[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3675":3,"related-3675":77},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":56,"slug":59,"view_count":50,"doi":60,"paper":61,"created_at":76},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。",null,"Environmental Research Communications","2026-09-25T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感监测","水质监测","埃塞俄比亚","湖泊生态","MODIS",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":55},[39,48],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":47},"I3",7,true,[],[45,46],"遥感","remote sensing",[27],{"code":49,"score":50,"supported":42,"title":51,"abstract":52,"tags":54},"H3",3,[],[53],"水资源",[],"农业相关性需要复核",[57,58],"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":60,"openalex_id":62,"authors":63,"venue":10,"cited_by_count":67,"oa_url":68,"card":69,"direction":73,"ingested_from":75},"W7214348540",[64],{"name":65,"orcid":66},"Maru Fentaw Getie","https:\u002F\u002Forcid.org\u002F0009-0003-9743-4365",0,"https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faeac9e\u002Fpdf",{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"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。","农业遥感与作物表型","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。","openalex","2026-09-28T23:30:30.946130Z",{"total":78,"page":22,"page_size":78,"items":79},6,[80,133,183,260,316,402],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":85,"content":9,"source_name":86,"source_url":83,"published_at":87,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":88,"score_detail":89,"sources":94,"tags":96,"primary_topic":9,"secondary_topics":99,"classification":100,"search_phrases":112,"slug":115,"view_count":50,"doi":116,"paper":117,"created_at":132},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":90,"substance":91,"depth":92,"authority":17,"freshness":21,"relevant":22,"comment":93},8,20,17,"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。",[95],{"name":86,"url":83},[97,27,28,98,30],"非洲农业","卫星遥感",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":101},{"candidates":102,"agricultural_evidence":14,"reason":55},[103,107],{"code":40,"score":90,"supported":42,"title":104,"abstract":105,"tags":106},[],[45,46],[98,27],{"code":108,"score":22,"supported":14,"title":109,"abstract":110,"tags":111},"J3",[],[],[97],[113,114],"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":116,"openalex_id":118,"authors":119,"venue":86,"cited_by_count":67,"oa_url":126,"card":127,"direction":73,"ingested_from":75},"W7213293788",[120,123],{"name":121,"orcid":122},"Osasere Abike Omoruyi","https:\u002F\u002Forcid.org\u002F0000-0003-4505-9551",{"name":124,"orcid":125},"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":128,"method":129,"finding":130,"direction":73,"opportunity":131},"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":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":67,"score_detail":141,"sources":143,"tags":145,"primary_topic":148,"secondary_topics":153,"classification":154,"search_phrases":162,"slug":165,"view_count":67,"doi":166,"paper":167,"created_at":182},5244,"Diurnal and Seasonal Surface Urban Heat Island Dynamics and Thermal Field Variance in Faridabad District, India: A MODIS-Based Assessment (2001–2025)","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fajgr\u002F2026\u002Fv9i4489","Aims: To examine the spatial and temporal dynamics of land surface temperature (LST), urban–rural biophysical indices (BUI and NDVI), daily Surface Urban Heat Island Intensity (SUHII), and the Urban Thermal Field Variance Index (UTFVI) on a diurnal and seasonal basis in Faridabad District of northern India. Study Design: Multi-temporal geospatial remote sensing and statistical modelling study. Place and Duration of Study: Faridabad District (Urban Core and Rural Hinterland), Haryana, India; Summer and Winter Seasons Analysed through Six Benchmark Periods (2001, 2005, 2010, 2015, 2020, and 2025). Methodology: MODIS optical (MOD09A1) and thermal infrared (MOD11A2) composite data were analysed using Google Earth Engine at the daytime (10:30 am) and night-time (10:30 pm) overpasses. LST values were grouped into UTFVI (Urban Thermal Field Variance Index) classes. Multivariable OLS regressions and empirical biophysical sensitivities such as Vegetation Cooling Efficiency (VCE) and Impervious Heating Effect (IHE) were evaluated in Python using a stratified random grid (N = 2,500 sampling locations; 235 n 872 pixels). Results: Over the 25-year study period, the built-up gradient for winter (BUI) increased from +0.096 to +0.233 in association with agricultural changes and urban development in the region, while the vegetative deficit for winter (NDVI) increased from -0.061 to -0.136. In terms of temperature, Faridabad showed a SUHI regime during summer (ranging from to ), where the urban core nocturnal LST increased from in 2001 to in 2025, together with regional warming and compression of the urban DTR. The pre-monsoon diurnal regimes showed the SUCI phenomenon (as low as in 2005) owing to the effects of sensible heating in the desiccated fallow rural lands after harvest season. Urban Core thermal field variance showed that moderate thermal stress (UTFVI Class 3) expanded from to , while severe stress tiers (Classes 4–6) concentrated within industrial estates. Daytime vegetation provided consistent cooling (VCE up to per ), whereas nocturnal VCE exhibited temporal variability and diminished transpirational cooling following stomatal closure. Conclusion: Urban greening offers a strong mechanism for daytime heat mitigation but is insufficient to address independently nocturnal heat trapping resulting from the high thermal inertia of construction materials. The sustainable heat-mitigation strategy in industrial nodes entails a combination of urban greening and reflective cool coatings on industrial sheds and impervious surfaces.","目的：考察印度北部法里达巴德县地表温度（LST）、城乡生物物理指数（BUI和NDVI）、日间地表城市热岛强度（SUHII）以及城市热场方差指数（UTFVI）在昼夜和季节尺度上的时空动态。研究设计：多时相地理空间遥感与统计建模研究。研究地点与时长：印度哈里亚纳邦法里达巴德县（城市核心区与乡村腹地）；通过六个基准时段（2001、2005、2010、2015、2020和2025年）分析夏季与冬季。方法：利用Google Earth Engine分析MODIS光学（MOD09A1）和热红外（MOD11A2）合成数据，分别对应白天（上午10：30）和夜间（晚上10：30）过境时刻。LST值按UTFVI（城市热场方差指数）类别分组。在Python中采用分层随机网格（N=2，500个采样点；235 n 872像素）评估多变量OLS回归及经验生物物理敏感性指标，如植被冷却效率（VCE）和不透水面增温效应（IHE）。结果：在25年研究期内，冬季建成区梯度（BUI）从+0.096增至+0.233，与该地区农业变化和城市发展相关；冬季植被亏缺（NDVI）从-0.061增至-0.136。温度方面，法里达巴德在夏季呈现SUHI态势（范围为至），城市核心区夜间LST从2001年的增至2025年的，同时伴随区域增暖和城市日较差（DTR）压缩。季风前昼夜态势因收获季后干燥休耕乡村土地的感热加热效应而呈现SUCI现象（2005年低至）。城市核心区热场方差显示，中度热胁迫（UTFVI第3类）从扩大至，而严重胁迫层级（第4—6类）集中于工业园区。白天植被提供持续冷却（VCE最高达每），而夜间VCE呈现时间变异性，且气孔关闭后蒸腾冷却减弱。结论：城市绿化是白天热缓解的有力机制，但不足以独立应对建筑材料高热惯性导致的夜间热量滞留。工业节点的可持续热缓解策略需要将城市绿化与反射性","Asian Journal of Geographical Research","2026-10-17T00:00:00Z",{"impact":67,"substance":67,"depth":67,"authority":67,"freshness":67,"relevant":67,"comment":142},"印度城市热岛遥感研究，属城市气候与环境领域，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。",[144],{"name":139,"url":136},[27,146,147,31],"地表温度","城市热岛",{"code":40,"slug":149,"name":150,"path":151,"parent_code":152},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":155,"locked":14,"version":35,"method":36,"evidence":156},"classified",{"candidates":157,"agricultural_evidence":42},[158],{"code":40,"score":90,"supported":42,"title":159,"abstract":160,"tags":161},[],[45,46],[146,27],[163,164],"Faridabad 城市热岛","MODIS 地表温度 遥感","Faridabad城市热岛-5244","10.9734\u002Fajgr\u002F2026\u002Fv9i4489",{"doi":166,"openalex_id":168,"authors":169,"venue":139,"cited_by_count":67,"oa_url":136,"card":177,"direction":73,"ingested_from":75},"W7222794736",[170,173,175],{"name":171,"orcid":172},"Abhishek Malik","https:\u002F\u002Forcid.org\u002F0009-0009-4878-0871",{"name":174,"orcid":9},"Shubham Mor",{"name":176,"orcid":9},"Gaurav Kalotra",{"tldr":178,"method":179,"finding":180,"direction":73,"opportunity":181},"基于MODIS分析印度Faridabad 2001–2025年昼夜与季节地表城市热岛及热场差异演变。","MODIS光学与热红外数据、Google Earth Engine、OLS回归及","白天绿化降温显著，但夜间建筑热惯性导致的热滞留难以单独缓解。","可结合城乡梯度与作物休耕期感热效应，研究农业用地变化对昼夜热岛的非对称贡献。","2026-10-18T23:30:31.211684Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":189,"source_url":186,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":67,"score_detail":191,"sources":193,"tags":195,"primary_topic":9,"secondary_topics":198,"classification":199,"search_phrases":225,"slug":228,"view_count":67,"doi":229,"paper":230,"created_at":259},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":67,"substance":67,"depth":67,"authority":67,"freshness":67,"relevant":67,"comment":192},"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。",[194],{"name":189,"url":186},[27,28,196,197],"战时污染","物联网传感器",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":200},{"candidates":201,"agricultural_evidence":14,"reason":55},[202,206,213,219],{"code":40,"score":41,"supported":42,"title":203,"abstract":204,"tags":205},[],[45,46],[27],{"code":207,"score":78,"supported":42,"title":208,"abstract":209,"tags":212},"I5",[],[210,211],"物联网","IoT",[],{"code":214,"score":50,"supported":42,"title":215,"abstract":216,"tags":218},"F4",[],[217],"农药",[],{"code":220,"score":50,"supported":42,"title":221,"abstract":222,"tags":224},"I2",[],[223],"decision support",[],[226,227],"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":229,"openalex_id":231,"authors":232,"venue":189,"cited_by_count":67,"oa_url":251,"card":252,"direction":258,"ingested_from":75},"W7221531247",[233,236,239,242,245,248],{"name":234,"orcid":235},"Світлана Олександрівна Кирій","https:\u002F\u002Forcid.org\u002F0000-0002-2805-7821",{"name":237,"orcid":238},"Khrystyna Hutsul","https:\u002F\u002Forcid.org\u002F0000-0002-4760-3605",{"name":240,"orcid":241},"Arcady Shakhnovsky","https:\u002F\u002Forcid.org\u002F0000-0003-2963-4026",{"name":243,"orcid":244},"Iryna V. Kosogina","https:\u002F\u002Forcid.org\u002F0000-0002-9795-7110",{"name":246,"orcid":247},"Марта Ігорівна Літинська","https:\u002F\u002Forcid.org\u002F0000-0001-7014-493X",{"name":249,"orcid":250},"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":253,"method":254,"finding":255,"direction":256,"opportunity":257},"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。","农业绿色发展与碳","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":261,"title":262,"url":263,"summary":264,"summary_zh":265,"content":9,"source_name":266,"source_url":263,"published_at":267,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":268,"score_detail":269,"sources":272,"tags":274,"primary_topic":9,"secondary_topics":278,"classification":279,"search_phrases":293,"slug":296,"view_count":22,"doi":297,"paper":298,"created_at":315},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":19,"substance":18,"depth":19,"authority":270,"freshness":78,"relevant":22,"comment":271},14,"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。",[273],{"name":266,"url":263},[275,276,27,28,277],"机器学习","无人机遥感","矿山生态修复",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":280},{"candidates":281,"agricultural_evidence":14,"reason":55},[282,288],{"code":40,"score":283,"supported":42,"title":284,"abstract":286,"tags":287},21,[46,285],"hyperspectral",[285],[276,27],{"code":220,"score":41,"supported":42,"title":289,"abstract":290,"tags":292},[],[275,291],"machine learning",[275],[294,295],"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":297,"openalex_id":299,"authors":300,"venue":266,"cited_by_count":67,"oa_url":309,"card":310,"direction":73,"ingested_from":75},"W7220716726",[301,303,306],{"name":302,"orcid":9},"Melisa Isgró",{"name":304,"orcid":305},"Maria  Dolores Basallote","https:\u002F\u002Forcid.org\u002F0000-0003-2011-3806",{"name":307,"orcid":308},"L. Barbero","https:\u002F\u002Forcid.org\u002F0000-0002-3513-2025","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2666017226001549\u002Fpdf",{"tldr":311,"method":312,"finding":313,"direction":73,"opportunity":314},"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":317,"title":318,"url":319,"summary":320,"summary_zh":321,"content":9,"source_name":322,"source_url":319,"published_at":323,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":324,"score_detail":325,"sources":327,"tags":329,"primary_topic":332,"secondary_topics":336,"classification":337,"search_phrases":361,"slug":364,"view_count":365,"doi":366,"paper":367,"created_at":401},2657,"Retrieval of Optically Active and Inactive Water Quality Parameters Using Remote Sensing and Machine Learning: Evidence from Water Hyacinth-Infested Lake Tana, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183185","Monitoring water quality is critical for protecting freshwater ecosystems and supporting sustainable water resource management. Lake Tana, Ethiopia’s largest freshwater lake, faces increasing agricultural and urban pressures, while conventional monitoring remains costly and spatially constrained. This study developed an integrated Sentinel-2 remote sensing and machine learning framework to estimate chlorophyll-a (Chl-a), turbidity (TU), total nitrogen (TN), and total phosphorus (TP) using 858 in situ observations and Google Earth Engine. Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Networks (ANN), and Support Vector Regression (SVR) were evaluated using spectral bands, band combinations, and indices. RF provided the best predictions for Chl-a (R2 = 0.94 ± 0.01; RMSE = 2.11 ± 0.18 µg L−1; MARE = 5%) and TP (R2 = 0.91 ± 0.01; RMSE = 0.26 ± 0.01 mg L−1; MARE = 8.7%), whereas XGB performed best for TU (R2 = 0.93 ± 0.01; RMSE = 5.17 ± 0.43 NTU; MARE = 7%) and TN (R2 = 0.94 ± 0.02; RMSE = 0.18 ± 0.02 mg L−1; MARE = 9.9%). The strong predictive performance of RF and XGB across both optically active and inactive parameters demonstrates the capability of the framework to capture complex spectral water quality relationships and support spatially continuous assessment. Significant seasonal differences (p \u003C 0.001) showed higher dry season Chl-a (137.1%) and higher wet season TP (21.7%), TU (7.5%), and TN (3.9%). Long-term paired observations further indicated increases in Chl-a (73.7%), TN (30%), and TP (14.3%) from December 2016 to December 2025 (p \u003C 0.001). Spatial hotspot analysis revealed strong clustering of TU, TN, and TP, particularly around tributary mouths and nearshore areas, highlighting priority zones for monitoring and intervention. Overall, integrating field observations, Sentinel-2 imagery, and machine learning provides an accurate, scalable, and cost-effective approach for monitoring diverse water quality parameters. The framework offers a transferable solution for strengthening freshwater monitoring in data-scarce regions and supporting sustainable management of lakes under increasing water quality pressures.","监测水质对于保护淡水生态系统和支撑可持续水资源管理至关重要。埃塞俄比亚最大的淡水湖——塔纳湖（Lake Tana）面临着日益加剧的农业和城市压力，而传统监测手段仍然成本高昂且受空间限制。本研究开发了一套集成Sentinel-2遥感与机器学习的框架，利用858个原位观测数据和Google Earth Engine估算叶绿素a（Chl-a）、浊度（TU）、总氮（TN）和总磷（TP）。采用光谱波段、波段组合和指数，评估了随机森林（RF）、极端梯度提升（XGB）、人工神经网络（ANN）和支持向量回归（SVR）的性能。RF对Chl-a（R2 = 0.94 ± 0.01；RMSE = 2.11 ± 0.18 µg L−1；MARE = 5%）和TP（R2 = 0.91 ± 0.01；RMSE = 0.26 ± 0.01 mg L−1；MARE = 8.7%）的预测效果最佳，而XGB对TU（R2 = 0.93 ± 0.01；RMSE = 5.17 ± 0.43 NTU；MARE = 7%）和TN（R2 = 0.94 ± 0.02；RMSE = 0.18 ± 0.02 mg L−1；MARE = 9.9%）的预测效果最佳。RF和XGB在光学活性和非光学活性参数上均表现出强大的预测性能，表明该框架能够捕捉复杂的光谱水质关系并支持空间连续评估。显著的季节性差异（p \u003C 0.001）显示旱季Chl-a较高（137.1%），雨季TP（21.7%）、TU（7.5%）和TN（3.9%）较高。长期配对观测进一步表明，从2016年12月至2025年12月，Chl-a（73.7%）、TN（30%）和TP（14.3%）均有所增加（p \u003C 0.001）。空间热点分析揭示了TU、TN和TP的强烈聚集性，尤其是在支流河口和近岸区域，凸显了优先监测和干预区域。总体而言，整合实地观测、Sentinel-2影像和机器学习为监测多种水质参数提供了一种准确、可扩展且具有成本效益的方法。该框架为加强数据稀缺地区的淡水监测以及支持在水质压力日益增大背景下湖泊的可持续管理提供了一种可迁移的解决方案。","Remote Sensing","2026-09-16T00:00:00Z",82,{"impact":19,"substance":18,"depth":19,"authority":270,"freshness":13,"relevant":22,"comment":326},"基于Sentinel-2与机器学习实现湖泊多参数水质反演，方法可迁移至国内农业面源污染与渔业水域监测，数据规模与精度均具参考价值。",[328],{"name":322,"url":319},[275,330,331,27,28],"农业面源污染","智慧渔业",{"code":152,"slug":333,"name":334,"path":335,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":338,"locked":14,"version":35,"method":36,"evidence":339},"partial",{"candidates":340,"agricultural_evidence":42,"reason":360},[341,346,350,355],{"code":220,"score":342,"supported":42,"title":343,"abstract":344,"tags":345},15,[291],[275,291],[275],{"code":40,"score":342,"supported":42,"title":347,"abstract":348,"tags":349},[46],[45,46],[27],{"code":49,"score":78,"supported":42,"title":351,"abstract":352,"tags":354},[],[53,353],"water resource",[],{"code":356,"score":22,"supported":14,"title":357,"abstract":358,"tags":359},"H5",[],[],[330],"一级领域明确，二级方向需要细分",[362,363],"农业面源污染 智慧渔业 机器学习 水质监测","农业面源污染 智慧渔业","农业面源污染智慧渔业机器学习水质监测-2657",2,"10.3390\u002Frs18183185",{"doi":366,"openalex_id":368,"authors":369,"venue":322,"cited_by_count":67,"oa_url":319,"card":396,"direction":73,"ingested_from":75},"W7213230686",[370,373,375,378,380,383,385,387,389,391,393],{"name":371,"orcid":372},"Lakachew Y. Alemneh","https:\u002F\u002Forcid.org\u002F0009-0004-3471-3778",{"name":374,"orcid":9},"Daganchew Aklog",{"name":376,"orcid":377},"Ann van Griensven","https:\u002F\u002Forcid.org\u002F0000-0002-2105-6287",{"name":379,"orcid":9},"Minychl G. Dersseh",{"name":381,"orcid":382},"Goraw Goshu","https:\u002F\u002Forcid.org\u002F0000-0001-9629-0126",{"name":384,"orcid":9},"Seleshi Yalew",{"name":386,"orcid":9},"Demesew A. Mhiret",{"name":388,"orcid":9},"Sisay B. Asress",{"name":390,"orcid":9},"Tigistu Wassie Agegnehu",{"name":392,"orcid":9},"Shawl Abebe Desta",{"name":394,"orcid":395},"Samuel Berihun Kassa","https:\u002F\u002Forcid.org\u002F0009-0004-5618-9743",{"tldr":397,"method":398,"finding":399,"direction":73,"opportunity":400},"用Sentinel-2与机器学习反演埃塞俄比亚塔纳湖四类水质参数并分析时空变化。","Sentinel-2影像、Google Earth Engine、858个实测点","RF与XGB精度最高（R²达0.91-0.94），水质参数呈显著季节与年际上升趋势。","可迁移至其他数据稀缺湖泊，探索水葫芦覆盖下水体光谱混合与多源遥感协同反演。","2026-09-16T23:30:28.415573Z",{"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":268,"score_detail":410,"sources":412,"tags":414,"primary_topic":9,"secondary_topics":419,"classification":420,"search_phrases":454,"slug":457,"view_count":67,"doi":458,"paper":459,"created_at":483},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",{"impact":19,"substance":18,"depth":92,"authority":17,"freshness":21,"relevant":22,"comment":411},"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。",[413],{"name":408,"url":405},[27,415,416,417,418],"土地利用","森林保护","亚马逊","CBERS-4",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":421},{"candidates":422,"agricultural_evidence":42,"reason":453},[423,430,434,441,447],{"code":424,"score":41,"supported":42,"title":425,"abstract":426,"tags":429},"H6",[],[427,428],"生物多样性","biodiversity",[415],{"code":40,"score":41,"supported":42,"title":431,"abstract":432,"tags":433},[],[45,46],[27],{"code":435,"score":78,"supported":42,"title":436,"abstract":437,"tags":440},"H7",[],[438,439],"气候变化","climate change",[],{"code":442,"score":50,"supported":42,"title":443,"abstract":444,"tags":446},"H2",[],[445],"soil",[],{"code":448,"score":50,"supported":42,"title":449,"abstract":450,"tags":452},"I7",[],[451],"研究方法",[],"多个产业证据相当，需要确认主归属",[455,456],"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":458,"openalex_id":460,"authors":461,"venue":408,"cited_by_count":67,"oa_url":405,"card":478,"direction":73,"ingested_from":75},"W7223141286",[462,465,467,470,473,476],{"name":463,"orcid":464},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":466,"orcid":9},"Gabriel Henrique Santos Silva",{"name":468,"orcid":469},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":471,"orcid":472},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":474,"orcid":475},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":477,"orcid":9},"Sylvana Melo dos Santos",{"tldr":479,"method":480,"finding":481,"direction":73,"opportunity":482},"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"]