[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4823":3,"related-4823":90},{"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":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":89},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。",null,"˜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","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"Sentinel-2","遥感监测","灾害评估","森林火灾","植被指数",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":62},[39,47,55],{"code":40,"score":21,"supported":41,"title":42,"abstract":43,"tags":46},"I3",true,[],[44,45],"遥感","remote sensing",[27,31,28],{"code":48,"score":49,"supported":41,"title":50,"abstract":51,"tags":54},"H7",8,[],[52,53],"气候变化","climate change",[30,29],{"code":56,"score":57,"supported":41,"title":58,"abstract":59,"tags":61},"H2",3,[],[60],"soil",[],"农业相关性需要复核",[64,65],"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",0,"10.5194\u002Fisprs-archives-xlviii-m-13-2026-1-2026",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":67,"oa_url":81,"card":82,"direction":86,"ingested_from":88},"W7221528348",[72,74,76,79],{"name":73,"orcid":9},"Giuseppina Costanzo",{"name":75,"orcid":9},"Gianluigi Ferrara",{"name":77,"orcid":78},"Claudio Parente","https:\u002F\u002Forcid.org\u002F0000-0002-8328-9764",{"name":80,"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":83,"method":84,"finding":85,"direction":86,"opportunity":87},"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.","农业遥感与作物表型","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。","openalex","2026-10-13T23:30:23.838067Z",{"total":91,"page":22,"page_size":91,"items":92},6,[93,141,199,268,339,401],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":104,"tags":106,"primary_topic":9,"secondary_topics":108,"classification":109,"search_phrases":120,"slug":123,"view_count":22,"doi":124,"paper":125,"created_at":140},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。","Turkish Journal of Forestry | Türkiye Ormancılık Dergisi","2026-10-08T00:00:00Z",73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":49,"relevant":22,"comment":103},"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。",[105],{"name":99,"url":96},[27,44,30,31,107],"Landsat-8",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":110},{"candidates":111,"agricultural_evidence":14,"reason":62},[112,116],{"code":40,"score":91,"supported":41,"title":113,"abstract":114,"tags":115},[],[44],[27,31,44],{"code":48,"score":22,"supported":14,"title":117,"abstract":118,"tags":119},[],[],[30],[121,122],"Çandır orman yangını","森林火灾 植被指数 遥感 Sentinel-2","Çandırormanyangını-4591","10.18182\u002Ftjf.1721381",{"doi":124,"openalex_id":126,"authors":127,"venue":99,"cited_by_count":67,"oa_url":134,"card":135,"direction":86,"ingested_from":88},"W7220904781",[128,131],{"name":129,"orcid":130},"Abdurrahman Cankut Göz","https:\u002F\u002Forcid.org\u002F0000-0003-1786-7658",{"name":132,"orcid":133},"HÜSEYİN OĞUZ ÇOBAN","https:\u002F\u002Forcid.org\u002F0000-0002-4037-4811","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F4966779",{"tldr":136,"method":137,"finding":138,"direction":86,"opportunity":139},"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","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。","2026-10-10T23:30:29.424999Z",{"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":67,"score_detail":149,"sources":151,"tags":153,"primary_topic":9,"secondary_topics":155,"classification":156,"search_phrases":174,"slug":177,"view_count":67,"doi":178,"paper":179,"created_at":198},5488,"Application of radar remote sensing for cyclone damage mapping in Bangladesh: a coherence-based approach","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fnhess-26-5103-2026","Abstract. Rapid and reliable assessment of cyclone-induced damage remains a major challenge in coastal regions where cloud cover and accessibility limit conventional approaches. This study presents a coherence-based Synthetic Aperture Radar (SAR) framework for identifying as well as quantifying cyclone-induced surface changes in coastal districts in Bangladesh. Multi-temporal Sentinel-1 data were analyzed using interferometric coherence to capture spatial and temporal variations associated with five major cyclones: Amphan (2020), Sitrang (2022), Mocha (2023), Hamoon (2023), and Remal (2024). A time-series approach incorporating multiple pre-, during-, and post-event image pairs was implemented to distinguish cyclone-driven changes from natural surface variability and to improve detection reliability. The results reveal a consistent decline in coherence during cyclone events followed by gradual recovery. Spatial analysis indicates that areas of significant coherence loss correspond to zones affected by flooding, vegetation damage, and structural disruption. Estimated damage extents varied across cyclones, with Remal (2024) showing the largest affected area (~601 km²), followed by Mocha (~474 km²), Amphan (~288 km²), Sitrang (~122 km²), and Hamoon (~117 km²). Validation using field observations and secondary data yielded an overall classification accuracy of 94 %, confirming the robustness of the coherence-based approach. Complementary inundation mapping further demonstrated that coherence captures a broader spectrum of cyclone impacts beyond flood extent alone, including wind-induced damage and geomorphic changes. This study establishes multi-temporal SAR coherence analysis as a scalable, cloud-independent, and operationally effective tool for rapid post-cyclone damage assessment. The proposed framework enhances the reliability of disaster monitoring in complex coastal environments and offers strong potential for integration into near-real-time disaster response systems. Future research should focus on integrating multi-sensor datasets and long-term coherence time series to further refine damage characterization and reduce uncertainty.","摘要：在云覆盖和可达性限制传统方法的沿海地区，快速可靠地评估气旋所致损害仍是一项重大挑战。本研究提出了一种基于相干性的合成孔径雷达（SAR）框架，用于识别并量化孟加拉国沿海地区气旋引起的地表变化。利用干涉相干性对多时相Sentinel-1数据进行分析，以捕捉与五次主要气旋相关的时间和空间变化：安潘（Amphan，2020年）、西特朗（Sitrang，2022年）、穆查（Mocha，2023年）、哈蒙（Hamoon，2023年）和雷马尔（Remal，2024年）。研究采用了一种整合多个事件前、事件期间和事件后影像对的时间序列方法，以将气旋驱动变化与自然地表的变异性区分开来，并提高检测可靠性。结果表明，气旋事件期间相干性持续下降，随后逐渐恢复。空间分析表明，相干性显著丧失的区域与受洪水、植被破坏和结构损毁影响的区域相对应。各气旋的估计受损范围有所不同，其中雷马尔（2024年）受影响面积最大（约601 km²），其次为穆查（约474 km²）、安潘（约288 km²）、西特朗（约122 km²）和哈蒙（约117 km²）。利用实地观测和二手数据进行的验证获得了94%的总体分类精度，证实了基于相干性方法的稳健性。补充的淹没制图进一步表明，相干性捕捉到了除洪水范围之外的更广泛的气旋影响，包括风致损害和地貌变化。本研究确立了多时相SAR相干性分析作为一种可扩展、不依赖云覆盖且业务上有效的工具，用于气旋后快速损害评估。所提出的框架提高了复杂沿海环境中灾害监测的可靠性，并具有将其集成到近实时灾害响应系统中的强大潜力。未来研究应侧重于整合多传感器数据集和长期相干性时间序列，以进一步完善损害表征并减少不确定性。","Natural hazards and earth system sciences","2026-10-19T00:00:00Z",{"impact":67,"substance":67,"depth":67,"authority":67,"freshness":67,"relevant":67,"comment":150},"该研究聚焦孟加拉国海岸带气旋灾害的SAR相干性制图，虽涉及遥感技术，但与三农、农业信息化、数字乡村等主题无直接关联，不宜进入每日精选。",[152],{"name":147,"url":144},[154,28,29],"SAR遥感",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":157},{"candidates":158,"agricultural_evidence":14,"reason":62},[159,163,170],{"code":40,"score":21,"supported":41,"title":160,"abstract":161,"tags":162},[45],[],[28],{"code":164,"score":91,"supported":41,"title":165,"abstract":166,"tags":169},"I4",[],[167,168],"空间分析","spatial analysis",[],{"code":48,"score":22,"supported":14,"title":171,"abstract":172,"tags":173},[],[],[29],[175,176],"Sentinel-1 气旋 灾害制图","雷达遥感 孟加拉湾 气旋","Sentinel-1气旋灾害制图-5488","10.5194\u002Fnhess-26-5103-2026",{"doi":178,"openalex_id":180,"authors":181,"venue":147,"cited_by_count":67,"oa_url":192,"card":193,"direction":86,"ingested_from":88},"W7160271999",[182,185,188,190],{"name":183,"orcid":184},"Shadmin Islam Prova","https:\u002F\u002Forcid.org\u002F0000-0001-8549-1758",{"name":186,"orcid":187},"Md. Shahoriar Sarker","https:\u002F\u002Forcid.org\u002F0009-0006-5131-1214",{"name":189,"orcid":9},"Rosdania Islam",{"name":191,"orcid":9},"Ridad Ibne Mahfuz","https:\u002F\u002Fnhess.copernicus.org\u002Farticles\u002F26\u002F5103\u002F2026\u002Fnhess-26-5103-2026.pdf",{"tldr":194,"method":195,"finding":196,"direction":86,"opportunity":197},"用Sentinel-1多时相干涉相干性识别并量化孟加拉湾五次气旋的地表破坏。","Sentinel-1多时相InSAR相干性时间序列，结合实地与二手数据验证。","气旋期间相干性显著下降后逐步恢复，分类精度94%，Remal受灾面积最大约601km²。","可将相干性框架迁移至农田风灾与洪涝受损制图，融合多源SAR与长时序相干性降低不确定性。","2026-10-21T23:30:23.509482Z",{"id":200,"title":201,"url":202,"summary":203,"summary_zh":204,"content":9,"source_name":10,"source_url":202,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":205,"score_detail":206,"sources":209,"tags":211,"primary_topic":9,"secondary_topics":215,"classification":216,"search_phrases":232,"slug":235,"view_count":67,"doi":236,"paper":237,"created_at":267},4886,"Mapping Fuel Categories in Mediterranean Regions with Sentinel-2 and PlanetScope Satellite Acquisitions","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fisprs-archives-xlviii-m-13-2026-9-2026","Accurate and spatially explicit mapping of forest fuel types is essential for developing integrated wildfire risk management strategies, estimating fire danger, and modeling fire propagation in fire-prone Mediterranean ecosystems. While time series of Copernicus Sentinel-2 multispectral imagery at 10 m spatial resolution have proven effective for regional fuel mapping, severe spectral and spatial ambiguities persist in distinguishing complex canopy structures, heterogeneous shrublands, and pastures. This study explores the synergistic integration of Sentinel-2 time series and high-resolution PlanetScope satellite acquisitions (3 m spatial resolution) to produce a 10 m fuel type map (9 classes) over a pilot area in Sardinia, Italy. A total of 114 features were synthesized, combining dynamic Sentinel-2 metrics (kNDVI, FAPAR, NDMI, ReNDVI harmonics), PlanetScope seasonal kNDVI zonal statistics and Gray-Level Co-occurrence Matrix (GLCM) textural features, and static terrain\u002Fcanopy parameters. Using an XGBoost machine learning classifier trained on 245 photo-interpreted Regions of Interest (80\u002F20 spatial split), three sets of features were compared. The combined model achieved a test overall accuracy of 74.46%, compared to 74.05% for Sentinel-2 alone and 74.66% for PlanetScope alone. Notably, incorporating PlanetScope textural features significantly improved classification metrics for dense canopy classes, increasing the F1-score of broadleaf evergreen trees and needleleaf evergreen trees. The PlanetScope GLCM kNDVI autumn entropy emerged as the most important feature overall. Conversely, PlanetScope performed poorly on the grass class. These findings highlight the critical trade-offs and high complementarity between fine spatial texture and broad spectral coverage for operational wildfire management.","在易发生火灾的地中海生态系统中，准确且具有空间明确性的森林可燃物类型制图对于制定综合野火风险管理策略、估算火险以及模拟火蔓延至关重要。尽管10 m空间分辨率的哥白尼Sentinel-2多光谱影像时间序列已被证明对区域可燃物制图有效，但在区分复杂冠层结构、异质性灌丛和草地时，仍存在严重的光谱和空间模糊性。本研究探索了Sentinel-2时间序列与高分辨率PlanetScope卫星影像（3 m空间分辨率）的协同集成，以在意大利撒丁岛的一个试验区生成10 m可燃物类型图（9类）。共合成114个特征，结合了动态Sentinel-2指标（kNDVI、FAPAR、NDMI、ReNDVI谐波）、PlanetScope季节性kNDVI分区统计和灰度共生矩阵（GLCM）纹理特征，以及静态地形\u002F冠层参数。使用在245个照片判读感兴趣区（80\u002F20空间划分）上训练的XGBoost机器学习分类器，比较了三组特征。组合模型的测试总体精度为74.46%，而仅使用Sentinel-2为74.05%，仅使用PlanetScope为74.66%。值得注意的是，纳入PlanetScope纹理特征显著改善了密集冠层类的分类指标，提高了阔叶常绿乔木和针叶常绿乔木的F1分数。PlanetScope GLCM kNDVI秋季熵成为总体最重要的特征。相反，PlanetScope在草地类上表现较差。这些发现凸显了精细空间纹理与广域光谱覆盖在业务化野火管理中的关键权衡和高互补性。",72,{"impact":17,"substance":18,"depth":207,"authority":20,"freshness":49,"relevant":22,"comment":208},17,"方法新颖、数据扎实的遥感制图论文，对林火风险管理有参考价值，但属细分领域研究，公共影响有限。",[210],{"name":10,"url":202},[27,212,28,213,214],"机器学习","地中海农业","林火风险",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":217},{"candidates":218,"agricultural_evidence":14,"reason":62},[219,226],{"code":220,"score":221,"supported":41,"title":222,"abstract":223,"tags":225},"I2",7,[],[212,224],"machine learning",[212],{"code":40,"score":227,"supported":41,"title":228,"abstract":229,"tags":231},5,[],[230],"multispectral",[27,28],[233,234],"Sentinel-2 PlanetScope 燃料类型制图","撒丁岛 林火燃料 遥感","Sentinel-2PlanetScope燃料类型制图-4886","10.5194\u002Fisprs-archives-xlviii-m-13-2026-9-2026",{"doi":236,"openalex_id":238,"authors":239,"venue":10,"cited_by_count":67,"oa_url":260,"card":261,"direction":266,"ingested_from":88},"W7221532579",[240,243,246,248,251,254,257],{"name":241,"orcid":242},"Federico Filipponi","https:\u002F\u002Forcid.org\u002F0000-0002-1419-3734",{"name":244,"orcid":245},"Daniela Stroppiana","https:\u002F\u002Forcid.org\u002F0000-0002-5619-4305",{"name":247,"orcid":9},"Forough Rajabi",{"name":249,"orcid":250},"Debora Voltolina","https:\u002F\u002Forcid.org\u002F0000-0001-9186-0644",{"name":252,"orcid":253},"Bachisio Arca","https:\u002F\u002Forcid.org\u002F0000-0002-2834-2753",{"name":255,"orcid":256},"Michele Salis","https:\u002F\u002Forcid.org\u002F0000-0002-0499-9726",{"name":258,"orcid":259},"Francesco Zucca","https:\u002F\u002Forcid.org\u002F0000-0003-4119-5484","https:\u002F\u002Fisprs-archives.copernicus.org\u002Farticles\u002FXLVIII-M-13-2026\u002F9\u002F2026\u002Fisprs-archives-XLVIII-M-13-2026-9-2026.pdf",{"tldr":262,"method":263,"finding":264,"direction":86,"opportunity":265},"融合Sentinel-2时序与PlanetScope高分影像，用XGBoost绘制地中海9类可燃物类","Sentinel-2时序谐波、PlanetScope kNDVI与GLCM纹理及","联合模型精度74.46%，PlanetScope纹理显著提升密冠层F1，但草地类表现差。","可探索多源高分纹理与光谱互补机制，优化稀疏植被与草地类别的特征设计。","智慧农业 \u002F 农业物联网","2026-10-14T23:30:11.734574Z",{"id":269,"title":270,"url":271,"summary":272,"summary_zh":273,"content":9,"source_name":274,"source_url":271,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":275,"score_detail":276,"sources":279,"tags":281,"primary_topic":285,"secondary_topics":290,"classification":296,"search_phrases":310,"slug":313,"view_count":67,"doi":314,"paper":315,"created_at":338},4826,"Multi-Index Remote Sensing Assessment of Agro-Climatic Change and Agricultural Responses in Temperate and Semi-Arid Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18203496","Diverse climatic regions exhibit unique agro-climatic vulnerabilities that are significantly shaped by climate stress. Understanding these distinct vulnerabilities is essential for developing effective policies to mitigate climate change impacts and ensuring sustainable environmental management. This study examines agro-climatic vulnerabilities in Nottinghamshire County (United Kingdom) and Udaipur District (India) from 2000 to 2024, comparing climate change impacts across temperate and semi-arid agricultural systems that represent fundamentally different system behaviours under contrasting climatic conditions. Using multi-temporal Landsat imagery and WorldClim climate data, nine environmental indices were analysed: the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Normalized Difference Water Index (NDWI), Moisture Stress Index (MSI), Land Surface Temperature (LST), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Drought Risk Index (DRI), and Crop Health Index (CHI). The results reveal contrasting regional trajectories that challenge conventional assumptions about climate vulnerability. Udaipur exhibited improved vegetation and moisture conditions across the observation period, whereas Nottinghamshire showed intensifying moisture stress and declining precipitation. Warming was more pronounced in Nottinghamshire, while strong inter-index correlations validated the monitoring approach. These findings indicate that Udaipur benefits from intensified monsoon patterns, requiring flood mitigation and water-storage infrastructure, whereas Nottinghamshire’s rising moisture stress underscores the need for sustained monitoring and adaptive management. This comparative framework provides evidence-based insights for region-specific adaptation strategies essential to sustainable agricultural development across diverse agro-climatic zones.","不同气候区域表现出独特的农业气候脆弱性，而这些脆弱性在很大程度上受到气候胁迫的影响。理解这些不同的脆弱性对于制定有效政策以减缓气候变化影响、确保可持续环境管理至关重要。本研究考察了2000年至2024年间英国诺丁汉郡与印度乌代布尔县的农业气候脆弱性，比较了温带与半干旱农业系统在气候变化影响下的差异，这两类系统在截然不同的气候条件下代表了根本不同的系统行为。研究利用多时相Landsat影像和WorldClim气候数据，分析了九个环境指数：归一化植被指数（NDVI）、植被状态指数（VCI）、归一化水体指数（NDWI）、水分胁迫指数（MSI）、地表温度（LST）、温度状态指数（TCI）、植被健康指数（VHI）、干旱风险指数（DRI）和作物健康指数（CHI）。结果揭示了截然不同的区域变化轨迹，挑战了关于气候脆弱性的传统假设。乌代布尔在观测期内表现出植被和水分条件的改善，而诺丁汉郡则显示出加剧的水分胁迫和降水减少。诺丁汉郡的增温更为显著，而指数间较强的相关性验证了该监测方法。这些发现表明，乌代布尔受益于季风模式的增强，需要防洪和水储存基础设施，而诺丁汉郡不断上升的水分胁迫则凸显了持续监测和适应性管理的必要性。这一比较框架为因地制宜的适应策略提供了循证依据，对跨不同农业气候区的可持续发展至关重要。","Remote Sensing",75,{"impact":17,"substance":18,"depth":19,"authority":277,"freshness":21,"relevant":22,"comment":278},14,"基于多时相遥感与九项环境指数的跨区域对比研究，方法扎实、结论具政策参考价值，但属学术论文，产业影响有限。",[280],{"name":274,"url":271},[28,31,282,283,284],"干旱风险","适应策略","农业气候",{"code":40,"slug":286,"name":287,"path":288,"parent_code":289},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[291],{"code":48,"slug":292,"name":293,"path":294,"parent_code":295},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":297,"locked":14,"version":35,"method":36,"evidence":298},"classified",{"candidates":299,"agricultural_evidence":41},[300,304],{"code":40,"score":13,"supported":41,"title":301,"abstract":302,"tags":303},[45],[],[31,28],{"code":48,"score":21,"supported":41,"title":305,"abstract":306,"tags":309},[],[52,307,53,308],"干旱","drought",[],[311,312],"Landsat 遥感 农业气候","诺丁汉郡 乌代布尔","Landsat遥感农业气候-4826","10.3390\u002Frs18203496",{"doi":314,"openalex_id":316,"authors":317,"venue":274,"cited_by_count":67,"oa_url":271,"card":333,"direction":86,"ingested_from":88},"W7221570035",[318,321,324,327,330],{"name":319,"orcid":320},"Bhanwar Vishvendra Raj Singh","https:\u002F\u002Forcid.org\u002F0000-0001-7750-5240",{"name":322,"orcid":323},"Vivek Agarwal","https:\u002F\u002Forcid.org\u002F0000-0003-1185-3802",{"name":325,"orcid":326},"Stuart Marsh","https:\u002F\u002Forcid.org\u002F0000-0002-1168-6760",{"name":328,"orcid":329},"Gajendra Singh Sisodiya","https:\u002F\u002Forcid.org\u002F0009-0006-4118-7491",{"name":331,"orcid":332},"Ajay Sharma","https:\u002F\u002Forcid.org\u002F0009-0001-6077-9875",{"tldr":334,"method":335,"finding":336,"direction":86,"opportunity":337},"用多时相遥感和九项指数对比英国诺丁汉郡与印度乌代浦2000—2024年农业气候脆弱性。","Landsat多时相影像与WorldClim气候数据，计算NDVI、VCI、ND","乌代浦植被与水分条件改善、季风增强，诺丁汉郡水分胁迫加剧、降水下降且升温更明显。","可拓展多区域长时序遥感指数耦合机器学习，构建跨气候带农业气候脆弱性预警与适应决策模型。","2026-10-13T23:30:24.062929Z",{"id":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":9,"source_name":345,"source_url":342,"published_at":346,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":347,"score_detail":348,"sources":351,"tags":353,"primary_topic":357,"secondary_topics":358,"classification":360,"search_phrases":382,"slug":385,"view_count":67,"doi":386,"paper":387,"created_at":400},4747,"Spatiotemporal mapping of agricultural drought using geospatial approach in the East Bale Zone, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fagg2.70475","Abstract Agricultural drought significantly threatens food security, livelihoods, and ecosystem stability, especially in regions prone to drought, such as the East Bale zone. This study investigates the spatial and temporal patterns of agricultural drought over 30 years (1993–2024) by combining meteorological station data with satellite‐derived vegetation and thermal indices. To assess meteorological drought, the standardized precipitation index (SPI) was calculated at 3‐, 6‐, and 12‐month intervals. For agricultural and thermal stress analysis, remote sensing‐based indices including the vegetation condition index (VCI), temperature condition index, and vegetation health index (VHI) were utilized. The results indicate that the years 2003, 2008, and 2018 experienced the most severe droughts, as shown consistently across all indices. Spatial analysis revealed that the central, southern, and eastern lowland areas faced the highest frequency and severity of drought, while the northwestern highlands remained comparatively resilient. Cross‐index correlation revealed strong agreement between SPI and VCI ( R 2 = 0.77), and between VHI and VCI ( R 2 = 0.83), supporting the effectiveness of integrated drought monitoring approaches. This research advances the field of drought assessment by enhancing spatial and temporal resolution and validating cross‐index relationships at the subregional level. The results provide critical insights for localized drought early warning systems, climate adaptation strategies, and future research to improve agricultural resilience under changing climatic conditions. Hence, agricultural drought risk mapping serves as a valuable tool for guiding decision‐making in drought monitoring and for mitigating its impacts on agricultural productivity.","摘要 农业干旱严重威胁粮食安全、生计和生态系统稳定性，尤其是在东巴莱区等易旱地区。本研究结合气象站数据与卫星反演的植被和热力指数，探讨了30年（1993—2024年）间农业干旱的时空格局。为评估气象干旱，计算了3个月、6个月和12个月尺度的标准化降水指数（SPI）。在农业与热胁迫分析方面，采用了基于遥感的植被状态指数（VCI）、温度状态指数和植被健康指数（VHI）。结果表明，2003年、2008年和2018年经历了最严重的干旱，各指数均一致反映了这一情况。空间分析显示，中部、南部和东部低地地区干旱发生频率最高、程度最严重，而西北部高地则相对具有较强恢复力。跨指数相关性分析表明，SPI与VCI之间（R²=0.77）以及VHI与VCI之间（R²=0.83）具有高度一致性，支持了综合干旱监测方法的有效性。本研究通过提高时空分辨率并在次区域尺度上验证跨指数关系，推进了干旱评估领域的发展。研究结果为地方性干旱预警系统、气候适应策略以及未来研究提供了重要见解，有助于在气候变化条件下提升农业韧性。因此，农业干旱风险制图为干旱监测决策及其对农业生产影响的减缓提供了有价值的工具。","Agrosystems Geosciences & Environment","2026-10-11T00:00:00Z",68,{"impact":49,"substance":349,"depth":207,"authority":20,"freshness":21,"relevant":22,"comment":350},21,"基于30年气象与遥感指数交叉验证的农业干旱时空制图研究，方法扎实、结论可靠，但属区域性案例，对国内三农信息化仅有方法借鉴价值。",[352],{"name":345,"url":342},[354,355,28,31,356],"粮食安全","防灾减灾","农业干旱",{"code":48,"slug":292,"name":293,"path":294,"parent_code":295},[359],{"code":40,"slug":286,"name":287,"path":288,"parent_code":289},{"status":297,"locked":14,"version":35,"method":36,"evidence":361},{"candidates":362,"agricultural_evidence":41},[363,368,372,378],{"code":48,"score":19,"supported":41,"title":364,"abstract":365,"tags":367},[308],[52,366,307,308],"气候适应",[355],{"code":40,"score":49,"supported":41,"title":369,"abstract":370,"tags":371},[],[44,45],[31,28],{"code":373,"score":221,"supported":41,"title":374,"abstract":375,"tags":377},"A8",[],[354,376],"food security",[354],{"code":164,"score":91,"supported":41,"title":379,"abstract":380,"tags":381},[],[167,168],[],[383,384],"埃塞俄比亚 East Bale 农业干旱","SPI VCI 干旱监测","埃塞俄比亚EastBale农业干旱-4747","10.1002\u002Fagg2.70475",{"doi":386,"openalex_id":388,"authors":389,"venue":345,"cited_by_count":67,"oa_url":342,"card":395,"direction":86,"ingested_from":88},"W7221455809",[390,392],{"name":391,"orcid":9},"Murad Hedo Haji",{"name":393,"orcid":394},"Karuturi Venkata Suryabhagavan","https:\u002F\u002Forcid.org\u002F0000-0003-2528-9106",{"tldr":396,"method":397,"finding":398,"direction":86,"opportunity":399},"结合气象站与卫星植被热指数，绘制埃塞俄比亚East Bale区30年农业干旱时空图。","用SPI（3\u002F6\u002F12月）与遥感VCI、TCI、VHI指数做交叉相关分析。","2003、2008、2018年干旱最重，中南部和东部低地最脆弱，SPI与VCI相关性R²=0.77。","可在小区域尺度融合多源指数构建干旱预警模型，并验证指数在不同作物区的适用性。","2026-10-12T23:30:24.342375Z",{"id":402,"title":403,"url":404,"summary":405,"summary_zh":406,"content":9,"source_name":407,"source_url":404,"published_at":408,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":409,"score_detail":410,"sources":412,"tags":414,"primary_topic":418,"secondary_topics":424,"classification":425,"search_phrases":451,"slug":454,"view_count":67,"doi":455,"paper":456,"created_at":471},4587,"Sentinel-2 NDVI as an indicator of apple orchard yield","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffhort.2026.1904481","Satellite data is increasingly being used as a basis for operational crop monitoring. However, satellite technologies for monitoring perennial crops remain underdeveloped. The aim of this study was to assess the relationship between apple yield and NDVI calculated using all available Sentinel-2 data for the growing seasons (March-November) from 2017 to 2024. Totally 81 cloud-free images were analyzed. The seasonal absolute maximum and area-weighted average NDVI were calculated based on analysis of all images for each growing season for each plot. Intensively irrigated, mixed-age apple orchards located in northern Azerbaijan served as the research site. Totally 30 plots with 6 apple tree varieties (Arian, Gala, Golden, Golden Parsi, Granny Smith, and Fuji) planted from 2007 to 2017 were analyzed. Apple yield for each plot for 2018–2024 were collected. The linear regression models between NDVI (the seasonal absolute maximum and area-weighted average) and apple yield was used as the relationship criterion. An analysis of the relationship was conducted for individual apple varieties, for orchard plots, and for plots of different ages. It was found that reliable relationships between apple yield and NDVI are only observed for specific plots with a single variety of the same age (R2cv higher than 0,8 with RPIQ more than 2,1). Grouping plots of the same variety or age does not demonstrate a reliable relationship between NDVI and yield. Only for groups of middle-aged orchards does such a relationship become stronger. These results should be considered when developing methods for satellite monitoring of apple orchards.","卫星数据正日益被用作业务化作物监测的基础。然而，用于监测多年生作物的卫星技术仍不发达。本研究旨在评估苹果产量与利用2017年至2024年生长季（3—11月）所有可用Sentinel-2数据计算的NDVI之间的关系。共分析了81幅无云图像。基于对每个生长季每块样地所有图像的分析，计算了季节绝对最大值NDVI和面积加权平均NDVI。研究地点位于阿塞拜疆北部集约化灌溉的混龄苹果园。共分析了30块样地，涵盖2007年至2017年种植的6个苹果品种（Arian、Gala、Golden、Golden Parsi、Granny Smith和Fuji）。收集了2018—2024年每块样地的苹果产量。以NDVI（季节绝对最大值和面积加权平均值）与苹果产量之间的线性回归模型作为关系评价标准。分别针对单个苹果品种、果园样地以及不同树龄样地进行了关系分析。研究发现，仅在特定同一品种、同树龄的样地中才能观察到苹果产量与NDVI之间可靠的关系（R2cv高于0.8，RPIQ大于2.1）。将同一品种或同一树龄的样地分组后，NDVI与产量之间并未表现出可靠的关系。仅对中年果园分组时，这种关系才变得更强。在开发苹果园卫星监测方法时，应考虑这些结果。","Frontiers in Horticulture","2026-10-09T00:00:00Z",71,{"impact":17,"substance":349,"depth":207,"authority":20,"freshness":49,"relevant":22,"comment":411},"基于2017—2024年81景Sentinel-2影像评估苹果园NDVI与产量的关系，方法扎实、结论有边界条件，对果园遥感监测有参考价值，但属细分领域研究，公共影响有限。",[413],{"name":407,"url":404},[415,416,27,28,417],"智慧农业","产量预测","苹果园",{"code":419,"slug":420,"name":421,"path":422,"parent_code":423},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":297,"locked":14,"version":35,"method":36,"evidence":426},{"candidates":427,"agricultural_evidence":41},[428,436,442,446],{"code":419,"score":429,"supported":41,"title":430,"abstract":433,"tags":435},25,[431,432],"orchard","apple",[434,431,432],"苹果",[],{"code":437,"score":57,"supported":41,"title":438,"abstract":439,"tags":441},"H3",[],[440],"灌溉",[],{"code":40,"score":57,"supported":14,"title":443,"abstract":444,"tags":445},[],[],[27,416,28],{"code":447,"score":22,"supported":14,"title":448,"abstract":449,"tags":450},"I1",[],[],[415],[452,453],"Sentinel-2 NDVI 苹果园 产量","阿塞拜疆 苹果园 遥感监测","Sentinel-2NDVI苹果园产量-4587","10.3389\u002Ffhort.2026.1904481",{"doi":455,"openalex_id":457,"authors":458,"venue":407,"cited_by_count":67,"oa_url":465,"card":466,"direction":86,"ingested_from":88},"W7221180616",[459,461,463],{"name":460,"orcid":9},"N. Gasimzade",{"name":462,"orcid":9},"A. Radzhabov",{"name":464,"orcid":9},"I. Savin","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fhorticulture\u002Farticles\u002F10.3389\u002Ffhort.2026.1904481\u002Fpdf",{"tldr":467,"method":468,"finding":469,"direction":86,"opportunity":470},"评估Sentinel-2 NDVI与苹果园产量的关系，发现仅单一品种同龄地块存在可靠相关。","用2017-2024年81景Sentinel-2影像计算NDVI，对30个苹果园","仅单一品种同龄地块NDVI与产量可靠相关（R²cv>0.8），按品种或树龄分组则关系不可靠。","多年生果园遥感估产需按品种、树龄精细分层建模，混合地块的普适性模型仍是研究空白。","2026-10-10T23:30:28.042882Z"]