[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2530":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":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":52},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。",null,"Sustainability","2026-09-14T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准农业","遥感","病虫害监测","作物估产",0,"10.3390\u002Fsu18189410",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":45,"direction":49,"ingested_from":51},"W7212851379",[37,40,43],{"name":38,"orcid":39},"Ghada A. Khdery","https:\u002F\u002Forcid.org\u002F0000-0002-1492-6029",{"name":41,"orcid":42},"Mohamed S. Shokr","https:\u002F\u002Forcid.org\u002F0000-0003-0328-7679",{"name":44,"orcid":9},"Aleksandra O. Utkina",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"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。","农业遥感与作物表型","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。","openalex","2026-09-15T23:30:20.628746Z"]