[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4582":3,"related-4582":85},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":56,"slug":59,"view_count":22,"doi":60,"paper":61,"created_at":84},4582,"Assessing mangrove sensitivity to thermal variations through a satellite-based analysis on the southern São Paulo coast, Brazil","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-16005-6","Abstract Mangroves near subtropical range limits may benefit from fewer cold constraints, yet warmer conditions can also increase atmospheric water demand. These competing possibilities were examined in the Cananéia-Iguape Coastal System, southeastern Brazil, by combining ERA5 2 m air temperature with MODIS normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI). Air-temperature trends were assessed for 1979–2019 with the Mann-Kendall test and Sen’s slope. Temperature-vegetation associations were calculated for 2003–2019 from mangrove-wide means and from individual grid cells at annual, monthly, and fixed calendar-quarter scales. Mean annual air temperature increased by about 0.02 °C year⁻ 1 ; the highest quarter-scale rate occurred in July–September (0.03 °C year⁻ 1 ). Annual temperature correlated positively with NDVI ( r = 0.61, R 2 = 0.38, p = 0.009) and EVI ( r = 0.55, R 2 = 0.30, p = 0.022), whereas the LAI relationship was not significant ( r = 0.27, R 2 = 0.07, p = 0.304). The sign of the association changed within the year: NDVI and EVI were negative in July–September, while positive EVI and LAI relationships occurred in October-December. Significant grid-cell relationships occupied limited and discontinuous areas. The temperature record shows regional warming, whereas canopy associations vary among metrics, periods, and locations. Given the bivariate design and coarse ERA5 grid, these associations should not be interpreted as causal or as responses to temperature alone.","摘要 靠近亚热带分布界限的红树林可能受益于较少的低温限制，但更温暖的条件也可能增加大气水分需求。为检验这两种相互竞争的的可能性，本研究在巴西东南部的卡纳内亚-伊瓜佩海岸系统，将ERA5 2米气温数据与MODIS归一化差异植被指数（NDVI）、增强植被指数（EVI）和叶面积指数（LAI）相结合进行了分析。采用Mann-Kendall检验和Sen斜率法评估了1979—2019年的气温趋势。基于2003—2019年红树林全域均值及单个网格单元，在年、月和固定日历季度尺度上计算了气温-植被关联。年平均气温上升约0.02 °C yr⁻¹；季度尺度最高升温速率出现在7—9月（0.03 °C yr⁻¹）。年气温与NDVI呈正相关（r = 0.61，R² = 0.38，p = 0.009），与EVI也呈正相关（r = 0.55，R² = 0.30，p = 0.022），而与LAI的关系不显著（r = 0.27，R² = 0.07，p = 0.304）。关联方向在年内发生变化：NDVI和EVI在7—9月为负，而EVI和LAI的正相关关系出现在10—12月。显著的网格单元关系占据有限且不连续的区域。气温记录显示区域变暖，而冠层关联因指标、时段和地点而异。鉴于双变量设计和ERA5网格较粗，这些关联不应被解释为因果关系或仅对温度的响应。",null,"Environmental Monitoring and Assessment","2026-10-09T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,16,13,9,1,"方法规范、结论审慎的区域性遥感研究，对农业信息化中的生态遥感监测有参考价值，但影响范围限于学术层面。",[25],{"name":10,"url":6},[27,28,29,30,31],"气候变化","遥感监测","植被指数","红树林","海岸带生态",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":55},[39,45,50],{"code":40,"score":41,"supported":14,"title":42,"abstract":43,"tags":44},"I3",2,[],[],[29,28],{"code":46,"score":22,"supported":14,"title":47,"abstract":48,"tags":49},"H6",[],[],[30],{"code":51,"score":22,"supported":14,"title":52,"abstract":53,"tags":54},"H7",[],[],[27],"农业相关性需要复核",[57,58],"巴西 圣保罗 红树林 遥感","MODIS NDVI 红树林 温度","巴西圣保罗红树林遥感-4582","10.1007\u002Fs10661-026-16005-6",{"doi":60,"openalex_id":62,"authors":63,"venue":10,"cited_by_count":75,"oa_url":76,"card":77,"direction":81,"ingested_from":83},"W7220983448",[64,67,70,72],{"name":65,"orcid":66},"Jakeline Baratto","https:\u002F\u002Forcid.org\u002F0000-0003-0311-2369",{"name":68,"orcid":69},"Paulo Miguel De Bodas Terassi","https:\u002F\u002Forcid.org\u002F0000-0002-8184-0348",{"name":71,"orcid":9},"Nádia Gilma Beserra de Lima",{"name":73,"orcid":74},"Emerson Galvani","https:\u002F\u002Forcid.org\u002F0000-0002-8082-5963",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10661-026-16005-6.pdf",{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"结合气温与植被指数分析巴西东南部红树林对温度变化的敏感性。","ERA5气温与MODIS NDVI\u002FEVI\u002FLAI，用Mann-Kendall趋","区域增温，但植被与温度关联随指标、时段和地点变化，非因果。","农业遥感与作物表型","可引入多源遥感与因果推断，探究亚热带红树林对增温的滞后与非线性响应。","openalex","2026-10-10T23:30:27.692383Z",{"total":86,"page":22,"page_size":86,"items":87},6,[88,175,226,347,410,495],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":9,"source_name":94,"source_url":91,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":101,"tags":103,"primary_topic":9,"secondary_topics":106,"classification":107,"search_phrases":127,"slug":130,"view_count":119,"doi":131,"paper":132,"created_at":174},3083,"Multi-index analysis reveals complexity of tundra greening and shrubification on Yamal Peninsula","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2752-664x\u002Faea99e","Abstract Arctic vegetation cover is undergoing rapid structural and spatial change under a warming climate. Shrubification is a major component of these changes, whereby shrubs grow outwards and upwards, infilling existing patches and spreading into new areas. Satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), show increasing trends across decades in many regions of the Arctic, a phenomenon referred to as greening, and interpreted as an indicator of compositional, structural, spatial and functional changes in vegetation. However, the direct contribution of shrubification on satellite-level greening remains poorly understood, partly due to the spectral limitations of single-index methods and the coarse spatial resolution of multi-decadal satellite records. Here we examine and compare the spatial patterns and magnitude of greening on Yamal Peninsula, Arctic Russia, measured with four commonly used vegetation indices derived from Landsat imagery (1987-2023): NDVI, Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI), and kernel NDVI (kNDVI). We also examine how willow (Salix lanata) cover (%) relates to greening, using cover data derived from field observations, and Unoccupied Aerial Vehicle (UAV) and Very High Resolution WorldView 3 satellite imagery. Vegetation greened in 12–25% of the study area, driven primarily by vegetation regeneration on cryogenic landslides and possibly due to shrub expansion. However, greening magnitude was dependent on the VI selected, as they showed only a maximum of 54% similarity in spatial distribution. Uncertainty in the relationship between spectral greening and shrubification adds complexity to interpreting drivers of vegetation change, including how herbivory, for example by reindeer (Rangifer tarandusi), inhibits shrub expansion and how abiotic and other biotic influences may be detected by different or some combination of VIs. We demonstrate that decadal vegetation greenness changes have not been homogenous on Yamal Peninsula, with contrasting Salix-dominated areas responding differently to shared changes in climate, likely due to herbivore dynamics, soil effects, as well as microclimatic or topographical differences in the area.","摘要 在气候变暖背景下，北极植被覆盖正经历快速的结构和空间变化。灌木化是这些变化的主要组成部分，表现为灌木向外和向上生长，填充现有斑块并向新区域扩散。基于卫星的植被指数（VIs），如归一化差异植被指数（NDVI），在北极许多地区显示出数十年间的增长趋势，这一现象被称为绿化，并被解释为植被组成、结构、空间和功能变化的指标。然而，灌木化对卫星尺度绿化的直接贡献仍知之甚少，部分原因在于单一指数方法的光谱局限性以及多年代际卫星记录的空间分辨率较粗。本研究利用1987—2023年Landsat影像衍生的四种常用植被指数——NDVI、增强植被指数2（EVI2）、土壤调节植被指数（SAVI）和核NDVI（kNDVI）——考察并比较了俄罗斯北极亚马尔半岛绿化的空间格局和幅度。我们还利用野外观测、无人驾驶飞行器（UAV）和甚高分辨率WorldView 3卫星影像获得的覆盖度数据，研究了柳树（Salix lanata）覆盖度（%）与绿化的关系。研究区12%—25%的区域出现植被绿化，主要由低温滑坡上的植被再生驱动，也可能源于灌木扩张。然而，绿化幅度取决于所选用的植被指数，因为它们在空间分布上的相似性最高仅为54%。光谱绿化与灌木化之间关系的不确定性增加了解释植被变化驱动因素的复杂性，包括植食作用（例如驯鹿Rangifer tarandus的啃食）如何抑制灌木扩张，以及非生物和其他生物影响如何通过不同植被指数或其组合被检测到。我们证明，亚马尔半岛数十年尺度的植被绿度变化并非均一，以柳树为主的对比区域对共同的气候变化响应不同，这可能归因于植食动物动态、土壤效应以及该地区微气候或地形的差异。","Environmental Research Ecology","2026-09-18T00:00:00Z",69,{"impact":17,"substance":98,"depth":99,"authority":20,"freshness":17,"relevant":22,"comment":100},22,18,"多指数遥感揭示北极苔原绿化与灌木扩张复杂性，方法新颖数据扎实，但属基础生态研究，与三农信息化关联间接，公共价值有限。",[102],{"name":94,"url":91},[27,28,29,104,105],"北极苔原","灌木扩张",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":108},{"candidates":109,"agricultural_evidence":14,"reason":55},[110,118,123],{"code":111,"score":86,"supported":112,"title":113,"abstract":114,"tags":117},"H2",true,[],[115,116],"土壤","soil",[],{"code":51,"score":119,"supported":112,"title":120,"abstract":121,"tags":122},4,[],[27],[27],{"code":40,"score":41,"supported":14,"title":124,"abstract":125,"tags":126},[],[],[29,28],[128,129],"北极苔原 灌木扩张 遥感","北极苔原 植被指数 气候变化 灌木扩张","北极苔原灌木扩张遥感-3083","10.1088\u002F2752-664x\u002Faea99e",{"doi":131,"openalex_id":133,"authors":134,"venue":94,"cited_by_count":75,"oa_url":168,"card":169,"direction":81,"ingested_from":83},"W7213598351",[135,138,141,143,145,148,151,154,157,160,162,165],{"name":136,"orcid":137},"Elias Koivisto","https:\u002F\u002Forcid.org\u002F0009-0007-6204-0963",{"name":139,"orcid":140},"Anton Kuzmin","https:\u002F\u002Forcid.org\u002F0000-0001-5066-5535",{"name":142,"orcid":9},"Logan Berner",{"name":144,"orcid":9},"Jeff T Kerby",{"name":146,"orcid":147},"Mariana Verdonen","https:\u002F\u002Forcid.org\u002F0000-0001-9780-0052",{"name":149,"orcid":150},"Anna Skarin","https:\u002F\u002Forcid.org\u002F0000-0003-3221-1024",{"name":152,"orcid":153},"Tiina H. M. Kolari","https:\u002F\u002Forcid.org\u002F0000-0003-0955-2402",{"name":155,"orcid":156},"Teemu Tahvanainen","https:\u002F\u002Forcid.org\u002F0000-0002-7856-299X",{"name":158,"orcid":159},"Pasi Korpelainen","https:\u002F\u002Forcid.org\u002F0009-0005-9956-6016",{"name":161,"orcid":9},"Miguel Villosada",{"name":163,"orcid":164},"Bruce C. Forbes","https:\u002F\u002Forcid.org\u002F0000-0002-4593-5083",{"name":166,"orcid":167},"Timo Kumpula","https:\u002F\u002Forcid.org\u002F0000-0002-2716-7420","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2752-664X\u002Faea99e\u002Fpdf",{"tldr":170,"method":171,"finding":172,"direction":81,"opportunity":173},"用四种植被指数分析亚马尔半岛苔原绿化，揭示灌木扩张与光谱绿化的复杂关系。","Landsat 1987-2023年NDVI、EVI2、SAVI、kNDVI及无","绿化面积12-25%，但不同指数空间分布相似度仅54%，灌木扩张与绿化关系不确定。","多指数遥感可揭示植被变化的异质性，需结合地面与高分辨率数据解析生物与非生物驱动机制。","2026-09-21T23:30:27.685080Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":9,"source_name":181,"source_url":178,"published_at":182,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":75,"score_detail":183,"sources":185,"tags":187,"primary_topic":9,"secondary_topics":190,"classification":191,"search_phrases":205,"slug":208,"view_count":75,"doi":209,"paper":210,"created_at":225},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":75,"substance":75,"depth":75,"authority":75,"freshness":75,"relevant":75,"comment":184},"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。",[186],{"name":181,"url":178},[27,28,188,189],"野火研究","西班牙",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":192},{"candidates":193,"agricultural_evidence":14,"reason":55},[194,201],{"code":51,"score":195,"supported":112,"title":196,"abstract":197,"tags":200},7,[],[198,199],"干旱","drought",[27],{"code":40,"score":22,"supported":14,"title":202,"abstract":203,"tags":204},[],[],[28],[206,207],"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":209,"openalex_id":211,"authors":212,"venue":181,"cited_by_count":75,"oa_url":218,"card":219,"direction":224,"ingested_from":83},"W7223002169",[213,215],{"name":214,"orcid":9},"Luis Hernández-Sánchez",{"name":216,"orcid":217},"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":220,"method":221,"finding":222,"direction":81,"opportunity":223},"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":227,"title":228,"url":229,"summary":230,"summary_zh":231,"content":9,"source_name":232,"source_url":229,"published_at":182,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":233,"sources":237,"tags":239,"primary_topic":243,"secondary_topics":248,"classification":257,"search_phrases":318,"slug":321,"view_count":75,"doi":322,"paper":323,"created_at":346},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",{"impact":20,"substance":99,"depth":234,"authority":235,"freshness":17,"relevant":22,"comment":236},15,12,"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。",[238],{"name":232,"url":229},[27,240,28,241,242],"盐碱地治理","智慧灌溉","土壤盐渍化",{"code":51,"slug":244,"name":245,"path":246,"parent_code":247},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[249,252],{"code":111,"slug":116,"name":250,"path":251,"parent_code":247},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":253,"slug":254,"name":255,"path":256,"parent_code":247},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":258,"locked":14,"version":35,"method":36,"evidence":259},"classified",{"candidates":260,"agricultural_evidence":112},[261,267,271,278,284,291,298,305,312],{"code":51,"score":99,"supported":112,"title":262,"abstract":264,"tags":266},[263],"climate change",[27,265,198,263],"气候适应",[27],{"code":111,"score":234,"supported":112,"title":268,"abstract":269,"tags":270},[116],[115,116],[240],{"code":253,"score":13,"supported":112,"title":272,"abstract":273,"tags":277},[],[274,275,254,276],"灌溉","水资源","evapotranspiration",[241],{"code":40,"score":195,"supported":112,"title":279,"abstract":280,"tags":283},[],[281,282],"遥感","remote sensing",[28],{"code":285,"score":86,"supported":112,"title":286,"abstract":287,"tags":290},"A8",[],[288,289],"粮食安全","food security",[],{"code":292,"score":86,"supported":112,"title":293,"abstract":294,"tags":297},"I1",[],[295,296],"精准农业","precision agriculture",[],{"code":299,"score":86,"supported":112,"title":300,"abstract":301,"tags":304},"I4",[],[302,303],"地理信息","geographic information",[],{"code":306,"score":307,"supported":112,"title":308,"abstract":309,"tags":311},"F3",3,[],[310],"养分管理",[],{"code":313,"score":307,"supported":112,"title":314,"abstract":315,"tags":317},"J1",[],[316],"农业政策",[],[319,320],"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":322,"openalex_id":324,"authors":325,"venue":232,"cited_by_count":75,"oa_url":229,"card":341,"direction":81,"ingested_from":83},"W7222999158",[326,328,330,332,334,336,339],{"name":327,"orcid":9},"Asifa Naz",{"name":329,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":331,"orcid":9},"Syed Asghar Hussain Shah",{"name":333,"orcid":9},"Shahrish Nawaz",{"name":335,"orcid":9},"Muhmmad Irfan",{"name":337,"orcid":338},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":340,"orcid":9},"Muhammad Nouman Khalid",{"tldr":342,"method":343,"finding":344,"direction":81,"opportunity":345},"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":348,"title":349,"url":350,"summary":351,"summary_zh":352,"content":9,"source_name":353,"source_url":350,"published_at":354,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":355,"score_detail":356,"sources":359,"tags":361,"primary_topic":365,"secondary_topics":366,"classification":367,"search_phrases":382,"slug":385,"view_count":75,"doi":386,"paper":387,"created_at":409},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":235,"substance":98,"depth":99,"authority":357,"freshness":17,"relevant":22,"comment":358},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。",[360],{"name":353,"url":350},[27,28,362,363,364],"植被生产力","森林物候","珠江流域",{"code":51,"slug":244,"name":245,"path":246,"parent_code":247},[],{"status":258,"locked":14,"version":35,"method":36,"evidence":368},{"candidates":369,"agricultural_evidence":112},[370,374,378],{"code":51,"score":195,"supported":112,"title":371,"abstract":372,"tags":373},[],[27,263],[27],{"code":111,"score":86,"supported":112,"title":375,"abstract":376,"tags":377},[],[115,116],[],{"code":40,"score":22,"supported":14,"title":379,"abstract":380,"tags":381},[],[],[28],[383,384],"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":386,"openalex_id":388,"authors":389,"venue":353,"cited_by_count":75,"oa_url":403,"card":404,"direction":81,"ingested_from":83},"W7221897424",[390,393,395,397,399,401],{"name":391,"orcid":392},"Shijie Yang","https:\u002F\u002Forcid.org\u002F0009-0001-7573-5509",{"name":394,"orcid":9},"Zhaoju Zheng",{"name":396,"orcid":9},"Xueming Zhao",{"name":398,"orcid":9},"Zhipeng Zhang",{"name":400,"orcid":9},"Xiaohui Cui",{"name":402,"orcid":9},"Yuan Zeng","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2738487?needAccess=true",{"tldr":405,"method":406,"finding":407,"direction":81,"opportunity":408},"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":411,"title":412,"url":413,"summary":414,"summary_zh":415,"content":9,"source_name":416,"source_url":413,"published_at":354,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":417,"score_detail":418,"sources":420,"tags":422,"primary_topic":426,"secondary_topics":432,"classification":434,"search_phrases":478,"slug":481,"view_count":22,"doi":482,"paper":483,"created_at":494},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":99,"substance":19,"depth":234,"authority":235,"freshness":21,"relevant":22,"comment":419},"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。",[421],{"name":416,"url":413},[423,424,425,27,28],"智慧农业","农业人工智能","可持续发展",{"code":427,"slug":428,"name":429,"path":430,"parent_code":431},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[433],{"code":51,"slug":244,"name":245,"path":246,"parent_code":247},{"status":258,"locked":14,"version":35,"method":36,"evidence":435},{"candidates":436,"agricultural_evidence":112},[437,446,450,454,458,465,471],{"code":427,"score":99,"supported":112,"title":438,"abstract":440,"tags":445},[439],"artificial intelligence",[441,442,443,444,439],"深度学习","机器学习","machine learning","deep learning",[424],{"code":51,"score":234,"supported":112,"title":447,"abstract":448,"tags":449},[263],[27,263],[27],{"code":292,"score":195,"supported":112,"title":451,"abstract":452,"tags":453},[],[295,296],[423],{"code":40,"score":195,"supported":112,"title":455,"abstract":456,"tags":457},[],[281,282],[28],{"code":459,"score":86,"supported":112,"title":460,"abstract":461,"tags":464},"H4",[],[462,463],"碳排放","carbon emission",[],{"code":46,"score":86,"supported":112,"title":466,"abstract":467,"tags":470},[],[468,469],"生物多样性","biodiversity",[],{"code":472,"score":86,"supported":112,"title":473,"abstract":474,"tags":477},"I5",[],[475,476],"物联网","internet of things",[],[479,480],"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":482,"openalex_id":484,"authors":485,"venue":416,"cited_by_count":75,"oa_url":413,"card":488,"direction":224,"ingested_from":83},"W7222092855",[486],{"name":487,"orcid":9},"Deepmala Suryavanshi",{"tldr":489,"method":490,"finding":491,"direction":492,"opportunity":493},"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":496,"title":497,"url":498,"summary":499,"summary_zh":500,"content":9,"source_name":501,"source_url":498,"published_at":502,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":503,"score_detail":504,"sources":506,"tags":508,"primary_topic":9,"secondary_topics":511,"classification":512,"search_phrases":524,"slug":527,"view_count":22,"doi":528,"paper":529,"created_at":540},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":17,"substance":99,"depth":234,"authority":235,"freshness":21,"relevant":22,"comment":505},"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。",[507],{"name":501,"url":498},[27,509,28,198,510],"NDVI","沙尘天气",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":513},{"candidates":514,"agricultural_evidence":14,"reason":55},[515,520],{"code":40,"score":516,"supported":112,"title":517,"abstract":518,"tags":519},5,[],[281],[509,28],{"code":51,"score":119,"supported":112,"title":521,"abstract":522,"tags":523},[],[198],[27],[525,526],"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":528,"openalex_id":530,"authors":531,"venue":501,"cited_by_count":75,"oa_url":534,"card":535,"direction":81,"ingested_from":83},"W7221436686",[532],{"name":533,"orcid":9},"حوراء راضي جاسم محمد","https:\u002F\u002Fbcchj.uobabylon.edu.iq\u002Findex.php\u002Fbcchj\u002Farticle\u002Fdownload\u002F4964\u002F5019",{"tldr":536,"method":537,"finding":538,"direction":81,"opportunity":539},"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"]