[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3603":3,"related-3603":88},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":87},3603,"Uncertainty propagation and intercomparison of multi-sensor measurements of vegetation stress in sub-optimal conditions","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10455-1","Abstract Purpose This study evaluates the metrological consistency and uncertainty propagation of spectral reflectance and vegetation indices derived from multiple remote sensing sensors under sub-optimal and variable illumination conditions. While multi-sensor data fusion is increasingly common in precision farming, the extent to which sensor differences arise from biological variation versus measurement uncertainty remains poorly quantified. Methods and results Field measurements were conducted on spring wheat ( Triticum aestivum L.) using three ground-based field spectroradiometers and two uncrewed aerial vehicles (UAV)-mounted multispectral cameras. Following a standardised Guide to the Expression of Uncertainty in Measurement (GUM) framework, uncertainties from sensor noise, spatial plot heterogeneity, and transient cloud cover were propagated using Monte Carlo simulations. The results indicate that total reflectance uncertainty peaked at approximately 11% in the red-edge and near-infrared regions, primarily driven by plot-level heterogeneity and fluctuating irradiance. Among the evaluated indices, the Optimized Soil-Adjusted Vegetation Index (OSAVI) demonstrated the highest stability across platforms, whereas the Enhanced Vegetation Index (EVI) proved highly sensitive to environmental noise, leading to metrological breakdown in sensor interoperability. The findings demonstrate that under unstable atmospheric conditions, the window for reliable multi-sensor data integration is limited to synchronous acquisitions within minutes. Conclusion This research provides a rigorous statistical foundation for identifying the limits of sensor agreement, ensuring that management decisions in precision agriculture are based on true crop signals rather than measurement artifacts.","摘要 目的 本研究评估了在次优和变化光照条件下，来自多个遥感传感器的光谱反射率和植被指数的计量一致性与不确定度传播。尽管多传感器数据融合在精准农业中日益普遍，但传感器差异在多大程度上源于生物变异而非测量不确定度，仍缺乏充分的量化。方法与结果 使用三台地面野外光谱辐射计和两台无人机（UAV）搭载的多光谱相机，对春小麦（Triticum aestivum L.）进行了田间测量。依据测量不确定度表示指南（GUM）的标准框架，采用蒙特卡洛模拟对传感器噪声、空间小区异质性和瞬时云覆盖所产生的不确定度进行了传播分析。结果表明，总反射率不确定度在红边和近红外区域达到约11%的峰值，主要由小区尺度异质性和辐照度波动驱动。在评估的指数中，优化土壤调节植被指数（OSAVI）在不同平台间表现出最高的稳定性，而增强植被指数（EVI）对环境噪声高度敏感，导致传感器互操作性在计量学上失效。研究结果表明，在不稳定大气条件下，可靠的多传感器数据整合窗口仅限于数分钟内的同步采集。结论 本研究为识别传感器一致性的限度提供了严格的统计学基础，确保精准农业中的管理决策基于真实的作物信号而非测量伪影。",null,"Precision Agriculture","2026-09-26T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,14,9,1,"该研究量化了多传感器植被胁迫测量中的不确定性传播，方法严谨、结论对精准农业多源数据融合具有实质指导价值，但属细分领域学术进展，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","遥感","植被指数","多光谱遥感","作物表性",[33,34],"春小麦 多光谱 遥感 不确定性","OSAVI EVI 传感器 互操作","春小麦多光谱遥感不确定性-3603",0,"10.1007\u002Fs11119-026-10455-1",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":80,"direction":84,"ingested_from":86},"W7214488126",[41,44,47,50,53,55,57,59,62,65,68,71,74,77],{"name":42,"orcid":43},"Mike Werfeli","https:\u002F\u002Forcid.org\u002F0000-0001-5768-8887",{"name":45,"orcid":46},"Michal Antala","https:\u002F\u002Forcid.org\u002F0000-0003-1294-9507",{"name":48,"orcid":49},"Abdallah Yussuf Ali Abdelmajeed","https:\u002F\u002Forcid.org\u002F0000-0002-3662-1824",{"name":51,"orcid":52},"Álvaro Sánchez-Virosta","https:\u002F\u002Forcid.org\u002F0000-0002-3842-3792",{"name":54,"orcid":9},"Zoe Halem",{"name":56,"orcid":9},"A. Merrington",{"name":58,"orcid":9},"Yousra El‐Mejjaouy",{"name":60,"orcid":61},"Bojana Petrović","https:\u002F\u002Forcid.org\u002F0000-0002-9901-3471",{"name":63,"orcid":64},"Andreas Hueni","https:\u002F\u002Forcid.org\u002F0000-0002-4283-2484",{"name":66,"orcid":67},"El Houssaine Bouras","https:\u002F\u002Forcid.org\u002F0000-0002-6973-6644",{"name":69,"orcid":70},"Shawn C. Kefauver","https:\u002F\u002Forcid.org\u002F0000-0002-1687-1965",{"name":72,"orcid":73},"Sahameh Shafiee","https:\u002F\u002Forcid.org\u002F0000-0002-3586-2327",{"name":75,"orcid":76},"Anshu Rastogi","https:\u002F\u002Forcid.org\u002F0000-0002-0953-7045",{"name":78,"orcid":79},"Laura Mihai","https:\u002F\u002Forcid.org\u002F0000-0003-3869-1890",{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"评估多传感器植被胁迫测量在非理想条件下的不确定度传播与一致性。","三种地面光谱仪与两架无人机多光谱相机，基于GUM框架和蒙特卡洛模拟传播不确定度。","红边与近红外总反射率不确定度达11%，OSAVI跨平台最稳定，EVI易受环境噪声影响。","农业遥感与作物表型","可研究分钟级同步采集协议与不确定度感知的传感器融合算法，提升多源数据互操作性。","openalex","2026-09-27T23:30:04.847804Z",{"total":89,"page":22,"page_size":89,"items":90},6,[91,146,196,245,284,322],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":106,"tags":108,"search_phrases":111,"slug":114,"view_count":36,"doi":115,"paper":116,"created_at":145},3620,"Assessment of corn chlorophyll content via UAV‐derived vegetative indices across growth stages","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fagg2.70437","Abstract Traditional chlorophyll (Chl) assessment methods are labor‐intensive and spatially limited. This study evaluated unmanned aerial vehicle (UAV) multispectral imagery for nondestructive field‐scale canopy Chl estimation in corn ( Zea mays L.) to support precision nutrient management. Field experiments were conducted at two Virginia Tech research farms under two nitrogen (N) rates, 112 and 224 kg N ha − 1 , and five phosphorus (P) rates, 0, 56, 112, 168, and 224 kg P 2 O 5 ha − 1 . Canopy Chl was measured using a SPAD‐502 Plus chlorophyll meter (where SPAD is soil plant analysis development). UAV imagery was collected at three sampling times using a DJI Mavic 3 M: early vegetative (Ve‐E), late vegetative (Ve‐L), and reproductive stages, corresponding to V4–V6, V11–V13, and R3–R5 growth stages, respectively. Eight vegetation indices (VIs) were derived from green, red, red‐edge, and near‐infrared bands. Relationships between SPAD and spectral predictors were evaluated using Pearson correlation, simple and multiple linear regression, Lasso, and Elastic Net. Model performance was assessed using fivefold cross‐validated (CV) estimates repeated 30 times with CV R 2 , root mean square error, and relative RMSE. Predictive accuracy was moderate and varied by site and time, with strongest relationships during Ve‐L. At Ve‐L, Green Normalized Difference Vegetation Index (GNDVI) and Chlorophyll Index Red Edge were the best predictors at Orange and Kentland, respectively. Multivariate and regularized models performed comparably but did not improve accuracy over the best single‐VI models. Overall, GNDVI and red‐edge‐based indices were the most useful indicators of canopy Chl variation. These findings indicate that UAV‐derived GNDVI and red‐edge indices can provide moderately accurate and interpretable estimates of SPAD‐based Chl status.","传统叶绿素（Chl）评估方法费时费力且空间覆盖有限。本研究评估了无人机（UAV）多光谱影像在玉米（Zea mays L.）田块尺度冠层叶绿素无损估测中的应用，以支持精准养分管理。田间试验在弗吉尼亚理工大学的两个研究农场进行，设置两个氮（N）水平，分别为112和224 kg N ha⁻¹，以及五个磷（P）水平，分别为0、56、112、168和224 kg P₂O₅ ha⁻¹。使用SPAD-502 Plus叶绿素仪（SPAD即土壤植物分析开发）测定冠层叶绿素。使用DJI Mavic 3 M在三个采样时期采集无人机影像：营养生长早期（Ve-E）、营养生长晚期（Ve-L）和生殖生长阶段，分别对应V4–V6、V11–V13和R3–R5生育时期。从绿、红、红边和近红外波段提取了8种植被指数（VIs）。采用Pearson相关、简单和多元线性回归、Lasso及弹性网（Elastic Net）评估SPAD与光谱预测变量之间的关系。模型性能通过重复30次的五折交叉验证（CV）估计进行评估，指标包括交叉验证R²、均方根误差和相对均方根误差。预测精度为中等水平，且因地点和时间而异，其中Ve-L时期关系最强。在Ve-L时期，绿归一化差异植被指数（GNDVI）和红边叶绿素指数分别在Orange和Kentland表现最佳。多变量模型和正则化模型表现相当，但未能比最佳单植被指数模型提高精度。总体而言，GNDVI和基于红边的指数是冠层叶绿素变异最有用的指标。这些结果表明，无人机获取的GNDVI和红边指数能够对基于SPAD的叶绿素状况提供中等精度且可解释的估测。","Agrosystems Geosciences & Environment","2026-09-24T00:00:00Z",69,{"impact":17,"substance":101,"depth":102,"authority":103,"freshness":104,"relevant":22,"comment":105},20,16,13,8,"基于无人机多光谱影像评估玉米冠层叶绿素，方法规范、结论明确，对精准养分管理有参考价值，但属细分领域研究，公共影响有限。",[107],{"name":97,"url":94},[27,109,28,110,29],"玉米","氮肥管理",[112,113],"无人机 玉米 叶绿素 遥感","Virginia Tech 玉米 氮肥 SPAD","无人机玉米叶绿素遥感-3620","10.1002\u002Fagg2.70437",{"doi":115,"openalex_id":117,"authors":118,"venue":97,"cited_by_count":36,"oa_url":139,"card":140,"direction":84,"ingested_from":86},"W7214232972",[119,122,125,128,130,132,134,136],{"name":120,"orcid":121},"Aarati Khulal","https:\u002F\u002Forcid.org\u002F0000-0003-4405-1320",{"name":123,"orcid":124},"Huijie Gan","https:\u002F\u002Forcid.org\u002F0000-0001-6634-5704",{"name":126,"orcid":127},"Jitender Rathore","https:\u002F\u002Forcid.org\u002F0009-0006-5764-1652",{"name":129,"orcid":9},"Sheetal Kumari",{"name":131,"orcid":9},"Ivy Flory",{"name":133,"orcid":9},"Caleb Bishop",{"name":135,"orcid":9},"Santosh Rijal",{"name":137,"orcid":138},"Olga S. Walsh","https:\u002F\u002Forcid.org\u002F0000-0002-2958-931X","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fagg2.70437",{"tldr":141,"method":142,"finding":143,"direction":84,"opportunity":144},"用无人机多光谱植被指数评估玉米不同生育期冠层叶绿素含量。","DJI Mavic 3 M多光谱影像，8个植被指数，SPAD实测，线性回归与La","GNDVI与红边指数在营养生长后期预测SPAD最准，多变量模型未优于单指数。","可探索多生育期时序融合与多源数据耦合，提升跨站点跨年份叶绿素估算泛化能力。","2026-09-27T23:30:35.468236Z",{"id":147,"title":148,"url":149,"summary":150,"summary_zh":151,"content":9,"source_name":152,"source_url":149,"published_at":153,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":154,"score_detail":155,"sources":157,"tags":159,"search_phrases":162,"slug":165,"view_count":36,"doi":166,"paper":167,"created_at":195},2660,"Red-Edge Information in Agricultural Remote Sensing: From Spectral Theory to Explainable Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183180","The red-edge (RE) spectral region has become a central component of agricultural remote sensing because it captures physiologically meaningful changes in chlorophyll content, canopy structure and vegetation functioning. The availability of dedicated RE bands on modern multispectral satellites and advances in hyperspectral sensing have stimulated widespread applications for crop monitoring, nutrient assessment, stress detection and yield prediction. However, reported improvements over conventional visible–near-infrared (VIS–NIR) approaches remain highly variable, and the mechanisms governing when and why RE information provides additional value are often poorly synthesised. This review presents a conceptual framework that links the physical and physiological basis of RE reflectance with its condition-dependent agronomic performance and its emerging role within modern machine learning (ML) systems. We first examine how pigment absorption, canopy structure and sensor characteristics jointly determine the representation of RE information from hyperspectral measurements to operational multispectral observations. We then synthesise evidence demonstrating that the agronomic value of RE information is strongly dependent on crop characteristics, phenological stage, environmental conditions and observation geometry, explaining much of the variability reported across previous studies. Finally, we show how recent advances in ML and explainable artificial intelligence have changed the interpretation of RE information. Rather than evaluating RE-derived vegetation indices in isolation, contemporary predictive frameworks integrate RE observations with complementary spectral, climatic, structural and temporal predictors, allowing their physiological contribution to be quantified within multidimensional models. We conclude that future value of RE remote sensing will require not only continued advances in spectral measurement and vegetation index development, but also improved interpretation, transferability and operational integration of physiologically meaningful RE information within explainable, multi-source agricultural monitoring systems.","红边（RE）光谱区已成为农业遥感的核心组成部分，因为它能够捕捉叶绿素含量、冠层结构和植被功能等方面具有生理意义的变化。现代多光谱卫星上专用红边波段的可用性以及高光谱传感技术的进步，推动了其在作物监测、养分评估、胁迫检测和产量预测中的广泛应用。然而，相较于传统可见光—近红外（VIS–NIR）方法所报道的改进效果仍高度可变，而关于红边信息何时以及为何提供额外价值的机制往往缺乏系统梳理。本综述提出了一个概念框架，将红边反射率的物理与生理基础与其条件依赖的农学表现及其在现代机器学习（ML）系统中新兴的作用联系起来。我们首先探讨色素吸收、冠层结构和传感器特性如何共同决定从高光谱测量到业务化多光谱观测中红边信息的表征。随后，我们综合证据表明，红边信息的农学价值强烈依赖于作物特征、物候阶段、环境条件和观测几何，这解释了以往研究中报道的大部分变异性。最后，我们展示了机器学习和可解释人工智能的最新进展如何改变了对红边信息的解读。当代预测框架不再孤立地评估红边衍生的植被指数，而是将红边观测与互补的光谱、气候、结构和时间预测因子相结合，从而在多维模型中量化其生理贡献。我们得出结论：红边遥感的未来价值不仅需要光谱测量和植被指数开发的持续进步，还需要在可解释的多源农业监测系统中改进对具有生理意义的红边信息的解读、可迁移性和业务化整合。","Remote Sensing","2026-09-16T00:00:00Z",82,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":156},"系统综述红边遥感从光谱机理到可解释机器学习的农学价值条件，方法框架新颖、结论可靠，对作物监测与产量预测有实质参考意义。",[158],{"name":152,"url":149},[27,160,28,161,29],"农业人工智能","作物监测",[163,164],"农业人工智能 作物监测 智慧农业 植被指数","农业人工智能 作物监测","农业人工智能作物监测智慧农业植被指数-2660","10.3390\u002Frs18183180",{"doi":166,"openalex_id":168,"authors":169,"venue":152,"cited_by_count":36,"oa_url":149,"card":190,"direction":84,"ingested_from":86},"W7213344870",[170,173,175,178,181,184,187],{"name":171,"orcid":172},"Ignacio Fuentes","https:\u002F\u002Forcid.org\u002F0000-0001-7066-7482",{"name":174,"orcid":9},"Nikolas Hoskin",{"name":176,"orcid":177},"Patrick Filippi","https:\u002F\u002Forcid.org\u002F0000-0003-3573-084X",{"name":179,"orcid":180},"Abhasha Joshi","https:\u002F\u002Forcid.org\u002F0000-0002-1422-465X",{"name":182,"orcid":183},"Yi Yu","https:\u002F\u002Forcid.org\u002F0000-0002-1140-2713",{"name":185,"orcid":186},"Thomas F. A. Bishop","https:\u002F\u002Forcid.org\u002F0000-0002-6723-7323",{"name":188,"orcid":189},"Dhahi Al-Shammari","https:\u002F\u002Forcid.org\u002F0000-0001-6608-8322",{"tldr":191,"method":192,"finding":193,"direction":84,"opportunity":194},"综述红边遥感在农业中的物理基础、条件依赖性与可解释机器学习中的新角色。","文献综述，整合光谱理论、多光谱\u002F高光谱数据与可解释AI方法。","红边信息的农学价值高度依赖作物、物候、环境与观测几何，需多源融合与可解释建模。","可研究红边信息在不同作物-物候-环境组合下的可迁移性，并构建可解释多源融合模型。","2026-09-16T23:30:28.719858Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":9,"content":9,"source_name":201,"source_url":199,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":202,"score_detail":203,"sources":206,"tags":208,"search_phrases":212,"slug":215,"view_count":36,"doi":216,"paper":217,"created_at":244},3619,"Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10767247\u002Fv1","Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee。Research Square","Research Square",51,{"impact":104,"substance":20,"depth":204,"authority":89,"freshness":104,"relevant":22,"comment":205},15,"咖啡园NDVI主动传感器与无人机多光谱对比研究，方法有参考价值但属细分作物技术验证，影响面有限。",[207],{"name":201,"url":199},[27,209,28,210,211],"无人机","NDVI","咖啡种植",[213,214],"咖啡 NDVI 无人机 多光谱","Research Square 咖啡 遥感","咖啡NDVI无人机多光谱-3619","10.21203\u002Frs.3.rs-10767247\u002Fv1",{"doi":216,"openalex_id":218,"authors":219,"venue":201,"cited_by_count":36,"oa_url":243,"card":9,"direction":84,"ingested_from":86},"W7214220732",[220,222,224,226,228,231,233,235,238,241],{"name":221,"orcid":9},"Gabriel de Morais Campos",{"name":223,"orcid":9},"Aline Bhering Silva",{"name":225,"orcid":9},"Cileimar Aparecida da Silva",{"name":227,"orcid":9},"Vanda Maria Salles Andrade",{"name":229,"orcid":230},"Thaline M. Pimenta","https:\u002F\u002Forcid.org\u002F0000-0002-5002-177X",{"name":232,"orcid":9},"Andressa Barcellos Silva",{"name":234,"orcid":9},"Marco Thúlio Gonçalves Vieira",{"name":236,"orcid":237},"Daniel Marçal de Queiroz","https:\u002F\u002Forcid.org\u002F0000-0003-0987-3855",{"name":239,"orcid":240},"Fábio Daniel Tancredi","https:\u002F\u002Forcid.org\u002F0000-0002-7619-2200",{"name":242,"orcid":9},"Flora Maria Melo Villar","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10767247\u002Flatest.pdf","2026-09-27T23:30:34.698079Z",{"id":246,"title":247,"url":248,"summary":249,"summary_zh":250,"content":9,"source_name":251,"source_url":248,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":253,"sources":255,"tags":257,"search_phrases":261,"slug":264,"view_count":36,"doi":265,"paper":266,"created_at":283},3607,"Analysis of drought carry-over effect on apple trees using the Leafiness-LiDAR index","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104603","Apple trees ( Malus × domestica Borkh.) are widely grown in Mediterranean regions, where drought-induced irrigation restrictions can impair canopy development and productivity. This study assessed the Leafiness-LiDAR Index (LLI), derived from terrestrial Light Detection and Ranging (LiDAR) point clouds, as a proxy for Leaf Area Index (LAI) and as a non-destructive indicator of canopy response and recovery following water deficit. An ‘Opal®’ apple orchard was monitored over three growing seasons (2023–2025), encompassing one drought year and two recovery years. In 2023, full irrigation (FI; 480 mm) was compared with deficit irrigation (DI; 300 mm; 37.5% less water) under two planting densities (0.5 and 1.0 m tree spacing). Mixed-effects models revealed significant Year × Irrigation interactions for LAI (p = 0.006) and LLI (p \u003C 0.001), but not for yield (p = 0.654), suggesting a partial decoupling between structural and productive recovery. During the drought year, DI reduced LLI by 37% and yield by 47%. Following irrigation restoration, LLI differences persisted, particularly in 2024, indicating lasting structural effects of water deficit. Higher planting density promoted canopy recovery and increased yield. LLI proved to be a valuable, non-destructive estimator of LAI for detecting drought legacy effects and tracking canopy recovery, supporting precision orchard management under Mediterranean water scarcity.","苹果树（Malus × domestica Borkh.）广泛种植于地中海地区，该地区因干旱导致的灌溉限制会损害树冠发育和生产力。本研究评估了基于地面激光雷达（LiDAR）点云衍生的叶量-激光雷达指数（Leafiness-LiDAR Index, LLI）作为叶面积指数（Leaf Area Index, LAI）的替代指标，以及作为水分亏缺后树冠响应与恢复的非破坏性指标的可行性。对一个‘Opal®’苹果园进行了三个生长季（2023—2025年）的监测，涵盖一个干旱年和两个恢复年。2023年，在两种种植密度（株距0.5 m和1.0 m）下，比较了充分灌溉（FI；480 mm）与亏缺灌溉（DI；300 mm；减少37.5%水量）。混合效应模型显示，LAI（p = 0.006）和LLI（p \u003C 0.001）存在显著的年×灌溉交互作用，而产量（p = 0.654）则无此交互作用，表明结构与产量恢复之间存在部分脱耦。在干旱年，DI使LLI降低37%，产量降低47%。灌溉恢复后，LLI差异持续存在，尤其在2024年，表明水分亏缺具有持久的结构性影响。较高的种植密度促进了树冠恢复并提高了产量。LLI被证明是一种有价值的、非破坏性的LAI估算指标，可用于检测干旱遗留效应并追踪树冠恢复，为地中海水资源短缺条件下的精准果园管理提供支持。","Biosystems Engineering",77,{"impact":204,"substance":18,"depth":19,"authority":20,"freshness":104,"relevant":22,"comment":254},"地中海苹果园三年试验证实LiDAR叶量指数可无损监测干旱遗留效应与冠层恢复，方法新颖、数据扎实，对果园精准灌溉有参考价值。",[256],{"name":251,"url":248},[27,258,28,259,260],"苹果","果园管理","精准灌溉",[262,263],"LiDAR 苹果园 干旱","Leafiness-LiDAR Index 苹果","LiDAR苹果园干旱-3607","10.1016\u002Fj.biosystemseng.2026.104603",{"doi":265,"openalex_id":267,"authors":268,"venue":251,"cited_by_count":36,"oa_url":248,"card":278,"direction":84,"ingested_from":86},"W7214458453",[269,272,275],{"name":270,"orcid":271},"Leire Sandonís-Pozo","https:\u002F\u002Forcid.org\u002F0000-0003-2472-0259",{"name":273,"orcid":274},"José Antonio Martínez-Casasnovas","https:\u002F\u002Forcid.org\u002F0000-0003-1480-3632",{"name":276,"orcid":277},"Miquel Pascual","https:\u002F\u002Forcid.org\u002F0000-0002-3329-4207",{"tldr":279,"method":280,"finding":281,"direction":84,"opportunity":282},"用LiDAR叶量指数追踪苹果园干旱遗留效应与冠层恢复。","三年果园试验，地面LiDAR点云提取LLI，混合效应模型分析。","亏缺灌溉使LLI降37%、产量降47%，复水后结构差异仍持续。","可探索LLI与多源遥感融合，构建果园干旱遗留效应早期预警与精准补灌决策模型。","2026-09-27T23:30:09.907348Z",{"id":285,"title":286,"url":287,"summary":288,"summary_zh":289,"content":9,"source_name":290,"source_url":287,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":291,"score_detail":292,"sources":294,"tags":296,"search_phrases":299,"slug":302,"view_count":36,"doi":303,"paper":304,"created_at":321},3515,"Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13198-026-03435-1","Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence。International Journal of Systems Assurance Engineering and Management","基于人工智能的精准农业土壤制图与肥料优化。《国际系统保障工程与管理杂志》","International Journal of Systems Assurance Engineering and Management",62,{"impact":17,"substance":20,"depth":204,"authority":103,"freshness":104,"relevant":22,"comment":293},"论文探讨AI用于土壤制图与施肥优化，属智慧农业细分方向，但摘要信息有限、影响面偏窄，暂不建议进入每日精选。",[295],{"name":290,"url":287},[27,160,297,28,298],"精准施肥","土壤制图",[300,301],"土壤制图 人工智能 精准施肥","精准农业 肥料优化 AI","土壤制图人工智能精准施肥-3515","10.1007\u002Fs13198-026-03435-1",{"doi":303,"openalex_id":305,"authors":306,"venue":290,"cited_by_count":36,"oa_url":9,"card":315,"direction":319,"ingested_from":86},"W7214144821",[307,310,312],{"name":308,"orcid":309},"Neetu Mittal","https:\u002F\u002Forcid.org\u002F0000-0002-2012-0523",{"name":308,"orcid":311},"https:\u002F\u002Forcid.org\u002F0000-0001-6923-0013",{"name":313,"orcid":314},"Pradeepta Kumar Sarangi","https:\u002F\u002Forcid.org\u002F0000-0003-3827-6208",{"tldr":316,"method":317,"finding":318,"direction":319,"opportunity":320},"利用人工智能进行土壤制图和肥料优化，以支持精准农业。","人工智能方法，用于土壤制图与肥料优化。","AI可提升土壤制图与肥料优化的精准性，促进精准农业。","农业人工智能与决策模型","可探索多源数据融合与实时决策模型，提升肥料推荐的自适应性和可解释性。","2026-09-25T23:30:49.869002Z",{"id":323,"title":324,"url":325,"summary":326,"summary_zh":327,"content":9,"source_name":152,"source_url":325,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":328,"score_detail":329,"sources":331,"tags":333,"search_phrases":337,"slug":340,"view_count":36,"doi":341,"paper":342,"created_at":360},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。",81,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":330},"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。",[332],{"name":152,"url":325},[27,334,335,28,336],"大豆","农情监测","作物表型",[338,339],"MODIS NDVI 大豆 物候","美国大豆 遥感 物候监测","MODISNDVI大豆物候-3501","10.3390\u002Frs18193300",{"doi":341,"openalex_id":343,"authors":344,"venue":152,"cited_by_count":36,"oa_url":325,"card":355,"direction":84,"ingested_from":86},"W7214203102",[345,347,350,353],{"name":346,"orcid":9},"Qiuxiang Yi",{"name":348,"orcid":349},"Siting Chen","https:\u002F\u002Forcid.org\u002F0000-0003-3468-9320",{"name":351,"orcid":352},"Fumin Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5078-358X",{"name":354,"orcid":9},"Qinyan Zhu",{"tldr":356,"method":357,"finding":358,"direction":84,"opportunity":359},"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"]