[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4598":3,"related-4598":113},{"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":32,"secondary_topics":38,"classification":45,"search_phrases":80,"slug":83,"view_count":22,"doi":84,"paper":85,"created_at":112},4598,"Seasonal compatibility of RGB and multispectral vegetation indices in Amazon Cacao","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12518-026-00815-x","Abstract Precision agriculture in cacao cultivation is constrained by multispectral sensor costs, limiting accessibility for smallholder farmers. This study evaluated the seasonal compatibility between RGB-derived visible reflectance and multispectral vegetation indices (NDVI, GNDVI, NDRE) to establish a low-cost monitoring approach for Eastern Amazon cacao plantations. We collected spectral data from 1,431 random sampling points across a 0.9-ha commercial cacao plantation during four seasonal periods (February, May, August, and November 2025) using UAV-mounted RGB and multispectral sensors. Pearson correlations and linear regression models assessed relationships between mean visible reflectance [(R + G+B)\u002F3] and multispectral indices. Geostatistical analysis characterized spatial dependence structure across seasons. GNDVI demonstrated superior compatibility across all seasons (mean R² = 59.3%; range: 52.5–62.9%), outperforming NDRE (44.2%) and NDVI (40.8%) by 15–19% points. Temporal stability was 2.9× higher for GNDVI (SD = 4.7 pp) than NDRE (14.1 pp) and NDVI (12.7 pp). Peak compatibility occurred in May (R² = 62.9%) during wet-to-dry transition. The conversion equation (GNDVI = 0.509–0.000012 × Visible Reflectance; RMSE = 0.077) enables reliable estimation, retaining 59% of multispectral information. Strong spatial dependence (Co\u002F(Co + C) \u003C 10%) was confirmed across all periods, with August providing optimal structure (range = 11.4 m) for management zone delineation. GNDVI is the most reliable RGB proxy for cacao monitoring, democratizing precision agriculture access through affordable technology while maintaining robust spatial and temporal performance year-round.","摘要 可可种植中的精准农业受到多光谱传感器成本的制约，限制了小农户的可及性。本研究评估了RGB衍生的可见光反射率与多光谱植被指数（NDVI、GNDVI、NDRE）之间的季节性兼容性，以建立一种面向东亚马逊可可种植园的低成本监测方法。我们使用无人机搭载的RGB和多光谱传感器，在四个季节时段（2025年2月、5月、8月和11月）从一片0.9公顷商业可可种植园的1，431个随机采样点采集了光谱数据。采用Pearson相关分析和线性回归模型评估平均可见光反射率[（R + G + B）\u002F3]与多光谱指数之间的关系。地统计学分析刻画了各季节的空间依赖结构。GNDVI在所有季节均表现出最优兼容性（平均R² = 59.3%；范围：52.5–62.9%），较NDRE（44.2%）和NDVI（40.8%）高出15–19个百分点。GNDVI的时间稳定性（SD = 4.7个百分点）是NDRE（14.1个百分点）和NDVI（12.7个百分点）的2.9倍。最高兼容性出现在5月（R² = 62.9%），即湿季向干季过渡期。转换方程（GNDVI = 0.509–0.000012 × 可见光反射率；RMSE = 0.077）可实现可靠估算，保留了59%的多光谱信息。所有时期均确认存在强空间依赖性（Co\u002F(Co + C) \u003C 10%），其中8月提供了最优结构（变程 = 11.4 m），适合用于管理分区划分。GNDVI是可可监测中最可靠的RGB替代指标，通过可负担的技术使精准农业更广泛可及，同时全年保持稳健的空间和时间性能。",null,"Applied Geomatics","2026-10-08T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,21,17,13,9,1,"以低成本RGB替代多光谱监测亚马逊可可，数据扎实、结论明确，对精准农业降本有参考价值，但属细分作物研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","遥感","植被指数","可可种植","无人机监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,59,64,72],{"code":33,"score":52,"supported":53,"title":54,"abstract":56,"tags":58},14,true,[55],"cacao",[57,55],"可可",[],{"code":40,"score":20,"supported":53,"title":60,"abstract":62,"tags":63},[61],"multispectral",[61],[29,28],{"code":65,"score":66,"supported":53,"title":67,"abstract":68,"tags":71},"I1",7,[],[69,70],"精准农业","precision agriculture",[27],{"code":73,"score":74,"supported":53,"title":75,"abstract":76,"tags":79},"G2",6,[],[77,78],"小农户","smallholder",[],[81,82],"亚马逊 可可 无人机 遥感","GNDVI RGB 植被指数 可可","亚马逊可可无人机遥感-4598","10.1007\u002Fs12518-026-00815-x",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":103,"oa_url":104,"card":105,"direction":109,"ingested_from":111},"W7221039911",[88,91,94,97,100],{"name":89,"orcid":90},"Charlyngton da Silva e Silva","https:\u002F\u002Forcid.org\u002F0009-0003-9332-4302",{"name":92,"orcid":93},"Luiz Antônio Soares Cardoso","https:\u002F\u002Forcid.org\u002F0000-0001-6868-7847",{"name":95,"orcid":96},"Ronilson de Souza Santos","https:\u002F\u002Forcid.org\u002F0000-0002-2683-456X",{"name":98,"orcid":99},"Carlos Rodrigo Tanajura Caldeira","https:\u002F\u002Forcid.org\u002F0000-0001-7102-7735",{"name":101,"orcid":102},"Norberto Cornejo Noronha","https:\u002F\u002Forcid.org\u002F0000-0001-7216-8687",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12518-026-00815-x.pdf",{"tldr":106,"method":107,"finding":108,"direction":109,"opportunity":110},"评估RGB与多光谱植被指数在亚马逊可可园的季节性兼容性，以低成本监测替代多光谱传感器。","无人机RGB与多光谱传感器采集1431个样点，用相关、回归和地统计分析四季数据。","GNDVI兼容性最佳（R²=59.3%），时空稳定性最高，5月表现最优，可保留59%多光谱信息。","农业遥感与作物表型","可探索RGB代理指数在其他热带作物和不同冠层结构下的普适性，并优化低成本管理分区方法。","openalex","2026-10-10T23:30:31.142887Z",{"total":74,"page":22,"page_size":74,"items":114},[115,188,255,313,369,464],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":9,"source_name":121,"source_url":118,"published_at":122,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":123,"score_detail":124,"sources":128,"tags":130,"primary_topic":133,"secondary_topics":134,"classification":136,"search_phrases":162,"slug":165,"view_count":22,"doi":166,"paper":167,"created_at":187},3893,"Modeling the productive potential of coffee trees in the Cerrado of Minas Gerais through hierarchical landscape analysis and remote sensing data","https:\u002F\u002Fdoi.org\u002F10.25186\u002F.v21i.2516","Brazil stands out as the world’s largest producer and exporter of Arabica coffee, one of the world’s most important commodities, which plays a crucial role in the country’s economy. In this context, this study develops a model to classify the productive potential of coffee trees based on hierarchical landscape analysis and coffee plant metrics, applied to different farms in the Córrego Feio watershed, Patrocínio, Minas Gerais. We employed a model based on landscape metrics (slope, water flow, vegetation, and slope shape), inspired by Ross’s (1994) proposal for Environmental Fragility. Each variable was reclassified according to hierarchical levels from the literature and empirical observations of the area. The data were integrated to generate two products: Factor T, which represents the potential of the area based on static soil and relief variables, and Calculated Potential, which combines Factor T with the NDVI vegetation index for a dynamic assessment. The application of the model demonstrated a strong capacity to interpret the qualitative hierarchy of productive potential, supporting the delineation of differentiated management zones. The results indicated that productive potential is more strongly associated with soil and topographic factors than with the vegetation index. It is concluded that the proposed method is a valuable tool for precision agriculture, allowing the delineation of management zones for the application of inputs in a more efficient and sustainable manner, contributing to resource conservation and environmental protection. Key words: Arabica coffee; environmental fragility; hierarchical analysis; vegetation index; watershed.","巴西是全球最大的阿拉比卡咖啡（Arabica coffee）生产国和出口国，阿拉比卡咖啡作为全球最重要的商品之一，在该国经济中发挥着至关重要的作用。在此背景下，本研究开发了一个基于层次景观分析和咖啡植株指标的咖啡树生产潜力分类模型，并将其应用于米纳斯吉拉斯州帕特罗西尼奥市Córrego Feio流域的不同农场。我们采用了一个基于景观指标（坡度、水流、植被和坡形）的模型，其灵感来源于Ross（1994）提出的环境脆弱性方案。每个变量根据文献和该区域经验观察所得的层次等级进行重新分类。数据经过整合生成了两个产品：因子T，代表基于静态土壤和地形变量的区域潜力；以及计算潜力，将因子T与NDVI植被指数相结合以进行动态评估。模型的应用展现出强大的能力，能够解释生产潜力的定性层次，支持差异化经营区的划分。结果表明，生产潜力与土壤和地形因子的关联性强于与植被指数的关联性。研究认为，所提出的方法是一种有价值的精准农业工具，能够以更高效和可持续的方式划定管理区以施用投入品，有助于资源保护和环境保护。关键词：阿拉比卡咖啡；环境脆弱性；层次分析；植被指数；流域。","Coffee Science","2026-09-30T00:00:00Z",70,{"impact":125,"substance":126,"depth":19,"authority":20,"freshness":17,"relevant":22,"comment":127},12,20,"基于景观分析与NDVI的咖啡生产潜力分级模型，方法新颖、结论可靠，对精准农业管理分区有实用价值，但属区域性研究，影响范围有限。",[129],{"name":121,"url":118},[27,69,28,131,132],"管理分区","咖啡种植",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[135],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":137},{"candidates":138,"agricultural_evidence":53},[139,146,151,155],{"code":33,"score":140,"supported":53,"title":141,"abstract":143,"tags":145},15,[142],"coffee",[144,142],"咖啡",[132],{"code":40,"score":21,"supported":53,"title":147,"abstract":149,"tags":150},[148],"remote sensing",[],[28],{"code":65,"score":17,"supported":53,"title":152,"abstract":153,"tags":154},[],[69,70],[27,69],{"code":156,"score":74,"supported":53,"title":157,"abstract":158,"tags":161},"H2",[],[159,160],"土壤","soil",[],[163,164],"Minas Gerais 咖啡 遥感","Córrego Feio 流域 咖啡 生产潜力","MinasGerais咖啡遥感-3893","10.25186\u002F.v21i.2516",{"doi":166,"openalex_id":168,"authors":169,"venue":121,"cited_by_count":103,"oa_url":118,"card":182,"direction":109,"ingested_from":111},"W7214888407",[170,173,176,179],{"name":171,"orcid":172},"Jorge Eduardo Ferreira Cunha","https:\u002F\u002Forcid.org\u002F0000-0002-3679-6376",{"name":174,"orcid":175},"Mateus Duarte Segismundo","https:\u002F\u002Forcid.org\u002F0009-0001-1289-406X",{"name":177,"orcid":178},"George Deroco Martins","https:\u002F\u002Forcid.org\u002F0000-0001-9738-7325",{"name":180,"orcid":181},"João Vítor Meza Bravo","https:\u002F\u002Forcid.org\u002F0000-0002-5457-3192",{"tldr":183,"method":184,"finding":185,"direction":109,"opportunity":186},"基于景观指标与NDVI构建模型，对巴西塞拉多咖啡树生产潜力进行分级分类。","层次化景观分析（坡度、水流、植被、坡形）结合NDVI，生成因子T与计算潜力。","生产潜力与土壤和地形因素关联强于植被指数，可划分差异化精准管理区。","可引入多时相遥感与机器学习，动态预测咖啡生产潜力并优化管理区划分。","2026-10-01T23:30:28.485534Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":9,"content":9,"source_name":193,"source_url":191,"published_at":194,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":198,"tags":200,"primary_topic":203,"secondary_topics":204,"classification":206,"search_phrases":221,"slug":224,"view_count":225,"doi":226,"paper":227,"created_at":254},3619,"Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10767247\u002Fv1","Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee。Research Square","Research Square","2026-09-24T00:00:00Z",51,{"impact":17,"substance":52,"depth":140,"authority":74,"freshness":17,"relevant":22,"comment":197},"咖啡园NDVI主动传感器与无人机多光谱对比研究，方法有参考价值但属细分作物技术验证，影响面有限。",[199],{"name":193,"url":191},[27,201,28,202,132],"无人机","NDVI",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[205],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":207},{"candidates":208,"agricultural_evidence":53},[209,213,217],{"code":40,"score":20,"supported":53,"title":210,"abstract":211,"tags":212},[61],[61],[202,28],{"code":33,"score":125,"supported":53,"title":214,"abstract":215,"tags":216},[142],[142],[132],{"code":65,"score":22,"supported":14,"title":218,"abstract":219,"tags":220},[],[],[27],[222,223],"咖啡 NDVI 无人机 多光谱","Research Square 咖啡 遥感","咖啡NDVI无人机多光谱-3619",5,"10.21203\u002Frs.3.rs-10767247\u002Fv1",{"doi":226,"openalex_id":228,"authors":229,"venue":193,"cited_by_count":103,"oa_url":253,"card":9,"direction":109,"ingested_from":111},"W7214220732",[230,232,234,236,238,241,243,245,248,251],{"name":231,"orcid":9},"Gabriel de Morais Campos",{"name":233,"orcid":9},"Aline Bhering Silva",{"name":235,"orcid":9},"Cileimar Aparecida da Silva",{"name":237,"orcid":9},"Vanda Maria Salles Andrade",{"name":239,"orcid":240},"Thaline M. Pimenta","https:\u002F\u002Forcid.org\u002F0000-0002-5002-177X",{"name":242,"orcid":9},"Andressa Barcellos Silva",{"name":244,"orcid":9},"Marco Thúlio Gonçalves Vieira",{"name":246,"orcid":247},"Daniel Marçal de Queiroz","https:\u002F\u002Forcid.org\u002F0000-0003-0987-3855",{"name":249,"orcid":250},"Fábio Daniel Tancredi","https:\u002F\u002Forcid.org\u002F0000-0002-7619-2200",{"name":252,"orcid":9},"Flora Maria Melo Villar","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10767247\u002Flatest.pdf","2026-09-27T23:30:34.698079Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":9,"content":9,"source_name":260,"source_url":9,"published_at":261,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":262,"score_detail":263,"sources":266,"tags":268,"primary_topic":271,"secondary_topics":272,"classification":279,"search_phrases":300,"slug":303,"view_count":22,"doi":9,"paper":304,"created_at":312},1367,"基于无人机多源遥感特征的茶树生长与氮素状况深度学习监测","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F17\u002F2923","南京农业大学园艺学院团队提出融合光谱、纹理与谐波特征的多域融合指标，结合无人机数据与深度学习模型，用于茶树生物量与氮素积累量估算。实验表明多域融合指数显著优于传统光谱指标，可实现对茶园生物量与氮素状况的高精度反演，为近实时茶园监测提供工具。","Remote Sensing · MDPI · 2026-09-01","2026-08-31T16:00:00Z",72,{"impact":140,"substance":126,"depth":264,"authority":125,"freshness":66,"relevant":22,"comment":265},18,"研究提出多域融合指标，提升茶树氮素监测精度，对智慧茶园有实用价值。",[267],{"name":260,"url":258},[27,201,28,269,270],"氮素监测","茶树",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[273,278],{"code":274,"slug":275,"name":276,"path":277,"parent_code":44},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":280},{"candidates":281,"agricultural_evidence":53},[282,287,292,296],{"code":33,"score":283,"supported":53,"title":284,"abstract":285,"tags":286},11,[270],[270],[],{"code":274,"score":283,"supported":53,"title":288,"abstract":290,"tags":291},[289],"深度学习",[289],[],{"code":40,"score":21,"supported":53,"title":293,"abstract":294,"tags":295},[28],[],[28],{"code":65,"score":22,"supported":14,"title":297,"abstract":298,"tags":299},[],[],[27],[301,302],"智慧农业 氮素监测 无人机 茶树","智慧农业 氮素监测","智慧农业氮素监测无人机茶树-1367",{"doi":9,"openalex_id":9,"authors":305,"venue":9,"cited_by_count":103,"oa_url":9,"card":306,"direction":109,"ingested_from":311},[],{"tldr":307,"method":308,"finding":309,"direction":109,"opportunity":310},"融合无人机多源遥感特征与深度学习，高精度估算茶树生物量与氮素积累量。","融合光谱、纹理、谐波特征的多域融合指标，结合无人机数据与深度学习模型。","多域融合指数显著优于传统光谱指标，实现茶园生物量与氮素高精度反演。","可探索多域融合特征在其他作物或不同生长阶段的适用性，或结合时序数据预测氮素动态。","agent","2026-09-02T00:05:07.021728Z",{"id":314,"title":315,"url":316,"summary":317,"summary_zh":318,"content":9,"source_name":319,"source_url":316,"published_at":320,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":321,"score_detail":322,"sources":324,"tags":326,"primary_topic":329,"secondary_topics":330,"classification":331,"search_phrases":342,"slug":345,"view_count":103,"doi":346,"paper":347,"created_at":368},5243,"Identificación de índices de vegetación relacionados con componentes de rendimiento de frijol común (Phaseolus vulgaris L.) mediante el uso de sensores multiespectrales","https:\u002F\u002Fdoi.org\u002F10.18845\u002Ftm.v39i4.8378","Bean production in Costa Rica supplies only 20 % of domestic consumption, requiring the importation of the remaining amount; this situation highlights the need to know in advance the actual productive capacity of local farmers to improve planning, agronomic management, and import regulation. Therefore, the objective of this study was to evaluate the use of multispectral data and vegetation indices to analyze their relationship with yield components in bean varieties cultivated in different production areas of Costa Rica and to identify the yield component with the greatest spectral association. The research was conducted at three sites located in Alajuela, Costa Rica. Multispectral reflectance data were collected during the R5 phenological stage using an airborne multispectral sensor. Subsequently, yield components were quantified per meter of crop row, including 100-grain weight, number of pods per meter, number of plants, number of grains, and total grain weight. The number of pods showed the highest association with spectral variables and was therefore identified as the yield component with the greatest potential for analysis through remote sensing. The SR (Simple Ratio) and GCI (Green Chlorophyll Index) indices showed correlations greater than 0.7 with this variable, being identified as the vegetation indices with the highest spectral association during the R5 stage of the crop.","哥斯达黎加的菜豆（bean）产量仅能满足国内消费量的20%，其余部分需要进口；这一状况凸显了提前了解当地农民实际生产能力的必要性，以改进生产规划、农艺管理和进口调控。因此，本研究旨在评估多光谱数据和植被指数（vegetation indices）的应用，分析其与哥斯达黎加不同产区菜豆品种产量构成因素（yield components）之间的关系，并确定与光谱关联性最强的产量构成因素。研究在哥斯达黎加阿拉胡埃拉（Alajuela）的三个地点开展。在R5物候期使用机载多光谱传感器采集多光谱反射率数据。随后，按每米作物行量化产量构成因素，包括百粒重、每米豆荚数、植株数、籽粒数和籽粒总重。豆荚数与光谱变量的关联性最高，因此被确定为最具遥感分析潜力的产量构成因素。SR（简单比值，Simple Ratio）和GCI（绿色叶绿素指数，Green Chlorophyll Index）与该变量的相关系数大于0.7，被确定为作物R5期光谱关联性最高的植被指数。","Revista Tecnología en Marcha","2026-10-16T00:00:00Z",62,{"impact":17,"substance":264,"depth":140,"authority":125,"freshness":21,"relevant":22,"comment":323},"该研究利用机载多光谱传感器识别与菜豆产量组分相关的植被指数，方法清晰、结论具体，但属于哥斯达黎加的小区域研究，公共影响有限，可作为遥感作物监测领域的技术性资讯收录。",[325],{"name":319,"url":316},[27,327,28,29,328],"产量预测","菜豆",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":332},{"candidates":333,"agricultural_evidence":53},[334,338],{"code":40,"score":125,"supported":53,"title":335,"abstract":336,"tags":337},[],[28,148,61],[327,29,28],{"code":65,"score":22,"supported":14,"title":339,"abstract":340,"tags":341},[],[],[27],[343,344],"哥斯达黎加 菜豆 多光谱 遥感","Phaseolus vulgaris 植被指数 产量","哥斯达黎加菜豆多光谱遥感-5243","10.18845\u002Ftm.v39i4.8378",{"doi":346,"openalex_id":348,"authors":349,"venue":319,"cited_by_count":103,"oa_url":362,"card":363,"direction":109,"ingested_from":111},"W7222720635",[350,353,356,359],{"name":351,"orcid":352},"Guillermo Vargas-Hernández","https:\u002F\u002Forcid.org\u002F0000-0003-1410-3898",{"name":354,"orcid":355},"Eddison José Araya-Morales","https:\u002F\u002Forcid.org\u002F0009-0001-0550-414X",{"name":357,"orcid":358},"Juan Carlos Hernández","https:\u002F\u002Forcid.org\u002F0000-0002-1915-3213",{"name":360,"orcid":361},"Ignacio Mora-Cordero","https:\u002F\u002Forcid.org\u002F0009-0007-0925-0099","https:\u002F\u002Frevistas.tec.ac.cr\u002Findex.php\u002Ftec_marcha\u002Farticle\u002Fdownload\u002F8378\u002F8641",{"tldr":364,"method":365,"finding":366,"direction":109,"opportunity":367},"用机载多光谱传感器评估菜豆产量构成与植被指数的关系。","机载多光谱遥感获取R5期反射率，结合地面产量构成测定与相关分析。","豆荚数与光谱变量关联最强，SR和GCI指数与其相关系数超过0.7。","可探索将SR\u002FGCI用于菜豆产量早期预测，并扩展到多品种、多生育期与无人机高光谱验证。","2026-10-18T23:30:20.259545Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":9,"source_name":375,"source_url":372,"published_at":376,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":377,"score_detail":378,"sources":381,"tags":383,"primary_topic":386,"secondary_topics":387,"classification":388,"search_phrases":410,"slug":413,"view_count":414,"doi":415,"paper":416,"created_at":463},3603,"Uncertainty propagation and intercomparison of multi-sensor measurements of vegetation stress in sub-optimal conditions","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10455-1","Abstract Purpose This study evaluates the metrological consistency and uncertainty propagation of spectral reflectance and vegetation indices derived from multiple remote sensing sensors under sub-optimal and variable illumination conditions. While multi-sensor data fusion is increasingly common in precision farming, the extent to which sensor differences arise from biological variation versus measurement uncertainty remains poorly quantified. Methods and results Field measurements were conducted on spring wheat ( Triticum aestivum L.) using three ground-based field spectroradiometers and two uncrewed aerial vehicles (UAV)-mounted multispectral cameras. Following a standardised Guide to the Expression of Uncertainty in Measurement (GUM) framework, uncertainties from sensor noise, spatial plot heterogeneity, and transient cloud cover were propagated using Monte Carlo simulations. The results indicate that total reflectance uncertainty peaked at approximately 11% in the red-edge and near-infrared regions, primarily driven by plot-level heterogeneity and fluctuating irradiance. Among the evaluated indices, the Optimized Soil-Adjusted Vegetation Index (OSAVI) demonstrated the highest stability across platforms, whereas the Enhanced Vegetation Index (EVI) proved highly sensitive to environmental noise, leading to metrological breakdown in sensor interoperability. The findings demonstrate that under unstable atmospheric conditions, the window for reliable multi-sensor data integration is limited to synchronous acquisitions within minutes. Conclusion This research provides a rigorous statistical foundation for identifying the limits of sensor agreement, ensuring that management decisions in precision agriculture are based on true crop signals rather than measurement artifacts.","摘要 目的 本研究评估了在次优和变化光照条件下，来自多个遥感传感器的光谱反射率和植被指数的计量一致性与不确定度传播。尽管多传感器数据融合在精准农业中日益普遍，但传感器差异在多大程度上源于生物变异而非测量不确定度，仍缺乏充分的量化。方法与结果 使用三台地面野外光谱辐射计和两台无人机（UAV）搭载的多光谱相机，对春小麦（Triticum aestivum L.）进行了田间测量。依据测量不确定度表示指南（GUM）的标准框架，采用蒙特卡洛模拟对传感器噪声、空间小区异质性和瞬时云覆盖所产生的不确定度进行了传播分析。结果表明，总反射率不确定度在红边和近红外区域达到约11%的峰值，主要由小区尺度异质性和辐照度波动驱动。在评估的指数中，优化土壤调节植被指数（OSAVI）在不同平台间表现出最高的稳定性，而增强植被指数（EVI）对环境噪声高度敏感，导致传感器互操作性在计量学上失效。研究结果表明，在不稳定大气条件下，可靠的多传感器数据整合窗口仅限于数分钟内的同步采集。结论 本研究为识别传感器一致性的限度提供了严格的统计学基础，确保精准农业中的管理决策基于真实的作物信号而非测量伪影。","Precision Agriculture","2026-09-26T00:00:00Z",75,{"impact":125,"substance":379,"depth":264,"authority":52,"freshness":21,"relevant":22,"comment":380},22,"该研究量化了多传感器植被胁迫测量中的不确定性传播，方法严谨、结论对精准农业多源数据融合具有实质指导价值，但属细分领域学术进展，影响范围有限。",[382],{"name":375,"url":372},[27,28,29,384,385],"多光谱遥感","作物表性",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":389},{"candidates":390,"agricultural_evidence":53},[391,395,399,406],{"code":40,"score":283,"supported":53,"title":392,"abstract":393,"tags":394},[],[28,148,61],[29,28],{"code":65,"score":66,"supported":53,"title":396,"abstract":397,"tags":398},[],[69,70],[27],{"code":400,"score":74,"supported":53,"title":401,"abstract":402,"tags":405},"A2",[],[403,404],"小麦","wheat",[],{"code":156,"score":74,"supported":53,"title":407,"abstract":408,"tags":409},[],[159,160],[],[411,412],"春小麦 多光谱 遥感 不确定性","OSAVI EVI 传感器 互操作","春小麦多光谱遥感不确定性-3603",4,"10.1007\u002Fs11119-026-10455-1",{"doi":415,"openalex_id":417,"authors":418,"venue":375,"cited_by_count":103,"oa_url":372,"card":458,"direction":109,"ingested_from":111},"W7214488126",[419,422,425,428,431,433,435,437,440,443,446,449,452,455],{"name":420,"orcid":421},"Mike Werfeli","https:\u002F\u002Forcid.org\u002F0000-0001-5768-8887",{"name":423,"orcid":424},"Michal Antala","https:\u002F\u002Forcid.org\u002F0000-0003-1294-9507",{"name":426,"orcid":427},"Abdallah Yussuf Ali Abdelmajeed","https:\u002F\u002Forcid.org\u002F0000-0002-3662-1824",{"name":429,"orcid":430},"Álvaro Sánchez-Virosta","https:\u002F\u002Forcid.org\u002F0000-0002-3842-3792",{"name":432,"orcid":9},"Zoe Halem",{"name":434,"orcid":9},"A. Merrington",{"name":436,"orcid":9},"Yousra El‐Mejjaouy",{"name":438,"orcid":439},"Bojana Petrović","https:\u002F\u002Forcid.org\u002F0000-0002-9901-3471",{"name":441,"orcid":442},"Andreas Hueni","https:\u002F\u002Forcid.org\u002F0000-0002-4283-2484",{"name":444,"orcid":445},"El Houssaine Bouras","https:\u002F\u002Forcid.org\u002F0000-0002-6973-6644",{"name":447,"orcid":448},"Shawn C. Kefauver","https:\u002F\u002Forcid.org\u002F0000-0002-1687-1965",{"name":450,"orcid":451},"Sahameh Shafiee","https:\u002F\u002Forcid.org\u002F0000-0002-3586-2327",{"name":453,"orcid":454},"Anshu Rastogi","https:\u002F\u002Forcid.org\u002F0000-0002-0953-7045",{"name":456,"orcid":457},"Laura Mihai","https:\u002F\u002Forcid.org\u002F0000-0003-3869-1890",{"tldr":459,"method":460,"finding":461,"direction":109,"opportunity":462},"评估多传感器植被胁迫测量在非理想条件下的不确定度传播与一致性。","三种地面光谱仪与两架无人机多光谱相机，基于GUM框架和蒙特卡洛模拟传播不确定度。","红边与近红外总反射率不确定度达11%，OSAVI跨平台最稳定，EVI易受环境噪声影响。","可研究分钟级同步采集协议与不确定度感知的传感器融合算法，提升多源数据互操作性。","2026-09-27T23:30:04.847804Z",{"id":465,"title":466,"url":467,"summary":468,"summary_zh":469,"content":9,"source_name":470,"source_url":467,"published_at":471,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":472,"score_detail":473,"sources":475,"tags":477,"primary_topic":480,"secondary_topics":481,"classification":483,"search_phrases":502,"slug":505,"view_count":414,"doi":506,"paper":507,"created_at":535},2660,"Red-Edge Information in Agricultural Remote Sensing: From Spectral Theory to Explainable Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183180","The red-edge (RE) spectral region has become a central component of agricultural remote sensing because it captures physiologically meaningful changes in chlorophyll content, canopy structure and vegetation functioning. The availability of dedicated RE bands on modern multispectral satellites and advances in hyperspectral sensing have stimulated widespread applications for crop monitoring, nutrient assessment, stress detection and yield prediction. However, reported improvements over conventional visible–near-infrared (VIS–NIR) approaches remain highly variable, and the mechanisms governing when and why RE information provides additional value are often poorly synthesised. This review presents a conceptual framework that links the physical and physiological basis of RE reflectance with its condition-dependent agronomic performance and its emerging role within modern machine learning (ML) systems. We first examine how pigment absorption, canopy structure and sensor characteristics jointly determine the representation of RE information from hyperspectral measurements to operational multispectral observations. We then synthesise evidence demonstrating that the agronomic value of RE information is strongly dependent on crop characteristics, phenological stage, environmental conditions and observation geometry, explaining much of the variability reported across previous studies. Finally, we show how recent advances in ML and explainable artificial intelligence have changed the interpretation of RE information. Rather than evaluating RE-derived vegetation indices in isolation, contemporary predictive frameworks integrate RE observations with complementary spectral, climatic, structural and temporal predictors, allowing their physiological contribution to be quantified within multidimensional models. We conclude that future value of RE remote sensing will require not only continued advances in spectral measurement and vegetation index development, but also improved interpretation, transferability and operational integration of physiologically meaningful RE information within explainable, multi-source agricultural monitoring systems.","红边（RE）光谱区已成为农业遥感的核心组成部分，因为它能够捕捉叶绿素含量、冠层结构和植被功能等方面具有生理意义的变化。现代多光谱卫星上专用红边波段的可用性以及高光谱传感技术的进步，推动了其在作物监测、养分评估、胁迫检测和产量预测中的广泛应用。然而，相较于传统可见光—近红外（VIS–NIR）方法所报道的改进效果仍高度可变，而关于红边信息何时以及为何提供额外价值的机制往往缺乏系统梳理。本综述提出了一个概念框架，将红边反射率的物理与生理基础与其条件依赖的农学表现及其在现代机器学习（ML）系统中新兴的作用联系起来。我们首先探讨色素吸收、冠层结构和传感器特性如何共同决定从高光谱测量到业务化多光谱观测中红边信息的表征。随后，我们综合证据表明，红边信息的农学价值强烈依赖于作物特征、物候阶段、环境条件和观测几何，这解释了以往研究中报道的大部分变异性。最后，我们展示了机器学习和可解释人工智能的最新进展如何改变了对红边信息的解读。当代预测框架不再孤立地评估红边衍生的植被指数，而是将红边观测与互补的光谱、气候、结构和时间预测因子相结合，从而在多维模型中量化其生理贡献。我们得出结论：红边遥感的未来价值不仅需要光谱测量和植被指数开发的持续进步，还需要在可解释的多源农业监测系统中改进对具有生理意义的红边信息的解读、可迁移性和业务化整合。","Remote Sensing","2026-09-16T00:00:00Z",82,{"impact":264,"substance":379,"depth":264,"authority":52,"freshness":13,"relevant":22,"comment":474},"系统综述红边遥感从光谱机理到可解释机器学习的农学价值条件，方法框架新颖、结论可靠，对作物监测与产量预测有实质参考意义。",[476],{"name":470,"url":467},[27,478,28,479,29],"农业人工智能","作物监测",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[482],{"code":274,"slug":275,"name":276,"path":277,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":484},{"candidates":485,"agricultural_evidence":53},[486,491,498],{"code":40,"score":126,"supported":53,"title":487,"abstract":488,"tags":490},[148],[28,148,61,489],"hyperspectral",[479,29,28],{"code":274,"score":264,"supported":53,"title":492,"abstract":494,"tags":497},[493],"machine learning",[495,493,496],"机器学习","artificial intelligence",[478],{"code":65,"score":22,"supported":14,"title":499,"abstract":500,"tags":501},[],[],[27],[503,504],"农业人工智能 作物监测 智慧农业 植被指数","农业人工智能 作物监测","农业人工智能作物监测智慧农业植被指数-2660","10.3390\u002Frs18183180",{"doi":506,"openalex_id":508,"authors":509,"venue":470,"cited_by_count":103,"oa_url":467,"card":530,"direction":109,"ingested_from":111},"W7213344870",[510,513,515,518,521,524,527],{"name":511,"orcid":512},"Ignacio Fuentes","https:\u002F\u002Forcid.org\u002F0000-0001-7066-7482",{"name":514,"orcid":9},"Nikolas Hoskin",{"name":516,"orcid":517},"Patrick Filippi","https:\u002F\u002Forcid.org\u002F0000-0003-3573-084X",{"name":519,"orcid":520},"Abhasha Joshi","https:\u002F\u002Forcid.org\u002F0000-0002-1422-465X",{"name":522,"orcid":523},"Yi Yu","https:\u002F\u002Forcid.org\u002F0000-0002-1140-2713",{"name":525,"orcid":526},"Thomas F. A. Bishop","https:\u002F\u002Forcid.org\u002F0000-0002-6723-7323",{"name":528,"orcid":529},"Dhahi Al-Shammari","https:\u002F\u002Forcid.org\u002F0000-0001-6608-8322",{"tldr":531,"method":532,"finding":533,"direction":109,"opportunity":534},"综述红边遥感在农业中的物理基础、条件依赖性与可解释机器学习中的新角色。","文献综述，整合光谱理论、多光谱\u002F高光谱数据与可解释AI方法。","红边信息的农学价值高度依赖作物、物候、环境与观测几何，需多源融合与可解释建模。","可研究红边信息在不同作物-物候-环境组合下的可迁移性，并构建可解释多源融合模型。","2026-09-16T23:30:28.719858Z"]