敏感性在综合分类中占主导地位，而降雨与温度的双重敏感性在该国中北部和东南部形成了独特的集群。这些结果为识别与年际水热波动相关的植被区域提供了全国尺度的空间基线。敏感性类别代表统计关联，而非因果归因。","Journal Of Babylon Center for Humanities Studies","2026-10-09T00:00:00Z",{"impact":121,"substance":275,"depth":192,"authority":121,"freshness":109,"relevant":22,"comment":276},22,"基于MODIS、CHIRPS与ERA5-Land长时序数据评估伊拉克气候与植被敏感性，方法规范、结论有区域参考价值，但属境外基础研究，公共影响有限。",[278],{"name":272,"url":269},[28,280,199,281,31],"NDVI","干旱区农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":285},{"candidates":286,"agricultural_evidence":47},[287,291,296],{"code":33,"score":21,"supported":47,"title":288,"abstract":289,"tags":290},[],[28,151],[281,28],{"code":60,"score":109,"supported":47,"title":292,"abstract":293,"tags":295},[],[294,63],"灌溉",[],{"code":72,"score":54,"supported":14,"title":297,"abstract":298,"tags":299},[],[],[280,31,199],[301,302],"伊拉克 NDVI 植被动态","MODIS CHIRPS ERA5-Land 遥感","伊拉克NDVI植被动态-4756","10.66026\u002Fy7543b35",{"doi":304,"openalex_id":306,"authors":307,"venue":272,"cited_by_count":98,"oa_url":314,"card":315,"direction":104,"ingested_from":106},"W7221306668",[308,310,312],{"name":309,"orcid":9},"خولة‌ حمه‌رشید عمر",{"name":311,"orcid":9},"سایە دلاوەر محمد",{"name":313,"orcid":9},"Daban Kadhim Omar","https:\u002F\u002Fbcchj.uobabylon.edu.iq\u002Findex.php\u002Fbcchj\u002Farticle\u002Fdownload\u002F4819\u002F4866",{"tldr":316,"method":317,"finding":318,"direction":104,"opportunity":319},"基于2001–2025年MODIS NDVI、CHIRPS降水与ERA5-Land温度数据，评估伊拉","MODIS NDVI、CHIRPS降水、ERA5-Land温度，0.1°网格去趋","植被绿化趋势多于褐化，降水敏感性主导，温度负相关集中于北部、东部和东南部。","可结合灌溉与土地覆盖数据，区分雨养与灌溉区植被对水热胁迫的差异化响应机制。","2026-10-12T23:30:27.995466Z",{"id":322,"title":323,"url":324,"summary":325,"summary_zh":326,"content":9,"source_name":327,"source_url":324,"published_at":328,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":329,"score_detail":330,"sources":332,"tags":334,"primary_topic":336,"secondary_topics":341,"classification":347,"search_phrases":386,"slug":389,"view_count":390,"doi":391,"paper":392,"created_at":414},3495,"Remote Sensing and GIS in Modern Drought Assessment: Bridging Conventional Methods and Emerging Technologies","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123","Drought is a complex and recurring hydroclimatic hazard that affects agricultural production, water resources, ecosystems and socioeconomic development. Effective drought assessment requires approaches capable of capturing its spatial and temporal variability and its multiple dimensions. This review examines the evolution of drought assessment from conventional drought indices to integrated approaches based on remote sensing and Geographic Information Systems (GIS), with an emphasis on their applications, strengths, limitations and emerging developments. Conventional indices, including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Reconnaissance Drought Index (RDI) and Percent of Normal Precipitation Index (PNPI), remain widely used because of their established methodologies and long-term applicability. However, their dependence on meteorological observations can limit spatial characterisation and the representation of vegetation, soil moisture and other land-surface responses. Remote sensing provides spatially extensive and repeated observations of vegetation condition, land surface temperature, soil moisture, evapotranspiration and water-related conditions, enabling the development of satellite-derived drought indicators and indices. GIS further facilitates the integration, spatial analysis, visualisation, and mapping of drought-related information from multiple sources. The review also discusses hybrid approaches that combine climate-based indices with satellite-derived indicators, as well as drought monitoring platforms and multi-source assessment frameworks. Despite substantial advances, challenges remain regarding cloud contamination, differences in spatial and temporal resolution, data continuity, ground-based validation and uncertainty associated with multi-source datasets. Emerging machine learning, deep learning and artificial intelligence approaches offer opportunities for integrating heterogeneous datasets and improving drought characterisation and early warning. Overall, the integration of conventional observations, remote sensing, GIS and advanced analytical approaches provides a promising framework for more comprehensive drought monitoring and risk assessment under increasing climate variability and change.","干旱是一种复杂且反复出现的水文气候灾害，影响农业生产、水资源、生态系统和社会经济发展。有效的干旱评估需要能够捕捉其时空变异性和多维特征的方法。本文综述了干旱评估从传统干旱指数到基于遥感与地理信息系统（GIS）的综合方法的演变，重点探讨其应用、优势、局限性和新兴发展。传统指数，包括标准化降水指数（SPI）、标准化降水蒸散指数（SPEI）、帕尔默干旱强度指数（PDSI）、侦察干旱指数（RDI）和降水距平百分率指数（PNPI），因其方法成熟且具有长期适用性而仍被广泛使用。然而，这些指数对气象观测的依赖可能限制其空间表征能力以及对植被、土壤水分和其他陆面响应的刻画。遥感提供了对植被状况、地表温度、土壤水分、蒸散量及与水相关状况的大范围重复观测，使得卫星衍生的干旱指标和指数得以发展。GIS进一步促进了多来源干旱相关信息的整合、空间分析、可视化和制图。本文还讨论了将基于气候的指数与卫星衍生指标相结合的混合方法，以及干旱监测平台和多源评估框架。尽管取得了实质性进展，但在云污染、时空分辨率差异、数据连续性、地面验证以及多源数据集相关的不确定性方面仍存在挑战。新兴的机器学习、深度学习和人工智能方法为整合异质数据集、改进干旱表征和预警提供了机遇。总体而言，在气候变异性和变化日益加剧的背景下，传统观测、遥感、GIS和先进分析方法的整合为更全面的干旱监测和风险评估提供了一个有前景的框架。","Journal of Geography Environment and Earth Science International","2026-09-24T00:00:00Z",66,{"impact":121,"substance":192,"depth":17,"authority":121,"freshness":21,"relevant":22,"comment":331},"综述系统梳理遥感与GIS在干旱评估中的应用演进，方法学价值明确，但属综述类论文、非国内落地事件，影响力有限。",[333],{"name":327,"url":324},[197,27,29,335,129],"GIS",{"code":337,"slug":338,"name":339,"path":340,"parent_code":134},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[342,343],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":72,"slug":344,"name":345,"path":346,"parent_code":134},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":348},{"candidates":349,"agricultural_evidence":47},[350,358,362,366,370,377,382],{"code":337,"score":192,"supported":47,"title":351,"abstract":352,"tags":357},[335],[353,354,335,355,356],"地理信息","空间分析","spatial analysis","geographic information",[335],{"code":33,"score":147,"supported":47,"title":359,"abstract":360,"tags":361},[149],[151,149],[129],{"code":72,"score":147,"supported":47,"title":363,"abstract":364,"tags":365},[156],[29,156],[29],{"code":83,"score":13,"supported":47,"title":367,"abstract":368,"tags":369},[],[210,144,142,211,208],[27],{"code":371,"score":109,"supported":47,"title":372,"abstract":373,"tags":376},"H2",[],[374,375],"土壤","soil",[],{"code":60,"score":109,"supported":47,"title":378,"abstract":379,"tags":381},[],[63,380],"evapotranspiration",[],{"code":218,"score":22,"supported":14,"title":383,"abstract":384,"tags":385},[],[],[197],[387,388],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495",5,"10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":391,"openalex_id":393,"authors":394,"venue":327,"cited_by_count":98,"oa_url":324,"card":409,"direction":104,"ingested_from":106},"W7214156857",[395,397,399,401,403,405,407],{"name":396,"orcid":9},"V. Dhanalakshmi",{"name":398,"orcid":9},"N. Manikandan",{"name":400,"orcid":9},"V. S. Jinsy",{"name":402,"orcid":9},"K. V. Sumesh",{"name":404,"orcid":9},"P. Nideesh",{"name":406,"orcid":9},"P. S. Manju",{"name":408,"orcid":9},"N. Gopika",{"tldr":410,"method":411,"finding":412,"direction":104,"opportunity":413},"综述了从传统干旱指数到遥感、GIS及AI集成的现代干旱评估方法演进。","文献综述，对比SPI、SPEI等传统指数与遥感、GIS及混合方法。","遥感与GIS弥补传统指数空间局限，但云污染、分辨率差异和验证仍是挑战。","可探索多源遥感与机器学习融合的干旱早期预警，重点解决数据不确定性与地面验证。","2026-09-25T23:30:30.576065Z",{"id":416,"title":417,"url":418,"summary":419,"summary_zh":420,"content":9,"source_name":421,"source_url":418,"published_at":422,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":423,"score_detail":424,"sources":426,"tags":428,"primary_topic":430,"secondary_topics":431,"classification":433,"search_phrases":454,"slug":457,"view_count":390,"doi":458,"paper":459,"created_at":474},3353,"Integrating artificial intelligence in climate change and sustainable development: A comprehensive bibliometric review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sftr.2026.102145","The integration of Artificial Intelligence (AI) with climate change and sustainable development (SD) studies has significant potential to enable actionable, data-driven, innovative, and long-term mitigation and adaptation strategies. However, there is a lack of a well-organized, comprehensive review of mapping that integrates AI, climate change, and the SD nexus. This study addresses this notable gap through a bibliometric review that dissects descriptive bibliometric, performance, and conceptual analyses. Utilizing the Web of Science Core Collection, 3291 publications from 2007 to 2025 were analyzed using Bibliometrix and VOSviewer software. The findings indicate a 38.29% annual publication growth rate, and original articles (80%) were the dominant publication type. The Chinese Academy of Sciences was the top contributor, and China and the USA were leaders in research output and international AI-integrated climate and SD research collaborations. Keyword analysis shows “artificial intelligence”, “climate change”, “deep learning”, “machine learning”, and “sustainability” as dominant keywords. Conceptual mapping identified four approaches: macro-level AI-driven digitalization; methodologically driven geospatial and machine-learning-based environmental monitoring; predictive modeling; and AI-driven smart agriculture applications. Thematic mapping reveals a shift from fundamental AI for climate change-focused research (2007–2013) to integrating AI with climate change and SD (2014–2019), and modeling and predicting approaches (2020–2025). Findings highlight substantial geographic asymmetries; data and research gaps persist in climate-vulnerable low-income areas. The extractivism rate for low-income countries was 41%, indicating that nearly half of their publications are led by high-income countries. Recommendations are grounded in theoretical, methodological, practical, and policy considerations, with an emphasis on SDGs 13 and 17. This study provides insights for researchers, practitioners, and policymakers to emphasize policies and technologies and implement a nexus-based framework.","人工智能（Artificial Intelligence, AI）与气候变化及可持续发展（Sustainable Development, SD）研究的融合，在推动可操作、数据驱动、创新性和长期性的减缓与适应策略方面具有巨大潜力。然而，目前缺乏对AI、气候变化与可持续发展三者交叉领域的系统化、综合性文献计量综述。本研究通过文献计量学综述填补了这一显著空白，从描述性文献计量分析、绩效分析和概念分析三个维度进行剖析。利用Web of Science核心合集，采用Bibliometrix和VOSviewer软件对2007年至2025年间的3291篇文献进行了分析。研究结果表明，年度发文增长率为38.29%，原创论文（80%）为主要文献类型。中国科学院是最大的贡献机构，中国和美国在研究产出及AI融合气候与可持续发展研究的国际合作方面处于领先地位。关键词分析显示，“人工智能”“气候变化”“深度学习”“机器学习”和“可持续性”是主导性关键词。概念图谱识别出四种研究路径：宏观层面的AI驱动数字化；方法论驱动的基于地理空间和机器学习的环境监测；预测建模；以及AI驱动的智慧农业应用。主题图谱揭示了研究重心的演变：从气候变化聚焦的基础AI研究（2007—2013年），到AI与气候变化及可持续发展的融合（2014—2019年），再到建模与预测方法（2020—2025年）。研究发现存在显著的地理不对称性；气候脆弱型低收入地区仍存在数据和研究缺口。低收入国家的提取主义率为41%，表明其近半数出版物由高收入国家主导。建议基于理论、方法论、实践和政策层面的考量，并着重关注可持续发展目标13和目标17。本研究为研究人员、实践者和政策制定者提供了洞见，以强调政策与技术的重要性，并实施基于交叉领域的框架。","Sustainable Futures","2026-09-23T00:00:00Z",80,{"impact":192,"substance":275,"depth":192,"authority":20,"freshness":122,"relevant":22,"comment":425},"基于3291篇文献的AI与气候变化及可持续发展文献计量综述，方法规范、数据规模大，对智慧农业与农业AI研究具有参考价值。",[427],{"name":421,"url":418},[197,27,198,28,429],"文献计量",{"code":83,"slug":131,"name":132,"path":133,"parent_code":134},[432],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":434},{"candidates":435,"agricultural_evidence":47},[436,440,444,449],{"code":83,"score":192,"supported":47,"title":437,"abstract":438,"tags":439},[208],[210,144,142,211,208],[27],{"code":33,"score":147,"supported":47,"title":441,"abstract":442,"tags":443},[50],[28,50],[28],{"code":66,"score":147,"supported":47,"title":445,"abstract":447,"tags":448},[446],"bibliometric",[429,446],[429],{"code":218,"score":46,"supported":47,"title":450,"abstract":451,"tags":453},[],[197,452],"smart agriculture",[197],[455,456],"农业人工智能 可持续发展 文献计量 智慧农业","农业人工智能 可持续发展","农业人工智能可持续发展文献计量智慧农业-3353","10.1016\u002Fj.sftr.2026.102145",{"doi":458,"openalex_id":460,"authors":461,"venue":421,"cited_by_count":98,"oa_url":418,"card":469,"direction":264,"ingested_from":106},"W7214099405",[462,465,467],{"name":463,"orcid":464},"H. B. T. P. Jayathilaka","https:\u002F\u002Forcid.org\u002F0009-0001-0589-6999",{"name":466,"orcid":9},"Shiyan Zhai",{"name":468,"orcid":9},"Yuke Feng",{"tldr":470,"method":471,"finding":472,"direction":179,"opportunity":473},"用文献计量法梳理2007-2025年AI与气候变化及可持续发展交叉研究，揭示主题演化与地域失衡。","Web of Science 3291篇文献，Bibliometrix与VOSv","年增38.29%，主题从基础AI转向建模预测，低收入国家41%论文由高收入国家主导。","可针对气候脆弱低收入地区，构建AI+智慧农业的本地化数据与决策模型，填补研究空白。","2026-09-24T23:30:10.248845Z",{"id":476,"title":477,"url":478,"summary":479,"summary_zh":480,"content":9,"source_name":481,"source_url":478,"published_at":482,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":483,"score_detail":484,"sources":486,"tags":488,"primary_topic":492,"secondary_topics":495,"classification":497,"search_phrases":523,"slug":526,"view_count":390,"doi":527,"paper":528,"created_at":545},3191,"Spatial prediction of soil organic carbon stocks in Sudanese clay soils using regression kriging","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fsjss.2026.16733","Soil organic carbon (SOC) stocks are a critical component of terrestrial carbon pools, influencing soil quality, agricultural productivity, and climate change mitigation. This study aimed to map and improve spatial estimation of SOC stocks in Sudan’s Blue Nile clay soils using regression kriging (RK). The model integrated 554 spatially unique soil profiles with nine environmental covariates: precipitation, temperature, relative humidity, normalized difference vegetation index (NDVI), land use\u002Fcover, bare soil index (BSI), digital elevation model (DEM), LS-factor, and aspect. Spectral indices were derived from Landsat 9 imagery (April 2024), while climate and terrain data were obtained from CHIRPS\u002FWorldClim and SRTM (30 m). RK performance was robust, with spatial cross-validation R 2 = 0.72, RMSE = 8.4 Mg C ha −1 (29% of mean observed stock), and mean bias = −0.8 Mg C ha −1 . Predicted SOC stocks (0–30 cm) ranged from 12.4 to 51.2 Mg C ha −1 (mean 28.6 Mg C ha −1 ). NDVI, clay content, and topographic wetness index were the most influential predictors. Agricultural lands exhibited the highest stocks (51.2 Mg C ha −1 ), while bare lands had the lowest (14.2 Mg C ha −1 ). This study (1) applies spatially explicit validation for SOC mapping in Sudan’s Blue Nile region, (2) harmonizes legacy and contemporary soil data using equivalent soil mass correction, and (3) provides high-resolution SOC maps for climate-resilient agricultural planning. Findings support soil carbon management and climate mitigation in semi-arid regions.","土壤有机碳（SOC）储量是陆地碳库的重要组成部分，影响土壤质量、农业生产力及气候变化减缓。本研究旨在利用回归克里金（RK）方法对苏丹青尼罗河黏土区SOC储量进行制图并改进其空间估算。该模型整合了554个空间独立土壤剖面与9个环境协变量：降水、温度、相对湿度、归一化植被指数（NDVI）、土地利用\u002F覆盖、裸土指数（BSI）、数字高程模型（DEM）、LS因子和坡向。光谱指数源自Landsat 9影像（2024年4月），气候与地形数据分别来自CHIRPS\u002FWorldClim和SRTM（30 m）。RK表现稳健，空间交叉验证R²=0.72，RMSE=8.4 Mg C ha⁻¹（为实测储量均值的29%），平均偏差=−0.8 Mg C ha⁻¹。预测SOC储量（0–30 cm）范围为12.4–51.2 Mg C ha⁻¹（均值28.6 Mg C ha⁻¹）。NDVI、黏粒含量和地形湿度指数是最具影响力的预测因子。农地储量最高（51.2 Mg C ha⁻¹），裸地最低（14.2 Mg C ha⁻¹）。本研究（1）对苏丹青尼罗河地区SOC制图采用空间显式验证，（2）利用等效土壤质量校正协调历史与当代土壤数据，（3）为气候韧性农业规划提供高分辨率SOC图。研究结果支持半干旱地区的土壤碳管理与气候减缓。","Spanish Journal of Soil Science","2026-09-22T00:00:00Z",68,{"impact":21,"substance":18,"depth":19,"authority":20,"freshness":122,"relevant":22,"comment":485},"基于554个土壤剖面与多源遥感协变量的回归克里金制图研究，方法规范、验证充分，对半干旱区土壤碳管理与气候适应型农业规划有参考价值，但属区域性学术成果，公共影响有限。",[487],{"name":481,"url":478},[489,28,29,490,491],"农业遥感","土壤碳汇","数字土壤制图",{"code":371,"slug":375,"name":493,"path":494,"parent_code":37},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[496],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":498},{"candidates":499,"agricultural_evidence":47},[500,505,509,515,519],{"code":371,"score":501,"supported":47,"title":502,"abstract":503,"tags":504},14,[375],[374,375],[],{"code":33,"score":13,"supported":47,"title":506,"abstract":507,"tags":508},[],[28,151,50],[28],{"code":510,"score":54,"supported":47,"title":511,"abstract":512,"tags":514},"J1",[],[513],"农业规划",[],{"code":72,"score":73,"supported":14,"title":516,"abstract":517,"tags":518},[],[],[489,29],{"code":229,"score":22,"supported":14,"title":520,"abstract":521,"tags":522},[],[],[490],[524,525],"苏丹青尼罗河 土壤有机碳 回归克里金","Landsat 9 土壤有机碳 空间预测","苏丹青尼罗河土壤有机碳回归克里金-3191","10.3389\u002Fsjss.2026.16733",{"doi":527,"openalex_id":529,"authors":530,"venue":481,"cited_by_count":98,"oa_url":478,"card":539,"direction":544,"ingested_from":106},"W7213971196",[531,533,535,537],{"name":532,"orcid":9},"Faroug A.H. Jadalla",{"name":534,"orcid":9},"Kolapo O. Oluwasemire",{"name":536,"orcid":9},"Abd Elmagid A. Elmobarak",{"name":538,"orcid":9},"Mohammed A. M. Mohammed Zein",{"tldr":540,"method":541,"finding":542,"direction":104,"opportunity":543},"用回归克里金结合多源环境协变量预测苏丹青尼罗河粘土区土壤有机碳储量。","554个土壤剖面与9个环境协变量，Landsat 9、CHIRPS\u002FWorldC","模型R²=0.72，NDVI、粘土含量和地形湿度指数影响最大，农地碳储量最高。","可引入时序遥感与机器学习提升半干旱区SOC动态预测，并耦合农业管理措施评估固碳潜力。","数字乡村与农业信息化","2026-09-22T23:30:31.028178Z"]