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Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z","论文",77,{"impact":94,"substance":95,"depth":96,"authority":97,"freshness":13,"relevant":21,"comment":98},16,21,17,13,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[100],{"name":89,"url":86},[102,103,104,105,106],"智慧农业","机器学习","遥感监测","土壤墒情","冬小麦",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[109,115],{"code":110,"slug":111,"name":112,"path":113,"parent_code":114},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":116,"slug":117,"name":118,"path":119,"parent_code":120},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":122},{"candidates":123,"agricultural_evidence":46},[124,130,136,141,149,156],{"code":32,"score":125,"supported":46,"title":126,"abstract":128,"tags":129},26,[127,33],"winter 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农业物联网","openalex","2026-10-22T11:20:18.887301Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":8,"content":8,"source_name":194,"source_url":8,"published_at":90,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":201,"tags":203,"primary_topic":208,"secondary_topics":209,"classification":219,"search_phrases":240,"slug":243,"view_count":21,"doi":8,"paper":244,"created_at":252},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21",82,{"impact":197,"substance":198,"depth":197,"authority":199,"freshness":13,"relevant":21,"comment":200},18,22,14,"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[202],{"name":194,"url":192},[102,204,205,206,207],"农业遥感","作物表型","无人机多光谱","小麦渍害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[210,214],{"code":142,"slug":211,"name":212,"path":213,"parent_code":120},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":215,"slug":216,"name":217,"path":218,"parent_code":114},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":39,"locked":14,"version":40,"method":41,"evidence":220},{"candidates":221,"agricultural_evidence":46},[222,227,231,236],{"code":32,"score":223,"supported":46,"title":224,"abstract":225,"tags":226},11,[34],[34],[],{"code":142,"score":223,"supported":46,"title":228,"abstract":229,"tags":230},[206],[],[205,204,206],{"code":215,"score":45,"supported":46,"title":232,"abstract":234,"tags":235},[233],"渍害",[],[],{"code":157,"score":21,"supported":14,"title":237,"abstract":238,"tags":239},[],[],[102],[241,242],"江苏省农科院 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日签约揭牌，作物所聘请科技成果转化推广专员刘洪彬担任技术指导员。基地以展示济麦系列优质高产及特色营养小麦新品种为核心，同步开展新品种新技术推广与人才培训，并从良种繁育前端与特色营养小麦加工后端两端拉长产业链。","10月20日，在位于阳信县农村技术协会的小麦播种田里，大型播种机在100多亩小麦品种展示基地回穿梭，不时传出技术人员下达的技术指令声，“播种机进地播种时一定检查好播种定量指针”“底肥和种子同期播种必须保持好安全距离”等技术措施不时从现场技术指导老师刘洪彬的嘴里发出。\n\n原来这是山东省农科院作物所山东省农业科学院作物研究所阳信小麦试验示范基地2026年的小麦品种播种现场，阳信县农技协会会长王保友在忙着记录数据、搬运种子，确保严格程序，完成高质量播种工作。\n\n今年9月15日，山东省农业科学院作物研究所阳信县小麦试验示范基地在阳信举行签约揭牌仪式。在随后召开的座谈会上，省农科院作物研究所小麦专家围绕济麦系列小麦新品种的特征特性、栽培要点等进行详细介绍。\n\n近年来，阳信县持续深化产学研合作，积极引进推广小麦新品种、新技术，粮食综合生产能力稳步提升。此次小麦试验示范基地的共建，将进一步推动济麦系列优良品种在该县规模化应用，打造农业科技创新与示范推广的县域样板，为全县小麦等粮食作物单产提升注入科技动能。\n\n基地由山东省农业科学院作物研究所与阳信农村专业技术协会共同打造建设，聘请山东省农科院作物所科技成果转化推广专员刘洪彬为小麦高质量栽培技术指导员。坚持“技术成果带动、试验示范结合、责任权利清晰、合作互利共赢”的原则，围绕山东省农业科学院作物研究所育成的小麦新品种（系）进行试验示范，开展新品种、新技术推广及人才培训，打造科技引领型小麦产业提质增效的阳信样板。旨在建设小麦新品种（系）展示平台，展示济麦系列优质高产及特色营养小麦新品种（系）。建立优质原粮生产基地，促进优质特色营养小麦产业化开发。以基地为核心，带动周边发展高产优质特色营养小麦生产，为当地面粉加工企业提供优质原料.延长小麦产业链，打造小麦产业提质增效的样板。\n\n在新品种展示示范的基础上，从两端拉长小麦产业链，前端引入种业企业，进一步发展小麦良种繁育，打造良种繁育基地；后端开发特色营养小麦加工，形成良种繁育—示范推广—产品加工完整产业链，不断提升小麦产业附加值。","山东省农业科学院 2026-10-20","2026-10-20T00:00:00Z",56,{"impact":19,"substance":97,"depth":13,"authority":19,"freshness":351,"relevant":21,"comment":352},9,"省级科研机构与县域共建小麦新品种示范基地的常规进展报道，有具体合作机制与产业链思路，但属区域性动态，公共影响有限。",[354],{"name":347,"url":344},[312,34,356,357,358],"济麦系列","院地合作","试验示范",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":362},{"candidates":363,"agricultural_evidence":46},[364,368,372],{"code":32,"score":97,"supported":46,"title":365,"abstract":366,"tags":367},[34],[34],[34,356],{"code":332,"score":158,"supported":46,"title":369,"abstract":370,"tags":371},[],[313],[357],{"code":326,"score":21,"supported":14,"title":373,"abstract":374,"tags":375},[],[],[312],[377,378],"山东农科院 阳信 小麦试验示范基地","济麦系列 小麦 示范推广","山东农科院阳信小麦试验示范基地-5431","2026-10-21T00:10:00.947422Z",{"id":382,"title":383,"url":384,"summary":385,"summary_zh":8,"content":386,"source_name":387,"source_url":8,"published_at":260,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":306,"score_detail":388,"sources":390,"tags":392,"primary_topic":395,"secondary_topics":396,"classification":397,"search_phrases":413,"slug":416,"view_count":166,"doi":8,"paper":8,"created_at":417},5429,"山东农科院作物所矮秆耐逆小麦新品种「济麦1806」生产经营权签约转让","http:\u002F\u002Fsdcrops.cn\u002Farticles\u002Fch11158\u002F202610\u002Fe4b9c977-8933-49d2-9adb-481fb88754ae.shtml","10 月 19 日，济麦 1806 生产经营权独占实施许可签约仪式在济南举办，山东省农科院作物所与山东东润种业有限公司签约。该品种是作物所育成的矮秆、耐逆、高产、广适小麦新品种，2026 年通过国家审定，两年区试全生育期 234.2 天，株高 68.3 厘米，倒伏达标点率 100%，亩穗数 35.8 万穗、穗粒数 35.7 粒、千粒重 40.7 克；生产试验平均亩产 346.6 公斤、增产 8.96%。品质稳定时间 5.3 至 6.4 分钟，适合制作馒头。","10月19日，济麦1806生产经营权独占实施许可签约仪式在济南举办。作物所理事长兼党委书记赵海军、山东东润种业有限公司总经理刘延福，小麦遗传育种团队、成果转化科以及东润种业相关人员参加活动。\n\n小麦遗传育种团队执行专家李豪圣介绍了济麦1806品种特征特性以及栽培技术要点。双方针对济麦1806的推广进行座谈交流，下一步将以该品种的转让为合作启点，加快新品种推广步伐，同时在项目申报、人才培养、成果培育上开展深入合作。\n\n济麦1806是作物所育成的矮秆、耐逆、高产、广适小麦新品种，2026年通过国家审定。两年区试全生育期234.2天，株高68.3厘米，倒伏达标点率100%；亩穗数35.8万穗，穗粒数35.7粒，千粒重40.7克，亩产435公斤，较对照增产4.6%；生产试验平均亩产346.6公斤，增产8.96%。品质较稳定，适合制作馒头。多年品质分析结果表明，容重792 g\u002FL-814 g\u002FL，蛋白质（干基）12.3%-13.4%，湿面筋（14%湿基）26.5% -29.6%，吸水率60.0%-62.0%，稳定时间5.3-6.4 min。（撰写：郭军 核稿：李峰）","山东省农业科学院 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