[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2788":3,"related-2788":94},{"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":93},2788,"Integrated satellite monitoring and field validation of the periodically outbursting glacier-dammed lake Nedre Demmevatnet, Norway","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fegusphere-2026-5025","Abstract. Glacial Lake Outburst Floods (GLOFs) present a significant hazard in warming alpine environments, but detailed, sub-seasonal studies of ice-dammed glacier lakes remain rare due to data scarcity. To overcome this challenge, we reconstruct nearly a decade (2016–2025) of drainage timings, outburst volumes, lake levels, and automated, machine learning-based sub-seasonal lake refilling cycles at the ice-dammed lake Nedre Demmevatnet (southwestern Norway), dammed by the glacier Rembesdalskåka, an outlet glacier of the Hardangerjøkulen ice cap. By integrating publicly available satellite and meteorological datasets, we evaluate remote sensing capabilities and establish an error budget for tracking a small and highly dynamic water body. We validate our spaceborne findings using field measurements, including water-level loggers, time-lapse cameras, a local automatic weather station, and high-resolution UAV photogrammetry, complemented by PlanetScope imagery. Combining Sentinel-1 and Sentinel-2 imagery, we constrain GLOF drainage windows to ± 2 days. Lake volume sensitivity tests revealed that while satellite outline errors are minor (2.6–4.2 %), using the static regional digital elevation model ArcticDEM (2014) causes a 25.5 % volume underestimation compared to our 2022 UAV bathymetry due to rapid ice-dam retreat and lakebed erosion. Crucially, the time gap between the last cloud-free satellite image and the GLOF introduces a relative daily volume underestimation of 1.7 %, which scales up significantly during cloudy periods. Our validated multi-sensor remote sensing approach enables valuable glaciological insights, revealing that GLOF timings shifted earlier by an average of 10 days, alongside shortened refilling periods over the past 9 years. Between 2016 and 2022, pre-GLOF lake levels reached 1236–1239 m a.s.l., closely matching the theoretical hydrostatic flotation threshold. In contrast, the 2023 event drained at approximately 1224 m a.s.l. – more than 10 m below this threshold – following a shortened melt period indicated by fewer positive degree days, possibly due to opening of subglacial channels through melt. Finally, we synthesize the workflow developed and tested on our case study into an operational framework that facilitates transferability to other rapidly changing ice-dammed lakes.","摘要：冰川湖溃决洪水（GLOFs）在变暖的高山环境中构成重大危害，但由于数据稀缺，针对冰坝冰川湖的详细次季节研究仍然很少。为克服这一挑战，我们重建了冰坝湖Nedre Demmevatnet（挪威西南部）近十年（2016—2025年）的排水时间、溃决水量、湖泊水位以及基于机器学习的自动化次季节湖泊再蓄水周期。该湖由Hardangerjøkulen冰帽的溢出冰川Rembesdalskåka所阻塞。通过整合公开可用的卫星和气象数据集，我们评估了遥感能力，并建立了追踪一个小型且高度动态水体的误差预算。我们利用实地测量验证了星载观测结果，包括水位记录仪、延时相机、当地自动气象站和高分辨率无人机摄影测量，并辅以PlanetScope影像。结合Sentinel-1和Sentinel-2影像，我们将GLOF排水窗口限定在±2天以内。湖泊体积敏感性测试表明，虽然卫星轮廓误差较小（2.6—4.2%），但由于冰坝快速退缩和湖床侵蚀，使用静态区域数字高程模型ArcticDEM（2014年）会导致体积较我们的2022年无人机测深结果低估25.5%。至关重要的是，最后一幅无云卫星影像与GLOF之间的时间间隔会导致相对日体积低估1.7%，在多云时段这一误差会显著放大。我们经过验证的多传感器遥感方法带来了有价值的冰川学认识，揭示了过去9年中GLOF发生时间平均提前了10天，同时再蓄水期缩短。2016年至2022年间，GLOF前湖泊水位达到海拔1236—1239 m，与理论静水浮力阈值密切吻合。相比之下，2023年事件在海拔约1224 m处排水——低于该阈值超过10 m——此前融化期缩短，表现为正积温日数减少，可能是由于融化导致冰下通道打开。最后，我们将本案例研究中开发和测试的工作流程综合为一个操作框架，以促进其向其他快速变化的冰坝湖推广应用。",null,"OpenAlex","2026-09-16T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"冰川湖溃决遥感监测研究，属冰川水文与灾害领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"遥感监测","卫星遥感","灾害预警","冰川湖",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":55},[32,41,49],{"code":33,"score":34,"supported":35,"title":36,"abstract":37,"tags":40},"I3",8,true,[],[38,39],"遥感","remote sensing",[22,21],{"code":42,"score":43,"supported":35,"title":44,"abstract":45,"tags":48},"I2",6,[],[46,47],"机器学习","machine learning",[],{"code":50,"score":51,"supported":14,"title":52,"abstract":53,"tags":54},"H7",1,[],[],[23],"农业相关性需要复核",[57,58],"卫星遥感 灾害预警 遥感监测 冰川湖","卫星遥感 灾害预警","卫星遥感灾害预警遥感监测冰川湖-2788",2,"10.5194\u002Fegusphere-2026-5025",{"doi":61,"openalex_id":63,"authors":64,"venue":9,"cited_by_count":15,"oa_url":85,"card":86,"direction":90,"ingested_from":92},"W7213228080",[65,67,70,73,76,79,82],{"name":66,"orcid":9},"Ronja Lappe",{"name":68,"orcid":69},"Ursula Enzenhofer","https:\u002F\u002Forcid.org\u002F0009-0005-4802-1243",{"name":71,"orcid":72},"Pascal E. Egli","https:\u002F\u002Forcid.org\u002F0000-0003-2549-8362",{"name":74,"orcid":75},"Yongmei Gong","https:\u002F\u002Forcid.org\u002F0000-0002-3839-5824",{"name":77,"orcid":78},"Liss M. Andreassen","https:\u002F\u002Forcid.org\u002F0000-0001-6494-4252",{"name":80,"orcid":81},"Andreas Kääb","https:\u002F\u002Forcid.org\u002F0000-0002-6017-6564",{"name":83,"orcid":84},"Martina Calovi","https:\u002F\u002Forcid.org\u002F0000-0002-2317-1190","https:\u002F\u002Fegusphere.copernicus.org\u002Fpreprints\u002F2026\u002Fegusphere-2026-5025\u002Fegusphere-2026-5025.pdf",{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"结合卫星与实地观测重建挪威冰坝湖近十年溃决周期与水量，并建立可迁移监测框架。","Sentinel-1\u002F2、PlanetScope、UAV摄影测量、水位记录仪与气","溃决时间提前约10天，2023年溃决水位低于浮力阈值10米以上。","农业遥感与作物表型","可将该多源遥感与误差预算框架迁移至其他冰坝湖，并探索云覆盖下水量估算的改进方法。","openalex","2026-09-17T23:30:34.612397Z",{"total":43,"page":51,"page_size":43,"items":95},[96,175,242,308,368,419],{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":9,"source_name":102,"source_url":99,"published_at":103,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":104,"score_detail":105,"sources":111,"tags":113,"primary_topic":117,"secondary_topics":122,"classification":123,"search_phrases":137,"slug":140,"view_count":15,"doi":141,"paper":142,"created_at":174},5318,"Lagged Effects and Mechanisms of Central Environmental Inspection Policies on Agricultural Straw Burning: Evidence from Remote Sensing Data in China","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10668-026-08214-3","Lagged Effects and Mechanisms of Central Environmental Inspection Policies on Agricultural Straw Burning: Evidence from Remote Sensing Data in China。Environment Development and Sustainability","中央环保督察政策对农业秸秆焚烧的滞后效应与作用机制：来自中国遥感数据的证据。《环境、发展与可持续性》","Environment Development and Sustainability","2026-10-19T00:00:00Z",84,{"impact":106,"substance":107,"depth":108,"authority":109,"freshness":13,"relevant":51,"comment":110},22,20,18,14,"基于全国遥感数据评估中央环保督察对秸秆焚烧的滞后效应，数据规模大、方法新颖，对农业环境政策有参考价值，值得进入每日精选。",[112],{"name":102,"url":99},[114,21,22,115,116],"农业政策","秸秆焚烧","环保督察",{"code":33,"slug":118,"name":119,"path":120,"parent_code":121},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":124,"locked":14,"version":28,"method":29,"evidence":125},"classified",{"candidates":126,"agricultural_evidence":35},[127,132],{"code":33,"score":128,"supported":35,"title":129,"abstract":130,"tags":131},16,[39],[38,39],[22,21],{"code":133,"score":51,"supported":14,"title":134,"abstract":135,"tags":136},"H5",[],[],[115],[138,139],"中央环保督察 秸秆焚烧","遥感数据 秸秆焚烧","中央环保督察秸秆焚烧-5318","10.1007\u002Fs10668-026-08214-3",{"doi":141,"openalex_id":143,"authors":144,"venue":102,"cited_by_count":15,"oa_url":9,"card":168,"direction":90,"ingested_from":92},"W7222877438",[145,147,149,151,153,155,157,160,162,164,166],{"name":146,"orcid":9},"Lu Dong",{"name":148,"orcid":9},"Linru Zhang",{"name":150,"orcid":9},"Na Liu",{"name":152,"orcid":9},"Xin Long",{"name":154,"orcid":9},"Ziyi Wang",{"name":156,"orcid":9},"Siyue Yang",{"name":158,"orcid":159},"Jie Tian","https:\u002F\u002Forcid.org\u002F0000-0001-6585-3643",{"name":161,"orcid":9},"Qiyuan Wang",{"name":163,"orcid":9},"Zhanfeng Dong",{"name":165,"orcid":9},"Long Wu",{"name":167,"orcid":9},"Yichen Wang",{"tldr":169,"method":170,"finding":171,"direction":172,"opportunity":173},"利用遥感数据评估中央环保督察政策对秸秆焚烧的滞后效应与作用机制。","基于中国遥感火点数据构建面板，采用滞后效应模型识别政策影响机制。","中央环保督察对秸秆焚烧具有显著但滞后的抑制效应，并存在区域异质性。","农业绿色发展与碳","可结合地块级遥感与农户调查，量化督察滞后期内秸秆离田利用与替代处置的路径。","2026-10-19T23:30:22.693590Z",{"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":183,"score_detail":184,"sources":191,"tags":193,"primary_topic":9,"secondary_topics":197,"classification":198,"search_phrases":207,"slug":210,"view_count":15,"doi":211,"paper":212,"created_at":241},5171,"Fuel load and potential fire behavior vary seasonally and across grassland plant communities","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs42408-026-00593-2","Abstract Grassland fuels can be patchy across space and time, especially on landscapes where multiple grassland plant communities co-occur. However, the standard fuel models inadequately capture the spatiotemporal variability in grassland fuels for local, strategic planning of fuel treatments. We aimed to better understand the seasonal variation in grassland fuels and fire behavior across grassland types. Here, we developed remote sensing-based grassland fuel maps using multispectral satellite data (10-m resolution) from Sentinel-2, compared seasonal fire behavior across grassland types, and performed a sensitivity analysis of fire behavior models to changes in litter, dead 1 h, and live herbaceous loads, and fuelbed depth estimates. We were able to best predict live herbaceous load (R2 = 0.85), followed by fuelbed depth (R2 = 0.81), dead 1 h fuel load (R2 = 0.76), and litter load (R2 = 0.67) based on remotely sensed predictors. Our spatially predicted grassland fuel maps showed seasonal variability in fuel loads in different places, reflecting the varying timing of curing by grassland types. In particular, we observed more accumulation of dead 1 h load between August and October in early senescing mixedgrass prairies than later senescing tallgrass prairies. We found an overall faster modeled rate of spread in smooth brome-dominated, invasive grasslands and native tallgrass prairies compared to mixedgrass and prairie dog-dominated grasslands. From the sensitivity analysis, we found that modeled fire behavior model is most sensitive to dead 1 h fuel load and least sensitive to litter load in this grassland system. At a time when grassland fires are increasing and resources for fuel management are limited, remote sensing approach to grassland fuel modeling may help land managers make decisions about where and when to prioritize fuel treatments to mitigate the risk of catastrophic wildfires.","摘要 草地可燃物在空间和时间上往往呈斑块状分布，在多种草地植物群落共存的景观中尤为如此。然而，标准可燃物模型难以充分刻画草地可燃物的时空变异性，无法满足局部可燃物处理策略性规划的需求。本研究旨在更好地理解不同草地类型间可燃物和火行为的季节性变化。为此，我们利用Sentinel-2多光谱卫星数据（10 m分辨率）构建了基于遥感的草地可燃物图，比较了不同草地类型间的季节性火行为，并对火行为模型进行了敏感性分析，考察凋落物、1小时时滞死可燃物、活草本可燃物载量及可燃物床深度估算值变化的影响。基于遥感预测因子，我们对活草本可燃物载量的预测效果最佳（R² = 0.85），其次为可燃物床深度（R² = 0.81）、1小时时滞死可燃物载量（R² = 0.76）和凋落物载量（R² = 0.67）。空间预测的草地可燃物图显示，不同地点的可燃物载量存在季节性变异，反映了不同草地类型枯萎时序的差异。特别是，我们观察到早枯的混合草草原在8月至10月间1小时时滞死可燃物载量的累积多于晚枯的高草草原。我们发现，与混合草草原和草原犬鼠主导的草地相比，无芒雀麦主导的入侵草地和原生高草草原的模拟蔓延速率总体更快。敏感性分析表明，在该草地系统中，火行为模型对1小时时滞死可燃物载量最为敏感，对凋落物载量最不敏感。在草地火灾日益增多且可燃物管理资源有限的背景下，基于遥感的草地可燃物建模方法可帮助土地管理者决策在何处、何时优先开展可燃物处理，以降低灾难性野火风险。","Fire Ecology","2026-10-15T00:00:00Z",72,{"impact":185,"substance":186,"depth":187,"authority":188,"freshness":189,"relevant":51,"comment":190},12,21,17,13,9,"基于 Sentinel-2 的草地可燃物遥感制图与火行为敏感性分析，方法新颖、结论可靠，对草原火险管理有实用价值，但属细分领域研究，公共影响有限。",[192],{"name":181,"url":178},[21,22,194,195,196],"草原火险","可燃物模型","草地管理",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":199},{"candidates":200,"agricultural_evidence":14,"reason":55},[201],{"code":33,"score":202,"supported":35,"title":203,"abstract":204,"tags":206},11,[],[38,39,205],"multispectral",[22,21],[208,209],"Sentinel-2 草地可燃物","草原火行为 遥感制图","Sentinel-2草地可燃物-5171","10.1186\u002Fs42408-026-00593-2",{"doi":211,"openalex_id":213,"authors":214,"venue":181,"cited_by_count":15,"oa_url":235,"card":236,"direction":90,"ingested_from":92},"W7221860401",[215,218,221,224,227,229,232],{"name":216,"orcid":217},"Lina Aoyama","https:\u002F\u002Forcid.org\u002F0000-0001-9677-7268",{"name":219,"orcid":220},"Katharine N. Suding","https:\u002F\u002Forcid.org\u002F0000-0002-5357-0176",{"name":222,"orcid":223},"Advyth Ramachandran","https:\u002F\u002Forcid.org\u002F0009-0005-1440-2726",{"name":225,"orcid":226},"Jordan T. Lee","https:\u002F\u002Forcid.org\u002F0000-0002-3188-9792",{"name":228,"orcid":9},"Paul Dennison",{"name":230,"orcid":231},"Thomas K. Merchant","https:\u002F\u002Forcid.org\u002F0000-0002-6905-0255",{"name":233,"orcid":234},"Jonathan J. Henn","https:\u002F\u002Forcid.org\u002F0000-0003-1551-9238","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1186\u002Fs42408-026-00593-2.pdf",{"tldr":237,"method":238,"finding":239,"direction":90,"opportunity":240},"用Sentinel-2遥感构建草地可燃物载量季节图谱并模拟火行为差异。","Sentinel-2多光谱数据回归预测可燃物载量，结合火行为模型敏感性分析。","早枯混草草原死1小时载量积累更多，入侵与高草草原蔓延速率更快。","可将遥感可燃物制图与火险预警、放牧管理结合，拓展到多草地类型时空动态监测。","2026-10-17T23:30:28.706068Z",{"id":243,"title":244,"url":245,"summary":246,"summary_zh":247,"content":9,"source_name":248,"source_url":245,"published_at":249,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":250,"sources":252,"tags":254,"primary_topic":9,"secondary_topics":257,"classification":258,"search_phrases":280,"slug":283,"view_count":60,"doi":284,"paper":285,"created_at":307},3832,"Applications of Earth Observation for Monitoring and Management of Natural Hazards and Emergency Situations in Central Asia","https:\u002F\u002Fdoi.org\u002F10.64599\u002Fmwdj4371","Central Asia is highly vulnerable to a wide range of natural hazards due to its complex mountainous terrain, glacierized catchments, and highly variable climate. Hazards such as glacial lake outburst floods (GLOFs), mudflows, snow avalanches, river floods, and droughts pose significant risks to communities, infrastructure, and water resources across the region. Many of these hazards originate in the high mountain systems of the Tien Shan and Pamir ranges, where climate change is accelerating glacier retreat, altering hydrological regimes, and increasing the likelihood of extreme events. Effective monitoring and management of these hazards require timely and spatially comprehensive information, which is often limited by sparse ground-based observations in remote mountain environments. Earth Observation (EO) technologies provide powerful tools for monitoring environmental conditions and supporting disaster risk management in such regions. Satellite remote sensing enables the detection of glacial lake expansion, monitoring of snow and ice dynamics, mapping of flood and mudflow events, and assessment of drought conditions over large areas. Radar and optical satellite data allow near-real-time hazard detection and provide critical information for early warning and emergency response. This short commentary article reviews the applications of EO technologies for monitoring major natural hazards in Central Asia and highlights their role in improving disaster preparedness and response. The study also discusses operational satellite platforms and emerging technologies that can support hazard assessment and risk reduction across the region.","中亚地区因其复杂的山地地形、冰川流域和高度多变的气候，极易受到多种自然灾害的影响。冰川湖溃决洪水（GLOFs）、泥石流、雪崩、河流洪水和干旱等灾害对该地区的社区、基础设施和水资源构成了重大风险。其中许多灾害源于天山和帕米尔山脉的高山系统，气候变化正在加速冰川退缩、改变水文情势并增加极端事件的发生概率。对这些灾害进行有效监测和管理需要及时且空间上全面的信息，而偏远山区稀疏的地面观测往往限制了信息的获取。地球观测（EO）技术为监测此类地区的环境状况和支持灾害风险管理提供了强有力的工具。卫星遥感能够探测冰川湖扩张、监测冰雪动态、绘制洪水和泥石流事件分布图，以及评估大范围区域的干旱状况。雷达和光学卫星数据可实现近实时的灾害探测，并为预警和应急响应提供关键信息。这篇简短的评论文章综述了地球观测技术在中亚主要自然灾害监测中的应用，并强调了其在改善灾害防备和响应方面的作用。该研究还讨论了可支持该地区灾害评估和风险减轻的业务化卫星平台及新兴技术。","International Journal of Sustainability and Risk Control","2026-09-28T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":251},"主题为地球观测与灾害管理，与三农及农业信息化关联弱，不建议进入每日精选。",[253],{"name":248,"url":245},[255,21,23,256],"气候变化","中亚农业",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":259},{"candidates":260,"agricultural_evidence":14,"reason":55},[261,268,273],{"code":50,"score":202,"supported":35,"title":262,"abstract":263,"tags":267},[],[255,264,265,266],"干旱","climate change","drought",[255,23],{"code":33,"score":269,"supported":35,"title":270,"abstract":271,"tags":272},7,[],[38,39],[21],{"code":274,"score":275,"supported":35,"title":276,"abstract":277,"tags":279},"H3",3,[],[278],"水资源",[],[281,282],"中亚 地球观测 自然灾害","天山 冰川湖 洪水 遥感","中亚地球观测自然灾害-3832","10.64599\u002Fmwdj4371",{"doi":284,"openalex_id":286,"authors":287,"venue":248,"cited_by_count":15,"oa_url":300,"card":301,"direction":306,"ingested_from":92},"W7214586653",[288,291,293,295,298],{"name":289,"orcid":290},"Ben Jarihani","https:\u002F\u002Forcid.org\u002F0000-0002-1728-0008",{"name":292,"orcid":9},"Behzod Pulatov",{"name":294,"orcid":9},"Vasila Sulaymonova",{"name":296,"orcid":297},"Aliyev Vugar","https:\u002F\u002Forcid.org\u002F0009-0008-9984-8349",{"name":299,"orcid":9},"Álvaro Salazar","https:\u002F\u002Fijsrc.com\u002Findex.php\u002Fijsrc\u002Farticle\u002Fdownload\u002F80\u002F56",{"tldr":302,"method":303,"finding":304,"direction":90,"opportunity":305},"综述地球观测技术在中亚自然灾害监测与应急管理中的应用。","综述卫星遥感（雷达与光学）用于冰川湖、洪水、泥石流、干旱监测。","地球观测可弥补地面观测不足，支持近实时预警与应急响应。","可探索多源遥感融合与AI方法，提升中亚跨境灾害预警的时效与精度。","智慧农业 \u002F 农业物联网","2026-09-30T23:30:16.535343Z",{"id":309,"title":310,"url":311,"summary":312,"summary_zh":9,"content":9,"source_name":313,"source_url":9,"published_at":314,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":315,"score_detail":316,"sources":320,"tags":322,"primary_topic":326,"secondary_topics":332,"classification":334,"search_phrases":355,"slug":358,"view_count":60,"doi":9,"paper":359,"created_at":367},3442,"《多因子监测实现小麦晚霜冻害大范围动态预警》——中国农科院资源区划所农业遥感团队","https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F9\u002F202692094936755155648.shtm","中国农科院农业资源与农业区划研究所农业遥感团队针对黄淮海平原冬小麦3至4月拔节至抽穗期频发的晚霜冻害，提出了多因子综合霜冻监测指数。该指数综合高精度气象预报和冬小麦生长进程信息，兼顾低温强度、持续时间及不同生育期敏感性，并利用遥感土壤水分数据构建非线性调节系数精准刻画湿润土壤对低温的缓冲效应。验证表明该指数对县级小麦产量具有显著指示作用，突破了气象站点的空间局限，可为农业主产区风险精细化管控和科学减灾提供技术支撑。","《欧洲农学杂志（European Journal of Agronomy）》","2026-09-20T00:00:00Z",82,{"impact":106,"substance":317,"depth":108,"authority":109,"freshness":318,"relevant":51,"comment":319},23,5,"国家级科研团队在核心期刊提出多因子霜冻监测指数，方法新颖、验证扎实，对黄淮海主产区减灾有实用价值，值得入选。",[321],{"name":313,"url":311},[323,21,324,23,325],"农业气象","冬小麦","晚霜冻害",{"code":327,"slug":328,"name":329,"path":330,"parent_code":331},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[333],{"code":33,"slug":118,"name":119,"path":120,"parent_code":121},{"status":124,"locked":14,"version":28,"method":29,"evidence":335},{"candidates":336,"agricultural_evidence":35},[337,341,345,351],{"code":327,"score":185,"supported":35,"title":338,"abstract":339,"tags":340},[329],[329],[324],{"code":33,"score":185,"supported":35,"title":342,"abstract":343,"tags":344},[38],[38],[21],{"code":346,"score":275,"supported":35,"title":347,"abstract":348,"tags":350},"H2",[],[349],"土壤",[],{"code":50,"score":60,"supported":14,"title":352,"abstract":353,"tags":354},[],[],[323,23],[356,357],"中国农科院 冬小麦 晚霜冻害","黄淮海 小麦 遥感监测","中国农科院冬小麦晚霜冻害-3442",{"doi":9,"openalex_id":9,"authors":360,"venue":9,"cited_by_count":15,"oa_url":9,"card":361,"direction":90,"ingested_from":366},[],{"tldr":362,"method":363,"finding":364,"direction":90,"opportunity":365},"提出多因子综合霜冻监测指数，实现黄淮海冬小麦晚霜冻害大范围动态预警。","融合高精度气象预报、小麦生长进程与遥感土壤水分，构建非线性调节系数。","该指数对县级小麦产量有显著指示作用，突破气象站点空间局限。","可结合多源遥感与机器学习，构建不同作物、区域的霜冻风险动态预警与保险定损模型。","agent","2026-09-25T00:09:34.300809Z",{"id":369,"title":370,"url":371,"summary":372,"summary_zh":373,"content":9,"source_name":374,"source_url":371,"published_at":375,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":376,"score_detail":377,"sources":379,"tags":381,"primary_topic":9,"secondary_topics":385,"classification":386,"search_phrases":398,"slug":401,"view_count":275,"doi":402,"paper":403,"created_at":418},2800,"Spatial Persistence and Interannual Variability of Floating Algae in Kainji Lake (Nigeria): Insights from Multi-Year Sentinel-2 Observations","https:\u002F\u002Fdoi.org\u002F10.62622\u002Fteiee.026.4.3.55-64","Background: Floating algae are increasingly recognized as indicators of ecological change in freshwater ecosystems. Although satellite remote sensing has been widely applied to monitor algal dynamics, little is known about the long-term spatial organization of floating algae in large tropical reservoirs in West Africa. Objectives: This study investigated the spatio-temporal dynamics of floating algae in Kainji Lake, Nigeria, from 2020 to 2025, with the aim of identifying recurrent hotspot zones, assessing interannual variability, and evaluating the long-term stability of floating algae distribution. Methods: Sentinel-2 Level-2A Surface Reflectance imagery was processed within the Google Earth Engine platform. Images with less than 10% cloud cover were selected and subjected to cloud masking and water extraction procedures. Floating algae were mapped using the Floating Algae Index (FAI), calculated from the red, near-infrared, and short-wave infrared spectral bands. Annual median FAI composites were generated for each year from 2020 to 2025. Descriptive statistics of positive FAI values were calculated to assess temporal variability. Multi-year persistence analysis was then performed to identify recurrent floating algae hotspot zones. Results: The results revealed pronounced spatial heterogeneity in floating algae occurrence across Kainji Lake. Floating algae were consistently concentrated within shoreline and embayment environments, whereas most open-water areas exhibited comparatively low occurrence. Mean annual positive FAI values ranged from 0.0656 to 0.0841, indicating interannual variability in floating algae intensity. Annual bloom extent varied from 139.93 to 168.64 km², representing 12 –15% of the lake surface, with the largest coverage recorded in 2022 and the smallest in 2025. Despite these temporal fluctuations, recurrent hotspot zones remained spatially consistent throughout the study period. Conclusion: Floating algae in Kainji Lake exhibit persistent spatial organization rather than random distribution. This study provides the first multi-year evidence of recurrent floating algae hotspot zones in the reservoir, establishing an important baseline for understanding floating algae dynamics, thereby addressing a significant knowledge gap in West African reservoir ecosystems and providing a foundation for future ecological assessments and monitoring programmes.","背景：浮游藻类日益被视为淡水生态系统生态变化的指示物。尽管卫星遥感已被广泛应用于监测藻类动态，但关于西非大型热带水库中浮游藻类长期空间格局的认识仍然有限。目标：本研究调查了2020年至2025年尼日利亚凯恩吉湖浮游藻类的时空动态，旨在识别反复出现的热点区域、评估年际变异性，并评价浮游藻类分布的长期稳定性。方法：在Google Earth Engine平台上处理Sentinel-2 Level-2A地表反射率影像。选取云量低于10%的影像，并进行去云和水体提取处理。利用浮游藻类指数（Floating Algae Index, FAI）对浮游藻类进行制图，该指数由红光、近红外和短波红外光谱波段计算得出。生成2020年至2025年各年的FAI年中值合成影像。计算FAI正值的描述性统计量以评估时间变异性。随后进行多年持续性分析，以识别反复出现的浮游藻类热点区域。结果：结果揭示了凯恩吉湖浮游藻类 occurrence 的显著空间异质性。浮游藻类持续集中于湖岸和湖湾环境，而大部分开阔水域的出现相对较低。年均FAI正值范围为0.0656至0.0841，表明浮游藻类强度存在年际变异性。年度藻华范围在139.93至168.64 km²之间变化，占湖泊表面积的12%–15%，其中2022年记录到最大覆盖面积，2025年最小。尽管存在这些时间波动，反复出现的热点区域在整个研究期间保持空间一致性。结论：凯恩吉湖的浮游藻类表现出持续的空间组织格局，而非随机分布。本研究首次提供了该水库浮游藻类热点区域反复出现的多年证据，为理解浮游藻类动态建立了重要基线，从而填补了西非水库生态系统中的重大知识空白，并为未来的生态评估和监测计划奠定了基础。","Trends in Ecological and Indoor Environmental Engineering","2026-09-15T00:00:00Z",66,{"impact":34,"substance":107,"depth":187,"authority":185,"freshness":189,"relevant":51,"comment":378},"基于Sentinel-2多年观测揭示尼日利亚Kainji湖浮藻热点空间稳定性，方法规范、数据扎实，但属区域生态研究，与国内三农信息化关联间接，可作遥感应用参考而非每日必读。",[380],{"name":374,"url":371},[382,21,383,22,384],"非洲农业","水质监测","湖泊生态",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":387},{"candidates":388,"agricultural_evidence":14,"reason":55},[389,393],{"code":33,"score":34,"supported":35,"title":390,"abstract":391,"tags":392},[],[38,39],[22,21],{"code":394,"score":51,"supported":14,"title":395,"abstract":396,"tags":397},"J3",[],[],[382],[399,400],"卫星遥感 水质监测 湖泊生态 遥感监测","卫星遥感 水质监测","卫星遥感水质监测湖泊生态遥感监测-2800","10.62622\u002Fteiee.026.4.3.55-64",{"doi":402,"openalex_id":404,"authors":405,"venue":374,"cited_by_count":15,"oa_url":412,"card":413,"direction":90,"ingested_from":92},"W7213293788",[406,409],{"name":407,"orcid":408},"Osasere Abike Omoruyi","https:\u002F\u002Forcid.org\u002F0000-0003-4505-9551",{"name":410,"orcid":411},"Ruby Asunomeh","https:\u002F\u002Forcid.org\u002F0009-0009-7529-254X","https:\u002F\u002Fwww.teiee.net\u002Fpdf-226150-145194?filename=Spatial-Persistence-and-I.pdf",{"tldr":414,"method":415,"finding":416,"direction":90,"opportunity":417},"利用2020-2025年Sentinel-2影像分析尼日利亚凯恩吉湖浮藻时空动态与热点区。","Google Earth Engine处理Sentinel-2 L2A影像，用F","浮藻集中于湖岸与湾口，年际面积波动但热点区位置持续稳定。","可结合水质、气象与人类活动数据，探究热带水库浮藻热点持续性的驱动机制与预警模型。","2026-09-17T23:30:38.937817Z",{"id":420,"title":421,"url":422,"summary":423,"summary_zh":424,"content":9,"source_name":425,"source_url":422,"published_at":426,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":427,"score_detail":428,"sources":430,"tags":432,"primary_topic":9,"secondary_topics":437,"classification":438,"search_phrases":468,"slug":471,"view_count":15,"doi":472,"paper":473,"created_at":497},5577,"Assessing Land Use and Land Cover Change in the Jamanxim River Region (Brazilian Amazon) from 2015 to 2025 Using CBERS-4\u002FPAN Imagery","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i4.269068","Land use and land cover (LULC) changes in the Amazon are among the main drivers of biodiversity loss, ecosystem degradation, and the intensification of climate change. This study aimed to analyze the multitemporal dynamics of LULC around the Jamanxim River, Pará, Brazilian Amazon, between 2015 and 2025, using high-resolution imagery from the CBERS-4\u002FPAN satellite. The study area covers 492.74 km², including part of the Jamanxim National Forest, a conservation unit under strong pressure from agricultural expansion and logging activities. The methodology included geometric correction of the images, Gram–Schmidt pansharpening, and supervised classification with the Random Forest algorithm, validated with historical imagery from Google Earth Pro. Thematic maps of land use and cover were generated, and performance metrics such as overall accuracy, omission and commission errors, and F1-scores were assessed. The results indicated a significant reduction in forest cover, from 412.28 km² in 2015 to 355.47 km² in 2025 (–13.78%). Non-vegetated areas increased by 132.47%, while agricultural areas expanded by 54.97%. The forest and water body classes showed high accuracy (F1-score > 0.95), while confusion persisted between agricultural and bare soil classes. Overall accuracy remained above 90% across all years, with the highest performance recorded in 2020 (96.31%). These results highlight the potential of CBERS-4\u002FPAN imagery for monitoring multitemporal changes in tropical forest landscapes. At the same time, they reveal the intensification of deforestation and the growing threats to conservation in the Amazon, reinforcing the importance of remote sensing for environmental management and policy support.","亚马逊地区的土地利用与土地覆盖（LULC）变化是生物多样性丧失、生态系统退化以及气候变化加剧的主要驱动因素之一。本研究旨在利用CBERS-4\u002FPAN卫星的高分辨率影像，分析2015年至2025年间巴西亚马孙州雅曼辛河（Jamanxim River）周边土地利用与土地覆盖的多时相动态。研究区面积为492.74 km²，包括雅曼辛国家森林（Jamanxim National Forest）的一部分，该保护区正面临农业扩张和采伐活动的强烈压力。研究方法包括影像几何校正、Gram–Schmidt全色锐化以及基于随机森林（Random Forest）算法的监督分类，并利用Google Earth Pro的历史影像进行验证。研究生成了土地利用与覆盖专题图，并评估了总体精度、漏分误差与错分误差以及F1分数等性能指标。结果表明，森林覆盖显著减少，从2015年的412.28 km²降至2025年的355.47 km²（–13.78%）。无植被区域增加了132.47%，农业区域扩张了54.97%。森林和水体类别表现出较高精度（F1分数 > 0.95），而农业类别与裸土类别之间仍存在混淆。各年份总体精度均保持在90%以上，其中2020年性能最高（96.31%）。这些结果凸显了CBERS-4\u002FPAN影像在监测热带森林景观多时相变化方面的潜力，同时揭示了亚马逊地区森林砍伐的加剧以及保护面临的日益增长的威胁，进一步强调了遥感在环境管理和政策支持中的重要性。","Journal of Hyperspectral Remote Sensing","2026-10-20T00:00:00Z",78,{"impact":108,"substance":106,"depth":187,"authority":185,"freshness":189,"relevant":51,"comment":429},"该研究利用CBERS-4\u002FPAN影像量化亚马逊保护区十年间森林减少13.78%，方法规范、结论可靠，对遥感监测与生态保护有参考价值，但地域性强、与国内三农直接关联有限。",[431],{"name":425,"url":422},[21,433,434,435,436],"土地利用","森林保护","亚马逊","CBERS-4",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":439},{"candidates":440,"agricultural_evidence":35,"reason":467},[441,448,452,456,461],{"code":442,"score":269,"supported":35,"title":443,"abstract":444,"tags":447},"H6",[],[445,446],"生物多样性","biodiversity",[433],{"code":33,"score":269,"supported":35,"title":449,"abstract":450,"tags":451},[],[38,39],[21],{"code":50,"score":43,"supported":35,"title":453,"abstract":454,"tags":455},[],[255,265],[],{"code":346,"score":275,"supported":35,"title":457,"abstract":458,"tags":460},[],[459],"soil",[],{"code":462,"score":275,"supported":35,"title":463,"abstract":464,"tags":466},"I7",[],[465],"研究方法",[],"多个产业证据相当，需要确认主归属",[469,470],"CBERS-4 亚马逊 土地利用","Jamanxim 森林覆盖 遥感","CBERS-4亚马逊土地利用-5577","10.29150\u002Fjhrs.v16i4.269068",{"doi":472,"openalex_id":474,"authors":475,"venue":425,"cited_by_count":15,"oa_url":422,"card":492,"direction":90,"ingested_from":92},"W7223141286",[476,479,481,484,487,490],{"name":477,"orcid":478},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":480,"orcid":9},"Gabriel Henrique Santos Silva",{"name":482,"orcid":483},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":485,"orcid":486},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":488,"orcid":489},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":491,"orcid":9},"Sylvana Melo dos Santos",{"tldr":493,"method":494,"finding":495,"direction":90,"opportunity":496},"用CBERS-4\u002FPAN影像分析亚马逊Jamanxim河流域2015-2025年土地利用变化。","几何校正、Gram-Schmidt融合、随机森林监督分类，Google Eart","森林覆盖减少13.78%，农业扩张54.97%，裸地增132.47%，总体精度超90%。","可探索中巴地球资源卫星在热带森林退化监测中的业务化应用与多源数据融合。","2026-10-22T11:20:42.901761Z"]