[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4026":3,"related-4026":125},{"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":32,"secondary_topics":38,"classification":45,"search_phrases":68,"slug":71,"view_count":72,"doi":73,"paper":74,"created_at":124},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。",null,"Remote Sensing of Environment","2026-10-01T00:00:00Z","论文",10,false,83,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,15,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","遥感","间作","作物表型","辐射传输模型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,57,63],{"code":33,"score":20,"supported":52,"title":53,"abstract":55,"tags":56},true,[54],"intercropping",[29,54],[29],{"code":40,"score":58,"supported":52,"title":59,"abstract":60,"tags":62},8,[],[28,61],"remote sensing",[30,28],{"code":64,"score":22,"supported":14,"title":65,"abstract":66,"tags":67},"I1",[],[],[27],[69,70],"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",4,"10.1016\u002Fj.rse.2026.115708",{"doi":73,"openalex_id":75,"authors":76,"venue":10,"cited_by_count":116,"oa_url":6,"card":117,"direction":121,"ingested_from":123},"W7215043648",[77,80,83,85,88,91,93,95,98,100,103,105,107,110,113],{"name":78,"orcid":79},"Run Zhong","https:\u002F\u002Forcid.org\u002F0009-0003-3067-388X",{"name":81,"orcid":82},"Dalei Hao","https:\u002F\u002Forcid.org\u002F0000-0003-3497-9774",{"name":84,"orcid":9},"Zhihong Li",{"name":86,"orcid":87},"Carlos Camino","https:\u002F\u002Forcid.org\u002F0000-0001-5188-4406",{"name":89,"orcid":90},"Si Gao","https:\u002F\u002Forcid.org\u002F0000-0001-6726-5693",{"name":92,"orcid":9},"Qing Li",{"name":94,"orcid":9},"Yanan Wei",{"name":96,"orcid":97},"Higor  de Queiroz Ribeiro","https:\u002F\u002Forcid.org\u002F0000-0002-8715-3290",{"name":99,"orcid":9},"Hong Sun",{"name":101,"orcid":102},"Feng Yang","https:\u002F\u002Forcid.org\u002F0000-0002-7499-9422",{"name":104,"orcid":9},"Matheus Pinheiro Ferreirad",{"name":106,"orcid":9},"Haoran Liu",{"name":108,"orcid":109},"Renan Caldas Umburanas","https:\u002F\u002Forcid.org\u002F0000-0002-4112-3598",{"name":111,"orcid":112},"Durval Dourado Neto","https:\u002F\u002Forcid.org\u002F0000-0002-8452-8288",{"name":114,"orcid":115},"Yelu Zeng","https:\u002F\u002Forcid.org\u002F0000-0003-4267-1841",0,{"tldr":118,"method":119,"finding":120,"direction":121,"opportunity":122},"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","农业遥感与作物表型","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。","openalex","2026-10-02T23:30:24.513706Z",{"total":126,"page":22,"page_size":126,"items":127},6,[128,223,288,378,442,510],{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":9,"source_name":134,"source_url":131,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":140,"tags":142,"primary_topic":146,"secondary_topics":147,"classification":149,"search_phrases":175,"slug":178,"view_count":116,"doi":179,"paper":180,"created_at":222},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",79,{"impact":17,"substance":18,"depth":17,"authority":138,"freshness":21,"relevant":22,"comment":139},12,"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[141],{"name":134,"url":131},[27,143,144,28,145],"无人机","育种","紫花苜蓿",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[148],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":150},{"candidates":151,"agricultural_evidence":52},[152,158,163,170],{"code":33,"score":20,"supported":52,"title":153,"abstract":155,"tags":157},[154],"alfalfa",[156,154],"苜蓿",[145],{"code":40,"score":138,"supported":52,"title":159,"abstract":161,"tags":162},[160],"multispectral",[160],[28],{"code":64,"score":164,"supported":52,"title":165,"abstract":166,"tags":169},7,[],[167,168],"精准农业","precision agriculture",[27],{"code":171,"score":72,"supported":52,"title":172,"abstract":173,"tags":174},"C4",[],[156],[145],[176,177],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":179,"openalex_id":181,"authors":182,"venue":134,"cited_by_count":116,"oa_url":131,"card":217,"direction":121,"ingested_from":123},"W7223083966",[183,186,189,192,195,197,199,202,205,208,211,214],{"name":184,"orcid":185},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":187,"orcid":188},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":190,"orcid":191},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":193,"orcid":194},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":196,"orcid":9},"Julie Hansen",{"name":198,"orcid":9},"Christopher Pierce",{"name":200,"orcid":201},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":203,"orcid":204},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":206,"orcid":207},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":209,"orcid":210},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":212,"orcid":213},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":215,"orcid":216},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":218,"method":219,"finding":220,"direction":121,"opportunity":221},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":9,"content":9,"source_name":228,"source_url":226,"published_at":229,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":230,"score_detail":231,"sources":235,"tags":237,"primary_topic":241,"secondary_topics":242,"classification":250,"search_phrases":276,"slug":279,"view_count":22,"doi":280,"paper":281,"created_at":287},4084,"Integrating Crop Rotation and Remote Sensing to Assess Soil and Water Conservation in Organic Soils of the Everglades Agricultural Area","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.31332.74887","Integrating Crop Rotation and Remote Sensing to Assess Soil and Water Conservation in Organic Soils of the Everglades Agricultural Area。OpenAlex","OpenAlex","2026-10-02T00:00:00Z",68,{"impact":138,"substance":17,"depth":232,"authority":233,"freshness":21,"relevant":22,"comment":234},16,13,"该论文将轮作与遥感结合评估有机土壤水土保持，方法有创新且数据可靠，但属区域研究，影响范围有限，时效性强，值得关注。",[236],{"name":228,"url":226},[27,238,28,239,240],"轮作","水土保持","有机土壤",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[243,244],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":245,"slug":246,"name":247,"path":248,"parent_code":249},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":251},{"candidates":252,"agricultural_evidence":52},[253,258,262,267,272],{"code":33,"score":138,"supported":52,"title":254,"abstract":256,"tags":257},[255],"crop rotation",[255],[238],{"code":40,"score":138,"supported":52,"title":259,"abstract":260,"tags":261},[61],[61],[28],{"code":245,"score":263,"supported":52,"title":264,"abstract":265,"tags":266},11,[246],[246],[],{"code":268,"score":22,"supported":14,"title":269,"abstract":270,"tags":271},"H6",[],[],[239],{"code":64,"score":22,"supported":14,"title":273,"abstract":274,"tags":275},[],[],[27],[277,278],"Everglades 农业区 遥感 轮作","有机土壤 水土保持 遥感","Everglades农业区遥感轮作-4084","10.13140\u002Frg.2.2.31332.74887",{"doi":280,"openalex_id":282,"authors":283,"venue":9,"cited_by_count":116,"oa_url":226,"card":9,"direction":121,"ingested_from":123},"W7216417358",[284],{"name":285,"orcid":286},"Bivek Bhusal","https:\u002F\u002Forcid.org\u002F0009-0001-6408-0033","2026-10-03T23:30:32.356375Z",{"id":289,"title":290,"url":291,"summary":292,"summary_zh":293,"content":9,"source_name":294,"source_url":291,"published_at":295,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":296,"score_detail":297,"sources":300,"tags":302,"primary_topic":306,"secondary_topics":307,"classification":314,"search_phrases":346,"slug":349,"view_count":116,"doi":350,"paper":351,"created_at":377},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",80,{"impact":232,"substance":18,"depth":17,"authority":298,"freshness":13,"relevant":22,"comment":299},14,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[301],{"name":294,"url":291},[27,303,304,305,145],"农业人工智能","农业遥感","氮素监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[308,309],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":171,"slug":310,"name":311,"path":312,"parent_code":313},"feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":46,"locked":14,"version":47,"method":48,"evidence":315},{"candidates":316,"agricultural_evidence":52},[317,322,326,332,342],{"code":40,"score":17,"supported":52,"title":318,"abstract":320,"tags":321},[319],"hyperspectral",[28,61,319],[304],{"code":33,"score":20,"supported":52,"title":323,"abstract":324,"tags":325},[154],[156,154],[145],{"code":171,"score":13,"supported":52,"title":327,"abstract":328,"tags":331},[],[329,156,330],"饲草","forage",[145],{"code":333,"score":13,"supported":52,"title":334,"abstract":335,"tags":341},"I2",[],[336,337,338,339,340],"深度学习","机器学习","machine learning","deep learning","decision support",[303],{"code":64,"score":22,"supported":14,"title":343,"abstract":344,"tags":345},[],[],[27],[347,348],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":350,"openalex_id":352,"authors":353,"venue":294,"cited_by_count":116,"oa_url":291,"card":372,"direction":121,"ingested_from":123},"W7223154576",[354,356,358,360,362,365,367,370],{"name":355,"orcid":9},"Shuai Fu",{"name":357,"orcid":9},"Yunhao Li",{"name":359,"orcid":9},"Chunli Miao",{"name":361,"orcid":9},"Jie Liu",{"name":363,"orcid":364},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":366,"orcid":9},"Yongjia Hu",{"name":368,"orcid":369},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":371,"orcid":9},"吴彩霞",{"tldr":373,"method":374,"finding":375,"direction":121,"opportunity":376},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":379,"title":380,"url":381,"summary":382,"summary_zh":383,"content":9,"source_name":384,"source_url":381,"published_at":385,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":386,"score_detail":387,"sources":389,"tags":391,"primary_topic":394,"secondary_topics":399,"classification":401,"search_phrases":417,"slug":420,"view_count":421,"doi":422,"paper":423,"created_at":441},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",81,{"impact":17,"substance":18,"depth":17,"authority":298,"freshness":21,"relevant":22,"comment":388},"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。",[390],{"name":384,"url":381},[27,392,393,28,30],"大豆","农情监测",{"code":395,"slug":396,"name":397,"path":398,"parent_code":37},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[400],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":402},{"candidates":403,"agricultural_evidence":52},[404,409,413],{"code":395,"score":20,"supported":52,"title":405,"abstract":407,"tags":408},[406],"soybean",[392,406],[392],{"code":40,"score":21,"supported":52,"title":410,"abstract":411,"tags":412},[],[28,61],[30,393,28],{"code":64,"score":22,"supported":14,"title":414,"abstract":415,"tags":416},[],[],[27],[418,419],"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":422,"openalex_id":424,"authors":425,"venue":384,"cited_by_count":116,"oa_url":381,"card":436,"direction":121,"ingested_from":123},"W7214203102",[426,428,431,434],{"name":427,"orcid":9},"Qiuxiang Yi",{"name":429,"orcid":430},"Siting Chen","https:\u002F\u002Forcid.org\u002F0000-0003-3468-9320",{"name":432,"orcid":433},"Fumin Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5078-358X",{"name":435,"orcid":9},"Qinyan Zhu",{"tldr":437,"method":438,"finding":439,"direction":121,"opportunity":440},"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":443,"title":444,"url":445,"summary":446,"summary_zh":447,"content":9,"source_name":448,"source_url":445,"published_at":449,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":296,"score_detail":450,"sources":452,"tags":454,"primary_topic":456,"secondary_topics":460,"classification":466,"search_phrases":487,"slug":490,"view_count":491,"doi":492,"paper":493,"created_at":509},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",{"impact":17,"substance":18,"depth":17,"authority":233,"freshness":21,"relevant":22,"comment":451},"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。",[453],{"name":448,"url":445},[27,303,455,28,30],"水稻",{"code":457,"slug":458,"name":455,"path":459,"parent_code":37},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice",[461,465],{"code":333,"slug":462,"name":463,"path":464,"parent_code":44},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":467},{"candidates":468,"agricultural_evidence":52},[469,474,478,483],{"code":457,"score":17,"supported":52,"title":470,"abstract":471,"tags":473},[458],[455,472,458],"稻田",[455],{"code":333,"score":20,"supported":52,"title":475,"abstract":476,"tags":477},[339],[336,339],[303],{"code":40,"score":58,"supported":52,"title":479,"abstract":480,"tags":482},[],[30,481],"crop phenotyping",[30,28],{"code":64,"score":164,"supported":52,"title":484,"abstract":485,"tags":486},[],[167,168],[27],[488,489],"无人机 RGB 水稻株高","Rice-STNet 水稻表型","无人机RGB水稻株高-3164",2,"10.3390\u002Fagriculture16182034",{"doi":492,"openalex_id":494,"authors":495,"venue":448,"cited_by_count":116,"oa_url":445,"card":504,"direction":121,"ingested_from":123},"W7213887432",[496,499,501],{"name":497,"orcid":498},"Weiguo Wang","https:\u002F\u002Forcid.org\u002F0009-0003-4028-9363",{"name":500,"orcid":9},"Noboru Noguchi",{"name":502,"orcid":503},"Liangliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5055-3987",{"tldr":505,"method":506,"finding":507,"direction":121,"opportunity":508},"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":511,"title":512,"url":513,"summary":514,"summary_zh":515,"content":9,"source_name":516,"source_url":517,"published_at":518,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":519,"score_detail":520,"sources":524,"tags":526,"primary_topic":528,"secondary_topics":529,"classification":530,"search_phrases":545,"slug":548,"view_count":72,"doi":549,"paper":550,"created_at":569},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","2026-09-15T00:00:00Z",74,{"impact":232,"substance":521,"depth":522,"authority":233,"freshness":58,"relevant":22,"comment":523},20,17,"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。",[525],{"name":516,"url":517},[27,303,28,30,527],"三维重建",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":531},{"candidates":532,"agricultural_evidence":52},[533,537,541],{"code":40,"score":232,"supported":52,"title":534,"abstract":535,"tags":536},[481],[30,481],[30,28],{"code":333,"score":491,"supported":14,"title":538,"abstract":539,"tags":540},[],[],[527,303],{"code":64,"score":22,"supported":14,"title":542,"abstract":543,"tags":544},[],[],[27],[546,547],"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":549,"openalex_id":551,"authors":552,"venue":516,"cited_by_count":116,"oa_url":563,"card":564,"direction":121,"ingested_from":123},"W7213397688",[553,556,558,561],{"name":554,"orcid":555},"Karanvir Singh","https:\u002F\u002Forcid.org\u002F0009-0003-0484-119X",{"name":557,"orcid":9},"Theo Morales",{"name":559,"orcid":560},"Binh‐Son Hua","https:\u002F\u002Forcid.org\u002F0000-0002-5706-8634",{"name":562,"orcid":9},"Mukesh Saini","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.16926",{"tldr":565,"method":566,"finding":567,"direction":121,"opportunity":568},"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"]