[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2800":3,"related-2800":78},{"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":53,"slug":56,"view_count":57,"doi":58,"paper":59,"created_at":77},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。",null,"Trends in Ecological and Indoor Environmental Engineering","2026-09-15T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,17,12,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"非洲农业","遥感监测","水质监测","卫星遥感","湖泊生态",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":52},[39,47],{"code":40,"score":17,"supported":41,"title":42,"abstract":43,"tags":46},"I3",true,[],[44,45],"遥感","remote sensing",[30,28],{"code":48,"score":22,"supported":14,"title":49,"abstract":50,"tags":51},"J3",[],[],[27],"农业相关性需要复核",[54,55],"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",3,"10.62622\u002Fteiee.026.4.3.55-64",{"doi":58,"openalex_id":60,"authors":61,"venue":10,"cited_by_count":68,"oa_url":69,"card":70,"direction":74,"ingested_from":76},"W7213293788",[62,65],{"name":63,"orcid":64},"Osasere Abike Omoruyi","https:\u002F\u002Forcid.org\u002F0000-0003-4505-9551",{"name":66,"orcid":67},"Ruby Asunomeh","https:\u002F\u002Forcid.org\u002F0009-0009-7529-254X",0,"https:\u002F\u002Fwww.teiee.net\u002Fpdf-226150-145194?filename=Spatial-Persistence-and-I.pdf",{"tldr":71,"method":72,"finding":73,"direction":74,"opportunity":75},"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。","农业遥感与作物表型","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。","openalex","2026-09-17T23:30:38.937817Z",{"total":79,"page":22,"page_size":79,"items":80},6,[81,133,209,272,348,402],{"id":82,"title":83,"url":84,"summary":85,"summary_zh":86,"content":9,"source_name":87,"source_url":84,"published_at":88,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":89,"score_detail":90,"sources":95,"tags":97,"primary_topic":9,"secondary_topics":100,"classification":101,"search_phrases":115,"slug":118,"view_count":57,"doi":119,"paper":120,"created_at":132},3675,"Long-term (2000-2023) Monitoring and Hotspot Identification of Total Suspended Solids and Turbidity in Lake Tana, Ethiopia, Using High Temporal Resolution MODIS Satellite Imagery","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faeac9e","Abstract Monitoring water quality is challenging in data-scarce regions. This study examines the use of remote sensing to assess key water quality parameters in Lake Tana, Northwestern Ethiopia, to support the sustainable management of Lake water resources. The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery was applied for long-term spatiotemporal assessment of total suspended solids (TSS) and turbidity. The results show that annual mean TSS increased from 350 mg\u002Fl in 2000 to 640 mg\u002Fl in 2020, while turbidity increased from 120 to about 500 NTU before both parameters declined during 2020-2023. Seasonal analysis indicates that the highest TSS and turbidity occurred during the peak rainy month of July. The spatial variation revealed that elevated TSS and turbidity levels were concentrated primarily at the inlets of the major feeder rivers to Lake Tana. This reflects a substantial influx of sediment into the Lake during rainy seasons. During post-rainy months, TSS is redistributed across different sections of the Lake by wind. The findings of this work demonstrate the ability of remote sensing to provide long-term monitoring of water quality parameters, offering valuable insights for the sustainable management of Lake water resources.","摘要 在数据稀缺地区，水质监测面临挑战。本研究探讨了利用遥感技术评估埃塞俄比亚西北部塔纳湖（Lake Tana）关键水质参数，以支持湖泊水资源的可持续管理。研究采用中分辨率成像光谱仪（MODIS）卫星影像，对总悬浮固体（TSS）和浊度进行了长期时空评估。结果表明，年均TSS从2000年的350 mg\u002Fl增至2020年的640 mg\u002Fl，浊度从120 NTU增至约500 NTU，随后两者在2020—2023年间均有所下降。季节性分析表明，TSS和浊度最高值出现在降雨高峰的7月。空间变化显示，较高的TSS和浊度水平主要集中在塔纳湖主要补给河流的入湖口处，反映了雨季大量泥沙流入湖泊。在雨后月份，TSS受风力作用在湖泊不同区域重新分布。本研究结果证明了遥感技术在水质参数长期监测方面的能力，为湖泊水资源的可持续管理提供了有价值的见解。","Environmental Research Communications","2026-09-25T00:00:00Z",74,{"impact":20,"substance":91,"depth":92,"authority":93,"freshness":21,"relevant":22,"comment":94},22,18,13,"利用MODIS长时序遥感监测塔纳湖水质，方法成熟、数据跨度长，对数据稀缺地区水质管理有参考价值，但属区域案例研究，公共影响有限。",[96],{"name":87,"url":84},[28,29,98,31,99],"埃塞俄比亚","MODIS",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":102},{"candidates":103,"agricultural_evidence":14,"reason":52},[104,109],{"code":40,"score":105,"supported":41,"title":106,"abstract":107,"tags":108},7,[],[44,45],[28],{"code":110,"score":57,"supported":41,"title":111,"abstract":112,"tags":114},"H3",[],[113],"水资源",[],[116,117],"Lake Tana MODIS 水质监测","埃塞俄比亚 塔纳湖 悬浮物","LakeTanaMODIS水质监测-3675","10.1088\u002F2515-7620\u002Faeac9e",{"doi":119,"openalex_id":121,"authors":122,"venue":87,"cited_by_count":68,"oa_url":126,"card":127,"direction":74,"ingested_from":76},"W7214348540",[123],{"name":124,"orcid":125},"Maru Fentaw Getie","https:\u002F\u002Forcid.org\u002F0009-0003-9743-4365","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faeac9e\u002Fpdf",{"tldr":128,"method":129,"finding":130,"direction":74,"opportunity":131},"利用MODIS影像长时序监测埃塞俄比亚塔纳湖悬浮物与浊度并识别热点区域。","MODIS高时间分辨率遥感影像，2000-2023年TSS与浊度反演及时空分析。","TSS与浊度2000-2020年显著上升，2020-2023年回落，高值集中于主要入湖河流口。","可结合降水与土地利用数据，量化流域农业侵蚀对湖泊水质贡献并构建预警模型。","2026-09-28T23:30:30.946130Z",{"id":134,"title":135,"url":136,"summary":137,"summary_zh":138,"content":9,"source_name":139,"source_url":136,"published_at":140,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":141,"score_detail":142,"sources":145,"tags":147,"primary_topic":151,"secondary_topics":156,"classification":157,"search_phrases":171,"slug":174,"view_count":68,"doi":175,"paper":176,"created_at":208},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":91,"substance":18,"depth":92,"authority":143,"freshness":13,"relevant":22,"comment":144},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。",[146],{"name":139,"url":136},[148,28,30,149,150],"农业政策","秸秆焚烧","环保督察",{"code":40,"slug":152,"name":153,"path":154,"parent_code":155},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":158,"locked":14,"version":35,"method":36,"evidence":159},"classified",{"candidates":160,"agricultural_evidence":41},[161,166],{"code":40,"score":162,"supported":41,"title":163,"abstract":164,"tags":165},16,[45],[44,45],[30,28],{"code":167,"score":22,"supported":14,"title":168,"abstract":169,"tags":170},"H5",[],[],[149],[172,173],"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":175,"openalex_id":177,"authors":178,"venue":139,"cited_by_count":68,"oa_url":9,"card":202,"direction":74,"ingested_from":76},"W7222877438",[179,181,183,185,187,189,191,194,196,198,200],{"name":180,"orcid":9},"Lu Dong",{"name":182,"orcid":9},"Linru Zhang",{"name":184,"orcid":9},"Na Liu",{"name":186,"orcid":9},"Xin Long",{"name":188,"orcid":9},"Ziyi Wang",{"name":190,"orcid":9},"Siyue Yang",{"name":192,"orcid":193},"Jie Tian","https:\u002F\u002Forcid.org\u002F0000-0001-6585-3643",{"name":195,"orcid":9},"Qiyuan Wang",{"name":197,"orcid":9},"Zhanfeng Dong",{"name":199,"orcid":9},"Long Wu",{"name":201,"orcid":9},"Yichen Wang",{"tldr":203,"method":204,"finding":205,"direction":206,"opportunity":207},"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":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":221,"tags":223,"primary_topic":9,"secondary_topics":227,"classification":228,"search_phrases":237,"slug":240,"view_count":68,"doi":241,"paper":242,"created_at":271},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":20,"substance":219,"depth":19,"authority":93,"freshness":21,"relevant":22,"comment":220},21,"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。",[222],{"name":215,"url":212},[28,30,224,225,226],"草原火险","可燃物模型","草地管理",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":229},{"candidates":230,"agricultural_evidence":14,"reason":52},[231],{"code":40,"score":232,"supported":41,"title":233,"abstract":234,"tags":236},11,[],[44,45,235],"multispectral",[30,28],[238,239],"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":241,"openalex_id":243,"authors":244,"venue":215,"cited_by_count":68,"oa_url":265,"card":266,"direction":74,"ingested_from":76},"W7221860401",[245,248,251,254,257,259,262],{"name":246,"orcid":247},"Lina Aoyama","https:\u002F\u002Forcid.org\u002F0000-0001-9677-7268",{"name":249,"orcid":250},"Katharine N. Suding","https:\u002F\u002Forcid.org\u002F0000-0002-5357-0176",{"name":252,"orcid":253},"Advyth Ramachandran","https:\u002F\u002Forcid.org\u002F0009-0005-1440-2726",{"name":255,"orcid":256},"Jordan T. Lee","https:\u002F\u002Forcid.org\u002F0000-0002-3188-9792",{"name":258,"orcid":9},"Paul Dennison",{"name":260,"orcid":261},"Thomas K. Merchant","https:\u002F\u002Forcid.org\u002F0000-0002-6905-0255",{"name":263,"orcid":264},"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":267,"method":268,"finding":269,"direction":74,"opportunity":270},"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":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":9,"source_name":278,"source_url":275,"published_at":279,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":68,"score_detail":280,"sources":282,"tags":284,"primary_topic":9,"secondary_topics":287,"classification":288,"search_phrases":314,"slug":317,"view_count":68,"doi":318,"paper":319,"created_at":347},4885,"Water quality monitoring challenges during the war in Ukraine: new contaminants and approaches to their detection","https:\u002F\u002Fdoi.org\u002F10.2166\u002Fwqrj.2026.128","ABSTRACT Graphical overview of water pollution and monitoring under conflict conditions. Water pollutants are grouped into conflict-related contaminants, including explosive compounds, ammunition additives, uranium and heavy metals; conventional pollutants, including nutrients, heavy metals and organic matter; and hazardous pollutants also occurring in civilian activities, including petroleum hydrocarbons, pesticides, phenols, pharmaceuticals, personal care products and microplastics. A hybrid monitoring concept combines laboratory ex situ analysis, field in situ measurements and remote monitoring. These monitoring approaches are supported by a four-level digital framework consisting of sensor networks, analytics and Big Data, decision support, and an integrative level. The article provides a critical assessment of the ability of routine water quality monitoring systems to operate under armed conflict conditions, using Ukraine as a case study. Conventional pollutants (metals, nutrients, organic contamination, and emerging environmental threats, including pharmaceuticals and microplastics), are provided. Particular attention is paid to substances that are specific to wartime conditions, including explosive compounds, fuel and lubricant components, and metals derived from munitions. It is shown that infrastructure damage, electricity shortages, restricted access to monitoring sites, and disruption of laboratory operations can distort results or fully halt classical ex situ monitoring, creating spatial and temporal data gaps. Against conflict specific requirements, including personnel safety, rapid diagnostics, and data continuity, the performance of laboratory, in situ, and remote sensing approaches are evaluated. The analysis supports implementation of hybrid monitoring architectures that combine satellite Earth observation, use of UAVs for sampling or reconnaissance, and mobile laboratories for confirmatory analysis, complemented by portable test systems for rapid screening. Priorities for digitalization of crisis monitoring are formulated, including deployment of IoT based sensors, data standardization, and development of a Big Data platform with an interactive map to support early warning systems and post war recovery planning.","摘要：冲突条件下水污染与监测的图形概览。水污染物被分为与冲突相关的污染物，包括爆炸性化合物、弹药添加剂、铀和重金属；常规污染物，包括营养物、重金属和有机物；以及民用活动中同样出现的有害污染物，包括石油烃、农药、酚类、药物、个人护理产品和微塑料。一种混合监测概念结合了实验室异位分析、现场原位测量和远程监测。这些监测方法由一个四级数字框架支撑，该框架由传感器网络、分析与大数据、决策支持以及综合层组成。本文以乌克兰为案例，对常规水质监测系统在武装冲突条件下运行的能力进行了批判性评估。文中提供了常规污染物（金属、营养物、有机污染以及包括药物和微塑料在内的新兴环境威胁）的相关内容。特别关注战时特有的物质，包括爆炸性化合物、燃料和润滑剂成分，以及源自弹药的金属。研究表明，基础设施损毁、电力短缺、监测站点准入受限以及实验室运行中断，可能使结果失真或使经典的异位监测完全停止，从而造成空间和时间数据缺口。针对冲突特定需求，包括人员安全、快速诊断和数据连续性，评估了实验室、原位和遥感方法的性能。该分析支持实施混合监测架构，即结合卫星地球观测、使用无人机进行采样或侦察，以及用于确证分析的移动实验室，并辅以用于快速筛查的便携式检测系统。文中提出了危机监测数字化的优先事项，包括部署基于物联网的传感器、数据标准化，以及开发带有交互式地图的大数据平台，以支持预警系统和战后恢复规划。","Water Quality Research Journal","2026-10-12T00:00:00Z",{"impact":68,"substance":68,"depth":68,"authority":68,"freshness":68,"relevant":68,"comment":281},"主题为乌克兰战时水质监测技术，属环境监测领域，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。",[283],{"name":278,"url":275},[28,29,285,286],"战时污染","物联网传感器",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":289},{"candidates":290,"agricultural_evidence":14,"reason":52},[291,295,302,308],{"code":40,"score":105,"supported":41,"title":292,"abstract":293,"tags":294},[],[44,45],[28],{"code":296,"score":79,"supported":41,"title":297,"abstract":298,"tags":301},"I5",[],[299,300],"物联网","IoT",[],{"code":303,"score":57,"supported":41,"title":304,"abstract":305,"tags":307},"F4",[],[306],"农药",[],{"code":309,"score":57,"supported":41,"title":310,"abstract":311,"tags":313},"I2",[],[312],"decision support",[],[315,316],"乌克兰 水质监测","战时水污染 检测","乌克兰水质监测-4885","10.2166\u002Fwqrj.2026.128",{"doi":318,"openalex_id":320,"authors":321,"venue":278,"cited_by_count":68,"oa_url":340,"card":341,"direction":346,"ingested_from":76},"W7221531247",[322,325,328,331,334,337],{"name":323,"orcid":324},"Світлана Олександрівна Кирій","https:\u002F\u002Forcid.org\u002F0000-0002-2805-7821",{"name":326,"orcid":327},"Khrystyna Hutsul","https:\u002F\u002Forcid.org\u002F0000-0002-4760-3605",{"name":329,"orcid":330},"Arcady Shakhnovsky","https:\u002F\u002Forcid.org\u002F0000-0003-2963-4026",{"name":332,"orcid":333},"Iryna V. Kosogina","https:\u002F\u002Forcid.org\u002F0000-0002-9795-7110",{"name":335,"orcid":336},"Марта Ігорівна Літинська","https:\u002F\u002Forcid.org\u002F0000-0001-7014-493X",{"name":338,"orcid":339},"Tetiana A. Dontsova","https:\u002F\u002Forcid.org\u002F0000-0001-8189-8665","https:\u002F\u002Fiwaponline.com\u002Fwqrj\u002Farticle-pdf\u002Fdoi\u002F10.2166\u002Fwqrj.2026.128\u002F1643189\u002Fwqrj2026128.pdf",{"tldr":342,"method":343,"finding":344,"direction":206,"opportunity":345},"以乌克兰战争为案例，评估冲突下水质监测挑战并提出混合监测与数字化框架。","文献综述与批判性评估，结合实验室、现场、遥感及物联网大数据框架。","基础设施破坏等致传统监测中断，需卫星、无人机、移动实验室与便携设备混合监测。","可研究冲突或灾害下农业面源污染应急监测与预警，融合遥感、无人机和物联网。","智慧农业 \u002F 农业物联网","2026-10-14T23:30:10.546829Z",{"id":349,"title":350,"url":351,"summary":352,"summary_zh":353,"content":9,"source_name":354,"source_url":351,"published_at":355,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":356,"score_detail":357,"sources":359,"tags":361,"primary_topic":9,"secondary_topics":365,"classification":366,"search_phrases":379,"slug":382,"view_count":22,"doi":383,"paper":384,"created_at":401},4519,"Predicting water quality in mine-affected environments: A hyperspectral remote sensing study in the Iberian pyrite Belt","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.srs.2026.100516","Monitoring water quality in Acid Mine Drainage (AMD) environments requires high-resolution data to capture the complex interplay between geochemical and hydrological dynamics. This study integrates unmanned aerial vehicle (UAV)-based hyperspectral imaging with machine learning to estimate pH, sulfate (SO 4 2- ), and iron (Fe) concentrations across nine water bodies at the Tharsis and Lagunazo abandoned mining sites in the Iberian Pyrite Belt (SW Iberian Peninsula), which span a broad hydrochemical gradient. Using in situ measurements for training, five algorithms: Random Forest (RF), k-nearest neighbors (KNN), Partial Least Squares Regression (PLSR), XGBoost, and Multilayer Perceptron (MLP), against dimensionality reduction techniques (LASSO and PCA) were evaluated. The MLP model using the full spectral set yielded the best performance for pH (R 2 = 0.952), while RF models using LASSO-selected features performed best for Fe (R 2 = 0.861) and SO 4 2- (R 2 = 0.880). SHapley Additive exPlanations (SHAP) confirmed that model decisions align with known spectral absorption features. Building on previous hyperspectral AMD studies, this work advances the state of the art by (i) exploiting UAV-scale hyperspectral data to resolve fine-scale spatial variability within individual water bodies, (ii) explicitly distinguishing between optically active and non-optically active water quality parameters, and (iii) systematically comparing multiple machine learning regression algorithms to retrieve pH, iron, and sulfate across chemically diverse systems. By integrating high-resolution imagery with hydrochemical data, this study provides a robust framework for monitoring AMD-impacted surface waters at spatial scales relevant for environmental management.","在酸性矿山排水（AMD）环境中监测水质需要高分辨率数据，以捕捉地球化学过程与水文动态之间复杂的相互作用。本研究将基于无人机（UAV）的高光谱成像与机器学习相结合，估算了伊比利亚黄铁矿带（伊比利亚半岛西南部）Tharsis和Lagunazo废弃矿区九个水体的pH、硫酸盐（SO₄²⁻）和铁（Fe）浓度，这些水体跨越了广泛的水化学梯度。利用原位测量数据进行训练，评估了五种算法：随机森林（RF）、k近邻（KNN）、偏最小二乘回归（PLSR）、XGBoost和多层感知机（MLP），并结合降维技术（LASSO和PCA）进行比较。使用全光谱集的MLP模型在pH估算中表现最佳（R² = 0.952），而使用LASSO筛选特征的RF模型在Fe（R² = 0.861）和SO₄²⁻（R² = 0.880）估算中表现最优。SHapley加法解释（SHAP）证实模型决策与已知的光谱吸收特征相一致。在以往高光谱AMD研究的基础上，本工作通过以下方面推进了该领域的技术水平：（i）利用无人机尺度的高光谱数据解析单个水体内部的精细空间变异性，（ii）明确区分光学活性与非光学活性水质参数，以及（iii）系统比较多种机器学习回归算法以反演化学组成多样系统中的pH、铁和硫酸盐。通过将高分辨率影像与水化学数据相结合，本研究为在与环境管理相关的空间尺度上监测受AMD影响的地表水提供了一个稳健的框架。","Science of Remote Sensing","2026-10-06T00:00:00Z",78,{"impact":92,"substance":91,"depth":92,"authority":143,"freshness":79,"relevant":22,"comment":358},"该研究将无人机高光谱与机器学习结合用于酸性矿山废水水质反演，方法新颖、数据扎实，对矿区农业水环境监测具有参考价值，但属细分领域国际论文，公共影响有限。",[360],{"name":354,"url":351},[362,363,28,29,364],"机器学习","无人机遥感","矿山生态修复",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":367},{"candidates":368,"agricultural_evidence":14,"reason":52},[369,374],{"code":40,"score":219,"supported":41,"title":370,"abstract":372,"tags":373},[45,371],"hyperspectral",[371],[363,28],{"code":309,"score":105,"supported":41,"title":375,"abstract":376,"tags":378},[],[362,377],"machine learning",[362],[380,381],"伊比利亚黄铁矿带 酸性矿山废水 高光谱","UAV 高光谱 水质反演","伊比利亚黄铁矿带酸性矿山废水高光谱-4519","10.1016\u002Fj.srs.2026.100516",{"doi":383,"openalex_id":385,"authors":386,"venue":354,"cited_by_count":68,"oa_url":395,"card":396,"direction":74,"ingested_from":76},"W7220716726",[387,389,392],{"name":388,"orcid":9},"Melisa Isgró",{"name":390,"orcid":391},"Maria  Dolores Basallote","https:\u002F\u002Forcid.org\u002F0000-0003-2011-3806",{"name":393,"orcid":394},"L. Barbero","https:\u002F\u002Forcid.org\u002F0000-0002-3513-2025","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2666017226001549\u002Fpdf",{"tldr":397,"method":398,"finding":399,"direction":74,"opportunity":400},"用无人机高光谱与机器学习反演矿区酸性废水pH、硫酸盐和铁浓度。","无人机高光谱成像+5种机器学习算法，结合LASSO\u002FPCA降维与SHAP解释。","MLP对pH最佳(R²=0.952)，RF结合LASSO对Fe和SO₄²⁻最佳。","可将该高光谱-机器学习框架迁移到农业面源污染水体监测，并探索跨区域迁移学习。","2026-10-09T23:30:30.251656Z",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":9,"source_name":408,"source_url":405,"published_at":409,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":410,"score_detail":411,"sources":413,"tags":415,"primary_topic":9,"secondary_topics":419,"classification":420,"search_phrases":462,"slug":465,"view_count":57,"doi":466,"paper":467,"created_at":478},4023,"Leveraging Artificial Intelligence for Climate Change Mitigation in Africa: A Sustainable Approach","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v9.no6.2025.pg212.229","As climate change intensifies environmental and socio-economic vulnerabilities across Africa, there is a growing need for innovative, scalable solutions to support climate resilience. This study critically examines the role of Artificial Intelligence (AI) in enhancing climate adaptation and mitigation efforts on the continent. Employing a qualitative, multi-source research methodology, the paper synthesizes academic literature, institutional frameworks, and applied case studies to assess the integration of AI into environmental monitoring and resource management systems. Particular emphasis is placed on the use of machine learning and remote sensing technologies to support real-time data analysis, land-use classification, water resource management, and early warning systems. The Enhancing National Climate Services (ENACTS) program is analyzed as a leading case, demonstrating how the fusion of satellite and groundbased data can significantly improve climate services in sectors such as agriculture and public health. The paper also identifies critical challenges—including data scarcity, inadequate infrastructure, limited technical capacity, and ethical risks such as data privacy and algorithmic bias—that hinder the effective deployment of AI. In response, the study offers strategic policy recommendations to strengthen climate data infrastructure, promote cross-sector collaboration, and build human capital in AI and climate science. The findings underscore the importance of African-led innovation, ethical governance, and context-specific solutions in ensuring that AI contributes meaningfully and equitably to sustainable development and climate resilience.","随着气候变化加剧非洲各地的环境和社会经济脆弱性，人们日益需要创新且可扩展的解决方案来支持气候韧性建设。本研究批判性地考察了人工智能（AI）在增强非洲大陆气候适应与减缓努力中的作用。本文采用定性、多来源的研究方法，综合学术文献、制度框架和应用案例研究，评估AI在环境监测和资源管理系统中的整合情况。研究特别强调利用机器学习和遥感技术支持实时数据分析、土地利用分类、水资源管理和预警系统。本文以“增强国家气候服务”（ENACTS）项目作为领先案例进行分析，展示卫星数据与地面数据的融合如何显著改善农业和公共卫生等部门的气候服务。本文还指出了阻碍AI有效部署的关键挑战，包括数据稀缺、基础设施不足、技术能力有限，以及数据隐私和算法偏见等伦理风险。针对这些问题，本研究提出了战略政策建议，以加强气候数据基础设施、促进跨部门合作，并建设AI与气候科学领域的人力资本。研究结果强调了非洲主导的创新、伦理治理和因地制宜的解决方案对于确保AI以有意义且公平的方式促进可持续发展和气候韧性的重要性。","WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY","2026-10-01T00:00:00Z",60,{"impact":20,"substance":92,"depth":162,"authority":79,"freshness":17,"relevant":22,"comment":412},"论文聚焦AI在非洲气候适应与农业气候服务中的应用，含ENACTS案例与政策建议，但期刊权威性一般、地域相关性偏弱，可作参考而非重点精选。",[414],{"name":408,"url":405},[416,417,27,418,28],"智慧农业","农业人工智能","气候韧性",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":421},{"candidates":422,"agricultural_evidence":41,"reason":461},[423,432,437,441,446,452,457],{"code":424,"score":92,"supported":41,"title":425,"abstract":427,"tags":431},"H7",[426],"climate change",[428,429,426,430],"气候变化","气候适应","climate resilience",[418],{"code":309,"score":92,"supported":41,"title":433,"abstract":435,"tags":436},[434],"artificial intelligence",[362,377,434],[417],{"code":40,"score":105,"supported":41,"title":438,"abstract":439,"tags":440},[],[44,45],[28],{"code":110,"score":79,"supported":41,"title":442,"abstract":443,"tags":445},[],[113,444],"water resource",[],{"code":447,"score":57,"supported":41,"title":448,"abstract":449,"tags":451},"I7",[],[450],"研究方法",[],{"code":453,"score":22,"supported":14,"title":454,"abstract":455,"tags":456},"I1",[],[],[416],{"code":48,"score":22,"supported":14,"title":458,"abstract":459,"tags":460},[],[],[27],"多个产业证据相当，需要确认主归属",[463,464],"ENACTS 气候服务 非洲","人工智能 气候适应 非洲","ENACTS气候服务非洲-4023","10.56201\u002Fwjimt.v9.no6.2025.pg212.229",{"doi":466,"openalex_id":468,"authors":469,"venue":408,"cited_by_count":22,"oa_url":472,"card":473,"direction":74,"ingested_from":76},"W7215085286",[470],{"name":471,"orcid":9},"Ogunade Josiah Ayodele","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 9 NO. 6 2025\u002FLeveraging Artificial Intelligence for Climate 212-229.pdf",{"tldr":474,"method":475,"finding":476,"direction":206,"opportunity":477},"综述AI在非洲气候变化适应与减缓中的作用，以ENACTS为例分析挑战并提出政策建议。","定性多源方法，综合文献、制度框架与案例，聚焦机器学习和遥感。","AI可提升气候服务，但受数据稀缺、基础设施和伦理风险制约，需非洲主导创新。","可研究小农户场景下AI气候服务的落地机制与公平性评估，填补非洲本地化实证空白。","2026-10-02T23:30:24.321762Z"]