[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5395":3,"related-5395":111},{"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":86,"slug":89,"view_count":90,"doi":91,"paper":92,"created_at":110},5395,"Strengthening Flood Resilience in Data-Sparse Agricultural Regions: A Planning Approach for Coastal Guyana","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23451582","Abstract. Coastal Guyana concentrates most of the country’s population and agricultural production in low-lying polder landscapes that are increasingly exposed to flooding from intense rainfall, sea-level rise, and deterioration of drainage and irrigation infrastructure. Engineering design and resilience planning in these settings are often constrained by limited hydrometric monitoring (discharge, water levels), incomplete documented drainage network, and restricted access to institutional datasets. This paper presents the value of using a low-information framework for flood resilience planning and preliminary hydraulic design in data-sparse agricultural regions, using the 364 km² part of Guyana’s Mahaica–Mahaicony catchment as a case study. The framework integrates (i) rainfall frequency analysis from long-term gauge records, (ii) remote sensing and global datasets (DEM and land-use\u002Fland-cover) to delineate sub-basins and screen flood impact, (iii) simplified runoff and volume-based drainage calculations consistent with rice-field storage and saturated soils, and (iv) structured stakeholder engagement to identify flood-prone areas, operational constraints, and feasible interventions. Six-sub-basins are used for developing a routing strategy to distribute runoff volumes and to explicitly incorporate upstream detention in the wetlands, thereby reducing peak flows conveyed northward through constrained canal corridors. High-level drainage canals, dedicated conveyance paths for critical sub-basins, and dual-purpose drains supporting both drainage and irrigation storage are used to determine preliminary cross-section designs, which are then assessed with 1D hydraulic modelling under tidal boundary conditions. The approach supports phased investment planning that improves flood safety, protects livelihoods, and increases agricultural reliability while aligning with national adaptation priorities.","摘要：圭亚那沿海地区集中了全国大部分人口和农业生产，地处低洼圩田景观，日益面临强降雨、海平面上升以及排水灌溉基础设施老化所带来的洪涝风险。在此类地区，工程设计与韧性规划往往受限于水文监测（流量、水位）资料不足、排水管网记录不完整以及机构数据集获取受限。本文以圭亚那马海卡—马海科尼（Mahaica–Mahaicony）流域364 km²的区域为案例，展示了在数据稀疏的农业地区采用低信息框架进行洪涝韧性规划与初步水力设计的价值。该框架整合了：(i) 基于长期测站记录的降雨频率分析；(ii) 利用遥感与全球数据集（DEM及土地利用\u002F土地覆盖）划分子流域并筛查洪涝影响；(iii) 与稻田蓄水和饱和土壤条件相适应的简化径流与基于水量的排水计算；(iv) 结构化利益相关方参与，以识别易涝区域、运行约束和可行干预措施。研究将流域划分为六个子流域，用于制定汇流路径策略，以分配径流量并明确纳入上游湿地滞蓄，从而削减通过受限渠廊向北输送的峰值流量。基于高位排水渠、关键子流域专用输水通道以及兼顾排水与灌溉蓄水的两用沟渠，确定初步断面设计，并在潮汐边界条件下采用一维水动力模型进行评估。该方法支持分阶段投资规划，在提升防洪安全、保护生计和提高农业可靠性的同时，与国家适应优先事项相衔接。",null,"Geo-Eco-Marina","2026-10-18T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"该论文提出一种低信息条件下的防洪韧性规划框架，方法新颖，但案例位于圭亚那，对我国三农信息化的直接参考价值有限，因此不宜进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","遥感","气候适应","防洪韧性","灌排设施",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,60,67,74,81],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},7,true,[],[28,50],"remote sensing",[28],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":59},"A1",6,[],[57,58],"稻田","rice",[],{"code":61,"score":54,"supported":47,"title":62,"abstract":63,"tags":66},"H3",[],[64,65],"灌溉","irrigation",[],{"code":68,"score":69,"supported":47,"title":70,"abstract":71,"tags":73},"H7",4,[],[72],"洪涝",[29],{"code":75,"score":76,"supported":47,"title":77,"abstract":78,"tags":80},"H2",3,[],[79],"土壤",[],{"code":82,"score":22,"supported":14,"title":83,"abstract":84,"tags":85},"I6",[],[],[27],[87,88],"Guyana Mahaica Mahaicony 洪水","沿海稻田 排水规划","GuyanaMahaicaMahaicony洪水-5395",0,"10.5281\u002Fzenodo.23451582",{"doi":91,"openalex_id":93,"authors":94,"venue":10,"cited_by_count":90,"oa_url":6,"card":102,"direction":108,"ingested_from":109},"W7222952224",[95,97,99],{"name":96,"orcid":9},"Omesh Persaud",{"name":98,"orcid":9},"Colis Allen",{"name":100,"orcid":101},"Ioana Popescu","https:\u002F\u002Forcid.org\u002F0000-0002-1009-5424",{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"提出低信息框架，为数据稀缺农业区规划洪水韧性并做初步水力设计。","降雨频率分析、遥感DEM与土地利用、简化径流计算、利益相关者参与及一维水力模型。","上游湿地滞蓄与分区排水可降低峰值流量，支撑分阶段投资规划。","农业绿色发展与碳","可探索低监测区遥感与参与式数据融合的洪水韧性规划方法及作物淹水风险量化。","农业遥感与作物表型","openalex","2026-10-20T23:30:19.345049Z",{"total":54,"page":22,"page_size":54,"items":112},[113,178,243,292,356,412],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":126,"tags":128,"primary_topic":132,"secondary_topics":133,"classification":139,"search_phrases":160,"slug":163,"view_count":164,"doi":165,"paper":166,"created_at":177},4102,"Application of GIS and Remote Sensing Techniques in Geospatial Mapping of Gully Statuses of an Alfisols in Yola South Local Government Area, Adamawa State","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v10.no1.2026.pg155.165","This research work saddled to study the application of GIS and Remote Sensing (RS) Techniques in geospatial mapping of gully erosion statuses of an Alfisols in Yola South LGA. Bole farmland was selected where expansion and development of gully was geospatially mapped out for the period of 20 years (2004-2024) at interval of five (5) years using GIS and RS techniques. The results reveal a concerning trend of increasing gully erosion, with significant growth in affected areas over the years. The highest percentage of increase occurred at 2014-2019 with about 58,069.00 m2 (65.5 %) and in 2019-2024was increased with 14,154.00 m2 (15.97 %). The total area affected in the area within the study period was 88,627.00 m2 caused by heavy rainfall and deforestation. Therefore, there are needs for effective land management practices, policy interventions, and community engagement to mitigate the effects of gully erosion.","本研究旨在探讨GIS与遥感（RS）技术在约拉南部地方政府区淋溶土冲沟侵蚀状况地理空间制图中的应用。选取博莱农田为研究区，利用GIS与遥感技术，以5年为间隔，对2004—2024年共20年间冲沟的扩展与发育进行了地理空间制图。结果表明，冲沟侵蚀呈令人担忧的加剧趋势，受影响面积逐年显著增长。增幅最大的时段为2014—2019年，约增加58 069.00 m²（65.5%）；2019—2024年增加14 154.00 m²（15.97%）。研究期内该区域受影响总面积为88 627.00 m²，主要由强降雨和森林砍伐所致。因此，需要采取有效的土地管理措施、政策干预和社区参与，以减轻冲沟侵蚀的影响。","WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY","2026-09-30T00:00:00Z",54,{"impact":21,"substance":123,"depth":124,"authority":54,"freshness":21,"relevant":22,"comment":125},18,14,"基于GIS与遥感的20年沟蚀动态监测，数据具体、结论明确，但属区域案例研究，公共影响有限。",[127],{"name":119,"url":116},[27,28,129,130,131],"土壤侵蚀","GIS","水土保持",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[134],{"code":135,"slug":136,"name":137,"path":138,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":40,"locked":14,"version":41,"method":42,"evidence":140},{"candidates":141,"agricultural_evidence":47},[142,147,151,156],{"code":33,"score":143,"supported":47,"title":144,"abstract":145,"tags":146},15,[50],[28,50],[28],{"code":135,"score":17,"supported":47,"title":148,"abstract":149,"tags":150},[130],[130],[130],{"code":152,"score":22,"supported":14,"title":153,"abstract":154,"tags":155},"H6",[],[],[131],{"code":82,"score":22,"supported":14,"title":157,"abstract":158,"tags":159},[],[],[27],[161,162],"Yola South 沟蚀 遥感","Alfisols 沟蚀 GIS 制图","YolaSouth沟蚀遥感-4102",2,"10.56201\u002Fwjimt.v10.no1.2026.pg155.165",{"doi":165,"openalex_id":167,"authors":168,"venue":119,"cited_by_count":90,"oa_url":171,"card":172,"direction":108,"ingested_from":109},"W7214920253",[169],{"name":170,"orcid":9},"Mohammed Pakra Umar","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 10 NO. 1 2026\u002FAPPLICATION OF GIS AND REMOTE 156-165.pdf",{"tldr":173,"method":174,"finding":175,"direction":108,"opportunity":176},"用GIS和遥感技术对尼日利亚Yola South地区20年沟蚀动态进行空间制图与监测。","基于GIS与遥感影像，按5年间隔提取2004-2024年沟蚀范围并计算面积变化。","沟蚀面积持续扩大，2014-2019年增幅最大达65.5%，20年累计影响88,627平方米。","可结合多源高分辨率遥感与机器学习，构建沟蚀风险预警模型并评估保护性耕作等治理措施效果。","2026-10-03T23:30:37.949646Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":9,"source_name":184,"source_url":181,"published_at":185,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":186,"score_detail":187,"sources":191,"tags":193,"primary_topic":197,"secondary_topics":198,"classification":199,"search_phrases":215,"slug":218,"view_count":69,"doi":219,"paper":220,"created_at":242},3492,"Compliance Algorithm for Socio-Environmental Safeguards in Legal Amazon: A Traceability and Risk Assessment Model for Agricultural Commodities Chain under Regulation (EU) 2023\u002F1115","https:\u002F\u002Fdoi.org\u002F10.66104\u002F5gcafj90","Regulation (EU) 2023\u002F1115 requires agricultural commodities placed on the European market to be deforestation-free from 31 December 2020, produced in line with laws in its country of origin, and covered by a due diligence statement identifying production plots by geolocation. Demonstrating compliance depends on a unit of analysis—rural property and plot—that falls between municipal-scale trade-flow models and pixel-scale remote sensing products, while relying on heterogeneous and consistently incomplete databases. This article presents COMPLY-SOY-MA, a model for assessing traceability, risk, and socio-environmental compliance across agricultural commodities supply chains in Legal Amazon. Its main contribution is a credibility estimator that separates two often-confused dimensions: substantive compliance, measured through a composite index of thirty-one indicators aggregated using power means with controlled compensability, and supporting-evidence quality, measured through a five-indicator index. Risk is estimated by shrinking observed deficiency toward a territorial prior, weighted by the power of the evidence index, so opaque units are not assessed using averages from units that disclose information. Under isolated changes in evidence, providing more evidence reduces risk only for genuinely compliant units. Traceability is modelled as a mass-flow-weighted multigraph, using origin entropy and a formal distinction between exposure and contamination index. Scope is stated clearly: the study delivers a formal model and reference implementation, with results based on mathematical derivation and domain characterization. It specifies but does not execute a validation protocol, and its risk score is not a calibrated probability.","欧盟第2023\u002F1115号条例要求，投放欧洲市场的农产品自2020年12月31日起不得涉及毁林，须依照原产国法律生产，并须通过尽职调查声明予以涵盖，该声明需以地理定位方式标明生产地块。证明合规取决于一个分析单元——农村地产与地块——它介于市级贸易流模型与像素级遥感产品之间，同时依赖于异质且始终不完整的数据库。本文提出COMPLY-SOY-MA，一个用于评估合法亚马逊地区农产品供应链可追溯性、风险及社会环境合规性的模型。其主要贡献在于一个可信度估计器，该估计器区分了两个常被混淆的维度：实质性合规，通过由三十一项指标构成的综合指数衡量，该指数采用可控补偿性的幂均值进行聚合；以及支撑性证据质量，通过一个五项指标指数衡量。风险估计通过将观测到的缺陷向地域先验收缩来实现，并以证据指数的幂进行加权，从而避免对不透明单元使用披露信息单元的平均值进行评估。在证据发生孤立变化的情况下，提供更多证据仅对真正合规的单元降低风险。可追溯性被建模为质量流加权的多重图，使用来源熵以及暴露指数与污染指数之间的形式化区分。研究范围已明确界定：本研究提供一个形式化模型及参考实现，其结果基于数学推导与领域刻画。它规定但并未执行验证方案，其风险评分并非经过校准的概率。","Revista Multidisciplinar do Nordeste Mineiro","2026-09-24T00:00:00Z",63,{"impact":21,"substance":188,"depth":123,"authority":54,"freshness":189,"relevant":22,"comment":190},22,9,"提出面向欧盟零毁林法规的农产品供应链可追溯与风险评估模型，方法新颖但仅属形式化模型、未经验证，且来源为地方性期刊，影响力有限。",[192],{"name":184,"url":181},[27,194,195,28,196],"农业供应链","农产品溯源","欧盟法规",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":200},{"candidates":201,"agricultural_evidence":47},[202,206,211],{"code":33,"score":46,"supported":47,"title":203,"abstract":204,"tags":205},[],[28,50],[28],{"code":207,"score":22,"supported":14,"title":208,"abstract":209,"tags":210},"E4",[],[],[195],{"code":82,"score":22,"supported":14,"title":212,"abstract":213,"tags":214},[],[],[27],[216,217],"EU 2023 1115 农产品 溯源","Legal Amazon 大豆 供应链","EU20231115农产品溯源-3492","10.66104\u002F5gcafj90",{"doi":219,"openalex_id":221,"authors":222,"venue":184,"cited_by_count":22,"oa_url":181,"card":237,"direction":108,"ingested_from":109},"W7214225231",[223,226,228,230,232,235],{"name":224,"orcid":225},"Felipe Oliveira Carvalho","https:\u002F\u002Forcid.org\u002F0000-0002-7540-286X",{"name":227,"orcid":9},"Allan Kardec Duailibe Barros Filho",{"name":229,"orcid":9},"José Artur Lima Cabral Marques",{"name":231,"orcid":9},"Rosângela Maria Guimarães Rosa",{"name":233,"orcid":234},"Paulo Fortes Neto","https:\u002F\u002Forcid.org\u002F0000-0001-5837-8450",{"name":236,"orcid":9},"Silvio Carlos Leite Mesquita",{"tldr":238,"method":239,"finding":240,"direction":106,"opportunity":241},"提出COMPLY-SOY-MA模型，评估合法亚马逊农产品供应链的可追溯性、风险与社会环境合规性。","构建可信度估计器，用31项指标复合指数与证据质量指数，结合质量流多重图与熵。","区分实质合规与证据质量，证据增加仅降低真正合规单元的风险，避免不透明单元被平均化。","可延伸至多品类供应链验证，并开发实证校准的风险概率模型与验证协议。","2026-09-25T23:30:30.374587Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":250,"category":12,"cover_url":9,"hotness":251,"is_selected":14,"score":252,"score_detail":253,"sources":255,"tags":259,"primary_topic":263,"secondary_topics":264,"classification":265,"search_phrases":277,"slug":280,"view_count":22,"doi":9,"paper":281,"created_at":291},2953,"Seasonal agricultural vulnerability in semi-arid Morocco: combining remote sensing and farmer knowledge to inform climate adaptation","https:\u002F\u002Fmel.cgiar.org\u002Freporting\u002Fdownloadmelspace\u002Fhash\u002F5daf70ceb0d05067d3d316f855cfa4f0","Seasonal agricultural vulnerability in semi-arid Morocco: combining remote sensing and farmer knowledge to inform climate adaptation。MELSpace (ICARDA (The International Center for Agricultural Research in Dry Areas))","半干旱摩洛哥的季节性农业脆弱性：结合遥感与农民知识为气候适应提供依据。MELSpace（ICARDA（国际干旱地区农业研究中心））","MELSpace (ICARDA (The International Center for Agricultural Research in Dry Areas))","2026-09-17T00:00:00Z",25,71,{"impact":17,"substance":18,"depth":123,"authority":20,"freshness":21,"relevant":22,"comment":254},"国际干旱农业研究机构成果，遥感与农户知识结合评估季节性脆弱性，方法有参考价值但属区域案例，影响力有限。",[256,257],{"name":249,"url":246},{"name":249,"url":258},"https:\u002F\u002Fhdl.handle.net\u002F20.500.11766\u002F70850",[28,29,260,261,262],"农户知识","干旱农业","摩洛哥",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":266},{"candidates":267,"agricultural_evidence":47},[268,272],{"code":33,"score":143,"supported":47,"title":269,"abstract":270,"tags":271},[50],[28,50],[28],{"code":68,"score":46,"supported":47,"title":273,"abstract":274,"tags":276},[],[29,275],"干旱",[29],[278,279],"ICARDA 摩洛哥 遥感 气候适应","农户知识 干旱农业 气候适应 摩洛哥","ICARDA摩洛哥遥感气候适应-2953",{"doi":9,"openalex_id":282,"authors":283,"venue":249,"cited_by_count":90,"oa_url":246,"card":286,"direction":108,"ingested_from":109},"W7213576262",[284],{"name":285,"orcid":9},"Cesar Ivan Alvarez",{"tldr":287,"method":288,"finding":289,"direction":108,"opportunity":290},"结合遥感与农户知识评估摩洛哥半干旱区季节性农业脆弱性，以支持气候适应。","遥感植被指数与农户访谈\u002F地方知识结合，分析季节性脆弱性。","遥感与农户知识互补，可更全面识别半干旱区季节性农业脆弱性。","可探索遥感指标与农户感知的定量耦合模型，用于气候适应决策支持。","2026-09-19T23:30:34.812798Z",{"id":293,"title":294,"url":295,"summary":296,"summary_zh":297,"content":9,"source_name":298,"source_url":295,"published_at":299,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":300,"score_detail":301,"sources":303,"tags":305,"primary_topic":309,"secondary_topics":310,"classification":311,"search_phrases":331,"slug":334,"view_count":90,"doi":335,"paper":336,"created_at":355},5571,"Temporal Multi-Scale Spatio-Temporal Network (TMST): Polarization–Temporal Feature Selection and Multi-Scale Spatiotemporal Fusion for Crop Mapping","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202068","Accurate and timely crop mapping supports agricultural resource management, yield prediction, and food security decision-making. In this study, we propose the Temporal Multi-scale Spatio-Temporal Network (TMST), a crop mapping method that integrates multi-source remote sensing time series through polarization–temporal feature selection and multi-scale spatiotemporal fusion. TMST combines Sentinel-1 dual-polarization synthetic aperture radar (SAR), Sentinel-2 multispectral data, and vegetation indices. Its Temporal Feature Importance Explainer (TFIExplainer) selects informative polarization–time features, the Multi-Scale Attention Aggregation (MSAA) module enhances spatial representations, and the Spatio-Temporal Feature Encoder (STFE) models long-range spatial dependencies and phenological dynamics; a composite loss improves minority-class and difficult-pixel recognition. On the Xinxiang dataset, TMST achieved an overall accuracy (OA) of 0.8625 and a mean F1-score (mF1) of 0.8329. Module and data-source ablations suggested benefits of the combined modules and multi-source fusion, while a cropland mask reduced non-cropland misclassification. In direct cross-dataset evaluation, models trained on a self-constructed Austrian dataset and tested on the radar-extended Panoptic Agricultural Satellite Time Series (PASTIS-R) dataset showed that TMST outperformed the evaluated baselines under the specified source–target setting. Annual mapping of Xinxiang for 2019–2023, using year-specific samples and independently trained models, provided map-based estimates of regional cropping structures and spatial patterns. Under the evaluated settings, TMST provides an effective approach to crop classification and annually updated crop mapping.","准确的作物制图有助于农业资源管理、产量预测和粮食安全决策。本研究提出了一种时间多尺度时空网络（Temporal Multi-scale Spatio-Temporal Network，TMST），这是一种通过极化—时间特征选择和多尺度时空融合来整合多源遥感时间序列的作物制图方法。TMST结合了Sentinel-1双极化合成孔径雷达（SAR）、Sentinel-2多光谱数据和植被指数。其时间特征重要性解释器（Temporal Feature Importance Explainer，TFIExplainer）用于选择信息量丰富的极化—时间特征，多尺度注意力聚合（Multi-Scale Attention Aggregation，MSAA）模块增强空间表征，时空特征编码器（Spatio-Temporal Feature Encoder，STFE）建模长程空间依赖关系和物候动态；复合损失函数改善了少数类和困难像元的识别。在新乡数据集上，TMST取得了0.8625的总体精度（OA）和0.8329的平均F1分数（mF1）。模块和数据源消融实验表明，组合模块和多源融合具有优势，而耕地掩膜减少了非耕地误分类。在直接的跨数据集评估中，在自行构建的奥地利数据集上训练并在雷达扩展的Panoptic农业卫星时间序列（PASTIS-R）数据集上测试的模型表明，在指定的源—目标设置下，TMST优于所评估的基线方法。使用逐年样本和独立训练的模型对新乡2019—2023年进行年度制图，提供了基于制图的区域种植结构和空间格局估计。在所评估的设置下，TMST为作物分类和年度更新的作物制图提供了一种有效方法。","Agronomy","2026-10-21T00:00:00Z",73,{"impact":17,"substance":18,"depth":19,"authority":124,"freshness":13,"relevant":22,"comment":302},"提出多源遥感融合的作物制图新方法，方法新颖、实验充分，但属细分技术进展，公共影响有限。",[304],{"name":298,"url":295},[306,307,28,308],"智慧农业","作物制图","SAR",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":312},{"candidates":313,"agricultural_evidence":47},[314,319,326],{"code":33,"score":13,"supported":47,"title":315,"abstract":316,"tags":318},[],[28,50,317],"multispectral",[28],{"code":320,"score":54,"supported":47,"title":321,"abstract":322,"tags":325},"A8",[],[323,324],"粮食安全","food security",[],{"code":327,"score":22,"supported":14,"title":328,"abstract":329,"tags":330},"I1",[],[],[306],[332,333],"Sentinel-1 Sentinel-2 作物分类","TMST 多尺度时空网络","Sentinel-1Sentinel-2作物分类-5571","10.3390\u002Fagronomy16202068",{"doi":335,"openalex_id":337,"authors":338,"venue":298,"cited_by_count":90,"oa_url":295,"card":350,"direction":108,"ingested_from":109},"W7223117203",[339,342,344,347],{"name":340,"orcid":341},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":343,"orcid":9},"Wentao Lv",{"name":345,"orcid":346},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":348,"orcid":349},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":351,"method":352,"finding":353,"direction":108,"opportunity":354},"提出TMST网络，融合SAR与多光谱时序数据实现作物制图。","Sentinel-1\u002F2时序、极化-时序特征选择、多尺度注意力与时空编码器。","新乡OA达0.8625，多源融合与模块组合有效，跨数据集优于基线。","可探索极化-时序特征选择在跨区域迁移与少样本作物制图中的泛化能力。","2026-10-22T11:20:39.359881Z",{"id":357,"title":358,"url":359,"summary":360,"summary_zh":361,"content":9,"source_name":362,"source_url":359,"published_at":299,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":363,"score_detail":364,"sources":366,"tags":368,"primary_topic":372,"secondary_topics":377,"classification":379,"search_phrases":396,"slug":399,"view_count":90,"doi":400,"paper":401,"created_at":411},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。","Zenodo (CERN European Organization for Nuclear Research)",67,{"impact":21,"substance":18,"depth":19,"authority":17,"freshness":13,"relevant":22,"comment":365},"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[367],{"name":362,"url":359},[306,369,370,28,371],"农业人工智能","产量预测","可解释性",{"code":373,"slug":374,"name":375,"path":376,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[378],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":380},{"candidates":381,"agricultural_evidence":47},[382,388,392],{"code":373,"score":17,"supported":47,"title":383,"abstract":385,"tags":387},[384],"computer vision",[386],"计算机视觉",[369],{"code":33,"score":21,"supported":47,"title":389,"abstract":390,"tags":391},[],[28,50],[370,28],{"code":327,"score":22,"supported":14,"title":393,"abstract":394,"tags":395},[],[],[306],[397,398],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":400,"openalex_id":402,"authors":403,"venue":362,"cited_by_count":90,"oa_url":359,"card":406,"direction":108,"ingested_from":109},"W7223114002",[404],{"name":405,"orcid":9},"Kadiatou Barry",{"tldr":407,"method":408,"finding":409,"direction":108,"opportunity":410},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":413,"title":414,"url":415,"summary":416,"summary_zh":417,"content":9,"source_name":418,"source_url":415,"published_at":419,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":420,"score_detail":421,"sources":423,"tags":425,"primary_topic":429,"secondary_topics":435,"classification":437,"search_phrases":461,"slug":464,"view_count":90,"doi":465,"paper":466,"created_at":508},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",79,{"impact":123,"substance":188,"depth":123,"authority":17,"freshness":189,"relevant":22,"comment":422},"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[424],{"name":418,"url":415},[306,426,427,28,428],"无人机","育种","紫花苜蓿",{"code":430,"slug":431,"name":432,"path":433,"parent_code":434},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[436],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":438},{"candidates":439,"agricultural_evidence":47},[440,446,450,456],{"code":430,"score":143,"supported":47,"title":441,"abstract":443,"tags":445},[442],"alfalfa",[444,442],"苜蓿",[428],{"code":33,"score":17,"supported":47,"title":447,"abstract":448,"tags":449},[317],[317],[28],{"code":327,"score":46,"supported":47,"title":451,"abstract":452,"tags":455},[],[453,454],"精准农业","precision agriculture",[306],{"code":457,"score":69,"supported":47,"title":458,"abstract":459,"tags":460},"C4",[],[444],[428],[462,463],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":465,"openalex_id":467,"authors":468,"venue":418,"cited_by_count":90,"oa_url":415,"card":503,"direction":108,"ingested_from":109},"W7223083966",[469,472,475,478,481,483,485,488,491,494,497,500],{"name":470,"orcid":471},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":473,"orcid":474},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":476,"orcid":477},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":479,"orcid":480},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":482,"orcid":9},"Julie Hansen",{"name":484,"orcid":9},"Christopher Pierce",{"name":486,"orcid":487},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":489,"orcid":490},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":492,"orcid":493},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":495,"orcid":496},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":498,"orcid":499},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":501,"orcid":502},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":504,"method":505,"finding":506,"direction":108,"opportunity":507},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z"]