[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3501":3,"related-3501":58},{"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":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":57},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。",null,"Remote Sensing","2026-09-24T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","大豆","农情监测","遥感","作物表型",[32,33],"MODIS NDVI 大豆 物候","美国大豆 遥感 物候监测","MODISNDVI大豆物候-3501",0,"10.3390\u002Frs18193300",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":50,"direction":54,"ingested_from":56},"W7214203102",[40,42,45,48],{"name":41,"orcid":9},"Qiuxiang Yi",{"name":43,"orcid":44},"Siting Chen","https:\u002F\u002Forcid.org\u002F0000-0003-3468-9320",{"name":46,"orcid":47},"Fumin Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5078-358X",{"name":49,"orcid":9},"Qinyan Zhu",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"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。","农业遥感与作物表型","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。","openalex","2026-09-25T23:30:31.075499Z",{"total":59,"page":21,"page_size":59,"items":60},6,[61,100,129,172,214,259],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":66,"content":9,"source_name":67,"source_url":64,"published_at":68,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":69,"score_detail":70,"sources":73,"tags":75,"search_phrases":78,"slug":81,"view_count":35,"doi":82,"paper":83,"created_at":99},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":17,"substance":18,"depth":17,"authority":71,"freshness":20,"relevant":21,"comment":72},13,"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。",[74],{"name":67,"url":64},[26,76,77,29,30],"农业人工智能","水稻",[79,80],"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":82,"openalex_id":84,"authors":85,"venue":67,"cited_by_count":35,"oa_url":64,"card":94,"direction":54,"ingested_from":56},"W7213887432",[86,89,91],{"name":87,"orcid":88},"Weiguo Wang","https:\u002F\u002Forcid.org\u002F0009-0003-4028-9363",{"name":90,"orcid":9},"Noboru Noguchi",{"name":92,"orcid":93},"Liangliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5055-3987",{"tldr":95,"method":96,"finding":97,"direction":54,"opportunity":98},"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",{"id":101,"title":102,"url":103,"summary":104,"summary_zh":9,"content":9,"source_name":105,"source_url":9,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":111,"tags":113,"search_phrases":116,"slug":119,"view_count":35,"doi":9,"paper":120,"created_at":128},3001,"UAV多光谱不同空间分辨率匹配春小麦多性状监测","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1811","天津师范大学张程程等联合天津市农科院农业资源与环境研究所，从原生0.07 m四波段UAV多光谱影像通过像素聚合重采样生成14种空间分辨率（0.07-3.03 m），耦合PROSAIL辐射传输模型与随机森林评估尺度依赖反演性能。研究揭示了叶面积指数（LAI）、叶绿素含量（Cab）和冠层水分含量（Cw）反演精度对空间分辨率的非单调响应，提出物候阶段自适应分辨率策略并开发Heterogeneity-Scale Game Model（HSGM）刻画最优聚合尺度形成机制。","MDPI Agronomy 16(18):1811","2026-09-15T00:00:00Z",74,{"impact":109,"substance":18,"depth":17,"authority":71,"freshness":59,"relevant":21,"comment":110},15,"方法新颖、数据扎实的作物遥感反演研究，对精准农业变量施药与无人机监测有参考价值，但属细分领域学术进展，公共影响有限。",[112],{"name":105,"url":103},[26,114,29,30,115],"精准农业","春小麦",[117,118],"天津师范大学 春小麦 多光谱","UAV 多光谱 空间分辨率","天津师范大学春小麦多光谱-3001",{"doi":9,"openalex_id":9,"authors":121,"venue":9,"cited_by_count":35,"oa_url":9,"card":122,"direction":54,"ingested_from":127},[],{"tldr":123,"method":124,"finding":125,"direction":54,"opportunity":126},"用无人机多光谱重采样14种分辨率，结合PROSAIL与随机森林，研究春小麦多性状反演的空间尺度效应。","UAV四波段多光谱像素聚合重采样，耦合PROSAIL模型与随机森林反演LAI、C","反演精度对空间分辨率呈非单调响应，提出物候自适应分辨率策略与HSGM模型。","可探索不同作物与物候下最优分辨率普适规律，并将尺度自适应策略嵌入实时无人机监测系统。","agent","2026-09-20T00:03:08.168753Z",{"id":130,"title":131,"url":132,"summary":133,"summary_zh":134,"content":9,"source_name":135,"source_url":136,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":137,"sources":143,"tags":145,"search_phrases":147,"slug":150,"view_count":35,"doi":151,"paper":152,"created_at":171},2781,"Evaluating Mesh Reconstruction Methods for Crop Phenotyping","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2609.16926","Phenotyping an agricultural crop is crucial for studying its entire life cycle, as it provides vital insights to improve yield and, ultimately, food production. Doing the same for crops grown on remote sites is a challenge for the specialists who cannot be available on-site. 3D reconstruction techniques offer a promising solution to this problem by enabling crop digitization, allowing specialists to access the resulting 3D crop models from anywhere at any time. In this work, we evaluate recent 3D reconstruction pipelines for crop phenotyping. We focus on 7 mesh reconstruction pipelines and measure the fidelity and consistency of their outputs qualitatively and quantitatively. Our results suggest that the meshes produced by the GGGS, PGSR, and 2DGS are preferable to the other pipelines, owing to their quantitative metrics and visually pleasing outputs. The GGGS pipeline is better than the second-best pipeline (2DGS) by about 27\\% on the radar chart with 5 dimensions, namely, User ratings, Chamfer distance, LPIPS, PSNR, and SSIM.","对农作物进行表型分析对于研究其整个生命周期至关重要，因为它为提高产量并最终提升粮食生产提供了关键见解。对于生长在偏远地区的作物而言，由于专家无法亲临现场，开展同样的表型分析是一项挑战。三维重建技术通过实现作物数字化，使专家能够随时随地访问生成的作物三维模型，从而为这一问题提供了有前景的解决方案。在本研究中，我们评估了近期用于作物表型分析的三维重建流程。我们聚焦于7种网格重建流程，并对其输出的保真度和一致性进行了定性和定量评估。结果表明，GGGS、PGSR和2DGS生成的网格在定量指标和视觉输出方面优于其他流程。在包含5个维度（用户评分、倒角距离、LPIPS、PSNR和SSIM）的雷达图上，GGGS流程比排名第二的2DGS流程高出约27%。","arXiv (Cornell University)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.16926",{"impact":138,"substance":139,"depth":140,"authority":71,"freshness":141,"relevant":21,"comment":142},16,20,17,8,"系统评测7种网格重建流程用于作物表型数字化，结论明确、指标可量化，对远程作物表型与三维数字化研究有实质参考价值。",[144],{"name":135,"url":136},[26,76,29,30,146],"三维重建",[148,149],"农业人工智能 三维重建 作物表型 智慧农业","农业人工智能 三维重建","农业人工智能三维重建作物表型智慧农业-2781","10.48550\u002Farxiv.2609.16926",{"doi":151,"openalex_id":153,"authors":154,"venue":135,"cited_by_count":35,"oa_url":165,"card":166,"direction":54,"ingested_from":56},"W7213397688",[155,158,160,163],{"name":156,"orcid":157},"Karanvir Singh","https:\u002F\u002Forcid.org\u002F0009-0003-0484-119X",{"name":159,"orcid":9},"Theo Morales",{"name":161,"orcid":162},"Binh‐Son Hua","https:\u002F\u002Forcid.org\u002F0000-0002-5706-8634",{"name":164,"orcid":9},"Mukesh Saini","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.16926",{"tldr":167,"method":168,"finding":169,"direction":54,"opportunity":170},"评估7种网格重建流程在作物表型三维数字化中的保真度与一致性。","对比7种3D重建流程，用Chamfer距离、LPIPS、PSNR、SSIM及用户","GGGS、PGSR和2DGS输出更优，GGGS在五维雷达图上比2DGS高约27%。","可探索轻量化、田间实时三维重建，并建立作物表型专用网格质量评价标准。","2026-09-17T23:30:27.096274Z",{"id":173,"title":174,"url":175,"summary":176,"summary_zh":177,"content":9,"source_name":178,"source_url":175,"published_at":179,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":181,"sources":183,"tags":185,"search_phrases":187,"slug":190,"view_count":35,"doi":191,"paper":192,"created_at":213},2280,"Predicting plant leaf functional traits using 2D spectral representation and multi-task learning with multi-gate mixture-of-experts","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112390","Predicting plant leaf functional traits using 2D spectral representation and multi-task learning with multi-gate mixture-of-experts。Computers and Electronics in Agriculture","利用二维光谱表示和多任务学习结合多门混合专家模型预测植物叶片功能性状。","Computers and Electronics in Agriculture","2026-09-12T00:00:00Z",75,{"impact":138,"substance":139,"depth":140,"authority":19,"freshness":141,"relevant":21,"comment":182},"核心期刊论文，提出二维光谱表征与多门专家混合多任务学习预测叶片功能性状，方法新颖、对作物表型与遥感监测有参考价值，但属细分方法进展，未达重大突破层级。",[184],{"name":178,"url":175},[26,76,29,30,186],"多任务学习",[188,189],"农业人工智能 多任务学习 作物表型 智慧农业","农业人工智能 多任务学习","农业人工智能多任务学习作物表型智慧农业-2280","10.1016\u002Fj.compag.2026.112390",{"doi":191,"openalex_id":193,"authors":194,"venue":178,"cited_by_count":35,"oa_url":9,"card":208,"direction":54,"ingested_from":56},"W7212395422",[195,197,199,201,203,205],{"name":196,"orcid":9},"Jianping Huang",{"name":198,"orcid":9},"Xin Zhang",{"name":200,"orcid":9},"Guanglai Wang",{"name":202,"orcid":9},"Chong Mo",{"name":204,"orcid":9},"Zhenghang Wang",{"name":206,"orcid":207},"Wenlong Song","https:\u002F\u002Forcid.org\u002F0000-0002-8810-532X",{"tldr":209,"method":210,"finding":211,"direction":54,"opportunity":212},"用二维光谱表示与多门混合专家多任务学习预测植物叶片功能性状。","二维光谱表示、多任务学习、多门混合专家模型。","该方法能同时准确预测多种叶片功能性状，优于单任务模型。","可探索将该多任务框架迁移到多作物、多时相的高光谱表型监测中。","2026-09-13T23:30:01.702074Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":9,"source_name":178,"source_url":217,"published_at":220,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":221,"score_detail":222,"sources":225,"tags":227,"search_phrases":230,"slug":233,"view_count":21,"doi":234,"paper":235,"created_at":258},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。","2026-09-10T00:00:00Z",68,{"impact":223,"substance":17,"depth":138,"authority":19,"freshness":141,"relevant":21,"comment":224},12,"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。",[226],{"name":178,"url":217},[26,29,30,228,229],"氮素营养","辐射传输模型",[231,232],"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","10.1016\u002Fj.compag.2026.112397",{"doi":234,"openalex_id":236,"authors":237,"venue":178,"cited_by_count":35,"oa_url":217,"card":253,"direction":54,"ingested_from":56},"W7212191131",[238,240,242,244,246,248,251],{"name":239,"orcid":9},"Shuang Xiang",{"name":241,"orcid":9},"Zixin Meng",{"name":243,"orcid":9},"Zhonghui Guo",{"name":245,"orcid":9},"Zhongyu Jin",{"name":247,"orcid":9},"Le Xu",{"name":249,"orcid":250},"Fenghua Yu","https:\u002F\u002Forcid.org\u002F0000-0001-6545-9873",{"name":252,"orcid":9},"Tongyu Xu",{"tldr":254,"method":255,"finding":256,"direction":54,"opportunity":257},"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。","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。","2026-09-11T23:30:02.027560Z",{"id":260,"title":261,"url":262,"summary":263,"summary_zh":264,"content":9,"source_name":265,"source_url":262,"published_at":266,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":267,"score_detail":268,"sources":271,"tags":273,"search_phrases":276,"slug":279,"view_count":35,"doi":280,"paper":281,"created_at":310},1990,"Scaling up soybean breeding: Satellite imagery delivers accurate maturity estimation across plot sizes","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppj2.70100","Abstract Accurate and scalable phenotyping is essential for accelerating genetic gain in soybean ( Glycine max (L.) Merr.) breeding programs. Traditional methods for estimating physiological maturity are labor‐intensive and prone to subjectivity, limiting throughput and consistency. This study evaluates the potential of high‐resolution satellite imagery as an alternative to unmanned aerial vehicle (UAV)‐based imaging for estimating soybean maturity across diverse environments and plot configurations. Using vegetation indices derived from both platforms, we applied logistic regression models to predict maturity dates and compared them to established maturity date assessments. Our results demonstrate strong correlations and concordance between satellite‐ and UAV‐derived maturity estimates ( R 2 up to 0.94), with high broad‐sense heritability values ( H 2 up to 0.98), indicating robust genetic control of the trait. Satellite imagery proved effective even in small plot settings (four‐ and eight‐row designs), though performance was higher in larger plots. These findings highlight the feasibility of satellite‐based phenotyping for soybean maturity, offering a cost‐effective, scalable, and reliable alternative to UAVs. The approach has broad implications for enhancing breeding efficiency and expanding remote sensing applications in crop improvement.","摘要：准确且可扩展的表型鉴定对于加速大豆（Glycine max (L.) Merr.）育种项目中的遗传增益至关重要。传统的生理成熟度估算方法劳动密集且易受主观性影响，限制了通量和一致性。本研究评估了高分辨率卫星影像作为无人机（UAV）影像替代方案，在不同环境和小区布局下估算大豆成熟度的潜力。利用两种平台获取的植被指数，我们应用逻辑回归模型预测成熟日期，并将其与既定的成熟度评估进行比较。结果表明，卫星与无人机估算的成熟度之间具有强相关性和一致性（R²最高达0.94），且具有较高的广义遗传力值（H²最高达0.98），表明该性状受稳健的遗传控制。即使在较小的小区设置（四行和八行设计）中，卫星影像也表现出有效性，但在较大小区中性能更优。这些发现凸显了基于卫星的大豆成熟度表型鉴定的可行性，为无人机提供了一种经济高效、可扩展且可靠的替代方案。该方法对提升育种效率及扩展遥感在作物改良中的应用具有广泛意义。","The Plant Phenome Journal","2026-09-07T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":71,"freshness":269,"relevant":21,"comment":270},7,"卫星遥感替代无人机用于大豆成熟期估算，方法新颖且数据可靠，对规模化育种表型鉴定有实质推动作用。",[272],{"name":265,"url":262},[26,274,27,29,275],"育种","表型鉴定",[277,278],"智慧农业 表型鉴定 大豆 育种","智慧农业 表型鉴定","智慧农业表型鉴定大豆育种-1990","10.1002\u002Fppj2.70100",{"doi":280,"openalex_id":282,"authors":283,"venue":265,"cited_by_count":35,"oa_url":304,"card":305,"direction":54,"ingested_from":56},"W7211946346",[284,287,290,293,295,298,301],{"name":285,"orcid":286},"Anastasios Mazis","https:\u002F\u002Forcid.org\u002F0000-0002-5024-7234",{"name":288,"orcid":289},"Sarah N. Anderson","https:\u002F\u002Forcid.org\u002F0000-0002-1671-2286",{"name":291,"orcid":292},"Guilherme Ferreira Simiqueli","https:\u002F\u002Forcid.org\u002F0000-0002-2867-0255",{"name":294,"orcid":9},"Adam Barbeau",{"name":296,"orcid":297},"Sara B. Tirado","https:\u002F\u002Forcid.org\u002F0000-0003-0432-091X",{"name":299,"orcid":300},"Julien F. Linares","https:\u002F\u002Forcid.org\u002F0000-0002-0083-0974",{"name":302,"orcid":303},"Nathan D. Coles","https:\u002F\u002Forcid.org\u002F0000-0002-2008-3283","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fppj2.70100",{"tldr":306,"method":307,"finding":308,"direction":54,"opportunity":309},"用卫星影像替代无人机估算大豆成熟期，实现规模化育种表型分析。","利用高分辨率卫星影像与无人机影像的植被指数，构建逻辑回归模型预测成熟期。","卫星与无人机估算成熟期相关性高（R²达0.94），遗传力高（H²达0.98），小小区也有效。","卫星影像在小区育种中的精度受限于小区大小，可探索优化算法或结合多源数据提升小尺度精度。","2026-09-09T23:30:17.932682Z"]