[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4441":3,"related-4441":80},{"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":24,"classification":25,"search_phrases":56,"slug":59,"view_count":60,"doi":61,"paper":62,"created_at":79},4441,"GIS and multi-temporal remote sensing assessment of heavy-metal redistribution susceptibility and monitoring priorities around the KAT refinery, Kirkuk, Iraq","https:\u002F\u002Fdoi.org\u002F10.12912\u002F27197050\u002F237407","Industrial soil contamination may persist long after initial deposition, yet the spatial conditions favoring subsequent redistribution are rarely evaluated together with historical chemical measurements.This study integrates an archived 2021 soil dataset from 21 locations around the KAT refinery in Kirkuk, Iraq, with multi-temporal Landsat 8 observations (2017, 2021, and 2025), SRTM terrain analysis, modeled surface-flow connectivity, and scenario-based susceptibility mapping.Eleven metals were available, with a pronounced Zn-Cu-Pb hotspot in the northwestern waste-valley sector.A standardized Zn-Cu-Pb hotspot composite (HCS) represented this multimetal pattern.SRTM elevations agreed strongly with an independent contour-derived terrain surface (Spearman rho approximately 0.962; RMSE approximately 1.82 m).Between 2017 and 2025, median NDVI declined from 0.127 to 0.112 and SAVI from 0.107 to 0.092, whereas NDBI increased from 0.039 to approximately 0.059.Long-term SAVI and NDVI changes showed the strongest exploratory associations with HCS (rho approximately 0.592 and 0.567), although neither crossed the multiple-testing-adjusted 0.05 threshold.Direct quantitative forecasting was rejected after poor leave-one-out cross-validation.Instead, a Pollution Redistribution Susceptibility Index (PESI) integrated source pressure, terrain\u002Fconnectivity, and long-term surface response under alternative weighting scenarios.Consensus PESI showed moderate internal spatial agreement with HCS (rho approximately 0.468, p approximately 0.033), but this is not independent validation because the documented MA-MB-MC sector contributes to source-pressure definition.The final 30-m priority raster covered 99.8001 km².Tier 1 occupied 8.7282 km² (8.746%) and Tier 2 occupied 2.9520 km² (2.958%), totaling 11.6802 km² (11.704%);MA-MB-MC occurred within Tier 1.The framework does not predict future metal concentrations; it identifies areas where documented contamination, environmental connectivity, and surface change jointly indicate greater susceptibility to persistence or redistribution.Independent repeat soil sampling is required for external validation.The study converts a sparse archived contamination baseline into a testable susceptibility framework without treating remote-sensing indices as direct metal measurements.","工业土壤污染在初始沉降后可能长期持续存在，但有利于后续再分布的空间条件很少与历史化学测量数据一起被评估。本研究将伊拉克基尔库克KAT炼油厂周边21个地点的2021年存档土壤数据集与多时相Landsat 8观测数据（2017年、2021年和2025年）、SRTM地形分析、模拟地表径流连通性以及基于情景的敏感性制图相结合。可获取的金属元素有11种，在西北部废物谷区段存在明显的Zn-Cu-Pb热点。采用标准化的Zn-Cu-Pb热点综合指数（HCS）来表征这种多金属模式。SRTM高程与独立等高线推导的地形表面高度吻合（Spearman rho约为0.962；RMSE约为1.82 m）。2017年至2025年间，NDVI中位数从0.127降至0.112，SAVI从0.107降至0.092，而NDBI从0.039升至约0.059。长期SAVI和NDVI变化与HCS表现出最强的探索性关联（rho分别约为0.592和0.567），但两者均未超过多重检验校正后的0.05阈值。由于留一法交叉验证表现不佳，直接定量预测被否决。取而代之的是，污染再分布敏感性指数（PESI）在替代权重情景下整合了源压力、地形\u002F连通性和长期地表响应。共识PESI与HCS表现出中等程度的内部空间一致性（rho约为0.468，p约为0.033），但这并非独立验证，因为已记录的MA-MB-MC区段参与了源压力的定义。最终30米优先栅格覆盖99.8001 km²。一级区占8.7282 km²（8.746%），二级区占2.9520 km²（2.958%），合计11.6802 km²（11.704%）；MA-MB-MC位于一级区内。该框架不预测未来金属浓度；它识别出已记录的污染、环境连通性和地表变化共同表明对持久存在或再分布具有更高敏感性的区域。需要独立的重复土壤采样进行外部验证。本研究将稀疏的存档污染基线转化为可检验的敏感性框架，而不将遥感指数视为直接的金属测量值。",null,"Ecological Engineering & Environmental Technology","2026-10-06T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦伊拉克炼油厂周边土壤重金属污染与遥感评估，属环境地学领域，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],"遥感监测","土壤重金属","污染风险评价",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":55},[31,40,47],{"code":32,"score":33,"supported":34,"title":35,"abstract":37,"tags":39},"I3",12,true,[36],"remote sensing",[38],"遥感",[21],{"code":41,"score":42,"supported":34,"title":43,"abstract":45,"tags":46},"I4",8,[44],"GIS",[],[],{"code":48,"score":49,"supported":34,"title":50,"abstract":51,"tags":54},"H2",7,[],[52,53],"土壤","soil",[22],"农业相关性需要复核",[57,58],"重金属 再分布 遥感","污染风险评价 土壤重金属 遥感监测","重金属再分布遥感-4441",3,"10.12912\u002F27197050\u002F237407",{"doi":61,"openalex_id":63,"authors":64,"venue":10,"cited_by_count":15,"oa_url":71,"card":72,"direction":76,"ingested_from":78},"W7220685993",[65,68],{"name":66,"orcid":67},"Noor Nabeel Abdulqader","https:\u002F\u002Forcid.org\u002F0000-0002-1338-8812",{"name":69,"orcid":70},"Hayder M. Jasim","https:\u002F\u002Forcid.org\u002F0009-0007-6357-2136","https:\u002F\u002Fwww.ecoeet.com\u002Fpdf-237407-149056?filename=GIS-and-multi-temporal-re.pdf",{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"结合存档土壤金属数据与多时相遥感，评估伊拉克KAT炼油厂周边重金属再分配敏感性与监测优先级。","整合2021年21点土壤金属数据、Landsat 8多时相影像、SRTM地形与地","PESI与金属热点复合指数中度一致，识别出11.7%高敏感区，但需独立土壤采样验证。","农业遥感与作物表型","可将该敏感性框架迁移至农田重金属风险监测，并引入独立重复采样与作物表型数据验证。","openalex","2026-10-08T23:30:31.031587Z",{"total":81,"page":82,"page_size":81,"items":83},6,1,[84,178,260,340,398,474],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":9,"source_name":90,"source_url":87,"published_at":91,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":92,"score_detail":93,"sources":98,"tags":100,"primary_topic":104,"secondary_topics":108,"classification":114,"search_phrases":137,"slug":140,"view_count":15,"doi":141,"paper":142,"created_at":177},4366,"Article topic: comprehensive assessment of soil and flora contamination by heavy metals in the zone of influence of the Nikolaevskaya enrichment plant","https:\u002F\u002Fdoi.org\u002F10.51886\u002F1999-740x_2026_3_46","Agricultural lands in East Kazakhstan region cover a significant area, with large portions of pasture and cropland situated in close proximity to operating industrial facilities. Despite this, no comprehensive integrated assessment combining remote sensing, ground-based soil monitoring, and flora survey had previously been conducted for the zone of influence of the Nikolaevskaya enrichment plant. This study aimed to fill that gap by a 40-year land cover analysis (1985-2024) using publicly available satellite data processed in Google Earth Engine with soil sampling and vegetation surveys across 17 sites. Soil analyses revealed not only elevated heavy metal concentrations but also a sharp pH decrease, conditions under which heavy metals become significantly more mobile and available for plant uptake. Technosol formation was observed at multiple sampling points, indicating a permanent transformation of the natural soil profile. The flora survey recorded 72 species from 62 genera, with ruderal and synanthropic species domina-ting disturbed habitats and native species persisting only at control points more distant from the facility. Taken together, these findings point to ongoing ecosystem degradation and raise concrete concerns for the regional food chain given the pastureland and cropland adjacent to the plant.","东哈萨克斯坦地区的农业用地面积广阔，其中大片牧场和农田与正在运营的工业设施相距不远。尽管如此，此前尚未针对尼古拉耶夫斯克选矿厂影响区域开展过将遥感、地面土壤监测与植物区系调查相结合的综合评估。本研究旨在填补这一空白，通过为期40年的土地覆盖分析（1985—2024年），利用Google Earth Engine处理的公开卫星数据，并结合17个样点的土壤采样与植被调查。土壤分析不仅揭示了重金属浓度升高，还发现pH值急剧下降，在这种条件下，重金属的迁移性和植物可利用性显著增强。在多个采样点观察到技术土（Technosol）的形成，表明自然土壤剖面发生了永久性改变。植物区系调查共记录到62属72种植物，其中扰动生境以杂草类和伴人植物物种为主，原生物种仅存于距设施较远的对照点。综合来看，这些发现表明生态系统正在持续退化，并且鉴于该厂邻近牧场和农田，对区域食物链构成了切实的担忧。","Pochvovedenie i agrokhimiya","2026-10-05T00:00:00Z",73,{"impact":33,"substance":94,"depth":95,"authority":96,"freshness":42,"relevant":82,"comment":97},22,18,13,"结合40年遥感与地面采样的重金属污染综合评估，方法扎实、结论具体，但属区域案例研究，公共影响有限。",[99],{"name":90,"url":87},[101,102,21,103,22],"耕地保护","农业生态","Google Earth Engine",{"code":48,"slug":53,"name":105,"path":106,"parent_code":107},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[109],{"code":32,"slug":110,"name":111,"path":112,"parent_code":113},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":115,"locked":14,"version":27,"method":28,"evidence":116},"classified",{"candidates":117,"agricultural_evidence":34},[118,123,127,132],{"code":48,"score":119,"supported":34,"title":120,"abstract":121,"tags":122},15,[53],[52,53],[22],{"code":32,"score":42,"supported":34,"title":124,"abstract":125,"tags":126},[],[38,36],[103,21],{"code":128,"score":82,"supported":14,"title":129,"abstract":130,"tags":131},"H1",[],[],[101],{"code":133,"score":82,"supported":14,"title":134,"abstract":135,"tags":136},"H6",[],[],[102],[138,139],"Nikolaevskaya 选矿厂 土壤污染","东哈萨克斯坦 农田 重金属","Nikolaevskaya选矿厂土壤污染-4366","10.51886\u002F1999-740x_2026_3_46",{"doi":141,"openalex_id":143,"authors":144,"venue":90,"cited_by_count":15,"oa_url":170,"card":171,"direction":76,"ingested_from":78},"W7220713350",[145,148,150,153,156,159,162,165,168],{"name":146,"orcid":147},"Yuliya Tugunova","https:\u002F\u002Forcid.org\u002F0009-0006-1037-1480",{"name":149,"orcid":9},"M. Altynbayeva",{"name":151,"orcid":152},"Жанар Абилда","https:\u002F\u002Forcid.org\u002F0000-0001-8505-5243",{"name":154,"orcid":155},"Рахим Канат","https:\u002F\u002Forcid.org\u002F0009-0009-2418-5920",{"name":157,"orcid":158},"Abylay Begaly","https:\u002F\u002Forcid.org\u002F0009-0009-2848-336X",{"name":160,"orcid":161},"D. Kairkhanov","https:\u002F\u002Forcid.org\u002F0009-0006-1017-1980",{"name":163,"orcid":164},"Iskander Isgandarov","https:\u002F\u002Forcid.org\u002F0009-0001-4386-3303",{"name":166,"orcid":167},"ELENA A. ISAKOVA","https:\u002F\u002Forcid.org\u002F0000-0002-9056-6504",{"name":169,"orcid":9},"Kh. Choi","https:\u002F\u002Fjournal.soil.kz\u002Fjour\u002Farticle\u002Fdownload\u002F1135\u002F802",{"tldr":172,"method":173,"finding":174,"direction":175,"opportunity":176},"综合遥感、土壤采样与植被调查，评估哈萨克斯坦一选矿厂影响区40年重金属污染与生态退化。","Google Earth Engine处理1985-2024卫星数据，结合17个","土壤重金属升高且pH骤降，重金属迁移性增强，形成技术土，植被以杂草为主，威胁食物链。","农业绿色发展与碳","可延伸研究重金属活化与pH耦合机制，并构建污染区农田食物链风险预警模型。","2026-10-07T23:30:29.146414Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":9,"source_name":184,"source_url":181,"published_at":185,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":186,"score_detail":187,"sources":191,"tags":193,"primary_topic":9,"secondary_topics":198,"classification":199,"search_phrases":230,"slug":233,"view_count":15,"doi":234,"paper":235,"created_at":259},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":95,"substance":94,"depth":188,"authority":33,"freshness":189,"relevant":82,"comment":190},17,9,"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。",[192],{"name":184,"url":181},[21,194,195,196,197],"土地利用","森林保护","亚马逊","CBERS-4",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":200},{"candidates":201,"agricultural_evidence":34,"reason":229},[202,208,212,219,223],{"code":133,"score":49,"supported":34,"title":203,"abstract":204,"tags":207},[],[205,206],"生物多样性","biodiversity",[194],{"code":32,"score":49,"supported":34,"title":209,"abstract":210,"tags":211},[],[38,36],[21],{"code":213,"score":81,"supported":34,"title":214,"abstract":215,"tags":218},"H7",[],[216,217],"气候变化","climate change",[],{"code":48,"score":60,"supported":34,"title":220,"abstract":221,"tags":222},[],[53],[],{"code":224,"score":60,"supported":34,"title":225,"abstract":226,"tags":228},"I7",[],[227],"研究方法",[],"多个产业证据相当，需要确认主归属",[231,232],"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":234,"openalex_id":236,"authors":237,"venue":184,"cited_by_count":15,"oa_url":181,"card":254,"direction":76,"ingested_from":78},"W7223141286",[238,241,243,246,249,252],{"name":239,"orcid":240},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":242,"orcid":9},"Gabriel Henrique Santos Silva",{"name":244,"orcid":245},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":247,"orcid":248},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":250,"orcid":251},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":253,"orcid":9},"Sylvana Melo dos Santos",{"tldr":255,"method":256,"finding":257,"direction":76,"opportunity":258},"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",{"id":261,"title":262,"url":263,"summary":264,"summary_zh":265,"content":9,"source_name":266,"source_url":263,"published_at":185,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":267,"score_detail":268,"sources":272,"tags":274,"primary_topic":279,"secondary_topics":280,"classification":281,"search_phrases":302,"slug":305,"view_count":15,"doi":306,"paper":307,"created_at":339},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA",79,{"impact":269,"substance":94,"depth":95,"authority":270,"freshness":189,"relevant":82,"comment":271},16,14,"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[273],{"name":266,"url":263},[21,275,276,277,278],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":48,"slug":53,"name":105,"path":106,"parent_code":107},[],{"status":115,"locked":14,"version":27,"method":28,"evidence":282},{"candidates":283,"agricultural_evidence":34},[284,288,292,298],{"code":48,"score":270,"supported":34,"title":285,"abstract":286,"tags":287},[53],[52,53],[],{"code":32,"score":49,"supported":34,"title":289,"abstract":290,"tags":291},[],[38,36],[21],{"code":213,"score":81,"supported":34,"title":293,"abstract":294,"tags":297},[],[295,296],"气候适应","干旱",[],{"code":133,"score":82,"supported":14,"title":299,"abstract":300,"tags":301},[],[],[277],[303,304],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":306,"openalex_id":308,"authors":309,"venue":266,"cited_by_count":15,"oa_url":263,"card":334,"direction":76,"ingested_from":78},"W7223069171",[310,312,314,316,319,321,324,327,329,331],{"name":311,"orcid":9},"Yue Li",{"name":313,"orcid":9},"Mengfei Shi",{"name":315,"orcid":9},"Fei Zhang",{"name":317,"orcid":318},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":320,"orcid":9},"Gowhar Meraj",{"name":322,"orcid":323},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":325,"orcid":326},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":328,"orcid":9},"Yuanfang Chai",{"name":330,"orcid":9},"Hongtao Xu",{"name":332,"orcid":333},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":335,"method":336,"finding":337,"direction":76,"opportunity":338},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z",{"id":341,"title":342,"url":343,"summary":344,"summary_zh":345,"content":9,"source_name":346,"source_url":343,"published_at":347,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":267,"score_detail":348,"sources":350,"tags":352,"primary_topic":357,"secondary_topics":363,"classification":364,"search_phrases":381,"slug":384,"view_count":15,"doi":385,"paper":386,"created_at":397},5565,"A Unified Survival-Based Energy Framework Linking Plant Color, Stress, and Productivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23540961","Plant coloration, expressed through leaf greenness, floral pigmentation, and fruit color intensity, reflects the integrated outcome of pigment biosynthesis, optical absorption, metabolic regulation, and energy–mass exchange with the environment. While the biochemical pathways governing the synthesis and degradation of major pigment classes—including chlorophylls, carotenoids, and anthocyanins—are well established, existing models largely interpret color expression as a localized biochemical phenomenon. Such approaches do not explicitly account for whole-plant energy balance, transpiration-driven losses, or thermodynamic constraints, limiting their ability to explain widely observed patterns of color saturation, stress-induced fading, and species-specific stability across environmental gradients. Here we develop a unified framework for plant color generation and stability that treats pigmentation as a survival-limited energetic process embedded within the broader dynamics of absorption, transport, metabolism, and entropy production. Central to the framework is a dimensionless energy survival factor, Ψ=AE\u002FTE+ε where AE represents absorbed and retained energetic and elemental resources relevant to pigment formation, TE denotes transpiration-linked, metabolic, and environmental dissipation losses, and ε captures irreducible entropy-producing degradation inherent to non-equilibrium biological systems. By construction, 0\u003CΨ\u003C1, ensuring full compliance with the second law of thermodynamics. When combined with pigment-specific optical absorption coefficients and physiological limitation functions, the framework yields a physically interpretable description of both pigment concentration and color stability. Empirical ranges for absorbed photosynthetically active radiation (≈45–70%), excitation survival (≈85–95%), and respiratory and transpiration losses (≈30–60% of assimilated resources) naturally constrain Ψ, predicting observed limits on sustained pigment accumulation without invoking biochemical inefficiency or ad hoc correction factors. The analysis demonstrates that increased light availability or enhanced biosynthetic capacity alone does not intensify coloration unless absorbed resources survive transport and loss processes. Stress-induced chlorosis, transient anthocyanin accumulation, and saturation of greenness under high irradiance emerge as direct consequences of reduced energy survival rather than impaired pigment synthesis. By explicitly separating survival from synthesis, the framework reconciles molecular-scale pigment biochemistry with whole-plant and ecosystem-scale color dynamics. This survival-based formulation provides a unified physical basis for interpreting plant color variation across species and environments and offers a scalable foundation for crop stress diagnostics, remote-sensing interpretation, and comparative plant phenotyping. More broadly, it extends survival-limited energy principles to visible plant traits, advancing plant science beyond isolated biochemical descriptions toward a systems-level understanding of how energy governs phenotypic expression.","植物色泽通过叶片绿色度、花色苷着色和果实颜色强度体现，反映了色素生物合成、光学吸收、代谢调控以及与环境的能量-物质交换的综合结果。尽管主要色素类别——包括叶绿素、类胡萝卜素和花青素——的合成与降解生化途径已较为明确，但现有模型大多将颜色表达解释为局部生化现象。此类方法未明确考虑整株植物能量平衡、蒸腾驱动的损耗或热力学约束，限制了其解释广泛观察到的颜色饱和度、胁迫诱导褪色以及跨环境梯度的物种特异性稳定性等模式的能力。本文构建了一个统一的植物颜色生成与稳定性框架，将色素沉着视为一种受生存限制的能量过程，并将其嵌入吸收、运输、代谢和熵产生的更广泛动力学之中。该框架的核心是一个无量纲能量生存因子Ψ=AE\u002FTE+ε，其中AE代表与色素形成相关的被吸收并保留的能量和元素资源，TE表示与蒸腾相关的、代谢的和环境的耗散损失，ε则捕捉非平衡生物系统固有的不可约熵产生降解。根据构建方式，0\u003CΨ\u003C1，确保完全符合热力学第二定律。当与色素特异性光学吸收系数和生理限制函数相结合时，该框架可对色素浓度和颜色稳定性给出具有物理可解释性的描述。吸收性光合有效辐射（约45–70%）、激发能存活率（约85–95%）以及呼吸和蒸腾损失（约占同化资源的30–60%）的经验范围自然约束了Ψ，从而预测出所观察到的持续色素积累限度，而无需引入生化低效性或特设校正因子。分析表明，仅增加光照可用性或增强生物合成能力并不会加深颜色，除非被吸收的资源能够在运输和损失过程中存活下来。胁迫诱导的黄化、瞬时花青素积累以及高辐照下绿色度饱和，均表现为能量生存率降低而非色素合成受损的直接后果。通过明确分离生存","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",{"impact":95,"substance":94,"depth":95,"authority":33,"freshness":189,"relevant":82,"comment":349},"提出以能量生存因子统一解释植物颜色与胁迫的物理框架，方法新颖且对遥感解译和作物诊断有潜在价值，但属预印本理论模型，尚缺田间验证。",[351],{"name":346,"url":343},[353,21,354,355,356],"智慧农业","植物表型","作物胁迫诊断","能量平衡",{"code":358,"slug":359,"name":360,"path":361,"parent_code":362},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":115,"locked":14,"version":27,"method":28,"evidence":365},{"candidates":366,"agricultural_evidence":34},[367,372,377],{"code":358,"score":60,"supported":34,"title":368,"abstract":369,"tags":371},[],[370],"萝卜",[],{"code":373,"score":82,"supported":14,"title":374,"abstract":375,"tags":376},"I1",[],[],[353],{"code":32,"score":82,"supported":14,"title":378,"abstract":379,"tags":380},[],[],[21],[382,383],"植物颜色 能量生存因子","作物胁迫 遥感诊断","植物颜色能量生存因子-5565","10.5281\u002Fzenodo.23540961",{"doi":385,"openalex_id":387,"authors":388,"venue":346,"cited_by_count":15,"oa_url":343,"card":392,"direction":76,"ingested_from":78},"W7223098474",[389],{"name":390,"orcid":391},"Mokhdum Azam Mashrafi","https:\u002F\u002Forcid.org\u002F0009-0002-1801-1130",{"tldr":393,"method":394,"finding":395,"direction":76,"opportunity":396},"提出基于生存的能量框架，用能量存活因子解释植物颜色生成与稳定性。","构建无量纲能量存活因子Ψ=AE\u002F(TE+ε)，结合色素吸收系数与生理限制函数。","颜色深浅取决于吸收资源经运输损耗后的存活率，而非单纯合成能力。","可将Ψ因子与冠层光谱\u002F表型遥感结合，发展胁迫早期诊断与生产力预测模型。","2026-10-22T11:20:31.195819Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":403,"content":9,"source_name":346,"source_url":401,"published_at":347,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":404,"score_detail":405,"sources":407,"tags":409,"primary_topic":413,"secondary_topics":417,"classification":418,"search_phrases":456,"slug":459,"view_count":15,"doi":460,"paper":461,"created_at":473},5563,"AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531525","Abstract: Crop diseases and pest infestations remain persistent threats to plantation productivity, with pests estimated to destroy up to 40% of global crop production and plant diseases and invasive insects imposing substantial economic losses worldwide (Food and Agriculture Organization of the United Nations [FAO], 2019). Climate change is intensifying these risks by altering the geographic range, seasonality, and population dynamics of pathogens and pests (Gullino et al., 2021; Skendžić et al., 2021). This paper examines how artificial intelligence (AI) — including convolutional neural networks (CNNs), vision transformers (ViTs), unmanned aerial vehicle (UAV)-based multispectral and hyperspectral sensing, and Internet of Things (IoT)-enabled monitoring — can strengthen the early detection of biotic stress in plantation crops and thereby support plantation risk management. Drawing on recent peer-reviewed and industry literature, the paper synthesises evidence on detection performance, deployment modalities, and pathways for integration into enterprise risk-management systems. It proposes a conceptual framework in which AI-enabled early-warning systems operate as a continuous, spatially explicit risk-sensing layer linking agronomic observations with operational, financial, and insurance decision-making. AI can improve the timeliness, spatial precision, and consistency of surveillance; however, its risk-management value depends on field validation, representative training data, reliable infrastructure, affordable deployment, and appropriate governance. AI should therefore currently be positioned as decision support that augments, rather than replaces, agronomic expertise and ground-truthing. Keywords: artificial intelligence; plant disease detection; pest infestation; precision agriculture; plantation risk management; machine learning; remote sensing. Title: AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management Author: Dr. Geetha Govindan K Gopalakrishnan International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 2, October 2026 - March 2027 Page No: 54-59 Research Publish Journals Website: www.researchpublish.com Published Date: 21-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-management","摘要：作物病害与虫害侵染始终是种植园生产力的持续威胁，据估计，害虫每年可摧毁全球高达40%的作物产量，植物病害与入侵昆虫在全球范围内造成重大经济损失（联合国粮食及农业组织[FAO]，2019）。气候变化通过改变病原体与害虫的地理分布范围、季节性规律及种群动态，正在加剧这些风险（Gullino等，2021；Skendžić等，2021）。本文探讨人工智能（AI）——包括卷积神经网络（CNN）、视觉变换器（ViT）、基于无人机（UAV）的多光谱与高光谱遥感，以及物联网（IoT）赋能的监测技术——如何强化种植园作物生物胁迫的早期检测，从而支撑种植园风险管理。本文基于近期同行评审文献与行业文献，综合梳理了检测性能、部署模式及其融入企业风险管理系统的路径等方面的证据。文章提出一个概念框架，其中AI赋能的预警系统作为持续运行、空间显性的风险感知层，将农艺观测与运营、财务及保险决策相衔接。AI能够提升监测的及时性、空间精度与一致性；然而，其风险管理价值取决于田间验证、具有代表性的训练数据、可靠的基础设施、可负担的部署成本以及适当的治理。因此，当前应将AI定位为决策支持工具，用以增强而非替代农艺专业知识与实地核验。关键词：人工智能；植物病害检测；虫害侵染；精准农业；种植园风险管理；机器学习；遥感。题目：AI驱动的作物病害与虫害侵染早期检测：种植园风险管理的新范式。作者：Geetha Govindan K Gopalakrishnan博士。《国际管理与商业创新期刊》ISSN 2348-7585（在线）第14卷第2期，2026年10月—2027年3月，第54-59页。研究出版期刊网站：www.researchpublish.com。发布日期：2026年10月21日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-ma",56,{"impact":33,"substance":269,"depth":96,"authority":81,"freshness":189,"relevant":82,"comment":406},"综述性论文，系统梳理AI与遥感在作物病虫害早期预警中的应用并提出风险管理框架，有参考价值但方法创新与数据规模有限，信源为普通出版平台。",[408],{"name":346,"url":401},[353,410,21,411,412],"农业人工智能","风险管理","病虫害监测",{"code":113,"slug":414,"name":415,"path":416,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":419,"locked":14,"version":27,"method":28,"evidence":420},"partial",{"candidates":421,"agricultural_evidence":34,"reason":455},[422,431,437,445,451],{"code":423,"score":13,"supported":34,"title":424,"abstract":425,"tags":430},"I2",[],[426,427,428,429],"机器学习","machine learning","artificial intelligence","decision support",[410],{"code":32,"score":13,"supported":34,"title":432,"abstract":433,"tags":436},[],[38,36,434,435],"multispectral","hyperspectral",[21],{"code":438,"score":189,"supported":34,"title":439,"abstract":440,"tags":444},"I5",[],[441,442,443],"物联网","internet of things","IoT",[],{"code":373,"score":49,"supported":34,"title":446,"abstract":447,"tags":450},[],[448,449],"精准农业","precision agriculture",[353],{"code":213,"score":81,"supported":34,"title":452,"abstract":453,"tags":454},[],[216,217],[],"一级领域明确，二级方向需要细分",[457,458],"AI 作物病害 早期检测","无人机 多光谱 病虫害监测","AI作物病害早期检测-5563","10.5281\u002Fzenodo.23531525",{"doi":460,"openalex_id":462,"authors":463,"venue":346,"cited_by_count":15,"oa_url":401,"card":466,"direction":472,"ingested_from":78},"W7223085400",[464],{"name":465,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":467,"method":468,"finding":469,"direction":470,"opportunity":471},"综述AI（CNN、ViT、无人机遥感、物联网）用于作物病虫害早期检测并融入种植园风险管理。","文献综述，整合CNN、ViT、无人机多\u002F高光谱与IoT监测的检测性能与部署方式。","AI可提升监测及时性与空间精度，但价值取决于田间验证、代表性数据与治理，应作决策支持而非替代专家。","农业人工智能与决策模型","可将AI预警层与保险、金融风控系统对接，研究跨域数据融合与可解释风险定价的空白。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.428080Z",{"id":475,"title":476,"url":477,"summary":478,"summary_zh":479,"content":9,"source_name":480,"source_url":477,"published_at":185,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":481,"sources":483,"tags":485,"primary_topic":9,"secondary_topics":488,"classification":489,"search_phrases":501,"slug":504,"view_count":15,"doi":505,"paper":506,"created_at":520},5560,"How do weather, topography and fuel condition drive the extreme behavior of a Mediterranean megafire? A case study of the 2025 Jarilla wildfire (western Spain)","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fffgc.2026.1953360","The 2025 wildfire season was the most destructive on record in the European Union, with more than 1 million hectares burned, and the Iberian Peninsula was the hardest-hit region, accounting for 43% of that area. The Jarilla wildfire (Cáceres, Spain; 17,317 ha, august 2025) exemplifies how meteorology, topography, and fuel condition jointly controlled the behavior of these Iberian megafires. Our aim was to analyze the role of these three factors in the fire’s behavior and to evaluate their interpretation against the post-fire severity footprint, within a reproducible workflow relying entirely on open-access data. We combined ERA5 hourly reanalysis, the fire weather index (FWI), Sentinel-2 spectral indices (NDVI, NDII, dNBR), VIIRS active-fire detections, and the 5 m PNOA digital elevation model. During the active phase (august 12–17), the atmosphere was extremely desiccating—vapor pressure deficit peaking at 5.9 kPa, minimum relative humidity near 15%, and FWI in the “extreme” class—acting on a dense but already water-stressed fuel cover (mean NDVI 0.53, mean NDII 0.05). The wind regime was bimodal: a frequent but weak nocturnal north-easterly flow and a diurnal south–south-westerly (SSW) flow that drove the observed SW–NE progression. Fire spread corridors—steep, wind-aligned, fuel-laden terrain delineated with the SSW driving wind—covered 13.7% of the burned area and showed higher mean severity (dNBR 665) than their surroundings (525); however, 84% of the high-severity area lay outside them. These results indicate that, in such “hot-drought” megafires, severity is driven primarily by extreme atmospheric drying acting on continuous, dry fuel, whereas topography and wind spatially organize its peaks rather than determining them. Beyond reconstructing a single event, the study provides a transferable, open-data framework for diagnosing megafire drivers and suggests that fuel- and weather-based prevention, more than topographic targeting, is key to mitigating Mediterranean megafires.","2025年野火季是欧盟有记录以来破坏性最强的一个野火季，过火面积超过100万公顷，其中伊比利亚半岛是受灾最严重的地区，占该面积的43%。Jarilla野火（西班牙卡塞雷斯，17,317公顷，2025年8月）体现了气象、地形和可燃物条件如何共同控制这些伊比利亚特大火灾的行为。我们的目标是分析这三个因素在火灾行为中的作用，并在完全依赖开放获取数据的可重复工作流程中，评估它们与火后严重程度足迹的对应解释。我们结合了ERA5逐小时再分析数据、火险天气指数（FWI）、Sentinel-2光谱指数（NDVI、NDII、dNBR）、VIIRS活跃火点检测以及5米PNOA数字高程模型。在活跃阶段（8月12日至17日），大气极度干燥——饱和水汽压差峰值达5.9 kPa，最低相对湿度接近15%，FWI处于“极端”等级——作用于密集但已受水分胁迫的可燃物覆盖层（平均NDVI为0.53，平均NDII为0.05）。风况呈双峰分布：夜间频繁但微弱的东北气流，以及白天驱动观测到的西南—东北方向蔓延的南—西南（SSW）气流。火势蔓延走廊——陡峭、与风向一致、可燃物载量高的地形，以SSW驱动风 delineated——覆盖了过火面积的13.7%，并显示出比周围区域更高的平均严重程度（dNBR 665对525）；然而，84%的高严重程度区域位于这些走廊之外。这些结果表明，在此类“高温—干旱”特大火灾中，严重程度主要由极端大气干燥作用于连续、干燥的可燃物所驱动，而地形和风在空间上组织其峰值而非决定峰值。除重建单一事件外，该研究提供了一个可迁移的开放数据框架，用于诊断特大火灾驱动因素，并表明基于可燃物和天气的预防，而非地形靶向，是缓解地中海特大火灾的关键。","Frontiers in Forests and Global Change",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":482},"该研究聚焦西班牙地中海野火的气象与地形驱动机制，属于林火生态与遥感应用，与三农、农业信息化、智慧农业主题无直接关联，不建议进入每日精选。",[484],{"name":480,"url":477},[216,21,486,487],"野火研究","西班牙",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":490},{"candidates":491,"agricultural_evidence":14,"reason":55},[492,497],{"code":213,"score":49,"supported":34,"title":493,"abstract":494,"tags":496},[],[296,495],"drought",[216],{"code":32,"score":82,"supported":14,"title":498,"abstract":499,"tags":500},[],[],[21],[502,503],"气候变化 遥感监测 野火研究 西班牙","气候变化 遥感监测","气候变化遥感监测野火研究西班牙-5560","10.3389\u002Fffgc.2026.1953360",{"doi":505,"openalex_id":507,"authors":508,"venue":480,"cited_by_count":15,"oa_url":514,"card":515,"direction":472,"ingested_from":78},"W7223002169",[509,511],{"name":510,"orcid":9},"Luis Hernández-Sánchez",{"name":512,"orcid":513},"Marcos Francos","https:\u002F\u002Forcid.org\u002F0000-0002-3311-5686","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fforests-and-global-change\u002Farticles\u002F10.3389\u002Fffgc.2026.1953360\u002Fpdf",{"tldr":516,"method":517,"finding":518,"direction":76,"opportunity":519},"分析2025年西班牙Jarilla特大火灾，揭示气象、地形与燃料条件如何共同驱动火行为。","融合ERA5再分析、FWI、Sentinel-2指数、VIIRS火点和5米DEM","极端大气干燥叠加连续干旱燃料主导火强度，地形与风仅空间组织高严重度峰值而非决定其分布。","可延伸至多源遥感融合的野火驱动因子归因与火险预警，并检验该开源框架在其他地中海火区的可迁移性。","2026-10-22T11:20:20.437834Z"]