[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2155":3,"related-2155":96},{"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,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":71,"slug":74,"view_count":21,"doi":75,"paper":76,"created_at":95},2155,"Bridging canopy light interception and absorption: Toward a multi-scale radiometric framework for precision irrigation in woody crops","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.srs.2026.100505",": Optimizing irrigation in orchards is increasingly challenged by climatic variability, water scarcity, and structural heterogeneity, requiring indicators that robustly link canopy architecture with transpiration and energy exchange processes. The fraction of intercepted photosynthetically active radiation (fIPAR) is widely used in agronomy to characterize canopy structure and radiation interception, whereas the fraction of absorbed PAR (fAPAR) is more commonly used in remote sensing, yet their relationship and relevance for orchard irrigation remain insufficiently clarified. This review critically examines the theoretical foundations, methodological developments, and irrigation applications of Canopy Light Interception in woody crops over the past three decades. We synthesize empirical formulations, geometric approaches, radiative transfer modelling, and advances in proximal and remote sensing, including hemispherical photography, LiDAR platforms, unmanned aerial vehicles, and satellite-derived products. The conditions under which fIPAR approximates fAPAR are examined, revealing that their divergence is more relevant in discontinuous orchard systems than in homogeneous canopies. We evaluate how Canopy Light Interception supports evapotranspiration estimation, crop coefficient refinement, and irrigation scheduling, while emphasizing that it constrains potential radiative demand rather than directly representing physiological regulation of transpiration. Despite substantial methodological progress, inconsistent terminology, heterogeneous protocols, and limited cross-validation across scales hinder comparability and deployment. We suggest a multi-scale framework in which fIPAR is a structural–radiative constraint on potential canopy water demand. This is embedded within integrated sensing and irrigation decision systems. We argue that clarifying the conceptual boundary between fIPAR and fAPAR is essential for translating canopy radiation assessments into decision-support tools for water-efficient orchard management under increasing climatic pressure.","果园灌溉优化正日益受到气候变异性、水资源短缺和结构异质性的挑战，亟需能够稳健地将冠层结构与蒸腾及能量交换过程联系起来的指标。截获光合有效辐射比例（fIPAR）在农学中被广泛用于表征冠层结构和辐射截获，而吸收光合有效辐射比例（fAPAR）则更常用于遥感领域，但二者之间的关系及其对果园灌溉的相关性仍未被充分阐明。本文综述了过去三十年间木本作物冠层光截获的理论基础、方法学发展及灌溉应用。我们综合了经验公式、几何方法、辐射传输模型以及近地和遥感技术的进展，包括半球摄影、LiDAR平台、无人机和卫星衍生产品。本文考察了fIPAR近似fAPAR的条件，揭示出二者的差异在不连续的果园系统中比在均质冠层中更为显著。我们评估了冠层光截获如何支持蒸散估算、作物系数优化和灌溉调度，同时强调其约束的是潜在辐射需求，而非直接表征蒸腾的生理调控。尽管方法学取得了实质性进展，但术语不一致、方案异质性和跨尺度交叉验证有限等问题阻碍了可比性和实际部署。我们提出一个多尺度框架，其中fIPAR作为冠层潜在水分需求的结构-辐射约束，并将其嵌入集成传感与灌溉决策系统。我们认为，厘清fIPAR与fAPAR之间的概念边界，对于将冠层辐射评估转化为在日益增大的气候压力下实现节水型果园管理的决策支持工具至关重要。",null,"Science of Remote Sensing","2026-09-08T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,21,14,8,1,"该综述系统梳理了木本作物冠层光截获与吸收的遥感方法，提出多尺度辐射框架以支撑精准灌溉决策，方法学新颖且对果园水分管理有直接指导价值，值得进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","遥感","果园管理","精准灌溉","冠层光截获",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,52,59,66],{"code":32,"score":17,"supported":45,"title":46,"abstract":47,"tags":51},true,[33],[48,49,33,50],"灌溉","水资源","evapotranspiration",[29],{"code":53,"score":54,"supported":45,"title":55,"abstract":56,"tags":58},"I3",7,[],[27,57],"remote sensing",[27],{"code":60,"score":61,"supported":45,"title":62,"abstract":63,"tags":65},"B2",3,[],[64],"orchard",[],{"code":67,"score":21,"supported":14,"title":68,"abstract":69,"tags":70},"I1",[],[],[26],[72,73],"冠层光截获 智慧农业 果园管理 精准灌溉","冠层光截获 智慧农业","冠层光截获智慧农业果园管理精准灌溉-2155","10.1016\u002Fj.srs.2026.100505",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":85,"oa_url":86,"card":87,"direction":93,"ingested_from":94},"W7211970568",[79,82],{"name":80,"orcid":81},"Mohamed Ibrahim Belaid","https:\u002F\u002Forcid.org\u002F0009-0002-9327-4598",{"name":83,"orcid":84},"Jaume Casadesús","https:\u002F\u002Forcid.org\u002F0000-0001-7036-8212",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2666017226001434\u002Fpdf",{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"综述木质作物冠层光截获与吸收关系，提出多尺度辐射框架支撑精准灌溉。","综述三十年经验公式、辐射传输模型及半球摄影、LiDAR、无人机、卫星遥感。","fIPAR与fAPAR在离散果园差异显著，fIPAR应作为潜在需水约束而非蒸腾调控。","农业遥感与作物表型","可研究果园fIPAR-fAPAR跨尺度转换模型，并耦合遥感与灌溉决策系统验证。","智慧农业 \u002F 农业物联网","openalex","2026-09-11T23:30:17.628726Z",{"total":97,"page":21,"page_size":97,"items":98},6,[99,178,294,382,454,542],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":112,"tags":114,"primary_topic":116,"secondary_topics":121,"classification":128,"search_phrases":154,"slug":157,"view_count":158,"doi":159,"paper":160,"created_at":177},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","2026-09-26T00:00:00Z",77,{"impact":109,"substance":110,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":111},15,22,"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。",[113],{"name":105,"url":102},[26,115,27,28,29],"苹果",{"code":60,"slug":117,"name":118,"path":119,"parent_code":120},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[122,127],{"code":123,"slug":124,"name":125,"path":126,"parent_code":36},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":129},{"candidates":130,"agricultural_evidence":45},[131,136,142,146,150],{"code":60,"score":17,"supported":45,"title":132,"abstract":134,"tags":135},[133],"apple",[118,115,64,133],[115],{"code":123,"score":19,"supported":45,"title":137,"abstract":139,"tags":141},[138],"drought",[140,138],"干旱",[],{"code":32,"score":13,"supported":45,"title":143,"abstract":144,"tags":145},[],[48,49,33],[29],{"code":67,"score":21,"supported":14,"title":147,"abstract":148,"tags":149},[],[],[26],{"code":53,"score":21,"supported":14,"title":151,"abstract":152,"tags":153},[],[],[27],[155,156],"LiDAR 苹果园 干旱","Leafiness-LiDAR Index 苹果","LiDAR苹果园干旱-3607",4,"10.1016\u002Fj.biosystemseng.2026.104603",{"doi":159,"openalex_id":161,"authors":162,"venue":105,"cited_by_count":85,"oa_url":102,"card":172,"direction":91,"ingested_from":94},"W7214458453",[163,166,169],{"name":164,"orcid":165},"Leire Sandonís-Pozo","https:\u002F\u002Forcid.org\u002F0000-0003-2472-0259",{"name":167,"orcid":168},"José Antonio Martínez-Casasnovas","https:\u002F\u002Forcid.org\u002F0000-0003-1480-3632",{"name":170,"orcid":171},"Miquel Pascual","https:\u002F\u002Forcid.org\u002F0000-0002-3329-4207",{"tldr":173,"method":174,"finding":175,"direction":91,"opportunity":176},"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":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":9,"source_name":184,"source_url":181,"published_at":185,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":186,"sources":190,"tags":192,"primary_topic":195,"secondary_topics":196,"classification":208,"search_phrases":265,"slug":268,"view_count":85,"doi":269,"paper":270,"created_at":293},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",{"impact":17,"substance":110,"depth":17,"authority":187,"freshness":188,"relevant":21,"comment":189},12,9,"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[191],{"name":184,"url":181},[26,193,27,194,29],"农业人工智能","作物水分胁迫",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[197,203],{"code":198,"slug":199,"name":200,"path":201,"parent_code":202},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":204,"slug":205,"name":206,"path":207,"parent_code":202},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":209},{"candidates":210,"agricultural_evidence":45},[211,215,222,229,235,239,246,253,260],{"code":32,"score":109,"supported":45,"title":212,"abstract":213,"tags":214},[33],[48,33],[29],{"code":198,"score":13,"supported":45,"title":216,"abstract":217,"tags":221},[],[218,219,220],"机器学习","machine learning","artificial intelligence",[193],{"code":204,"score":188,"supported":45,"title":223,"abstract":224,"tags":228},[],[225,226,227],"物联网","internet of things","IoT",[],{"code":67,"score":54,"supported":45,"title":230,"abstract":231,"tags":234},[],[232,233],"数字孪生","digital twin",[26],{"code":53,"score":54,"supported":45,"title":236,"abstract":237,"tags":238},[],[27,57],[27],{"code":240,"score":97,"supported":45,"title":241,"abstract":242,"tags":245},"A2",[],[243,244],"小麦","wheat",[],{"code":247,"score":97,"supported":45,"title":248,"abstract":249,"tags":252},"A3",[],[250,251],"玉米","maize",[],{"code":254,"score":97,"supported":45,"title":255,"abstract":256,"tags":259},"A5",[],[257,258],"马铃薯","potato",[],{"code":60,"score":61,"supported":45,"title":261,"abstract":262,"tags":264},[],[263],"葡萄",[],[266,267],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":269,"openalex_id":271,"authors":272,"venue":184,"cited_by_count":85,"oa_url":181,"card":288,"direction":93,"ingested_from":94},"W7223088921",[273,276,279,282,285],{"name":274,"orcid":275},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":277,"orcid":278},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":280,"orcid":281},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":283,"orcid":284},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":286,"orcid":287},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":289,"method":290,"finding":291,"direction":91,"opportunity":292},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z",{"id":295,"title":296,"url":297,"summary":298,"summary_zh":299,"content":9,"source_name":300,"source_url":297,"published_at":301,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":302,"score_detail":303,"sources":305,"tags":307,"primary_topic":310,"secondary_topics":311,"classification":322,"search_phrases":350,"slug":353,"view_count":61,"doi":354,"paper":355,"created_at":381},2318,"Estimation of field-scale crop evapotranspiration from the mechanistic SIF-ET model using the UAV hyperspectral imagery","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128332","Accurate estimation of evapotranspiration (ET) is critical for agricultural water management and understanding land-atmosphere interactions. Conventional thermal and optical remote sensing is a powerful tool for ET estimation, but existing methods remain constrained by empirical parameterization, calibration complexity, and limited applicability. As a direct indicator of vegetation photosynthesis, solar-induced chlorophyll fluorescence (SIF) enables consistent and accurate ET estimation through a mechanistically grounded framework that does not require site-specific empirical calibration of the SIF-GPP relationship. However, current SIF-based ET estimation mainly rely on coarse-resolution satellite data and its capacity to resolve high-resolution ET dynamics over farmland remains unexplored. To address this, the mechanistic light response model and water-carbon relationship was integrated to construct a SIF-ET model at field-scale using UAV narrow-band hyperspectra imagery. The principal conclusions are: (1) High-resolution crop (canola,soybean and wheat) canopy SIF 740 at 1-nm SR was retrieved from UAV hyperspectra imagery using the Photochemistry and Energy Fluxes (SCOPE) model; (2)The daily ET estimates from SIF-ET model were validated against the in-situ measurements across multiple growth stages and water-nitrogen treatments, with wheat exhibiting the highest precision (R²=0.72–0.94, RMSE = 0.04–0.11 mm\u002Fd, MAPE = 1.70–5.89%), followed by canola and soybean; (3) Cumulative ET errors across repeated UAV observations within each growth stage remained within acceptable bounds, indicating the stability of SIF-ET model in capturing long-term ET dynamics. Overall, the SIF-ET model from UAV hyperspectra imagery revealed fine-scale cropland ET spatial-temporal heterogeneity. These findings provide support for precision irrigation, rational water-nitrogen management, and stress diagnostics under changing environmental conditions.","准确估算蒸散发（ET）对农业水资源管理和理解陆气相互作用至关重要。传统的热红外与光学遥感是估算ET的有力工具，但现有方法仍受限于经验参数化、校准复杂性和适用性有限等问题。作为植被光合作用的直接指示因子，日光诱导叶绿素荧光（SIF）能够通过机理明确的框架实现一致且准确的ET估算，且无需对SIF-GPP关系进行站点特定的经验校准。然而，当前基于SIF的ET估算主要依赖粗分辨率卫星数据，其解析农田高分辨率ET动态的能力尚未得到探索。为此，本研究整合机理光响应模型与水碳关系，利用无人机窄波段高光谱影像构建了田块尺度的SIF-ET模型。主要结论如下：（1）利用光化学与能量通量（SCOPE）模型，从无人机高光谱影像中反演了1 nm光谱分辨率下油菜、大豆和小麦的高分辨率冠层SIF₇₄₀；（2）基于SIF-ET模型的日ET估算值在多个生育期和水氮处理下与原位观测进行了验证，其中小麦精度最高（R²=0.72–0.94，RMSE=0.04–0.11 mm\u002Fd，MAPE=1.70–5.89%），油菜和大豆次之；（3）各生育期内重复无人机观测的累积ET误差均在可接受范围内，表明SIF-ET模型在捕捉长期ET动态方面具有稳定性。总体而言，基于无人机高光谱影像的SIF-ET模型揭示了精细尺度的农田ET时空异质性。这些发现为精准灌溉、合理水氮管理以及变化环境条件下的胁迫诊断提供了支持。","European Journal of Agronomy","2026-09-12T00:00:00Z",81,{"impact":17,"substance":110,"depth":17,"authority":19,"freshness":188,"relevant":21,"comment":304},"该研究基于无人机高光谱与SIF-ET机理模型实现田块尺度作物蒸散发高精度估算，方法新颖、验证充分，对精准灌溉与水氮管理有实质参考价值，值得进入每日精选。",[306],{"name":300,"url":297},[26,308,27,29,309],"无人机","蒸散发",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[312,316],{"code":53,"slug":313,"name":314,"path":315,"parent_code":202},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":317,"slug":318,"name":319,"path":320,"parent_code":321},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",{"status":39,"locked":14,"version":40,"method":41,"evidence":323},{"candidates":324,"agricultural_evidence":45},[325,330,335,342,346],{"code":32,"score":326,"supported":45,"title":327,"abstract":328,"tags":329},19,[50],[48,49,309,33,50],[29,309],{"code":53,"score":109,"supported":45,"title":331,"abstract":333,"tags":334},[332],"hyperspectral",[27,57],[27],{"code":317,"score":188,"supported":45,"title":336,"abstract":337,"tags":341},[],[338,339,340],"大豆","油菜","soybean",[],{"code":240,"score":97,"supported":45,"title":343,"abstract":344,"tags":345},[],[243,244],[],{"code":67,"score":21,"supported":14,"title":347,"abstract":348,"tags":349},[],[],[26],[351,352],"智慧农业 精准灌溉 无人机 蒸散发","智慧农业 精准灌溉","智慧农业精准灌溉无人机蒸散发-2318","10.1016\u002Fj.eja.2026.128332",{"doi":354,"openalex_id":356,"authors":357,"venue":300,"cited_by_count":85,"oa_url":297,"card":376,"direction":91,"ingested_from":94},"W7212242584",[358,360,362,365,368,370,373],{"name":359,"orcid":9},"Ruiqi Du",{"name":361,"orcid":9},"Yonghong Zhang",{"name":363,"orcid":364},"Youzhen Xiang","https:\u002F\u002Forcid.org\u002F0000-0002-8268-1609",{"name":366,"orcid":367},"Fucang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6659-3262",{"name":369,"orcid":9},"Jian Gao",{"name":371,"orcid":372},"Qiliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-3274-2119",{"name":374,"orcid":375},"Xianghui Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0638-3068",{"tldr":377,"method":378,"finding":379,"direction":91,"opportunity":380},"利用无人机高光谱影像构建田间尺度SIF-ET机理模型，实现作物蒸散发高精度估算。","无人机窄波段高光谱结合SCOPE模型反演SIF，耦合光响应与水碳关系构建SIF-","小麦估算精度最高（R²=0.72–0.94），模型能稳定捕捉多生育期蒸散发动态与空间异质性。","可探索多作物多环境下的SIF-ET普适性，并融合热红外与机器学习提升胁迫诊断能力。","2026-09-13T23:30:22.677041Z",{"id":383,"title":384,"url":385,"summary":386,"summary_zh":387,"content":9,"source_name":388,"source_url":385,"published_at":389,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":390,"score_detail":391,"sources":393,"tags":395,"primary_topic":397,"secondary_topics":399,"classification":401,"search_phrases":432,"slug":435,"view_count":61,"doi":436,"paper":437,"created_at":453},1905,"A novel surface reflectance calibration approach to improve the estimation of maize actual evapotranspiration in a semi-arid USA region","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01431161.2026.2727171","This study aimed to develop a novel surface reflectance (SR) data calibration approach to improve maize actual crop evapotranspiration (ETa, mm h−1) predictions in agricultural settings. The remote sensing (RS) platforms used in the study included Landsat-8 (30 m spatial resolution), Sentinel-2 (10 m), Planet CubeSat (3 m), a handheld radiometer (1 m), and a small uncrewed aerial system or sUAS (0.03 m). A one-source surface energy balance (OSEB) was applied to estimate ETa and assess any improvements when using original and calibrated SR data. Five machine-learning (ML) algorithms were evaluated as part of the calibration process. The ML approaches were linear regression, regression tree, random forest, support vector machine, and Gaussian process regression. The SR calibration approach incorporated a novel dual-source pixel decomposition model that accounts for the contributions of soil and vegetation light reflectance captured by a given SR band. Two maize research sites, one in Greeley and another in Fort Collins, Colorado, U.S.A. provided the grounds for data collection over a four-year period. Results indicated that the best ML model, for a given RS sensor, depended on the SR contributions from plants and bare soil. Among the evaluated algorithms, the Regression Tree and Gaussian Process Regression provided the most accurate pixel decomposition and reflectance adjustments, consistently demonstrating the highest statistical agreement (R2 = 0.70–0.94) and lowest residual errors (RMSE = 0.004–0.022). Improvements in maize hourly ETa had a 34% error reduction in maize ETa estimation for data from the handheld radiometer and sUAS, compared to a 12% error reduction for the spaceborne sensors, on average.","本研究旨在开发一种新型地表反射率（SR）数据校准方法，以提高农业环境中玉米实际作物蒸散量（ETa, mm h⁻¹）的预测精度。研究中使用的遥感（RS）平台包括Landsat-8（空间分辨率30米）、Sentinel-2（10米）、Planet CubeSat（3米）、手持式辐射计（1米）以及小型无人机系统（sUAS，0.03米）。采用单源地表能量平衡（OSEB）模型估算ETa，并评估使用原始与校准SR数据时的改进效果。校准过程中评估了五种机器学习（ML）算法，包括线性回归、回归树、随机森林、支持向量机和高斯过程回归。SR校准方法引入了一种新型双源像元分解模型，该模型考虑了特定SR波段所捕获的土壤与植被光反射的贡献。研究选取了美国科罗拉多州格里利和柯林斯堡的两个玉米研究站点，在四年期间收集数据。结果表明，对于给定的遥感传感器，最佳ML模型取决于植被和裸土的SR贡献。在所评估的算法中，回归树和高斯过程回归提供了最精确的像元分解和反射率调整，始终表现出最高的一致性统计指标（R² = 0.70–0.94）和最低的残差误差（RMSE = 0.004–0.022）。在玉米小时ETa估算改进方面，手持式辐射计和sUAS数据的ETa估算误差平均降低了34%，而星载传感器的误差平均降低了12%。","International Journal of Remote Sensing","2026-09-06T00:00:00Z",72,{"impact":109,"substance":110,"depth":17,"authority":19,"freshness":61,"relevant":21,"comment":392},"研究提出地表反射率校准新方法，结合多源遥感与机器学习提升玉米蒸散估算精度，对农业水资源管理有参考价值。",[394],{"name":388,"url":385},[26,218,27,396,29],"作物模型",{"code":247,"slug":251,"name":250,"path":398,"parent_code":321},"\u002Ftopic\u002Ffield-crops\u002Fmaize",[400],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":402},{"candidates":403,"agricultural_evidence":45},[404,408,412,416,423,428],{"code":247,"score":19,"supported":45,"title":405,"abstract":406,"tags":407},[251],[250,251],[],{"code":32,"score":187,"supported":45,"title":409,"abstract":410,"tags":411},[50],[50],[29],{"code":53,"score":54,"supported":45,"title":413,"abstract":414,"tags":415},[],[27,57],[27],{"code":417,"score":97,"supported":45,"title":418,"abstract":419,"tags":422},"H2",[],[420,421],"土壤","soil",[],{"code":198,"score":424,"supported":45,"title":425,"abstract":426,"tags":427},5,[],[218],[396,218],{"code":67,"score":21,"supported":14,"title":429,"abstract":430,"tags":431},[],[],[26],[433,434],"作物模型 智慧农业 机器学习 精准灌溉","作物模型 智慧农业","作物模型智慧农业机器学习精准灌溉-1905","10.1080\u002F01431161.2026.2727171",{"doi":436,"openalex_id":438,"authors":439,"venue":388,"cited_by_count":85,"oa_url":9,"card":448,"direction":91,"ingested_from":94},"W7210284621",[440,443,446],{"name":441,"orcid":442},"Edson Costa‐Filho","https:\u002F\u002Forcid.org\u002F0000-0001-5610-7382",{"name":444,"orcid":445},"José L. Chávez","https:\u002F\u002Forcid.org\u002F0000-0001-6456-0822",{"name":447,"orcid":9},"Huihui Zhang",{"tldr":449,"method":450,"finding":451,"direction":91,"opportunity":452},"提出地表反射率校准方法，结合机器学习与双源像素分解，提高半干旱区玉米蒸散发估算精度。","多源遥感数据，OSEB模型，五种机器学习算法，双源像素分解模型。","回归树和高斯过程回归校准效果最佳，误差降低12%-34%，高分辨率传感器提升更显著。","可探索将校准方法应用于其他作物或区域，或结合深度学习提升低分辨率卫星数据的校准精度。","2026-09-08T23:30:20.154043Z",{"id":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":9,"source_name":460,"source_url":457,"published_at":461,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":462,"score_detail":463,"sources":465,"tags":467,"primary_topic":469,"secondary_topics":470,"classification":473,"search_phrases":506,"slug":509,"view_count":61,"doi":510,"paper":511,"created_at":541},1895,"Continuous monitoring of deficit irrigation in avocado across two contrasting rainfall years using sensor networks, telemetry, and machine learning","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.03.745400","Water scarcity and increasingly irregular rainfall threaten avocado production in Mediterranean regions, yet the long term physiological responses of mature trees to sustained deficit irrigation remain poorly understood. We conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping in a mature avocado orchard subjected to three irrigation regimes. The two study years differed markedly in rainfall, providing a unique opportunity to evaluate how environmental conditions modulate tree responses to water limitation. Trees under severe deficit irrigation showed depletion of water in deeper soil layers and a flattened physiological profile, with near-zero diel variation in leaf thickness and trunk water potential, indicating minimal transpiration and decoupling of tree water status from environmental demand. Drone telemetry via NDVI detected stress during fruit growth and maturation, but not during flowering or the new summer leaf flush, revealing greater drought sensitivity at later maturation stages. Although canopy area did not differ among irrigation treatments, canopy surface roughness increased significantly under deficit irrigation, thereby identifying a novel structural indicator of drought stress. Despite large physiological differences among treatments, fruit number remained stable, while fruit weight decreased significantly under severe deficit irrigation, particularly in the wetter year, suggesting that annual rainfall modulates the trade-off between fruit retention and fruit growth. This study provides the first continuous, multi-scale characterization of avocado performance under sustained deficit irrigation in Mediterranean conditions. By integrating plant-based sensors, remote sensing, and artificial intelligence, we reveal previously undescribed stress dynamics and identify new indicators for precision irrigation management in fruit crops.","水资源短缺和日益不规律的降雨威胁着地中海地区鳄梨的生产，然而，长期亏缺灌溉下成熟树木的生理响应仍知之甚少。我们开展了一项为期两年的田间研究，在成熟鳄梨园中设置了三种灌溉制度，整合了土壤-植物-大气连续体的连续监测、无人机多光谱成像、冠层结构分析以及果实表型鉴定。两个研究年份的降雨量差异显著，为评估环境条件如何调节树木对水分限制的响应提供了独特机会。严重亏缺灌溉下的树木表现出深层土壤水分耗竭和生理特征趋于平缓的现象，叶片厚度和树干水势的昼夜变化接近于零，表明蒸腾作用极低，树木水分状态与环境需求脱钩。通过NDVI进行的无人机遥测在果实生长和成熟期检测到胁迫，但在开花期或夏季新梢生长期未检测到，揭示了后期成熟阶段对干旱的敏感性更高。尽管各灌溉处理间冠层面积无显著差异，但亏缺灌溉下冠层表面粗糙度显著增加，从而识别出一种新的干旱胁迫结构指标。尽管各处理间生理差异显著，果实数量保持稳定，而严重亏缺灌溉下果实重量显著下降，尤其在较湿润的年份更为明显，表明年降雨量调节着果实保留与果实生长之间的权衡。本研究首次提供了地中海条件下持续亏缺灌溉下鳄梨表现的多尺度连续表征。通过整合基于植物的传感器、遥感技术和人工智能，我们揭示了此前未被描述的胁迫动态，并为果树精准灌溉管理识别了新指标。","bioRxiv (Cold Spring Harbor Laboratory)","2026-09-07T00:00:00Z",73,{"impact":17,"substance":110,"depth":17,"authority":187,"freshness":61,"relevant":21,"comment":464},"研究利用传感器网络与机器学习持续监测牛油果缺水胁迫，揭示新结构指标，对精准灌溉有重要参考价值。",[466],{"name":460,"url":457},[26,193,27,29,468],"牛油果",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[471,472],{"code":198,"slug":199,"name":200,"path":201,"parent_code":202},{"code":53,"slug":313,"name":314,"path":315,"parent_code":202},{"status":39,"locked":14,"version":40,"method":41,"evidence":474},{"candidates":475,"agricultural_evidence":45},[476,480,484,490,494,498,502],{"code":32,"score":17,"supported":45,"title":477,"abstract":478,"tags":479},[33],[48,49,33],[29],{"code":198,"score":187,"supported":45,"title":481,"abstract":482,"tags":483},[219],[220],[193],{"code":53,"score":13,"supported":45,"title":485,"abstract":486,"tags":489},[],[27,487,57,488],"无人机多光谱","multispectral",[27],{"code":60,"score":54,"supported":45,"title":491,"abstract":492,"tags":493},[],[118,64],[468],{"code":417,"score":97,"supported":45,"title":495,"abstract":496,"tags":497},[],[420,421],[],{"code":123,"score":97,"supported":45,"title":499,"abstract":500,"tags":501},[],[140,138],[],{"code":67,"score":21,"supported":14,"title":503,"abstract":504,"tags":505},[],[],[26],[507,508],"农业人工智能 智慧农业 精准灌溉 牛油果","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉牛油果-1895","10.64898\u002F2026.09.03.745400",{"doi":510,"openalex_id":512,"authors":513,"venue":460,"cited_by_count":85,"oa_url":535,"card":536,"direction":91,"ingested_from":94},"W7211887459",[514,517,520,523,526,529,532],{"name":515,"orcid":516},"Alberto Férez-Gómez","https:\u002F\u002Forcid.org\u002F0000-0002-8545-9237",{"name":518,"orcid":519},"Lucia Soler-Escamez","https:\u002F\u002Forcid.org\u002F0009-0004-1361-6928",{"name":521,"orcid":522},"Cristina Ferrer-Blanco","https:\u002F\u002Forcid.org\u002F0000-0002-3456-917X",{"name":524,"orcid":525},"Adrián Pérez Aguilar","https:\u002F\u002Forcid.org\u002F0000-0001-7012-1551",{"name":527,"orcid":528},"J.I. Hormaza","https:\u002F\u002Forcid.org\u002F0000-0001-5449-7444",{"name":530,"orcid":531},"Almudena Díaz Zayas","https:\u002F\u002Forcid.org\u002F0000-0002-1226-6135",{"name":533,"orcid":534},"Juan M. Losada","https:\u002F\u002Forcid.org\u002F0000-0002-7966-5018","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F09\u002F07\u002F2026.09.03.745400.full.pdf",{"tldr":537,"method":538,"finding":539,"direction":93,"opportunity":540},"通过传感器、遥感和机器学习连续监测两年不同降雨下鳄梨的亏缺灌溉响应。","集成土壤-植物-大气连续体监测、无人机多光谱、冠层分析及机器学习。","严重亏缺灌溉导致深层土壤水分耗尽、生理活动减弱，果实重量下降，冠层表面粗糙度增加。","可研究不同降雨年份下亏缺灌溉对果实品质的影响，或开发基于冠层粗糙度的干旱胁迫预警模型。","2026-09-08T23:30:14.886372Z",{"id":543,"title":544,"url":545,"summary":546,"summary_zh":547,"content":9,"source_name":548,"source_url":545,"published_at":549,"category":12,"cover_url":9,"hotness":550,"is_selected":14,"score":462,"score_detail":551,"sources":555,"tags":559,"primary_topic":562,"secondary_topics":563,"classification":565,"search_phrases":595,"slug":598,"view_count":85,"doi":599,"paper":600,"created_at":617},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","Zenodo (CERN European Organization for Nuclear Research)","2026-11-25T00:00:00Z",25,{"impact":110,"substance":18,"depth":552,"authority":553,"freshness":85,"relevant":21,"comment":554},17,13,"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[556,557],{"name":548,"url":545},{"name":548,"url":558},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[26,193,560,561,29],"农业物联网","区块链溯源",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[564],{"code":204,"slug":205,"name":206,"path":207,"parent_code":202},{"status":39,"locked":14,"version":40,"method":41,"evidence":566},{"candidates":567,"agricultural_evidence":45},[568,572,576,580,586,591],{"code":32,"score":109,"supported":45,"title":569,"abstract":570,"tags":571},[33],[48,33],[29],{"code":204,"score":13,"supported":45,"title":573,"abstract":574,"tags":575},[],[225,226,227],[560],{"code":198,"score":158,"supported":45,"title":577,"abstract":578,"tags":579},[],[220],[193],{"code":581,"score":61,"supported":45,"title":582,"abstract":583,"tags":585},"G2",[],[584],"小农户",[],{"code":587,"score":21,"supported":14,"title":588,"abstract":589,"tags":590},"E4",[],[],[561],{"code":67,"score":21,"supported":14,"title":592,"abstract":593,"tags":594},[],[],[26],[596,597],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":599,"openalex_id":601,"authors":602,"venue":548,"cited_by_count":85,"oa_url":545,"card":611,"direction":93,"ingested_from":94},"W7223034914",[603,606,608],{"name":604,"orcid":605},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":607,"orcid":9},"Dr.T.Baskar",{"name":609,"orcid":610},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":612,"method":613,"finding":614,"direction":615,"opportunity":616},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z"]