[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2665":3,"related-2665":86},{"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":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":85},2665,"Assessing historical climatic impacts on wetland change using satellite imagery: a transferable framework demonstrated on the Texas coast","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmars.2026.1911273","Coastal wetlands provide critical ecosystem services worldwide but are increasingly threatened by climate-driven stressors, including sea-level rise, coastal flooding, drought, wave erosion, and extreme temperature events. Long-term, spatially consistent monitoring is needed to quantify wetland transformation and identify ecosystems most vulnerable to these stressors. The objective of the study was to develop tools that harmonize historical Landsat (5, 7, 8, 9) and Sentinel (1, 2) satellite datasets to map wetlands and identify wetland areas most vulnerable to climate-driven stress, while being readily transferable to other coastal regions. The Texas Coastal Bend Region is presented as a case study to demonstrate the utility of these tools in evaluating wetland change. To do this, the Multi Decadal Wetland Extent Tool (WET-MUD) was developed in Google Earth Engine, along with the Wetland Health Analysis: Multi-Source (WHAMS). WET-MUD is a semi-automated wetland classification tool that produces annual maps with pixels classified as wetland, upland, or water from 1984 to 2024. WHAMS is a Google Earth Engine tool and framework that evaluates changes to wetland plant health (using Normalized Difference Vegetation Index (NDVI) as a proxy) at a monthly level for the 41-year period of record. Total classification accuracy for the Coastal Bend Region of Texas averaged 93% for the 40-year study period, and wetland pixel classification averaged 82%. Annual wetland extent maps show that since 1984, the region has experienced approximately 193 km 2 of net wetland loss, representing 17% of the initial wetland extent, with most of these losses (95%) occurring between 2008 and 2016. Analyses of NDVI for wetland areas show that 43 km 2 of coastal palustrine (low-lying, freshwater) wetlands were especially sensitive to repeated drought events (2008-2010, 2011-2015), representing 22% of the total coastal wetland losses. The publicly available tools developed in this study provide insight into wetland resilience by rapidly measuring both wetland extents and health at a regional, multi-decadal scale in a way that can be reproduced for areas beyond the Coastal Bend of Texas.","沿海湿地为全球提供关键的生态系统服务，但日益受到气候驱动压力的威胁，包括海平面上升、海岸洪水、干旱、波浪侵蚀和极端温度事件。需要长期、空间一致的监测来量化湿地变化并识别最易受这些压力影响的生态系统。本研究的目标是开发工具，以协调历史Landsat（5、7、8、9）和Sentinel（1、2）卫星数据集，从而绘制湿地分布图并识别最易受气候驱动压力影响的湿地区域，同时能够方便地推广到其他沿海地区。本文以德克萨斯州沿海湾岸地区为案例研究，展示这些工具在评估湿地变化方面的实用性。为此，在Google Earth Engine中开发了多年代际湿地范围工具（WET-MUD），以及湿地健康分析：多源工具（WHAMS）。WET-MUD是一种半自动化湿地分类工具，可生成1984年至2024年期间像元被分类为湿地、高地或水体的年度地图。WHAMS是一个Google Earth Engine工具和框架，用于在41年记录期内按月评估湿地植物健康状况的变化（以归一化差异植被指数（NDVI）作为代理指标）。在40年研究期内，德克萨斯州沿海湾岸地区的总分类精度平均为93%，湿地像元分类精度平均为82%。年度湿地范围地图显示，自1984年以来，该地区经历了约193 km²的净湿地损失，占初始湿地范围的17%，其中大部分损失（95%）发生在2008年至2016年间。对湿地区域NDVI的分析表明，43 km²的沿海沼泽湿地（低洼淡水湿地）对反复干旱事件（2008-2010年、2011-2015年）尤为敏感，占沿海湿地总损失的22%。本研究开发的公开可用工具通过快速测量区域多年代际尺度上的湿地范围和健康状况，为理解湿地恢复力提供了见解，其方法可在德克萨斯州沿海湾岸以外的地区重现。",null,"Frontiers in Marine Science","2026-09-15T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于Landsat与Sentinel长时序影像构建可迁移湿地制图与健康评估框架，方法新颖、数据规模大，对沿海湿地监测与农业生态信息化有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","气候变化","遥感监测","湿地保护","海岸带",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":60},[38,47,54],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":46},"H7",7,true,[],[44,45],"干旱","drought",[27],{"code":48,"score":49,"supported":41,"title":50,"abstract":51,"tags":53},"H6",4,[],[52],"生态系统服务",[29],{"code":55,"score":56,"supported":14,"title":57,"abstract":58,"tags":59},"I3",2,[],[],[26,28],"农业相关性需要复核",[62,63],"农业遥感 气候变化 湿地保护 遥感监测","农业遥感 气候变化","农业遥感气候变化湿地保护遥感监测-2665",3,"10.3389\u002Ffmars.2026.1911273",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":76,"oa_url":77,"card":78,"direction":82,"ingested_from":84},"W7213245622",[70,72,74],{"name":71,"orcid":9},"John Malito",{"name":73,"orcid":9},"Vanessa Valenti",{"name":75,"orcid":9},"Katie Swanson",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fmarine-science\u002Farticles\u002F10.3389\u002Ffmars.2026.1911273\u002Fpdf",{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"开发可迁移框架，用卫星影像评估历史气候对湿地变化的影响。","Google Earth Engine中构建WET-MUD和WHAMS工具，融合","1984-2024年德州海岸湿地净损失193 km²，95%发生在2008-2016年，淡水湿地对干","农业遥感与作物表型","可将该可迁移框架应用于其他沿海湿地，结合气候数据量化不同压力源的贡献。","openalex","2026-09-16T23:30:29.083410Z",{"total":87,"page":21,"page_size":87,"items":88},6,[89,165,213,333,398,460],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":95,"source_url":92,"published_at":96,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":97,"score_detail":98,"sources":103,"tags":105,"primary_topic":9,"secondary_topics":108,"classification":109,"search_phrases":136,"slug":139,"view_count":65,"doi":140,"paper":141,"created_at":164},2941,"Drought dynamics and climatic drivers in the Tarim Basin using remote sensing indices and pixel-wise machine learning","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72142-5","Abstract Drought characterization in hyper-arid endorheic basins requires multi-index approaches that capture distinct hydrometeorological processes. This study investigates spatio-temporal drought dynamics in the Tarim Basin (TB)—China’s largest inland arid region—using two complementary remote sensing indices: the Temperature Vegetation Dryness Index (TVDI) for landscape-scale moisture and the Crop Water Stress Index (CWSI) for agricultural drought. Based on 2000–2024 remote sensing and meteorological data, we employed a pixel-wise Random Forest framework with spatial cross-validation and permutation importance analysis to quantify climatic drivers across the TB. Results reveal a fundamental “core-periphery” dichotomy: TVDI identifies persistent extreme drought in the Taklamakan Desert core, while CWSI reveals alleviating water stress in peripheral oasis farmlands (73.21% showing significant decrease, p \u003C 0.05). Despite regional warming-wetting trends, TVDI exhibited an insignificant decrease (54.72% of the basin), contrasting with CWSI's significant agricultural drought alleviation. Vapor Pressure Deficit (VPD)—a key atmospheric dryness indicator—exhibited high relative permutation importance for both drought indices (72–75%), considerably exceeding the values obtained for precipitation (6–8%) within the Tarim Basin. Secondary drivers diverge by land surface type: TVDI responds to Relative Humidity (8.2%) and Precipitation (6.1%), while CWSI is modulated by Land Surface Temperature (9.4%) and Sunshine Hours (7.8%). Partial correlation analyses controlling for topography and temperature confirm VPD’s independent effect on drought severity. Large-scale climate oscillations, particularly the Arctic Oscillation (AO) and ENSO-PDO interactions, significantly modulate interannual drought variability (r = 0.74–0.75, p \u003C 0.01). This study provides the first pixel-scale quantification of the relative dominance of atmospheric water demand over precipitation in driving drought evolution in the Tarim Basin, with VPD contributing 72–75% of the total permutation importance compared to 6–8% for precipitation. This quantitative benchmark offers actionable parameters for drought monitoring systems in arid regions and underscores the need to integrate VPD and large-scale climate signals into early warning frameworks.","摘要 极端干旱内流盆地的干旱特征刻画需要能够捕捉不同水文气象过程的多指标方法。本研究利用两个互补的遥感指数——用于景观尺度土壤湿度的温度植被干旱指数（TVDI）和用于农业干旱的作物水分胁迫指数（CWSI）——探讨了塔里木盆地（TB）——中国最大的内陆干旱区——干旱的时空动态。基于2000—2024年遥感与气象数据，我们采用逐像元随机森林框架，结合空间交叉验证和置换重要性分析，量化了塔里木盆地气候驱动因子的作用。结果揭示了一种根本性的“核心—边缘”二分格局：TVDI识别出塔克拉玛干沙漠核心区持续存在的极端干旱，而CWSI则显示外围绿洲农田的水分胁迫正在缓解（73.21%呈显著下降，p \u003C 0.05）。尽管区域呈现暖湿化趋势，TVDI却表现出不显著的下降（占流域面积的54.72%），这与CWSI所反映的农业干旱显著缓解形成对比。饱和水汽压差（VPD）——一个关键的大气干燥度指标——对两个干旱指数均表现出较高的相对置换重要性（72%—75%），远超塔里木盆地降水所对应的值（6%—8%）。次要驱动因子因地表类型而异：TVDI响应相对湿度（8.2%）和降水（6.1%），而CWSI受地表温度（9.4%）和日照时数（7.8%）调控。控制地形和温度后的偏相关分析证实了VPD对干旱严重程度的独立影响。大尺度气候振荡，尤其是北极涛动（AO）和ENSO-PDO相互作用，显著调控着年际干旱变率（r = 0.74—0.75，p \u003C 0.01）。本研究首次在像元尺度上量化了大气需水量相对于降水在驱动塔里木盆地干旱演变中的相对主导地位，其中VPD贡献了总置换重要性的72%—75%，而降水仅贡献6%—8%。这一定量基准为干旱区干旱监测系统提供了可操作的参数，并凸显了将VPD和大尺度气候信号纳入预警框架的必要性。","Scientific Reports","2026-09-18T00:00:00Z",81,{"impact":17,"substance":99,"depth":17,"authority":100,"freshness":101,"relevant":21,"comment":102},23,14,8,"首次在像元尺度量化VPD对干旱的主导作用，方法新颖、数据跨度长，对干旱预警系统建设有实质参考价值。",[104],{"name":95,"url":92},[26,27,28,106,107],"干旱预警","塔里木盆地",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":110},{"candidates":111,"agricultural_evidence":41,"reason":135},[112,117,123,129],{"code":39,"score":113,"supported":41,"title":114,"abstract":115,"tags":116},16,[45],[44,45],[106,27],{"code":55,"score":113,"supported":41,"title":118,"abstract":120,"tags":122},[119],"remote sensing",[121,119],"遥感",[26,28],{"code":124,"score":101,"supported":41,"title":125,"abstract":127,"tags":128},"I2",[126],"machine learning",[],[],{"code":130,"score":65,"supported":41,"title":131,"abstract":132,"tags":134},"H2",[],[133],"土壤",[],"多个产业证据相当，需要确认主归属",[137,138],"塔里木盆地 遥感 干旱","TVDI CWSI 干旱监测","塔里木盆地遥感干旱-2941","10.1038\u002Fs41598-026-72142-5",{"doi":140,"openalex_id":142,"authors":143,"venue":95,"cited_by_count":76,"oa_url":92,"card":159,"direction":82,"ingested_from":84},"W7213539719",[144,146,148,150,153,155,157],{"name":145,"orcid":9},"Mutallip Sattar",{"name":147,"orcid":9},"Alim Abbas",{"name":149,"orcid":9},"Sardar Parhat",{"name":151,"orcid":152},"Alimujiang Yasen","https:\u002F\u002Forcid.org\u002F0000-0002-9860-7921",{"name":154,"orcid":9},"Muhemaiti Wahafu",{"name":156,"orcid":9},"Akida Salam",{"name":158,"orcid":9},"Batur Bake",{"tldr":160,"method":161,"finding":162,"direction":82,"opportunity":163},"基于遥感指数与逐像元机器学习，量化塔里木盆地2000—2024年干旱动态及气候驱动因子。","TVDI与CWSI双指数，逐像元随机森林、空间交叉验证与置换重要性分析。","干旱呈核心—边缘分异，VPD贡献72–75%远超降水的6–8%，主导干旱演变。","可将VPD与大尺度气候振荡纳入干旱预警，并拓展至其他干旱内陆盆地的逐像元归因研究。","2026-09-19T23:30:32.698746Z",{"id":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":9,"source_name":171,"source_url":168,"published_at":172,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":76,"score_detail":173,"sources":175,"tags":177,"primary_topic":9,"secondary_topics":180,"classification":181,"search_phrases":192,"slug":195,"view_count":76,"doi":196,"paper":197,"created_at":212},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":76,"substance":76,"depth":76,"authority":76,"freshness":76,"relevant":76,"comment":174},"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。",[176],{"name":171,"url":168},[27,28,178,179],"野火研究","西班牙",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":182},{"candidates":183,"agricultural_evidence":14,"reason":60},[184,188],{"code":39,"score":40,"supported":41,"title":185,"abstract":186,"tags":187},[],[44,45],[27],{"code":55,"score":21,"supported":14,"title":189,"abstract":190,"tags":191},[],[],[28],[193,194],"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":196,"openalex_id":198,"authors":199,"venue":171,"cited_by_count":76,"oa_url":205,"card":206,"direction":211,"ingested_from":84},"W7223002169",[200,202],{"name":201,"orcid":9},"Luis Hernández-Sánchez",{"name":203,"orcid":204},"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":207,"method":208,"finding":209,"direction":82,"opportunity":210},"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":214,"title":215,"url":216,"summary":217,"summary_zh":218,"content":9,"source_name":219,"source_url":216,"published_at":172,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":220,"score_detail":221,"sources":225,"tags":227,"primary_topic":231,"secondary_topics":236,"classification":246,"search_phrases":304,"slug":307,"view_count":76,"doi":308,"paper":309,"created_at":332},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":19,"substance":17,"depth":222,"authority":223,"freshness":101,"relevant":21,"comment":224},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。",[226],{"name":219,"url":216},[27,228,28,229,230],"盐碱地治理","智慧灌溉","土壤盐渍化",{"code":39,"slug":232,"name":233,"path":234,"parent_code":235},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[237,241],{"code":130,"slug":238,"name":239,"path":240,"parent_code":235},"soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":242,"slug":243,"name":244,"path":245,"parent_code":235},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":247,"locked":14,"version":34,"method":35,"evidence":248},"classified",{"candidates":249,"agricultural_evidence":41},[250,256,260,267,271,278,285,292,298],{"code":39,"score":17,"supported":41,"title":251,"abstract":253,"tags":255},[252],"climate change",[27,254,44,252],"气候适应",[27],{"code":130,"score":222,"supported":41,"title":257,"abstract":258,"tags":259},[238],[133,238],[228],{"code":242,"score":13,"supported":41,"title":261,"abstract":262,"tags":266},[],[263,264,243,265],"灌溉","水资源","evapotranspiration",[229],{"code":55,"score":40,"supported":41,"title":268,"abstract":269,"tags":270},[],[121,119],[28],{"code":272,"score":87,"supported":41,"title":273,"abstract":274,"tags":277},"A8",[],[275,276],"粮食安全","food security",[],{"code":279,"score":87,"supported":41,"title":280,"abstract":281,"tags":284},"I1",[],[282,283],"精准农业","precision agriculture",[],{"code":286,"score":87,"supported":41,"title":287,"abstract":288,"tags":291},"I4",[],[289,290],"地理信息","geographic information",[],{"code":293,"score":65,"supported":41,"title":294,"abstract":295,"tags":297},"F3",[],[296],"养分管理",[],{"code":299,"score":65,"supported":41,"title":300,"abstract":301,"tags":303},"J1",[],[302],"农业政策",[],[305,306],"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":308,"openalex_id":310,"authors":311,"venue":219,"cited_by_count":76,"oa_url":216,"card":327,"direction":82,"ingested_from":84},"W7222999158",[312,314,316,318,320,322,325],{"name":313,"orcid":9},"Asifa Naz",{"name":315,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":317,"orcid":9},"Syed Asghar Hussain Shah",{"name":319,"orcid":9},"Shahrish Nawaz",{"name":321,"orcid":9},"Muhmmad Irfan",{"name":323,"orcid":324},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":326,"orcid":9},"Muhammad Nouman Khalid",{"tldr":328,"method":329,"finding":330,"direction":82,"opportunity":331},"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":334,"title":335,"url":336,"summary":337,"summary_zh":338,"content":9,"source_name":339,"source_url":336,"published_at":340,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":341,"score_detail":342,"sources":345,"tags":347,"primary_topic":351,"secondary_topics":352,"classification":357,"search_phrases":373,"slug":376,"view_count":76,"doi":377,"paper":378,"created_at":397},5392,"Wetland Loss Associated with Hydro-Agricultural Development in North African Floodplains: Insights from a Historical Cartographic Analysis of the Gharb Plain (Morocco)","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fd18100642","Mediterranean floodplain wetlands are among the most productive ecosystems, yet they are increasingly threatened by agricultural intensification and hydrological modifications. In North Africa, most of these wetlands have been converted to agricultural lands using an extensive ‘drainage–irrigation’ process that has irreversibly degraded them. This study documents this loss through the illustrative example of the Gharb Plain in the northwest of Morocco. The agricultural development process of this plain, initiated in 1927 and only recently completed, is documented by both historical maps and remote sensing data, which we combined to reconstruct the simultaneous wetland decline and agriculture extension. The results reveal a dramatic decline in marshland ecosystems, covering about 88% of the original marshlands in less than one cycle. Several wetlands, including large ones, have entirely disappeared, particularly within the Sebou and Baht river floodplains. To ensure their human occupation, the drainage process and river canalization have been supported by a large program of dam construction upstream of the Gharb Plain. The wetland loss estimate highlights the substantial ecological costs of agricultural intensification and underscores the need for integrated management strategies balancing food production and patrimony conservation. Future research should focus on wetland restoration, ecosystem service assessment, and climate-resilient water management in Mediterranean floodplains.","地中海洪泛平原湿地是最具生产力的生态系统之一，却日益受到农业集约化与水文改造的威胁。在北非，大多数此类湿地已通过大规模的“排水—灌溉”过程被转变为农业用地，并因此遭到不可逆的退化。本研究以摩洛哥西北部加尔卜平原（Gharb Plain）为例，记录了这一丧失过程。该平原的农业开发进程始于1927年，直至近期才告完成，我们结合历史地图与遥感数据，重建了湿地衰退与农业扩张同步发生的过程。结果表明，沼泽生态系统急剧减少，在不到一个周期内约88%的原生沼泽地消失。包括大型湿地在内的多处湿地已完全消失，尤其是在塞布河（Sebou）与巴赫特河（Baht）洪泛平原内。为保障人类占用，排水过程与河流渠化得到了加尔卜平原上游大规模水坝建设计划的支持。湿地丧失的估算凸显了农业集约化所带来的巨大生态代价，并强调需要制定兼顾粮食生产与遗产保护的综合管理策略。未来研究应聚焦于湿地恢复、生态系统服务评估以及地中海洪泛平原的气候韧性水资源管理。","Diversity","2026-10-19T00:00:00Z",76,{"impact":113,"substance":343,"depth":17,"authority":19,"freshness":101,"relevant":21,"comment":344},21,"该研究结合历史地图与遥感数据，量化了北非洪泛平原湿地因农业开发而丧失的情况，方法扎实、结论明确，对农业生态与土地管理有参考价值，但属区域性案例研究，公共影响有限。",[346],{"name":339,"url":336},[348,28,349,350,29],"生态修复","灌溉农业","土地利用变化",{"code":242,"slug":243,"name":244,"path":245,"parent_code":235},[353],{"code":48,"slug":354,"name":355,"path":356,"parent_code":235},"biodiversity","生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity",{"status":247,"locked":14,"version":34,"method":35,"evidence":358},{"candidates":359,"agricultural_evidence":41},[360,364,369],{"code":242,"score":13,"supported":41,"title":361,"abstract":362,"tags":363},[],[263,264,243],[349],{"code":48,"score":20,"supported":41,"title":365,"abstract":366,"tags":368},[],[52,367],"ecosystem service",[350,29,348],{"code":55,"score":40,"supported":41,"title":370,"abstract":371,"tags":372},[],[121,119],[28],[374,375],"Gharb平原 湿地 遥感","摩洛哥 湿地 灌溉","Gharb平原湿地遥感-5392","10.3390\u002Fd18100642",{"doi":377,"openalex_id":379,"authors":380,"venue":339,"cited_by_count":76,"oa_url":336,"card":392,"direction":82,"ingested_from":84},"W7222966485",[381,384,386,389],{"name":382,"orcid":383},"Mohamed Dakki","https:\u002F\u002Forcid.org\u002F0000-0002-8042-3972",{"name":385,"orcid":9},"Said El Falah",{"name":387,"orcid":388},"Ibrahim Makrane","https:\u002F\u002Forcid.org\u002F0009-0002-9201-206X",{"name":390,"orcid":391},"Ismail Mansouri","https:\u002F\u002Forcid.org\u002F0000-0002-3679-6050",{"tldr":393,"method":394,"finding":395,"direction":82,"opportunity":396},"用历史地图与遥感重建摩洛哥Gharb平原1927年以来的湿地丧失与农业扩张过程。","历史地图与遥感数据结合，重建湿地退缩与农田扩张的时空过程。","不到一个开发周期内约88%的原生沼泽消失，大型湿地完全丧失。","可延伸至地中海洪泛平原湿地恢复优先区识别与生态系统服务权衡研究。","2026-10-20T23:30:19.135602Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":403,"content":9,"source_name":404,"source_url":401,"published_at":405,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":406,"score_detail":407,"sources":409,"tags":411,"primary_topic":415,"secondary_topics":416,"classification":417,"search_phrases":432,"slug":435,"view_count":76,"doi":436,"paper":437,"created_at":459},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":223,"substance":18,"depth":17,"authority":100,"freshness":101,"relevant":21,"comment":408},"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。",[410],{"name":404,"url":401},[27,28,412,413,414],"植被生产力","森林物候","珠江流域",{"code":39,"slug":232,"name":233,"path":234,"parent_code":235},[],{"status":247,"locked":14,"version":34,"method":35,"evidence":418},{"candidates":419,"agricultural_evidence":41},[420,424,428],{"code":39,"score":40,"supported":41,"title":421,"abstract":422,"tags":423},[],[27,252],[27],{"code":130,"score":87,"supported":41,"title":425,"abstract":426,"tags":427},[],[133,238],[],{"code":55,"score":21,"supported":14,"title":429,"abstract":430,"tags":431},[],[],[28],[433,434],"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":436,"openalex_id":438,"authors":439,"venue":404,"cited_by_count":76,"oa_url":453,"card":454,"direction":82,"ingested_from":84},"W7221897424",[440,443,445,447,449,451],{"name":441,"orcid":442},"Shijie Yang","https:\u002F\u002Forcid.org\u002F0009-0001-7573-5509",{"name":444,"orcid":9},"Zhaoju Zheng",{"name":446,"orcid":9},"Xueming Zhao",{"name":448,"orcid":9},"Zhipeng Zhang",{"name":450,"orcid":9},"Xiaohui Cui",{"name":452,"orcid":9},"Yuan Zeng","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2738487?needAccess=true",{"tldr":455,"method":456,"finding":457,"direction":82,"opportunity":458},"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":461,"title":462,"url":463,"summary":464,"summary_zh":465,"content":9,"source_name":466,"source_url":463,"published_at":405,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":467,"score_detail":468,"sources":470,"tags":472,"primary_topic":476,"secondary_topics":481,"classification":483,"search_phrases":525,"slug":528,"view_count":21,"doi":529,"paper":530,"created_at":541},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":17,"substance":113,"depth":222,"authority":223,"freshness":20,"relevant":21,"comment":469},"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。",[471],{"name":466,"url":463},[473,474,475,27,28],"智慧农业","农业人工智能","可持续发展",{"code":124,"slug":477,"name":478,"path":479,"parent_code":480},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[482],{"code":39,"slug":232,"name":233,"path":234,"parent_code":235},{"status":247,"locked":14,"version":34,"method":35,"evidence":484},{"candidates":485,"agricultural_evidence":41},[486,494,498,502,506,513,518],{"code":124,"score":17,"supported":41,"title":487,"abstract":489,"tags":493},[488],"artificial intelligence",[490,491,126,492,488],"深度学习","机器学习","deep learning",[474],{"code":39,"score":222,"supported":41,"title":495,"abstract":496,"tags":497},[252],[27,252],[27],{"code":279,"score":40,"supported":41,"title":499,"abstract":500,"tags":501},[],[282,283],[473],{"code":55,"score":40,"supported":41,"title":503,"abstract":504,"tags":505},[],[121,119],[28],{"code":507,"score":87,"supported":41,"title":508,"abstract":509,"tags":512},"H4",[],[510,511],"碳排放","carbon emission",[],{"code":48,"score":87,"supported":41,"title":514,"abstract":515,"tags":517},[],[516,354],"生物多样性",[],{"code":519,"score":87,"supported":41,"title":520,"abstract":521,"tags":524},"I5",[],[522,523],"物联网","internet of things",[],[526,527],"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":529,"openalex_id":531,"authors":532,"venue":466,"cited_by_count":76,"oa_url":463,"card":535,"direction":211,"ingested_from":84},"W7222092855",[533],{"name":534,"orcid":9},"Deepmala Suryavanshi",{"tldr":536,"method":537,"finding":538,"direction":539,"opportunity":540},"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"]