[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2672":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":54},2672,"Estimating Small Farming Plots’ Key Crop Production at a Regional Level Utilizing Sentinel Imagery in Southern Europe","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8090388","This study utilizes Sentinel imaging to monitor small farming plots (\u003C5 ha) and objectively estimate their significance in agricultural output. It develops, tests, and evaluates methodologies within and across ten southern European NUTS-3 regions—commonly referred to as prefectures, provinces, or departments—located in Greece, Italy, France, Portugal, and Spain. The study provides stakeholders with the necessary tools to estimate their spatial distribution, crop diversity, crop area extent, and agricultural yields. The approach is expanded as needed to similar landscapes, serving as a model for other European NUTS-3 regions and providing a comprehensive view of the remote sensing solution. It employs random forest crop identification and classification analysis to build crop-type maps and record the countless small agricultural plots. The Sentinel data utilized are from 20 NUTS-3 pilot regions selected across 11 EU countries, focusing on 29 estimated key crop areas. The research demonstrates remarkable classification accuracy for a previously identified wide range of key crop data across each of the selected NUTS-3 regions. Crop area estimates derived from field data were adjusted and stratified. The RS-based methodology reduces propagation errors in estimating the area and production of crop categories with low accuracy levels, enhancing the overall understanding of the crucial role of small farming plots. For highly accurate key crop products (per crop-type categorization, with FScore > 75%), production estimates are calculated by multiplying predicted self-reported crop yields by unbiased key crop small farming plot area estimates. However, for the referenced period of cultivation, only 16 out of the above 29 estimated key crop areas are recorded for SFs by the regional official statistics. In addition, of the 16 reported key crop areas listed above, eleven are from nine NUTS-3 regions spread across four southern EU countries, with the remaining five coming from four NUTS-3 regions of two non-southern EU countries. The analysis shows a strong correlation between estimated key crop areas from SFs reported in regional official statistics and Sentinel-based key crop area estimates obtained from small farming plots, with R2=0.96 for 16 estimated key crop area datasets across all NUTS-3 regions in the EU and R2=0.98 for 11 estimated key crop area datasets across all southern NUTS-3 regions. As a result, in NUTS-3 regions lacking regional official statistics data, RS can serve as a reliable alternative source for estimating the extent of key crop areas.","本研究利用Sentinel影像监测小型农田地块（\u003C5公顷），并客观评估其在农业产出中的重要性。研究在位于希腊、意大利、法国、葡萄牙和西班牙的十个南欧NUTS-3区域（通常称为省、郡或专区）内部及之间开发、测试和评估了相关方法。研究为利益相关者提供了估算其空间分布、作物多样性、作物面积范围和农业产量所需的工具。该方法根据需要扩展至类似景观，为其他欧洲NUTS-3区域提供模型，并呈现遥感解决方案的全面视角。研究采用随机森林作物识别与分类分析来构建作物类型图并记录众多小型农田地块。所使用的Sentinel数据来自11个欧盟国家中选定的20个NUTS-3试点区域，重点关注29个估算的主要作物区域。研究表明，在每个选定的NUTS-3区域中，对先前确定的一系列广泛主要作物数据均取得了显著的分类精度。从实地数据得出的作物面积估算经过了调整和分层处理。基于遥感的方法减少了在估算精度较低的作物类别面积和产量时的传播误差，增强了对小型农田地块关键作用的整体理解。对于高精度的主要作物产品（按作物类型分类，FScore>75%），产量估算通过将预测的自报作物产量乘以无偏的主要作物小型农田地块面积估算值来计算。然而，在上述29个估算的主要作物区域中，仅有16个在参考种植期内被区域官方统计记录为小型农田。此外，在上述16个已报告的主要作物区域中，有11个来自分布在四个南欧欧盟国家的九个NUTS-3区域，其余5个来自两个非南欧欧盟国家的四个NUTS-3区域。分析表明，区域官方统计中报告的小型农田主要作物面积估算值与基于Sentinel从小型农田地块获得的主要作物面积估算值之间存在强相关性，欧盟所有NUTS-3区域中16个估算主要作物面积数据集的R²=0.96，所有南欧NUTS-3区域中11个估算主要作物面积数据集的R²=0.98。因此，在缺乏区域官方统计的NUTS-3区域中",null,"AgriEngineering","2026-09-15T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于Sentinel影像对南欧小地块作物面积与产量估算，方法新颖、数据覆盖10国20个NUTS-3区域，R²达0.96-0.98，对遥感替代官方统计具有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"精准农业","小农户","遥感监测","欧盟农业","作物估产",0,"10.3390\u002Fagriengineering8090388",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":6,"card":47,"direction":51,"ingested_from":53},"W7213308546",[36,39,42,44],{"name":37,"orcid":38},"Theodore Tsiligiridis","https:\u002F\u002Forcid.org\u002F0000-0003-2183-4760",{"name":40,"orcid":41},"Sérgio Godinho","https:\u002F\u002Forcid.org\u002F0000-0003-1457-9810",{"name":43,"orcid":9},"Katerina Ainali",{"name":45,"orcid":46},"Rui Machado","https:\u002F\u002Forcid.org\u002F0000-0002-4345-6636",{"tldr":48,"method":49,"finding":50,"direction":51,"opportunity":52},"利用Sentinel影像估算南欧小农地块关键作物面积与产量，并与官方统计对比验证。","随机森林分类Sentinel影像，覆盖10个南欧NUTS-3区域，构建作物类型图","小农地块Sentinel估算面积与官方统计高度相关，R²达0.96（全欧）和0.98（南欧）。","农业遥感与作物表型","可扩展至缺乏官方统计的区域，研究小农地块遥感估算的不确定性传播与多源数据融合。","openalex","2026-09-16T23:30:30.646809Z"]