[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3325":3,"related-3325":89},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":74,"slug":77,"view_count":19,"doi":8,"paper":78,"created_at":88},3325,"MBF-HybridNet：在极端气候下仍可提前一月预测冬小麦产量的多分支AI模型——Qingdao六县R² 0.756-0.765 MAPE 4.2%","https:\u002F\u002Fbioengineer.org\u002Fnew-multi-branch-ai-model-predicts-winter-wheat-yields-weeks-before-harvest-even-under-extreme-weather\u002F","MBF-HybridNet由青岛六县研究团队开发和测试：采用多分支并行架构，包括处理日常遥感和气象数据的动态变量模块、处理年度尺度极端气候指数数据的动态ECI模块以及处理土壤属性的静态变量模块。动态模块堆叠三个二维卷积层，插入自注意力机制；静态模块独立处理土壤有机碳、阳离子交换容量、pH、砂和粘土含量。研究团队计算了九个极端气候指数（热日、热应力强度、连续热日、霜日、冷应力强度、连续冷日、强降水日、连续湿日和连续干日）用于每个生长阶段。2004至2019年留一年交叉验证，MBF-HybridNet在三个累积生长阶段的R²达到0.756-0.765，平均绝对百分比误差约为4.2%。",null,"MBF-HybridNet 2026 (Qingdao)","2026-09-17T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,12,8,1,"多分支AI融合遥感气象与极端气候指数，提前一月预测冬小麦产量且精度可靠，方法新颖、数据扎实，对农业信息化与智慧农业有较高参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29,30],"智慧农业","农业人工智能","产量预测","遥感监测","冬小麦","极端气候",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,57,64,69],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},9,true,[34],[],[29],{"code":51,"score":52,"supported":46,"title":53,"abstract":54,"tags":56},"I3",5,[],[55],"遥感",[27,28],{"code":58,"score":59,"supported":46,"title":60,"abstract":61,"tags":63},"H2",3,[],[62],"土壤",[],{"code":65,"score":20,"supported":13,"title":66,"abstract":67,"tags":68},"I1",[],[],[25],{"code":70,"score":20,"supported":13,"title":71,"abstract":72,"tags":73},"I2",[],[],[26],[75,76],"MBF-HybridNet 冬小麦 产量预测","青岛 冬小麦 遥感 极端气候","MBF-HybridNet冬小麦产量预测-3325",{"doi":8,"openalex_id":8,"authors":79,"venue":8,"cited_by_count":80,"oa_url":8,"card":81,"direction":85,"ingested_from":87},[],0,{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"提出多分支AI模型MBF-HybridNet，融合遥感、气象与土壤数据，提前一月预测冬小麦产量。","多分支并行架构，含2D卷积、自注意力与极端气候指数，2004-2019年留一年交","在青岛六县三个累积生长阶段R²达0.756-0.765，MAPE约4.2%，极端气候下仍可提前一月预","农业人工智能与决策模型","可探索极端气候指数与深度学习结合在其他作物或区域的泛化能力，并提升可解释性。","agent","2026-09-24T00:04:02.748442Z",{"total":90,"page":20,"page_size":90,"items":91},6,[92,187,256,313,374,433],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":8,"source_name":98,"source_url":95,"published_at":99,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":100,"score_detail":101,"sources":107,"tags":109,"primary_topic":112,"secondary_topics":113,"classification":124,"search_phrases":161,"slug":164,"view_count":80,"doi":165,"paper":166,"created_at":186},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. 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",77,{"impact":102,"substance":103,"depth":104,"authority":105,"freshness":12,"relevant":20,"comment":106},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。",[108],{"name":98,"url":95},[25,110,28,111,29],"机器学习","土壤墒情",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[114,119],{"code":58,"slug":115,"name":116,"path":117,"parent_code":118},"soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":70,"slug":120,"name":121,"path":122,"parent_code":123},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":125},{"candidates":126,"agricultural_evidence":46},[127,133,138,143,149,156],{"code":32,"score":128,"supported":46,"title":129,"abstract":131,"tags":132},26,[130,33],"winter wheat",[34,130,33],[29],{"code":58,"score":134,"supported":46,"title":135,"abstract":136,"tags":137},15,[115],[62,115],[111],{"code":70,"score":134,"supported":46,"title":139,"abstract":141,"tags":142},[140],"machine learning",[110,140],[110],{"code":51,"score":12,"supported":46,"title":144,"abstract":145,"tags":148},[],[55,146,147],"remote sensing","multispectral",[28],{"code":150,"score":90,"supported":46,"title":151,"abstract":152,"tags":155},"H3",[],[153,154],"灌溉","irrigation",[],{"code":65,"score":157,"supported":46,"title":158,"abstract":159,"tags":160},4,[],[25],[25],[162,163],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":165,"openalex_id":167,"authors":168,"venue":98,"cited_by_count":80,"oa_url":95,"card":178,"direction":184,"ingested_from":185},"W7223156204",[169,172,174,176],{"name":170,"orcid":171},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":173,"orcid":8},"Yi Zhang",{"name":175,"orcid":8},"WEI Lei",{"name":177,"orcid":8},"Chengcai Zhang",{"tldr":179,"method":180,"finding":181,"direction":182,"opportunity":183},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","农业遥感与作物表型","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","openalex","2026-10-22T11:20:18.887301Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":8,"source_name":193,"source_url":190,"published_at":194,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":100,"score_detail":195,"sources":197,"tags":199,"primary_topic":202,"secondary_topics":203,"classification":208,"search_phrases":227,"slug":230,"view_count":80,"doi":231,"paper":232,"created_at":255},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",{"impact":102,"substance":17,"depth":16,"authority":105,"freshness":19,"relevant":20,"comment":196},"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。",[198],{"name":193,"url":190},[25,200,28,29,201],"无人机","生物量估算",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[204],{"code":51,"slug":205,"name":206,"path":207,"parent_code":123},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":13,"version":40,"method":41,"evidence":209},{"candidates":210,"agricultural_evidence":46},[211,215,219,223],{"code":32,"score":128,"supported":46,"title":212,"abstract":213,"tags":214},[130,33],[34,130,33],[29],{"code":51,"score":102,"supported":46,"title":216,"abstract":217,"tags":218},[146],[55,146],[201,28],{"code":70,"score":90,"supported":46,"title":220,"abstract":221,"tags":222},[],[110,140],[],{"code":65,"score":20,"supported":13,"title":224,"abstract":225,"tags":226},[],[],[25],[228,229],"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":231,"openalex_id":233,"authors":234,"venue":193,"cited_by_count":80,"oa_url":190,"card":250,"direction":182,"ingested_from":185},"W7222806066",[235,237,239,241,244,246,248],{"name":236,"orcid":8},"Guangtao Wang",{"name":238,"orcid":8},"Sumei Yao",{"name":240,"orcid":8},"Yanzheng Xie",{"name":242,"orcid":243},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":245,"orcid":8},"Xiangdong Chen",{"name":247,"orcid":8},"Yuhao Zhang",{"name":249,"orcid":8},"Lina Jiang",{"tldr":251,"method":252,"finding":253,"direction":182,"opportunity":254},"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",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":8,"content":8,"source_name":261,"source_url":259,"published_at":262,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":263,"score_detail":264,"sources":267,"tags":269,"primary_topic":272,"secondary_topics":273,"classification":275,"search_phrases":296,"slug":299,"view_count":59,"doi":300,"paper":301,"created_at":312},3807,"Reliability Auditing and Capacity-Constrained Review for Wheat-Yield Decision Support under Temporal and Spatial Shift","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10698325\u002Fv1","Reliability Auditing and Capacity-Constrained Review for Wheat-Yield Decision Support under Temporal and Spatial Shift。Research Square","Research Square","2026-09-28T00:00:00Z",52,{"impact":19,"substance":265,"depth":134,"authority":90,"freshness":45,"relevant":20,"comment":266},14,"预印本论文，聚焦小麦产量决策支持系统的可靠性审计与时空迁移问题，方法有一定新意但尚未经同行评审，公共影响有限。",[268],{"name":261,"url":259},[25,26,270,28,271],"决策支持","小麦产量",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[274],{"code":70,"slug":120,"name":121,"path":122,"parent_code":123},{"status":39,"locked":13,"version":40,"method":41,"evidence":276},{"candidates":277,"agricultural_evidence":46},[278,283,288,292],{"code":70,"score":105,"supported":46,"title":279,"abstract":281,"tags":282},[280],"decision support",[280],[26,270],{"code":32,"score":284,"supported":46,"title":285,"abstract":286,"tags":287},11,[33],[33],[],{"code":65,"score":20,"supported":13,"title":289,"abstract":290,"tags":291},[],[],[25],{"code":51,"score":20,"supported":13,"title":293,"abstract":294,"tags":295},[],[],[28],[297,298],"小麦产量 决策支持 遥感","Research Square 小麦 产量预测","小麦产量决策支持遥感-3807","10.21203\u002Frs.3.rs-10698325\u002Fv1",{"doi":300,"openalex_id":302,"authors":303,"venue":261,"cited_by_count":80,"oa_url":259,"card":8,"direction":85,"ingested_from":185},"W7214624863",[304,307,310],{"name":305,"orcid":306},"Xin Li","https:\u002F\u002Forcid.org\u002F0009-0005-0670-5006",{"name":308,"orcid":309},"Bojian Guo","https:\u002F\u002Forcid.org\u002F0009-0005-8353-2436",{"name":311,"orcid":8},"Asel Kartanova","2026-09-29T23:31:02.997580Z",{"id":314,"title":315,"url":316,"summary":317,"summary_zh":318,"content":8,"source_name":319,"source_url":316,"published_at":320,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":321,"score_detail":322,"sources":324,"tags":326,"primary_topic":329,"secondary_topics":330,"classification":331,"search_phrases":353,"slug":356,"view_count":20,"doi":357,"paper":358,"created_at":373},3483,"АГРОМЕТЕОРОЛОГІЧНІ УМОВИ ТА ДИНАМІКА ВЕГЕТАЦІЙНИХ ІНДЕКСІВ ПШЕНИЦІ ОЗИМОЇ У ПІВДЕННОМУ СТЕПУ УКРАЇНИ","https:\u002F\u002Fdoi.org\u002F10.32848\u002Fagrar.innov.2026.37.23","Комплексно оцінити агрометеорологічні умови вегетаційних періодів 2021–2025 рр. для пшениці озимої в умовах Південного Степу України та встановити закономірності динаміки вегетаційних індексів NDVI, NDRE і NDMI під впливом сезонної мінливості температурного режиму, опадів і дефіциту вологи. Методи. Дослідження проведено на базі Навчально-науково- практичного центру Миколаївського національного аграрного університету. Використано метеорологічні дані автоматичної станції Pessl Instruments (iMETOS) за 2021–2025 рр. та супутникові дані Sentinel‑2 SR Harmonized, оброблені в середовищі Google Earth Engine. Для оцінки стану посівів розраховано індекси NDVI, NDRE і NDMI та виконано їх порівняльний аналіз у взаємозв’язку з температурним режимом, кількістю опадів і перебігом основних фенологічних фаз розвитку пшениці озимої. Результати. Встановлено істотні міжрічні відмінності агрометеорологічних умов, які визначали характер формування та сезонної динаміки вегетаційних індексів. Найсприятливішими для розвитку рослин були вегетаційні періоди 2021–2022 та 2022–2023 рр., коли значення NDVI досягали 0,8–0,9, а NDRE – 0,6–0,7. Вегетаційний період 2023–2024 рр. характеризувався пригніченим осіннім розвитком і компенсаторним весняним наростанням біомаси, тоді як у 2024–2025 рр. добрі умови осінньої вегетації не забезпечили тривалого весняного розвитку через холодну й відносно суху весну. Встановлено, що найбільш критичним періодом розвитку водного стресу був червень, коли рослини переходили від фази колосіння до наливу зерна. Індекс NDMI найраніше реагував на погіршення водного режиму посівів і був найбільш чутливим індикатором розвитку водного стресу, тоді як скорочення тривалості плато NDVI відображало обмеження реалізації продуктивного потенціалу посівів. Висновки. Динаміка вегетаційних індексів пшениці озимої тісно пов’язана з особливостями сезонного перебігу температурного режиму та зволоження. Вирішальне значення для формування фізіологічного стану рослин мали умови весняного періоду, а найбільш критичним щодо розвитку водного стресу був перехід від колосіння до наливу зерна. Індекс NDMI доцільно використовувати як ранній індикатор водного стресу, тоді як комплексне застосування NDVI, NDRE і NDMI забезпечує ефективний моніторинг стану посівів, а скорочення тривалості плато NDVI може бути використане як індикатор обмеження реалізації продуктивного потенціалу озимої пшениці","综合评估2021—2025年乌克兰南部草原冬小麦（пшениця озима）生育期农业气象条件，并揭示NDVI、NDRE和NDMI植被指数在温度状况、降水和水分亏缺季节性变化影响下的动态规律。方法。研究在尼古拉耶夫国立农业大学教学-科学-实践中心基地进行。使用了Pessl Instruments自动气象站（iMETOS）2021—2025年的气象数据，以及经Google Earth Engine环境处理的Sentinel-2 SR Harmonized卫星数据。为评估作物状况，计算了NDVI、NDRE和NDMI指数，并结合温度状况、降水量及冬小麦主要物候发育阶段进程对其进行了比较分析。结果。确定了农业气象条件的显著年际差异，这些差异决定了植被指数的形成特征和季节性动态。2021—2022年和2022—2023年生育期对植株发育最为有利，NDVI值达到0.8—0.9，NDRE为0.6—0.7。2023—2024年生育期的特点是秋季发育受抑制，春季生物量补偿性增长，而2024—2025年良好的秋季生长条件未能保障春季的持续发育，原因是春季寒冷且相对干燥。确定水分胁迫发展的最关键时期为6月，此时植株从抽穗期过渡到籽粒灌浆期。NDMI指数对作物水分状况恶化反应最早，是水分胁迫发展最敏感的指标，而NDVI平台期持续时间的缩短则反映了作物生产潜力实现的受限。结论。冬小麦植被指数动态与温度状况和湿润条件的季节性进程特征密切相关。春季条件对植株生理状态的形成具有决定性意义，而就水分胁迫发展而言，从抽穗到籽粒灌浆的过渡期最为关键。NDMI指数宜作为水分胁迫的早期指标使用，而NDVI、NDRE和NDMI的综合应用可有效监测作物状况，NDVI平台期持续时间的缩短可作为冬小麦生产潜力实现受限的指标。","Аграрні інновації","2026-09-23T00:00:00Z",70,{"impact":18,"substance":103,"depth":104,"authority":18,"freshness":19,"relevant":20,"comment":323},"基于五年气象站与Sentinel-2数据系统分析冬小麦植被指数动态，方法规范、结论明确，对遥感作物监测有参考价值，但属区域性研究，公共影响有限。",[325],{"name":319,"url":316},[25,28,327,29,328],"水分胁迫","植被指数",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":332},{"candidates":333,"agricultural_evidence":46},[334,338,344,349],{"code":32,"score":157,"supported":46,"title":335,"abstract":336,"tags":337},[],[34],[29],{"code":339,"score":59,"supported":46,"title":340,"abstract":341,"tags":343},"H7",[],[342],"农业气象",[],{"code":51,"score":345,"supported":13,"title":346,"abstract":347,"tags":348},2,[],[],[328,28],{"code":65,"score":20,"supported":13,"title":350,"abstract":351,"tags":352},[],[],[25],[354,355],"乌克兰 南方草原 冬小麦 NDVI","Sentinel-2 NDMI 冬小麦 水分胁迫","乌克兰南方草原冬小麦NDVI-3483","10.32848\u002Fagrar.innov.2026.37.23",{"doi":357,"openalex_id":359,"authors":360,"venue":319,"cited_by_count":80,"oa_url":367,"card":368,"direction":182,"ingested_from":185},"W7214071557",[361,364],{"name":362,"orcid":363},"Антонина Панфилова","https:\u002F\u002Forcid.org\u002F0000-0003-0006-4090",{"name":365,"orcid":366},"Dmytro Koshkin","https:\u002F\u002Forcid.org\u002F0000-0002-6927-8487","https:\u002F\u002Fagrarian-innovations.izpr.ks.ua\u002Findex.php\u002Fagrarian\u002Farticle\u002Fdownload\u002F1168\u002F1166",{"tldr":369,"method":370,"finding":371,"direction":182,"opportunity":372},"评估乌克兰南部2021–2025年冬小麦生育期农业气象条件与NDVI、NDRE、NDMI植被指数动态","iMETOS自动气象站数据与Sentinel-2影像，在Google Earth","NDMI对水分胁迫响应最早最敏感，NDVI平台期缩短可指示产量潜力受限，抽穗至灌浆期为关键期。","可探索多指数融合的冬小麦水分胁迫早期预警阈值及平台期缩短与产量的定量模型。","2026-09-25T23:30:21.548558Z",{"id":375,"title":376,"url":377,"summary":378,"summary_zh":8,"content":8,"source_name":379,"source_url":8,"published_at":380,"category":11,"cover_url":8,"hotness":381,"is_selected":13,"score":382,"score_detail":383,"sources":385,"tags":390,"primary_topic":393,"secondary_topics":394,"classification":395,"search_phrases":421,"slug":424,"view_count":345,"doi":8,"paper":425,"created_at":432},2998,"物候阶段渐进式多源数据融合的县域冬小麦产量预测","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-09-18\u002Fdoc-inisheny8322115.shtml","西安财经大学王毅等联合江苏大学张立元副教授、西安理工大学西北旱区生态水利国家重点实验室、中国科学院重庆绿色智能技术研究院团队，以河南省100个冬小麦主产县为研究区融合2013—2022年遥感变量、气象变量和日光诱导叶绿素荧光光合变量，构建覆盖冬小麦分蘖期至成熟期的多源时序特征集。提出物候阶段渐进式多源数据融合方法明确不同物候阶段信息累积对县域冬小麦估产性能的影响。","智慧农业(中英文)2026,8(4):70-84","2026-09-18T00:00:00Z",25,80,{"impact":16,"substance":17,"depth":16,"authority":265,"freshness":19,"relevant":20,"comment":384},"以河南100个主产县2013—2022年遥感、气象与SIF数据构建物候阶段渐进式融合估产方法，数据规模与方法新颖性突出，对县域粮食产量预测有参考价值。",[386,387],{"name":379,"url":377},{"name":388,"url":389},"《智慧农业（中英文）》2026年第4期","https:\u002F\u002Ffinance.sina.cn\u002F2026-09-18\u002Fdetail-inisheny8322115.d.html",[25,26,391,29,392],"遥感估产","多源数据融合",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":396},{"candidates":397,"agricultural_evidence":46},[398,402,408,412,416],{"code":32,"score":18,"supported":46,"title":399,"abstract":400,"tags":401},[34],[34],[29],{"code":51,"score":403,"supported":46,"title":404,"abstract":405,"tags":407},7,[],[55,406],"估产",[391],{"code":65,"score":20,"supported":13,"title":409,"abstract":410,"tags":411},[],[],[25],{"code":70,"score":20,"supported":13,"title":413,"abstract":414,"tags":415},[],[],[26],{"code":417,"score":20,"supported":13,"title":418,"abstract":419,"tags":420},"I5",[],[],[392],[422,423],"河南 冬小麦 遥感估产","物候阶段 多源数据融合 产量预测","河南冬小麦遥感估产-2998",{"doi":8,"openalex_id":8,"authors":426,"venue":8,"cited_by_count":80,"oa_url":8,"card":427,"direction":182,"ingested_from":87},[],{"tldr":428,"method":429,"finding":430,"direction":182,"opportunity":431},"融合遥感、气象与SIF数据，提出物候阶段渐进式融合方法预测河南县域冬小麦产量。","2013—2022年河南100县遥感、气象、SIF多源时序特征，按物候阶段渐进融","明确不同物候阶段信息累积对县域冬小麦估产性能的影响，提升预测精度。","可探索物候自适应加权与深度学习融合，并迁移至其他作物及极端气候情景下的县域估产。","2026-09-20T00:03:07.932461Z",{"id":434,"title":435,"url":436,"summary":437,"summary_zh":8,"content":8,"source_name":438,"source_url":8,"published_at":439,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":440,"score_detail":441,"sources":443,"tags":445,"primary_topic":448,"secondary_topics":449,"classification":452,"search_phrases":476,"slug":479,"view_count":345,"doi":8,"paper":480,"created_at":487},2854,"面向冬小麦水分含量的无人机遥感自动机器学习预测——MDPI Remote Sensing","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3161","本研究探索了无人机遥感快速准确评估冬小麦水分含量的潜力。在开花期和灌浆期使用配备多光谱、RGB和热红外相机的无人机获取高分辨率冠层遥感图像。集成地面真值采样数据与无人机遥感数据,使用自动机器学习(AutoML)框架建立回归模型预测冬小麦水分含量(MC)。结果表明,MC预测在灌浆期表现最佳,TIR传感器精度最高(R²=0.812,MAE=0.0204,RMSE=0.0274)。多传感器融合相比单传感器方法进一步提升预测性能,MC预测的R²达0.876、MAE 0.0191、RMSE 0.0259。来自中国农业科学院农田灌溉研究所。","MDPI Remote Sensing","2026-09-15T00:00:00Z",74,{"impact":134,"substance":103,"depth":104,"authority":105,"freshness":19,"relevant":20,"comment":442},"中国农科院团队用AutoML融合多光谱、RGB与热红外无人机数据预测冬小麦水分含量，多传感器融合R²达0.876，方法新颖、结论可靠，对精准灌溉有实用价值，值得进入每日精选。",[444],{"name":438,"url":436},[25,26,446,29,447],"无人机遥感","多传感器融合",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[450,451],{"code":51,"slug":205,"name":206,"path":207,"parent_code":123},{"code":70,"slug":120,"name":121,"path":122,"parent_code":123},{"status":39,"locked":13,"version":40,"method":41,"evidence":453},{"candidates":454,"agricultural_evidence":46},[455,460,464,468,472],{"code":51,"score":456,"supported":46,"title":457,"abstract":458,"tags":459},20,[55,146],[55],[446],{"code":32,"score":18,"supported":46,"title":461,"abstract":462,"tags":463},[34],[34],[29],{"code":70,"score":18,"supported":46,"title":465,"abstract":466,"tags":467},[110],[110],[26],{"code":150,"score":59,"supported":46,"title":469,"abstract":470,"tags":471},[],[153],[],{"code":65,"score":20,"supported":13,"title":473,"abstract":474,"tags":475},[],[],[25],[477,478],"农业人工智能 多传感器融合 无人机遥感 智慧农业","农业人工智能 多传感器融合","农业人工智能多传感器融合无人机遥感智慧农业-2854",{"doi":8,"openalex_id":8,"authors":481,"venue":8,"cited_by_count":80,"oa_url":8,"card":482,"direction":182,"ingested_from":87},[],{"tldr":483,"method":484,"finding":485,"direction":182,"opportunity":486},"用无人机多传感器遥感结合AutoML预测冬小麦水分含量。","无人机多光谱、RGB、热红外图像+地面真值，AutoML回归建模。","灌浆期热红外精度最高，多传感器融合将R²提升至0.876。","可探索不同生育期与品种的泛化性，及将水分预测接入灌溉决策系统。","2026-09-18T00:03:30.758419Z"]