[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2121":3,"related-2121":85},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":52,"slug":55,"view_count":56,"doi":57,"paper":58,"created_at":84},2121,"Considering the allocation of photosynthetic and non-photosynthetic nitrogen to improve the accuracy of radiative transfer model for estimating leaf biochemical traits","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112397","Considering the allocation of photosynthetic and non-photosynthetic nitrogen to improve the accuracy of radiative transfer model for estimating leaf biochemical traits。Computers and Electronics in Agriculture","考虑光合氮与非光合氮的分配以提高辐射传输模型估算叶片生化特性的准确性。",null,"Computers and Electronics in Agriculture","2026-09-10T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,16,14,8,1,"核心期刊论文，提出区分光合与非光合氮分配以提升叶片生化参数遥感反演精度，方法有新意但属细分领域进展，适合主题聚合而非头条精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","遥感","作物表型","氮素营养","辐射传输模型",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"unclassified","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":50,"reason":51},[39,45],{"code":40,"score":41,"supported":14,"title":42,"abstract":43,"tags":44},"I3",2,[],[],[29,28],{"code":46,"score":22,"supported":14,"title":47,"abstract":48,"tags":49},"I1",[],[],[27],true,"缺少足够的主题证据",[53,54],"辐射传输模型 作物表型 智慧农业 氮素营养","辐射传输模型 作物表型","辐射传输模型作物表型智慧农业氮素营养-2121",4,"10.1016\u002Fj.compag.2026.112397",{"doi":57,"openalex_id":59,"authors":60,"venue":10,"cited_by_count":76,"oa_url":6,"card":77,"direction":81,"ingested_from":83},"W7212191131",[61,63,65,67,69,71,74],{"name":62,"orcid":9},"Shuang Xiang",{"name":64,"orcid":9},"Zixin Meng",{"name":66,"orcid":9},"Zhonghui Guo",{"name":68,"orcid":9},"Zhongyu Jin",{"name":70,"orcid":9},"Le Xu",{"name":72,"orcid":73},"Fenghua Yu","https:\u002F\u002Forcid.org\u002F0000-0001-6545-9873",{"name":75,"orcid":9},"Tongyu Xu",0,{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"区分光合与非光合氮分配，改进辐射传输模型以提升叶片生化参数估算精度。","基于叶片辐射传输模型，引入光合\u002F非光合氮分配参数进行改进与验证。","考虑氮分配可显著提高叶片氮含量等生化性状的遥感估算精度。","农业遥感与作物表型","可将氮分配机制耦合到冠层尺度模型，并探索多物种、多生育期的普适性。","openalex","2026-09-11T23:30:02.027560Z",{"total":86,"page":22,"page_size":86,"items":87},6,[88,190,257,314,388,451],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":9,"source_name":94,"source_url":91,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":103,"tags":105,"primary_topic":107,"secondary_topics":113,"classification":119,"search_phrases":137,"slug":140,"view_count":56,"doi":141,"paper":142,"created_at":189},4026,"RTAS: A radiative transfer model for agricultural strip-intercropping structures","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115708","Agricultural strip-intercropping, characterized by alternating tall and short crops, poses substantial challenges for current radiative transfer (RT) modeling due to complex mutual shadowing and heterogeneous canopy optical and structural properties. Most existing geometric-optical models for row crops are restricted to monoculture configurations, unable to account for the asymmetric radiative interactions arising from the coexistence of two distinct crop species. In this study, we developed the Radiative Transfer Model for Agricultural Strip-intercropping Structures (RTAS), a hybrid analytical model integrating geometric-optical (GO) principles and turbid-medium RT theory under periodic boundary conditions. RTAS showed strong agreement with 3D reference simulations from the LESS (Large-scale Remote Sensing Data and Image Simulation Framework) ray-tracing model in both red and near-infrared (NIR) bands ( R 2 ≥ 0.93, RRMSE ≤3.96%) and with multi-angular unmanned aerial vehicle (UAV) observations ( R 2 = 0.94, RMSE ≤0.0252, RRMSE ≤12.44%). Structural sensitivity analysis showed stable performance across the tested intercropping configurations, with the RRMSE remaining below 8%. RTAS also outperformed the Spectral Linear Mixture (SLM) model and GO baseline, whose red-band errors exceeded 14% and reached nearly 27% at large solar zenith angles. Furthermore, component-level analysis revealed a wavelength-dependent difference between geometric visibility and radiative contribution in the NIR band, associated with enhanced multiple scattering and non-negligible contributions from shaded vegetation. RTAS provides an efficient forward simulation framework with potential for future retrieval studies in complex strip-intercropping structures.","农业条带间作以高矮作物交替种植为特征，由于复杂的相互遮蔽以及异质冠层的光学和结构属性，给当前的辐射传输（RT）建模带来了巨大挑战。现有的大多数行播作物几何光学模型仅限于单作配置，无法考虑两种不同作物共存所产生的非对称辐射相互作用。本研究开发了农业条带间作结构辐射传输模型（RTAS），这是一个在周期性边界条件下整合几何光学（GO）原理与浑浊介质辐射传输理论的混合解析模型。RTAS在红光和近红外（NIR）波段均与LESS（大规模遥感数据与图像模拟框架）光线追踪模型的三维参考模拟结果高度一致（R² ≥ 0.93，RRMSE ≤ 3.96%），并与多角度无人机（UAV）观测结果吻合良好（R² = 0.94，RMSE ≤ 0.0252，RRMSE ≤ 12.44%）。结构敏感性分析表明，该模型在测试的间作配置下性能稳定，RRMSE保持在8%以下。RTAS还优于光谱线性混合（SLM）模型和GO基线模型，后两者在红光波段的误差超过14%，在大太阳天顶角时接近27%。此外，组分层面分析揭示了近红外波段中几何可见度与辐射贡献之间存在波长依赖性差异，这与增强的多重散射及阴影植被不可忽略的贡献有关。RTAS提供了一个高效的前向模拟框架，有望用于未来复杂条带间作结构的反演研究。","Remote Sensing of Environment","2026-10-01T00:00:00Z",83,{"impact":18,"substance":98,"depth":99,"authority":100,"freshness":101,"relevant":22,"comment":102},22,19,15,9,"提出面向条带间作结构的混合辐射传输模型RTAS，验证充分、精度可靠，对农业遥感定量反演有实质推进。",[104],{"name":94,"url":91},[27,28,106,29,31],"间作",{"code":108,"slug":109,"name":110,"path":111,"parent_code":112},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[114],{"code":40,"slug":115,"name":116,"path":117,"parent_code":118},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":120,"locked":14,"version":35,"method":36,"evidence":121},"classified",{"candidates":122,"agricultural_evidence":50},[123,128,133],{"code":108,"score":100,"supported":50,"title":124,"abstract":126,"tags":127},[125],"intercropping",[106,125],[106],{"code":40,"score":21,"supported":50,"title":129,"abstract":130,"tags":132},[],[28,131],"remote sensing",[29,28],{"code":46,"score":22,"supported":14,"title":134,"abstract":135,"tags":136},[],[],[27],[138,139],"RTAS 辐射传输模型 间作","条带间作 遥感 冠层","RTAS辐射传输模型间作-4026","10.1016\u002Fj.rse.2026.115708",{"doi":141,"openalex_id":143,"authors":144,"venue":94,"cited_by_count":76,"oa_url":91,"card":184,"direction":81,"ingested_from":83},"W7215043648",[145,148,151,153,156,159,161,163,166,168,171,173,175,178,181],{"name":146,"orcid":147},"Run Zhong","https:\u002F\u002Forcid.org\u002F0009-0003-3067-388X",{"name":149,"orcid":150},"Dalei Hao","https:\u002F\u002Forcid.org\u002F0000-0003-3497-9774",{"name":152,"orcid":9},"Zhihong Li",{"name":154,"orcid":155},"Carlos Camino","https:\u002F\u002Forcid.org\u002F0000-0001-5188-4406",{"name":157,"orcid":158},"Si Gao","https:\u002F\u002Forcid.org\u002F0000-0001-6726-5693",{"name":160,"orcid":9},"Qing Li",{"name":162,"orcid":9},"Yanan Wei",{"name":164,"orcid":165},"Higor  de Queiroz Ribeiro","https:\u002F\u002Forcid.org\u002F0000-0002-8715-3290",{"name":167,"orcid":9},"Hong Sun",{"name":169,"orcid":170},"Feng Yang","https:\u002F\u002Forcid.org\u002F0000-0002-7499-9422",{"name":172,"orcid":9},"Matheus Pinheiro Ferreirad",{"name":174,"orcid":9},"Haoran Liu",{"name":176,"orcid":177},"Renan Caldas Umburanas","https:\u002F\u002Forcid.org\u002F0000-0002-4112-3598",{"name":179,"orcid":180},"Durval Dourado Neto","https:\u002F\u002Forcid.org\u002F0000-0002-8452-8288",{"name":182,"orcid":183},"Yelu Zeng","https:\u002F\u002Forcid.org\u002F0000-0003-4267-1841",{"tldr":185,"method":186,"finding":187,"direction":81,"opportunity":188},"提出RTAS混合辐射传输模型，模拟条带间作农田的反射率并验证其精度。","融合几何光学与浑浊介质理论，用LESS三维模拟和无人机多角度观测验证。","RTAS精度高（R²≥0.93），优于线性混合和几何光学基线，并揭示近红外几何可见度与辐射贡献的差异","可基于RTAS发展条带间作结构参数反演，并耦合多物种冠层异质性提升遥感监测能力。","2026-10-02T23:30:24.513706Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":201,"tags":203,"primary_topic":206,"secondary_topics":211,"classification":212,"search_phrases":236,"slug":239,"view_count":76,"doi":240,"paper":241,"created_at":256},5561,"Growth Stage and Cultivar Influence the Interpretation of UAV-Derived NDVI for Potato Yield Assessment","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11540-026-10161-5","Abstract Single-date UAV vegetation indices are widely used to assess potato crop performance and estimate yield. However, whether their biological interpretation remains consistent across cultivars and growth stages is rarely examined. This study investigated how the relationships between UAV-derived multispectral and thermal canopy indicators and potato yield changed during crop development under contrasting irrigation in two cultivars with contrasting maturity classes (Anouk and Celandine). UAV surveys were conducted in July and August, and canopy metrics were related to final yield and tuber traits. The relationship between NDVI and yield differed markedly between cultivars and growth stages. In the early-maturing cultivar Anouk, July NDVI showed a strong positive correlation with yield ( r = 0.87, p = 0.002), whereas August NDVI was not informative. In the late-maturing cultivar Celandine, the relationship reversed from a positive correlation in July ( r = 0.56) to a strong negative correlation in August ( r = −0.80, p = 0.009), indicating that greater late-season canopy greenness was associated with lower yield. Exploratory LOOCV Random Forest models achieved the highest yield prediction accuracy using July features for Anouk ( R 2 = 0.72) and August features for Celandine ( R 2 = 0.47), while multi-temporal features provided similar predictive performance. Greater temporal declines in NDVI were associated with higher yield in both cultivars, consistent with the change in canopy greenness acting as a phenological indicator of the transition from vegetative growth to tuber bulking. These results demonstrate that the interpretation of UAV-derived vegetation indices is strongly dependent on cultivar maturity and growth stage. A single UAV acquisition may therefore provide misleading assessments of crop performance when canopy phenology is not considered. Rather than serving as fixed proxies for yield, canopy vegetation indices should be interpreted within cultivar-specific and temporal contexts when used for potato monitoring and agronomic decision-making.","摘要 单期无人机植被指数被广泛用于评估马铃薯作物表现和估算产量。然而，其生物学解释在不同品种和生育期之间是否保持一致，鲜有研究加以检验。本研究探讨了在两种成熟性类别不同的品种（Anouk和Celandine）中，不同灌溉条件下，无人机获取的多光谱与热红外冠层指标与马铃薯产量之间的关系在作物发育过程中如何变化。无人机调查于7月和8月进行，并将冠层指标与最终产量及块茎性状进行关联分析。NDVI与产量之间的关系在品种和生育期之间差异显著。在早熟品种Anouk中，7月NDVI与产量呈强正相关（r = 0.87，p = 0.002），而8月NDVI则不具有指示意义。在晚熟品种Celandine中，该关系由7月的正相关（r = 0.56）转变为8月的强负相关（r = −0.80，p = 0.009），表明后期冠层绿度越高与产量越低相关。探索性留一交叉验证（LOOCV）随机森林模型在使用Anouk的7月特征（R² = 0.72）和Celandine的8月特征（R² = 0.47）时取得了最高的产量预测精度，而多时相特征提供了相似的预测表现。两个品种中NDVI随时间下降幅度越大均与产量越高相关，这与冠层绿度变化作为从营养生长向块茎膨大转变的物候指示相一致。这些结果表明，无人机获取的植被指数的解释强烈依赖于品种成熟性和生育期。因此，在未考虑冠层物候的情况下，单次无人机采集可能对作物表现作出误导性评估。冠层植被指数不应作为产量的固定替代指标，而应在品种特异性和时间背景下加以解释，方可用于马铃薯监测和农艺决策。","Potato Research","2026-10-20T00:00:00Z",79,{"impact":19,"substance":98,"depth":18,"authority":20,"freshness":101,"relevant":22,"comment":200},"该研究揭示UAV-NDVI与马铃薯产量的关系随品种熟期和生育阶段而反转，对智慧农业遥感监测具有实质方法学与决策参考价值。",[202],{"name":196,"url":193},[27,204,205,28,29],"马铃薯","产量预测",{"code":207,"slug":208,"name":209,"path":210,"parent_code":112},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains",[],{"status":120,"locked":14,"version":35,"method":36,"evidence":213},{"candidates":214,"agricultural_evidence":50},[215,220,227,232],{"code":207,"score":100,"supported":50,"title":216,"abstract":218,"tags":219},[217],"potato",[204,217],[204],{"code":221,"score":86,"supported":50,"title":222,"abstract":223,"tags":226},"H3",[],[224,225],"灌溉","irrigation",[],{"code":40,"score":86,"supported":50,"title":228,"abstract":229,"tags":231},[],[230],"multispectral",[205,29,28],{"code":46,"score":22,"supported":14,"title":233,"abstract":234,"tags":235},[],[],[27],[237,238],"UAV NDVI 马铃薯 产量","马铃薯 品种 生育期 遥感","UAVNDVI马铃薯产量-5561","10.1007\u002Fs11540-026-10161-5",{"doi":240,"openalex_id":242,"authors":243,"venue":196,"cited_by_count":76,"oa_url":249,"card":250,"direction":255,"ingested_from":83},"W7223067378",[244,247],{"name":245,"orcid":246},"Jiangsan Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-3916-7388",{"name":248,"orcid":9},"Jaroslaw S. Grodek","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs11540-026-10161-5.pdf",{"tldr":251,"method":252,"finding":253,"direction":81,"opportunity":254},"研究不同品种和生育期下无人机NDVI与马铃薯产量的关系差异。","两个熟期品种、多时相无人机多光谱与热红外冠层指标，结合随机森林建模。","NDVI与产量的关系随品种和时期反转，晚熟品种8月NDVI与产量强负相关。","可构建融合品种熟期与冠层物候期的时相自适应产量估算模型，避免单次飞行误判。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:21.975818Z",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":9,"content":9,"source_name":262,"source_url":260,"published_at":263,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":264,"score_detail":265,"sources":268,"tags":270,"primary_topic":273,"secondary_topics":277,"classification":278,"search_phrases":291,"slug":294,"view_count":22,"doi":295,"paper":296,"created_at":313},4787,"3D modeling for crop applications in precision agriculture: a review","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10466-y","3D modeling for crop applications in precision agriculture: a review。Precision Agriculture","Precision Agriculture","2026-10-12T00:00:00Z",78,{"impact":18,"substance":266,"depth":18,"authority":20,"freshness":21,"relevant":22,"comment":267},20,"核心期刊发表的作物三维建模综述，方法学价值高，对精准农业与表型研究有参考意义。",[269],{"name":262,"url":260},[27,271,28,29,272],"精准农业","三维建模",{"code":46,"slug":274,"name":275,"path":276,"parent_code":118},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":120,"locked":14,"version":35,"method":36,"evidence":279},{"candidates":280,"agricultural_evidence":50},[281,287],{"code":46,"score":282,"supported":50,"title":283,"abstract":285,"tags":286},13,[284],"precision agriculture",[284],[27,271],{"code":40,"score":41,"supported":14,"title":288,"abstract":289,"tags":290},[],[],[29,28],[292,293],"三维建模 作物表型 智慧农业 精准农业","三维建模 作物表型","三维建模作物表型智慧农业精准农业-4787","10.1007\u002Fs11119-026-10466-y",{"doi":295,"openalex_id":297,"authors":298,"venue":262,"cited_by_count":76,"oa_url":260,"card":9,"direction":255,"ingested_from":83},"W7221570971",[299,302,305,307,310],{"name":300,"orcid":301},"Valeria Maritano","https:\u002F\u002Forcid.org\u002F0000-0002-5844-3184",{"name":303,"orcid":304},"Orou Berme Herve Gonroudobou","https:\u002F\u002Forcid.org\u002F0000-0003-0614-2731",{"name":306,"orcid":9},"Offer Rozenstein",{"name":308,"orcid":309},"Paolo Gay","https:\u002F\u002Forcid.org\u002F0000-0002-8342-425X",{"name":311,"orcid":312},"Lorenzo Comba","https:\u002F\u002Forcid.org\u002F0000-0001-5466-0918","2026-10-13T23:30:02.534333Z",{"id":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":9,"source_name":10,"source_url":317,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":320,"score_detail":321,"sources":323,"tags":325,"primary_topic":328,"secondary_topics":332,"classification":334,"search_phrases":354,"slug":357,"view_count":41,"doi":358,"paper":359,"created_at":387},3973,"A physical-context-conditioned multimodal fusion network for estimating maize canopy nitrogen content using multi-source UAV remote sensing data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112465","A physical-context-conditioned multimodal fusion network for estimating maize canopy nitrogen content using multi-source UAV remote sensing data。Computers and Electronics in Agriculture","一种基于物理情境条件调节的多模态融合网络，用于利用多源无人机遥感数据估算玉米冠层氮含量。《计算机与电子农业》",81,{"impact":18,"substance":98,"depth":18,"authority":20,"freshness":101,"relevant":22,"comment":322},"多源无人机遥感与物理上下文多模态融合估算玉米冠层氮素，方法新颖、数据可靠，对精准施肥有参考价值。",[324],{"name":10,"url":317},[27,326,327,28,30],"农业人工智能","玉米",{"code":329,"slug":330,"name":327,"path":331,"parent_code":112},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize",[333],{"code":40,"slug":115,"name":116,"path":117,"parent_code":118},{"status":120,"locked":14,"version":35,"method":36,"evidence":335},{"candidates":336,"agricultural_evidence":50},[337,341,345,349],{"code":329,"score":100,"supported":50,"title":338,"abstract":339,"tags":340},[330],[327,330],[327],{"code":40,"score":100,"supported":50,"title":342,"abstract":343,"tags":344},[131],[28,131],[28],{"code":46,"score":22,"supported":14,"title":346,"abstract":347,"tags":348},[],[],[27],{"code":350,"score":22,"supported":14,"title":351,"abstract":352,"tags":353},"I2",[],[],[326],[355,356],"无人机 遥感 玉米 氮含量","多模态融合 冠层氮素","无人机遥感玉米氮含量-3973","10.1016\u002Fj.compag.2026.112465",{"doi":358,"openalex_id":360,"authors":361,"venue":10,"cited_by_count":76,"oa_url":9,"card":382,"direction":81,"ingested_from":83},"W7215084428",[362,364,366,369,372,374,376,379],{"name":363,"orcid":9},"Wang Baoju",{"name":365,"orcid":9},"Jing Zhao",{"name":367,"orcid":368},"Yu Yan","https:\u002F\u002Forcid.org\u002F0009-0004-4202-6935",{"name":370,"orcid":371},"Junke Zhu","https:\u002F\u002Forcid.org\u002F0009-0005-7778-9614",{"name":373,"orcid":9},"Xinlong Li",{"name":375,"orcid":9},"Huizheng Wang",{"name":377,"orcid":378},"Weiguang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9862-5839",{"name":380,"orcid":381},"Yubin Lan","https:\u002F\u002Forcid.org\u002F0000-0001-6664-5571",{"tldr":383,"method":384,"finding":385,"direction":81,"opportunity":386},"提出物理上下文条件化多模态融合网络，用多源无人机遥感估算玉米冠层氮含量。","多源无人机遥感数据与物理上下文条件化多模态融合网络。","该网络能提升玉米冠层氮含量估算精度，融合多源数据优于单一模态。","可探索物理先验与多模态融合在不同作物、生育期及跨区域迁移中的泛化能力。","2026-10-02T23:30:02.413294Z",{"id":389,"title":390,"url":391,"summary":392,"summary_zh":393,"content":9,"source_name":394,"source_url":391,"published_at":395,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":320,"score_detail":396,"sources":398,"tags":400,"primary_topic":403,"secondary_topics":408,"classification":410,"search_phrases":426,"slug":429,"view_count":430,"doi":431,"paper":432,"created_at":450},3501,"A Dual-Phenological-Characteristic Weighting Method to Reconcile Time Discrepancies in Soybean Phenology Estimation from MODIS NDVI Time Series","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193300","Accurate large-scale monitoring of crop phenology is essential for optimizing agricultural management. Remote sensing has been widely used for estimating crop phenological stages, yet time discrepancies often exist between remotely sensed phenological metrics and ground-observed growth stages. Moreover, phenological parameters derived from different characterization models exhibit varying degrees of deviation from field observations. The primary goal of this study was to develop a novel method that fully exploits the deviation patterns of diverse phenological parameters to enhance the accuracy of soybean phenology retrieval. To this end, we extracted 11 phenological parameters for six key growth stages—emerged, blooming, pod-setting, turning yellow, dropping leaf, and harvest—of soybean across 16 U.S. states using MODIS NDVI (normalized difference vegetation index) time-series data from 2000 to 2020, employing GU-, curvature-, and derivative-based phenological modeling methods. The study design centered on proposing a dual-phenological-characteristic weighting (DPCW) method that leverages the deviation features of different phenological parameters relative to ground-observed growth stages, generating composite phenological characteristics by pairing two distinct parameters. The key innovation of this paper is the use of dual-feature weighting to improve the correspondence between satellite-derived phenometrics and field observations, offering an alternative to conventional phenological estimation. The results demonstrated that the optimal DPCW-based combinations for the six growth stages were SOS (start of season) and GREEN, SOS and POS (peak of season), MATURITY and POS, EOS (end of season) and SENES (senescence), RD (recession date) and DD (downturn date), and EOS and DORM (dormancy), respectively. The coefficient of determination (R2) between the retrieved transition dates and ground observations exceeded 0.65 for most stages, with the emerged stage improving to 0.47 from 0.052 and 0.357 of the unadjusted and offset-adjusted benchmarks. The average root mean square error (RMSE) was less than 5 days in most cases, representing a reduction of over 40%, with the most substantial improvement at the turning yellow stage, where RMSE dropped from 12.8 days to 2.8 days. A strength of this study lies in its multi-state, multi-decade validation, demonstrating the robustness and temporal consistency of the DPCW method within the major U.S. soybean-growing region. However, a limitation is that the method’s performance may vary with different satellite sensors or crop types, warranting further investigation. The proposed approach is expected to enhance the accuracy of remote sensing-based crop phenology monitoring and offers an effective alternative for calibrating remotely sensed phenological parameters.","准确的大尺度作物物候监测对于优化农业管理至关重要。遥感已被广泛用于估算作物物候阶段，但遥感物候指标与地面观测生育阶段之间常存在时间差异。此外，不同特征化模型衍生的物候参数与田间观测之间存在不同程度的偏差。本研究的主要目标是开发一种新方法，充分利用多种物候参数的偏差模式，以提高大豆物候反演精度。为此，我们利用2000—2020年MODIS NDVI（归一化差异植被指数）时间序列数据，采用基于GU、曲率和导数的方法提取了美国16个州大豆六个关键生育阶段——出苗、开花、结荚、黄化、落叶和收获——的11个物候参数。研究设计的核心是提出一种双物候特征加权（dual-phenological-characteristic weighting，DPCW）方法，该方法利用不同物候参数相对于地面观测生育阶段的偏差特征，通过配对两个不同参数生成复合物候特征。本文的关键创新在于利用双特征加权提高卫星衍生物候指标与田间观测之间的对应关系，为传统物候估算提供了一种替代方案。结果表明，六个生育阶段基于DPCW的最优组合分别为SOS（生长季开始）与GREEN、SOS与POS（生长季峰值）、MATURITY与POS、EOS（生长季结束）与SENES（衰老）、RD（衰退日期）与DD（下降日期）以及EOS与DORM（休眠）。反演得到的转换日期与地面观测之间的决定系数（R²）在大多数阶段超过0.65，其中出苗阶段从基准的0.052和偏移调整后的0.357提高至0.47。大多数情况下平均均方根误差（RMSE）小于5天，降幅超过40%，其中黄化阶段改善最为显著，RMSE从12.8天降至2.8天。本研究的一个优势在于其多州、多年代际验证，证明了DPCW方法在美国主要大豆种植区内的稳健性和时间一致性。","Remote Sensing","2026-09-24T00:00:00Z",{"impact":18,"substance":98,"depth":18,"authority":20,"freshness":101,"relevant":22,"comment":397},"提出双物候特征加权方法，用MODIS NDVI长时序数据校正大豆物候遥感估算偏差，方法新颖、验证扎实，对农情遥感监测有参考价值。",[399],{"name":394,"url":391},[27,401,402,28,29],"大豆","农情监测",{"code":404,"slug":405,"name":406,"path":407,"parent_code":112},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[409],{"code":40,"slug":115,"name":116,"path":117,"parent_code":118},{"status":120,"locked":14,"version":35,"method":36,"evidence":411},{"candidates":412,"agricultural_evidence":50},[413,418,422],{"code":404,"score":100,"supported":50,"title":414,"abstract":416,"tags":417},[415],"soybean",[401,415],[401],{"code":40,"score":101,"supported":50,"title":419,"abstract":420,"tags":421},[],[28,131],[29,402,28],{"code":46,"score":22,"supported":14,"title":423,"abstract":424,"tags":425},[],[],[27],[427,428],"MODIS NDVI 大豆 物候","美国大豆 遥感 物候监测","MODISNDVI大豆物候-3501",3,"10.3390\u002Frs18193300",{"doi":431,"openalex_id":433,"authors":434,"venue":394,"cited_by_count":76,"oa_url":391,"card":445,"direction":81,"ingested_from":83},"W7214203102",[435,437,440,443],{"name":436,"orcid":9},"Qiuxiang Yi",{"name":438,"orcid":439},"Siting Chen","https:\u002F\u002Forcid.org\u002F0000-0003-3468-9320",{"name":441,"orcid":442},"Fumin Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5078-358X",{"name":444,"orcid":9},"Qinyan Zhu",{"tldr":446,"method":447,"finding":448,"direction":81,"opportunity":449},"提出双物候特征加权法，校正MODIS NDVI大豆物候估计与地面观测的时间偏差。","用MODIS NDVI 2000-2020数据，结合GU、曲率、导数三类物候模型","多数生育期R²超0.65，RMSE多小于5天，降幅超40%，转黄期RMSE从12.8天降至2.8天。","可将该加权校正思路迁移到其他作物与多源遥感数据，并探索自适应权重与深度学习融合的物候反演。","2026-09-25T23:30:31.075499Z",{"id":452,"title":453,"url":454,"summary":455,"summary_zh":456,"content":9,"source_name":457,"source_url":454,"published_at":458,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":459,"score_detail":460,"sources":462,"tags":464,"primary_topic":466,"secondary_topics":470,"classification":476,"search_phrases":500,"slug":503,"view_count":41,"doi":504,"paper":505,"created_at":521},3164,"Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16182034","Accurate plant height estimation is important for monitoring crop growth and supporting precision agricultural management. Manual measurements are labor-intensive, while LiDAR-based methods are expensive and require complex processing. UAV photogrammetry provides a lower-cost alternative but remains challenging in flooded rice paddies because of canopy deformation and difficulties in terrain extraction. This study proposes Rice-STNet, a spatiotemporal deep learning framework for end-to-end rice plant height estimation using multi-temporal UAV RGB imagery. Rice-STNet integrates a convolutional neural network for spatial feature extraction, Time2Vec for temporal encoding, and a gated recurrent unit network for modeling temporal dependencies across observation dates. The framework was evaluated using field data collected from rice paddies over two growing seasons. Rice-STNet achieved an R2 of 0.97, a root mean squared error of 1.97 cm, and a mean absolute error of 1.14 cm. It outperformed random forest, support vector regression, a CNN-only baseline, and a UAV photogrammetry-based point-cloud approach. In addition, the framework generated high-resolution plant height maps for field-scale analysis of spatial growth variability. These results underscore the importance of jointly modeling spatial and temporal characteristics for continuously evolving crop traits. The proposed framework offers an accurate, scalable, and non-destructive solution for large-scale crop phenotyping and precision agriculture.","准确的株高估算对于监测作物生长和支持精准农业管理具有重要意义。人工测量劳动强度大，而基于激光雷达（LiDAR）的方法成本高昂且需要复杂的处理。无人机摄影测量提供了一种成本较低的替代方案，但在淹水稻田中仍面临挑战，原因在于冠层变形和地形提取困难。本研究提出了Rice-STNet，一种时空深度学习框架，用于利用多时相无人机RGB影像进行端到端水稻株高估算。Rice-STNet集成了用于空间特征提取的卷积神经网络、用于时间编码的Time2Vec，以及用于建模观测日期之间时间依赖关系的门控循环单元网络。该框架利用两个生长季从稻田采集的田间数据进行了评估。Rice-STNet取得了R²为0.97、均方根误差为1.97 cm、平均绝对误差为1.14 cm的结果。其性能优于随机森林、支持向量回归、仅使用CNN的基线方法以及基于无人机摄影测量的点云方法。此外，该框架生成了高分辨率株高图，用于田块尺度空间生长变异性分析。这些结果凸显了联合建模空间与时间特征对于持续变化的作物性状的重要性。所提出的框架为大规模作物表型分析和精准农业提供了一种准确、可扩展且非破坏性的解决方案。","Agriculture","2026-09-21T00:00:00Z",80,{"impact":18,"substance":98,"depth":18,"authority":282,"freshness":101,"relevant":22,"comment":461},"提出时空深度学习框架Rice-STNet，用多时相无人机RGB影像实现水稻株高高精度估算，方法新颖、数据跨两个生长季，对作物表型与精准农业有实用价值。",[463],{"name":457,"url":454},[27,326,465,28,29],"水稻",{"code":467,"slug":468,"name":465,"path":469,"parent_code":112},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice",[471,475],{"code":350,"slug":472,"name":473,"path":474,"parent_code":118},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":40,"slug":115,"name":116,"path":117,"parent_code":118},{"status":120,"locked":14,"version":35,"method":36,"evidence":477},{"candidates":478,"agricultural_evidence":50},[479,484,490,495],{"code":467,"score":18,"supported":50,"title":480,"abstract":481,"tags":483},[468],[465,482,468],"稻田",[465],{"code":350,"score":100,"supported":50,"title":485,"abstract":487,"tags":489},[486],"deep learning",[488,486],"深度学习",[326],{"code":40,"score":21,"supported":50,"title":491,"abstract":492,"tags":494},[],[29,493],"crop phenotyping",[29,28],{"code":46,"score":496,"supported":50,"title":497,"abstract":498,"tags":499},7,[],[271,284],[27],[501,502],"无人机 RGB 水稻株高","Rice-STNet 水稻表型","无人机RGB水稻株高-3164","10.3390\u002Fagriculture16182034",{"doi":504,"openalex_id":506,"authors":507,"venue":457,"cited_by_count":76,"oa_url":454,"card":516,"direction":81,"ingested_from":83},"W7213887432",[508,511,513],{"name":509,"orcid":510},"Weiguo Wang","https:\u002F\u002Forcid.org\u002F0009-0003-4028-9363",{"name":512,"orcid":9},"Noboru Noguchi",{"name":514,"orcid":515},"Liangliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5055-3987",{"tldr":517,"method":518,"finding":519,"direction":81,"opportunity":520},"提出Rice-STNet时空深度学习框架，用多时相无人机RGB影像估算水稻株高。","CNN提取空间特征，Time2Vec编码时间，GRU建模时序依赖，两季稻田数据验","R²达0.97、RMSE 1.97cm，优于随机森林、SVR、纯CNN及点云方法。","可迁移至其他作物与多源遥感融合，探索轻量化模型及实时田间部署。","2026-09-22T23:30:18.545958Z"]