[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4602":3,"related-4602":126},{"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":85,"slug":88,"view_count":76,"doi":89,"paper":90,"created_at":125},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。",null,"Water Practice & Technology","2026-10-09T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,17,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","遥感","作物识别","Google Earth Engine","灌溉农业",{"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},[45,52,60,67,74,80],{"code":33,"score":13,"supported":46,"title":47,"abstract":48,"tags":51},true,[],[49,50,34],"灌溉","水资源",[31],{"code":53,"score":54,"supported":46,"title":55,"abstract":56,"tags":59},"A2",6,[],[57,58],"小麦","wheat",[],{"code":61,"score":54,"supported":46,"title":62,"abstract":63,"tags":66},"B1",[],[64,65],"番茄","tomato",[],{"code":68,"score":69,"supported":46,"title":70,"abstract":71,"tags":73},"G2",3,[],[72],"smallholder",[],{"code":75,"score":76,"supported":14,"title":77,"abstract":78,"tags":79},"I3",2,[],[],[30,28],{"code":81,"score":22,"supported":14,"title":82,"abstract":83,"tags":84},"I1",[],[],[27],[86,87],"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":89,"openalex_id":91,"authors":92,"venue":10,"cited_by_count":116,"oa_url":6,"card":117,"direction":123,"ingested_from":124},"W7221136167",[93,96,99,102,105,108,111,113],{"name":94,"orcid":95},"Belete Berhanu Kidanewold","https:\u002F\u002Forcid.org\u002F0000-0002-9876-8549",{"name":97,"orcid":98},"Jemal Mohammed Hassen","https:\u002F\u002Forcid.org\u002F0000-0002-7417-173X",{"name":100,"orcid":101},"Rahel Sintayehu Tessema","https:\u002F\u002Forcid.org\u002F0009-0006-2798-1752",{"name":103,"orcid":104},"Saba Kidane","https:\u002F\u002Forcid.org\u002F0009-0002-7403-9939",{"name":106,"orcid":107},"Mulugeta Melese","https:\u002F\u002Forcid.org\u002F0009-0008-3539-910X",{"name":109,"orcid":110},"Tadesse Shimels","https:\u002F\u002Forcid.org\u002F0009-0009-3351-5952",{"name":112,"orcid":9},"Mussie Alemayehu",{"name":114,"orcid":115},"Daneal Fekersillassie","https:\u002F\u002Forcid.org\u002F0009-0006-6194-0903",0,{"tldr":118,"method":119,"finding":120,"direction":121,"opportunity":122},"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","农业遥感与作物表型","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","openalex","2026-10-10T23:30:36.165502Z",{"total":54,"page":22,"page_size":54,"items":127},[128,221,337,403,481,550],{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":9,"source_name":134,"source_url":131,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":143,"tags":145,"primary_topic":148,"secondary_topics":149,"classification":153,"search_phrases":188,"slug":191,"view_count":192,"doi":193,"paper":194,"created_at":220},2527,"Extracting Summer-Harvested Crops in the Baojixia Irrigation District Using CycleGAN and Transfer Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183155","Remote sensing-based mapping of crop planting structures in irrigation districts plays a vital role in forecasting regional production and optimizing water resource allocation. However, optical satellite imagery is limited by insufficient spatial resolution when applied to fragmented farmland landscapes, while unmanned aerial vehicle (UAV) imagery is limited by spatial coverage and high data processing costs. To address these bottlenecks, this study proposed a cross-scale collaborative extraction framework utilizing the CycleGAN network and transfer learning to map summer harvest crops (winter wheat and rapeseed) in the Baojixia Irrigation District for the year 2023. First, multiple semantic segmentation models—including U-Net, DeepLabv3+, SegFormer, and HRNet—were evaluated on a joint satellite–UAV dataset, with U-Net selected as the optimal backbone. Next, CycleGAN was introduced to perform style translation from the UAV domain to the satellite domain. This step generated high-fidelity, satellite-like images that preserve UAV-derived high-resolution spatial details, which were subsequently used to pre-train the U-Net backbone, significantly reducing the labor of manual annotation. Finally, the model was fine-tuned with real satellite images to achieve precise crop extraction. Results indicated that this framework accelerates model convergence and improves segmentation accuracy. The proposed method achieved an mIoU of 85.09%, an mPA (Recall) of 91.63%, a Precision of 91.97%, an Accuracy of 93.57%, and an F1-Score of 91.80%, outperforming the baseline U-Net model by 2.98%, 2.00%, 1.66%, 1.52%, and 1.83%, respectively. By successfully transferring high-resolution prior knowledge into the satellite feature space, this study provides a cost-effective and highly accurate solution for crop identification in complex agricultural landscapes, breaking the spatial limitations of UAV remote sensing.","基于遥感的灌区作物种植结构制图对于区域产量预测与水资源优化配置具有重要作用。然而，光学卫星影像在应用于破碎化农田景观时受限于空间分辨率不足，而无人机（UAV）影像则受限于空间覆盖范围和数据获取成本高。为解决这些瓶颈问题，本研究提出了一种利用CycleGAN网络和迁移学习的跨尺度协同提取框架，用于绘制2023年宝鸡峡灌区夏收作物（冬小麦和油菜）分布图。首先，在卫星—无人机联合数据集上评估了多种语义分割模型——包括U-Net、DeepLabv3+、SegFormer和HRNet——并选择U-Net作为最优主干网络。其次，引入CycleGAN进行从无人机域到卫星域的风格转换。该步骤生成了高保真、类卫星影像，同时保留了无人机来源的高分辨率空间细节，随后用于预训练U-Net主干网络，显著减少了人工标注的工作量。最后，利用真实卫星影像对模型进行微调，以实现精确的作物提取。结果表明，该框架加速了模型收敛并提高了分割精度。所提方法达到了85.09%的mIoU、91.63%的mPA（召回率）、91.97%的精确率、93.57%的总体精度和91.80%的F1分数，分别优于基线U-Net模型2.98%、2.00%、1.66%、1.52%和1.83%。通过成功将高分辨率先验知识迁移至卫星特征空间，本研究为复杂农业景观中的作物识别提供了一种经济高效且高精度的解决方案，突破了无人机遥感的空间局限性。","Remote Sensing","2026-09-14T00:00:00Z",78,{"impact":138,"substance":139,"depth":140,"authority":141,"freshness":17,"relevant":22,"comment":142},16,22,18,14,"提出CycleGAN跨尺度协同框架，将无人机高分辨率先验迁移至卫星影像，实现灌溉区夏收作物高精度提取，方法新颖、指标扎实，对农业遥感监测有实用价值。",[144],{"name":134,"url":131},[27,28,29,146,147],"迁移学习","灌溉区",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[150],{"code":53,"slug":58,"name":57,"path":151,"parent_code":152},"\u002Ftopic\u002Ffield-crops\u002Fwheat","A",{"status":40,"locked":14,"version":41,"method":42,"evidence":154},{"candidates":155,"agricultural_evidence":46},[156,161,166,172,179,183],{"code":33,"score":19,"supported":46,"title":157,"abstract":158,"tags":160},[34],[50,34,159],"water resource",[],{"code":53,"score":21,"supported":46,"title":162,"abstract":163,"tags":165},[],[57,164,58],"winter wheat",[],{"code":75,"score":167,"supported":46,"title":168,"abstract":169,"tags":171},7,[],[28,170],"remote sensing",[28],{"code":173,"score":54,"supported":46,"title":174,"abstract":175,"tags":178},"A4",[],[176,177],"油菜","rapeseed",[],{"code":81,"score":22,"supported":14,"title":180,"abstract":181,"tags":182},[],[],[27],{"code":184,"score":22,"supported":14,"title":185,"abstract":186,"tags":187},"I2",[],[],[146],[189,190],"作物识别 智慧农业 迁移学习 灌溉区","作物识别 智慧农业","作物识别智慧农业迁移学习灌溉区-2527",4,"10.3390\u002Frs18183155",{"doi":193,"openalex_id":195,"authors":196,"venue":134,"cited_by_count":116,"oa_url":131,"card":215,"direction":121,"ingested_from":124},"W7212518427",[197,199,201,203,205,207,210,212],{"name":198,"orcid":9},"Zili Chen",{"name":200,"orcid":9},"Zhilong Gao",{"name":202,"orcid":9},"Zefeng Jia",{"name":204,"orcid":9},"Pengjie Pan",{"name":206,"orcid":9},"Wen Gao",{"name":208,"orcid":209},"Jun Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-6972-6828",{"name":211,"orcid":9},"Zijie Niu",{"name":213,"orcid":214},"Dongyan Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-3509-7482",{"tldr":216,"method":217,"finding":218,"direction":121,"opportunity":219},"用CycleGAN和迁移学习融合无人机与卫星影像，提取宝鸡峡灌区夏收作物。","CycleGAN风格迁移+U-Net迁移学习，联合卫星-无人机数据集。","框架提升分割精度，mIoU达85.09%，优于基线U-Net。","可探索跨尺度迁移学习在更多作物和区域的应用，降低标注成本。","2026-09-15T23:30:20.433643Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":9,"source_name":227,"source_url":224,"published_at":228,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":229,"score_detail":230,"sources":233,"tags":235,"primary_topic":239,"secondary_topics":240,"classification":251,"search_phrases":307,"slug":310,"view_count":116,"doi":311,"paper":312,"created_at":336},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":140,"substance":139,"depth":140,"authority":231,"freshness":21,"relevant":22,"comment":232},12,"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。",[234],{"name":227,"url":224},[27,236,28,237,238],"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},[241,246],{"code":184,"slug":242,"name":243,"path":244,"parent_code":245},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":247,"slug":248,"name":249,"path":250,"parent_code":245},"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":252},{"candidates":253,"agricultural_evidence":46},[254,259,266,273,279,283,287,294,301],{"code":33,"score":255,"supported":46,"title":256,"abstract":257,"tags":258},15,[34],[49,34],[238],{"code":184,"score":13,"supported":46,"title":260,"abstract":261,"tags":265},[],[262,263,264],"机器学习","machine learning","artificial intelligence",[236],{"code":247,"score":21,"supported":46,"title":267,"abstract":268,"tags":272},[],[269,270,271],"物联网","internet of things","IoT",[],{"code":81,"score":167,"supported":46,"title":274,"abstract":275,"tags":278},[],[276,277],"数字孪生","digital twin",[27],{"code":75,"score":167,"supported":46,"title":280,"abstract":281,"tags":282},[],[28,170],[28],{"code":53,"score":54,"supported":46,"title":284,"abstract":285,"tags":286},[],[57,58],[],{"code":288,"score":54,"supported":46,"title":289,"abstract":290,"tags":293},"A3",[],[291,292],"玉米","maize",[],{"code":295,"score":54,"supported":46,"title":296,"abstract":297,"tags":300},"A5",[],[298,299],"马铃薯","potato",[],{"code":302,"score":69,"supported":46,"title":303,"abstract":304,"tags":306},"B2",[],[305],"葡萄",[],[308,309],"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":311,"openalex_id":313,"authors":314,"venue":227,"cited_by_count":116,"oa_url":224,"card":330,"direction":335,"ingested_from":124},"W7223088921",[315,318,321,324,327],{"name":316,"orcid":317},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":319,"orcid":320},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":322,"orcid":323},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":325,"orcid":326},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":328,"orcid":329},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":331,"method":332,"finding":333,"direction":121,"opportunity":334},"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":338,"title":339,"url":340,"summary":341,"summary_zh":342,"content":9,"source_name":343,"source_url":344,"published_at":345,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":346,"score_detail":347,"sources":350,"tags":352,"primary_topic":356,"secondary_topics":357,"classification":358,"search_phrases":379,"slug":382,"view_count":22,"doi":383,"paper":384,"created_at":402},4913,"Factors Influencing the Adoption of Precision Agriculture Technologies by Nebraska Producers","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.413436","An ever-increasing world population and increasingly-volatile commodity prices have charged producers with the task of becoming more efficient. To combat this, precision agriculture technologies aimed at increasing production efficiency are continually being developed, but their adoption is not yet widespread. A survey regarding the usage of these technologies was distributed to a sample of row crop producers across the state of Nebraska and a Poisson regression was used to determine the factors influencing adoption. Results of the study indicate that larger, more tech-savvy producers and those using irrigation are more likely to adopt a higher number of precision agriculture technologies, while operator age and gross farm income were found to be noninfluential factors.","世界人口不断增长以及大宗商品价格日益波动，使生产者肩负起提高效率的任务。为应对这一挑战，旨在提高生产效率的精准农业（precision agriculture）技术不断被开发出来，但其采用尚未普及。本研究向内布拉斯加州大田作物生产者样本发放了关于这些技术使用情况的调查问卷，并采用泊松回归（Poisson regression）确定影响采用的因素。研究结果表明，规模更大、更精通技术的生产者以及使用灌溉的生产者更可能采用更多数量的精准农业技术，而经营者年龄和农场总收入则被发现在影响因素中不起作用。","Lincoln (University of Nebraska)","https:\u002F\u002Fdigitalcommons.unl.edu\u002Fageconworkpap\u002F49","2026-10-13T00:00:00Z",75,{"impact":138,"substance":348,"depth":19,"authority":20,"freshness":17,"relevant":22,"comment":349},21,"基于内布拉斯加州种植者调查的实证研究，用泊松回归揭示规模、技术素养与灌溉对精准农业技术采纳的影响，对国内智慧农业推广有参考价值。",[351],{"name":343,"url":344},[27,353,354,31,355],"精准农业","农业技术采纳","玉米大豆",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":359},{"candidates":360,"agricultural_evidence":46},[361,366,370,374],{"code":81,"score":138,"supported":46,"title":362,"abstract":364,"tags":365},[363],"precision agriculture",[353,363],[27,353],{"code":33,"score":167,"supported":46,"title":367,"abstract":368,"tags":369},[],[49,34],[31],{"code":288,"score":22,"supported":14,"title":371,"abstract":372,"tags":373},[],[],[355],{"code":375,"score":22,"supported":14,"title":376,"abstract":377,"tags":378},"G1",[],[],[354],[380,381],"内布拉斯加 精准农业 采纳","精准农业技术 采纳因素","内布拉斯加精准农业采纳-4913","10.22004\u002Fag.econ.413436",{"doi":383,"openalex_id":385,"authors":386,"venue":343,"cited_by_count":394,"oa_url":395,"card":396,"direction":401,"ingested_from":124},"W2326051080",[387,389,391],{"name":388,"orcid":9},"Michael H Castle",{"name":390,"orcid":9},"Bradley Lubben",{"name":392,"orcid":393},"Joe D. Luck","https:\u002F\u002Forcid.org\u002F0000-0002-8278-9812",32,"http:\u002F\u002Fdigitalcommons.unl.edu\u002Fcgi\u002Fviewcontent.cgi?article=1050&context=ageconworkpap",{"tldr":397,"method":398,"finding":399,"direction":123,"opportunity":400},"基于内布拉斯加州调查，用泊松回归分析影响精准农业技术采纳的因素。","对行作物生产者问卷调查，采用泊松回归模型分析采纳数量。","规模大、技术熟练及有灌溉的生产者更易采纳更多精准农业技术，年龄和收入无显著影响。","可探究小规模或非灌溉农户采纳障碍，以及技术熟练度的中介机制与干预策略。","农业人工智能与决策模型","2026-10-14T23:30:55.751323Z",{"id":404,"title":405,"url":406,"summary":407,"summary_zh":408,"content":9,"source_name":409,"source_url":406,"published_at":410,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":411,"score_detail":412,"sources":414,"tags":416,"primary_topic":419,"secondary_topics":424,"classification":431,"search_phrases":458,"slug":461,"view_count":192,"doi":462,"paper":463,"created_at":480},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":255,"substance":139,"depth":140,"authority":141,"freshness":17,"relevant":22,"comment":413},"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。",[415],{"name":409,"url":406},[27,417,28,418,238],"苹果","果园管理",{"code":302,"slug":420,"name":421,"path":422,"parent_code":423},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[425,430],{"code":426,"slug":427,"name":428,"path":429,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":432},{"candidates":433,"agricultural_evidence":46},[434,440,446,450,454],{"code":302,"score":140,"supported":46,"title":435,"abstract":437,"tags":439},[436],"apple",[421,417,438,436],"orchard",[417],{"code":426,"score":141,"supported":46,"title":441,"abstract":443,"tags":445},[442],"drought",[444,442],"干旱",[],{"code":33,"score":13,"supported":46,"title":447,"abstract":448,"tags":449},[],[49,50,34],[238],{"code":81,"score":22,"supported":14,"title":451,"abstract":452,"tags":453},[],[],[27],{"code":75,"score":22,"supported":14,"title":455,"abstract":456,"tags":457},[],[],[28],[459,460],"LiDAR 苹果园 干旱","Leafiness-LiDAR Index 苹果","LiDAR苹果园干旱-3607","10.1016\u002Fj.biosystemseng.2026.104603",{"doi":462,"openalex_id":464,"authors":465,"venue":409,"cited_by_count":116,"oa_url":406,"card":475,"direction":121,"ingested_from":124},"W7214458453",[466,469,472],{"name":467,"orcid":468},"Leire Sandonís-Pozo","https:\u002F\u002Forcid.org\u002F0000-0003-2472-0259",{"name":470,"orcid":471},"José Antonio Martínez-Casasnovas","https:\u002F\u002Forcid.org\u002F0000-0003-1480-3632",{"name":473,"orcid":474},"Miquel Pascual","https:\u002F\u002Forcid.org\u002F0000-0002-3329-4207",{"tldr":476,"method":477,"finding":478,"direction":121,"opportunity":479},"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":482,"title":483,"url":484,"summary":485,"summary_zh":486,"content":9,"source_name":134,"source_url":484,"published_at":487,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":346,"score_detail":488,"sources":490,"tags":492,"primary_topic":496,"secondary_topics":497,"classification":503,"search_phrases":528,"slug":531,"view_count":192,"doi":532,"paper":533,"created_at":549},3190,"Comparing Irrigation Identification Methods in Colorado: Limitations of Downscaled SMAP Soil Moisture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183223","With limited freshwater resources and growing water demands, it is imperative to identify and monitor water needs. Agricultural irrigation is the world’s largest water user, comprising 45–90% of freshwater withdrawals. Identifying and tracking changes in irrigated land is necessary for sustainable water management and forecasting agricultural water needs and patterns; however, the low resolution of available remote sensing observations hinders field-scale analysis. Downscaling soil moisture observations has been offered as a solution to this problem. Our study compares the performance of five irrigation identification methods using a newly developed downscaled deep soil moisture extrapolation method used to estimate Soil Moisture Active Passive (SMAP) soil moisture (SM) at a spatial resolution of 400 m for 5 cm, 20 cm, and 50 cm depths. Using this data along with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation and Landsat Normalized Difference Vegetation Index (NDVI), we evaluate these methods with respect to crop type, irrigation type, and observation depth on agricultural fields in Colorado using the irrigation maps provided by the Colorado Decision Support System from 2015 through 2024. With no crop\u002Firrigation type–observation depth combination exceeding an F1 score of 0.149 or MCC value of 0.163, we find that none of the methods can accurately identify irrigated land regardless of crop type, irrigation type, and observation depth. Because these methods succeeded in earlier small-area studies, and because the classified maps resolved into large, spatially uniform blocks, we interpret this as a limitation specific to field-scale detection over large, heterogeneous regions rather than a defect in the dataset. These findings highlight a limitation of this downscaled SM dataset and raise the question of whether other downscaled soil moisture products share this limitation.","在淡水资源有限且用水需求不断增长的背景下，识别和监测水资源需求势在必行。农业灌溉是全球最大的用水部门，占淡水取水量的45%–90%。识别和追踪灌溉土地的变化对于可持续水资源管理以及预测农业用水需求和模式至关重要；然而，现有遥感观测的低分辨率阻碍了田块尺度的分析。土壤湿度观测数据的降尺度被提出作为解决这一问题的方法。本研究比较了五种灌溉识别方法的性能，所用数据基于新开发的降尺度深层土壤湿度外推方法，用于估算土壤湿度主动被动（SMAP）卫星在400 m空间分辨率下5 cm、20 cm和50 cm深度的土壤湿度（SM）。利用该数据以及气候灾害组红外降水与站点数据（CHIRPS）降水和Landsat归一化植被指数（NDVI），我们结合科罗拉多决策支持系统提供的2015年至2024年灌溉地图，在科罗拉多州的农田上就作物类型、灌溉类型和观测深度对这些方法进行了评估。在没有任何作物\u002F灌溉类型–观测深度组合的F1分数超过0.149或MCC值超过0.163的情况下，我们发现无论作物类型、灌溉类型和观测深度如何，这些方法均无法准确识别灌溉土地。由于这些方法在早期小区域研究中取得了成功，且分类地图呈现为大的、空间均一的斑块，我们将此解释为大规模异质区域上田块尺度检测所特有的局限性，而非数据集本身的缺陷。这些发现凸显了该降尺度SM数据集的局限性，并提出其他降尺度土壤湿度产品是否也存在这一局限性的问题。","2026-09-19T00:00:00Z",{"impact":231,"substance":139,"depth":140,"authority":141,"freshness":21,"relevant":22,"comment":489},"该研究通过大区域对比实验揭示降尺度SMAP土壤水分在田块尺度灌溉识别上的局限，方法严谨、结论明确，对农业遥感与水资源管理有参考价值，但属细分领域学术进展，公共影响有限。",[491],{"name":134,"url":484},[27,28,493,494,495],"水资源管理","土壤水分","灌溉识别",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[498],{"code":499,"slug":500,"name":501,"path":502,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":40,"locked":14,"version":41,"method":42,"evidence":504},{"candidates":505,"agricultural_evidence":46},[506,510,515,519,524],{"code":33,"score":140,"supported":46,"title":507,"abstract":508,"tags":509},[34],[49,50,34],[493],{"code":499,"score":255,"supported":46,"title":511,"abstract":512,"tags":514},[500],[513,500],"土壤",[494],{"code":75,"score":167,"supported":46,"title":516,"abstract":517,"tags":518},[],[28,170],[28],{"code":184,"score":69,"supported":46,"title":520,"abstract":521,"tags":523},[],[522],"decision support",[],{"code":81,"score":22,"supported":14,"title":525,"abstract":526,"tags":527},[],[],[27],[529,530],"SMAP 土壤水分 灌溉识别","科罗拉多 灌溉制图 遥感","SMAP土壤水分灌溉识别-3190","10.3390\u002Frs18183223",{"doi":532,"openalex_id":534,"authors":535,"venue":134,"cited_by_count":116,"oa_url":484,"card":544,"direction":121,"ingested_from":124},"W7213934204",[536,539,541],{"name":537,"orcid":538},"Annelise M. Turman","https:\u002F\u002Forcid.org\u002F0009-0000-2392-3631",{"name":540,"orcid":9},"Bin Fang",{"name":542,"orcid":543},"V. Vijaya Lakshmi","https:\u002F\u002Forcid.org\u002F0000-0001-9522-7897",{"tldr":545,"method":546,"finding":547,"direction":121,"opportunity":548},"比较五种灌溉识别方法，发现降尺度SMAP土壤湿度在科罗拉多田间尺度无法准确识别灌溉。","用400米降尺度SMAP土壤湿度、CHIRPS降水和Landsat NDVI，对","所有方法F1最高仅0.149，无法准确识别灌溉地，归因于大区域异质性而非数据缺陷。","可检验其他降尺度土壤湿度产品是否同样受大区域异质性限制，并探索融合多源数据提升田间尺度灌溉识别。","2026-09-22T23:30:26.627420Z",{"id":551,"title":552,"url":553,"summary":554,"summary_zh":555,"content":9,"source_name":556,"source_url":553,"published_at":557,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":558,"score_detail":559,"sources":561,"tags":563,"primary_topic":567,"secondary_topics":571,"classification":573,"search_phrases":609,"slug":612,"view_count":76,"doi":613,"paper":614,"created_at":625},2667,"A systematic review of remote sensing applications for agricultural water management in a water-stressed South Africa","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04728-y","Abstract Recent advancements in earth observation and computational technologies have enabled the prudent use of remote sensing (RS) for monitoring and managing water resources. RS is a spatially explicit and effective approach for field-level to large-scale monitoring through data-driven insights on key agricultural water management (AWM) aspects of water consumption, productivity, agricultural drought and irrigated area extent mapping. However, the progress made in the South African context is not well documented. Hence, this review aimed to assess how RS is utilized for AWM in South Africa. Article search was conducted in the Scopus and Web of Science databases using keywords remote sensing, earth observation, agricultural water management, irrigation, evapotranspiration, water use, water productivity, agricultural drought, and South Africa. The bibliometric analysis from 165 articles revealed that number of publications have a 8.3% annual growth over 26 years. RS data have been utilized to assess evapotranspiration (ET), crop water productivity (CWP), agricultural drought, scheme performance, and irrigation water quality. Data limitations, insufficient technical capacity, and limited computational infrastructure were the notable challenges. Future research should focus on integrated use of multi-temporal and multi-source data to minimize gaps across sensors and improve accuracy. Overall, RS has substantial potential to address water management issues in the agricultural sector of South Africa. Addressing the challenges through continued innovation, data integration, and capacity building is key to utilizing RS for sustainable agriculture and water security.","摘要 地球观测与计算技术的最新进展使得遥感（RS）能够被审慎地应用于水资源监测与管理。遥感是一种空间显式且有效的方法，可通过数据驱动的洞察，从田块尺度到大规模监测农业水资源管理（AWM）的关键方面，包括耗水量、生产力、农业干旱及灌溉面积范围制图。然而，南非在这一领域的进展尚未得到充分记录。因此，本综述旨在评估遥感在南非农业水资源管理中的应用情况。研究在Scopus和Web of Science数据库中使用遥感、地球观测、农业水资源管理、灌溉、蒸散发、用水量、水分生产力、农业干旱和南非等关键词进行文献检索。对165篇文章的文献计量分析显示，过去26年间发表数量年增长率为8.3%。遥感数据已被用于评估蒸散发（ET）、作物水分生产力（CWP）、农业干旱、灌区性能及灌溉水质。数据局限性、技术能力不足和计算基础设施有限是显著挑战。未来研究应侧重于多时相和多源数据的集成使用，以缩小传感器之间的差距并提高精度。总体而言，遥感在解决南非农业部门水资源管理问题方面具有巨大潜力。通过持续创新、数据集成和能力建设来应对这些挑战，是利用遥感实现可持续农业和水安全的关键。","Discover Sustainability","2026-09-15T00:00:00Z",74,{"impact":138,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":22,"comment":560},"以南非为对象的遥感农业水资源管理系统性综述，方法规范、数据规模明确，对干旱区灌溉与遥感应用有参考价值，但属区域案例研究，公共影响层级有限。",[562],{"name":556,"url":553},[27,564,28,565,566],"农业水资源","灌溉管理","农业干旱",{"code":75,"slug":568,"name":569,"path":570,"parent_code":245},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[572],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":574},{"candidates":575,"agricultural_evidence":46},[576,580,587,591,598,605],{"code":75,"score":255,"supported":46,"title":577,"abstract":578,"tags":579},[170],[28,170],[28],{"code":33,"score":581,"supported":46,"title":582,"abstract":583,"tags":586},11,[],[49,50,584,34,585],"蒸散发","evapotranspiration",[564,565],{"code":426,"score":54,"supported":46,"title":588,"abstract":589,"tags":590},[],[444,442],[],{"code":592,"score":54,"supported":46,"title":593,"abstract":594,"tags":597},"H8",[],[595,596],"可持续农业","sustainable agriculture",[],{"code":599,"score":54,"supported":46,"title":600,"abstract":601,"tags":604},"I7",[],[602,603],"文献计量","bibliometric",[],{"code":81,"score":22,"supported":14,"title":606,"abstract":607,"tags":608},[],[],[27],[610,611],"农业水资源 农业干旱 智慧农业 灌溉管理","农业水资源 农业干旱","农业水资源农业干旱智慧农业灌溉管理-2667","10.1007\u002Fs43621-026-04728-y",{"doi":613,"openalex_id":615,"authors":616,"venue":556,"cited_by_count":116,"oa_url":553,"card":620,"direction":121,"ingested_from":124},"W7213266434",[617],{"name":618,"orcid":619},"Yilkal Gebeyehu Mekonnen","https:\u002F\u002Forcid.org\u002F0000-0001-9450-1081",{"tldr":621,"method":622,"finding":623,"direction":121,"opportunity":624},"系统综述遥感在南非农业水资源管理中的应用进展与挑战。","Scopus与Web of Science检索165篇文献，做文献计量分析。","遥感用于蒸散、水分生产力、干旱与灌溉监测，但受数据与能力限制。","多源多时相遥感数据融合以填补传感器空缺、提升精度，是值得切入的方向。","2026-09-16T23:30:29.241938Z"]