[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4591":3,"related-4591":76},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":52,"slug":55,"view_count":22,"doi":56,"paper":57,"created_at":75},4591,"Uydu görüntüleri ile yanan orman alanının değerlendirilmesi: Çandır orman yangını örneği","https:\u002F\u002Fdoi.org\u002F10.18182\u002Ftjf.1721381","Bu çalışmada, 2021 yılında Çandır Orman İşletme Şefliğinde meydana gelen orman yangınında zarar gören orman alanlarının uzaktan algılama veri ve yöntemleriyle belirlenmesi ve değerlendirilmesi amaçlanmıştır. Bu kapsamda, yangın öncesi ve yangın sonrası Landsat-8 ve Sentinel-2 uydu görüntülerinden Normalleştirilmiş Fark Vejetasyon İndeksi (NDVI) ve Normalleştirilmiş Yanma Oranı (NBR) spektral indeksleri üretilmiştir. Yangının etkisini nicel olarak ortaya koymak amacıyla yangın öncesi ve sonrası indeks görüntüleri arasındaki fark alınarak dNDVI ve dNBR görüntüleri elde edilmiştir. Bu fark görüntülerine eşikleme yöntemi uygulanarak yanan orman alanları belirlenmiştir. Ayrıca Landsat-8 ve Sentinel-2 görüntüleri üzerinde kontrollü sınıflandırma yöntemi uygulanmış ve Maximum Likelihood algoritmasıyla tematik haritalar üretilmiştir. Elde edilen sonuçlara göre en yüksek sınıflandırma doğrulukları Sentinel-2 verilerinden üretilen dNBR ve dNDVI görüntülerinde elde edilmiş olup, genel doğruluk değerleri sırasıyla 0.92 ve 0.88, kappa değerleri ise 0.83 ve 0.77 olarak hesaplanmıştır. Landsat-8 verilerinden üretilen dNBR ve dNDVI görüntülerinde ise genel doğruluk değerleri sırasıyla 0.87 ve 0.85, kappa değerleri ise 0.73 ve 0.7 olarak belirlenmiştir. Kontrollü sınıflandırma sonucunda üretilen tematik haritalarda Sentinel-2 ve Landsat-8 için genel doğruluk değerleri sırasıyla 0.88 ve 0.85, kappa değerleri ise 0.85 ve 0.82 olarak bulunmuştur. Farklı yöntemlerden elde edilen sonuçların ortalamasına göre yanan orman alanının büyüklüğü 760 hektar hesaplanmıştır. Sonuç olarak, Sentinel-2 ve Landsat-8 uydu verilerinden elde edilen dNDVI ve dNBR görüntülerinin, yanan orman alanlarının belirlenmesi ve yangın etkisinin değerlendirilmesinde verimli, güvenilir ve uygulanabilir sonuçlar üretme potansiyeline sahip olduğu ortaya konulmuştur.","本研究旨在利用遥感数据和方法，确定并评估2021年昌德尔森林经营管理局辖区内发生的森林火灾中受损的森林面积。在此范围内，利用火灾前后的Landsat-8和Sentinel-2卫星影像生成了归一化差异植被指数（NDVI）和归一化燃烧比率（NBR）光谱指数。为了定量揭示火灾的影响，通过计算火灾前后指数影像之间的差值，获得了dNDVI和dNBR影像。对这些差值影像应用阈值法，确定了过火森林区域。此外，对Landsat-8和Sentinel-2影像应用了监督分类方法，并利用最大似然法（Maximum Likelihood）生成了专题地图。根据所得结果，最高的分类精度来自Sentinel-2数据生成的dNBR和dNDVI影像，其总体精度分别为0.92和0.88，Kappa系数分别为0.83和0.77。在Landsat-8数据生成的dNBR和dNDVI影像中，总体精度分别为0.87和0.85，Kappa系数分别为0.73和0.7。在监督分类生成的专题地图中，Sentinel-2和Landsat-8的总体精度分别为0.88和0.85，Kappa系数分别为0.85和0.82。根据不同方法所得结果的平均值，过火森林面积计算为760公顷。综上所述，由Sentinel-2和Landsat-8卫星数据获得的dNDVI和dNBR影像在确定过火森林区域和评估火灾影响方面具有产生高效、可靠且可应用结果的潜力。",null,"Turkish Journal of Forestry | Türkiye Ormancılık Dergisi","2026-10-08T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,8,1,"该论文利用Landsat-8与Sentinel-2遥感数据评估土耳其Çandır森林火灾过火面积，方法可靠、结论具体，对农业信息化中的遥感监测有参考价值，但属区域性案例研究，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"Sentinel-2","遥感","森林火灾","植被指数","Landsat-8",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":51},[39,46],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":45},"I3",6,true,[],[28],[27,30,28],{"code":47,"score":22,"supported":14,"title":48,"abstract":49,"tags":50},"H7",[],[],[29],"农业相关性需要复核",[53,54],"Çandır orman yangını","森林火灾 植被指数 遥感 Sentinel-2","Çandırormanyangını-4591","10.18182\u002Ftjf.1721381",{"doi":56,"openalex_id":58,"authors":59,"venue":10,"cited_by_count":66,"oa_url":67,"card":68,"direction":72,"ingested_from":74},"W7220904781",[60,63],{"name":61,"orcid":62},"Abdurrahman Cankut Göz","https:\u002F\u002Forcid.org\u002F0000-0003-1786-7658",{"name":64,"orcid":65},"HÜSEYİN OĞUZ ÇOBAN","https:\u002F\u002Forcid.org\u002F0000-0002-4037-4811",0,"https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F4966779",{"tldr":69,"method":70,"finding":71,"direction":72,"opportunity":73},"利用Landsat-8和Sentinel-2影像评估2021年Çandır森林火灾过火面积。","计算火灾前后NDVI、NBR及dNDVI、dNBR，结合阈值法和最大似然监督分类","Sentinel-2的dNBR精度最高（总体精度0.92，Kappa 0.83），过火面积约760公","农业遥感与作物表型","可探索多源卫星时序数据融合与深度学习，提升火烧区制图精度并拓展至植被恢复监测。","openalex","2026-10-10T23:30:29.424999Z",{"total":41,"page":22,"page_size":41,"items":77},[78,141,206,281,334,384],{"id":79,"title":80,"url":81,"summary":82,"summary_zh":83,"content":9,"source_name":84,"source_url":81,"published_at":85,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":86,"score_detail":87,"sources":90,"tags":92,"primary_topic":9,"secondary_topics":95,"classification":96,"search_phrases":117,"slug":120,"view_count":66,"doi":121,"paper":122,"created_at":140},4823,"Comparing Sentinel-2 Spectral Indices for Mapping Forest Fire Damage in Pollino National Park, Italy","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fisprs-archives-xlviii-m-13-2026-1-2026","In recent years, climate change has significantly increased the severity and frequency of wildfires across Europe, with the 2025 season marking an unprecedented peak of one million hectares burned. Rapid and cost-effective mapping of these damaged areas is crucial for environmental recovery, making satellite remote sensing an indispensable tool. This study evaluates and compares the performance of four Sentinel-2 spectral burn indices for mapping forest fire damage within the Pollino National Park (Italy) occurred in September 2025, an area severely affected by the devastating summer fires of 2025. The analyzed indices include the Normalized Burn Ratio (NBR), Normalized Burn Ratio Plus (NBR+), Normalized Burn Ratio Short Wave Infrared (NBR-SWIR), and Normalized Difference Short Wave Infrared (ND-SWIR). An image differencing technique was applied to pre-fire (June 7, 2025) and post-fire (November 14, 2025) datasets, followed by a supervised Maximum Likelihood classification to map burned and unburned classes. Accuracy assessment via confusion matrices showed that NBR and NBR+ delivered the highest performance, achieving an Overall Accuracy (OA) of 0.976 and 0.974, respectively. ND-SWIR performed closely at 0.966, whereas NBR-SWIR yielded a lower OA of 0.873 due to the misclassification of bare soil transitions as burned areas. These findings highlight the critical role of specific SWIR-band combinations in optimizing automated wildfire monitoring systems.","近年来，气候变化显著加剧了欧洲野火的严重程度和发生频率，2025年火灾季烧毁面积达到一百万公顷，创下历史新高。快速且经济高效地绘制这些受损区域对于环境恢复至关重要，使卫星遥感成为不可或缺的工具。本研究评估并比较了四种哨兵2号（Sentinel-2）光谱燃烧指数在绘制2025年9月发生于意大利波利诺国家公园（Pollino National Park）的森林火灾损害中的表现，该地区受2025年夏季毁灭性火灾的严重影响。所分析的指数包括归一化燃烧比率（NBR）、归一化燃烧比率增强版（NBR+）、归一化燃烧比率短波红外（NBR-SWIR）以及归一化差值短波红外（ND-SWIR）。研究对火灾前（2025年6月7日）和火灾后（2025年11月14日）数据集应用了图像差值技术，随后采用监督最大似然分类法绘制燃烧与未燃烧类别。通过混淆矩阵进行的精度评估表明，NBR和NBR+表现最佳，总体精度（OA）分别达到0.976和0.974。ND-SWIR紧随其后，为0.966，而NBR-SWIR由于将裸土转变误分类为燃烧区域，总体精度较低，为0.873。这些发现凸显了特定短波红外波段组合在优化自动化野火监测系统中的关键作用。","˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences\u002FInternational archives of the photogrammetry, remote sensing and spatial information sciences","2026-10-12T00:00:00Z",74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":88,"relevant":22,"comment":89},9,"方法对比扎实、结论明确，但属区域案例研究，对农业信息化有间接参考价值，可入精选但非必选。",[91],{"name":84,"url":81},[27,93,94,29,30],"遥感监测","灾害评估",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":97},{"candidates":98,"agricultural_evidence":14,"reason":51},[99,104,110],{"code":40,"score":88,"supported":42,"title":100,"abstract":101,"tags":103},[],[28,102],"remote sensing",[27,30,93],{"code":47,"score":21,"supported":42,"title":105,"abstract":106,"tags":109},[],[107,108],"气候变化","climate change",[29,94],{"code":111,"score":112,"supported":42,"title":113,"abstract":114,"tags":116},"H2",3,[],[115],"soil",[],[118,119],"Sentinel-2 森林火灾 遥感","Pollino 国家公园 火烧迹地","Sentinel-2森林火灾遥感-4823","10.5194\u002Fisprs-archives-xlviii-m-13-2026-1-2026",{"doi":121,"openalex_id":123,"authors":124,"venue":84,"cited_by_count":66,"oa_url":134,"card":135,"direction":72,"ingested_from":74},"W7221528348",[125,127,129,132],{"name":126,"orcid":9},"Giuseppina Costanzo",{"name":128,"orcid":9},"Gianluigi Ferrara",{"name":130,"orcid":131},"Claudio Parente","https:\u002F\u002Forcid.org\u002F0000-0002-8328-9764",{"name":133,"orcid":9},"Vincenzo Verde","https:\u002F\u002Fisprs-archives.copernicus.org\u002Farticles\u002FXLVIII-M-13-2026\u002F1\u002F2026\u002Fisprs-archives-XLVIII-M-13-2026-1-2026.pdf",{"tldr":136,"method":137,"finding":138,"direction":72,"opportunity":139},"比较四种 Sentinel-2 光谱指数在意大利 Pollino 国家公园 2025 年林火损伤制图","Sentinel-2 火前火后影像差值 + 最大似然监督分类，用混淆矩阵评估四种","NBR 与 NBR+ 精度最高（OA 0.976、0.974），NBR-SWIR 因裸土误判仅 0.","可探索多指数融合与机器学习分类器，提升复杂地表下火烧迹地自动制图鲁棒性。","2026-10-13T23:30:23.838067Z",{"id":142,"title":143,"url":144,"summary":145,"summary_zh":146,"content":9,"source_name":147,"source_url":144,"published_at":148,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":149,"score_detail":150,"sources":153,"tags":155,"primary_topic":159,"secondary_topics":164,"classification":165,"search_phrases":179,"slug":182,"view_count":66,"doi":183,"paper":184,"created_at":205},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":21,"substance":19,"depth":151,"authority":17,"freshness":88,"relevant":22,"comment":152},15,"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。",[154],{"name":147,"url":144},[156,157,28,30,158],"智慧农业","产量预测","菜豆",{"code":40,"slug":160,"name":161,"path":162,"parent_code":163},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":166,"locked":14,"version":35,"method":36,"evidence":167},"classified",{"candidates":168,"agricultural_evidence":42},[169,174],{"code":40,"score":17,"supported":42,"title":170,"abstract":171,"tags":173},[],[28,102,172],"multispectral",[157,30,28],{"code":175,"score":22,"supported":14,"title":176,"abstract":177,"tags":178},"I1",[],[],[156],[180,181],"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":183,"openalex_id":185,"authors":186,"venue":147,"cited_by_count":66,"oa_url":199,"card":200,"direction":72,"ingested_from":74},"W7222720635",[187,190,193,196],{"name":188,"orcid":189},"Guillermo Vargas-Hernández","https:\u002F\u002Forcid.org\u002F0000-0003-1410-3898",{"name":191,"orcid":192},"Eddison José Araya-Morales","https:\u002F\u002Forcid.org\u002F0009-0001-0550-414X",{"name":194,"orcid":195},"Juan Carlos Hernández","https:\u002F\u002Forcid.org\u002F0000-0002-1915-3213",{"name":197,"orcid":198},"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":201,"method":202,"finding":203,"direction":72,"opportunity":204},"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":207,"title":208,"url":209,"summary":210,"summary_zh":211,"content":9,"source_name":212,"source_url":209,"published_at":213,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":86,"score_detail":214,"sources":216,"tags":218,"primary_topic":9,"secondary_topics":222,"classification":223,"search_phrases":261,"slug":264,"view_count":66,"doi":265,"paper":266,"created_at":280},4893,"Systematic vegetation index discovery for phenology-based crop mapping","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1911652","Introduction The Systematic Vegetation Index Discovery (SVID) framework generates and evaluates large numbers of vegetation indices (VIs) by combining spectral bands with basic arithmetic operations. We evaluated whether systematically discovered indices could improve agricultural land-cover classification compared with conventional indices such as NDVI while reducing the dimensionality of multitemporal spectral data. Methods We applied SVID to a 323.9 ha agricultural landscape in Andong, South Korea, containing rice paddies, pepper fields, and plastic-covered greenhouses, using 32 cloud‐free Sentinel‐2A images acquired during 2022. Of 1,328 candidate expressions, 1,144 produced numerically valid rasters. A separate Random Forest model was trained for each index using its 32‐date temporal profile and evaluated against 500 independent reference points withheld from training. Before ranking, 388 expressions were identified as algebraically identical to another expression, and a further 355 were strictly monotone transformations and therefore indistinguishable to a rank‐based classifier. Results The 1,144 indices consequently represented 401 distinct features. Several near‐infrared‐to‐visible ratio families outperformed NDVI. The best‐performing index, (NIR – Red)\u002F(Green × Blue), achieved 97.0% overall accuracy and a Kappa coefficient of 0.873, compared with 94.4% and 0.734 for NDVI; this difference was significant by McNemar's test (p = 0.002). The improvement was concentrated in the greenhouse class, whose producer's accuracy increased from 58.6% to 84.5%. A model using all four raw bands across all dates (128 features) achieved 96.0% accuracy and was statistically indistinguishable from the best single index, demonstrating a four‐fold reduction in dimensionality without a detectable loss in accuracy. Rank positions were nevertheless unstable: ten of the 401 features were statistically tied with the best, and mathematically identical formulations differed by up to 3.4 percentage points because of finite‐precision effects. Discussion SVID can identify compact spectral representations that perform as well as or better than conventional indices and full multiband temporal models. However, the instability among top‐ranked indices suggests that results should be reported as index families with confidence intervals rather than as a single winner. Limitations include the use of one site, one year, and four spectral bands; broader testing across sites, years, and spectral configurations is needed.","引言 系统性植被指数发现（Systematic Vegetation Index Discovery, SVID）框架通过将光谱波段与基本算术运算相结合，生成并评估大量植被指数（vegetation indices, VIs）。我们评估了系统性发现的指数能否在降低多时相光谱数据维度的同时，相比NDVI等传统指数提升农业土地覆盖分类性能。方法 我们将SVID应用于韩国安东一处323.9公顷的农业景观，该区域包含稻田、辣椒田和覆膜温室，使用2022年获取的32景无云Sentinel-2A影像。在1,328个候选表达式中，1,144个生成了数值有效栅格。针对每个指数，利用其32个时相的时间剖面分别训练随机森林模型，并在500个未参与训练的独立参考点上进行评估。在排序之前，388个表达式被识别为与另一表达式代数等价，另有355个为严格单调变换，因而对基于秩的分类器不可区分。结果 这1,144个指数因此代表401个不同特征。若干近红外与可见光比值族的表现优于NDVI。表现最佳的指数(NIR – Red)\u002F(Green × Blue)总体精度达到97.0%，Kappa系数为0.873，而NDVI分别为94.4%和0.734；该差异经McNemar检验显著（p = 0.002）。提升主要集中在温室类别，其生产者精度从58.6%提高至84.5%。使用所有时相全部四个原始波段（128个特征）的模型精度为96.0%，与最佳单一指数在统计上不可区分，表明在未检测到精度损失的情况下实现了四倍的维度缩减。然而，排名位置并不稳定：401个特征中有10个与最佳特征在统计上并列，且由于有限精度效应，数学上等价的公式之间差异可达3.4个百分点。讨论 SVID能够识别出性能与常规指数及全多波段时间模型相当或更优的紧凑光谱表示。然而，排名靠前指数之间的不稳定性表明，结果应以指数族及置信区间的形式报告，而非单一最优指数。局限性包括仅使用一个站点、一年数据和四个光谱波段；需要在不同站点、年份和s","Frontiers in Remote Sensing","2026-10-13T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":88,"relevant":22,"comment":215},"提出系统性植被指数发现框架，在韩国安洞农田验证优于NDVI，方法新颖但仅单点单年，属细分领域方法学进展。",[217],{"name":212,"url":209},[219,220,28,221,30],"农业人工智能","精准农业","作物识别",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":224},{"candidates":225,"agricultural_evidence":42,"reason":260},[226,233,240,246,251,255],{"code":227,"score":41,"supported":42,"title":228,"abstract":229,"tags":232},"A1",[],[230,231],"稻田","rice",[],{"code":234,"score":41,"supported":42,"title":235,"abstract":236,"tags":239},"B7",[],[237,238],"温室","greenhouse",[],{"code":241,"score":112,"supported":42,"title":242,"abstract":243,"tags":245},"B1",[],[244],"辣椒",[],{"code":40,"score":247,"supported":14,"title":248,"abstract":249,"tags":250},2,[],[],[30,28],{"code":175,"score":22,"supported":14,"title":252,"abstract":253,"tags":254},[],[],[220],{"code":256,"score":22,"supported":14,"title":257,"abstract":258,"tags":259},"I2",[],[],[219],"多个产业证据相当，需要确认主归属",[262,263],"Sentinel-2 作物分类 植被指数","SVID 植被指数 水稻 辣椒","Sentinel-2作物分类植被指数-4893","10.3389\u002Ffrsen.2026.1911652",{"doi":265,"openalex_id":267,"authors":268,"venue":212,"cited_by_count":66,"oa_url":274,"card":275,"direction":72,"ingested_from":74},"W7221623694",[269,272],{"name":270,"orcid":271},"Ehsan Rahimi","https:\u002F\u002Forcid.org\u002F0000-0002-8401-472X",{"name":273,"orcid":9},"Chuleui Jung","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1911652\u002Fpdf",{"tldr":276,"method":277,"finding":278,"direction":72,"opportunity":279},"系统生成并评估大量植被指数，用于基于物候的农作物分类，发现优于NDVI的指数。","Sentinel-2A时序影像，组合光谱波段生成1328个指数，随机森林分类。","最佳指数(NIR-Red)\u002F(Green×Blue)精度97.0%，优于NDVI的94.4%，且维度","可探索更多波段组合与多站点验证，并研究指数排名不稳定的置信区间报告方法。","2026-10-14T23:30:20.231693Z",{"id":282,"title":283,"url":284,"summary":285,"summary_zh":286,"content":9,"source_name":287,"source_url":284,"published_at":288,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":289,"score_detail":290,"sources":293,"tags":295,"primary_topic":299,"secondary_topics":300,"classification":301,"search_phrases":313,"slug":316,"view_count":22,"doi":317,"paper":318,"created_at":333},4749,"Beyond site-specific archaeological prospection: Spectral-geometry reveals cross-site cropmark signals in satellite data","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fpnasnexus\u002Fpgag336","Abstract Remote sensing has long supported archaeological prospection, mainly through the detection of cropmarks, a key archaeological proxy expressed as spectral vegetation anomalies over subsurface features. Yet, cropmark detection is still assumed in the literature to be highly site-specific, governed by local agronomic and environmental conditions. Using multispectral Sentinel-2 data from three European archaeological sites, we show that models trained in one landscape can recover meaningful archaeological proxy signals in other locations. By relying strictly on satellite spectral signatures for training, our models achieve measurable cross-location transferability without incorporating ancillary environmental data. We find that cross-site performance arises not from geographic proximity but from the spectral-geometric similarity of vegetation responses, and that transferability is strongly directional. We quantify this relationship through a Directional Strength Score (DSS), derived from monthly subcluster distances under multiple norms, which captures directional geometric similarity and correlates strongly with machine-learning performance. To keep the analysis grounded in the underlying spectral signal, we relied on architecturally restrained nonlinear classifiers (SVM and constrained XGBoost) and used NDVI-identified phenophases to align training and evaluation with comparable seasonal windows. Taken together, these results demonstrate that partial, spectrally mediated cross-location cropmark detection is feasible and that the underlying vegetation responses exhibit stable spectral structure, with spectral geometry offering a clear interpretive lens on why these signals can transfer across sites. Leveraging globally harmonized satellite data, this approach offers a scalable complement to uneven historical aerial archives and supports systematic regional archaeological prioritization to be deployed within integrated archaeological workflows.","摘要 遥感长期支持考古勘探，主要通过检测作物痕迹（cropmarks）——一种关键的考古代用指标，表现为地下特征上方植被的光谱异常。然而，文献中仍普遍认为作物痕迹检测高度依赖于具体遗址，受当地农艺和环境条件制约。利用来自三个欧洲考古遗址的多光谱Sentinel-2数据，我们表明在一个景观中训练的模型可以在其他地点恢复有意义的考古代用信号。通过严格依赖卫星光谱特征进行训练，我们的模型在不纳入辅助环境数据的情况下实现了可测量的跨地点迁移能力。我们发现，跨遗址性能并非源于地理邻近性，而是源于植被响应的光谱-几何相似性，并且迁移能力具有强烈的方向性。我们通过方向强度评分（Directional Strength Score, DSS）量化这一关系，该评分基于多种范数下的逐月子簇距离推导而来，捕捉方向性几何相似性并与机器学习性能强烈相关。为使分析立足于底层光谱信号，我们采用架构受限的非线性分类器（SVM和约束XGBoost），并使用NDVI识别的物候期来对齐训练和评估，使其处于可比较的季节窗口内。综合来看，这些结果表明，部分、以光谱为中介的跨地点作物痕迹检测是可行的，且底层植被响应表现出稳定的光谱结构，光谱几何为理解这些信号为何能够跨遗址迁移提供了清晰的解释视角。利用全球协调的卫星数据，该方法为不均衡的历史航空档案提供了可扩展的补充，并支持在综合考古工作流程中部署系统性的区域考古优先级排序。","PNAS Nexus","2026-10-10T00:00:00Z",82,{"impact":19,"substance":18,"depth":291,"authority":151,"freshness":21,"relevant":22,"comment":292},19,"PNAS Nexus 论文，用 Sentinel-2 多光谱数据证明作物标记信号可跨遗址迁移，方法新颖、结论可靠，对农业遥感与考古信息化有参考价值，但属细分领域突破，未达全国性产业事件层级。",[294],{"name":287,"url":284},[27,28,296,297,298],"NDVI","考古遥感","作物标记",{"code":40,"slug":160,"name":161,"path":162,"parent_code":163},[],{"status":166,"locked":14,"version":35,"method":36,"evidence":302},{"candidates":303,"agricultural_evidence":42},[304,308],{"code":40,"score":17,"supported":42,"title":305,"abstract":306,"tags":307},[],[28,102,172],[296,27,28],{"code":256,"score":112,"supported":42,"title":309,"abstract":310,"tags":312},[],[311],"机器学习",[],[314,315],"Sentinel-2 作物标记 考古","遥感 光谱几何 跨遗址","Sentinel-2作物标记考古-4749","10.1093\u002Fpnasnexus\u002Fpgag336",{"doi":317,"openalex_id":319,"authors":320,"venue":287,"cited_by_count":66,"oa_url":327,"card":328,"direction":72,"ingested_from":74},"W7221319130",[321,324],{"name":322,"orcid":323},"Elias Gravanis","https:\u002F\u002Forcid.org\u002F0000-0002-5331-6661",{"name":325,"orcid":326},"Άθως Αγαπίου","https:\u002F\u002Forcid.org\u002F0000-0001-9106-6766","https:\u002F\u002Facademic.oup.com\u002Fpnasnexus\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fpnasnexus\u002Fpgag336\u002F71838194\u002Fpgag336.pdf",{"tldr":329,"method":330,"finding":331,"direction":72,"opportunity":332},"用Sentinel-2多光谱数据证明作物标记模型可跨遗址迁移，且迁移性由光谱几何相似性决定。","Sentinel-2多光谱、SVM与约束XGBoost、NDVI物候对齐、方向强","跨遗址作物标记检测可行，迁移性源于植被光谱几何相似而非地理邻近，且具方向性。","可将光谱几何迁移性指标引入作物表型与病害遥感，构建跨区域可迁移的植被异常检测模型。","2026-10-12T23:30:24.489835Z",{"id":335,"title":336,"url":337,"summary":338,"summary_zh":339,"content":9,"source_name":340,"source_url":337,"published_at":341,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":149,"score_detail":342,"sources":344,"tags":346,"primary_topic":350,"secondary_topics":351,"classification":352,"search_phrases":366,"slug":369,"view_count":66,"doi":370,"paper":371,"created_at":383},4748,"Study of Territories Using Geoinformatics and Remote Sensing Technologies: Spectral Band Combinations Based on Sentinel-2 Data (A Case Study of Parkent District)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23297593","This scientific paper analyzes the combination of spectral bands based on data from the European Space Agency's (ESA) Sentinel-2 satellite, using geoinformatics and remote sensing technologies. The Parkent district of the Tashkent region, Republic of Uzbekistan, was selected as the object of study. The research primarily compares the characteristics of the NIR-RED-GREEN (Near-Infrared, Red, Green) and SWIR-NIR-GREEN (Shortwave Infrared, Near-Infrared, Green) band combinations, as well as their advantages in identifying agricultural lands, forest cover, and assessing moisture levels.","本文基于欧洲空间局（ESA）Sentinel-2卫星数据，运用地理信息学与遥感技术，对光谱波段组合进行了分析。研究选取乌兹别克斯坦共和国塔什干州帕尔肯特区作为研究对象。研究主要比较了近红外-红-绿（NIR-RED-GREEN）与短波红外-近红外-绿（SWIR-NIR-GREEN）波段组合的特征，以及二者在识别农业用地、森林覆盖和评估湿度水平方面的优势。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-11T00:00:00Z",{"impact":21,"substance":19,"depth":151,"authority":17,"freshness":88,"relevant":22,"comment":343},"基于Sentinel-2多光谱波段组合的遥感应用研究，方法具体、数据来源权威，但研究对象为乌兹别克斯坦地方区域，对国内三农信息化的直接参考价值有限。",[345],{"name":340,"url":337},[347,27,28,348,349],"农业遥感","乌兹别克斯坦","光谱波段",{"code":40,"slug":160,"name":161,"path":162,"parent_code":163},[],{"status":166,"locked":14,"version":35,"method":36,"evidence":353},{"candidates":354,"agricultural_evidence":42},[355,360],{"code":40,"score":356,"supported":42,"title":357,"abstract":358,"tags":359},17,[102],[28,102],[27,347,28],{"code":361,"score":112,"supported":42,"title":362,"abstract":363,"tags":365},"I4",[],[364],"地理信息",[],[367,368],"Sentinel-2 光谱波段 组合","Parkent 遥感 农业用地","Sentinel-2光谱波段组合-4748","10.5281\u002Fzenodo.23297593",{"doi":370,"openalex_id":372,"authors":373,"venue":340,"cited_by_count":66,"oa_url":337,"card":378,"direction":72,"ingested_from":74},"W7221467289",[374,376],{"name":375,"orcid":9},"Xalilov Doniyor",{"name":377,"orcid":9},"Allambergenova Venera",{"tldr":379,"method":380,"finding":381,"direction":72,"opportunity":382},"基于Sentinel-2数据比较NIR-RED-GREEN与SWIR-NIR-GREEN波段组合，用","使用ESA Sentinel-2影像与地理信息学、遥感技术，对比两种光谱波段组合","NIR-RED-GREEN和SWIR-NIR-GREEN组合在识别农业用地、森林覆盖和评估湿度上各有","可进一步量化不同波段组合在干旱区作物分类与土壤湿度反演中的精度差异，并构建自动化优选流程。","2026-10-12T23:30:24.415736Z",{"id":385,"title":386,"url":387,"summary":388,"summary_zh":389,"content":9,"source_name":390,"source_url":387,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":391,"score_detail":392,"sources":395,"tags":397,"primary_topic":400,"secondary_topics":406,"classification":408,"search_phrases":435,"slug":438,"view_count":22,"doi":439,"paper":440,"created_at":464},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。","Applied Geomatics",68,{"impact":21,"substance":393,"depth":356,"authority":20,"freshness":88,"relevant":22,"comment":394},21,"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。",[396],{"name":390,"url":387},[156,28,30,398,399],"可可种植","无人机监测",{"code":401,"slug":402,"name":403,"path":404,"parent_code":405},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[407],{"code":40,"slug":160,"name":161,"path":162,"parent_code":163},{"status":166,"locked":14,"version":35,"method":36,"evidence":409},{"candidates":410,"agricultural_evidence":42},[411,418,422,428],{"code":401,"score":412,"supported":42,"title":413,"abstract":415,"tags":417},14,[414],"cacao",[416,414],"可可",[],{"code":40,"score":20,"supported":42,"title":419,"abstract":420,"tags":421},[172],[172],[30,28],{"code":175,"score":423,"supported":42,"title":424,"abstract":425,"tags":427},7,[],[220,426],"precision agriculture",[156],{"code":429,"score":41,"supported":42,"title":430,"abstract":431,"tags":434},"G2",[],[432,433],"小农户","smallholder",[],[436,437],"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":439,"openalex_id":441,"authors":442,"venue":390,"cited_by_count":66,"oa_url":458,"card":459,"direction":72,"ingested_from":74},"W7221039911",[443,446,449,452,455],{"name":444,"orcid":445},"Charlyngton da Silva e Silva","https:\u002F\u002Forcid.org\u002F0009-0003-9332-4302",{"name":447,"orcid":448},"Luiz Antônio Soares Cardoso","https:\u002F\u002Forcid.org\u002F0000-0001-6868-7847",{"name":450,"orcid":451},"Ronilson de Souza Santos","https:\u002F\u002Forcid.org\u002F0000-0002-2683-456X",{"name":453,"orcid":454},"Carlos Rodrigo Tanajura Caldeira","https:\u002F\u002Forcid.org\u002F0000-0001-7102-7735",{"name":456,"orcid":457},"Norberto Cornejo Noronha","https:\u002F\u002Forcid.org\u002F0000-0001-7216-8687","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12518-026-00815-x.pdf",{"tldr":460,"method":461,"finding":462,"direction":72,"opportunity":463},"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。","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。","2026-10-10T23:30:31.142887Z"]