[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1626":3,"related-1626":89},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":71,"slug":74,"view_count":21,"doi":75,"paper":76,"created_at":88},1626,"Assessing and Mapping Drought Vulnerability Areas in Homa Bay County, Kenya","https:\u002F\u002Fdoi.org\u002F10.59765\u002Fkfw6y","Drought is a recurring environmental hazard in Homa Bay County, Kenya, affecting agriculture, water resources, and community livelihoods.Despite its impacts, there is limited spatially explicit information on drought-prone areas within the county.This study aimed to map drought vulnerability in Homa Bay County using remote sensing and GIS techniques.The study was anchored on two theories; Disaster risk reduction theory and Vulnerability theory.The study used MODIS NDVI data (2020-2023) to map drought severity, while ESA World Cover land use and CHIRPS rainfall data were integrated as vulnerability factors.A weighted overlay analysis was run in QGIS to combine three factors: drought severity (50%), land use (30%), and rainfall (20%).The final vulnerability map was classified into four classes: low, moderate, mild, and severe.The results show that 0.91% of the county falls under severe vulnerability, concentrated in Mbita and Karachuonyo sub-counties.Moderate vulnerability dominates 53.69% of the county, followed by mild (31.22%) and low (14.18%).The study provides spatially explicit information to support county-level drought planning and intervention strategies.The paper recommends development in severe vulnerable counties; Mbita and Karachuonyo with carefully drought planning and intervention strategies.","干旱是肯尼亚霍马贝县反复出现的环境灾害，影响农业、水资源和社区生计。尽管其影响重大，但该县内关于干旱易发区域的空间明确信息仍然有限。本研究旨在利用遥感和GIS技术绘制霍马贝县的干旱脆弱性地图。研究基于两大理论：灾害风险减轻理论和脆弱性理论。研究使用MODIS NDVI数据（2020-2023年）绘制干旱严重程度图，同时整合ESA World Cover土地利用数据和CHIRPS降雨数据作为脆弱性因素。在QGIS中进行了加权叠加分析，以综合三个因素：干旱严重程度（50%）、土地利用（30%）和降雨量（20%）。最终脆弱性地图分为四个等级：低、中、轻度和严重。结果显示，该县0.91%的面积属于严重脆弱性，集中在姆比塔和卡拉丘奥尼分区。中度脆弱性占全县面积的53.69%，其次是轻度（31.22%）和低度（14.18%）。本研究提供了空间明确的信息，以支持县级干旱规划和干预策略。论文建议在严重脆弱的分区——姆比塔和卡拉丘奥尼，制定周密的干旱规划和干预策略。",null,"Journal of Research Innovation and Implications in Education","2026-09-02T00:00:00Z","论文",10,false,55,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},8,18,16,3,1,"研究利用遥感与GIS技术绘制肯尼亚Homa Bay县干旱脆弱性地图，为当地农业规划提供空间信息，但影响范围局限于县级，时效性较低。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","肯尼亚","GIS","干旱监测","灾害评估",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,51,59,65],{"code":32,"score":19,"supported":45,"title":46,"abstract":48,"tags":50},true,[47],"drought",[49,47],"干旱",[29,30],{"code":52,"score":53,"supported":45,"title":54,"abstract":55,"tags":58},"I3",7,[],[56,57],"遥感","remote sensing",[26],{"code":60,"score":61,"supported":45,"title":62,"abstract":63,"tags":64},"I4",4,[],[28],[28],{"code":66,"score":20,"supported":45,"title":67,"abstract":68,"tags":70},"H3",[],[69],"水资源",[],[72,73],"农业遥感 干旱监测 灾害评估 肯尼亚","农业遥感 干旱监测","农业遥感干旱监测灾害评估肯尼亚-1626","10.59765\u002Fkfw6y",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":79,"oa_url":6,"card":80,"direction":86,"ingested_from":87},"W7206217503",[],0,{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"利用遥感和GIS技术绘制肯尼亚霍马贝县干旱脆弱性地图，识别脆弱区域。","MODIS NDVI、ESA土地覆盖和CHIRPS降雨数据，加权叠加分析。","严重脆弱区占0.91%，集中在Mbita和Karachuonyo；中度脆弱占53.69%。","农业遥感与作物表型","可扩展至其他地区，结合社会经济数据，提高干旱脆弱性评估精度，并制定针对性适应策略。","智慧农业 \u002F 农业物联网","openalex","2026-09-04T23:30:13.864272Z",{"total":90,"page":21,"page_size":90,"items":91},6,[92,165,266,353,411,474],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":9,"content":9,"source_name":97,"source_url":95,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":104,"tags":106,"primary_topic":110,"secondary_topics":116,"classification":118,"search_phrases":140,"slug":143,"view_count":144,"doi":145,"paper":146,"created_at":164},4650,"A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105011","A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact。Agricultural Systems","Agricultural Systems","2026-10-11T00:00:00Z",81,{"impact":18,"substance":101,"depth":18,"authority":102,"freshness":13,"relevant":21,"comment":103},21,14,"核心期刊提出的面向过程的夏玉米干旱逐日动态监测框架，方法新颖、面向农业信息化应用，时效性强，值得进入每日精选。",[105],{"name":97,"url":95},[107,26,108,109,29],"智慧农业","夏玉米","农业气象",{"code":111,"slug":112,"name":113,"path":114,"parent_code":115},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[117],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":119},{"candidates":120,"agricultural_evidence":45},[121,126,131,136],{"code":32,"score":122,"supported":45,"title":123,"abstract":124,"tags":125},13,[47],[47],[109,29],{"code":111,"score":127,"supported":45,"title":128,"abstract":129,"tags":130},12,[112],[112],[108],{"code":132,"score":21,"supported":14,"title":133,"abstract":134,"tags":135},"I1",[],[],[107],{"code":52,"score":21,"supported":14,"title":137,"abstract":138,"tags":139},[],[],[26],[141,142],"夏玉米 干旱 动态监测","气象胁迫 农业影响 监测框架","夏玉米干旱动态监测-4650",2,"10.1016\u002Fj.agsy.2026.105011",{"doi":145,"openalex_id":147,"authors":148,"venue":97,"cited_by_count":79,"oa_url":95,"card":9,"direction":84,"ingested_from":87},"W7221308782",[149,151,153,155,158,160,162],{"name":150,"orcid":9},"Yuanda Zhang",{"name":152,"orcid":9},"Peijuan Wang",{"name":154,"orcid":9},"Jun Zhu",{"name":156,"orcid":157},"Yang Li","https:\u002F\u002Forcid.org\u002F0000-0001-9021-0201",{"name":159,"orcid":9},"Dianchen Han",{"name":161,"orcid":9},"Dingrong Wu",{"name":163,"orcid":9},"Jianping Guo","2026-10-11T23:30:03.734429Z",{"id":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":9,"source_name":171,"source_url":168,"published_at":172,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":173,"score_detail":174,"sources":176,"tags":178,"primary_topic":180,"secondary_topics":185,"classification":191,"search_phrases":237,"slug":240,"view_count":241,"doi":242,"paper":243,"created_at":265},3495,"Remote Sensing and GIS in Modern Drought Assessment: Bridging Conventional Methods and Emerging Technologies","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123","Drought is a complex and recurring hydroclimatic hazard that affects agricultural production, water resources, ecosystems and socioeconomic development. Effective drought assessment requires approaches capable of capturing its spatial and temporal variability and its multiple dimensions. This review examines the evolution of drought assessment from conventional drought indices to integrated approaches based on remote sensing and Geographic Information Systems (GIS), with an emphasis on their applications, strengths, limitations and emerging developments. Conventional indices, including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Reconnaissance Drought Index (RDI) and Percent of Normal Precipitation Index (PNPI), remain widely used because of their established methodologies and long-term applicability. However, their dependence on meteorological observations can limit spatial characterisation and the representation of vegetation, soil moisture and other land-surface responses. Remote sensing provides spatially extensive and repeated observations of vegetation condition, land surface temperature, soil moisture, evapotranspiration and water-related conditions, enabling the development of satellite-derived drought indicators and indices. GIS further facilitates the integration, spatial analysis, visualisation, and mapping of drought-related information from multiple sources. The review also discusses hybrid approaches that combine climate-based indices with satellite-derived indicators, as well as drought monitoring platforms and multi-source assessment frameworks. Despite substantial advances, challenges remain regarding cloud contamination, differences in spatial and temporal resolution, data continuity, ground-based validation and uncertainty associated with multi-source datasets. Emerging machine learning, deep learning and artificial intelligence approaches offer opportunities for integrating heterogeneous datasets and improving drought characterisation and early warning. Overall, the integration of conventional observations, remote sensing, GIS and advanced analytical approaches provides a promising framework for more comprehensive drought monitoring and risk assessment under increasing climate variability and change.","干旱是一种复杂且反复出现的水文气候灾害，影响农业生产、水资源、生态系统和社会经济发展。有效的干旱评估需要能够捕捉其时空变异性和多维特征的方法。本文综述了干旱评估从传统干旱指数到基于遥感与地理信息系统（GIS）的综合方法的演变，重点探讨其应用、优势、局限性和新兴发展。传统指数，包括标准化降水指数（SPI）、标准化降水蒸散指数（SPEI）、帕尔默干旱强度指数（PDSI）、侦察干旱指数（RDI）和降水距平百分率指数（PNPI），因其方法成熟且具有长期适用性而仍被广泛使用。然而，这些指数对气象观测的依赖可能限制其空间表征能力以及对植被、土壤水分和其他陆面响应的刻画。遥感提供了对植被状况、地表温度、土壤水分、蒸散量及与水相关状况的大范围重复观测，使得卫星衍生的干旱指标和指数得以发展。GIS进一步促进了多来源干旱相关信息的整合、空间分析、可视化和制图。本文还讨论了将基于气候的指数与卫星衍生指标相结合的混合方法，以及干旱监测平台和多源评估框架。尽管取得了实质性进展，但在云污染、时空分辨率差异、数据连续性、地面验证以及多源数据集相关的不确定性方面仍存在挑战。新兴的机器学习、深度学习和人工智能方法为整合异质数据集、改进干旱表征和预警提供了机遇。总体而言，在气候变异性和变化日益加剧的背景下，传统观测、遥感、GIS和先进分析方法的整合为更全面的干旱监测和风险评估提供了一个有前景的框架。","Journal of Geography Environment and Earth Science International","2026-09-24T00:00:00Z",66,{"impact":127,"substance":18,"depth":19,"authority":127,"freshness":17,"relevant":21,"comment":175},"综述系统梳理遥感与GIS在干旱评估中的应用演进，方法学价值明确，但属综述类论文、非国内落地事件，影响力有限。",[177],{"name":171,"url":168},[107,179,56,28,29],"农业人工智能",{"code":60,"slug":181,"name":182,"path":183,"parent_code":184},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[186,187],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":52,"slug":188,"name":189,"path":190,"parent_code":184},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":192},{"candidates":193,"agricultural_evidence":45},[194,202,207,211,221,228,233],{"code":60,"score":18,"supported":45,"title":195,"abstract":196,"tags":201},[28],[197,198,28,199,200],"地理信息","空间分析","spatial analysis","geographic information",[28],{"code":32,"score":203,"supported":45,"title":204,"abstract":205,"tags":206},15,[47],[49,47],[29],{"code":52,"score":203,"supported":45,"title":208,"abstract":209,"tags":210},[57],[56,57],[56],{"code":212,"score":13,"supported":45,"title":213,"abstract":214,"tags":220},"I2",[],[215,216,217,218,219],"深度学习","机器学习","machine learning","deep learning","artificial intelligence",[179],{"code":222,"score":90,"supported":45,"title":223,"abstract":224,"tags":227},"H2",[],[225,226],"土壤","soil",[],{"code":66,"score":90,"supported":45,"title":229,"abstract":230,"tags":232},[],[69,231],"evapotranspiration",[],{"code":132,"score":21,"supported":14,"title":234,"abstract":235,"tags":236},[],[],[107],[238,239],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495",5,"10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":242,"openalex_id":244,"authors":245,"venue":171,"cited_by_count":79,"oa_url":168,"card":260,"direction":84,"ingested_from":87},"W7214156857",[246,248,250,252,254,256,258],{"name":247,"orcid":9},"V. Dhanalakshmi",{"name":249,"orcid":9},"N. Manikandan",{"name":251,"orcid":9},"V. S. Jinsy",{"name":253,"orcid":9},"K. V. Sumesh",{"name":255,"orcid":9},"P. Nideesh",{"name":257,"orcid":9},"P. S. Manju",{"name":259,"orcid":9},"N. Gopika",{"tldr":261,"method":262,"finding":263,"direction":84,"opportunity":264},"综述了从传统干旱指数到遥感、GIS及AI集成的现代干旱评估方法演进。","文献综述，对比SPI、SPEI等传统指数与遥感、GIS及混合方法。","遥感与GIS弥补传统指数空间局限，但云污染、分辨率差异和验证仍是挑战。","可探索多源遥感与机器学习融合的干旱早期预警，重点解决数据不确定性与地面验证。","2026-09-25T23:30:30.576065Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":271,"content":9,"source_name":272,"source_url":269,"published_at":273,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":274,"score_detail":275,"sources":278,"tags":280,"primary_topic":283,"secondary_topics":287,"classification":289,"search_phrases":319,"slug":322,"view_count":90,"doi":323,"paper":324,"created_at":352},1898,"Connecting earth observation anomalies to farmer surveys for monitoring impacts of agricultural drought on rainfed rice yields in Nigeria","https:\u002F\u002Fdoi.org\u002F10.5194\u002Feo-1-77-2026","Abstract. Agricultural drought threatens rainfed rice production in Nigeria, where smallholder farmers depend on rainfall and have limited capacity to buffer climate shocks. While meteorological drought indices such as the SPI and the SPEI are widely used in national early warning systems, their ability to capture the impacts of droughts on rainfed rice yields at the smallholder field-level remains uncertain. This study evaluates the added value of earth observation (EO)-derived vegetation and soil moisture anomalies for monitoring and predicting drought impacts on rainfed rice yields in Nigeria. Satellite-based Normalized Difference Vegetation Index anomalies (NDVIA) and Soil Water Index anomalies (SWIA) were derived using a zonal clustering and thresholding approach and combined with farmer survey and yield data collected from 146 rainfed rice farmers across four major rice-growing states between 2019 and 2024. Multivariate regression models were used to assess the relationships between EO indicator anomalies and annual yield changes, and the effects of different zonal clustering and anomaly thresholds on anomaly calculation were evaluated. Results show that SPI and SPEI explain a substantial share of yield variability in some years, particularly when droughts coincide with sensitive phenological stages. However, EO-based anomaly indicators, especially SWIA (maximum improved R2 = 0.25), provide complementary information and significantly improve yield predictions in years when meteorological indices alone perform poorly. The timing of anomalies relative to rice phenology was critical, with droughts during panicle initiation having the largest yield impacts. Integrating EO-based vegetation and soil moisture anomaly indicators with existing meteorological indices can contribute to the monitoring of agricultural droughts and improve the operational relevance of early warning systems for rainfed rice farmers in Nigeria.","摘要：农业干旱威胁着尼日利亚依赖雨养的水稻生产，当地小农户依靠降雨为生，缓冲气候冲击的能力有限。尽管诸如标准化降水指数（SPI）和标准化降水蒸散指数（SPEI）等气象干旱指数被广泛应用于国家早期预警系统，但它们在捕捉小农田间尺度上干旱对雨养水稻产量影响的能力仍不确定。本研究评估了基于地球观测（EO）的植被和土壤湿度异常在监测和预测尼日利亚干旱对雨养水稻产量影响方面的附加价值。采用分区聚类和阈值方法，推导出基于卫星的归一化差异植被指数异常（NDVIA）和土壤水分指数异常（SWIA），并结合2019年至2024年间从四个主要水稻种植州的146位雨养水稻农户收集的农户调查和产量数据。使用多元回归模型评估EO指标异常与年度产量变化之间的关系，并评估不同分区聚类和异常阈值对异常计算的影响。结果表明，在某些年份，SPI和SPEI解释了产量变异的很大一部分，特别是当干旱与敏感物候阶段重合时。然而，基于EO的异常指标，尤其是SWIA（最大改进R²=0.25），提供了补充信息，并在气象指数单独表现不佳的年份显著改善了产量预测。异常相对于水稻物候的时机至关重要，穗分化期发生的干旱对产量影响最大。将基于EO的植被和土壤湿度异常指标与现有气象指数相结合，有助于监测农业干旱，并提高尼日利亚雨养水稻农户早期预警系统的业务相关性。","Earth Observation","2026-09-07T00:00:00Z",74,{"impact":203,"substance":276,"depth":18,"authority":127,"freshness":53,"relevant":21,"comment":277},22,"研究结合遥感与农户调查，提升雨养水稻干旱监测精度，对非洲小农预警有参考价值。",[279],{"name":272,"url":269},[107,26,281,29,282],"水稻","尼日利亚",{"code":284,"slug":285,"name":281,"path":286,"parent_code":115},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice",[288],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":290},{"candidates":291,"agricultural_evidence":45},[292,296,300,307,311,315],{"code":284,"score":203,"supported":45,"title":293,"abstract":294,"tags":295},[285],[281,285],[281],{"code":32,"score":203,"supported":45,"title":297,"abstract":298,"tags":299},[47],[49,47],[29],{"code":301,"score":90,"supported":45,"title":302,"abstract":303,"tags":306},"G2",[],[304,305],"小农户","smallholder",[],{"code":222,"score":90,"supported":45,"title":308,"abstract":309,"tags":310},[],[225,226],[],{"code":132,"score":21,"supported":14,"title":312,"abstract":313,"tags":314},[],[],[107],{"code":52,"score":21,"supported":14,"title":316,"abstract":317,"tags":318},[],[],[26],[320,321],"农业遥感 尼日利亚 干旱监测 智慧农业","农业遥感 尼日利亚","农业遥感尼日利亚干旱监测智慧农业-1898","10.5194\u002Feo-1-77-2026",{"doi":323,"openalex_id":325,"authors":326,"venue":272,"cited_by_count":79,"oa_url":269,"card":347,"direction":84,"ingested_from":87},"W7162756810",[327,330,332,335,337,340,343,345],{"name":328,"orcid":329},"Nick Gutkin","https:\u002F\u002Forcid.org\u002F0000-0003-4708-3483",{"name":331,"orcid":9},"Chiamaka I. Ehiemere",{"name":333,"orcid":334},"Koen De Vos","https:\u002F\u002Forcid.org\u002F0000-0002-4607-7877",{"name":336,"orcid":9},"Nnamdi Ehiemere",{"name":338,"orcid":339},"Jeroen Degerickx","https:\u002F\u002Forcid.org\u002F0000-0001-9022-9623",{"name":341,"orcid":342},"Sarah Gebruers","https:\u002F\u002Forcid.org\u002F0000-0001-8426-4158",{"name":344,"orcid":9},"Uchechukwu Nwafor",{"name":346,"orcid":9},"Anne Gobin",{"tldr":348,"method":349,"finding":350,"direction":84,"opportunity":351},"评估EO异常指标监测尼日利亚雨养水稻干旱影响，补充气象指数，提高产量预测。","结合NDVI和土壤水分指数异常、聚类阈值法、农户调查与回归模型。","EO指标（尤其SWIA）补充气象指数，显著提高干旱年份产量预测，关键物候期干旱影响最大。","可探索将EO异常与物候模型结合，开发针对小农的干旱预警系统，并验证其他作物和区域。","2026-09-08T23:30:18.761711Z",{"id":354,"title":355,"url":356,"summary":357,"summary_zh":9,"content":9,"source_name":358,"source_url":9,"published_at":359,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":360,"score_detail":361,"sources":363,"tags":365,"primary_topic":369,"secondary_topics":374,"classification":377,"search_phrases":398,"slug":401,"view_count":21,"doi":9,"paper":402,"created_at":410},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21","2026-10-21T00:00:00Z",82,{"impact":18,"substance":276,"depth":18,"authority":102,"freshness":13,"relevant":21,"comment":362},"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[364],{"name":358,"url":356},[107,26,366,367,368],"作物表型","无人机多光谱","小麦渍害",{"code":370,"slug":371,"name":372,"path":373,"parent_code":115},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[375,376],{"code":52,"slug":188,"name":189,"path":190,"parent_code":184},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":378},{"candidates":379,"agricultural_evidence":45},[380,385,389,394],{"code":370,"score":381,"supported":45,"title":382,"abstract":383,"tags":384},11,[372],[372],[],{"code":52,"score":381,"supported":45,"title":386,"abstract":387,"tags":388},[367],[],[366,26,367],{"code":32,"score":17,"supported":45,"title":390,"abstract":392,"tags":393},[391],"渍害",[],[],{"code":132,"score":21,"supported":14,"title":395,"abstract":396,"tags":397},[],[],[107],[399,400],"江苏省农科院 小麦渍害 多光谱","VGG21 小麦 硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":9,"openalex_id":9,"authors":403,"venue":9,"cited_by_count":79,"oa_url":9,"card":404,"direction":84,"ingested_from":409},[],{"tldr":405,"method":406,"finding":407,"direction":84,"opportunity":408},"用无人机多光谱图像和VGG21模型识别小麦渍害后硅肥与氨基酸调控效果。","大疆精灵4采集冠层多光谱影像，构建VGG21深度学习分类模型。","VGG21对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","可扩展多生育期、多调控组合与轻量化模型，探索渍害胁迫等级与调控效果的同步识别。","agent","2026-10-22T00:07:10.070916Z",{"id":412,"title":413,"url":414,"summary":415,"summary_zh":416,"content":9,"source_name":417,"source_url":414,"published_at":418,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":419,"score_detail":420,"sources":424,"tags":426,"primary_topic":429,"secondary_topics":433,"classification":435,"search_phrases":450,"slug":453,"view_count":79,"doi":454,"paper":455,"created_at":473},5320,"Machine Learning for Drought Analysis and Forecasting: A Critical Review of Data Fusion, Extreme-Event Sensitivity, Explainability and Generalization","https:\u002F\u002Fdoi.org\u002F10.17485\u002Fijst\u002Fv19i36.1263","Background: Machine learning is increasingly applied to drought monitoring and forecasting, but studies differ widely in how drought is defined, what data are used, and how models are validated, which makes reported performance difficult to compare directly. Objectives: To explain why reported machine-learning drought-model performance varies across definitions, data sources and validation schemes, and to assess whether recent studies adequately address rare severe events and cross-domain transferability. Method: Journal articles published January 2018 to August 2026 were reviewed, starting from a 70-record high-relevance subset of a 359-record discovery export and extended through citation chaining. Publisher and DOI verification produced a final evidence base of 55 articles: 48 primary studies and 7 reviews. Target definition, input data, model family, validation design, metrics, class-imbalance treatment, explainability and external testing were compared across studies. Findings: Reported performance varied with the modelling task rather than with any single algorithm: Random Forest reached 97.7% AUC-ROC for agricultural-drought mapping in Queensland, SVM reached average R²=0.90 for hydrological drought in Algeria, and a CatBoost stacking model reached R²=0.9065 for remote-sensing-based drought monitoring. These scores are not directly comparable because targets, data and validation protocols differ. Recent studies increasingly test cross-product and cross-region transfer, use SHAP\u002FLIME explainability, and adopt extreme-aware losses, yet class-wise reporting of severe and extreme drought, ordinal-aware evaluation, and leakage-resistant temporal validation remain inconsistently applied across the 48 primary studies. Novelty: The review jointly evaluates predictive accuracy alongside rare-event sensitivity, ordinal drought severity, leakage-resistant validation, explainability, uncertainty and cross-domain generalization, criteria largely absent from earlier machine-learning drought reviews. Keywords: Drought Forecasting, Machine Learning, Remote Sensing, Explainable AI, Extreme Drought","背景：机器学习正日益应用于干旱监测与预报，但各研究在干旱定义、所用数据和模型验证方式上差异很大，导致所报告的模型性能难以直接比较。目标：解释为何已报告的机器学习干旱模型性能会因定义、数据来源和验证方案的不同而变化，并评估近期研究是否充分处理了罕见的严重事件和跨域可迁移性问题。方法：综述了2018年1月至2026年8月发表的期刊论文，起始于359条发现导出记录中70条高相关性记录的子集，并通过引文追溯加以扩展。经出版商和DOI核验，最终证据基础为55篇文章：48篇原始研究和7篇综述。比较了各研究的目标定义、输入数据、模型族、验证设计、指标、类别不平衡处理、可解释性和外部测试。发现：所报告的性能随建模任务而非任何单一算法而变化：随机森林在昆士兰农业干旱制图中达到97.7%的AUC-ROC，SVM在阿尔及利亚水文干旱中达到平均R²=0.90，CatBoost堆叠模型在基于遥感的干旱监测中达到R²=0.9065。这些分数不能直接比较，因为目标、数据和验证方案不同。近期研究越来越多地测试跨产品和跨区域迁移，使用SHAP\u002FLIME可解释性，并采用极端事件感知损失函数，然而在48篇原始研究中，严重和极端干旱的分类别报告、序数感知评估以及防泄漏的时间验证仍应用不一致。新颖性：本综述将预测准确性与罕见事件敏感性、序数干旱严重程度、防泄漏验证、可解释性、不确定性和跨域泛化等标准共同评估，这些标准在以往的机器学习干旱综述中大多缺失。关键词：干旱预报，机器学习，遥感，可解释人工智能，极端干旱","Indian Journal of Science and Technology","2026-10-18T00:00:00Z",71,{"impact":127,"substance":101,"depth":421,"authority":127,"freshness":422,"relevant":21,"comment":423},17,9,"系统综述机器学习干旱分析与预测，指出评估口径不一、极端事件敏感性与可解释性不足，对农业旱情监测信息化有参考价值，但属学术综述、影响面偏细分领域。",[425],{"name":417,"url":414},[179,427,428,56,29],"可解释AI","极端天气",{"code":212,"slug":430,"name":431,"path":432,"parent_code":184},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[434],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":436},{"candidates":437,"agricultural_evidence":45},[438,442,446],{"code":212,"score":19,"supported":45,"title":439,"abstract":440,"tags":441},[217],[216,217],[179,427],{"code":32,"score":203,"supported":45,"title":443,"abstract":444,"tags":445},[47],[49,47],[29],{"code":52,"score":53,"supported":45,"title":447,"abstract":448,"tags":449},[],[56,57],[56],[451,452],"机器学习 干旱预测","遥感 干旱监测","机器学习干旱预测-5320","10.17485\u002Fijst\u002Fv19i36.1263",{"doi":454,"openalex_id":456,"authors":457,"venue":417,"cited_by_count":79,"oa_url":414,"card":467,"direction":84,"ingested_from":87},"W7222882764",[458,461,464],{"name":459,"orcid":460},"Ritik Mallik","https:\u002F\u002Forcid.org\u002F0009-0000-2785-0265",{"name":462,"orcid":463},"Dayal Kumar Behera","https:\u002F\u002Forcid.org\u002F0000-0002-9740-8066",{"name":465,"orcid":466},"Satarupa Mohanty","https:\u002F\u002Forcid.org\u002F0000-0002-1434-7158",{"tldr":468,"method":469,"finding":470,"direction":471,"opportunity":472},"综述55篇文献，比较机器学习干旱监测预报在定义、数据与验证上的差异。","系统综述2018-2026年55篇文献，比较目标定义、数据、模型、验证与可解释性","模型性能差异主要源于任务与验证设计而非算法，极端事件与泄漏防护评估仍不足。","农业人工智能与决策模型","可构建面向极端干旱的序数感知评估与抗泄漏时序验证框架，提升跨区域泛化。","2026-10-19T23:30:22.840943Z",{"id":475,"title":476,"url":477,"summary":478,"summary_zh":9,"content":9,"source_name":479,"source_url":9,"published_at":480,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":481,"score_detail":482,"sources":484,"tags":486,"primary_topic":487,"secondary_topics":488,"classification":491,"search_phrases":510,"slug":513,"view_count":79,"doi":9,"paper":514,"created_at":521},5296,"雷添杰研究员团队《基于无人机多光谱图像和VGG21模型的小麦渍害调控效果识别》","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-10-16\u002Fdoc-inivmprm8363597.shtml","《智慧农业(中英文)》2026年第8卷第4期刊载梁万杰、刘晓君、雷添杰等论文,在小麦拔节—抽穗和抽穗—灌浆两个生长阶段进行对照、渍水胁迫、硅肥调控、氨基酸调控控制试验;利用大疆精灵4多光谱无人机采集小麦冠层多光谱图像建立数据集,基于VGG21模型构建识别模型,发现硅肥调控样本识别性能最好,识别精度最高,具良好鲁棒性和可扩展性,可为小麦渍害综合防控提供理论与技术支持。","智慧农业(中英文)(2026,8(4))","2026-10-16T00:00:00Z",76,{"impact":19,"substance":101,"depth":421,"authority":102,"freshness":17,"relevant":21,"comment":483},"核心期刊论文，方法新颖、结论可靠，对小麦渍害防控有实用价值，值得进入每日精选。",[485],{"name":479,"url":477},[107,26,366,367,368],{"code":370,"slug":371,"name":372,"path":373,"parent_code":115},[489,490],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":52,"slug":188,"name":189,"path":190,"parent_code":184},{"status":39,"locked":14,"version":40,"method":41,"evidence":492},{"candidates":493,"agricultural_evidence":45},[494,498,502,506],{"code":370,"score":381,"supported":45,"title":495,"abstract":496,"tags":497},[372],[372],[],{"code":32,"score":381,"supported":45,"title":499,"abstract":500,"tags":501},[391],[391],[],{"code":52,"score":381,"supported":45,"title":503,"abstract":504,"tags":505},[367],[],[366,26,367],{"code":132,"score":61,"supported":45,"title":507,"abstract":508,"tags":509},[],[107],[107],[511,512],"雷添杰 小麦渍害 无人机多光谱","VGG21 小麦渍害 识别模型","雷添杰小麦渍害无人机多光谱-5296",{"doi":9,"openalex_id":9,"authors":515,"venue":9,"cited_by_count":79,"oa_url":9,"card":516,"direction":84,"ingested_from":409},[],{"tldr":517,"method":518,"finding":519,"direction":84,"opportunity":520},"用无人机多光谱图像和VGG21模型识别小麦渍害调控效果。","大疆精灵4采集冠层多光谱图像，构建VGG21识别模型。","硅肥调控样本识别性能最好、精度最高，模型鲁棒可扩展。","可探索多生育期多光谱特征与不同调控措施的耦合机制，并扩展到其他作物渍害监测。","2026-10-19T00:07:48.071055Z"]