[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4683":3,"related-4683":116},{"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":39,"search_phrases":92,"slug":95,"view_count":96,"doi":97,"paper":98,"created_at":115},4683,"Linking NDVI to METRIC‐Based Stress‐Adjusted Crop Coefficients ( K c_adj ) for Operational Evapotranspiration Estimation Under Semiarid Conditions","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fird.70246","ABSTRACT Accurate estimation of evapotranspiration (ET) is essential for sustainable water management, particularly in arid and semiarid regions. This study aimed to establish relationships between the normalised difference vegetation index (NDVI) derived from satellite imagery and stress‐adjusted crop coefficients ( K c_adj ) obtained using the METRIC (mapping ET at high resolution with internalised calibration) model and to develop efficient models for rapid ET estimation. The study was conducted during the 2023 and 2024 growing seasons in Ergani (Diyarbakır, Türkiye). The water stress coefficient ( K s ) was derived from METRIC‐based ET and used to obtain K c_adj values. Regression analysis was performed to quantify the relationships between NDVI and K c_adj , and NDVI‐based K c ( K c_NDVI ) values were developed. The results indicated strong relationships between NDVI and K c_adj for maize and tobacco ( R 2 = 0.90–0.93 and 0.89–0.95, respectively), whereas the resulting NDVI‐based ET estimates showed strong internal consistency with the METRIC‐informed stress‐adjusted estimates. Conversely, weaker relationships were observed for potato and onion ( R 2 = 0.52–0.58 and 0.0003–0.56, respectively), likely driven by planting–harvest timing heterogeneity, incomplete satellite coverage and the influence of stress factors. Overall, the approach is most suitable for crops showing strong and stable NDVI– K c_adj relationships under adequate temporal data coverage conditions.","精确估算蒸散发（ET）对于可持续水资源管理至关重要，尤其是在干旱和半干旱地区。本研究旨在建立由卫星影像获取的归一化差异植被指数（NDVI）与利用METRIC（高分辨率内部校准蒸散发制图）模型得到的胁迫调整作物系数（K_c_adj）之间的关系，并开发用于快速估算蒸散发的有效模型。研究于2023年和2024年生长季在埃尔加尼（土耳其迪亚巴克尔）开展。水分胁迫系数（K_s）由基于METRIC的蒸散发推导得出，并用于获取K_c_adj值。通过回归分析量化NDVI与K_c_adj之间的关系，并建立了基于NDVI的作物系数（K_c_NDVI）值。结果表明，玉米和烟草的NDVI与K_c_adj之间存在强相关关系（R²分别为0.90–0.93和0.89–0.95），而由此得到的基于NDVI的蒸散发估算值与METRIC-informed胁迫调整估算值之间表现出较强的内部一致性。相反，马铃薯和洋葱的关系较弱（R²分别为0.52–0.58和0.0003–0.56），这可能由种植—收获时间异质性、卫星覆盖不完整以及胁迫因素的影响所致。总体而言，该方法最适用于在充足时间数据覆盖条件下表现出强且稳定NDVI–K_c_adj关系的作物。",null,"Irrigation and Drainage","2026-10-09T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,8,1,"基于METRIC模型与NDVI建立胁迫调整作物系数关系，为半干旱区精准灌溉提供新方法，但属细分领域研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"精准农业","遥感","蒸散发","智慧灌溉","作物系数",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,61,68,75,82,87],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":52},19,true,[49],"evapotranspiration",[51,29,49],"水资源",[30,29],{"code":54,"score":55,"supported":47,"title":56,"abstract":57,"tags":60},"A3",6,[],[58,59],"玉米","maize",[],{"code":62,"score":55,"supported":47,"title":63,"abstract":64,"tags":67},"A5",[],[65,66],"马铃薯","potato",[],{"code":69,"score":55,"supported":47,"title":70,"abstract":71,"tags":74},"A6",[],[72,73],"烟草","tobacco",[],{"code":76,"score":77,"supported":47,"title":78,"abstract":79,"tags":81},"H7",3,[],[80],"干旱",[],{"code":83,"score":22,"supported":14,"title":84,"abstract":85,"tags":86},"I1",[],[],[27],{"code":88,"score":22,"supported":14,"title":89,"abstract":90,"tags":91},"I3",[],[],[28],[93,94],"METRIC 模型 NDVI 蒸散发","Ergani Diyarbakır 作物系数","METRIC模型NDVI蒸散发-4683",0,"10.1002\u002Fird.70246",{"doi":97,"openalex_id":99,"authors":100,"venue":10,"cited_by_count":96,"oa_url":107,"card":108,"direction":112,"ingested_from":114},"W7221125586",[101,104],{"name":102,"orcid":103},"Kıvanc Hayri Doganay","https:\u002F\u002Forcid.org\u002F0000-0002-6977-4982",{"name":105,"orcid":106},"Bekir S. KARATAŞ","https:\u002F\u002Forcid.org\u002F0000-0002-1424-8870","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fird.70246",{"tldr":109,"method":110,"finding":111,"direction":112,"opportunity":113},"建立NDVI与METRIC模型应力调整作物系数Kc_adj的关系，实现半干旱区蒸散发快速估算。","2023-2024年土耳其半干旱区玉米、烟草等作物，用METRIC模型和NDVI","玉米和烟草的NDVI-Kc_adj关系强（R²达0.90-0.95），马铃薯和洋葱较弱。","农业遥感与作物表型","可探索多源遥感与时间序列数据融合，提升异质性作物NDVI-Kc_adj关系的稳健性。","openalex","2026-10-11T23:30:31.633015Z",{"total":55,"page":22,"page_size":55,"items":117},[118,218,298,390,446,534],{"id":119,"title":120,"url":121,"summary":122,"summary_zh":123,"content":9,"source_name":124,"source_url":121,"published_at":125,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":126,"score_detail":127,"sources":132,"tags":134,"primary_topic":137,"secondary_topics":138,"classification":139,"search_phrases":166,"slug":169,"view_count":96,"doi":170,"paper":171,"created_at":217},4597,"Evaluating OpenET evapotranspiration estimates in California vineyards: Insights from the GRAPEX experiment","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agrformet.2026.111505","California agriculture faces escalating water-management challenges from drought and heat while adapting to groundwater-sustainability policies. Accurate field-scale evapotranspiration (ET) values are essential for irrigation decisions and water conservation. OpenET integrates six satellite-based ET models in Google Earth Engine to provide near–real-time actual ET (ET a ) at 30 m and daily resolution. We evaluated OpenET against eddy-covariance ET a from 11 wine-grape vineyards monitored by the Grape Remote sensing Atmospheric Profile & Evapotranspiration eXperiment (GRAPEX). We compared individual-model and ensemble ET a to tower ET a and conducted site-focused analyses to diagnose performance under key microclimate and management conditions. The OpenET ensemble ET a value closely matched tower ET a (daily R 2 = 0.82; MAE = 0.63 mm d -1 ; monthly R 2 = 0.89; MAE = 14.8 mm month -1 ) with negligible bias and generally outperformed individual models. Performance improved with temporal aggregation, consistent with reduced sensitivity to Landsat overpass timing and reference ET-based interpolation at the daily timestep. Skill was highest at monthly and seasonal scales, while daily errors increased during advective conditions and short-lived irrigation deficits, and tower energy-balance closure influenced inferred bias at advection-prone sites. Controlled experiments showed that thermal-based models detected imposed vine water-stress responses directionally (but were dampened in the ensemble) and that winter cover crops, and vine-row orientation produced consistent ET a shifts that propagated into thermal-based OpenET values. These results quantify when OpenET is most reliable for precision viticulture and provide guidance for using satellite ET a to support irrigation scheduling and water accounting in practice.","加利福尼亚农业在适应地下水可持续性政策的同时，面临着干旱和高温带来的日益加剧的水资源管理挑战。准确的田块尺度蒸散发（ET）值对于灌溉决策和节水至关重要。OpenET在Google Earth Engine中集成了六个基于卫星的ET模型，以30米空间分辨率和日时间分辨率提供近实时的实际蒸散发（ETa）。我们利用葡萄遥感大气廓线与蒸散发实验（GRAPEX）监测的11个酿酒葡萄园的通量观测ETa对OpenET进行了评估。我们将单个模型和集合ETa与塔站ETa进行了比较，并开展了站点聚焦分析，以诊断其在关键微气候和管理条件下的表现。OpenET集合ETa值与塔站ETa高度吻合（日尺度R²=0.82；MAE=0.63 mm d⁻¹；月尺度R²=0.89；MAE=14.8 mm month⁻¹），偏差可忽略不计，且总体优于单个模型。性能随时间聚合而提升，这与日时间步长下对Landsat过境时间和基于参考ET插值的敏感性降低相一致。在月和季节尺度上表现最佳，而日尺度误差在平流条件和短期灌溉亏缺期间增大，塔站能量平衡闭合影响了易发生平流站点推断偏差。控制实验表明，基于热红外的模型能够方向性地检测到施加的葡萄藤水分胁迫响应（但在集合中被削弱），且冬季覆盖作物和葡萄行向产生了持续的ETa变化，并传递至基于热红外的OpenET值中。这些结果量化了OpenET在精准葡萄栽培中最可靠的时机，并为在实践中利用卫星ETa支持灌溉调度和水核算提供了指导。","Agricultural and Forest Meteorology","2026-10-08T00:00:00Z",80,{"impact":128,"substance":129,"depth":128,"authority":130,"freshness":21,"relevant":22,"comment":131},18,22,14,"基于GRAPEX试验11个葡萄园涡度相关数据系统评估OpenET蒸散发产品精度，为卫星遥感支撑精准灌溉与农业水核算提供实证依据，方法扎实、结论可靠，对智慧农业水资源管理有参考价值。",[133],{"name":124,"url":121},[135,28,136,29,30],"农业水资源","葡萄园",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":140},{"candidates":141,"agricultural_evidence":47},[142,148,156,161],{"code":33,"score":143,"supported":47,"title":144,"abstract":145,"tags":147},20,[49],[146,51,29,34,49],"灌溉",[135,30,29],{"code":149,"score":150,"supported":47,"title":151,"abstract":152,"tags":155},"B2",7,[],[153,154],"葡萄","grape",[136],{"code":88,"score":150,"supported":47,"title":157,"abstract":158,"tags":160},[],[28,159],"remote sensing",[28],{"code":76,"score":55,"supported":47,"title":162,"abstract":163,"tags":165},[],[80,164],"drought",[],[167,168],"OpenET 蒸散发 葡萄园","GRAPEX 加州 葡萄园","OpenET蒸散发葡萄园-4597","10.1016\u002Fj.agrformet.2026.111505",{"doi":170,"openalex_id":172,"authors":173,"venue":124,"cited_by_count":96,"oa_url":121,"card":212,"direction":112,"ingested_from":114},"W7221033125",[174,177,179,181,183,185,187,189,191,193,195,198,200,202,204,206,208,210],{"name":175,"orcid":176},"Kyle  R. Knipper","https:\u002F\u002Forcid.org\u002F0000-0003-0889-8129",{"name":178,"orcid":9},"N. Bambach",{"name":180,"orcid":9},"M. Anderson",{"name":182,"orcid":9},"W. Kustas",{"name":184,"orcid":9},"A. J. McElrone",{"name":186,"orcid":9},"M. Swintek",{"name":188,"orcid":9},"F. Melton",{"name":190,"orcid":9},"D. Salazar-Martinez",{"name":192,"orcid":9},"L. Hipps",{"name":194,"orcid":9},"J. Alfieri",{"name":196,"orcid":197},"John H. Prueger","https:\u002F\u002Forcid.org\u002F0000-0002-7768-5760",{"name":199,"orcid":9},"L. McKee",{"name":201,"orcid":9},"S. Castro",{"name":203,"orcid":9},"P. Tolentino",{"name":205,"orcid":9},"I. Wright",{"name":207,"orcid":9},"A. Gal",{"name":209,"orcid":9},"N. Dokoozlian",{"name":211,"orcid":9},"M.M. Alsina",{"tldr":213,"method":214,"finding":215,"direction":112,"opportunity":216},"评估OpenET在加州葡萄园的蒸散发估算精度，并与涡度协方差观测对比。","利用GRAPEX实验11个葡萄园的涡度协方差数据，对比六种卫星模型及集合的ET。","OpenET集合在月\u002F季尺度精度高，日尺度在平流和灌溉亏缺时误差增大。","可研究平流条件下卫星ET校正方法，或融合地面微气象数据提升日尺度精度。","2026-10-10T23:30:31.071040Z",{"id":219,"title":220,"url":221,"summary":222,"summary_zh":223,"content":9,"source_name":224,"source_url":221,"published_at":225,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":226,"score_detail":227,"sources":231,"tags":233,"primary_topic":237,"secondary_topics":238,"classification":249,"search_phrases":281,"slug":284,"view_count":96,"doi":285,"paper":286,"created_at":297},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",75,{"impact":128,"substance":143,"depth":228,"authority":17,"freshness":229,"relevant":22,"comment":230},16,9,"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[232],{"name":224,"url":221},[234,235,28,30,236],"农业人工智能","农业物联网","节水农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[239,245],{"code":240,"slug":241,"name":242,"path":243,"parent_code":244},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":88,"slug":246,"name":247,"path":248,"parent_code":244},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":250},{"candidates":251,"agricultural_evidence":47},[252,256,263,268,274],{"code":33,"score":46,"supported":47,"title":253,"abstract":254,"tags":255},[34],[146,51,34],[30,236],{"code":240,"score":128,"supported":47,"title":257,"abstract":259,"tags":262},[258],"IoT",[260,261,258],"物联网","internet of things",[235],{"code":88,"score":264,"supported":47,"title":265,"abstract":266,"tags":267},15,[159],[28,159],[28],{"code":269,"score":17,"supported":47,"title":270,"abstract":272,"tags":273},"I2",[271],"artificial intelligence",[271],[234],{"code":275,"score":55,"supported":47,"title":276,"abstract":277,"tags":280},"H8",[],[278,279],"可持续农业","sustainable agriculture",[],[282,283],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":285,"openalex_id":287,"authors":288,"venue":224,"cited_by_count":96,"oa_url":221,"card":291,"direction":295,"ingested_from":114},"W7223090389",[289],{"name":290,"orcid":9},"Patil",{"tldr":292,"method":293,"finding":294,"direction":295,"opportunity":296},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","智慧农业 \u002F 农业物联网","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":299,"title":300,"url":301,"summary":302,"summary_zh":303,"content":9,"source_name":304,"source_url":301,"published_at":305,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":126,"score_detail":306,"sources":308,"tags":310,"primary_topic":314,"secondary_topics":319,"classification":321,"search_phrases":352,"slug":355,"view_count":348,"doi":356,"paper":357,"created_at":389},3370,"A National Depth-Resolved Soil-Moisture-to-Electromagnetic Proxy Database for Hydrogeophysical Monitoring","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-2026-563","Abstract. Soil moisture is a key control on drought, recharge, crop water availability, agricultural water management, and land-atmosphere exchange, but its depth-resolved spatial variability remains difficult to monitor over large areas. Non-invasive methods such as ground-penetrating radar (GPR), electrical resistivity, and electromagnetic surveys can support soil-moisture estimation, yet their interpretation depends strongly on soil texture and hydraulic-retention properties. This study develops a national six-depth soil-moisture-to-electromagnetic proxy database to support future GPR- and resistivity-based root-zone monitoring and decision support. We used multi-year soil-moisture, porosity, saturation, texture, and hydraulic-retention data from 117 monitoring stations across Hungary at 10, 20, 30, 45, 60, and 75 cm depth. Dielectric permittivity, EM-wave velocity, electrical conductivity, attenuation, and apparent resistivity were derived as proxy variables from observed soil moisture using established petrophysical relationships, including the Topp equation and an Archie-type formulation. The proxy database identified a consistent 30-45 cm buffering zone, where soil moisture increased to 22.25%, dielectric permittivity peaked at 12.29, EM-wave velocity reached a minimum of 0.0935 m ns⁻¹, and apparent resistivity decreased to 545 Ω m. Texture strongly shaped the translated EM response: sandy soils were drier and more resistive, whereas clayey soils retained more water and showed higher dielectric response. Hydraulic-retention variables provided calibration-relevant prior information for explaining why similar EM or resistivity signals may represent different true soil-moisture values across soil types. The framework provides a transferable national calibration layer for drought-monitoring agencies, irrigation planners, precision-agriculture users, and future drone-based geophysical surveys in regions with comparable monitoring and soil databases.","摘要：土壤水分是干旱、补给、作物可用水量、农业水资源管理和陆气交换的关键控制因素，但其深度分辨的空间变异性在大范围区域仍难以监测。探地雷达（GPR）、电阻率和电磁勘探等非侵入式方法可支持土壤水分估算，但其解译在很大程度上取决于土壤质地和水力保持特性。本研究构建了一个全国尺度的六深度土壤水分—电磁代用指标数据库，以支持未来基于GPR和电阻率的根区监测与决策支持。我们使用了匈牙利全国117个监测站多年在10、20、30、45、60和75 cm深度的土壤水分、孔隙度、饱和度、质地和水力保持数据。基于观测土壤水分，利用成熟的地球物理关系（包括Topp方程和Archie型公式），推导出介电常数、电磁波速度、电导率、衰减和视电阻率作为代用变量。该代用指标数据库识别出一致的30–45 cm缓冲带，其中土壤水分增至22.25%，介电常数峰值达12.29，电磁波速度最低为0.0935 m ns⁻¹，视电阻率降至545 Ω m。质地对转换后的电磁响应具有强烈影响：砂质土壤更干、电阻率更高，而黏质土壤持水更多并表现出更高的介电响应。水力保持变量为解释为何相似的电磁或电阻率信号在不同土壤类型中可能代表不同真实土壤水分值提供了与校准相关的先验信息。该框架为干旱监测机构、灌溉规划者、精准农业用户以及未来在具有可比监测和土壤数据库区域开展的无人机地球物理调查提供了一个可迁移的全国校准层。","Earth System Science Data Discussions","2026-09-23T00:00:00Z",{"impact":128,"substance":129,"depth":128,"authority":20,"freshness":229,"relevant":22,"comment":307},"基于匈牙利117个站点六层深度构建的土壤水分—电磁代理数据库，为根区墒情遥感与精准灌溉决策提供可迁移校准层，方法新颖、数据扎实，对农业信息化有参考价值。",[309],{"name":304,"url":301},[311,27,312,313,30],"农业遥感","遥感监测","土壤墒情",{"code":315,"slug":316,"name":317,"path":318,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[320],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":322},{"candidates":323,"agricultural_evidence":47},[324,329,333,337,342,347],{"code":315,"score":264,"supported":47,"title":325,"abstract":326,"tags":328},[316],[327,316],"土壤",[313],{"code":33,"score":13,"supported":47,"title":330,"abstract":331,"tags":332},[],[146,51,34],[30],{"code":76,"score":55,"supported":47,"title":334,"abstract":335,"tags":336},[],[80,164],[],{"code":83,"score":338,"supported":47,"title":339,"abstract":340,"tags":341},4,[],[27],[27],{"code":269,"score":77,"supported":47,"title":343,"abstract":344,"tags":346},[],[345],"decision support",[],{"code":88,"score":348,"supported":14,"title":349,"abstract":350,"tags":351},2,[],[],[311,312],[353,354],"匈牙利 土壤水分 电磁代理数据库","探地雷达 根区土壤水分 监测","匈牙利土壤水分电磁代理数据库-3370","10.5194\u002Fessd-2026-563",{"doi":356,"openalex_id":358,"authors":359,"venue":304,"cited_by_count":96,"oa_url":301,"card":383,"direction":388,"ingested_from":114},"W7214076150",[360,363,365,367,370,372,374,377,380],{"name":361,"orcid":362},"Diaa Sheishah","https:\u002F\u002Forcid.org\u002F0000-0003-2050-6474",{"name":364,"orcid":9},"Enas Abdelsamei",{"name":366,"orcid":9},"Viktoria Blanka-Vegi",{"name":368,"orcid":369},"Károly Barta","https:\u002F\u002Forcid.org\u002F0000-0001-9450-5277",{"name":371,"orcid":9},"Ahmed M. Ali",{"name":373,"orcid":9},"Khaldoun Abualhin",{"name":375,"orcid":376},"Djamil Al‐Halbouni","https:\u002F\u002Forcid.org\u002F0000-0003-2254-3914",{"name":378,"orcid":379},"Wouter Arnoud Dorigo","https:\u002F\u002Forcid.org\u002F0000-0001-8054-7572",{"name":381,"orcid":382},"György Sípos","https:\u002F\u002Forcid.org\u002F0000-0001-6224-2361",{"tldr":384,"method":385,"finding":386,"direction":112,"opportunity":387},"构建匈牙利全国六深度土壤水分-电磁代理数据库，支持根区水文地球物理监测。","利用117站多年土壤水分、质地与水力参数，经Topp和Archie模型推导电磁代","发现30-45 cm缓冲带水分与介电常数峰值，质地显著影响电磁响应解释。","可扩展至无人机地球物理调查与多区域迁移校准，解决土壤质地导致的电磁信号歧义。","农业人工智能与决策模型","2026-09-24T23:30:36.864649Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":9,"content":9,"source_name":395,"source_url":9,"published_at":396,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":397,"score_detail":398,"sources":400,"tags":402,"primary_topic":406,"secondary_topics":407,"classification":409,"search_phrases":433,"slug":436,"view_count":348,"doi":9,"paper":437,"created_at":445},3049,"土壤压实与灌溉管理：对精准农业中土壤水力变化的启示","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1853","意大利帕多瓦大学A.C.与L.B.评估土壤压实通过改变土壤水力特性对精准农业灌溉管理的综合影响。研究维护土壤结构作为维持土壤水力功能、提升灌溉效率与农业系统长期可持续性最有效途径，使用HYPROP水力特性分析仪测定田间持水量（FC）、永久萎蔫点（PWP）、饱和水力传导度（Ksat）等关键参数，结合无人机遥感（UAV）与决策支持系统（DSS）实现精准灌溉调度。研究获SOILWAT（BIRD 2026）项目资助，为精准农业管理决策提供可量化水力参数基础。","MDPI Agronomy 16(18):1853","2026-09-20T00:00:00Z",68,{"impact":17,"substance":128,"depth":228,"authority":20,"freshness":229,"relevant":22,"comment":399},"学术论文，方法结合HYPROP与无人机遥感，对精准灌溉有参考价值，但属细分领域研究，公共影响有限。",[401],{"name":395,"url":393},[403,27,404,30,405],"决策支持系统","无人机遥感","土壤压实",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[408],{"code":315,"slug":316,"name":317,"path":318,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":410},{"candidates":411,"agricultural_evidence":47},[412,416,420,425,429],{"code":33,"score":17,"supported":47,"title":413,"abstract":414,"tags":415},[146],[146],[30],{"code":83,"score":17,"supported":47,"title":417,"abstract":418,"tags":419},[27],[27],[27],{"code":315,"score":421,"supported":47,"title":422,"abstract":423,"tags":424},11,[327],[327],[],{"code":88,"score":338,"supported":47,"title":426,"abstract":427,"tags":428},[],[28],[404],{"code":269,"score":22,"supported":14,"title":430,"abstract":431,"tags":432},[],[],[403],[434,435],"帕多瓦大学 土壤压实 灌溉","HYPROP 水力特性 精准灌溉","帕多瓦大学土壤压实灌溉-3049",{"doi":9,"openalex_id":9,"authors":438,"venue":9,"cited_by_count":96,"oa_url":9,"card":439,"direction":295,"ingested_from":444},[],{"tldr":440,"method":441,"finding":442,"direction":295,"opportunity":443},"评估土壤压实改变水力特性对精准灌溉管理的影响，并提出维护土壤结构的对策。","用HYPROP测FC、PWP、Ksat，结合无人机遥感与决策支持系统调度灌溉。","维护土壤结构是保持水力功能、提升灌溉效率与长期可持续性的最有效途径。","可探索压实-水力参数-遥感反演耦合模型，实现压实风险与灌溉调度的实时协同优化。","agent","2026-09-21T00:04:39.395594Z",{"id":447,"title":448,"url":449,"summary":450,"summary_zh":451,"content":9,"source_name":452,"source_url":449,"published_at":453,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":454,"score_detail":455,"sources":457,"tags":459,"primary_topic":463,"secondary_topics":464,"classification":472,"search_phrases":502,"slug":505,"view_count":77,"doi":506,"paper":507,"created_at":533},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":128,"substance":129,"depth":128,"authority":130,"freshness":229,"relevant":22,"comment":456},"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。",[458],{"name":452,"url":449},[460,461,28,462,29],"智慧农业","无人机","精准灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[465,466],{"code":88,"slug":246,"name":247,"path":248,"parent_code":244},{"code":467,"slug":468,"name":469,"path":470,"parent_code":471},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",{"status":40,"locked":14,"version":41,"method":42,"evidence":473},{"candidates":474,"agricultural_evidence":47},[475,479,484,491,498],{"code":33,"score":46,"supported":47,"title":476,"abstract":477,"tags":478},[49],[146,51,29,34,49],[462,29],{"code":88,"score":264,"supported":47,"title":480,"abstract":482,"tags":483},[481],"hyperspectral",[28,159],[28],{"code":467,"score":229,"supported":47,"title":485,"abstract":486,"tags":490},[],[487,488,489],"大豆","油菜","soybean",[],{"code":492,"score":55,"supported":47,"title":493,"abstract":494,"tags":497},"A2",[],[495,496],"小麦","wheat",[],{"code":83,"score":22,"supported":14,"title":499,"abstract":500,"tags":501},[],[],[460],[503,504],"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":506,"openalex_id":508,"authors":509,"venue":452,"cited_by_count":96,"oa_url":449,"card":528,"direction":112,"ingested_from":114},"W7212242584",[510,512,514,517,520,522,525],{"name":511,"orcid":9},"Ruiqi Du",{"name":513,"orcid":9},"Yonghong Zhang",{"name":515,"orcid":516},"Youzhen Xiang","https:\u002F\u002Forcid.org\u002F0000-0002-8268-1609",{"name":518,"orcid":519},"Fucang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6659-3262",{"name":521,"orcid":9},"Jian Gao",{"name":523,"orcid":524},"Qiliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-3274-2119",{"name":526,"orcid":527},"Xianghui Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0638-3068",{"tldr":529,"method":530,"finding":531,"direction":112,"opportunity":532},"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":535,"title":536,"url":537,"summary":538,"summary_zh":539,"content":9,"source_name":540,"source_url":537,"published_at":225,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":541,"score_detail":542,"sources":544,"tags":546,"primary_topic":548,"secondary_topics":549,"classification":555,"search_phrases":598,"slug":601,"view_count":96,"doi":602,"paper":603,"created_at":626},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",79,{"impact":128,"substance":129,"depth":128,"authority":17,"freshness":229,"relevant":22,"comment":543},"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。",[545],{"name":540,"url":537},[460,234,28,547,462],"作物水分胁迫",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[550,554],{"code":269,"slug":551,"name":552,"path":553,"parent_code":244},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":240,"slug":241,"name":242,"path":243,"parent_code":244},{"status":40,"locked":14,"version":41,"method":42,"evidence":556},{"candidates":557,"agricultural_evidence":47},[558,562,568,572,578,582,586,590,594],{"code":33,"score":264,"supported":47,"title":559,"abstract":560,"tags":561},[34],[146,34],[462],{"code":269,"score":13,"supported":47,"title":563,"abstract":564,"tags":567},[],[565,566,271],"机器学习","machine learning",[234],{"code":240,"score":229,"supported":47,"title":569,"abstract":570,"tags":571},[],[260,261,258],[],{"code":83,"score":150,"supported":47,"title":573,"abstract":574,"tags":577},[],[575,576],"数字孪生","digital twin",[460],{"code":88,"score":150,"supported":47,"title":579,"abstract":580,"tags":581},[],[28,159],[28],{"code":492,"score":55,"supported":47,"title":583,"abstract":584,"tags":585},[],[495,496],[],{"code":54,"score":55,"supported":47,"title":587,"abstract":588,"tags":589},[],[58,59],[],{"code":62,"score":55,"supported":47,"title":591,"abstract":592,"tags":593},[],[65,66],[],{"code":149,"score":77,"supported":47,"title":595,"abstract":596,"tags":597},[],[153],[],[599,600],"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":602,"openalex_id":604,"authors":605,"venue":540,"cited_by_count":96,"oa_url":537,"card":621,"direction":295,"ingested_from":114},"W7223088921",[606,609,612,615,618],{"name":607,"orcid":608},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":610,"orcid":611},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":613,"orcid":614},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":616,"orcid":617},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":619,"orcid":620},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":622,"method":623,"finding":624,"direction":112,"opportunity":625},"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"]