[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-Sentinel-2":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":8,"page_size":9,"items":10},"Sentinel-2","tag",[4],16,1,100,[11,100,165,223,273,322,394,442,502,577,647,716,789,850,916,969],{"id":12,"title":13,"url":14,"summary":15,"summary_zh":16,"content":17,"source_name":18,"source_url":14,"published_at":19,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":23,"score_detail":24,"sources":31,"tags":33,"primary_topic":17,"secondary_topics":38,"classification":39,"search_phrases":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":99},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。",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,72,{"impact":25,"substance":26,"depth":27,"authority":28,"freshness":29,"relevant":8,"comment":30},12,22,17,13,8,"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。",[32],{"name":18,"url":14},[4,34,35,36,37],"机器学习","遥感监测","地中海农业","林火风险",[],{"status":40,"locked":22,"version":41,"method":42,"evidence":43},"needs_review","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":22,"reason":60},[45,53],{"code":46,"score":47,"supported":48,"title":49,"abstract":50,"tags":52},"I2",7,true,[],[34,51],"machine learning",[34],{"code":54,"score":55,"supported":48,"title":56,"abstract":57,"tags":59},"I3",5,[],[58],"multispectral",[4,35],"农业相关性需要复核",[62,63],"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",0,"10.5194\u002Fisprs-archives-xlviii-m-13-2026-9-2026",{"doi":66,"openalex_id":68,"authors":69,"venue":18,"cited_by_count":65,"oa_url":90,"card":91,"direction":97,"ingested_from":98},"W7221532579",[70,73,76,78,81,84,87],{"name":71,"orcid":72},"Federico Filipponi","https:\u002F\u002Forcid.org\u002F0000-0002-1419-3734",{"name":74,"orcid":75},"Daniela Stroppiana","https:\u002F\u002Forcid.org\u002F0000-0002-5619-4305",{"name":77,"orcid":17},"Forough Rajabi",{"name":79,"orcid":80},"Debora Voltolina","https:\u002F\u002Forcid.org\u002F0000-0001-9186-0644",{"name":82,"orcid":83},"Bachisio Arca","https:\u002F\u002Forcid.org\u002F0000-0002-2834-2753",{"name":85,"orcid":86},"Michele Salis","https:\u002F\u002Forcid.org\u002F0000-0002-0499-9726",{"name":88,"orcid":89},"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":92,"method":93,"finding":94,"direction":95,"opportunity":96},"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。","农业遥感与作物表型","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","openalex","2026-10-14T23:30:11.734574Z",{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":17,"source_name":18,"source_url":103,"published_at":19,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":106,"score_detail":107,"sources":111,"tags":113,"primary_topic":17,"secondary_topics":117,"classification":118,"search_phrases":141,"slug":144,"view_count":65,"doi":145,"paper":146,"created_at":164},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。",74,{"impact":25,"substance":26,"depth":108,"authority":28,"freshness":109,"relevant":8,"comment":110},18,9,"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。",[112],{"name":18,"url":103},[4,35,114,115,116],"灾害评估","森林火灾","植被指数",[],{"status":40,"locked":22,"version":41,"method":42,"evidence":119},{"candidates":120,"agricultural_evidence":22,"reason":60},[121,127,134],{"code":54,"score":109,"supported":48,"title":122,"abstract":123,"tags":126},[],[124,125],"遥感","remote sensing",[4,116,35],{"code":128,"score":29,"supported":48,"title":129,"abstract":130,"tags":133},"H7",[],[131,132],"气候变化","climate change",[115,114],{"code":135,"score":136,"supported":48,"title":137,"abstract":138,"tags":140},"H2",3,[],[139],"soil",[],[142,143],"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":145,"openalex_id":147,"authors":148,"venue":18,"cited_by_count":65,"oa_url":158,"card":159,"direction":95,"ingested_from":98},"W7221528348",[149,151,153,156],{"name":150,"orcid":17},"Giuseppina Costanzo",{"name":152,"orcid":17},"Gianluigi Ferrara",{"name":154,"orcid":155},"Claudio Parente","https:\u002F\u002Forcid.org\u002F0000-0002-8328-9764",{"name":157,"orcid":17},"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":160,"method":161,"finding":162,"direction":95,"opportunity":163},"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":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":17,"source_name":171,"source_url":168,"published_at":172,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":173,"score_detail":174,"sources":178,"tags":180,"primary_topic":184,"secondary_topics":189,"classification":190,"search_phrases":202,"slug":205,"view_count":8,"doi":206,"paper":207,"created_at":222},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":108,"substance":26,"depth":175,"authority":176,"freshness":29,"relevant":8,"comment":177},19,15,"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。",[179],{"name":171,"url":168},[4,124,181,182,183],"NDVI","考古遥感","作物标记",{"code":54,"slug":185,"name":186,"path":187,"parent_code":188},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":191,"locked":22,"version":41,"method":42,"evidence":192},"classified",{"candidates":193,"agricultural_evidence":48},[194,198],{"code":54,"score":25,"supported":48,"title":195,"abstract":196,"tags":197},[],[124,125,58],[181,4,124],{"code":46,"score":136,"supported":48,"title":199,"abstract":200,"tags":201},[],[34],[],[203,204],"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":206,"openalex_id":208,"authors":209,"venue":171,"cited_by_count":65,"oa_url":216,"card":217,"direction":95,"ingested_from":98},"W7221319130",[210,213],{"name":211,"orcid":212},"Elias Gravanis","https:\u002F\u002Forcid.org\u002F0000-0002-5331-6661",{"name":214,"orcid":215},"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":218,"method":219,"finding":220,"direction":95,"opportunity":221},"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":224,"title":225,"url":226,"summary":227,"summary_zh":228,"content":17,"source_name":229,"source_url":226,"published_at":230,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":231,"score_detail":232,"sources":234,"tags":236,"primary_topic":240,"secondary_topics":241,"classification":242,"search_phrases":255,"slug":258,"view_count":65,"doi":259,"paper":260,"created_at":272},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",62,{"impact":29,"substance":108,"depth":176,"authority":25,"freshness":109,"relevant":8,"comment":233},"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。",[235],{"name":229,"url":226},[237,4,124,238,239],"农业遥感","乌兹别克斯坦","光谱波段",{"code":54,"slug":185,"name":186,"path":187,"parent_code":188},[],{"status":191,"locked":22,"version":41,"method":42,"evidence":243},{"candidates":244,"agricultural_evidence":48},[245,249],{"code":54,"score":27,"supported":48,"title":246,"abstract":247,"tags":248},[125],[124,125],[4,237,124],{"code":250,"score":136,"supported":48,"title":251,"abstract":252,"tags":254},"I4",[],[253],"地理信息",[],[256,257],"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":259,"openalex_id":261,"authors":262,"venue":229,"cited_by_count":65,"oa_url":226,"card":267,"direction":95,"ingested_from":98},"W7221467289",[263,265],{"name":264,"orcid":17},"Xalilov Doniyor",{"name":266,"orcid":17},"Allambergenova Venera",{"tldr":268,"method":269,"finding":270,"direction":95,"opportunity":271},"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":274,"title":275,"url":276,"summary":277,"summary_zh":278,"content":17,"source_name":279,"source_url":276,"published_at":280,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":281,"score_detail":282,"sources":284,"tags":286,"primary_topic":17,"secondary_topics":288,"classification":289,"search_phrases":301,"slug":304,"view_count":8,"doi":305,"paper":306,"created_at":321},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":25,"substance":26,"depth":108,"authority":28,"freshness":29,"relevant":8,"comment":283},"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。",[285],{"name":279,"url":276},[4,124,115,116,287],"Landsat-8",[],{"status":40,"locked":22,"version":41,"method":42,"evidence":290},{"candidates":291,"agricultural_evidence":22,"reason":60},[292,297],{"code":54,"score":293,"supported":48,"title":294,"abstract":295,"tags":296},6,[],[124],[4,116,124],{"code":128,"score":8,"supported":22,"title":298,"abstract":299,"tags":300},[],[],[115],[302,303],"Çandır orman yangını","森林火灾 植被指数 遥感 Sentinel-2","Çandırormanyangını-4591","10.18182\u002Ftjf.1721381",{"doi":305,"openalex_id":307,"authors":308,"venue":279,"cited_by_count":65,"oa_url":315,"card":316,"direction":95,"ingested_from":98},"W7220904781",[309,312],{"name":310,"orcid":311},"Abdurrahman Cankut Göz","https:\u002F\u002Forcid.org\u002F0000-0003-1786-7658",{"name":313,"orcid":314},"HÜSEYİN OĞUZ ÇOBAN","https:\u002F\u002Forcid.org\u002F0000-0002-4037-4811","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F4966779",{"tldr":317,"method":318,"finding":319,"direction":95,"opportunity":320},"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":323,"title":324,"url":325,"summary":326,"summary_zh":327,"content":17,"source_name":328,"source_url":325,"published_at":329,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":330,"score_detail":331,"sources":334,"tags":336,"primary_topic":340,"secondary_topics":346,"classification":347,"search_phrases":373,"slug":376,"view_count":65,"doi":377,"paper":378,"created_at":393},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":25,"substance":332,"depth":27,"authority":28,"freshness":29,"relevant":8,"comment":333},21,"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。",[335],{"name":328,"url":325},[337,338,4,35,339],"智慧农业","产量预测","苹果园",{"code":341,"slug":342,"name":343,"path":344,"parent_code":345},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":191,"locked":22,"version":41,"method":42,"evidence":348},{"candidates":349,"agricultural_evidence":48},[350,358,364,368],{"code":341,"score":351,"supported":48,"title":352,"abstract":355,"tags":357},25,[353,354],"orchard","apple",[356,353,354],"苹果",[],{"code":359,"score":136,"supported":48,"title":360,"abstract":361,"tags":363},"H3",[],[362],"灌溉",[],{"code":54,"score":136,"supported":22,"title":365,"abstract":366,"tags":367},[],[],[4,338,35],{"code":369,"score":8,"supported":22,"title":370,"abstract":371,"tags":372},"I1",[],[],[337],[374,375],"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":377,"openalex_id":379,"authors":380,"venue":328,"cited_by_count":65,"oa_url":387,"card":388,"direction":95,"ingested_from":98},"W7221180616",[381,383,385],{"name":382,"orcid":17},"N. Gasimzade",{"name":384,"orcid":17},"A. Radzhabov",{"name":386,"orcid":17},"I. Savin","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fhorticulture\u002Farticles\u002F10.3389\u002Ffhort.2026.1904481\u002Fpdf",{"tldr":389,"method":390,"finding":391,"direction":95,"opportunity":392},"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",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":17,"source_name":400,"source_url":397,"published_at":329,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":330,"score_detail":401,"sources":403,"tags":405,"primary_topic":408,"secondary_topics":409,"classification":410,"search_phrases":423,"slug":426,"view_count":65,"doi":427,"paper":428,"created_at":441},4583,"An Efficient Parcel-Center Sampling Strategy for Parcel-Scale Crop Classification Using Multiresolution Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18203463","Parcel-scale crop classification is fundamental to agricultural remote sensing and precision agriculture. The integration of detailed parcel information with medium-spatial-resolution remote sensing data has become an important technical approach to parcel-scale crop classification. For medium-spatial-resolution imagery, the mismatch between parcel boundaries and pixel size makes the parcel–pixel matching strategy a critical step in constructing parcel-level classification features. Existing full-pixels, pixel-center inclusion, and area-weighted strategies generally require identifying pixels associated with each parcel, determining spatial relationships, and performing statistical aggregation. When applied to thousands of parcels across multiple acquisition dates, repeated spatial overlay and multipixel aggregation can become a computational bottleneck in parcel-level feature construction. To address this issue, and assuming sufficient within-parcel feature consistency, a single pixel corresponding to an interior center point or a local window around that point is used to construct parcel-level classification features, thereby reducing the number of pixels involved in spatial matching and feature aggregation. Pringsewu Regency, Lampung Province, Indonesia, which has a highly fragmented parcel pattern, was selected as a case study. Sentinel-2, Landsat-8, Sentinel-1, and topographic data were integrated for parcel-scale crop classification. The results show that the parcel-center sampling strategy substantially reduced the cost of parcel-level feature construction under both the Sentinel-2 and Landsat-8 configurations. Compared with the three multipixel strategies, under the experimental conditions of this study, PCS achieved speedups of approximately 5.2–13.3-fold under Sentinel-2 and 7.3–17.1-fold under Landsat-8. Relative to the full-pixels strategy, OA increased by 0.55 percentage points under the Sentinel-2 configuration and decreased by 1.30 percentage points under the Landsat-8 configuration. Across parcel scales, the parcel-center sampling strategy generally maintained classification performance similar to that of the conventional multipixel strategies, and the resulting spatial patterns of the major crops were broadly consistent. Using a parcel-center pixel or local window can therefore retain sufficient classification information while substantially reducing the cost of parcel-level feature construction. This approach provides a simplified solution for efficient parcel-scale crop classification mapping in fragmented agricultural landscapes using medium-resolution imagery.","地块尺度作物分类是农业遥感和精准农业的基础。将详细的地块信息与中空间分辨率遥感数据相结合，已成为地块尺度作物分类的重要技术途径。对于中空间分辨率影像而言，地块边界与像元大小之间的不匹配使得地块—像元匹配策略成为构建地块级分类特征的关键步骤。现有的全像元策略、像元中心包含策略和面积加权策略通常需要识别与每个地块相关的像元、确定空间关系并进行统计聚合。当应用于多个获取日期下的数千个地块时，重复的空间叠加和多像元聚合可能成为地块级特征构建中的计算瓶颈。为解决这一问题，在假设地块内部特征具有足够一致性的前提下，采用与内部中心点相对应的单个像元或该点周围的局部窗口来构建地块级分类特征，从而减少参与空间匹配和特征聚合的像元数量。选取地块格局高度破碎的印度尼西亚楠榜省普林塞乌县作为案例研究区，集成Sentinel-2、Landsat-8、Sentinel-1和地形数据用于地块尺度作物分类。结果表明，在Sentinel-2和Landsat-8两种配置下，地块中心采样策略均大幅降低了地块级特征构建的成本。与三种多像元策略相比，在本研究的实验条件下，PCS在Sentinel-2下实现了约5.2至13.3倍的加速，在Landsat-8下实现了约7.3至17.1倍的加速。相对于全像元策略，总体精度（OA）在Sentinel-2配置下提高了0.55个百分点，在Landsat-8配置下降低了1.30个百分点。在不同地块尺度下，地块中心采样策略总体上保持了与传统多像元策略相似的分类性能，且主要作物的空间格局基本一致。因此，使用地块中心像元或局部窗口能够在保留足够分类信息的同时，大幅降低地块级特征构建的成本。该方法提供了一种简化的解决方案","Remote Sensing",{"impact":25,"substance":332,"depth":27,"authority":28,"freshness":29,"relevant":8,"comment":402},"提出地块中心采样策略，在破碎地块区大幅提速且精度基本持平，方法新颖、结论可靠，对农业遥感工程化有参考价值。",[404],{"name":400,"url":397},[337,4,124,406,407],"作物分类","地块尺度",{"code":54,"slug":185,"name":186,"path":187,"parent_code":188},[],{"status":191,"locked":22,"version":41,"method":42,"evidence":411},{"candidates":412,"agricultural_evidence":48},[413,417],{"code":54,"score":27,"supported":48,"title":414,"abstract":415,"tags":416},[125],[124,125],[4,406,124],{"code":369,"score":47,"supported":48,"title":418,"abstract":419,"tags":422},[],[420,421],"精准农业","precision agriculture",[337],[424,425],"Sentinel-2 地块尺度 作物分类","Pringsewu 遥感 作物制图","Sentinel-2地块尺度作物分类-4583","10.3390\u002Frs18203463",{"doi":427,"openalex_id":429,"authors":430,"venue":400,"cited_by_count":65,"oa_url":397,"card":436,"direction":95,"ingested_from":98},"W7221068088",[431,434],{"name":432,"orcid":433},"Zhihua Wang","https:\u002F\u002Forcid.org\u002F0000-0002-6776-2910",{"name":435,"orcid":17},"Qin Yang",{"tldr":437,"method":438,"finding":439,"direction":95,"opportunity":440},"提出地块中心采样策略，用单像素或局部窗口构建地块级特征，大幅加速地块尺度作物分类。","以印尼Pringsewu为案例，融合Sentinel-2、Landsat-8、S","中心采样较全像素等策略提速5.2–17.1倍，精度基本相当，主要作物空间格局一致。","可探索中心采样在破碎地块、多时相与不同分辨率下的自适应窗口选择及精度-效率权衡机制。","2026-10-10T23:30:27.757031Z",{"id":443,"title":444,"url":445,"summary":446,"summary_zh":447,"content":17,"source_name":448,"source_url":445,"published_at":449,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":450,"score_detail":451,"sources":454,"tags":456,"primary_topic":458,"secondary_topics":459,"classification":464,"search_phrases":481,"slug":484,"view_count":485,"doi":486,"paper":487,"created_at":501},4365,"An Index-Based Algorithm for Detecting Water Soil Erosion Indicators from Sentinel-2 Multispectral Data and Its Verification on Field Data of Rostov Oblast and Krasnodar Krai","https:\u002F\u002Fdoi.org\u002F10.23947\u002F2587-8999-2026-10-3-49-57","Introduction . The paper presents the development and testing of an index-based algorithm for detecting indicators of soil erosion by water from Sentinel-2 remote sensing data, considered as the first practical stage in constructing a hybrid physics-based mass-transport model with neural-network parameter identification. Materials and Methods . The algorithm relies on joint analysis of time series of the Normalized Difference Vegetation Index (NDVI) and the Bare Soil Index (BSI), followed by spatial change detection (ΔBSI) with threshold filtering at the 90th percentile. The algorithm was first validated on synthetic data and then applied to real Sentinel-2 imagery for two test sites – the Kamensky District of Rostov Oblast and the vicinity of Moldovanskoye village, Krymsky District, Krasnodar Krai – covering 10 dates within 2024. Results. The algorithm is shown to consistently identify spatially localised zones of increasing bare-soil exposure. Discussion . A key limitation – the inability to distinguish erosion-related exposure from ordinary agrotechnical bare soil — is discussed, along with directions for resolving it (digital elevation model overlay, multi-year analysis). Conclusion . An index-based algorithm for detecting indicators of water-induced soil erosion from Sentinel-2 multispectral time series was developed and validated. Verification on synthetic data and application to two real test areas in southern Russia confirmed its operability while demonstrating the need to separate erosion-related changes from agricultural soil exposure in subsequent model development.","引言。本文介绍了一种基于指数的算法开发与测试，该算法用于从Sentinel-2遥感数据中检测水蚀土壤侵蚀指标，被视为构建基于物理的质量输运混合模型并结合神经网络参数辨识的第一个实践阶段。材料与方法。该算法依赖于对归一化植被指数（NDVI）和裸土指数（BSI）时间序列的联合分析，随后进行空间变化检测（ΔBSI），并在第90百分位数处进行阈值滤波。该算法首先在合成数据上进行了验证，然后应用于两个测试点的真实Sentinel-2影像——罗斯托夫州卡缅斯基区和克拉斯诺达尔边疆区克里姆斯克区摩尔多瓦诺夫斯科耶村附近——覆盖2024年内的10个日期。结果。结果表明，该算法能够一致地识别出裸土暴露增加的空间局部区域。讨论。讨论了关键局限性——无法区分与侵蚀相关的裸土暴露和普通农技作业造成的裸土——以及解决该问题的方向（叠加数字高程模型、多年分析）。结论。开发并验证了一种基于指数的算法，用于从Sentinel-2多光谱时间序列中检测水蚀土壤侵蚀指标。在合成数据上的验证以及在俄罗斯南部两个真实测试区域的应用证实了其可操作性，同时表明在后续模型开发中有必要将与侵蚀相关的变化与农业裸土暴露区分开来。","COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES","2026-10-05T00:00:00Z",70,{"impact":25,"substance":452,"depth":27,"authority":28,"freshness":29,"relevant":8,"comment":453},20,"提出基于NDVI与BSI时序的土壤水蚀指标遥感识别算法，并在俄罗斯南部两个试验区验证，方法有新意但属区域尺度研究，对国内农业遥感监测有参考价值。",[455],{"name":448,"url":445},[337,4,181,35,457],"土壤侵蚀",{"code":54,"slug":185,"name":186,"path":187,"parent_code":188},[460],{"code":135,"slug":139,"name":461,"path":462,"parent_code":463},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":191,"locked":22,"version":41,"method":42,"evidence":465},{"candidates":466,"agricultural_evidence":48},[467,471,477],{"code":54,"score":452,"supported":48,"title":468,"abstract":469,"tags":470},[58],[124,125,58],[181,4,35],{"code":135,"score":472,"supported":48,"title":473,"abstract":474,"tags":476},14,[139],[475,139],"土壤",[],{"code":369,"score":8,"supported":22,"title":478,"abstract":479,"tags":480},[],[],[337],[482,483],"Sentinel-2 土壤侵蚀 遥感","罗斯托夫州 克拉斯诺达尔 土壤侵蚀","Sentinel-2土壤侵蚀遥感-4365",2,"10.23947\u002F2587-8999-2026-10-3-49-57",{"doi":486,"openalex_id":488,"authors":489,"venue":448,"cited_by_count":65,"oa_url":445,"card":496,"direction":95,"ingested_from":98},"W7220651513",[490,493],{"name":491,"orcid":492},"V. A. Blagin","https:\u002F\u002Forcid.org\u002F0009-0003-3239-6150",{"name":494,"orcid":495},"V. A. Beraia","https:\u002F\u002Forcid.org\u002F0009-0006-2270-5011",{"tldr":497,"method":498,"finding":499,"direction":95,"opportunity":500},"开发基于Sentinel-2时序NDVI与BSI指数的水土流失指标检测算法，并在俄罗斯南部实地验证。","联合NDVI与BSI时序，用ΔBSI阈值（90百分位）检测裸土变化，合成与真实数","算法能稳定识别裸土暴露增加区域，但无法区分侵蚀与农事裸土，需DEM和多年度分析。","可融合DEM与多年度时序，构建区分侵蚀与农事裸土的分类模型，并接入神经网络参数识别。","2026-10-07T23:30:29.080156Z",{"id":503,"title":504,"url":505,"summary":506,"summary_zh":507,"content":17,"source_name":508,"source_url":505,"published_at":509,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":510,"score_detail":511,"sources":513,"tags":515,"primary_topic":518,"secondary_topics":519,"classification":520,"search_phrases":547,"slug":550,"view_count":551,"doi":552,"paper":553,"created_at":576},4025,"Remote estimation of plastic distribution in California agriculture using Sentinel-2 imagery: a case study","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1927646","Global application of agricultural plastic mulch is increasing. While these films offer benefits for crop production, they also are a large source of plastic waste and pollution in both terrestrial and aquatic systems. The current scale of global mulch application and its deleterious impacts are unclear. Current estimates of agricultural plastic use remain limited by field-specific growing patterns and data limitations. Satellite spectroscopy may offer a means to reduce this uncertainty and model agricultural plastic use on large spatial and temporal scales, though temporally variable crop canopy growth and spectrally diverse plastic targets complicate this approach. To build toward improved plasticulture estimates, this study presents an interpretable, spectral-based classification of bulk plastic use in a California agricultural site using Sentinel-2 imagery. In situ reflectance measurements of soil and white, green, and black plastic mulch endmembers were collected using a Spectra Vista Corporation HR-1024i Spectroradiometer from 350–2500 nm. A combination of field observations and high spatial resolution NearMap imagery (n = 416 spectra) were compiled and divided into 60% training and 40% testing data using a stratified sampling approach. Informed by field reflectance data, mixture analyses, and Sentinel-2 reflectance imagery, an empirical decision tree classification was parameterized to classify dark and bright plastic debris from soil and crop (accuracy = 0.982, kappa coefficient = 0.973, macro-averaged F1-score = 0.967). Modeled agricultural plastic distributions were then retrieved through a time series of Sentinel-2 imagery over Santa Maria, CA, from April 2023 to January 2025, elucidating patterns in plastic application and crop canopy growth without the need for prior knowledge of field growing and management patterns. These results are encouraging for the utility of optical remote sensing in modeling plastic use patterns and identifying contamination hotspots throughout California and, potentially, similarly mulched agroecological systems.","全球农业塑料地膜的应用日益增加。尽管这些薄膜为作物生产带来了益处，但它们同时也是陆地和水生系统中塑料废弃物和污染的重要来源。目前全球地膜应用的规模及其有害影响尚不明确。当前对农业塑料使用量的估算仍受限于特定田块的种植模式和数据局限性。卫星光谱学可能为减少这种不确定性并在大时空尺度上模拟农业塑料使用提供一种手段，尽管作物冠层的时序变化和光谱多样的塑料目标使这一方法变得复杂。为推进对塑料农业（plasticulture）估算的改进，本研究利用Sentinel-2影像，对加利福尼亚州一个农业站点的大宗塑料使用进行了可解释的、基于光谱的分类。使用Spectra Vista Corporation HR-1024i光谱辐射仪在350–2500 nm范围内采集了土壤以及白色、绿色和黑色塑料地膜端元的原位反射率测量数据。结合野外观测和高空间分辨率NearMap影像（n = 416条光谱），采用分层抽样方法将数据分为60%训练集和40%测试集。在野外反射率数据、混合分析及Sentinel-2反射率影像的指导下，参数化了一棵经验决策树分类器，以从土壤和作物中分类暗色和亮色塑料残膜（精度 = 0.982，kappa系数 = 0.973，宏平均F1分数 = 0.967）。随后通过2023年4月至2025年1月期间覆盖加利福尼亚州圣玛丽亚的Sentinel-2影像时间序列，反演了模拟的农业塑料分布，阐明了塑料施用和作物冠层生长的模式，而无需事先了解田块种植和管理模式。这些结果令人鼓舞，表明光学遥感在模拟加利福尼亚州乃至其他类似覆膜农业生态系统的塑料使用模式和识别污染热点方面具有实用价值。","Frontiers in Remote Sensing","2026-10-01T00:00:00Z",78,{"impact":7,"substance":26,"depth":108,"authority":28,"freshness":109,"relevant":8,"comment":512},"该研究利用Sentinel-2影像实现农膜分布遥感估算，方法新颖、精度高，对农业面源污染监测有参考价值，但属区域案例，影响范围有限。",[514],{"name":508,"url":505},[337,4,124,516,517],"农膜污染","加州农业",{"code":54,"slug":185,"name":186,"path":187,"parent_code":188},[],{"status":191,"locked":22,"version":41,"method":42,"evidence":521},{"candidates":522,"agricultural_evidence":48},[523,527,531,537,543],{"code":54,"score":29,"supported":48,"title":524,"abstract":525,"tags":526},[],[124,125],[4,124],{"code":135,"score":293,"supported":48,"title":528,"abstract":529,"tags":530},[],[475,139],[],{"code":532,"score":136,"supported":48,"title":533,"abstract":534,"tags":536},"H6",[],[535],"农业生态",[],{"code":538,"score":136,"supported":48,"title":539,"abstract":540,"tags":542},"J3",[],[541],"全球农业",[],{"code":369,"score":8,"supported":22,"title":544,"abstract":545,"tags":546},[],[],[337],[548,549],"Sentinel-2 农膜 遥感","加州 农业塑料 遥感监测","Sentinel-2农膜遥感-4025",4,"10.3389\u002Ffrsen.2026.1927646",{"doi":552,"openalex_id":554,"authors":555,"venue":508,"cited_by_count":65,"oa_url":505,"card":571,"direction":95,"ingested_from":98},"W7215027304",[556,558,560,563,565,568],{"name":557,"orcid":17},"Eva Scrivner",{"name":559,"orcid":17},"Dexter Hamilton",{"name":561,"orcid":562},"Daniel J. Sousa","https:\u002F\u002Forcid.org\u002F0000-0002-1632-1955",{"name":564,"orcid":17},"Margot Mattson",{"name":566,"orcid":567},"Nicholas E. Williams","https:\u002F\u002Forcid.org\u002F0000-0003-4013-9296",{"name":569,"orcid":570},"Seeta A. Sistla","https:\u002F\u002Forcid.org\u002F0000-0002-6345-462X",{"tldr":572,"method":573,"finding":574,"direction":95,"opportunity":575},"利用Sentinel-2影像和实地光谱，分类并制图加州农业塑料覆盖分布。","Sentinel-2影像、实地光谱与决策树分类，结合高分辨率影像验证。","决策树分类精度达0.982，可无需先验知识反演塑料覆盖时空格局。","可扩展至全球地膜污染热点监测，并融合多源遥感提升复杂作物区塑料识别。","2026-10-02T23:30:24.457168Z",{"id":578,"title":579,"url":580,"summary":581,"summary_zh":582,"content":17,"source_name":583,"source_url":580,"published_at":509,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":510,"score_detail":584,"sources":586,"tags":588,"primary_topic":592,"secondary_topics":593,"classification":594,"search_phrases":619,"slug":622,"view_count":136,"doi":623,"paper":624,"created_at":646},3892,"Monitoring sand encroachment in south-western Algeria (2019-2025) using Sentinel-2 imagery and a Random Forest classifier on Google Earth Engine","https:\u002F\u002Fdoi.org\u002F10.15243\u002Fjdmlm.2026.134.11367","Sand encroachment is among the most significant processes of land degradation in arid and semi-arid regions, producing complex spatial and temporal dynamics as dune formations expand at the expense of pastoral and agricultural land. This study used Sentinel-2 imagery and Random Forest classification on the Google Earth Engine (GEE) platform to map and analyse land-cover change in the wilayas of El-Bayadh and Naâma (south-western Algeria) between 2019 and 2025. Supervised classification of Sentinel-2A imagery produced land-cover maps for 2019 and 2025 (overall accuracy 88.9% and 91.5%; Kappa 0.87 and 0.90, respectively). Transition matrices quantified flows to and from the 'Dunes and Shifting Sands' class. Dune formations remained relatively stable, with 5.37 million ha unchanged, while the class expanded by 219,458 ha (+3.7%) overall. This expansion was predominantly fed by conversion from grazing land (627,335 ha), bare soil (57,395 ha) and agricultural land (32,959 ha), reflecting intensifying desertification linked to climate variability, overgrazing, deforestation and cropland abandonment. Conversely, 455,993 ha of former dune surfaces reverted to grazing land and 86,631 ha to bare soil, corresponding mainly to areas where natural vegetation recovered or where biological dune-fixation works were implemented. These results confirm the value of multi-temporal remote sensing combined with GEE for monitoring large-scale land degradation. Critically, the study identifies management opportunities: areas undergoing dune stabilization through vegetation recovery or anti-desertification engineering provide model zones for scaled intervention.","沙丘入侵是干旱和半干旱地区最重要的土地退化过程之一，随着沙丘地貌以牺牲牧草地和农用地为代价不断扩张，产生了复杂的时空动态。本研究利用Sentinel-2影像和随机森林分类方法，在Google Earth Engine（GEE）平台上对阿尔及利亚西南部埃尔贝亚德省和纳马省2019年至2025年间的土地覆盖变化进行了制图与分析。对Sentinel-2A影像进行监督分类，生成了2019年和2025年的土地覆盖图（总体精度分别为88.9%和91.5%；Kappa系数分别为0.87和0.90）。通过转移矩阵量化了“沙丘与流沙”类别的流入与流出。沙丘地貌总体保持相对稳定，537万公顷未发生变化，但该类别整体扩张了219,458公顷（+3.7%）。这一扩张主要来源于牧草地（627,335公顷）、裸土（57,395公顷）和农用地（32,959公顷）的转化，反映出与气候变率、过度放牧、森林砍伐和耕地撂荒相关的荒漠化加剧。相反，455,993公顷的原沙丘表面恢复为牧草地，86,631公顷恢复为裸土，主要对应自然植被恢复或生物固沙工程实施的区域。这些结果证实了多时相遥感结合GEE在大尺度土地退化监测中的价值。重要的是，本研究识别了管理机遇：通过植被恢复或防沙治沙工程实现沙丘固定的区域，为规模化干预提供了示范样区。","Journal of Degraded and Mining Lands Management",{"impact":7,"substance":26,"depth":108,"authority":28,"freshness":109,"relevant":8,"comment":585},"基于Sentinel-2与随机森林的阿尔及利亚西南部沙丘入侵监测研究，方法成熟、数据详实，对荒漠化遥感监测有参考价值，但区域性强、与国内三农信息化关联较弱。",[587],{"name":583,"url":580},[4,589,35,590,591],"土地退化","Google Earth Engine","荒漠化防治",{"code":54,"slug":185,"name":186,"path":187,"parent_code":188},[],{"status":191,"locked":22,"version":41,"method":42,"evidence":595},{"candidates":596,"agricultural_evidence":48},[597,601,605,610,615],{"code":54,"score":109,"supported":48,"title":598,"abstract":599,"tags":600},[],[124,125],[590,4,35],{"code":135,"score":136,"supported":48,"title":602,"abstract":603,"tags":604},[],[139],[],{"code":128,"score":136,"supported":48,"title":606,"abstract":607,"tags":609},[],[608],"干旱",[],{"code":611,"score":8,"supported":22,"title":612,"abstract":613,"tags":614},"H1",[],[],[589],{"code":532,"score":8,"supported":22,"title":616,"abstract":617,"tags":618},[],[],[591],[620,621],"阿尔及利亚 沙丘入侵 Sentinel-2","El-Bayadh Naâma 土地覆盖变化","阿尔及利亚沙丘入侵Sentinel-2-3892","10.15243\u002Fjdmlm.2026.134.11367",{"doi":623,"openalex_id":625,"authors":626,"venue":583,"cited_by_count":65,"oa_url":580,"card":641,"direction":95,"ingested_from":98},"W7214937065",[627,630,633,636,638],{"name":628,"orcid":629},"Naîmi Bendouina","https:\u002F\u002Forcid.org\u002F0000-0002-9776-1660",{"name":631,"orcid":632},"Lakhdar Guérine","https:\u002F\u002Forcid.org\u002F0000-0003-0836-2703",{"name":634,"orcid":635},"Nassira Boukaya","https:\u002F\u002Forcid.org\u002F0000-0002-6201-3523",{"name":637,"orcid":17},"Djallal Moumen",{"name":639,"orcid":640},"Omar Belabid","https:\u002F\u002Forcid.org\u002F0000-0002-7736-2259",{"tldr":642,"method":643,"finding":644,"direction":95,"opportunity":645},"基于Sentinel-2与随机森林监测阿尔及利亚西南部2019-2025年沙丘侵占与土地覆盖变化。","Sentinel-2影像、随机森林分类、Google Earth Engine、","沙丘面积增21.9万公顷（+3.7%），主要来自牧草地、裸土和耕地，同时部分沙地经植被恢复固定。","可结合多源时序遥感与治沙工程数据，量化生物固沙措施的长期成效并识别可推广的修复优先区。","2026-10-01T23:30:27.844225Z",{"id":648,"title":649,"url":650,"summary":651,"summary_zh":652,"content":17,"source_name":653,"source_url":650,"published_at":654,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":655,"score_detail":656,"sources":658,"tags":660,"primary_topic":664,"secondary_topics":665,"classification":671,"search_phrases":693,"slug":696,"view_count":136,"doi":697,"paper":698,"created_at":715},3845,"Problems of soil degradation assessment using spectral indices from space images","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71433-1","This article presents an integrated method of spectral indices for assessing soil degradation using Sentinel-2 Earth Remote Sensing (ERS) data. Unlike approaches based on individual indices, the method combines indicators of vegetation, humidity, thermal state, and salinity, increasing the accuracy of mapping saline and degraded lands while minimizing errors caused by variations in natural conditions. The application of the method in the Tashkent region of Uzbekistan (41.1465°N, 69.3009°E) showed that the category of “extremely high” and “high” degradation accounts for 3.29% and 8.28% of pixels, “medium” and “low” for 9.4% and 9.52%, while the remaining 69.51% belong to other category (class) of land cover, confirming the concentration of degradation foci in lowland areas. Comparison with 2020 showed a notable increase in the share of critical degradation (growth from 10.46% to 11.57%), while the share of the “extremely high” category increased from 2.80% to 3.29%, indicating a shift toward more severe conditions that aligns with official regional salinization statistics. Analysis of meteorological data (2013–2025) revealed an inverse relationship between temperature and precipitation, confirming the climatic nature of salinization; four stages of salinization were identified—latent, progressive, severe, and complete degradation and the main components method (PCA) was applied to reduce the size of the data. These results correspond to the expansion of irrigated lands in Uzbekistan from 3.5 to 8.5 million hectares and the associated increase in groundwater levels. Consequently, this method is not the final means of delimiting degraded lands, but represents a quick, inexpensive preliminary check before field surveys: it marks potentially degraded areas for priority ground surveys, supplementing official surveys and government statistics rather than replacing them. The method is applicable in agriculture, land cadastre, and environmental monitoring as an auxiliary tool for climate adaptation and water resource management.","本文提出了一种利用Sentinel-2地球遥感（ERS）数据评估土壤退化的光谱指数集成方法。与基于单一指数的方法不同，该方法综合了植被、湿度、热状态和盐分指标，提高了盐碱化和退化土地制图的精度，同时最大限度地减少了自然条件变化引起的误差。该方法在乌兹别克斯坦塔什干地区（北纬41.1465°，东经69.3009°）的应用表明，“极高”和“高”退化类别分别占像元的3.29%和8.28%，“中等”和“低”分别占9.4%和9.52%，其余69.51%属于其他土地覆盖类别（等级），证实了退化热点集中在低地地区。与2020年相比，临界退化比例显著增加（从10.46%增至11.57%），而“极高”类别比例从2.80%增至3.29%，表明退化状况趋于严重，这与官方区域盐渍化统计数据一致。气象数据（2013—2025年）分析揭示了气温与降水之间的反比关系，证实了盐渍化的气候成因；识别出盐渍化的四个阶段——潜伏期、进展期、严重期和完全退化期，并应用主成分分析方法（PCA）对数据进行了降维处理。这些结果与乌兹别克斯坦灌溉面积从350万公顷扩展至850万公顷以及相关的地下水位上升相吻合。因此，该方法并非划定退化土地的最终手段，而是实地调查前一种快速、低成本的初步筛查：它标记出潜在退化区域以供优先开展地面调查，是对官方调查和政府统计的补充而非替代。该方法适用于农业、土地地籍和环境监测领域，可作为气候适应和水资源管理的辅助工具。","Scientific Reports","2026-09-29T00:00:00Z",79,{"impact":7,"substance":26,"depth":108,"authority":472,"freshness":109,"relevant":8,"comment":657},"基于Sentinel-2多光谱指数的土壤退化综合评估方法，方法新颖、数据翔实，对盐渍化遥感监测与耕地保护有参考价值。",[659],{"name":653,"url":650},[4,35,661,662,663],"土壤退化","盐渍化","土地退化评估",{"code":135,"slug":139,"name":461,"path":462,"parent_code":463},[666,670],{"code":359,"slug":667,"name":668,"path":669,"parent_code":463},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"code":54,"slug":185,"name":186,"path":187,"parent_code":188},{"status":191,"locked":22,"version":41,"method":42,"evidence":672},{"candidates":673,"agricultural_evidence":48},[674,678,684,688],{"code":135,"score":176,"supported":48,"title":675,"abstract":676,"tags":677},[139],[475,139],[662],{"code":359,"score":109,"supported":48,"title":679,"abstract":680,"tags":683},[],[362,681,682],"水资源","water resource",[],{"code":54,"score":29,"supported":48,"title":685,"abstract":686,"tags":687},[],[124,125],[4,35],{"code":128,"score":136,"supported":48,"title":689,"abstract":690,"tags":692},[],[691],"气候适应",[],[694,695],"乌兹别克斯坦 塔什干 土壤盐渍化","Sentinel-2 光谱指数 土壤退化","乌兹别克斯坦塔什干土壤盐渍化-3845","10.1038\u002Fs41598-026-71433-1",{"doi":697,"openalex_id":699,"authors":700,"venue":653,"cited_by_count":65,"oa_url":709,"card":710,"direction":95,"ingested_from":98},"W7214801255",[701,704,707],{"name":702,"orcid":703},"Z. Z. Shamsiev","https:\u002F\u002Forcid.org\u002F0000-0002-0323-9741",{"name":705,"orcid":706},"L. F. Sulyukova","https:\u002F\u002Forcid.org\u002F0000-0002-7150-4359",{"name":708,"orcid":17},"Rasul Shamsiev","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71433-1_reference.pdf",{"tldr":711,"method":712,"finding":713,"direction":95,"opportunity":714},"提出融合多光谱指数的Sentinel-2土壤退化评估方法，在乌兹别克斯坦验证并识别盐渍化阶段。","Sentinel-2影像多指数融合（植被、湿度、热、盐分）结合PCA与气象数据分","退化集中于低地，2020年以来极重度退化占比由2.80%升至3.29%，与官方盐渍化统计一致。","可将该快速预筛方法与地面调查、机器学习结合，构建多区域可迁移的退化早期预警与制图框架。","2026-09-30T23:30:29.386707Z",{"id":717,"title":718,"url":719,"summary":720,"summary_zh":721,"content":17,"source_name":722,"source_url":719,"published_at":723,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":724,"score_detail":725,"sources":727,"tags":729,"primary_topic":733,"secondary_topics":739,"classification":742,"search_phrases":761,"slug":764,"view_count":485,"doi":765,"paper":766,"created_at":788},3792,"Estimation of soil organic carbon under rice-fallow system using Sentinel-2 derivatives","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.11182","The evaluation of soil properties under a century-old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of North-east (NE) India in the Eastern Himalaya faces multiple challenges in conventional studies of soil properties. Alternatively, remote sensing offers a viable solution. An attempt was made to study the soil organic carbon (SOC) under century old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of Bhoirymbong, Meghalaya, NE India in Eastern Himalaya using remote sensing indices derived from Sentinel-2. The objective was to develop a suitable regression model for rice-fallow systems across different hill slopes in northeastern India in the Eastern Himalaya. A total of 100 composite soil samples from the 0–15 cm depth were collected from rice-fallow areas on various slopes (nearly level: 0–3 %, gentle: 3–8 %, moderate: 8–15 %, steep: 15–30 % and very steep: > 30 %) during November 2020. Each composite sample was prepared from 10 randomly collected soil samples within a 10 × 10 m area and analysed for SOC using standard protocols in the analytical laboratory of School of Natural Resource Management, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal. Nearly synchronised, cloud-free (\u003C 10 % cloud cover) Sentinel-2 data consistent with soil sampling period were accessed from the US Geological Survey (USGS) official website to derive 15 indices. Results showed that brightness index (BI), BI2 and the soil bareness index (SBI) were the most influential indices for SOC. The highest and lowest SOC values were found in gently undulating topography (2–5 % slope) and rolling topography (10–15 % slope), at 2.86 % and 1.17 %, respectively. After rice crop harvesting, the red-NIR-based indices did not show a significant difference, whereas the green-NIR-based indices were significantly different at p \u003C 0.05. Hence, it is concluded that green-NIR-based indices are the most suitable for SOC prediction in NE India in the Eastern Himalaya.","在印度东北部东喜马拉雅地区高度脆弱的地形（即山坡和低地）下，对百年传统水稻休耕实践下的土壤性质进行评估，在常规土壤性质研究中面临多重挑战。遥感则提供了一种可行的替代方案。本研究尝试利用Sentinel-2衍生的遥感指数，研究印度东北部东喜马拉雅地区梅加拉亚邦Bhoirymbong在高度脆弱地形（即山坡和低地）下百年传统水稻休耕实践中的土壤有机碳（SOC）。目标是建立一个适用于东喜马拉雅地区印度东北部不同山坡水稻休耕系统的合适回归模型。2020年11月，在不同坡度（近水平：0–3%、缓坡：3–8%、中坡：8–15%、陡坡：15–30%和极陡坡：>30%）的水稻休耕区采集了100个0–15 cm深度的复合土壤样品。每个复合样品由10 × 10 m区域内随机采集的10个土壤样品制备而成，并在Central Agricultural University, Imphal农业科学研究生院自然资源管理学院分析实验室采用标准方法分析SOC。从美国地质调查局（USGS）官方网站获取与土壤采样期基本同步、无云（云量\u003C10%）的Sentinel-2数据，以衍生15个指数。结果表明，亮度指数（BI）、BI2和土壤裸露指数（SBI）是对SOC影响最大的指数。SOC最高值和最低值分别出现在缓起伏地形（2–5%坡度）和波状地形（10–15%坡度），分别为2.86%和1.17%。水稻收获后，基于红-近红外的指数未显示出显著差异，而基于绿-近红外的指数在p \u003C 0.05水平上差异显著。因此，得出结论：基于绿-近红外的指数最适合东喜马拉雅地区印度东北部的SOC预测。","Plant Science Today","2026-09-28T00:00:00Z",69,{"impact":25,"substance":452,"depth":7,"authority":28,"freshness":29,"relevant":8,"comment":726},"基于Sentinel-2遥感指数估算稻作休耕系统土壤有机碳，方法有新意但属区域案例研究，对国内智慧农业遥感应用有参考价值。",[728],{"name":722,"url":719},[730,4,731,35,732],"数字农业","土壤有机碳","水稻休耕",{"code":734,"slug":735,"name":736,"path":737,"parent_code":738},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[740,741],{"code":135,"slug":139,"name":461,"path":462,"parent_code":463},{"code":54,"slug":185,"name":186,"path":187,"parent_code":188},{"status":191,"locked":22,"version":41,"method":42,"evidence":743},{"candidates":744,"agricultural_evidence":48},[745,749,753,757],{"code":135,"score":176,"supported":48,"title":746,"abstract":747,"tags":748},[139],[475,139],[731],{"code":734,"score":472,"supported":48,"title":750,"abstract":751,"tags":752},[735],[736,735],[],{"code":54,"score":29,"supported":48,"title":754,"abstract":755,"tags":756},[],[124,125],[4,35],{"code":369,"score":8,"supported":22,"title":758,"abstract":759,"tags":760},[],[],[730],[762,763],"印度东北部 水稻休耕 土壤有机碳","Sentinel-2 遥感 土壤有机碳","印度东北部水稻休耕土壤有机碳-3792","10.14719\u002Fpst.11182",{"doi":765,"openalex_id":767,"authors":768,"venue":722,"cited_by_count":65,"oa_url":719,"card":783,"direction":95,"ingested_from":98},"W7214617000",[769,772,774,777,780],{"name":770,"orcid":771},"Janaki Singh Naorem","https:\u002F\u002Forcid.org\u002F0000-0001-9534-0339",{"name":773,"orcid":17},"D Priya",{"name":775,"orcid":776},"Deepak Kumar Dwivedi","https:\u002F\u002Forcid.org\u002F0009-0003-5408-887X",{"name":778,"orcid":779},"J S Rajkumar","https:\u002F\u002Forcid.org\u002F0009-0003-7863-277X",{"name":781,"orcid":782},"Tamang Basant","https:\u002F\u002Forcid.org\u002F0000-0001-6856-7298",{"tldr":784,"method":785,"finding":786,"direction":95,"opportunity":787},"用Sentinel-2指数估算印度东北部稻作休耕系统不同坡度的土壤有机碳。","采集100个0-15cm土样，结合Sentinel-2的15个遥感指数建立回归模","绿光-近红外指数最适合预测SOC，缓坡SOC最高（2.86%），陡坡最低（1.17%）。","可探索多时相Sentinel-2与地形因子耦合，提升复杂地形区SOC制图精度与普适性。","2026-09-29T23:30:28.690009Z",{"id":790,"title":791,"url":792,"summary":793,"summary_zh":794,"content":17,"source_name":795,"source_url":792,"published_at":796,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":23,"score_detail":797,"sources":799,"tags":801,"primary_topic":803,"secondary_topics":807,"classification":808,"search_phrases":829,"slug":832,"view_count":485,"doi":833,"paper":834,"created_at":849},3568,"Interpretable multi-index framework for extracting dry-season built-up areas: benchmarking machine learning with Sentinel-2","https:\u002F\u002Fdoi.org\u002F10.4995\u002Fraet.2027.25885","Spectral similarity between built-up surfaces and exposed dry soil significantly constrains built-up area extraction during dry seasons. Although machine-learning techniques can address this challenge by modeling complex spectral relationships, they generally require sufficiently large and representative labeled training datasets. This study therefore investigates a less data-demanding, rule-based multi-index approach for dry-season built-up mapping, aiming to reduce reliance on large labeled training datasets while maintaining effective classification performance. Sentinel-2A imagery acquired in April 2024 was analyzed, and temporal validation was conducted for 2020, 2022, and 2024. Among the tested combinations, the Built-up Area Extraction Index (BAEI), Dry BareSoil Index (DBSI), and Normalized Difference Vegetation Index (NDVI) achieved the highest performance, yielding 95 % overall accuracy and a Kappa coefficient of 0.89. This represents a substantial improvement over singleindex BAEI (80 % overall accuracy; Kappa 0.55), increasing the built-up user’s accuracy from 61 % to 92 %. Multitemporal validation confirmed that the optimized BAEI–DBSI–NDVI decision rules remained stable across the 2020–2024 dry-season images without recalibration, achieving 93–96 % overall accuracy and a built-up F1-score of 84–93 %. Spatial analysis demonstrated robust performance across peripheral and agricultural zones, with moderate variability in dense urban cores due to spectral heterogeneity. To evaluate the operational robustness of the proposed framework, it was benchmarked against machine-learning classifiers, specifically Support Vector Machine and Random Forest (RF). Under the specific conditions of this study, the proposed rule-based method (95 % accuracy) marginally outperformed both SVM (93 %) and RF (92 %), while offering greater transparency and reducing dependency on large, labeled training datasets. Furthermore, feature importance analysis confirmed the critical role of these selected indices in resolving spectral confusion. These findings suggest that the proposed framework offers a transparent and computationally efficient approach for dry-season urban monitoring in tropical coastal environments similar to Visakhapatnam.","建成表面与裸露干土之间的光谱相似性显著制约了旱季建成区提取。尽管机器学习技术可通过建模复杂光谱关系来应对这一挑战，但其通常需要足够大且具有代表性的标记训练数据集。因此，本研究探讨了一种对数据需求较低、基于规则的多指数方法用于旱季建成区制图，旨在减少对大规模标记训练数据集的依赖，同时保持有效的分类性能。研究分析了2024年4月获取的Sentinel-2A影像，并对2020年、2022年和2024年进行了时间验证。在测试的组合中，建成区提取指数（BAEI）、干裸土指数（DBSI）和归一化差异植被指数（NDVI）表现最佳，总体精度达95%，Kappa系数为0.89。相较于单一指数BAEI（总体精度80%；Kappa 0.55），这一结果有显著提升，建成区用户精度从61%提高至92%。多时相验证证实，优化后的BAEI–DBSI–NDVI决策规则在2020—2024年旱季影像上无需重新校准即可保持稳定，总体精度达93%—96%，建成区F1分数为84%—93%。空间分析表明，该方法在外围和农业区域表现稳健，而在密集城市核心区由于光谱异质性存在中等程度变异。为评估所提框架的业务化稳健性，将其与机器学习分类器进行了基准比较，具体为支持向量机（SVM）和随机森林（RF）。在本研究的特定条件下，所提出的基于规则的方法（95%精度）略优于SVM（93%）和RF（92%），同时具有更高的透明性并减少了对大规模标记训练数据集的依赖。此外，特征重要性分析证实了所选指数在解决光谱混淆方面的关键作用。这些发现表明，所提框架为类似维沙卡帕特南的热带沿海环境旱季城市监测提供了一种透明且计算高效的方法。","Revista de teledetección: Revista de la Asociación Española de Teledetección","2026-09-25T00:00:00Z",{"impact":25,"substance":332,"depth":27,"authority":28,"freshness":109,"relevant":8,"comment":798},"该研究提出可解释的多指数规则框架，在旱季建成区提取上以更少标注数据达到95%精度并优于SVM\u002FRF，方法新颖、验证充分，对农业遥感与乡村土地利用监测有参考价值。",[800],{"name":795,"url":792},[4,34,181,35,802],"建成区提取",{"code":46,"slug":804,"name":805,"path":806,"parent_code":188},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":191,"locked":22,"version":41,"method":42,"evidence":809},{"candidates":810,"agricultural_evidence":48},[811,815,821,825],{"code":46,"score":25,"supported":48,"title":812,"abstract":813,"tags":814},[51],[34],[34],{"code":250,"score":293,"supported":48,"title":816,"abstract":817,"tags":820},[],[818,819],"空间分析","spatial analysis",[],{"code":135,"score":136,"supported":48,"title":822,"abstract":823,"tags":824},[],[139],[],{"code":54,"score":136,"supported":22,"title":826,"abstract":827,"tags":828},[],[],[181,4,35],[830,831],"Sentinel-2 建成区提取","BAEI DBSI NDVI 旱季","Sentinel-2建成区提取-3568","10.4995\u002Fraet.2027.25885",{"doi":833,"openalex_id":835,"authors":836,"venue":795,"cited_by_count":65,"oa_url":792,"card":843,"direction":848,"ingested_from":98},"W7214385607",[837,840],{"name":838,"orcid":839},"Sarah Sejari","https:\u002F\u002Forcid.org\u002F0009-0002-7300-9084",{"name":841,"orcid":842},"Vazeer Mahammood","https:\u002F\u002Forcid.org\u002F0000-0003-1667-5232",{"tldr":844,"method":845,"finding":846,"direction":95,"opportunity":847},"提出可解释多指数规则框架，用Sentinel-2提取旱季建成区，性能优于机器学习。","Sentinel-2A影像，BAEI、DBSI、NDVI组合规则，对比SVM与随","BAEI-DBSI-NDVI规则达95%精度、Kappa 0.89，优于单指数及SVM\u002FRF且无需重","可探索该规则框架向其他气候带与传感器迁移，并结合少量样本的半监督学习提升城市核心区精度。","农业人工智能与决策模型","2026-09-26T23:30:53.200163Z",{"id":851,"title":852,"url":853,"summary":854,"summary_zh":855,"content":17,"source_name":856,"source_url":853,"published_at":857,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":106,"score_detail":858,"sources":860,"tags":862,"primary_topic":866,"secondary_topics":867,"classification":870,"search_phrases":887,"slug":890,"view_count":485,"doi":891,"paper":892,"created_at":915},3504,"WaterFormer-MS: A multi-stream deep learning framework for surface water segmentation from satellite imagery","https:\u002F\u002Fdoi.org\u002F10.24850\u002Fj-tyca-18-03-08","Accurate mapping of surface water bodies is essential for dam monitoring, irrigation planning, and sustainable water resource management. Conventional methods based on spectral indices, such as NDWI, can face challenges in complex environments due to spectral ambiguity, seasonal variability, and urban or vegetated surfaces. Deep learning approaches such as U-Net and DeepLabv3+ have improved water-body segmentation; however, accurately delineating fine boundaries and detecting small water bodies remain challenging. To address these limitations, this study proposes WaterFormer-MS, a multi-stream deep learning framework for surface water segmentation from Sentinel-2 imagery. The architecture integrates spectral, geometric, and contextual information through dedicated processing streams and combines the extracted features using a convolutional fusion module enhanced with CBAM attention. This design aims to improve feature representation and support accurate water-body delineation. The proposed model was trained and evaluated using Sentinel-2 imagery from a publicly available Kaggle dataset containing diverse land-cover conditions, including urban, vegetated, and agricultural environments. At 300 epochs, WaterFormer-MS achieved an overall accuracy of 97.4%, a precision of 93.2%, a recall of 94.2%, an F1-score of 93.7%, and an IoU of 89.7%. Within the experimental framework, WaterFormer-MS achieved higher values than the other proposed variants. Comparisons with conventional NDWI-based approaches and published deep learning methods suggest competitive performance; however, these comparisons should be interpreted cautiously because datasets, preprocessing procedures, training protocols, and evaluation settings may differ. The results support the potential of the proposed framework for surface water segmentation and environmental monitoring applications in diverse remote sensing scenarios.","准确绘制地表水体分布对于大坝监测、灌溉规划和水资源可持续管理至关重要。基于NDWI等光谱指数的传统方法在复杂环境中可能因光谱模糊性、季节性变化以及城市或植被覆盖地表而面临挑战。U-Net和DeepLabv3+等深度学习方法改善了水体分割效果，但精确描绘精细边界和检测小型水体仍然具有挑战性。为解决这些局限，本研究提出WaterFormer-MS，一种用于Sentinel-2影像地表水分割的多流深度学习框架。该架构通过专门的处理流整合光谱、几何和上下文信息，并利用经CBAM注意力增强的卷积融合模块将提取的特征进行融合。该设计旨在改善特征表征并支持精确的水体描绘。所提模型使用来自公开Kaggle数据集的Sentinel-2影像进行训练和评估，该数据集包含城市、植被和农业环境等多种土地覆盖条件。在300个训练轮次时，WaterFormer-MS取得了97.4%的总体精度、93.2%的精确率、94.2%的召回率、93.7%的F1分数和89.7%的IoU。在实验框架内，WaterFormer-MS取得了高于其他所提变体的数值。与传统基于NDWI的方法及已发表的深度学习方法进行比较，结果表明其性能具有竞争力；然而，这些比较应谨慎解读，因为数据集、预处理流程、训练方案和评估设置可能存在差异。研究结果支持所提框架在多样化遥感场景中用于地表水分割和环境监测应用的潜力。","Tecnología y Ciencias del Agua","2026-09-24T00:00:00Z",{"impact":176,"substance":26,"depth":108,"authority":21,"freshness":109,"relevant":8,"comment":859},"提出多流深度学习框架用于卫星影像地表水提取，精度指标明确，对灌溉规划与水资源监测有参考价值，但属方法类论文，产业影响有限。",[861],{"name":856,"url":853},[863,4,124,864,865],"农业人工智能","水资源管理","地表水体提取",{"code":46,"slug":804,"name":805,"path":806,"parent_code":188},[868,869],{"code":359,"slug":667,"name":668,"path":669,"parent_code":463},{"code":54,"slug":185,"name":186,"path":187,"parent_code":188},{"status":191,"locked":22,"version":41,"method":42,"evidence":871},{"candidates":872,"agricultural_evidence":48},[873,879,883],{"code":46,"score":176,"supported":48,"title":874,"abstract":876,"tags":878},[875],"deep learning",[877,875],"深度学习",[863],{"code":359,"score":21,"supported":48,"title":880,"abstract":881,"tags":882},[],[362,681,667,682],[864],{"code":54,"score":29,"supported":48,"title":884,"abstract":885,"tags":886},[],[124,125],[4,124],[888,889],"WaterFormer-MS 地表水 分割","Sentinel-2 水体提取 深度学习","WaterFormer-MS地表水分割-3504","10.24850\u002Fj-tyca-18-03-08",{"doi":891,"openalex_id":893,"authors":894,"venue":856,"cited_by_count":65,"oa_url":853,"card":910,"direction":95,"ingested_from":98},"W7214228825",[895,898,901,904,907],{"name":896,"orcid":897},"Ayoub Benali","https:\u002F\u002Forcid.org\u002F0000-0002-8989-755X",{"name":899,"orcid":900},"Yesma Bendaha","https:\u002F\u002Forcid.org\u002F0000-0002-1347-856X",{"name":902,"orcid":903},"Malika Seddik Bouchouicha","https:\u002F\u002Forcid.org\u002F0009-0001-6092-784X",{"name":905,"orcid":906},"Zakaria Bellahcene","https:\u002F\u002Forcid.org\u002F0000-0003-4353-7871",{"name":908,"orcid":909},"Rachid Rimani","https:\u002F\u002Forcid.org\u002F0000-0002-1923-4312",{"tldr":911,"method":912,"finding":913,"direction":95,"opportunity":914},"提出多流深度学习框架WaterFormer-MS，从Sentinel-2影像中精准分割地表水体。","多流架构融合光谱、几何与上下文特征，CBAM注意力卷积融合，Sentinel-2","300轮训练下总体精度97.4%、IoU 89.7%，优于NDWI及对比深度学习方法。","可探索多流框架在农业灌溉水体与小型水塘精细提取中的迁移，并引入时序Sentinel-2提升季节鲁棒性。","2026-09-25T23:30:31.338437Z",{"id":917,"title":918,"url":919,"summary":920,"summary_zh":921,"content":17,"source_name":922,"source_url":919,"published_at":923,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":924,"score_detail":925,"sources":927,"tags":929,"primary_topic":17,"secondary_topics":933,"classification":934,"search_phrases":949,"slug":952,"view_count":485,"doi":953,"paper":954,"created_at":968},2795,"Statistical evaluation of sentinel-2 spectral indices as environmental indicators of shallow soil resistivity for soil corrosion assessment in a semi-arid region of northwestern Nigeria","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44288-026-00732-x","Abstract Soil corrosion poses a significant threat to buried engineering infrastructure, particularly in semi-arid environments where variations in soil properties and surface environmental conditions influence electrochemical processes. This study integrated Sentinel-2-derived environmental indices with shallow-layer electrical resistivity and soil pH measurements to evaluate environmental factors associated with soil corrosion susceptibility in Sabon Gida, Katsina State, northwestern Nigeria. Sentinel-2 Level-2 A imagery acquired during the 2026 dry season was processed to derive the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), and Salinity Index (SI). Spectral index values were extracted at 44 Vertical Electrical Sounding (VES) locations, where shallow-layer resistivity and soil pH data were available. Descriptive statistics, Pearson correlation analysis, and multiple linear regression were employed to investigate the relationships between the remotely sensed environmental variables and field-measured soil properties. The results indicate that the study area is characterized by sparse vegetation cover, low surface moisture, limited bare-soil exposure, and relatively higher salinity-index values that may indicate spatial variations in salinity-related environmental conditions during the dry-season image acquisition period. Strong correlations were observed among the Sentinel-2-derived indices, whereas only weak relationships were found between the spectral indices and shallow-layer resistivity. Multiple linear regression analysis yielded an R² value of 0.197, indicating that approximately 19.7% of the variation in soil resistivity was explained by the model. Although the salinity index was identified as the only statistically significant predictor, the overall regression model was not statistically significant (F = 1.867, p = 0.123). These findings suggest that the remotely sensed variables provide only limited explanatory power with respect to shallow soil resistivity. Nevertheless, the results indicate that Sentinel-2-derived environmental indices provide useful information for characterizing surface environmental conditions associated with soil corrosion but should be used as complementary indicators rather than direct substitutes for field-based geophysical investigations. The study demonstrates that integrating remote sensing with geophysical measurements provides a more robust framework for preliminary soil corrosion susceptibility assessment and offers valuable support for infrastructure planning and environmental monitoring in data-scarce semi-arid regions.","摘要 土壤腐蚀对埋地工程基础设施构成重大威胁，尤其是在半干旱环境中，土壤性质与地表环境条件的变化会影响电化学过程。本研究将Sentinel-2衍生的环境指数与浅层电阻率及土壤pH测量相结合，评估尼日利亚西北部卡齐纳州Sabon Gida地区与土壤腐蚀敏感性相关的环境因素。研究处理了2026年旱季获取的Sentinel-2 Level-2A影像，以提取归一化差异植被指数（NDVI）、归一化差异水体指数（NDWI）、裸土指数（BSI）和盐分指数（SI）。在44个垂直电测深（VES）点位提取了光谱指数值，这些点位具备浅层电阻率和土壤pH数据。研究采用描述性统计、Pearson相关分析和多元线性回归，探讨遥感环境变量与实地测量土壤性质之间的关系。结果表明，研究区植被覆盖稀疏、地表湿度较低、裸土暴露有限，且盐分指数值相对较高，这可能指示旱季影像获取期间盐分相关环境条件的空间变化。Sentinel-2衍生的各指数之间观察到较强相关性，而光谱指数与浅层电阻率之间仅存在弱关系。多元线性回归分析得出R²值为0.197，表明模型可解释约19.7%的土壤电阻率变异。尽管盐分指数被识别为唯一具有统计学意义的预测因子，但整体回归模型未达到统计学显著性（F = 1.867，p = 0.123）。这些发现表明，遥感变量对浅层土壤电阻率的解释力有限。然而，结果也显示Sentinel-2衍生的环境指数可为表征与土壤腐蚀相关的地表环境条件提供有用信息，但应作为补充性指标，而非实地地球物理调查的直接替代。本研究表明，将遥感与地球物理测量相结合可提供更稳健的框架","Discover Geoscience","2026-09-16T00:00:00Z",61,{"impact":29,"substance":108,"depth":472,"authority":25,"freshness":109,"relevant":8,"comment":926},"该研究将Sentinel-2遥感指数与土壤电阻率结合评估土壤腐蚀风险，方法有创新但解释力有限，对农业基础设施规划有参考价值，属细分领域进展。",[928],{"name":922,"url":919},[4,124,930,931,932],"土壤监测","土壤腐蚀","半干旱农业",[],{"status":40,"locked":22,"version":41,"method":42,"evidence":935},{"candidates":936,"agricultural_evidence":22,"reason":60},[937,941,945],{"code":135,"score":472,"supported":48,"title":938,"abstract":939,"tags":940},[139],[475,139],[],{"code":54,"score":29,"supported":48,"title":942,"abstract":943,"tags":944},[],[124,125],[4,124],{"code":128,"score":136,"supported":48,"title":946,"abstract":947,"tags":948},[],[608],[],[950,951],"半干旱农业 土壤监测 土壤腐蚀 遥感","半干旱农业 土壤监测","半干旱农业土壤监测土壤腐蚀遥感-2795","10.1007\u002Fs44288-026-00732-x",{"doi":953,"openalex_id":955,"authors":956,"venue":922,"cited_by_count":65,"oa_url":962,"card":963,"direction":95,"ingested_from":98},"W7213365338",[957,960],{"name":958,"orcid":959},"Abdulhakim Ahmad","https:\u002F\u002Forcid.org\u002F0009-0004-1030-8313",{"name":961,"orcid":17},"A. F. Akpaneno","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44288-026-00732-x.pdf",{"tldr":964,"method":965,"finding":966,"direction":95,"opportunity":967},"用Sentinel-2光谱指数与电法电阻率、pH对比，评估尼日利亚半干旱区土壤腐蚀环境指示能力。","Sentinel-2 L2A影像提取NDVI、NDWI、BSI、SI，结合44个","光谱指数与浅层电阻率仅弱相关，回归R²=0.197且不显著，遥感只能作辅助指标。","可引入多时相\u002F土壤水分与纹理特征，或结合机器学习提升遥感对地下土壤腐蚀属性的间接反演能力。","2026-09-17T23:30:36.379942Z",{"id":970,"title":971,"url":972,"summary":973,"summary_zh":17,"content":974,"source_name":975,"source_url":17,"published_at":976,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":977,"score_detail":978,"sources":980,"tags":982,"primary_topic":986,"secondary_topics":987,"classification":988,"search_phrases":1003,"slug":1006,"view_count":472,"doi":17,"paper":17,"created_at":1007},55,"卫星数据+专用视觉Transformer的甜菜早期产量预测（Sentinel-2影像应用）","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.17661","基于Sentinel-2光学影像的早期甜菜产量预测研究。通过紧耦合领域知识与机器学习，使用极小Vision Transformer patch size与全部Sentinel-2谱段，提升了模型表现。研究通过调整训练设置与基于排名的低产田检测方法，能在生长周期早期识别出大量低产田，为精准农业田间管理提供早期决策依据。","[Skip to main content](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.17661#content)[](https:\u002F\u002Farxiv.org\u002FIgnoreMe)[![Image 1: archive](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Farxiv-logo-primary-light.svg)](https:\u002F\u002Farxiv.org\u002F)[Search](https:\u002F\u002Farxiv.org\u002Fsearch)[Submit](https:\u002F\u002Farxiv.org\u002Fuser\u002Fcreate)[Donate](https:\u002F\u002Finfo.arxiv.org\u002Fabout\u002Fdonate.html)[Log in](https:\u002F\u002Farxiv.org\u002Flogin)\n\nSearch arXiv \n\n Press Enter to search · [Advanced search](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fadvanced)\n\n# Computer Science > Computer Vision and Pattern Recognition\n\n**arXiv:2607.17661** (cs) \n\n [Submitted on 20 Jul 2026]\n\n# Title:Early Yield Prediction for Sugar Beet Fields using Satellite Data -- Learnings from Specialized Vision Transformers\n\nAuthors:[Philipp Vaeth](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Vaeth,+P), [Bhumika Laxman Sadbhave](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Sadbhave,+B+L), [Denise Dejon](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Dejon,+D), [Gunther Schorcht](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Schorcht,+G), [Magda Gregorova](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Gregorova,+M)\n\nView a PDF of the paper titled Early Yield Prediction for Sugar Beet Fields using Satellite Data -- Learnings from Specialized Vision Transformers, by Philipp Vaeth and 4 other authors\n\n[View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2607.17661)[HTML (experimental)](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2607.17661v1)\n> Abstract:Remote sensing has become an increasingly valuable tool for agricultural monitoring, particularly through the use of publicly available satellite imagery. However, effectively integrating domain knowledge into machine learning methods remains challenging. This study presents a real-world example of early sugar beet harvest yield forecasting from purely optical Sentinel-2 imagery, demonstrating how a tight integration of domain knowledge and machine learning can lead to synergistic gains. We empirically find that using very small vision transformer patch sizes and all available Sentinel-2 spectral bands improves our model despite being uncommon design choices in the domain. As a practical contribution, we were able to identify a large fraction of low-yield fields in a different year early on in the growth cycle through a modified training setup and a ranking-based detection of underperforming fields.\n\nSubjects:Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)\nCite as:[arXiv:2607.17661](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.17661) [cs.CV]\n(or [arXiv:2607.17661v1](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.17661v1) [cs.CV] for this version)\n[https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.17661](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.17661)\n\nFocus to learn more\n\n arXiv-issued DOI via DataCite (pending registration)\n\n## Submission history\n\n From: Philipp Vaeth [[view email](https:\u002F\u002Farxiv.org\u002Fshow-email\u002Ff02ba8e5\u002F2607.17661)] \n\n**[v1]** Mon, 20 Jul 2026 08:11:07 UTC (4,067 KB)\n\n[](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.17661)Full-text links:\n## 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[**Learn more about arXivLabs**](https:\u002F\u002Finfo.arxiv.org\u002Flabs\u002Findex.html).\n\n[Which authors of this paper are endorsers?](https:\u002F\u002Farxiv.org\u002Fauth\u002Fshow-endorsers\u002F2607.17661) | [Disable MathJax](javascript:setMathjaxCookie()) ([What is MathJax?](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fmathjax.html)) \n\n We gratefully acknowledge support from our **major funders**, [**member institutions**](https:\u002F\u002Finfo.arxiv.org\u002Fabout\u002Fourmembers.html), , and all contributors. \n\n[About](https:\u002F\u002Finfo.arxiv.org\u002Fabout)·[Help](https:\u002F\u002Finfo.arxiv.org\u002Fhelp)·[Contact](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fcontact.html)·[Subscribe](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fsubscribe)·[Copyright](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Flicense\u002Findex.html)·[Privacy](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fpolicies\u002Fprivacy_policy.html)·[Accessibility](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fweb_accessibility.html)·[Operational Status (opens in new tab)](https:\u002F\u002Fstatus.arxiv.org\u002F)\n\nMajor funding support from\n\n[![Image 4: Simons Foundation](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fsimons-foundation.png)](https:\u002F\u002Fwww.simonsfoundation.org\u002F)[![Image 5: Simons Foundation International](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fsimons-foundation-international.png)](https:\u002F\u002Fwww.sfi.org.bm\u002F)[![Image 6: Schmidt Sciences](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fschmidt-sciences.png)](https:\u002F\u002Fwww.schmidtsciences.org\u002F)","arXiv:2607.17661v1 [cs.CV] · 2026-07-20","2026-07-20T00:11:00Z",76,{"impact":452,"substance":26,"depth":108,"authority":25,"freshness":551,"relevant":8,"comment":979},"利用Sentinel-2卫星影像和专用视觉Transformer进行甜菜早期产量预测，方法新颖，有实际应用价值，但发表于8天前，时效性较低。",[981],{"name":975,"url":972},[237,20,983,338,4,984,985],"甜菜","视觉Transformer","预印本",{"code":46,"slug":804,"name":805,"path":806,"parent_code":188},[],{"status":191,"locked":22,"version":41,"method":42,"evidence":989},{"candidates":990,"agricultural_evidence":48},[991,995,999],{"code":369,"score":136,"supported":48,"title":992,"abstract":993,"tags":994},[],[420],[],{"code":46,"score":136,"supported":48,"title":996,"abstract":997,"tags":998},[],[34],[],{"code":54,"score":136,"supported":22,"title":1000,"abstract":1001,"tags":1002},[],[],[4,338,237],[1004,1005],"产量预测 农业遥感 预印本 视觉Transformer","产量预测 农业遥感","产量预测农业遥感预印本视觉Transformer-55","2026-07-28T23:53:31.313063Z"]