[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4083":3,"related-4083":164},{"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,"primary_topic":33,"secondary_topics":39,"classification":46,"search_phrases":112,"slug":115,"view_count":116,"doi":117,"paper":118,"created_at":163},4083,"Wheat Rust Diseases: Global Impact, Integrated Disease Management, and Emerging Biotechnological Innovations for Sustainable Crop Protection","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fbiology15191756","Wheat (Triticum aestivum L.) remains one of the most important staple crops worldwide and serves as a primary source of calories and protein for billions of people. Global wheat production, however, continues to be threatened by rust diseases caused by Puccinia triticina, Puccinia striiformis f. sp. tritici and Puccinia graminis f. sp. tritici. These pathogens are highly adaptive and frequently give rise to new virulent races, causing resistance in wheat cultivars to breakdown. Under favorable environmental conditions, severe epidemics can develop and substantial yield losses may occur. In response to these challenges, traditional management approaches such as resistant cultivars, crop rotation, sanitation and fungicide application have long been employed in wheat production systems. Yet these strategies have increasingly been constrained by resistance breakdown, genetic uniformity in modern cultivars, and climate-driven changes in pathogen distribution. Conventional management practices are evaluated alongside emerging technological solutions including genomic-assisted breeding, gene pyramiding, CRISPR-based genome editing, biological control agents and precision agriculture tools. In addition, nanotechnology has emerged as a promising tool in plant disease management, where nanoparticles such as silver, copper and zinc oxide exhibit strong antifungal activity and enable targeted delivery of agrochemicals. Nano-enabled formulations can enhance fungicide efficiency, reduce chemical inputs and improve plant defense responses, offering a sustainable complement to existing wheat rust management strategies. Particular attention is given to integrated disease management strategies, which combine traditional agronomic practices with modern surveillance technologies such as remote sensing, artificial intelligence-based forecasting models, and pathogen genomics. These combined approaches can improve resistance durability and build more resilient wheat production systems. Future directions emphasize climate adaptation, global surveillance and collaborative breeding. In an era of increasing climatic uncertainty and evolving pathogen populations, durable rust management will depend on coordinated global efforts that align scientific innovation, farmer adoption, and policy support to safeguard wheat productivity and global food security.","小麦（Triticum aestivum L.）仍是全球最重要的主粮作物之一，是数十亿人热量和蛋白质的主要来源。然而，全球小麦生产持续受到由小麦叶锈菌（Puccinia triticina）、小麦条锈菌（Puccinia striiformis f. sp. tritici）和小麦秆锈菌（Puccinia graminis f. sp. tritici）引起的锈病威胁。这些病原菌适应性极强，频繁产生新的毒性小种，导致小麦品种抗性丧失。在有利的环境条件下，病害可严重流行并造成大幅产量损失。为应对这些挑战，小麦生产体系中长期采用抗病品种、轮作、田园卫生和杀菌剂施用等传统管理措施。然而，这些策略日益受到抗性丧失、现代品种遗传单一化以及气候驱动的病原菌分布变化的制约。本文在评估传统管理实践的同时，也探讨了新兴技术解决方案，包括基因组辅助育种、基因聚合、基于CRISPR的基因组编辑、生物防治制剂和精准农业工具。此外，纳米技术已成为植物病害管理中有前景的工具，其中银、铜和氧化锌等纳米颗粒表现出强抗真菌活性，并能够实现农化品的靶向递送。纳米制剂可提高杀菌剂效率、减少化学投入并改善植物防御反应，为现有小麦锈病管理策略提供了可持续的补充。本文特别关注病害综合管理策略，即将传统农艺措施与遥感、基于人工智能的预测模型和病原菌基因组学等现代监测技术相结合。这些综合方法可提高抗性持久性并构建更具韧性的小麦生产体系。未来方向强调气候适应、全球监测和协作育种。在气候不确定性日益加剧和病原菌群体不断演化的时代，持久的锈病管理将依赖于协调一致的全球努力，将科学创新、农民采纳和政策支持相结合，以保障小麦生产和全球粮食安全。",null,"Biology","2026-10-02T00:00:00Z","论文",10,true,82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,21,18,13,8,1,"系统综述小麦锈病全球影响与基因组育种、纳米农药、遥感与AI预测等前沿防控手段，专业性强，对保障粮食安全具参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"智慧农业","粮食安全","基因编辑","纳米农药","遥感监测","小麦锈病",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[40],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",{"status":47,"locked":48,"version":49,"method":50,"evidence":51},"classified",false,"2026-10-22.2","rules",{"candidates":52,"agricultural_evidence":14},[53,58,65,72,79,86,94,101,107],{"code":34,"score":54,"supported":14,"title":55,"abstract":56,"tags":57},14,[35],[36,35],[],{"code":41,"score":59,"supported":14,"title":60,"abstract":62,"tags":64},12,[61],"crop protection",[63],"生物防治",[30],{"code":66,"score":67,"supported":14,"title":68,"abstract":69,"tags":71},"A8",7,[],[28,70],"food security",[28],{"code":73,"score":67,"supported":14,"title":74,"abstract":75,"tags":78},"I1",[],[76,77],"精准农业","precision agriculture",[27],{"code":80,"score":67,"supported":14,"title":81,"abstract":82,"tags":85},"I3",[],[83,84],"遥感","remote sensing",[31],{"code":87,"score":88,"supported":14,"title":89,"abstract":90,"tags":93},"A7",6,[],[91,92],"轮作","crop rotation",[],{"code":95,"score":96,"supported":14,"title":97,"abstract":98,"tags":100},"H7",3,[],[99],"气候适应",[],{"code":102,"score":96,"supported":14,"title":103,"abstract":104,"tags":106},"I2",[],[105],"artificial intelligence",[],{"code":108,"score":22,"supported":48,"title":109,"abstract":110,"tags":111},"F2",[],[],[29],[113,114],"小麦锈病 综合防控","Puccinia triticina 抗性育种","小麦锈病综合防控-4083",2,"10.3390\u002Fbiology15191756",{"doi":117,"openalex_id":119,"authors":120,"venue":10,"cited_by_count":154,"oa_url":6,"card":155,"direction":161,"ingested_from":162},"W7215995869",[121,124,127,130,133,136,138,140,142,145,148,151],{"name":122,"orcid":123},"Mingzheng Duan","https:\u002F\u002Forcid.org\u002F0000-0002-3486-024X",{"name":125,"orcid":126},"Muhammad Asif Shabbir","https:\u002F\u002Forcid.org\u002F0009-0002-5452-5040",{"name":128,"orcid":129},"Mustansar Mubeen","https:\u002F\u002Forcid.org\u002F0009-0003-9231-9560",{"name":131,"orcid":132},"Yasir Iftikhar","https:\u002F\u002Forcid.org\u002F0000-0002-4182-8969",{"name":134,"orcid":135},"Naureen Anwar","https:\u002F\u002Forcid.org\u002F0000-0003-1254-1720",{"name":137,"orcid":9},"Shiping Jiang",{"name":139,"orcid":9},"Zhifang He",{"name":141,"orcid":9},"Sirui Han",{"name":143,"orcid":144},"Ahmed Mahmoud Ismail","https:\u002F\u002Forcid.org\u002F0000-0001-9679-640X",{"name":146,"orcid":147},"Khalid Mushabab Al Syaad","https:\u002F\u002Forcid.org\u002F0000-0002-2372-9975",{"name":149,"orcid":150},"Ahmed Ezzat Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-4532-0636",{"name":152,"orcid":153},"Muhammad Umer","https:\u002F\u002Forcid.org\u002F0000-0002-3957-0196",0,{"tldr":156,"method":157,"finding":158,"direction":159,"opportunity":160},"综述小麦锈病全球影响与综合防控，并展望基因编辑、纳米技术、遥感与AI预测等新兴手段。","文献综述，整合抗病育种、CRISPR、纳米制剂、遥感与AI预测模型。","综合传统农艺与现代监测技术可提升抗性持久性，纳米与基因编辑是可持续补充。","农业人工智能与决策模型","可探索遥感与AI预测模型同病原基因组数据融合的实时预警系统，并评估纳米制剂田间长期生态效应。","农业遥感与作物表型","openalex","2026-10-03T23:30:32.282761Z",{"total":88,"page":22,"page_size":88,"items":165},[166,254,332,416,516,585],{"id":167,"title":168,"url":169,"summary":170,"summary_zh":171,"content":9,"source_name":172,"source_url":169,"published_at":173,"category":12,"cover_url":9,"hotness":13,"is_selected":48,"score":174,"score_detail":175,"sources":180,"tags":182,"primary_topic":185,"secondary_topics":186,"classification":192,"search_phrases":217,"slug":220,"view_count":221,"doi":222,"paper":223,"created_at":253},2670,"A knowledge-guided machine learning framework for cross-scale wheat harvest monitoring via sample augmentation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115671","Accurate monitoring of wheat harvest is crucial for precision agriculture and ensuring food security. However, rapid changes in land surface composition during the harvest period in intensive agricultural regions make it difficult to obtain sufficiently high-confidence ground samples, limiting the performance and generalization of data-driven remote sensing methods. Therefore, this study proposes a Knowledge-Guided Machine Learning (KGML) framework that integrates multi-satellite Earth observation data (PlanetScope, Sentinel-2, and MODIS) to monitor harvest from the field to regional scales. Ground data were collected using vehicle-mounted cameras and smartphones during the 2023 and 2024 wheat harvest periods. The results showed that combining spectral knowledge rules with a Random Forest model (regional accuracy >0.80) generated numerous high-confidence augmented samples from PlanetScope imagery. The augmented dataset was used to train a Hybrid CNN-Transformer-LSTM (HCTL) model with two pathways: Sentinel-2 classification for field-level harvest mapping (overall accuracy = 0.93) and MODIS regression for sub-pixel harvest fraction estimation, which showed high agreement with PlanetScope-derived harvest fractions (R 2 = 0.97, RMSE = 0.07, rRMSE = 0.15). The harvest dates derived from the MODIS harvest fraction time series showed high consistency with field observations (R 2 = 0.82, RMSE = 1.30 days). This framework provides an effective solution for wheat harvest monitoring by bridging the gap between limited ground-truth data and multi-scale satellite observations, thereby supporting food security assessments and informed agricultural management decisions.","准确监测小麦收获对精准农业和保障粮食安全至关重要。然而，在集约化农业区域，收获期地表组成的快速变化使得获取足够高置信度的地面样本变得困难，限制了数据驱动遥感方法的性能和泛化能力。因此，本研究提出了一种知识引导机器学习（KGML）框架，集成多卫星地球观测数据（PlanetScope、Sentinel-2和MODIS），实现从田块到区域尺度的收获监测。地面数据通过车载摄像头和智能手机在2023年和2024年小麦收获期采集。结果表明，将光谱知识规则与随机森林模型相结合（区域精度>0.80），可从PlanetScope影像中生成大量高置信度增强样本。利用该增强数据集训练了混合CNN-Transformer-LSTM（HCTL）模型，该模型包含两条路径：Sentinel-2分类用于田块尺度收获制图（总体精度=0.93），MODIS回归用于亚像元收获比例估算，其结果与PlanetScope-derived收获比例高度一致（R²=0.97，RMSE=0.07，rRMSE=0.15）。由MODIS收获比例时间序列提取的收获日期与田间观测结果高度一致（R²=0.82，RMSE=1.30天）。该框架通过弥合有限地面真值数据与多尺度卫星观测之间的差距，为小麦收获监测提供了有效解决方案，从而支持粮食安全评估和农业管理决策。","Remote Sensing of Environment","2026-09-15T00:00:00Z",87,{"impact":17,"substance":176,"depth":19,"authority":177,"freshness":178,"relevant":22,"comment":179},23,15,9,"提出知识引导机器学习框架，融合多源卫星数据实现田块到区域尺度的跨尺度小麦收获监测，方法新颖、精度可靠，对精准农业与粮食安全评估有实质参考价值。",[181],{"name":172,"url":169},[27,183,28,31,184],"农业人工智能","小麦收获",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[187],{"code":102,"slug":188,"name":189,"path":190,"parent_code":191},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":47,"locked":48,"version":49,"method":50,"evidence":193},{"candidates":194,"agricultural_evidence":14},[195,201,205,209,213],{"code":102,"score":177,"supported":14,"title":196,"abstract":198,"tags":200},[197],"machine learning",[199,197],"机器学习",[183],{"code":34,"score":54,"supported":14,"title":202,"abstract":203,"tags":204},[35],[36,35],[],{"code":66,"score":67,"supported":14,"title":206,"abstract":207,"tags":208},[],[28,70],[28],{"code":73,"score":67,"supported":14,"title":210,"abstract":211,"tags":212},[],[76,77],[27],{"code":80,"score":67,"supported":14,"title":214,"abstract":215,"tags":216},[],[83,84],[31],[218,219],"农业人工智能 小麦收获 智慧农业 粮食安全","农业人工智能 小麦收获","农业人工智能小麦收获智慧农业粮食安全-2670",4,"10.1016\u002Fj.rse.2026.115671",{"doi":222,"openalex_id":224,"authors":225,"venue":172,"cited_by_count":154,"oa_url":169,"card":248,"direction":161,"ingested_from":162},"W7213296259",[226,229,231,233,235,237,239,241,244,246],{"name":227,"orcid":228},"Mingchao Shao","https:\u002F\u002Forcid.org\u002F0000-0003-2619-4272",{"name":230,"orcid":9},"Chongya Jiang",{"name":232,"orcid":9},"Jingwei An",{"name":234,"orcid":9},"Haokai Zhu",{"name":236,"orcid":9},"Yue Li",{"name":238,"orcid":9},"Xia Yao",{"name":240,"orcid":9},"Tao Cheng",{"name":242,"orcid":243},"Hengbiao Zheng","https:\u002F\u002Forcid.org\u002F0009-0008-4778-0450",{"name":245,"orcid":9},"Weixing Cao",{"name":247,"orcid":9},"Yan Zhu",{"tldr":249,"method":250,"finding":251,"direction":161,"opportunity":252},"提出知识引导机器学习框架，用样本增强实现田块到区域尺度的冬小麦收获监测。","融合PlanetScope、Sentinel-2、MODIS与车载相机地面数据，","增强样本训练的HCTL模型田块分类精度0.93，区域收获比例R²=0.97，收获日期误差约1.3天。","可迁移至其他作物收获监测，并探索知识规则自动化构建与跨区域泛化能力。","2026-09-16T23:30:30.474537Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":257,"published_at":261,"category":12,"cover_url":9,"hotness":13,"is_selected":48,"score":262,"score_detail":263,"sources":265,"tags":267,"primary_topic":272,"secondary_topics":273,"classification":275,"search_phrases":302,"slug":305,"view_count":96,"doi":306,"paper":307,"created_at":331},1823,"Integrating a pesticide risk indicator into a multi-criteria assessment tool for comparing crop protection scenarios","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104945","CONTEXT Chemical pesticides remain central to agricultural productivity but pose well documented risks to human health and ecosystems. While Integrated Pest Management (IPM) offers a more sustainable alternative, its adoption remains limited, partly due to the complex trade-offs that are not easily quantified and compared. OBJECTIVE This study presents the IPM Self-Assessment Tool (ISAT), a web-based application designed to enable transparent comparison of crop protection scenarios at the field level, integrating environmental, human health, agronomic, and economic indicators. METHODS A new harmonized pesticide risk indicator for Europe (PL EU – Pesticide Load Indicator for Europe) was developed. The PL EU covers 41 hazard metrics across three sub-indicators (Environmental Fate, Ecotoxicity, and Human Health). The PL EU was integrated into the ISAT alongside indicators for greenhouse gas (GHG) emissions and production costs, calculated using life cycle based methods. The tool was developed using data from ten European countries covering eight crops, with scenarios described by regional specialists and validated by farm advisors. RESULTS AND CONCLUSIONS The PL EU was calculated for 502 active ingredients (441 synthetic, 61 biopesticides) across eight pesticide groups. A case study on winter wheat in Germany showed that, relative to conventional practice, an IPM scenario replacing herbicide use by mechanical weeding reduced pesticide risk by up to 75%, but at the cost of GHG emissions comparable to the conventional scenario. The ISAT enables users to identify risk-driving active ingredients and explore lower-risk alternatives, supporting informed decision-making for IPM adoption. SIGNIFICANCE To our knowledge, ISAT is the first freely accessible, web-based tool integrating a harmonized pesticide risk indicator with GHG emissions and economic outcomes within a single, comprehensive field level scenario comparison. It provides a transparent decision-support platform that can help farmers, advisors, and researchers make informed decisions when designing and comparing crop protection strategies across diverse European cropping systems.","化学农药仍是农业生产力的核心，但对人类健康和生态系统构成的风险已有充分记录。尽管有害生物综合治理（IPM）提供了更具可持续性的替代方案，但其采用率仍然有限，部分原因在于复杂的权衡关系难以量化和比较。目的 本研究介绍了IPM自我评估工具（ISAT），这是一个基于网络的应用程序，旨在实现田间层面作物保护方案的透明比较，整合环境、人类健康、农学和经济指标。方法 开发了欧洲统一农药风险指标（PL EU——欧洲农药负荷指标）。PL EU涵盖三个子指标（环境归趋、生态毒性和人类健康）下的41项危害指标。PL EU被整合到ISAT中，同时还包括基于生命周期方法计算的温室气体（GHG）排放和生产成本指标。该工具利用覆盖八个作物、来自十个欧洲国家的数据开发，情景由区域专家描述并由农场顾问验证。结果与结论 针对八个农药类别中的502种有效成分（441种合成农药，61种生物农药）计算了PL EU。德国冬小麦案例研究表明，相对于常规实践，用机械除草替代除草剂使用的IPM情景可将农药风险降低高达75%，但代价是温室气体排放与常规情景相当。ISAT使用户能够识别风险驱动型有效成分并探索低风险替代方案，为IPM采用提供知情决策支持。意义 据我们所知，ISAT是首个免费访问的基于网络的工具，将统一的农药风险指标与温室气体排放和经济结果整合在单一、全面的田间层面情景比较中。它提供了一个透明的决策支持平台，可帮助农民、顾问和研究人员在设计和比较欧洲多样化种植制度中的作物保护策略时做出明智决策。","Agricultural Systems","2026-09-06T00:00:00Z",79,{"impact":19,"substance":17,"depth":19,"authority":54,"freshness":67,"relevant":22,"comment":264},"论文提出首个整合农药风险、温室气体与成本的田间级评估工具，数据覆盖广，对IPM决策有实质参考价值。",[266],{"name":260,"url":257},[183,268,269,270,271],"决策支持","欧洲","农药风险","作物保护",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},[274],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":47,"locked":48,"version":49,"method":50,"evidence":276},{"candidates":277,"agricultural_evidence":14},[278,286,291,298],{"code":41,"score":279,"supported":14,"title":280,"abstract":282,"tags":285},25,[281,61],"pesticide",[283,284,281,61],"农药","除草",[],{"code":34,"score":178,"supported":14,"title":287,"abstract":288,"tags":290},[],[36,289,35],"winter wheat",[],{"code":292,"score":88,"supported":14,"title":293,"abstract":294,"tags":297},"B7",[],[295,296],"温室","greenhouse",[],{"code":102,"score":116,"supported":48,"title":299,"abstract":300,"tags":301},[],[],[183,268],[303,304],"农业人工智能 作物保护 农药风险 决策支持","农业人工智能 作物保护","农业人工智能作物保护农药风险决策支持-1823","10.1016\u002Fj.agsy.2026.104945",{"doi":306,"openalex_id":308,"authors":309,"venue":260,"cited_by_count":154,"oa_url":325,"card":326,"direction":159,"ingested_from":162},"W7168097222",[310,312,314,317,320,323],{"name":311,"orcid":9},"Vera Krause",{"name":313,"orcid":9},"Jörn Strassemeyer",{"name":315,"orcid":316},"Virginia Nichols","https:\u002F\u002Forcid.org\u002F0000-0003-1850-8243",{"name":318,"orcid":319},"Per Kudsk","https:\u002F\u002Forcid.org\u002F0000-0003-2431-3610",{"name":321,"orcid":322},"Noé Vandevoorde","https:\u002F\u002Forcid.org\u002F0000-0003-1959-7524",{"name":324,"orcid":9},"Daniel Daehmlow","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0308521X26003136\u002Fpdf",{"tldr":327,"method":328,"finding":329,"direction":159,"opportunity":330},"开发了IPM自评估工具ISAT，集成新的农药风险指标PL EU，比较作物保护情景的环境、健康和经济影","开发欧洲统一农药风险指标PL EU，覆盖41种危害指标，集成到网络工具ISAT中","德国冬小麦案例中，IPM机械除草替代除草剂可降低农药风险75%，但温室气体排放与传统相当。","可扩展ISAT至更多作物和地区，或开发更精细的生态毒理模型，并考虑生物农药的长期风险。","2026-09-07T23:30:05.419008Z",{"id":333,"title":334,"url":335,"summary":336,"summary_zh":337,"content":9,"source_name":338,"source_url":335,"published_at":339,"category":12,"cover_url":9,"hotness":13,"is_selected":48,"score":340,"score_detail":341,"sources":345,"tags":347,"primary_topic":350,"secondary_topics":351,"classification":359,"search_phrases":392,"slug":395,"view_count":154,"doi":396,"paper":397,"created_at":415},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":342,"substance":18,"depth":343,"authority":20,"freshness":13,"relevant":22,"comment":344},16,17,"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。",[346],{"name":338,"url":335},[27,199,31,348,349],"土壤墒情","冬小麦",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[352,358],{"code":353,"slug":354,"name":355,"path":356,"parent_code":357},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":102,"slug":188,"name":189,"path":190,"parent_code":191},{"status":47,"locked":48,"version":49,"method":50,"evidence":360},{"candidates":361,"agricultural_evidence":14},[362,367,372,376,381,388],{"code":34,"score":363,"supported":14,"title":364,"abstract":365,"tags":366},26,[289,35],[36,289,35],[349],{"code":353,"score":177,"supported":14,"title":368,"abstract":369,"tags":371},[354],[370,354],"土壤",[348],{"code":102,"score":177,"supported":14,"title":373,"abstract":374,"tags":375},[197],[199,197],[199],{"code":80,"score":13,"supported":14,"title":377,"abstract":378,"tags":380},[],[83,84,379],"multispectral",[31],{"code":382,"score":88,"supported":14,"title":383,"abstract":384,"tags":387},"H3",[],[385,386],"灌溉","irrigation",[],{"code":73,"score":221,"supported":14,"title":389,"abstract":390,"tags":391},[],[27],[27],[393,394],"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":396,"openalex_id":398,"authors":399,"venue":338,"cited_by_count":154,"oa_url":335,"card":409,"direction":414,"ingested_from":162},"W7223156204",[400,403,405,407],{"name":401,"orcid":402},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":404,"orcid":9},"Yi Zhang",{"name":406,"orcid":9},"WEI Lei",{"name":408,"orcid":9},"Chengcai Zhang",{"tldr":410,"method":411,"finding":412,"direction":161,"opportunity":413},"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。","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","2026-10-22T11:20:18.887301Z",{"id":417,"title":418,"url":419,"summary":420,"summary_zh":421,"content":9,"source_name":422,"source_url":419,"published_at":423,"category":12,"cover_url":9,"hotness":13,"is_selected":48,"score":424,"score_detail":425,"sources":428,"tags":430,"primary_topic":433,"secondary_topics":434,"classification":435,"search_phrases":478,"slug":481,"view_count":154,"doi":482,"paper":483,"created_at":515},5480,"Sustainable Plant Disease Management: Current Advances, Challenges, and Future Directions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010661","Sustainable plant disease management is essential for maintaining agricultural productivity, environmental health, and global food security. Climate change has increased crop vulnerability to emerging and re-emerging plant diseases, posing significant challenges for agricultural sustainability. Although synthetic chemical pesticides have long been effective for disease control, their extensive use is increasingly questioned because of environmental contamination, pathogen resistance, and potential risks to human and ecosystem health. Consequently, plant disease management (PDM) is shifting toward more sustainable and integrated approaches. Future strategies are expected to incorporate multiple disciplines, including: (i) biological control and microbiome engineering to enhance natural plant defense mechanisms; (ii) advanced molecular and genomic technologies, such as CRISPR\u002FCas systems, RNA interference (RNAi), and epigenome editing, to develop durable disease resistance; (iii) smart delivery systems for the precise application of antimicrobial compounds and defense elicitors; (iv) nanotechnology-based platforms for the targeted delivery of nutrients, pesticides, and biocontrol agents, improving efficacy while reducing chemical inputs; (v) digital and precision agriculture technologies, including remote sensing, unmanned aerial vehicles (UAVs), and artificial intelligence (AI)-based diagnostics, to facilitate real-time disease surveillance and targeted interventions; and (vi) agroecological practices such as crop diversification, soil health restoration, and climate-smart farming to enhance agroecosystem resilience. These innovations aim not only to suppress plant pathogens but also to build sustainable, adaptive, and resilient agricultural systems that address future environmental and biological challenges.","可持续的植物病害管理对于维持农业生产力、环境健康和全球粮食安全至关重要。气候变化加剧了作物对新发和再发植物病害的脆弱性，给农业可持续性带来了重大挑战。尽管合成化学农药长期以来在病害防控中行之有效，但其广泛使用正日益受到质疑，原因包括环境污染、病原菌抗药性以及对人类和生态系统健康的潜在风险。因此，植物病害管理（plant disease management, PDM）正转向更加可持续和综合的途径。未来的策略预计将融合多个学科，包括：(i) 生物防治和微生物组工程，以增强植物天然防御机制；(ii) 先进的分子和基因组技术，如CRISPR\u002FCas系统、RNA干扰（RNAi）和表观基因组编辑，以培育持久抗病性；(iii) 用于精准施用抗菌化合物和防御诱导子的智能递送系统；(iv) 基于纳米技术的平台，用于靶向递送养分、农药和生物防治剂，在提高功效的同时减少化学投入；(v) 数字和精准农业技术，包括遥感、无人机（UAVs）和基于人工智能（AI）的诊断，以促进实时病害监测和靶向干预；以及(vi) 农业生态实践，如作物多样化、土壤健康恢复和气候智慧型农业，以增强农业生态系统的韧性。这些创新不仅旨在抑制植物病原菌，还旨在构建可持续、适应性强且具有韧性的农业系统，以应对未来的环境和生物挑战。","Sustainability","2026-10-20T00:00:00Z",81,{"impact":17,"substance":426,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":427},20,"系统综述可持续植物病害管理六大技术方向，涵盖AI诊断、纳米递送与基因编辑，对智慧农业与绿色防控具参考价值。",[429],{"name":422,"url":419},[27,76,29,431,63,432],"植物病害","纳米技术",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},[],{"status":47,"locked":48,"version":49,"method":50,"evidence":436},{"candidates":437,"agricultural_evidence":14},[438,442,446,450,454,460,464,470,474],{"code":73,"score":21,"supported":14,"title":439,"abstract":440,"tags":441},[],[76,77],[27,76],{"code":41,"score":67,"supported":14,"title":443,"abstract":444,"tags":445},[],[283,63],[63],{"code":66,"score":88,"supported":14,"title":447,"abstract":448,"tags":449},[],[28,70],[],{"code":353,"score":88,"supported":14,"title":451,"abstract":452,"tags":453},[],[370,354],[],{"code":95,"score":88,"supported":14,"title":455,"abstract":456,"tags":459},[],[457,458],"气候变化","climate change",[],{"code":80,"score":88,"supported":14,"title":461,"abstract":462,"tags":463},[],[83,84],[],{"code":465,"score":96,"supported":14,"title":466,"abstract":467,"tags":469},"H6",[],[468],"农业生态",[],{"code":102,"score":96,"supported":14,"title":471,"abstract":472,"tags":473},[],[105],[],{"code":108,"score":22,"supported":48,"title":475,"abstract":476,"tags":477},[],[],[29],[479,480],"CRISPR RNAi 作物抗病","基因编辑 智慧农业 植物病害 生物防治","CRISPRRNAi作物抗病-5480","10.3390\u002Fsu182010661",{"doi":482,"openalex_id":484,"authors":485,"venue":422,"cited_by_count":154,"oa_url":419,"card":510,"direction":414,"ingested_from":162},"W7223044776",[486,489,492,495,498,501,504,507],{"name":487,"orcid":488},"Karim M. Hassan","https:\u002F\u002Forcid.org\u002F0000-0002-1241-2307",{"name":490,"orcid":491},"Basma T. Abd-Elhalim","https:\u002F\u002Forcid.org\u002F0000-0002-3009-332X",{"name":493,"orcid":494},"Mohamed K. Abou El-Nasr","https:\u002F\u002Forcid.org\u002F0000-0003-1451-833X",{"name":496,"orcid":497},"Essam Yosef Abdul-Hafeez","https:\u002F\u002Forcid.org\u002F0000-0003-4744-4672",{"name":499,"orcid":500},"Ahmed N. Abdelhamid","https:\u002F\u002Forcid.org\u002F0000-0002-1326-7885",{"name":502,"orcid":503},"Mostafa Abdelkader","https:\u002F\u002Forcid.org\u002F0000-0003-2339-5087",{"name":505,"orcid":506},"Mohamed Abd El-Hamed Nasser","https:\u002F\u002Forcid.org\u002F0000-0002-0328-2969",{"name":508,"orcid":509},"Mahmoud A. A. Ali","https:\u002F\u002Forcid.org\u002F0000-0002-9062-0048",{"tldr":511,"method":512,"finding":513,"direction":414,"opportunity":514},"综述植物病害可持续管理的多学科前沿进展、挑战与未来方向。","文献综述，整合生物防治、基因编辑、纳米递送、数字农业与生态农艺。","未来病害管理需融合生物、分子、纳米与数字技术构建韧性农业系统。","遥感、无人机与AI诊断的实时病害监测闭环仍缺田间验证与低成本落地研究。","2026-10-21T23:30:09.242484Z",{"id":517,"title":518,"url":519,"summary":520,"summary_zh":521,"content":9,"source_name":522,"source_url":519,"published_at":523,"category":12,"cover_url":9,"hotness":13,"is_selected":48,"score":340,"score_detail":524,"sources":526,"tags":528,"primary_topic":531,"secondary_topics":532,"classification":537,"search_phrases":556,"slug":559,"view_count":154,"doi":560,"paper":561,"created_at":584},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":342,"substance":17,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":525},"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。",[527],{"name":522,"url":519},[27,529,31,349,530],"无人机","生物量估算",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[533],{"code":80,"slug":534,"name":535,"path":536,"parent_code":191},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":47,"locked":48,"version":49,"method":50,"evidence":538},{"candidates":539,"agricultural_evidence":14},[540,544,548,552],{"code":34,"score":363,"supported":14,"title":541,"abstract":542,"tags":543},[289,35],[36,289,35],[349],{"code":80,"score":342,"supported":14,"title":545,"abstract":546,"tags":547},[84],[83,84],[530,31],{"code":102,"score":88,"supported":14,"title":549,"abstract":550,"tags":551},[],[199,197],[],{"code":73,"score":22,"supported":48,"title":553,"abstract":554,"tags":555},[],[],[27],[557,558],"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":560,"openalex_id":562,"authors":563,"venue":522,"cited_by_count":154,"oa_url":519,"card":579,"direction":161,"ingested_from":162},"W7222806066",[564,566,568,570,573,575,577],{"name":565,"orcid":9},"Guangtao Wang",{"name":567,"orcid":9},"Sumei Yao",{"name":569,"orcid":9},"Yanzheng Xie",{"name":571,"orcid":572},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":574,"orcid":9},"Xiangdong Chen",{"name":576,"orcid":9},"Yuhao Zhang",{"name":578,"orcid":9},"Lina Jiang",{"tldr":580,"method":581,"finding":582,"direction":161,"opportunity":583},"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":586,"title":587,"url":588,"summary":589,"summary_zh":590,"content":9,"source_name":172,"source_url":588,"published_at":591,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":592,"score_detail":593,"sources":595,"tags":597,"primary_topic":601,"secondary_topics":602,"classification":604,"search_phrases":632,"slug":635,"view_count":21,"doi":636,"paper":637,"created_at":667},4087,"Improving regional winter wheat yield estimation by assimilating cross-scale heterogeneity information from UAV and satellite remote sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115701","Accurate high-resolution winter wheat yield estimation remains challenging in heterogeneous agricultural systems. Conventional yield estimation approaches remain limited by high uncertainty (process-based crop models) or weak transferability due to insufficient physical knowledge and scarce sample sizes (statistical models based on remote sensing data). Data assimilation offers a promising approach to overcome these limitations, but its performance is constrained by the neglect of heterogeneous information within assimilation grids, particularly in low-intensity agricultural systems. Therefore, we developed a novel cross-scale data assimilation framework to enhance winter wheat yield estimation by integrating unmanned aerial vehicle (UAV) and satellite remote sensing with the WOFOST crop model. First, UAV-derived crop-soil parameters, including leaf area index (LAI), aboveground biomass (AGB), and soil moisture at 10 and 20 cm depths (SMC-10 and SMC-20), served as a bridge linking in situ sampling data with Sentinel satellite images. Next, a mixture density network (MDN) was employed to construct an upscaling model for predicting probability distributions, thereby obtaining the distribution of crop-soil parameters within each satellite pixel as field heterogeneity information. Finally, these heterogeneity distributions were incorporated into WOFOST through an improved particle filter to generate high-resolution yield (10 m × 10 m) estimates at the regional scale. The results showed that robust crop-soil parameter inversion models were constructed using random forest with multispectral and thermal UAV images (R 2 : 0.73–0.88; NRMSE: 12.30%–22.83%). The MDN quantified within-pixel heterogeneity of crop-soil parameters through a UAV-satellite upscaling approach. The joint assimilation of LAI, AGB, and SMC-10 achieved optimal winter wheat yield estimation performance for MDN-based cross-scale data assimilation (R 2 = 0.83, NRMSE = 15.70% in 2024–2025), outperforming satellite-based and mean-based cross-scale assimilation. The proposed framework showed stable interannual performance in Xianyang (RMSE ≤758 kg ha −1 , NRMSE ≤17.20% in 2017–2023) and reasonable transferability in two external winter wheat regions (RMSE ≤943 kg ha −1 ; NRMSE ≤18.29% in Baoding and Changji). Overall, the cross-scale data assimilation framework exhibited strong performance in wheat yield estimation and would provide scientific support for intensive agricultural development.","在异质性农业系统中，实现准确的高分辨率冬小麦产量估算仍具挑战性。传统产量估算方法受限于较高不确定性（基于过程的作物模型）或可迁移性较弱（基于遥感数据的统计模型），后者源于物理知识不足和样本量稀缺。数据同化为克服这些局限提供了一种有前景的途径，但其性能受到同化网格内异质性信息被忽略的制约，尤其是在低强度农业系统中。因此，我们开发了一种新的跨尺度数据同化框架，通过将无人机（UAV）和卫星遥感与WOFOST作物模型相结合，提升冬小麦产量估算。首先，无人机获取的作物-土壤参数，包括叶面积指数（LAI）、地上生物量（AGB）以及10和20 cm深度土壤水分（SMC-10和SMC-20），作为连接原位采样数据与Sentinel卫星影像的桥梁。接下来，采用混合密度网络（MDN）构建升尺度模型以预测概率分布，从而获得每个卫星像元内作物-土壤参数的分布，作为田块异质性信息。最后，通过改进的粒子滤波器将这些异质性分布纳入WOFOST，以生成区域尺度的高分辨率产量（10 m × 10 m）估算。结果表明，利用随机森林结合多光谱和热红外无人机影像构建了稳健的作物-土壤参数反演模型（R²：0.73–0.88；NRMSE：12.30%–22.83%）。MDN通过无人机-卫星升尺度方法量化了作物-土壤参数的像元内异质性。LAI、AGB和SMC-10的联合同化使基于MDN的跨尺度数据同化取得了最优的冬小麦产量估算性能（2024–2025年R² = 0.83，NRMSE = 15.70%），优于基于卫星和基于均值的跨尺度同化。所提出的框架在咸阳表现出稳定的年际性能（2017–2023年RMSE ≤758 kg ha⁻¹，NRMSE ≤17.20%），并在两个外部冬小麦区域具有合理的可迁移性（保定和昌吉RMSE ≤943 kg ha⁻¹；NRMSE ≤18.29%）。总体而言，该跨尺度数据同化框架在小麦产量估算中表现出较强性能，可为集约化农业发展提供科学支持。","2026-10-01T00:00:00Z",83,{"impact":19,"substance":176,"depth":19,"authority":177,"freshness":178,"relevant":22,"comment":594},"该研究提出跨尺度数据同化框架，融合无人机与卫星遥感提升区域冬小麦估产精度，方法新颖、验证充分，对精准农业与农业遥感应用具有较高参考价值。",[596],{"name":172,"url":588},[27,598,31,599,600],"作物模型","数据同化","小麦产量",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[603],{"code":80,"slug":534,"name":535,"path":536,"parent_code":191},{"status":47,"locked":48,"version":49,"method":50,"evidence":605},{"candidates":606,"agricultural_evidence":14},[607,611,615,619,623,627],{"code":34,"score":279,"supported":14,"title":608,"abstract":609,"tags":610},[289,35],[36,289,35],[],{"code":80,"score":19,"supported":14,"title":612,"abstract":613,"tags":614},[84],[83,84,379],[31],{"code":353,"score":88,"supported":14,"title":616,"abstract":617,"tags":618},[],[370,354],[],{"code":73,"score":22,"supported":48,"title":620,"abstract":621,"tags":622},[],[],[27],{"code":102,"score":22,"supported":48,"title":624,"abstract":625,"tags":626},[],[],[598],{"code":628,"score":22,"supported":48,"title":629,"abstract":630,"tags":631},"I5",[],[],[599],[633,634],"无人机 卫星 遥感 冬小麦 估产","WOFOST 数据同化 小麦","无人机卫星遥感冬小麦估产-4087","10.1016\u002Fj.rse.2026.115701",{"doi":636,"openalex_id":638,"authors":639,"venue":172,"cited_by_count":154,"oa_url":588,"card":662,"direction":161,"ingested_from":162},"W7215051418",[640,642,644,646,649,651,654,656,658,660],{"name":641,"orcid":9},"Zhikai Cheng",{"name":643,"orcid":9},"Xiaobo Gu",{"name":645,"orcid":9},"Yang Xu",{"name":647,"orcid":648},"Xiaohai Fang","https:\u002F\u002Forcid.org\u002F0000-0003-1322-1366",{"name":650,"orcid":9},"Zhengtao Zhang",{"name":652,"orcid":653},"Penglin Li","https:\u002F\u002Forcid.org\u002F0009-0005-2706-1326",{"name":655,"orcid":9},"Yupeng Li",{"name":657,"orcid":9},"Shikun Sun",{"name":659,"orcid":9},"Yadan Du",{"name":661,"orcid":9},"Huanjie Cai",{"tldr":663,"method":664,"finding":665,"direction":161,"opportunity":666},"构建跨尺度数据同化框架，融合无人机与卫星遥感提升区域冬小麦产量估算精度。","UAV-Sentinel数据、混合密度网络升尺度、改进粒子滤波同化WOFOST模","联合同化LAI、AGB与SMC-10最优，R²=0.83，跨年与跨区表现稳定。","可探索异质农田下多源遥感与作物模型同化的普适性及低成本无人机采样优化。","2026-10-03T23:30:33.709814Z"]