[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2872":3,"related-2872":85},{"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":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":84},2872,"Simulation of Groundwater Recharge and Surface Runoff in Ungauged basin of Northern India Using SWAT Model","https:\u002F\u002Fdoi.org\u002F10.1139\u002Fcjce-2026-0128","Rapid agricultural expansion, urbanization, and increasing anthropogenic water demands are exerting significant pressure on surface and groundwater resources, necessitating robust hydrological assessments, particularly in ungauged basins. This study quantifies groundwater recharge and surface runoff in the Yamuna–Krishni interfluve of Northern India using the SWAT model. Due to unavailability of observed streamflow data, MODIS-based evapotranspiration data was employed for model calibration and validation. During the calibration period, the model achieved an NSE of 0.73 and an R² of 0.77, while the validation results demonstrated consistent predictive capability, with corresponding NSE and R² values of 0.71 and 0.72, respectively confirming a good statistical agreement. Simulated groundwater recharge ranged from 104.71 to 201.25 mm (14.67–23.88% of rainfall), while surface runoff varied between 8.15 and 14.86% supported by Groundwater Estimation Committee norms as well as hydrological model outcomes from adjacent basins. The results highlight the effectiveness of remote sensing–driven SWAT modeling for hydrological assessment in ungauged basins, providing a scientific basis for sustainable water resource management and infrastructure planning.","快速的农业扩张、城市化以及不断增加的人为用水需求，正对地表水和地下水资源造成巨大压力，因此亟需开展可靠的水文评估，尤其是在无测站流域。本研究利用SWAT模型，对印度北部亚穆纳河—克里希尼河河间地带的地下水补给与地表径流进行了定量评估。由于缺乏实测径流数据，研究采用基于MODIS的蒸散发数据对模型进行率定与验证。在率定期，模型取得了NSE为0.73、R²为0.77的结果；验证结果则显示出稳定的预测能力，相应的NSE和R²分别为0.71和0.72，证实了良好的统计一致性。模拟的地下水补给量为104.71至201.25 mm（占降雨量的14.67%–23.88%），而地表径流占比则在8.15%至14.86%之间，这一结果得到了地下水估算委员会规范以及邻近流域水文模型成果的支持。研究结果凸显了遥感驱动的SWAT模型在无测站流域水文评估中的有效性，为可持续水资源管理和基础设施规划提供了科学依据。",null,"Canadian Journal of Civil Engineering","2026-09-17T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"以MODIS蒸散发驱动SWAT在无资料流域完成率定验证，方法可迁移，对农业水资源管理有参考价值，但属区域案例研究，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","水文模型","地下水补给","地表径流","无资料流域",{"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":46},[45,54],{"code":33,"score":13,"supported":46,"title":47,"abstract":48,"tags":53},true,[],[49,50,51,52],"水资源","蒸散发","water resource","evapotranspiration",[28],{"code":55,"score":56,"supported":46,"title":57,"abstract":58,"tags":60},"I3",7,[],[27,59],"remote sensing",[27],[62,63],"SWAT模型 无资料流域 地下水补给","Yamuna-Krishni 地表径流 模拟","SWAT模型无资料流域地下水补给-2872",2,"10.1139\u002Fcjce-2026-0128",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":76,"oa_url":9,"card":77,"direction":81,"ingested_from":83},"W7213515648",[70,73],{"name":71,"orcid":72},"Praveen Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-8311-1800",{"name":74,"orcid":75},"Raj Mohan Singh","https:\u002F\u002Forcid.org\u002F0000-0002-8029-4584",0,{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"用SWAT模型结合MODIS蒸散发数据，模拟印度北部无观测流域的地下水补给与地表径流。","SWAT水文模型，以MODIS蒸散发数据替代实测径流进行率定与验证。","率定期NSE=0.73、R²=0.77，验证期NSE=0.71、R²=0.72；地下水补给占降雨14","农业遥感与作物表型","无观测流域可用遥感蒸散发替代径流率定水文模型，可延伸至多源遥感融合与灌溉用水估算。","openalex","2026-09-18T23:30:29.296252Z",{"total":86,"page":22,"page_size":86,"items":87},6,[88,213,281,359,432,519],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":9,"source_name":94,"source_url":91,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":101,"tags":103,"primary_topic":108,"secondary_topics":109,"classification":121,"search_phrases":183,"slug":186,"view_count":76,"doi":187,"paper":188,"created_at":212},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",79,{"impact":98,"substance":99,"depth":98,"authority":17,"freshness":21,"relevant":22,"comment":100},18,22,"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。",[102],{"name":94,"url":91},[104,105,27,106,107],"智慧农业","农业人工智能","作物水分胁迫","精准灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[110,116],{"code":111,"slug":112,"name":113,"path":114,"parent_code":115},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":117,"slug":118,"name":119,"path":120,"parent_code":115},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":40,"locked":14,"version":41,"method":42,"evidence":122},{"candidates":123,"agricultural_evidence":46},[124,130,137,144,151,155,162,169,176],{"code":33,"score":125,"supported":46,"title":126,"abstract":127,"tags":129},15,[34],[128,34],"灌溉",[107],{"code":111,"score":13,"supported":46,"title":131,"abstract":132,"tags":136},[],[133,134,135],"机器学习","machine learning","artificial intelligence",[105],{"code":117,"score":21,"supported":46,"title":138,"abstract":139,"tags":143},[],[140,141,142],"物联网","internet of things","IoT",[],{"code":145,"score":56,"supported":46,"title":146,"abstract":147,"tags":150},"I1",[],[148,149],"数字孪生","digital twin",[104],{"code":55,"score":56,"supported":46,"title":152,"abstract":153,"tags":154},[],[27,59],[27],{"code":156,"score":86,"supported":46,"title":157,"abstract":158,"tags":161},"A2",[],[159,160],"小麦","wheat",[],{"code":163,"score":86,"supported":46,"title":164,"abstract":165,"tags":168},"A3",[],[166,167],"玉米","maize",[],{"code":170,"score":86,"supported":46,"title":171,"abstract":172,"tags":175},"A5",[],[173,174],"马铃薯","potato",[],{"code":177,"score":178,"supported":46,"title":179,"abstract":180,"tags":182},"B2",3,[],[181],"葡萄",[],[184,185],"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":187,"openalex_id":189,"authors":190,"venue":94,"cited_by_count":76,"oa_url":91,"card":206,"direction":211,"ingested_from":83},"W7223088921",[191,194,197,200,203],{"name":192,"orcid":193},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":195,"orcid":196},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":198,"orcid":199},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":201,"orcid":202},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":204,"orcid":205},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":207,"method":208,"finding":209,"direction":81,"opportunity":210},"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。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":214,"title":215,"url":216,"summary":217,"summary_zh":218,"content":9,"source_name":219,"source_url":216,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":220,"score_detail":221,"sources":225,"tags":227,"primary_topic":231,"secondary_topics":232,"classification":238,"search_phrases":265,"slug":268,"view_count":76,"doi":269,"paper":270,"created_at":280},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)",75,{"impact":98,"substance":222,"depth":223,"authority":17,"freshness":21,"relevant":22,"comment":224},20,16,"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。",[226],{"name":219,"url":216},[105,228,27,229,230],"农业物联网","智慧灌溉","节水农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[233,234],{"code":117,"slug":118,"name":119,"path":120,"parent_code":115},{"code":55,"slug":235,"name":236,"path":237,"parent_code":115},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":239},{"candidates":240,"agricultural_evidence":46},[241,246,250,254,258],{"code":33,"score":242,"supported":46,"title":243,"abstract":244,"tags":245},19,[34],[128,49,34],[229,230],{"code":117,"score":98,"supported":46,"title":247,"abstract":248,"tags":249},[142],[140,141,142],[228],{"code":55,"score":125,"supported":46,"title":251,"abstract":252,"tags":253},[59],[27,59],[27],{"code":111,"score":17,"supported":46,"title":255,"abstract":256,"tags":257},[135],[135],[105],{"code":259,"score":86,"supported":46,"title":260,"abstract":261,"tags":264},"H8",[],[262,263],"可持续农业","sustainable agriculture",[],[266,267],"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":269,"openalex_id":271,"authors":272,"venue":219,"cited_by_count":76,"oa_url":216,"card":275,"direction":211,"ingested_from":83},"W7223090389",[273],{"name":274,"orcid":9},"Patil",{"tldr":276,"method":277,"finding":278,"direction":211,"opportunity":279},"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。","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":282,"title":283,"url":284,"summary":285,"summary_zh":286,"content":9,"source_name":287,"source_url":284,"published_at":288,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":289,"score_detail":290,"sources":293,"tags":295,"primary_topic":300,"secondary_topics":305,"classification":308,"search_phrases":338,"slug":341,"view_count":76,"doi":342,"paper":343,"created_at":358},4753,"Mapping groundwater potential zones using GIS, remote sensing and analytical hierarchy process (AHP) in Pader and Kitgum Districts, Uganda","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04943-7","Groundwater is a crucial source of freshwater in semi-arid regions, particularly in water-stressed areas, such as northern Uganda, where the surface water is scarce and unreliable precipitation patterns changes. This province lacks sufficient clean water for human consumption and only 20% of the water for agriculture extracted from the ground due to inaccessibility and insufficient information regarding the groundwater. With regard to these difficulties in water that are faced by the local communities, it has not been yet studied the distribution of groundwater in Kitgum and Pader districts. Therefore, the result of this research contributes preliminary groundwater investigation for contacting this indispensable source of water. The study employed a geospatial approach integrated with multi-criteria analysis to identify groundwater potential zones in Kitgum and Pader districts of Acholi subregion. The Utilization of Geographic Information Systems (GIS), remote sensing, and the Analytic Hierarchy Process (AHP) and six thematic layers; rainfall, drainage density, slope, soil type, geology, and land use\u002Fland cover were selected based on their hydrogeological influence and the accessibility of the data. After standardizing, and assigning normalized weights,a weighted overlay analysis in ArcGIS Pro version 3.1.0 was used to synthesize these layers into a comprehensive groundwater potential zonation map. The result categorized the study area into five zones; very low, low, medium, high, and very high potential. The analysis revealed that Kitgum District is predominantly characterized by very low to low groundwater potential zones of 67.24% when it is combined both, constrained by factors such as higher drainage density, and steeper slopes. Conversely, Pader District showed favorable conditions, with 85.5% of its area classified as high to very high groundwater potential, attributed to gentle slopes, and higher rainfall, that enhance infiltration capacity. Validation against yield data from 18 existing boreholes demonstrated a 75.0% alignment between the model and actual well performance, confirming the model’s reliability at acceptable result. This study demonstrated the effectiveness of GIS-based and AHP as a cost-effective tool for preliminary groundwater exploration in data-scarce environments. The resulting groundwater potential map provides a vital scientific foundation for sustainable water resource management, enabling targeted borehole siting, informed aquifer development, and resilience-building strategies to enhance water security for communities and livelihoods in the Acholi sub-region.","地下水是半干旱地区重要的淡水来源，尤其是在乌干达北部等水资源紧张地区，那里地表水稀缺且降水模式变化不可靠。该地区缺乏足够的清洁饮用水供人类消费，由于地下水难以获取且信息不足，仅有20%的农业用水来自地下开采。鉴于当地社区面临这些水资源困难，基特古姆和帕德尔地区的地下水分布尚未得到研究。因此，本研究结果为接触这一不可或缺的水源提供了初步的地下水调查。本研究采用地理空间方法与多准则分析相结合，识别阿乔利次区域基特古姆和帕德尔地区的地下水潜力区。利用地理信息系统（GIS）、遥感技术和层次分析法（AHP），基于其水文地质影响和数据可获取性，选取了六个主题图层：降雨量、河网密度、坡度、土壤类型、地质和土地利用\u002F土地覆盖。经过标准化和归一化权重赋值后，使用ArcGIS Pro 3.1.0版本中的加权叠加分析将这些图层综合成一张全面的地下水潜力分区图。结果将研究区划分为五个区域：极低、低、中等、高和极高潜力。分析表明，基特古姆区主要以极低至低地下水潜力区为主，两者合计占67.24%，受较高河网密度和较陡坡度等因素制约。相反，帕德尔区表现出有利条件，其85.5%的面积被归类为高至极高地下水潜力，这归因于平缓的坡度和较高的降雨量，增强了入渗能力。利用18口现有钻孔的出水量数据进行验证，模型与实际井表现之间的一致率达到75.0%，证实了模型在可接受结果范围内的可靠性。本研究证明了基于GIS和AHP的方法作为数据稀缺环境中初步地下水勘探的成本效益工具的有效性。所生成的地下水潜力图为可持续水资源管理提供了重要的科学基础，有助于有针对性地选址钻孔，为知情决策提供依据。","Discover Sustainability","2026-10-10T00:00:00Z",67,{"impact":291,"substance":222,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":292},8,"基于GIS、遥感与AHP的乌干达北部地下水潜力制图研究，方法成熟、验证可靠，对缺水地区农业水资源规划有参考价值，但属区域性初步研究，公共影响有限。",[294],{"name":287,"url":284},[27,296,297,298,299],"水资源管理","GIS","地下水潜力","乌干达农业",{"code":301,"slug":302,"name":303,"path":304,"parent_code":115},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[306,307],{"code":55,"slug":235,"name":236,"path":237,"parent_code":115},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":309},{"candidates":310,"agricultural_evidence":46},[311,317,321,325,332],{"code":301,"score":98,"supported":46,"title":312,"abstract":313,"tags":316},[297],[314,297,315],"地理信息","geographic information",[297],{"code":55,"score":125,"supported":46,"title":318,"abstract":319,"tags":320},[59],[27,59],[27],{"code":33,"score":291,"supported":46,"title":322,"abstract":323,"tags":324},[],[49,51],[298,296],{"code":326,"score":86,"supported":46,"title":327,"abstract":328,"tags":331},"H2",[],[329,330],"土壤","soil",[],{"code":333,"score":178,"supported":46,"title":334,"abstract":335,"tags":337},"H7",[],[336],"干旱",[],[339,340],"乌干达 Kitgum Pader 地下水","GIS AHP 地下水潜力","乌干达KitgumPader地下水-4753","10.1007\u002Fs43621-026-04943-7",{"doi":342,"openalex_id":344,"authors":345,"venue":287,"cited_by_count":76,"oa_url":284,"card":353,"direction":81,"ingested_from":83},"W7221339030",[346,349,351],{"name":347,"orcid":348},"Ahmed Abdirizak Dirie","https:\u002F\u002Forcid.org\u002F0009-0002-8109-0724",{"name":350,"orcid":9},"Abdulkadir Ahmed Mohamed",{"name":352,"orcid":9},"Ambiga Kannapiran",{"tldr":354,"method":355,"finding":356,"direction":81,"opportunity":357},"用GIS、遥感和AHP制图评估乌干达两区地下水潜力并验证。","GIS与遥感结合AHP，加权叠加6个主题图层，用18口井数据验证。","Kitgum区67.24%为低潜力，Pader区85.5%为高潜力，模型与井数据吻合75%。","可将该地下水潜力制图方法迁移到其他数据稀缺区，并结合农业灌溉需求优化井位。","2026-10-12T23:30:24.761508Z",{"id":360,"title":361,"url":362,"summary":363,"summary_zh":364,"content":9,"source_name":365,"source_url":362,"published_at":366,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":367,"score_detail":368,"sources":370,"tags":372,"primary_topic":375,"secondary_topics":376,"classification":377,"search_phrases":411,"slug":414,"view_count":76,"doi":415,"paper":416,"created_at":431},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。","Irrigation and Drainage","2026-10-09T00:00:00Z",71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":291,"relevant":22,"comment":369},"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。",[371],{"name":365,"url":362},[373,27,50,229,374],"精准农业","作物系数",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":378},{"candidates":379,"agricultural_evidence":46},[380,384,388,392,399,403,407],{"code":33,"score":242,"supported":46,"title":381,"abstract":382,"tags":383},[52],[49,50,52],[229,50],{"code":163,"score":86,"supported":46,"title":385,"abstract":386,"tags":387},[],[166,167],[],{"code":170,"score":86,"supported":46,"title":389,"abstract":390,"tags":391},[],[173,174],[],{"code":393,"score":86,"supported":46,"title":394,"abstract":395,"tags":398},"A6",[],[396,397],"烟草","tobacco",[],{"code":333,"score":178,"supported":46,"title":400,"abstract":401,"tags":402},[],[336],[],{"code":145,"score":22,"supported":14,"title":404,"abstract":405,"tags":406},[],[],[373],{"code":55,"score":22,"supported":14,"title":408,"abstract":409,"tags":410},[],[],[27],[412,413],"METRIC 模型 NDVI 蒸散发","Ergani Diyarbakır 作物系数","METRIC模型NDVI蒸散发-4683","10.1002\u002Fird.70246",{"doi":415,"openalex_id":417,"authors":418,"venue":365,"cited_by_count":76,"oa_url":425,"card":426,"direction":81,"ingested_from":83},"W7221125586",[419,422],{"name":420,"orcid":421},"Kıvanc Hayri Doganay","https:\u002F\u002Forcid.org\u002F0000-0002-6977-4982",{"name":423,"orcid":424},"Bekir S. KARATAŞ","https:\u002F\u002Forcid.org\u002F0000-0002-1424-8870","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fird.70246",{"tldr":427,"method":428,"finding":429,"direction":81,"opportunity":430},"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。","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。","2026-10-11T23:30:31.633015Z",{"id":433,"title":434,"url":435,"summary":436,"summary_zh":437,"content":9,"source_name":438,"source_url":435,"published_at":366,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":289,"score_detail":439,"sources":441,"tags":443,"primary_topic":447,"secondary_topics":448,"classification":449,"search_phrases":481,"slug":484,"view_count":65,"doi":485,"paper":486,"created_at":518},4602,"Phenology-driven multisensor unsupervised crop mapping in data-scarce irrigated agriculture: a case of Adea District in Ethiopia","https:\u002F\u002Fdoi.org\u002F10.2166\u002Fwpt.2026.492","ABSTRACT Accurate irrigation planning, water allocation, and agricultural productivity assessment are frequently constrained in data-scare regions where reliable ground-based crop information and spatial coverage data are limited. This study addresses these gaps using a phenology-driven, multisensor unsupervised crop-mapping framework implemented within Google Earth Engine (GEE). The approach integrates monthly normalized difference vegetation index (NDVI) profiles from Sentinel-2, Sentinel-1VV backscatter, and digital elevation model–derived terrain slope into unsupervised K-means clustering algorithm. Adea District, Ethiopia, served as a demonstration site to evaluate and interpret the phenological signatures of key irrigated crops. The identified crop types were mapped to estimate spatial coverage and compared with the ground truth observations of irrigated wheat and tomato fields. The approach successfully distinguished irrigated wheat and tomato fields with an overall classification accuracy of 91%, a Kappa coefficient of 0.82, and estimated cultivated areas of 666.93 and 812.35 ha, respectively. Combining phenological profiles with multisensor satellite observations significantly enhance unsupervised classification performance in heterogeneous, smallholder-dominated agricultural landscapes. The cloud-based framework offers a computationally scalable and cost-effective solution for crop mapping, and water productivity monitoring in data-scarce agricultural regions.","在数据稀缺地区，可靠的基于地面作物信息和空间覆盖数据往往有限，精准灌溉规划、水资源分配和农业生产力评估因此常受制约。本研究针对这些不足，采用了一种由物候驱动、多传感器协同的无监督作物制图框架，并在Google Earth Engine（GEE）平台上实现。该方法将Sentinel-2的月度归一化植被指数（NDVI）时序、Sentinel-1 VV后向散射以及数字高程模型衍生的地形坡度整合到无监督K均值聚类算法中。以埃塞俄比亚Adea区为示范区，评估和解读主要灌溉作物的物候特征。对识别出的作物类型进行制图以估算空间覆盖范围，并与灌溉小麦和番茄田的地面实测数据进行对比。该方法成功区分了灌溉小麦和番茄田，总体分类精度达91%，Kappa系数为0.82，估算种植面积分别为666.93公顷和812.35公顷。将物候特征与多传感器卫星观测相结合，可显著提升异质性小农主导农业景观中的无监督分类性能。该基于云的框架为数据稀缺农业地区的作物制图和水分生产力监测提供了一种计算可扩展且成本效益高的解决方案。","Water Practice & Technology",{"impact":291,"substance":222,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":440},"方法新颖、精度可靠，但属区域案例研究，产业影响有限，可作为技术参考入选。",[442],{"name":438,"url":435},[104,27,444,445,446],"作物识别","Google Earth Engine","灌溉农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":450},{"candidates":451,"agricultural_evidence":46},[452,456,460,467,473,477],{"code":33,"score":13,"supported":46,"title":453,"abstract":454,"tags":455},[],[128,49,34],[446],{"code":156,"score":86,"supported":46,"title":457,"abstract":458,"tags":459},[],[159,160],[],{"code":461,"score":86,"supported":46,"title":462,"abstract":463,"tags":466},"B1",[],[464,465],"番茄","tomato",[],{"code":468,"score":178,"supported":46,"title":469,"abstract":470,"tags":472},"G2",[],[471],"smallholder",[],{"code":55,"score":65,"supported":14,"title":474,"abstract":475,"tags":476},[],[],[445,27],{"code":145,"score":22,"supported":14,"title":478,"abstract":479,"tags":480},[],[],[104],[482,483],"埃塞俄比亚 Adea 灌溉作物 遥感","Sentinel-2 NDVI 作物分类","埃塞俄比亚Adea灌溉作物遥感-4602","10.2166\u002Fwpt.2026.492",{"doi":485,"openalex_id":487,"authors":488,"venue":438,"cited_by_count":76,"oa_url":435,"card":512,"direction":517,"ingested_from":83},"W7221136167",[489,492,495,498,501,504,507,509],{"name":490,"orcid":491},"Belete Berhanu Kidanewold","https:\u002F\u002Forcid.org\u002F0000-0002-9876-8549",{"name":493,"orcid":494},"Jemal Mohammed Hassen","https:\u002F\u002Forcid.org\u002F0000-0002-7417-173X",{"name":496,"orcid":497},"Rahel Sintayehu Tessema","https:\u002F\u002Forcid.org\u002F0009-0006-2798-1752",{"name":499,"orcid":500},"Saba Kidane","https:\u002F\u002Forcid.org\u002F0009-0002-7403-9939",{"name":502,"orcid":503},"Mulugeta Melese","https:\u002F\u002Forcid.org\u002F0009-0008-3539-910X",{"name":505,"orcid":506},"Tadesse Shimels","https:\u002F\u002Forcid.org\u002F0009-0009-3351-5952",{"name":508,"orcid":9},"Mussie Alemayehu",{"name":510,"orcid":511},"Daneal Fekersillassie","https:\u002F\u002Forcid.org\u002F0009-0006-6194-0903",{"tldr":513,"method":514,"finding":515,"direction":81,"opportunity":516},"基于物候与多传感器数据，在埃塞俄比亚缺数据灌区实现无监督作物制图。","GEE平台融合Sentinel-2 NDVI、Sentinel-1 VV及DEM","成功区分灌溉小麦和番茄，总体精度91%，Kappa 0.82，面积分别为666.93和812.35公","可探索物候特征与多源遥感在缺数据小农灌区的迁移学习及作物水生产力监测。","数字乡村与农业信息化","2026-10-10T23:30:36.165502Z",{"id":520,"title":521,"url":522,"summary":523,"summary_zh":524,"content":9,"source_name":525,"source_url":522,"published_at":526,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":527,"score_detail":528,"sources":531,"tags":533,"primary_topic":536,"secondary_topics":537,"classification":538,"search_phrases":559,"slug":562,"view_count":76,"doi":563,"paper":564,"created_at":610},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":98,"substance":99,"depth":98,"authority":529,"freshness":291,"relevant":22,"comment":530},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。",[532],{"name":525,"url":522},[534,27,535,50,229],"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":539},{"candidates":540,"agricultural_evidence":46},[541,545,550,554],{"code":33,"score":222,"supported":46,"title":542,"abstract":543,"tags":544},[52],[128,49,50,34,52],[534,229,50],{"code":177,"score":56,"supported":46,"title":546,"abstract":547,"tags":549},[],[181,548],"grape",[535],{"code":55,"score":56,"supported":46,"title":551,"abstract":552,"tags":553},[],[27,59],[27],{"code":333,"score":86,"supported":46,"title":555,"abstract":556,"tags":558},[],[336,557],"drought",[],[560,561],"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":563,"openalex_id":565,"authors":566,"venue":525,"cited_by_count":76,"oa_url":522,"card":605,"direction":81,"ingested_from":83},"W7221033125",[567,570,572,574,576,578,580,582,584,586,588,591,593,595,597,599,601,603],{"name":568,"orcid":569},"Kyle  R. Knipper","https:\u002F\u002Forcid.org\u002F0000-0003-0889-8129",{"name":571,"orcid":9},"N. Bambach",{"name":573,"orcid":9},"M. Anderson",{"name":575,"orcid":9},"W. Kustas",{"name":577,"orcid":9},"A. J. McElrone",{"name":579,"orcid":9},"M. Swintek",{"name":581,"orcid":9},"F. Melton",{"name":583,"orcid":9},"D. Salazar-Martinez",{"name":585,"orcid":9},"L. Hipps",{"name":587,"orcid":9},"J. Alfieri",{"name":589,"orcid":590},"John H. Prueger","https:\u002F\u002Forcid.org\u002F0000-0002-7768-5760",{"name":592,"orcid":9},"L. McKee",{"name":594,"orcid":9},"S. Castro",{"name":596,"orcid":9},"P. Tolentino",{"name":598,"orcid":9},"I. Wright",{"name":600,"orcid":9},"A. Gal",{"name":602,"orcid":9},"N. Dokoozlian",{"name":604,"orcid":9},"M.M. Alsina",{"tldr":606,"method":607,"finding":608,"direction":81,"opportunity":609},"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"]