[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2672":3,"related-2672":97},{"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,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":69,"slug":72,"view_count":21,"doi":73,"paper":74,"created_at":96},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",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,59,64],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":51},8,true,[],[49,50],"遥感","remote sensing",[30,28],{"code":53,"score":54,"supported":46,"title":55,"abstract":56,"tags":58},"B2",3,[],[57],"葡萄",[],{"code":60,"score":21,"supported":14,"title":61,"abstract":62,"tags":63},"G2",[],[],[27],{"code":65,"score":21,"supported":14,"title":66,"abstract":67,"tags":68},"I1",[],[],[26],[70,71],"作物估产 欧盟农业 精准农业 遥感监测","作物估产 欧盟农业","作物估产欧盟农业精准农业遥感监测-2672","10.3390\u002Fagriengineering8090388",{"doi":73,"openalex_id":75,"authors":76,"venue":10,"cited_by_count":88,"oa_url":6,"card":89,"direction":93,"ingested_from":95},"W7213308546",[77,80,83,85],{"name":78,"orcid":79},"Theodore Tsiligiridis","https:\u002F\u002Forcid.org\u002F0000-0003-2183-4760",{"name":81,"orcid":82},"Sérgio Godinho","https:\u002F\u002Forcid.org\u002F0000-0003-1457-9810",{"name":84,"orcid":9},"Katerina Ainali",{"name":86,"orcid":87},"Rui Machado","https:\u002F\u002Forcid.org\u002F0000-0002-4345-6636",0,{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"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",{"total":98,"page":21,"page_size":98,"items":99},6,[100,189,276,329,380,457],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":112,"tags":114,"primary_topic":118,"secondary_topics":123,"classification":130,"search_phrases":159,"slug":162,"view_count":54,"doi":163,"paper":164,"created_at":188},3167,"Temporal trend analysis and multi-temporal satellite feature integration for mango orchard acreage estimation using machine learning algorithms approach","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-65828-3","Accurate estimation of horticultural crop acreage is essential for agricultural planning, market forecasting and evidence-based policy formulation. The present study investigated long-term trends in mango cultivation and developed a multi-temporal remote sensing framework for mango orchard acreage estimation in Navsari District using integrated optical, SAR and machine learning approaches. Time-series (temporal) data spanning 23 years (2001–02 to 2023–24) were analyzed using polynomial regression models to evaluate trends in mango area and production. Linear regression best represented area expansion trends (Adj. R² = 0.969), whereas cubic regression better captured production variability (Adj. R² = 0.634), indicating climatic and seasonal influences on productivity. For orchard classification and acreage estimation, multi-temporal Sentinel-2 imagery acquired from November 2023 to March 2024 was processed within a phenology-guided framework. Monthly composites were generated and integrated with Sentinel-1 SAR backscatter data, vegetation indices (NDVI, GNDVI, NDRE, SAVI, EVI and NDMI) and texture metrics derived from Gray Level Co-occurrence Matrix (GLCM) analysis. A comprehensive 72-band feature stack was developed for classification. Four machine learning algorithms, namely Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM) and Multinomial Logistic Regression (MNLR) were evaluated for orchard discrimination. Among the tested models, RF achieved the highest classification performance with an Overall Accuracy of 99.80% and a Kappa coefficient of 0.997, followed by SVM (99.30%), MNLR (98.21%) and XGB (9.20%). The RF model estimated mango orchard area at 36,099.94 ha, showing the closest agreement with official horticultural statistics (34,363 ha) with only 5.05% estimation error. In contrast, SVM and MNLR overestimated orchard extent by 16.03% and 43.37%, respectively. The proposed framework provides a reliable and scalable methodology for operational horticultural monitoring, crop inventory generation and precision agricultural planning in tropical orchard ecosystems.","准确估算园艺作物种植面积对于农业规划、市场预测和循证政策制定至关重要。本研究探讨了芒果种植的长期趋势，并开发了一个多时相遥感框架，结合光学、合成孔径雷达（SAR）和机器学习方法，用于纳夫萨里县芒果园种植面积估算。利用多项式回归模型分析了跨越23年（2001—02年至2023—24年）的时间序列数据，以评估芒果面积和产量的变化趋势。线性回归最能表征面积扩张趋势（调整R² = 0.969），而三次回归更能捕捉产量变异性（调整R² = 0.634），表明气候和季节性因素对生产力具有影响。在果园分类和面积估算方面，基于物候指导框架处理了2023年11月至2024年3月获取的多时相Sentinel-2影像。生成了月度合成影像，并将其与Sentinel-1 SAR后向散射数据、植被指数（NDVI、GNDVI、NDRE、SAVI、EVI和NDMI）以及基于灰度共生矩阵（GLCM）分析提取的纹理指标进行整合。构建了一个包含72个波段的综合特征集用于分类。评估了四种机器学习算法，即随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）和多项逻辑回归（MNLR），用于果园判别。在测试的模型中，RF取得了最高的分类性能，总体精度为99.80%，Kappa系数为0.997，其次是SVM（99.30%）、MNLR（98.21%）和XGB（9.20%）。RF模型估算的芒果园面积为36,099.94公顷，与官方园艺统计数据（34,363公顷）最为接近，估算误差仅为5.05%。相比之下，SVM和MNLR分别高估了果园面积16.03%和43.37%。所提出的框架为热带果园生态系统中的业务化园艺监测、作物清单生成和精准农业规划提供了一种可靠且可扩展的方法。","Scientific Reports","2026-09-22T00:00:00Z",78,{"impact":110,"substance":18,"depth":17,"authority":110,"freshness":13,"relevant":21,"comment":111},14,"方法扎实、数据规模大且精度高，但属区域性作物遥感估产研究，产业影响有限，可作为技术方法类精选。",[113],{"name":106,"url":103},[115,116,117,28,30],"智慧农业","机器学习","芒果",{"code":53,"slug":119,"name":120,"path":121,"parent_code":122},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[124,129],{"code":125,"slug":126,"name":127,"path":128,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":131},{"candidates":132,"agricultural_evidence":46},[133,139,144,148,153],{"code":53,"score":134,"supported":46,"title":135,"abstract":137,"tags":138},15,[136],"orchard",[117,136],[117],{"code":125,"score":134,"supported":46,"title":140,"abstract":142,"tags":143},[141],"machine learning",[116,141],[116],{"code":32,"score":45,"supported":46,"title":145,"abstract":146,"tags":147},[],[49,50],[30,28],{"code":65,"score":149,"supported":46,"title":150,"abstract":151,"tags":152},4,[],[26],[115],{"code":154,"score":54,"supported":46,"title":155,"abstract":156,"tags":158},"J1",[],[157],"农业规划",[],[160,161],"Navsari 芒果 遥感估产","Sentinel-2 芒果 果园面积","Navsari芒果遥感估产-3167","10.1038\u002Fs41598-026-65828-3",{"doi":163,"openalex_id":165,"authors":166,"venue":106,"cited_by_count":88,"oa_url":103,"card":183,"direction":93,"ingested_from":95},"W7213920056",[167,169,172,174,176,179,181],{"name":168,"orcid":9},"V. Raju",{"name":170,"orcid":171},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":173,"orcid":9},"Dr. V. S. Thorat",{"name":175,"orcid":9},"V. T. Shinde",{"name":177,"orcid":178},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":180,"orcid":9},"Alok Shrivastava",{"name":182,"orcid":9},"A. P. Chaudhary",{"tldr":184,"method":185,"finding":186,"direction":93,"opportunity":187},"融合多时相Sentinel-1\u002F2与机器学习，估算印度芒果园面积并分析23年种植趋势。","23年时序回归分析；Sentinel-2月合成+SAR+植被指数+GLCM纹理共","RF精度最高（总体精度99.80%，Kappa 0.997），面积估算误差仅5.05%，优于SVM和","可迁移该多时相SAR-光学特征框架至其他热带果园，并探索深度学习与物候自适应特征优化。","2026-09-22T23:30:22.619289Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":9,"source_name":195,"source_url":192,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":196,"sources":199,"tags":201,"primary_topic":204,"secondary_topics":205,"classification":212,"search_phrases":240,"slug":243,"view_count":21,"doi":244,"paper":245,"created_at":275},2664,"Soil Salinity Mapping from UAV-Borne Hyperspectral Imagery with Soil Moisture Correction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181812","Soil salinization poses a significant threat to sustainable agricultural development and ecological security worldwide, resulting in considerable crop losses annually. The advent of drone-based hyperspectral remote sensing offers promising solutions for monitoring soil salinity, due to its high spatial resolution and versatile data acquisition capabilities. However, soil moisture alters both the scattering and absorption characteristics of electromagnetic radiation, thereby modifying soil spectral reflectance and masking salinity-related diagnostic spectral features, which can reduce the accuracy of conventional salinity estimation models. This study evaluates six spectral transformation methods—raw reflectance data (Ref), first derivative (FDR), Piecewise Direct Standardization (PDS), Orthogonal Signal Correction (OSC), FDR + PDS, and FDR + OSC—in conjunction with three machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). A Stacking ensemble model integrating these base learners was further developed to improve soil salinity inversion under moisture interference. The results demonstrated that the Stacking model achieved the highest accuracy and stability among the evaluated models. Additional comparisons with XGBoost and Random Forest (RF) further confirmed the competitive performance of the proposed Stacking framework. The FDR + OSC–Stacking combination achieved the best validation performance, with Rp2 = 0.87, RMSEP = 0.67 mS·cm−1, and RPD = 2.93. Compared with the Ref–Stacking model, Rp2 increased by 0.32 (from 0.55 to 0.87), while RMSEP decreased by 0.58 mS·cm−1 (from 1.25 to 0.67 mS·cm−1). The results showed that PDS had limited effectiveness in correcting moisture-related spectral variation, whereas OSC more effectively mitigated moisture interference while preserving spectral information relevant to salinity estimation. Among the machine learning models evaluated, the Stacking ensemble model achieved better predictive performance than MLP, SVR, and KNN. Furthermore, the FDR + OSC–Stacking combination provided the best performance among the evaluated modeling frameworks and was successfully applied to UAV hyperspectral imagery for spatial mapping of EC1:5. These findings demonstrate the potential of combining appropriate spectral correction with Stacking for UAV-based soil salinity assessment and provide useful technical support for site-specific salinity management in precision agriculture.","土壤盐渍化对全球农业可持续发展和生态安全构成重大威胁，每年造成大量作物损失。基于无人机的高光谱遥感凭借其高空间分辨率和灵活的数据获取能力，为土壤盐分监测提供了有前景的解决方案。然而，土壤水分会改变电磁辐射的散射和吸收特性，从而改变土壤光谱反射率并掩盖与盐分相关的诊断性光谱特征，这可能降低传统盐分估算模型的精度。本研究评估了六种光谱变换方法——原始反射率数据（Ref）、一阶导数（FDR）、分段直接标准化（PDS）、正交信号校正（OSC）、FDR + PDS和FDR + OSC——并结合三种机器学习算法：K近邻（KNN）、支持向量回归（SVR）和多层感知机（MLP）。进一步构建了集成这些基学习器的Stacking集成模型，以提高水分干扰下的土壤盐分反演精度。结果表明，Stacking模型在所评估的模型中达到了最高的精度和稳定性。与XGBoost和随机森林（RF）的额外比较进一步证实了所提出的Stacking框架的竞争性性能。FDR + OSC–Stacking组合取得了最佳的验证表现，Rp2 = 0.87，RMSEP = 0.67 mS·cm−1，RPD = 2.93。与Ref–Stacking模型相比，Rp2提高了0.32（从0.55增至0.87），而RMSEP降低了0.58 mS·cm−1（从1.25降至0.67 mS·cm−1）。结果表明，PDS在校正水分相关光谱变异方面效果有限，而OSC在保留与盐分估算相关的光谱信息的同时，更有效地减轻了水分干扰。在所评估的机器学习模型中，Stacking集成模型取得了优于MLP、SVR和KNN的预测性能。此外，FDR + OSC–Stacking组合在所评估的建模框架中表现最佳，并成功应用于无人机高光谱影像进行EC1:5的空间制图。这些发现证明了将适当的光谱校正与Stacking相结合用于基于无人机的土壤盐分评估的潜力，并为精准农业中的针对性盐分管理提供了有用的技术支持。","Agronomy",{"impact":197,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":198},16,"该研究提出FDR+OSC光谱校正与Stacking集成模型相结合的无人机高光谱土壤盐分反演方法，验证精度显著提升（Rp²=0.87），为盐渍化农田精准管理提供了可落地的技术方案，专业深度与信息增量均较突出。",[200],{"name":195,"url":192},[115,202,26,28,203],"无人机","土壤盐渍化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[206],{"code":207,"slug":208,"name":209,"path":210,"parent_code":211},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":213},{"candidates":214,"agricultural_evidence":46},[215,220,225,230,234],{"code":32,"score":17,"supported":46,"title":216,"abstract":218,"tags":219},[217],"hyperspectral",[49,50,217],[28],{"code":207,"score":110,"supported":46,"title":221,"abstract":222,"tags":224},[208],[223,208],"土壤",[],{"code":65,"score":45,"supported":46,"title":226,"abstract":227,"tags":229},[],[26,228],"precision agriculture",[115,26],{"code":125,"score":98,"supported":46,"title":231,"abstract":232,"tags":233},[],[116,141],[],{"code":235,"score":54,"supported":46,"title":236,"abstract":237,"tags":239},"J3",[],[238],"全球农业",[],[241,242],"土壤盐渍化 智慧农业 精准农业 遥感监测","土壤盐渍化 智慧农业","土壤盐渍化智慧农业精准农业遥感监测-2664","10.3390\u002Fagronomy16181812",{"doi":244,"openalex_id":246,"authors":247,"venue":195,"cited_by_count":88,"oa_url":192,"card":270,"direction":93,"ingested_from":95},"W7213244904",[248,250,252,254,256,258,260,262,265,267],{"name":249,"orcid":9},"Haiye Yu",{"name":251,"orcid":9},"Muyan Yu",{"name":253,"orcid":9},"Ranzhe Jiang",{"name":255,"orcid":9},"Xin Zhang",{"name":257,"orcid":9},"Zhu Guo",{"name":259,"orcid":9},"Yaohui Fu",{"name":261,"orcid":9},"Xingbang Liu",{"name":263,"orcid":264},"Xingyu Sun","https:\u002F\u002Forcid.org\u002F0009-0008-8684-498X",{"name":266,"orcid":9},"Bingze Li",{"name":268,"orcid":269},"Yuanyuan Sui","https:\u002F\u002Forcid.org\u002F0000-0003-4849-9077",{"tldr":271,"method":272,"finding":273,"direction":93,"opportunity":274},"用无人机高光谱结合水分校正与Stacking集成模型反演土壤盐分。","六种光谱变换与KNN\u002FSVR\u002FMLP及Stacking，对比XGBoost、RF","FDR+OSC-Stacking最优，Rp²=0.87，OSC比PDS更能消除水分干扰。","可探索水分校正与深度学习结合、多时相无人机盐分动态监测及跨区域迁移。","2026-09-16T23:30:29.016721Z",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":281,"content":9,"source_name":195,"source_url":279,"published_at":282,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":283,"score_detail":284,"sources":286,"tags":288,"primary_topic":290,"secondary_topics":291,"classification":292,"search_phrases":304,"slug":307,"view_count":308,"doi":309,"paper":310,"created_at":328},2538,"Multitemporal UAV-Based Estimation of Kenaf (Hibiscus cannabinus L.) Plant Height Under Nitrogen and Compost Treatments","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181805","This study aimed to evaluate the growth responses of kenaf (Hibiscus cannabinus L.) under nitrogen and compost treatments and to develop a UAV-based plant height estimation model. Ground-measured plant height differences were not significant at harvest (110 days after sowing, DAS), highlighting the need for multitemporal monitoring. Multispectral drone imagery was acquired at five growth stages (20–110 DAS). Object-based image segmentation was applied to extract pure vegetation areas, and digital surface model differencing (ΔDSMt) was used to reduce micro-topographic effects. A UAV-based multiple linear regression (UAV-MLR) model was developed using ΔDSMt, NDVI, GNDVI, and NGRDI to integrate complementary structural and spectral information. Evaluated on the calibration dataset, the UAV-MLR model demonstrated high fitting performance (adjusted R2 = 0.9858, RMSE = 14.95 cm, MAE = 11.31 cm), outperforming the ground-based simple linear regression (G-SLR) model based on stem diameter (adjusted R2 = 0.9615, RMSE = 39.68 cm, MAE = 29.16 cm). By integrating structural and spectral information, the proposed approach reduced RMSE by 62.3%, offering a highly accurate, non-destructive tool for crop monitoring and precision agriculture.","本研究旨在评估在氮肥和堆肥处理下洋麻（Hibiscus cannabinus L.）的生长响应，并构建基于无人机（UAV）的株高估测模型。在收获期（播种后110天，DAS），地面实测株高差异不显著，凸显了多时相监测的必要性。在5个生长阶段（20–110 DAS）获取了多光谱无人机影像。采用基于对象的图像分割提取纯植被区域，并利用数字表面模型差值（ΔDSMt）以降低微地形效应。利用ΔDSMt、NDVI、GNDVI和NGRDI构建了基于无人机的多元线性回归（UAV-MLR）模型，以整合互补的结构与光谱信息。在标定数据集上评估，UAV-MLR模型表现出较高的拟合性能（调整R² = 0.9858，RMSE = 14.95 cm，MAE = 11.31 cm），优于基于茎粗的地面简单线性回归（G-SLR）模型（调整R² = 0.9615，RMSE = 39.68 cm，MAE = 29.16 cm）。通过整合结构与光谱信息，所提方法将RMSE降低了62.3%，为作物监测和精准农业提供了一种高精度、非破坏性工具。","2026-09-14T00:00:00Z",76,{"impact":134,"substance":18,"depth":17,"authority":19,"freshness":45,"relevant":21,"comment":285},"该研究提出融合结构与光谱信息的无人机多时相株高估测模型，精度显著优于地面模型，对作物无损监测与精准农业具有实质参考价值。",[287],{"name":195,"url":279},[115,202,26,28,289],"作物长势",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":293},{"candidates":294,"agricultural_evidence":46},[295,299],{"code":65,"score":45,"supported":46,"title":296,"abstract":297,"tags":298},[],[26,228],[115,26],{"code":32,"score":149,"supported":46,"title":300,"abstract":301,"tags":303},[],[302],"multispectral",[28],[305,306],"作物长势 智慧农业 精准农业 遥感监测","作物长势 智慧农业","作物长势智慧农业精准农业遥感监测-2538",2,"10.3390\u002Fagronomy16181805",{"doi":309,"openalex_id":311,"authors":312,"venue":195,"cited_by_count":88,"oa_url":279,"card":322,"direction":327,"ingested_from":95},"W7213116925",[313,316,319],{"name":314,"orcid":315},"TaekJin Yoon","https:\u002F\u002Forcid.org\u002F0009-0003-6507-8415",{"name":317,"orcid":318},"Tae Wan Kim","https:\u002F\u002Forcid.org\u002F0000-0002-1742-1982",{"name":320,"orcid":321},"Sung Yung Yoo","https:\u002F\u002Forcid.org\u002F0000-0002-7889-3924",{"tldr":323,"method":324,"finding":325,"direction":93,"opportunity":326},"用多时相无人机影像估算氮肥与堆肥处理下红麻株高，构建高精度回归模型。","五期多光谱无人机影像，对象分割提取植被，ΔDSMt结合NDVI等建多元线性回归。","UAV-MLR模型拟合优度达0.9858，RMSE比地面模型降低62.3%。","可推广至其他纤维\u002F能源作物，并融合机器学习与多源遥感提升跨生育期泛化能力。","数字乡村与农业信息化","2026-09-15T23:30:26.678242Z",{"id":330,"title":331,"url":332,"summary":333,"summary_zh":334,"content":9,"source_name":335,"source_url":332,"published_at":282,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":283,"score_detail":336,"sources":340,"tags":342,"primary_topic":344,"secondary_topics":345,"classification":346,"search_phrases":358,"slug":361,"view_count":308,"doi":362,"paper":363,"created_at":379},2530,"Seeing the Green from Above: A Review of Remote Sensing Techniques for Vegetation Cover Discrimination","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189410","This review synthesizes recent regional applications of satellite, unmanned aerial vehicle (UAV), and hyperspectral\u002Fspectroradiometric remote sensing for vegetation cover discrimination, including crop and natural vegetation discrimination, plant disease and pest detection, and yield assessment. The reviewed evidence demonstrates complementary rather than universally superior capabilities among sensing platforms. Satellite observations provide repeated large-area monitoring but remain constrained by spatial resolution, cloud interference, and spectral mixing, whereas UAVs offer very-high-resolution and flexible field-scale observations at the expense of spatial coverage and greater acquisition and processing requirements. Hyperspectral and spectroradiometric approaches provide detailed spectral information for distinguishing subtle vegetation differences, but are limited by data complexity and operational scalability. The quantitative results reported in the reviewed studies illustrate this variability: satellite-based crop discrimination achieved approximately 90% overall accuracy with QuickBird and 81% overall accuracy (κ = 0.74) with Sentinel-2 at a 10 m resolution, while a UAV hyperspectral vegetation classification study achieved 94.5% accuracy. However, these values are not directly comparable because the vegetation targets, sensors, acquisition conditions, and analytical methods differed among studies. Recent evidence also indicates that the phenological timing, spectral band selection, spatial resolution, and representative training data strongly influence the discrimination performance, while the transfer of disease detection models from controlled experiments to operational field conditions remains a major challenge. By integrating evidence from satellite, UAV, and ground-based spectroradiometric approaches, this review provides a comprehensive framework for understanding the complementary capabilities of these technologies for vegetation cover discrimination and highlights their importance for improving vegetation monitoring, precision agriculture, and sustainable ecosystem management.","本文综述了近年来卫星、无人机（UAV）及高光谱\u002F光谱辐射遥感在植被覆盖判别中的区域应用，包括作物与自然植被判别、植物病虫害检测及产量评估。所综述的证据表明，各传感平台之间的能力是互补的，而非普遍存在优劣之分。卫星观测可提供重复的大面积监测，但仍受空间分辨率、云层干扰及光谱混合的限制；无人机则能够提供超高分辨率且灵活的田块尺度观测，但代价是空间覆盖范围有限，且采集与处理要求更高。高光谱与光谱辐射方法可提供精细的光谱信息，用于区分细微的植被差异，但受数据复杂性及业务化可扩展性的制约。所综述研究报告的定量结果体现了这种差异性：基于卫星的作物判别在QuickBird下总体精度约为90%，在10 m分辨率的Sentinel-2下总体精度为81%（κ = 0.74），而一项无人机高光谱植被分类研究达到了94.5%的精度。然而，这些数值之间并不能直接比较，因为各研究中的植被目标、传感器、采集条件及分析方法均不相同。近期证据还表明，物候时序、光谱波段选择、空间分辨率及代表性训练数据对判别性能有显著影响，而将病害检测模型从受控实验推广至实际田间条件仍是一项重大挑战。通过整合来自卫星、无人机及地面光谱辐射方法的证据，本综述为理解这些技术在植被覆盖判别中的互补能力提供了一个综合框架，并强调了其对改进植被监测、精准农业及可持续生态系统管理的重要意义。","Sustainability",{"impact":17,"substance":337,"depth":338,"authority":19,"freshness":45,"relevant":21,"comment":339},20,17,"系统综述卫星、无人机与高光谱遥感在植被识别、病虫害检测与估产中的互补能力，数据翔实、结论审慎，对农业遥感应用有参考价值。",[341],{"name":335,"url":332},[115,26,49,343,30],"病虫害监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":347},{"candidates":348,"agricultural_evidence":46},[349,354],{"code":32,"score":350,"supported":46,"title":351,"abstract":352,"tags":353},19,[50],[49,50,217],[30,49],{"code":65,"score":45,"supported":46,"title":355,"abstract":356,"tags":357},[],[26,228],[115,26],[359,360],"病虫害监测 作物估产 智慧农业 精准农业","病虫害监测 作物估产","病虫害监测作物估产智慧农业精准农业-2530","10.3390\u002Fsu18189410",{"doi":362,"openalex_id":364,"authors":365,"venue":335,"cited_by_count":88,"oa_url":332,"card":374,"direction":93,"ingested_from":95},"W7212851379",[366,369,372],{"name":367,"orcid":368},"Ghada A. Khdery","https:\u002F\u002Forcid.org\u002F0000-0002-1492-6029",{"name":370,"orcid":371},"Mohamed S. Shokr","https:\u002F\u002Forcid.org\u002F0000-0003-0328-7679",{"name":373,"orcid":9},"Aleksandra O. Utkina",{"tldr":375,"method":376,"finding":377,"direction":93,"opportunity":378},"综述卫星、无人机与高光谱遥感在植被覆盖判别中的区域应用与互补能力。","综述卫星、无人机、高光谱\u002F光谱辐射遥感在作物与植被判别中的案例。","各平台能力互补而非普遍优越，物候、波段、分辨率与训练数据显著影响精度。","可探索跨平台遥感数据融合与病害检测模型从受控实验到田间业务的迁移。","2026-09-15T23:30:20.628746Z",{"id":381,"title":382,"url":383,"summary":384,"summary_zh":385,"content":9,"source_name":386,"source_url":383,"published_at":387,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":88,"score_detail":388,"sources":390,"tags":392,"primary_topic":396,"secondary_topics":397,"classification":398,"search_phrases":423,"slug":426,"view_count":88,"doi":427,"paper":428,"created_at":456},5390,"Land use and socio-economic changes caused by riverbank erosion - case study: Obrenovac municipality, Serbia","https:\u002F\u002Fdoi.org\u002F10.59954\u002Fstnv.797","Lateral bank erosion of meandering rivers represents a significant geomorphological process in the lower part of the Kolubara River Basin in Serbia. This process directly impacts land-use and agricultural productivity and lead to broader environmental, socioeconomic, and demographic changes. Historically, the Kolubara riverbanks have been intensively used for agriculture. Today, lateral channel migration and riverbank erosion, driven by the interaction between natural process and human activities, directly threaten traditionally active land plots along the river. This study investigates the long-term dynamics of riverbank erosion along the four active meanders of the Kolubara River in Obrenovac Municipality over a 97-year period (1930–2026). Main tasks include quantification of lateral channel migration and land loss rates, analysis of land-use changes within the endangered plots, and an investigation of socioeconomic consequences and demographic dynamics in affected settlements. The methodological framework integrates cadastral maps analysis, remote sensing techniques (UAVs), and GIS software, with analysis of demographic and land-use data. The results indicate that maximum lateral channel migration reached 259.3 m during the observed period, while the total land loss amounted to 17.6 ha, consisting mostly of arable land. This reduction has directly affected the local agricultural sector, resulting in an estimated land loss value of approximately 24,122 USD. Demographic analysis reveals population decline exceeding 40% in the analysed settlements (Draževac, Konatice and Poljane) between 1961 and 2022, from over 90% of the economically active population in 1961 to less than 2% in 2022. Ultimately, by highlighting the socioeconomic impacts of riverbank erosion such as irreversible agricultural land loss and rural livelihood disruption, these findings offer valuable insights for spatial planning and sustainable land management.","蜿蜒河流的侧向河岸侵蚀是塞尔维亚科卢巴拉河（Kolubara River）流域下游一个重要的地貌过程。该过程直接影响土地利用和农业生产力，并引发更广泛的环境、社会经济和人口变化。历史上，科卢巴拉河岸曾被密集用于农业生产。如今，在自然过程与人类活动相互作用驱动下的侧向河道迁移和河岸侵蚀，直接威胁着沿河传统上活跃的地块。本研究考察了奥布雷诺瓦茨市（Obrenovac Municipality）科卢巴拉河四个活跃曲流段在97年期间（1930—2026年）河岸侵蚀的长期动态。主要任务包括：量化侧向河道迁移和土地流失速率，分析受威胁地块内的土地利用变化，以及调查受影响居民点的社会经济后果和人口动态。方法框架整合了地籍图分析、遥感技术（无人机，UAVs）和GIS软件，并结合人口与土地利用数据分析。结果表明，在观测期内侧向河道迁移最大距离达259.3 m，土地流失总量为17.6 ha，其中大部分为耕地。这一减少直接影响了当地农业部门，估计土地流失价值约为24,122美元。人口分析显示，1961年至2022年间，所分析居民点（Draževac、Konatice和Poljane）的人口下降幅度超过40%，经济活动人口比例从1961年的超过90%降至2022年的不足2%。最终，通过突出河岸侵蚀的社会经济影响，如不可逆的农业土地流失和农村生计破坏，这些发现为空间规划和可持续土地管理提供了有价值的见解。","Stanovnistvo","2026-10-19T00:00:00Z",{"impact":88,"substance":88,"depth":88,"authority":88,"freshness":88,"relevant":88,"comment":389},"塞尔维亚河岸侵蚀案例研究，属地理与土地利用领域，与三农及农业信息化主题关联弱，不建议进入每日精选。",[391],{"name":386,"url":383},[28,393,394,395],"土地利用","土壤侵蚀","乡村人口",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":399},{"candidates":400,"agricultural_evidence":46},[401,406,412,418],{"code":32,"score":402,"supported":46,"title":403,"abstract":404,"tags":405},7,[],[49,50],[28],{"code":407,"score":54,"supported":46,"title":408,"abstract":409,"tags":411},"I4",[],[410],"GIS",[],{"code":413,"score":54,"supported":46,"title":414,"abstract":415,"tags":417},"I7",[],[416],"methodological framework",[],{"code":419,"score":21,"supported":14,"title":420,"abstract":421,"tags":422},"H6",[],[],[393],[424,425],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":427,"openalex_id":429,"authors":430,"venue":386,"cited_by_count":88,"oa_url":383,"card":450,"direction":93,"ingested_from":95},"W7222945959",[431,434,437,439,442,445,448],{"name":432,"orcid":433},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":435,"orcid":436},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":438,"orcid":9},"Милан Радовић",{"name":440,"orcid":441},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":443,"orcid":444},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":446,"orcid":447},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":449,"orcid":9},"Manojlović Stefan",{"tldr":451,"method":452,"finding":453,"direction":454,"opportunity":455},"研究塞尔维亚Kolubara河岸侵蚀97年间的河道迁移、耕地损失及社会经济影响。","结合地籍图、UAV遥感、GIS与人口土地利用数据分析四条曲流。","最大侧向迁移259.3米，损失17.6公顷耕地约2.4万美元，村落人口降超40%。","其他","可将河岸侵蚀遥感监测与农户生计、土地价值评估耦合，构建侵蚀风险预警与补偿决策模型。","2026-10-20T23:30:18.982943Z",{"id":458,"title":459,"url":460,"summary":461,"summary_zh":462,"content":9,"source_name":463,"source_url":460,"published_at":464,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":465,"score_detail":466,"sources":468,"tags":470,"primary_topic":473,"secondary_topics":479,"classification":481,"search_phrases":502,"slug":505,"view_count":88,"doi":506,"paper":507,"created_at":530},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","Smart Agricultural Technology","2026-10-17T00:00:00Z",77,{"impact":197,"substance":18,"depth":17,"authority":19,"freshness":45,"relevant":21,"comment":467},"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[469],{"name":463,"url":460},[115,202,28,471,472],"冬小麦","生物量估算",{"code":474,"slug":475,"name":476,"path":477,"parent_code":478},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[480],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":482},{"candidates":483,"agricultural_evidence":46},[484,490,494,498],{"code":474,"score":485,"supported":46,"title":486,"abstract":488,"tags":489},26,[487,475],"winter wheat",[476,487,475],[471],{"code":32,"score":197,"supported":46,"title":491,"abstract":492,"tags":493},[50],[49,50],[472,28],{"code":125,"score":98,"supported":46,"title":495,"abstract":496,"tags":497},[],[116,141],[],{"code":65,"score":21,"supported":14,"title":499,"abstract":500,"tags":501},[],[],[115],[503,504],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":506,"openalex_id":508,"authors":509,"venue":463,"cited_by_count":88,"oa_url":460,"card":525,"direction":93,"ingested_from":95},"W7222806066",[510,512,514,516,519,521,523],{"name":511,"orcid":9},"Guangtao Wang",{"name":513,"orcid":9},"Sumei Yao",{"name":515,"orcid":9},"Yanzheng Xie",{"name":517,"orcid":518},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":520,"orcid":9},"Xiangdong Chen",{"name":522,"orcid":9},"Yuhao Zhang",{"name":524,"orcid":9},"Lina Jiang",{"tldr":526,"method":527,"finding":528,"direction":93,"opportunity":529},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z"]