[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4594":3,"related-4594":76},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":56,"slug":59,"view_count":60,"doi":61,"paper":62,"created_at":75},4594,"Glacial Lake Outburst Flood Hazard Assessment in the Upper Indus Basin, Ladakh Himalaya Using Remote Sensing and Hydrodynamic Modeling","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fegusphere-2026-5906","Abstract. The Indian Himalayan Region (IHR), often called the “Third Pole,” contains the largest concentration of glaciers and perennial snow outside the polar regions and is highly sensitive to climate change. Rapid glacier retreat has accelerated the formation and expansion of glacial lakes, increasing the likelihood of Glacial Lake Outburst Floods (GLOFs). This study presents a multi-site GLOF hazard assessment of four glacier-fed lakes associated with the Gya, Raru, and Panikhar glaciers in the Upper Indus Basin, Ladakh. Spatio-temporal lake-area changes were mapped using Sentinel-2 imagery and geographic information system (GIS) techniques, while lake depth and volume were estimated using empirical relationships supported by digital elevation model analysis. Dam-break simulations using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) were conducted for 25 %, 50 %, 75 %, and 100 % lake-volume release scenarios for the Gya, Raru, and Panikhar lakes. Simulation outputs provided peak discharge, flow velocity, flood depth, and inundation extent. Results show that worst-case scenarios could affect downstream infrastructure such as bridges and roads, whereas the Rumbak lake poses limited hazard potential because of its low volume and natural moraine buffering. The hazard maps developed in this study support risk prioritization, early-warning planning, and climate-resilient disaster management in the Indian Himalaya.","摘要：印度喜马拉雅地区（IHR）常被称为“第三极”，拥有除极地以外规模最大的冰川和常年积雪集中区，对气候变化高度敏感。冰川快速退缩加速了冰湖的形成与扩张，增加了冰湖溃决洪水（GLOFs）的发生概率。本研究对拉达克上印度河流域与Gya、Raru和Panikhar冰川相关的四个冰川补给湖泊开展了多站点GLOF危险性评估。利用Sentinel-2影像和地理信息系统（GIS）技术绘制了湖泊面积的时空变化，并借助数字高程模型分析，采用经验关系估算了湖泊深度和体积。基于水文工程中心河流分析系统（HEC-RAS），对Gya、Raru和Panikhar湖泊分别开展了25%、50%、75%和100%湖水量释放情景下的溃坝模拟。模拟输出提供了峰值流量、流速、淹没深度和淹没范围。结果表明，最坏情景可能影响桥梁和道路等下游基础设施，而Rumbak湖因其水量较小且具有天然冰碛缓冲，危险性潜力有限。本研究编制的危险性图可为印度喜马拉雅地区的风险优先级排序、预警规划和气候韧性灾害管理提供支持。",null,"OpenAlex","2026-10-08T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦喜马拉雅冰川湖溃决洪水灾害评估，属自然地理与灾害风险领域，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"气候变化","遥感监测","灾害风险评估","冰川灾害",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":55},[32,40,48],{"code":33,"score":34,"supported":35,"title":36,"abstract":38,"tags":39},"I3",9,true,[37],"remote sensing",[],[22],{"code":41,"score":34,"supported":35,"title":42,"abstract":43,"tags":47},"I4",[],[44,45,46],"地理信息","GIS","geographic information",[],{"code":49,"score":50,"supported":35,"title":51,"abstract":52,"tags":54},"H7",7,[],[21,53],"climate change",[21],"农业相关性需要复核",[57,58],"上印度河流域 冰川湖 洪水","灾害风险评估 冰川灾害 气候变化 遥感监测","上印度河流域冰川湖洪水-4594",1,"10.5194\u002Fegusphere-2026-5906",{"doi":61,"openalex_id":63,"authors":64,"venue":9,"cited_by_count":15,"oa_url":67,"card":68,"direction":72,"ingested_from":74},"W7220955925",[65],{"name":66,"orcid":9},"Jigmet Angmo","https:\u002F\u002Fegusphere.copernicus.org\u002Fpreprints\u002F2026\u002Fegusphere-2026-5906\u002Fegusphere-2026-5906.pdf",{"tldr":69,"method":70,"finding":71,"direction":72,"opportunity":73},"用遥感和水动力模型评估拉达克上印度河盆地四个冰湖的溃决洪水风险。","Sentinel-2影像、GIS、DEM估算湖深体积，HEC-RAS做四种溃决情","最坏情景可威胁下游桥梁道路，Rumbak湖因体积小和冰碛缓冲风险有限。","农业遥感与作物表型","可延伸至冰湖溃决对下游农业区与灌溉设施的淹没风险及预警研究。","openalex","2026-10-10T23:30:29.697412Z",{"total":77,"page":60,"page_size":77,"items":78},6,[79,129,252,316,403,450],{"id":80,"title":81,"url":82,"summary":83,"summary_zh":84,"content":9,"source_name":85,"source_url":82,"published_at":86,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":87,"sources":89,"tags":91,"primary_topic":9,"secondary_topics":94,"classification":95,"search_phrases":108,"slug":111,"view_count":15,"doi":112,"paper":113,"created_at":128},5560,"How do weather, topography and fuel condition drive the extreme behavior of a Mediterranean megafire? A case study of the 2025 Jarilla wildfire (western Spain)","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fffgc.2026.1953360","The 2025 wildfire season was the most destructive on record in the European Union, with more than 1 million hectares burned, and the Iberian Peninsula was the hardest-hit region, accounting for 43% of that area. The Jarilla wildfire (Cáceres, Spain; 17,317 ha, august 2025) exemplifies how meteorology, topography, and fuel condition jointly controlled the behavior of these Iberian megafires. Our aim was to analyze the role of these three factors in the fire’s behavior and to evaluate their interpretation against the post-fire severity footprint, within a reproducible workflow relying entirely on open-access data. We combined ERA5 hourly reanalysis, the fire weather index (FWI), Sentinel-2 spectral indices (NDVI, NDII, dNBR), VIIRS active-fire detections, and the 5 m PNOA digital elevation model. During the active phase (august 12–17), the atmosphere was extremely desiccating—vapor pressure deficit peaking at 5.9 kPa, minimum relative humidity near 15%, and FWI in the “extreme” class—acting on a dense but already water-stressed fuel cover (mean NDVI 0.53, mean NDII 0.05). The wind regime was bimodal: a frequent but weak nocturnal north-easterly flow and a diurnal south–south-westerly (SSW) flow that drove the observed SW–NE progression. Fire spread corridors—steep, wind-aligned, fuel-laden terrain delineated with the SSW driving wind—covered 13.7% of the burned area and showed higher mean severity (dNBR 665) than their surroundings (525); however, 84% of the high-severity area lay outside them. These results indicate that, in such “hot-drought” megafires, severity is driven primarily by extreme atmospheric drying acting on continuous, dry fuel, whereas topography and wind spatially organize its peaks rather than determining them. Beyond reconstructing a single event, the study provides a transferable, open-data framework for diagnosing megafire drivers and suggests that fuel- and weather-based prevention, more than topographic targeting, is key to mitigating Mediterranean megafires.","2025年野火季是欧盟有记录以来破坏性最强的一个野火季，过火面积超过100万公顷，其中伊比利亚半岛是受灾最严重的地区，占该面积的43%。Jarilla野火（西班牙卡塞雷斯，17,317公顷，2025年8月）体现了气象、地形和可燃物条件如何共同控制这些伊比利亚特大火灾的行为。我们的目标是分析这三个因素在火灾行为中的作用，并在完全依赖开放获取数据的可重复工作流程中，评估它们与火后严重程度足迹的对应解释。我们结合了ERA5逐小时再分析数据、火险天气指数（FWI）、Sentinel-2光谱指数（NDVI、NDII、dNBR）、VIIRS活跃火点检测以及5米PNOA数字高程模型。在活跃阶段（8月12日至17日），大气极度干燥——饱和水汽压差峰值达5.9 kPa，最低相对湿度接近15%，FWI处于“极端”等级——作用于密集但已受水分胁迫的可燃物覆盖层（平均NDVI为0.53，平均NDII为0.05）。风况呈双峰分布：夜间频繁但微弱的东北气流，以及白天驱动观测到的西南—东北方向蔓延的南—西南（SSW）气流。火势蔓延走廊——陡峭、与风向一致、可燃物载量高的地形，以SSW驱动风 delineated——覆盖了过火面积的13.7%，并显示出比周围区域更高的平均严重程度（dNBR 665对525）；然而，84%的高严重程度区域位于这些走廊之外。这些结果表明，在此类“高温—干旱”特大火灾中，严重程度主要由极端大气干燥作用于连续、干燥的可燃物所驱动，而地形和风在空间上组织其峰值而非决定峰值。除重建单一事件外，该研究提供了一个可迁移的开放数据框架，用于诊断特大火灾驱动因素，并表明基于可燃物和天气的预防，而非地形靶向，是缓解地中海特大火灾的关键。","Frontiers in Forests and Global Change","2026-10-20T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":88},"该研究聚焦西班牙地中海野火的气象与地形驱动机制，属于林火生态与遥感应用，与三农、农业信息化、智慧农业主题无直接关联，不建议进入每日精选。",[90],{"name":85,"url":82},[21,22,92,93],"野火研究","西班牙",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":96},{"candidates":97,"agricultural_evidence":14,"reason":55},[98,104],{"code":49,"score":50,"supported":35,"title":99,"abstract":100,"tags":103},[],[101,102],"干旱","drought",[21],{"code":33,"score":60,"supported":14,"title":105,"abstract":106,"tags":107},[],[],[22],[109,110],"气候变化 遥感监测 野火研究 西班牙","气候变化 遥感监测","气候变化遥感监测野火研究西班牙-5560","10.3389\u002Fffgc.2026.1953360",{"doi":112,"openalex_id":114,"authors":115,"venue":85,"cited_by_count":15,"oa_url":121,"card":122,"direction":127,"ingested_from":74},"W7223002169",[116,118],{"name":117,"orcid":9},"Luis Hernández-Sánchez",{"name":119,"orcid":120},"Marcos Francos","https:\u002F\u002Forcid.org\u002F0000-0002-3311-5686","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fforests-and-global-change\u002Farticles\u002F10.3389\u002Fffgc.2026.1953360\u002Fpdf",{"tldr":123,"method":124,"finding":125,"direction":72,"opportunity":126},"分析2025年西班牙Jarilla特大火灾，揭示气象、地形与燃料条件如何共同驱动火行为。","融合ERA5再分析、FWI、Sentinel-2指数、VIIRS火点和5米DEM","极端大气干燥叠加连续干旱燃料主导火强度，地形与风仅空间组织高严重度峰值而非决定其分布。","可延伸至多源遥感融合的野火驱动因子归因与火险预警，并检验该开源框架在其他地中海火区的可迁移性。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:20.437834Z",{"id":130,"title":131,"url":132,"summary":133,"summary_zh":134,"content":9,"source_name":135,"source_url":132,"published_at":86,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":144,"tags":146,"primary_topic":150,"secondary_topics":155,"classification":166,"search_phrases":223,"slug":226,"view_count":15,"doi":227,"paper":228,"created_at":251},5483,"Soil Salinity under Climate Change in Pakistan: Emerging Challenges and Sustainable Management Approaches for Agriculture","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.883","Soil salinity is a major constraint to agricultural productivity, soil health, and food security in Pakistan, particularly under changing climatic conditions. Rising temperatures, increased evapotranspiration, irregular precipitation, droughts, flooding, and sea-level rise can intensify salt accumulation and alter soil–water interactions. This review examines the emerging challenges associated with climate change and soil salinity in Pakistan, focusing on their impacts on soil properties, crop physiology, agricultural production, and rural livelihoods. It discusses the mechanisms of soil salinity and sodicity, their spatial distribution, and the interactions among climate variability, irrigation practices, groundwater quality, and secondary salinization. Furthermore, sustainable management approaches are evaluated, including gypsum application, organic amendments, integrated nutrient management, efficient irrigation, drainage improvement, and appropriate leaching strategies. Climate-resilient approaches, such as conventional crop breeding, modern biotechnology, halophyte utilization, plant growth-promoting microorganisms, and mycorrhizal associations, are also explored. The review highlights the potential of remote sensing, geographic information systems, precision agriculture, and predictive modelling to support salinity monitoring and site-specific management. Despite these opportunities, important challenges remain, including insufficient long-term monitoring, limited region-specific data, economic constraints, and gaps in farmer awareness and institutional coordination. Addressing these challenges requires integrated research, effective agricultural policies, improved extension services, and collaboration among researchers, policymakers, and farming communities. Prioritizing locally adapted, economically feasible, and environmentally sustainable interventions can strengthen agricultural resilience, restore salt-affected soils, and protect freshwater resources. Overall, integrating climate adaptation with sustainable soil and water management is essential for maintaining agricultural productivity and improving long-term food security in Pakistan.","土壤盐渍化是制约巴基斯坦农业生产力、土壤健康和粮食安全的主要因素，在气候变化条件下尤为突出。气温升高、蒸散加剧、降水不规律、干旱、洪水以及海平面上升可能加剧盐分积累并改变土壤—水相互作用。本文综述了巴基斯坦气候变化与土壤盐渍化相关的新兴挑战，重点关注其对土壤性质、作物生理、农业生产和农村生计的影响。文章讨论了土壤盐渍化和钠质化的机制、其空间分布，以及气候变率、灌溉实践、地下水质量和次生盐渍化之间的相互作用。此外，评估了可持续管理途径，包括施用石膏、有机改良、综合养分管理、高效灌溉、排水改良和适宜的淋洗策略。还探讨了气候韧性方法，如常规作物育种、现代生物技术、盐生植物利用、植物促生微生物和菌根共生。本文强调了遥感、地理信息系统、精准农业和预测建模在支持盐渍化监测和因地制宜管理方面的潜力。尽管存在这些机遇，重要挑战依然存在，包括长期监测不足、区域特定数据有限、经济制约以及农民意识和制度协调方面的差距。应对这些挑战需要综合研究、有效的农业政策、改进的推广服务以及研究人员、政策制定者和农业社区之间的协作。优先采取适应当地、经济可行且环境可持续的干预措施，可增强农业韧性、修复盐渍化土壤并保护淡水资源。总体而言，将气候适应与可持续水土管理相结合，对于维持巴基斯坦农业生产力和改善长期粮食安全至关重要。","Kashmir Journal of Academic Research and Development",66,{"impact":138,"substance":139,"depth":140,"authority":141,"freshness":142,"relevant":60,"comment":143},13,18,15,12,8,"综述性论文，系统梳理气候变化下巴基斯坦土壤盐渍化成因与可持续治理路径，兼具遥感与精准农业视角，对盐碱地治理议题有参考价值，但属区域性综述、非突破性成果。",[145],{"name":135,"url":132},[21,147,22,148,149],"盐碱地治理","智慧灌溉","土壤盐渍化",{"code":49,"slug":151,"name":152,"path":153,"parent_code":154},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[156,161],{"code":157,"slug":158,"name":159,"path":160,"parent_code":154},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":162,"slug":163,"name":164,"path":165,"parent_code":154},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":167,"locked":14,"version":28,"method":29,"evidence":168},"classified",{"candidates":169,"agricultural_evidence":35},[170,175,180,187,192,199,206,210,217],{"code":49,"score":139,"supported":35,"title":171,"abstract":172,"tags":174},[53],[21,173,101,53],"气候适应",[21],{"code":157,"score":140,"supported":35,"title":176,"abstract":177,"tags":179},[158],[178,158],"土壤",[147],{"code":162,"score":13,"supported":35,"title":181,"abstract":182,"tags":186},[],[183,184,163,185],"灌溉","水资源","evapotranspiration",[148],{"code":33,"score":50,"supported":35,"title":188,"abstract":189,"tags":191},[],[190,37],"遥感",[22],{"code":193,"score":77,"supported":35,"title":194,"abstract":195,"tags":198},"A8",[],[196,197],"粮食安全","food security",[],{"code":200,"score":77,"supported":35,"title":201,"abstract":202,"tags":205},"I1",[],[203,204],"精准农业","precision agriculture",[],{"code":41,"score":77,"supported":35,"title":207,"abstract":208,"tags":209},[],[44,46],[],{"code":211,"score":212,"supported":35,"title":213,"abstract":214,"tags":216},"F3",3,[],[215],"养分管理",[],{"code":218,"score":212,"supported":35,"title":219,"abstract":220,"tags":222},"J1",[],[221],"农业政策",[],[224,225],"巴基斯坦 土壤盐渍化","盐碱地 遥感监测","巴基斯坦土壤盐渍化-5483","10.71317\u002Fkjard.2.10.2026.883",{"doi":227,"openalex_id":229,"authors":230,"venue":135,"cited_by_count":15,"oa_url":132,"card":246,"direction":72,"ingested_from":74},"W7222999158",[231,233,235,237,239,241,244],{"name":232,"orcid":9},"Asifa Naz",{"name":234,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":236,"orcid":9},"Syed Asghar Hussain Shah",{"name":238,"orcid":9},"Shahrish Nawaz",{"name":240,"orcid":9},"Muhmmad Irfan",{"name":242,"orcid":243},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":245,"orcid":9},"Muhammad Nouman Khalid",{"tldr":247,"method":248,"finding":249,"direction":72,"opportunity":250},"综述气候变化下巴基斯坦土壤盐渍化的挑战与可持续管理途径。","文献综述，整合盐渍化机制、空间分布及遥感GIS与预测模型应用。","需将气候适应与水土管理结合，优先本地化、经济可行的干预措施。","可填补长期盐渍化监测与区域数据空白，发展遥感+预测模型的精准分区治理。","2026-10-21T23:30:23.059448Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":265,"tags":267,"primary_topic":271,"secondary_topics":272,"classification":273,"search_phrases":288,"slug":291,"view_count":15,"doi":292,"paper":293,"created_at":315},5172,"Forest green-up in the Pearl River Basin: trends, driving factors, and relationships with net primary productivity","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17538947.2026.2738487","The spring green-up date (GUD), a critical indicator of ecosystem response to climate change, profoundly influences terrestrial productivity. However, the complex drivers of GUD remain elusive, while the contentious relationship between GUD and net primary productivity (NPP) challenges the universal validity of the hypothesis that earlier green-up enhances productivity. Using MODIS NDVI data (2000-2020), we quantified forest GUD dynamics in the Pearl River Basin and combined random forest, SHAP, and structural equation modelling to identify drivers and assess GUD-NPP relationships. GUD was advanced by approximately 5 days, with 81.8% of forestland pixels advancing and 18.2% delaying. Only 5.39% of advances showed significant trends, whereas 30.45% of delays were concentrated near croplands and developed areas. The random forest model explained 58.2% of GUD variation. Elevation, precipitation, and soil potassium and phosphorus were dominant; elevation-induced thermal gradients outweighed ambient temperature effects, with elevations above 1,200 m exhibiting markedly delayed GUD. Spatially, GUD had a positive correlation with NPP, indicating that later GUD was associated with higher forest NPP, possibly because of early-spring cold snaps and frost. Temporally, areas with stronger GUD delays tended to show greater NPP increases. This study identified key controls on subtropical forest phenology and revealed a GUD-NPP trade-off.","春季返青期（GUD）是生态系统响应气候变化的关键指标，深刻影响陆地生产力。然而，GUD的复杂驱动机制仍不明确，且GUD与净初级生产力（NPP）之间存在争议性关系，挑战了“提前返青提高生产力”这一假说的普适性。本研究利用MODIS NDVI数据（2000—2020年），量化了珠江流域森林GUD动态，并结合随机森林、SHAP和结构方程模型识别驱动因子并评估GUD-NPP关系。GUD整体提前约5天，其中81.8%的林地像元提前、18.2%推迟。仅5.39%的提前像元呈显著趋势，而30.45%的推迟像元集中在农田和建成区附近。随机森林模型解释了58.2%的GUD变异。海拔、降水以及土壤钾和磷为主导因子；海拔引起的热梯度效应超过环境温度的影响，海拔1200 m以上区域GUD显著推迟。空间上，GUD与NPP呈正相关，表明GUD越晚森林NPP越高，这可能与早春寒潮和霜冻有关。时间上，GUD推迟越强的区域往往表现为NPP增加越大。本研究识别了亚热带森林物候的关键控制因子，揭示了GUD-NPP之间的权衡关系。","International Journal of Digital Earth","2026-10-15T00:00:00Z",74,{"impact":141,"substance":262,"depth":139,"authority":263,"freshness":142,"relevant":60,"comment":264},22,14,"基于MODIS长时序数据的区域森林物候研究，方法组合新颖、结论有增量，但属区域基础研究，产业与政策影响有限，可作专业补充而非每日精选头条。",[266],{"name":258,"url":255},[21,22,268,269,270],"植被生产力","森林物候","珠江流域",{"code":49,"slug":151,"name":152,"path":153,"parent_code":154},[],{"status":167,"locked":14,"version":28,"method":29,"evidence":274},{"candidates":275,"agricultural_evidence":35},[276,280,284],{"code":49,"score":50,"supported":35,"title":277,"abstract":278,"tags":279},[],[21,53],[21],{"code":157,"score":77,"supported":35,"title":281,"abstract":282,"tags":283},[],[178,158],[],{"code":33,"score":60,"supported":14,"title":285,"abstract":286,"tags":287},[],[],[22],[289,290],"珠江流域 森林 返青期","MODIS NDVI 植被生产力","珠江流域森林返青期-5172","10.1080\u002F17538947.2026.2738487",{"doi":292,"openalex_id":294,"authors":295,"venue":258,"cited_by_count":15,"oa_url":309,"card":310,"direction":72,"ingested_from":74},"W7221897424",[296,299,301,303,305,307],{"name":297,"orcid":298},"Shijie Yang","https:\u002F\u002Forcid.org\u002F0009-0001-7573-5509",{"name":300,"orcid":9},"Zhaoju Zheng",{"name":302,"orcid":9},"Xueming Zhao",{"name":304,"orcid":9},"Zhipeng Zhang",{"name":306,"orcid":9},"Xiaohui Cui",{"name":308,"orcid":9},"Yuan Zeng","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2738487?needAccess=true",{"tldr":311,"method":312,"finding":313,"direction":72,"opportunity":314},"基于MODIS NDVI量化珠江流域森林返青期动态，并解析其驱动因子及与NPP的关系。","MODIS NDVI（2000-2020）、随机森林、SHAP与结构方程模型。","返青期整体提前约5天，但延迟区NPP更高，揭示返青期与NPP的权衡关系。","可探究早春寒潮霜冻对返青-NPP权衡的调控机制，并拓展至其他亚热带流域验证普适性。","2026-10-17T23:30:29.750744Z",{"id":317,"title":318,"url":319,"summary":320,"summary_zh":321,"content":9,"source_name":322,"source_url":319,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":323,"score_detail":324,"sources":327,"tags":329,"primary_topic":333,"secondary_topics":339,"classification":341,"search_phrases":386,"slug":389,"view_count":60,"doi":390,"paper":391,"created_at":402},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)",70,{"impact":139,"substance":325,"depth":140,"authority":141,"freshness":34,"relevant":60,"comment":326},16,"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。",[328],{"name":322,"url":319},[330,331,332,21,22],"智慧农业","农业人工智能","可持续发展",{"code":334,"slug":335,"name":336,"path":337,"parent_code":338},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[340],{"code":49,"slug":151,"name":152,"path":153,"parent_code":154},{"status":167,"locked":14,"version":28,"method":29,"evidence":342},{"candidates":343,"agricultural_evidence":35},[344,353,357,361,365,372,379],{"code":334,"score":139,"supported":35,"title":345,"abstract":347,"tags":352},[346],"artificial intelligence",[348,349,350,351,346],"深度学习","机器学习","machine learning","deep learning",[331],{"code":49,"score":140,"supported":35,"title":354,"abstract":355,"tags":356},[53],[21,53],[21],{"code":200,"score":50,"supported":35,"title":358,"abstract":359,"tags":360},[],[203,204],[330],{"code":33,"score":50,"supported":35,"title":362,"abstract":363,"tags":364},[],[190,37],[22],{"code":366,"score":77,"supported":35,"title":367,"abstract":368,"tags":371},"H4",[],[369,370],"碳排放","carbon emission",[],{"code":373,"score":77,"supported":35,"title":374,"abstract":375,"tags":378},"H6",[],[376,377],"生物多样性","biodiversity",[],{"code":380,"score":77,"supported":35,"title":381,"abstract":382,"tags":385},"I5",[],[383,384],"物联网","internet of things",[],[387,388],"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":390,"openalex_id":392,"authors":393,"venue":322,"cited_by_count":15,"oa_url":319,"card":396,"direction":127,"ingested_from":74},"W7222092855",[394],{"name":395,"orcid":9},"Deepmala Suryavanshi",{"tldr":397,"method":398,"finding":399,"direction":400,"opportunity":401},"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。","2026-10-16T23:30:15.059867Z",{"id":404,"title":405,"url":406,"summary":407,"summary_zh":408,"content":9,"source_name":409,"source_url":406,"published_at":410,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":411,"score_detail":412,"sources":414,"tags":416,"primary_topic":9,"secondary_topics":419,"classification":420,"search_phrases":433,"slug":436,"view_count":60,"doi":437,"paper":438,"created_at":449},4827,"تحليل العلاقة بين الجفاف الموسمي وتناقص الغطاء النباتي واثره في تكرار الظواهر الغبارية في محافظة المثنى للمدة (1980-2025)","https:\u002F\u002Fdoi.org\u002F10.66026\u002Fksfnen31","تعتبر الظواهر الغبارية من الظواهر المناخية التي لها العديد من التأثيرات السلبية على البيئة والاقتصاد والصحة. والغرض من هذه الدراسة التحقق من العلاقة بين تكرار العواصف الرملية والغبار المتطاير والغبار العالق والعوامل المناخية خاصة في المواسم الجافة والرطبة ، وتحليل ودراسة إمكانية حدوث الظواهر الغبارية في منطقة الدراسة وتأثير العوامل المناخية على تكرار حدوثها. اتبعت الدراسة المنهج الوصفي التحليلي، وذلك من خلال الاعتماد على بيانات محطة السماوة المناخية خلال مدة الدراسة من ( 1980- 2025 ) والمحطات الضابطة النجف والديوانية وايضا على بيانات الاستشعار عن بعد الخاصة بالغطاء النباتي الـ NDVI . حيث بينت نتائج الدراسة ان الظروف المناخية تؤثر على التوزيع المكاني للظواهر الغبارية وتكرارها، كما واستخدمت الدراسة معاملات الارتباط لمعالجة البيانات وبيان اتجاه وقوة العلاقة بين الظاهرة المدروسة وأيهما اكثر تأثير من خلال المتغيرات المستقلة والعوامل المناخية المؤثرة فيها. وتوصلت الدراسة إلى ان مصفوفة الارتباط ذات علاقة موجبة قوية بين سرعة الرياح و العاصفة الغبارية أكثر من ارتباطها بأي متغير آخر إذ بلغ معامل الارتباط ( r= 0.79 )، كما ارتبطت إيجابياً بالغبار المتصاعد (r=0.72) والغبار العالق( (r=0.61اما العلاقة الارتباطية بين الرطوبة النسبية والامطار علاقة عك تمثلت منطقة الدراسة بمحافظة المثنى التي تقع في الجزء الجنوبي من العراق يحدها من الشمال محافظة القادسية ومن الشرق محافظتي البصرة وذي قار ومن الغرب محافظة النجف ومن الجنوب المملكة العربية السعودية ، تقع فلكياً بين دائرتي عرض(¢5 29°) ( ¢42 31°) شمالاً وخطي طول (¢50- 43°) و (¢32 -46°)[i] شرقاً كما في الخريطة (1) ، في حين تمثلت المدة الزمنية للظواهر الغبارية من (1980-2025) بالاعتماد على البيانات المناخية لمحطة السماوة و محطة النجف والديوانية كمحطات ضابطة . سية قوية . [i] - اشواق حسن حميد ، الاتجاه الحديث في عناصر المناخ وظواهره لمحافظة المثنى واثره في الجفاف للمدة ( 1987- 2020) ، مجلة الجامعة العراقية ، المجلد 46 ، العدد 2 ، 2023 ، ص 313.","沙尘现象是一种气候现象，对环境、经济和健康具有诸多负面影响。本研究旨在探讨沙尘暴、扬尘和悬浮尘埃发生频率与气候因素之间的关系，尤其是在干旱季和湿润季，并分析和研究研究区域内沙尘现象发生的可能性以及气候因素对其发生频率的影响。本研究采用描述性分析方法，依据萨马沃气候站在研究期（1980—2025年）的数据以及纳杰夫和迪瓦尼耶对照站的数据，同时依据植被归一化植被指数（NDVI）的遥感数据。研究结果表明，气候条件影响沙尘现象的空间分布及其发生频率。本研究还使用相关系数处理数据，说明所研究现象与各独立变量及影响气候因素之间关系的方向和强度，并判断哪个因素影响更大。研究发现，相关矩阵显示风速与沙尘暴之间存在强正相关关系，其相关性高于与其他任何变量的相关性，相关系数达到（r=0.79）；风速与扬尘（r=0.72）和悬浮尘埃（r=0.61）也呈正相关。至于相对湿度与降雨之间的相关关系则呈强负相关。研究区域为穆萨纳省，位于伊拉克南部，北邻卡迪西亚省，东接巴士拉省和济加尔省，西连纳杰夫省，南与沙特阿拉伯王国接壤。该省在天文位置上位于北纬（29°5′）至（31°42′）之间，东经（43°50′）至（46°32′）之间，如图1所示。沙尘现象的时间跨度则为（1980—2025年），依据萨马沃站以及作为对照站的纳杰夫站和迪瓦尼耶站的气候数据。","Journal Of Babylon Center for Humanities Studies","2026-10-11T00:00:00Z",62,{"impact":142,"substance":139,"depth":140,"authority":141,"freshness":34,"relevant":60,"comment":413},"基于1980-2025年气候与NDVI数据定量分析干旱、植被退化与沙尘频次关系，方法规范但属区域气候研究，与农业信息化关联偏弱。",[415],{"name":409,"url":406},[21,417,22,101,418],"NDVI","沙尘天气",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":421},{"candidates":422,"agricultural_evidence":14,"reason":55},[423,428],{"code":33,"score":424,"supported":35,"title":425,"abstract":426,"tags":427},5,[],[190],[417,22],{"code":49,"score":429,"supported":35,"title":430,"abstract":431,"tags":432},4,[],[101],[21],[434,435],"穆萨纳省 沙尘 植被覆盖","NDVI 干旱 沙尘暴 伊拉克","穆萨纳省沙尘植被覆盖-4827","10.66026\u002Fksfnen31",{"doi":437,"openalex_id":439,"authors":440,"venue":409,"cited_by_count":15,"oa_url":443,"card":444,"direction":72,"ingested_from":74},"W7221436686",[441],{"name":442,"orcid":9},"حوراء راضي جاسم محمد","https:\u002F\u002Fbcchj.uobabylon.edu.iq\u002Findex.php\u002Fbcchj\u002Farticle\u002Fdownload\u002F4964\u002F5019",{"tldr":445,"method":446,"finding":447,"direction":72,"opportunity":448},"分析伊拉克穆萨纳省1980-2025年季节性干旱、植被减少与沙尘现象频率的关系。","利用气象站数据与NDVI遥感数据，采用描述性分析和相关系数法。","风速与沙尘暴相关性最强(r=0.79)，相对湿度和降雨与沙尘现象呈强负相关。","可结合多源遥感与机器学习，构建干旱-植被-沙尘耦合预警模型，并拓展到其他干旱区。","2026-10-13T23:30:25.135328Z",{"id":451,"title":452,"url":453,"summary":454,"summary_zh":455,"content":9,"source_name":409,"source_url":453,"published_at":456,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":323,"score_detail":457,"sources":459,"tags":461,"primary_topic":464,"secondary_topics":465,"classification":466,"search_phrases":481,"slug":484,"view_count":60,"doi":485,"paper":486,"created_at":501},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。","2026-10-09T00:00:00Z",{"impact":141,"substance":262,"depth":139,"authority":141,"freshness":77,"relevant":60,"comment":458},"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。",[460],{"name":409,"url":453},[21,417,22,462,463],"干旱区农业","植被覆盖",{"code":49,"slug":151,"name":152,"path":153,"parent_code":154},[],{"status":167,"locked":14,"version":28,"method":29,"evidence":467},{"candidates":468,"agricultural_evidence":35},[469,473,477],{"code":49,"score":142,"supported":35,"title":470,"abstract":471,"tags":472},[],[21,101],[462,21],{"code":162,"score":77,"supported":35,"title":474,"abstract":475,"tags":476},[],[183,184],[],{"code":33,"score":212,"supported":14,"title":478,"abstract":479,"tags":480},[],[],[417,463,22],[482,483],"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":485,"openalex_id":487,"authors":488,"venue":409,"cited_by_count":15,"oa_url":495,"card":496,"direction":72,"ingested_from":74},"W7221306668",[489,491,493],{"name":490,"orcid":9},"خولة‌ حمه‌رشید عمر",{"name":492,"orcid":9},"سایە دلاوەر محمد",{"name":494,"orcid":9},"Daban Kadhim Omar","https:\u002F\u002Fbcchj.uobabylon.edu.iq\u002Findex.php\u002Fbcchj\u002Farticle\u002Fdownload\u002F4819\u002F4866",{"tldr":497,"method":498,"finding":499,"direction":72,"opportunity":500},"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"]