[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2159":3,"related-2159":81},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":55,"slug":58,"view_count":59,"doi":60,"paper":61,"created_at":80},2159,"Remote sensing, fairness and institutional capacity in European agricultural governance","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs43016-026-01427-9","Remote sensing, fairness and institutional capacity in European agricultural governance。Nature Food",null,"Nature Food","2026-09-10T00:00:00Z","论文",10,false,79,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,20,15,8,1,"Nature Food 论文，探讨遥感技术在欧洲农业治理中的公平性与制度能力，属农业信息化与数字治理交叉议题，方法新颖、信源权威，但偏学术且地域限于欧洲，公共影响有限。",[23],{"name":9,"url":6},[25,26,27,28,29],"数字农业","遥感","农业治理","欧盟农业","数据公平",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},12,true,[47],"remote sensing",[47],[26],{"code":51,"score":20,"supported":13,"title":52,"abstract":53,"tags":54},"I1",[],[],[25],[56,57],"农业治理 数字农业 数据公平 欧盟农业","农业治理 数字农业","农业治理数字农业数据公平欧盟农业-2159",5,"10.1038\u002Fs43016-026-01427-9",{"doi":60,"openalex_id":62,"authors":63,"venue":9,"cited_by_count":77,"oa_url":8,"card":8,"direction":78,"ingested_from":79},"W7212112693",[64,66,69,72,74],{"name":65,"orcid":8},"Yeying Zhou",{"name":67,"orcid":68},"Andrea Fiorini","https:\u002F\u002Forcid.org\u002F0000-0002-5601-2954",{"name":70,"orcid":71},"Michele Croci","https:\u002F\u002Forcid.org\u002F0000-0001-7356-2774",{"name":73,"orcid":8},"Xiaotong Tang",{"name":75,"orcid":76},"Davide Cammarano","https:\u002F\u002Forcid.org\u002F0000-0003-0918-550X",0,"农业遥感与作物表型","openalex","2026-09-11T23:30:29.468515Z",{"total":82,"page":20,"page_size":82,"items":83},6,[84,202,292,336,397,451],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":8,"source_name":90,"source_url":87,"published_at":91,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":92,"score_detail":93,"sources":98,"tags":100,"primary_topic":104,"secondary_topics":110,"classification":112,"search_phrases":154,"slug":157,"view_count":77,"doi":158,"paper":159,"created_at":201},5102,"Integrating Proximal and Remote Sensing to Improve Characterization and Mapping of Soil Organic Carbon Stocks under Contrasting Land Uses","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42729-026-03629-3","Abstract Soils are fundamental to support ecosystem functions. Therefore, understanding soil organic carbon stock (SCS) as a strategic environmental component for climate regulation, sustainable agricultural practices, and maximizing fertilizer application is essential. This study aimed to predict and map the SCS at Palmital Farm, Minas Gerais state, using land use, parent materials, soil classes, proximal and remote sensing data as predictor variables. A total of 76 soil samples were collected in the surface (A) and subsurface (B) horizons using a regular grid design (200 × 200 m). Carbon content was determined using the Walkley–Black method. Prediction models were created via Random Forest algorithm and validated through leave-one-out cross-validation. Environmental and anthropogenic influences on SCS were analyzed statistically and spatially. The highest mean SCS values were found at the surface horizon under native vegetation and pasture areas (~ 33 Mg ha⁻¹), due to continuous organic matter deposition and lack of disturbance. Charnockite-derived soils showed higher SCS (31.24 Mg ha⁻¹, surface). Among soil classes, Argissolos stood out reaching the highest SCS (35.86 Mg ha⁻¹). Remote sensing data and terrain attributes yielded the best SCS prediction (R²=0.43). The main factors influencing SCS were land use\u002Fmanagement and soil texture. Also, strong correlations were observed between vegetation indices, some terrain attributes and SCS. This approach, combining multiple variables, indicated a moderate potential to achieve accurate SCS predictions using these data, demonstrating the high variability and complexity of SCS spatial variation in detailed-scale mapping.","摘要 土壤是支撑生态系统功能的基础。因此，理解土壤有机碳储量（SCS）作为气候调节、可持续农业实践和最大化肥料施用的战略性环境组成部分至关重要。本研究旨在利用土地利用、成土母质、土壤类别、近端和遥感数据作为预测变量，对米纳斯吉拉斯州Palmital农场的SCS进行预测和制图。采用规则网格设计（200 × 200 m），在表层（A）和亚表层（B）共采集76个土壤样品。碳含量采用Walkley–Black法测定。通过随机森林算法建立预测模型，并采用留一交叉验证进行验证。对环境和人为因素对SCS的影响进行了统计和空间分析。表层中原生植被和牧草地利用方式下的SCS均值最高（约33 Mg ha⁻¹），这归因于有机质的持续沉积和缺乏扰动。源自紫苏花岗岩的土壤表现出较高的SCS（31.24 Mg ha⁻¹，表层）。在土壤类别中，Argissolos表现突出，达到最高SCS（35.86 Mg ha⁻¹）。遥感数据和地形属性获得了最佳的SCS预测效果（R²=0.43）。影响SCS的主要因素为土地利用\u002F管理和土壤质地。此外，植被指数、部分地形属性与SCS之间观察到较强的相关性。这种结合多变量的方法表明，利用这些数据实现准确SCS预测具有中等潜力，同时也揭示了详细尺度制图中SCS空间变异的高变异性和复杂性。","Journal of soil science and plant nutrition","2026-10-14T00:00:00Z",67,{"impact":19,"substance":17,"depth":94,"authority":95,"freshness":96,"relevant":20,"comment":97},17,13,9,"该研究结合近地与遥感数据及随机森林算法预测土壤有机碳储量，方法新颖、数据扎实，对农业信息化与精准农业有参考价值，但属区域性案例研究，影响范围有限。",[99],{"name":90,"url":87},[25,101,102,26,103],"随机森林","土壤有机碳","土壤制图",{"code":105,"slug":106,"name":107,"path":108,"parent_code":109},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[111],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":113},{"candidates":114,"agricultural_evidence":45},[115,121,125,132,139,145,149],{"code":105,"score":116,"supported":45,"title":117,"abstract":118,"tags":120},16,[106],[119,106],"土壤",[103,102],{"code":31,"score":18,"supported":45,"title":122,"abstract":123,"tags":124},[47],[26,47],[26],{"code":126,"score":82,"supported":45,"title":127,"abstract":128,"tags":131},"F3",[],[129,130],"肥料","fertilizer",[],{"code":133,"score":134,"supported":45,"title":135,"abstract":136,"tags":138},"H8",3,[],[137],"可持续农业",[],{"code":140,"score":134,"supported":45,"title":141,"abstract":142,"tags":144},"I4",[],[143],"空间分析",[],{"code":51,"score":20,"supported":13,"title":146,"abstract":147,"tags":148},[],[],[25],{"code":150,"score":20,"supported":13,"title":151,"abstract":152,"tags":153},"I2",[],[],[101],[155,156],"Palmital Farm 土壤有机碳","Minas Gerais 遥感 土壤碳","PalmitalFarm土壤有机碳-5102","10.1007\u002Fs42729-026-03629-3",{"doi":158,"openalex_id":160,"authors":161,"venue":90,"cited_by_count":77,"oa_url":195,"card":196,"direction":78,"ingested_from":79},"W7222335269",[162,165,168,171,174,177,180,183,186,189,192],{"name":163,"orcid":164},"Leonardo Augusto de Sousa","https:\u002F\u002Forcid.org\u002F0009-0003-1560-5600",{"name":166,"orcid":167},"Fernanda Almeida Bócoli","https:\u002F\u002Forcid.org\u002F0000-0001-5476-6886",{"name":169,"orcid":170},"Eduane José de Pádua","https:\u002F\u002Forcid.org\u002F0009-0003-1782-2756",{"name":172,"orcid":173},"Luiza Maria Pereira Pierangeli","https:\u002F\u002Forcid.org\u002F0000-0002-9218-3718",{"name":175,"orcid":176},"Junior César Avanzi","https:\u002F\u002Forcid.org\u002F0000-0003-2455-0325",{"name":178,"orcid":179},"Bruno Montoani Silva","https:\u002F\u002Forcid.org\u002F0000-0002-8240-8987",{"name":181,"orcid":182},"Carlos Victor Lima Albuquerque","https:\u002F\u002Forcid.org\u002F0009-0009-4597-4443",{"name":184,"orcid":185},"Luiz Roberto Guimarães Guilherme","https:\u002F\u002Forcid.org\u002F0000-0002-5387-6028",{"name":187,"orcid":188},"Marco Aurélio Carbone Carneiro","https:\u002F\u002Forcid.org\u002F0000-0003-4349-3071",{"name":190,"orcid":191},"Nilton Curi","https:\u002F\u002Forcid.org\u002F0000-0002-2604-0866",{"name":193,"orcid":194},"Sérgio Henrique Godinho Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2750-5976","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs42729-026-03629-3.pdf",{"tldr":197,"method":198,"finding":199,"direction":78,"opportunity":200},"结合近地与遥感数据及随机森林预测并制图巴西农场不同土地利用下的土壤有机碳储量。","76个样点、Walkley-Black法测碳，随机森林建模，留一交叉验证。","遥感与地形因子预测最佳（R²=0.43），土地利用和土壤质地主导碳储量空间变异。","可探索多源遥感与深度学习方法提升小尺度土壤碳制图精度，并耦合管理措施情景。","2026-10-16T23:30:31.433840Z",{"id":203,"title":204,"url":205,"summary":206,"summary_zh":207,"content":8,"source_name":208,"source_url":205,"published_at":209,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":210,"score_detail":211,"sources":214,"tags":216,"primary_topic":220,"secondary_topics":225,"classification":231,"search_phrases":275,"slug":278,"view_count":20,"doi":279,"paper":280,"created_at":291},3901,"Geospatial analysis of irrigation suitability in Nigeria using remote sensing and GIS","https:\u002F\u002Fdoi.org\u002F10.53030\u002Ftjags.1937471","This research offers a comprehensive geospatial evaluation of land suitability for irrigation across Nigeria. It leverages 2020 Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, processed within the Google Earth Engine (GEE) cloud-based computational environment. Given growing concerns about food security and climate variability, ascertaining areas amenable to irrigation is paramount for fostering sustainable agricultural development within Nigeria. This investigation integrated multi-temporal remote sensing observations, including the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Water Index - NDWI, to construct an Irrigation Suitability Index (ISI). These datasets underwent analysis at a 500-meter spatial resolution, encompassing the entirety of the nation. Weighted overlay analysis, performed within the GEE environment, generated a nationwide map delineating irrigation potential across five distinct categories: Very Low, Low, Moderate, High, and Very High suitability. The findings reveal suitability scores ranging from a minimum of 0.11 (very low) to a maximum of 1 (very high). Specifically, northern Nigerian states such as Sokoto, Kebbi, Yobe, and Borno demonstrated high to very high suitability. In contrast, southern states, including Lagos, Rivers, Cross River, and Delta, were characterized by very low suitability. The study underscores significant geographical disparities, particularly in arid northern regions which experience greater constraints due to lower precipitation and elevated evapotranspiration. These findings offer actionable directives for agricultural planners, policymakers, and stakeholders seeking to enhance irrigation capacity and strengthen food security resilience amidst evolving climatic conditions.","本研究对尼日利亚全国灌溉土地适宜性进行了综合地理空间评估。研究利用2020年中分辨率成像光谱仪（MODIS）卫星数据，在谷歌地球引擎（GEE）云端计算环境中进行处理。鉴于粮食安全和气候变率日益受到关注，确定适宜灌溉的区域对于促进尼日利亚可持续农业发展至关重要。本研究整合了多时相遥感观测数据，包括归一化植被指数（NDVI）、地表温度（LST）和归一化水体指数（NDWI），以构建灌溉适宜性指数（ISI）。这些数据集以500米空间分辨率进行分析，覆盖全国范围。在GEE环境中进行的加权叠加分析生成了全国灌溉潜力分布图，划分为五个类别：极低、低、中等、高和极高适宜性。研究结果显示，适宜性评分从最低0.11（极低）到最高1（极高）不等。具体而言，尼日利亚北部各州如索科托、凯比、约贝和博尔诺表现出高至极高适宜性。相比之下，南部各州包括拉各斯、河流州、十字河州和三角洲州则以极低适宜性为特征。研究强调了显著的地理差异，特别是干旱的北部地区因降水量较低和蒸散量较高而面临更大限制。这些发现为农业规划者、政策制定者和利益相关者提供了可操作的指导，以在气候条件不断变化的情况下增强灌溉能力并提升粮食安全韧性。","OpenAlex","2026-09-30T00:00:00Z",78,{"impact":16,"substance":212,"depth":94,"authority":95,"freshness":19,"relevant":20,"comment":213},22,"基于MODIS与GEE的全国尺度灌溉适宜性制图，方法规范、结论具体，对农业规划有参考价值，但属他国案例，国内落地性有限。",[215],{"name":208,"url":205},[25,217,26,218,219],"粮食安全","GIS","灌溉适宜性",{"code":221,"slug":222,"name":223,"path":224,"parent_code":109},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[226,227],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":140,"slug":228,"name":229,"path":230,"parent_code":35},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":38,"locked":13,"version":39,"method":40,"evidence":232},{"candidates":233,"agricultural_evidence":45},[234,240,244,248,255,261,265,271],{"code":221,"score":94,"supported":45,"title":235,"abstract":236,"tags":239},[222],[237,222,238],"灌溉","evapotranspiration",[],{"code":31,"score":18,"supported":45,"title":241,"abstract":242,"tags":243},[47],[26,47],[26],{"code":140,"score":96,"supported":45,"title":245,"abstract":246,"tags":247},[218],[],[218],{"code":249,"score":250,"supported":45,"title":251,"abstract":252,"tags":254},"A8",7,[],[217,253],"food security",[217],{"code":256,"score":134,"supported":45,"title":257,"abstract":258,"tags":260},"H7",[],[259],"干旱",[],{"code":133,"score":134,"supported":45,"title":262,"abstract":263,"tags":264},[],[137],[],{"code":266,"score":134,"supported":45,"title":267,"abstract":268,"tags":270},"J1",[],[269],"农业规划",[],{"code":51,"score":20,"supported":13,"title":272,"abstract":273,"tags":274},[],[],[25],[276,277],"尼日利亚 灌溉适宜性 遥感","Google Earth Engine 灌溉","尼日利亚灌溉适宜性遥感-3901","10.53030\u002Ftjags.1937471",{"doi":279,"openalex_id":281,"authors":282,"venue":8,"cited_by_count":77,"oa_url":8,"card":286,"direction":78,"ingested_from":79},"W7214955379",[283],{"name":284,"orcid":285},"Emmanuel M. Menegbo","https:\u002F\u002Forcid.org\u002F0000-0002-6694-3226",{"tldr":287,"method":288,"finding":289,"direction":78,"opportunity":290},"用遥感与GIS评估尼日利亚全国灌溉适宜性并绘制五级分区图。","2020年MODIS数据在GEE中计算NDVI、LST、NDWI并加权叠加。","北部各州适宜性高，南部低，全国适宜性得分0.11至1。","可结合土壤、地下水与作物需水数据，将静态适宜性图升级为动态灌溉决策支持系统。","2026-10-01T23:30:29.159371Z",{"id":293,"title":294,"url":295,"summary":296,"summary_zh":8,"content":297,"source_name":298,"source_url":8,"published_at":299,"category":300,"cover_url":8,"hotness":12,"is_selected":13,"score":301,"score_detail":302,"sources":305,"tags":307,"primary_topic":311,"secondary_topics":312,"classification":313,"search_phrases":330,"slug":333,"view_count":334,"doi":8,"paper":8,"created_at":335},1787,"第四届数字农业学术研讨会在郑州召开——空天院等主办聚焦\"天空地一体化\"农情监测","https:\u002F\u002Faircas.cas.cn\u002Fdtxw\u002Fhzjl\u002F202609\u002Ft20260904_8278690.html","由中国科学院空天信息创新研究院、数字农业专业委员会主办的第四届数字农业学术研讨会在郑州召开。空天院吴炳方研究员强调数字农业需实现\"高分辨率化、平台化、终端化\"三个转化；西北农林科技大学副校长张宏鸣建议全球水蚀评价与FAO\u002FGSP等国际组织协同；北京师范大学潘耀忠教授剖析大尺度遥感作物制图工程化瓶颈。8月7-9日举办首届农情遥感技术培训班以CropWatch云平台开展实战教学。","8月9至11日，第四届数字农业学术研讨会在河南郑州召开。本次研讨会由国际数字地球学会中国国家委员会数字农业专业委员会、中国农学会农业信息分会、中国科学院空天信息创新研究院（空天院）、中国地理信息产业协会农业农村地理信息工作委员会、河南省农业科学院联合主办，河南省农业科学院农业信息技术研究所、遥感与数字地球全国重点实验室承办，以“数字技术驱动农业系统转型”为主题，吸引了来自全国150余家科研院所、高等院校及相关企事业单位的200余名专家学者、行业部门及企业代表参会，是国内数字农业领域极具影响力的一次学术盛会。\n\n河南省农业科学院党委副书记、副院长王强出席会议开幕式并致欢迎辞，他在致辞中表示全球农业正迎来数字化、智能化变革窗口期，生成式AI、空天信息组网、自主无人系统等新质生产力核心技术，正在颠覆传统农业生产模式、重构现代农业发展范式。他指出，河南作为农业大省，亟需数字科创赋能农业提质升级，希望以本次研讨会为纽带，深化全国产学研交流合作，加速数字农业科技创新成果落地转化，助力中原农业现代化建设，赋能全国农业高质量发展。\n\n会议主席、数字农业专业委员会主任委员、空天院研究员吴炳方，以及会议共同主席、中国农学会农业信息分会主任委员、中国农业科学院农业资源与农业区划研究所所长、研究员吴文斌先后致开幕辞，立足行业发展全局，阐述了数字技术在保障粮食安全、优化水土资源配置、推动农食体系升级中的核心价值与重要意义，并展望了“十五五”数字农业领域的发展方向与前景。开幕式由河南省农业科学院农业信息技术研究所所长曾凯主持。\n\n本次研讨会聚焦行业前沿、深耕技术落地，搭建了高水平学术交流平台。会上，8位特邀专家紧扣“数字技术驱动农业系统转型”的核心议题作主旨报告。在宏观战略层面，联合国粮农组织原副总干事、国际欧亚科学院院士何昌垂指出AI赋能食农体系已是战略必答题，需强化顶层设计与“政产学研用”协同创新；空天院研究员吴炳方强调，提出数字农业需实现“高分辨率化、平台化、终端化”三个转化的观点，以便推动数字农业技术从研究走向落地应用；西北农林科技大学副校长张宏鸣建议全球水蚀评价需与FAO\u002FGSP等国际组织协同，共同构建土壤健康风险防控体系建设；北京师范大学教授潘耀忠则直指痛点，明确了大尺度遥感作物制图面临的工程化瓶颈，深度剖析数据覆盖、AI勾绘与模型泛化等核心难题；中国水利水电科学研究院水利研究所研究员吴文勇呈现数字技术在优化水土资源配置、高效灌溉、保障粮食安全中的关键作用；中国科学院东北地理与农业生态研究所研究员刘焕军构建了基于天空地立体监测体系与“地块画像”平台，提出大模型与AI赋能黑土地质量监测与保护的新路径；西南交通大学教授黄健熙立足学术前瞻，展望了遥感数据同化与深度学习耦合、灾害胁迫下产量预测及时空谱农业遥感大模型等未来趋势；中国科学院地理科学与资源研究所研究员董金玮系统剖析了气候变化下洪旱灾害频发及跨境流域水资源统筹对农业气候韧性的关键影响。上述系列报告精准回应了行业痛点、预判了发展态势，为数字农业技术创新和产业应用提供了具有高度实操价值的理论指引与实践路径。\n\n为拓宽交流维度、覆盖多元研究方向，会议设置5个专题分会场与2个学生快闪报告专场，共聚焦遥感技术、地理信息系统、人工智能、大数据等核心数字技术在农业服务中的创新应用，为不同层级、不同领域的科研人员提供思想碰撞、成果展示、经验互通的平台。\n\n会议期间，数字农业专业委员会同步召开2026年度工作会议，系统复盘年度工作成果，审议专项工作报告，深入研讨农情智慧监测、农业大数据AI应用、作物遥感建模、智能农机装备研发、农业系统集成服务等五大核心工作组发展方案，并完成委员会人员增补选举工作，进一步完善专业委员会组织架构，夯实行业科研统筹与服务能力。\n\n本次研讨会配套预热活动成果丰硕，8月7日至9日举办的首届农情遥感技术培训班，以“理论教学+平台实操”的立体化教学模式，依托CropWatch云平台开展实战教学，精准提升农业科研从业者、基层技术人员的遥感数据处理与农情监测实操能力，有效打通了前沿技术落地应用的“最后一公里”。\n\n此次研讨会的圆满召开，有效打通了数字农业领域产学研用沟通壁垒，推动空天信息、智能大数据技术与农业可持续发展深度融合，不仅集中展示了我国数字农业领域的前沿科研成果，更为培育农业新质生产力、推动传统农业转型升级、保障现代农业高质量发展注入了强劲科创动能。\n\n![Image 1](https:\u002F\u002Faircas.cas.cn\u002Fdtxw\u002Fhzjl\u002F202609\u002FW020260904583592815639.png)\n\n大会合影","中国科学院空天信息创新研究院2026年9月4日","2026-09-04T00:00:00Z","报道",84,{"impact":303,"substance":17,"depth":16,"authority":18,"freshness":82,"relevant":20,"comment":304},25,"全国性数字农业学术盛会，汇聚顶尖专家，聚焦天空地一体化农情监测，具有行业引领价值。",[306],{"name":298,"url":295},[25,308,26,309,310],"农情监测","天空地一体化","学术研讨会",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":314},{"candidates":315,"agricultural_evidence":45},[316,320,324],{"code":31,"score":95,"supported":45,"title":317,"abstract":318,"tags":319},[308],[26],[308,26],{"code":51,"score":44,"supported":45,"title":321,"abstract":322,"tags":323},[25],[25],[25],{"code":325,"score":134,"supported":45,"title":326,"abstract":327,"tags":329},"G1",[],[328],"技术培训",[],[331,332],"天空地一体化 学术研讨会 农情监测 数字农业","天空地一体化 学术研讨会","天空地一体化学术研讨会农情监测数字农业-1787",2,"2026-09-07T00:04:37.580467Z",{"id":337,"title":338,"url":339,"summary":340,"summary_zh":341,"content":8,"source_name":342,"source_url":339,"published_at":343,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":344,"score_detail":345,"sources":348,"tags":350,"primary_topic":354,"secondary_topics":355,"classification":356,"search_phrases":372,"slug":375,"view_count":77,"doi":376,"paper":377,"created_at":396},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":44,"substance":17,"depth":94,"authority":346,"freshness":12,"relevant":20,"comment":347},14,"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。",[349],{"name":342,"url":339},[351,352,26,353],"智慧农业","作物制图","SAR",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":357},{"candidates":358,"agricultural_evidence":45},[359,364,368],{"code":31,"score":12,"supported":45,"title":360,"abstract":361,"tags":363},[],[26,47,362],"multispectral",[26],{"code":249,"score":82,"supported":45,"title":365,"abstract":366,"tags":367},[],[217,253],[],{"code":51,"score":20,"supported":13,"title":369,"abstract":370,"tags":371},[],[],[351],[373,374],"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":376,"openalex_id":378,"authors":379,"venue":342,"cited_by_count":77,"oa_url":339,"card":391,"direction":78,"ingested_from":79},"W7223117203",[380,383,385,388],{"name":381,"orcid":382},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":384,"orcid":8},"Wentao Lv",{"name":386,"orcid":387},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":389,"orcid":390},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":392,"method":393,"finding":394,"direction":78,"opportunity":395},"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":398,"title":399,"url":400,"summary":401,"summary_zh":402,"content":8,"source_name":403,"source_url":400,"published_at":343,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":92,"score_detail":404,"sources":406,"tags":408,"primary_topic":412,"secondary_topics":416,"classification":418,"search_phrases":435,"slug":438,"view_count":77,"doi":439,"paper":440,"created_at":450},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)",{"impact":19,"substance":17,"depth":94,"authority":44,"freshness":12,"relevant":20,"comment":405},"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。",[407],{"name":403,"url":400},[351,409,410,26,411],"农业人工智能","产量预测","可解释性",{"code":150,"slug":413,"name":414,"path":415,"parent_code":35},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[417],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":419},{"candidates":420,"agricultural_evidence":45},[421,427,431],{"code":150,"score":44,"supported":45,"title":422,"abstract":424,"tags":426},[423],"computer vision",[425],"计算机视觉",[409],{"code":31,"score":19,"supported":45,"title":428,"abstract":429,"tags":430},[],[26,47],[410,26],{"code":51,"score":20,"supported":13,"title":432,"abstract":433,"tags":434},[],[],[351],[436,437],"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":439,"openalex_id":441,"authors":442,"venue":403,"cited_by_count":77,"oa_url":400,"card":445,"direction":78,"ingested_from":79},"W7223114002",[443],{"name":444,"orcid":8},"Kadiatou Barry",{"tldr":446,"method":447,"finding":448,"direction":78,"opportunity":449},"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":452,"title":453,"url":454,"summary":455,"summary_zh":456,"content":8,"source_name":457,"source_url":454,"published_at":458,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":459,"sources":461,"tags":463,"primary_topic":467,"secondary_topics":473,"classification":475,"search_phrases":500,"slug":503,"view_count":77,"doi":504,"paper":505,"created_at":547},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",{"impact":16,"substance":212,"depth":16,"authority":44,"freshness":96,"relevant":20,"comment":460},"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。",[462],{"name":457,"url":454},[351,464,465,26,466],"无人机","育种","紫花苜蓿",{"code":468,"slug":469,"name":470,"path":471,"parent_code":472},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[474],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":476},{"candidates":477,"agricultural_evidence":45},[478,484,488,494],{"code":468,"score":18,"supported":45,"title":479,"abstract":481,"tags":483},[480],"alfalfa",[482,480],"苜蓿",[466],{"code":31,"score":44,"supported":45,"title":485,"abstract":486,"tags":487},[362],[362],[26],{"code":51,"score":250,"supported":45,"title":489,"abstract":490,"tags":493},[],[491,492],"精准农业","precision agriculture",[351],{"code":495,"score":496,"supported":45,"title":497,"abstract":498,"tags":499},"C4",4,[],[482],[466],[501,502],"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":504,"openalex_id":506,"authors":507,"venue":457,"cited_by_count":77,"oa_url":454,"card":542,"direction":78,"ingested_from":79},"W7223083966",[508,511,514,517,520,522,524,527,530,533,536,539],{"name":509,"orcid":510},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":512,"orcid":513},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":515,"orcid":516},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":518,"orcid":519},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":521,"orcid":8},"Julie Hansen",{"name":523,"orcid":8},"Christopher Pierce",{"name":525,"orcid":526},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":528,"orcid":529},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":531,"orcid":532},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":534,"orcid":535},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":537,"orcid":538},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":540,"orcid":541},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":543,"method":544,"finding":545,"direction":78,"opportunity":546},"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"]