[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1787":3,"related-1787":70},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":64,"slug":67,"view_count":68,"doi":8,"paper":8,"created_at":69},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。",null,"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",10,false,84,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},25,20,18,15,6,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字农业","农情监测","遥感","天空地一体化","学术研讨会",{"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},[45,51,57],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":50},13,true,[28],[29],[28,29],{"code":52,"score":53,"supported":47,"title":54,"abstract":55,"tags":56},"I1",12,[27],[27],[27],{"code":58,"score":59,"supported":47,"title":60,"abstract":61,"tags":63},"G1",3,[],[62],"技术培训",[],[65,66],"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",{"total":21,"page":22,"page_size":21,"items":71},[72,194,284,350,402,434],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":77,"content":8,"source_name":78,"source_url":75,"published_at":79,"category":80,"cover_url":8,"hotness":13,"is_selected":14,"score":81,"score_detail":82,"sources":87,"tags":89,"primary_topic":93,"secondary_topics":99,"classification":101,"search_phrases":143,"slug":146,"view_count":147,"doi":148,"paper":149,"created_at":193},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","论文",67,{"impact":83,"substance":18,"depth":84,"authority":46,"freshness":85,"relevant":22,"comment":86},8,17,9,"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。",[88],{"name":78,"url":75},[27,90,91,29,92],"随机森林","土壤有机碳","土壤制图",{"code":94,"slug":95,"name":96,"path":97,"parent_code":98},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[100],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":102},{"candidates":103,"agricultural_evidence":47},[104,110,115,122,128,134,138],{"code":94,"score":105,"supported":47,"title":106,"abstract":107,"tags":109},16,[95],[108,95],"土壤",[92,91],{"code":33,"score":20,"supported":47,"title":111,"abstract":113,"tags":114},[112],"remote sensing",[29,112],[29],{"code":116,"score":21,"supported":47,"title":117,"abstract":118,"tags":121},"F3",[],[119,120],"肥料","fertilizer",[],{"code":123,"score":59,"supported":47,"title":124,"abstract":125,"tags":127},"H8",[],[126],"可持续农业",[],{"code":129,"score":59,"supported":47,"title":130,"abstract":131,"tags":133},"I4",[],[132],"空间分析",[],{"code":52,"score":22,"supported":14,"title":135,"abstract":136,"tags":137},[],[],[27],{"code":139,"score":22,"supported":14,"title":140,"abstract":141,"tags":142},"I2",[],[],[90],[144,145],"Palmital Farm 土壤有机碳","Minas Gerais 遥感 土壤碳","PalmitalFarm土壤有机碳-5102",0,"10.1007\u002Fs42729-026-03629-3",{"doi":148,"openalex_id":150,"authors":151,"venue":78,"cited_by_count":147,"oa_url":185,"card":186,"direction":190,"ingested_from":192},"W7222335269",[152,155,158,161,164,167,170,173,176,179,182],{"name":153,"orcid":154},"Leonardo Augusto de Sousa","https:\u002F\u002Forcid.org\u002F0009-0003-1560-5600",{"name":156,"orcid":157},"Fernanda Almeida Bócoli","https:\u002F\u002Forcid.org\u002F0000-0001-5476-6886",{"name":159,"orcid":160},"Eduane José de Pádua","https:\u002F\u002Forcid.org\u002F0009-0003-1782-2756",{"name":162,"orcid":163},"Luiza Maria Pereira Pierangeli","https:\u002F\u002Forcid.org\u002F0000-0002-9218-3718",{"name":165,"orcid":166},"Junior César Avanzi","https:\u002F\u002Forcid.org\u002F0000-0003-2455-0325",{"name":168,"orcid":169},"Bruno Montoani Silva","https:\u002F\u002Forcid.org\u002F0000-0002-8240-8987",{"name":171,"orcid":172},"Carlos Victor Lima Albuquerque","https:\u002F\u002Forcid.org\u002F0009-0009-4597-4443",{"name":174,"orcid":175},"Luiz Roberto Guimarães Guilherme","https:\u002F\u002Forcid.org\u002F0000-0002-5387-6028",{"name":177,"orcid":178},"Marco Aurélio Carbone Carneiro","https:\u002F\u002Forcid.org\u002F0000-0003-4349-3071",{"name":180,"orcid":181},"Nilton Curi","https:\u002F\u002Forcid.org\u002F0000-0002-2604-0866",{"name":183,"orcid":184},"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":187,"method":188,"finding":189,"direction":190,"opportunity":191},"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。","农业遥感与作物表型","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。","openalex","2026-10-16T23:30:31.433840Z",{"id":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":8,"source_name":200,"source_url":197,"published_at":201,"category":80,"cover_url":8,"hotness":13,"is_selected":14,"score":202,"score_detail":203,"sources":206,"tags":208,"primary_topic":212,"secondary_topics":217,"classification":223,"search_phrases":267,"slug":270,"view_count":22,"doi":271,"paper":272,"created_at":283},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":19,"substance":204,"depth":84,"authority":46,"freshness":83,"relevant":22,"comment":205},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。",[207],{"name":200,"url":197},[27,209,29,210,211],"粮食安全","GIS","灌溉适宜性",{"code":213,"slug":214,"name":215,"path":216,"parent_code":98},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[218,219],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":129,"slug":220,"name":221,"path":222,"parent_code":37},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":40,"locked":14,"version":41,"method":42,"evidence":224},{"candidates":225,"agricultural_evidence":47},[226,232,236,240,247,253,257,263],{"code":213,"score":84,"supported":47,"title":227,"abstract":228,"tags":231},[214],[229,214,230],"灌溉","evapotranspiration",[],{"code":33,"score":20,"supported":47,"title":233,"abstract":234,"tags":235},[112],[29,112],[29],{"code":129,"score":85,"supported":47,"title":237,"abstract":238,"tags":239},[210],[],[210],{"code":241,"score":242,"supported":47,"title":243,"abstract":244,"tags":246},"A8",7,[],[209,245],"food security",[209],{"code":248,"score":59,"supported":47,"title":249,"abstract":250,"tags":252},"H7",[],[251],"干旱",[],{"code":123,"score":59,"supported":47,"title":254,"abstract":255,"tags":256},[],[126],[],{"code":258,"score":59,"supported":47,"title":259,"abstract":260,"tags":262},"J1",[],[261],"农业规划",[],{"code":52,"score":22,"supported":14,"title":264,"abstract":265,"tags":266},[],[],[27],[268,269],"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":271,"openalex_id":273,"authors":274,"venue":8,"cited_by_count":147,"oa_url":8,"card":278,"direction":190,"ingested_from":192},"W7214955379",[275],{"name":276,"orcid":277},"Emmanuel M. Menegbo","https:\u002F\u002Forcid.org\u002F0000-0002-6694-3226",{"tldr":279,"method":280,"finding":281,"direction":190,"opportunity":282},"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":285,"title":286,"url":287,"summary":288,"summary_zh":289,"content":8,"source_name":290,"source_url":287,"published_at":291,"category":80,"cover_url":8,"hotness":13,"is_selected":14,"score":292,"score_detail":293,"sources":296,"tags":298,"primary_topic":302,"secondary_topics":308,"classification":310,"search_phrases":326,"slug":329,"view_count":59,"doi":330,"paper":331,"created_at":349},3501,"A Dual-Phenological-Characteristic Weighting Method to Reconcile Time Discrepancies in Soybean Phenology Estimation from MODIS NDVI Time Series","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193300","Accurate large-scale monitoring of crop phenology is essential for optimizing agricultural management. Remote sensing has been widely used for estimating crop phenological stages, yet time discrepancies often exist between remotely sensed phenological metrics and ground-observed growth stages. Moreover, phenological parameters derived from different characterization models exhibit varying degrees of deviation from field observations. The primary goal of this study was to develop a novel method that fully exploits the deviation patterns of diverse phenological parameters to enhance the accuracy of soybean phenology retrieval. To this end, we extracted 11 phenological parameters for six key growth stages—emerged, blooming, pod-setting, turning yellow, dropping leaf, and harvest—of soybean across 16 U.S. states using MODIS NDVI (normalized difference vegetation index) time-series data from 2000 to 2020, employing GU-, curvature-, and derivative-based phenological modeling methods. The study design centered on proposing a dual-phenological-characteristic weighting (DPCW) method that leverages the deviation features of different phenological parameters relative to ground-observed growth stages, generating composite phenological characteristics by pairing two distinct parameters. The key innovation of this paper is the use of dual-feature weighting to improve the correspondence between satellite-derived phenometrics and field observations, offering an alternative to conventional phenological estimation. The results demonstrated that the optimal DPCW-based combinations for the six growth stages were SOS (start of season) and GREEN, SOS and POS (peak of season), MATURITY and POS, EOS (end of season) and SENES (senescence), RD (recession date) and DD (downturn date), and EOS and DORM (dormancy), respectively. The coefficient of determination (R2) between the retrieved transition dates and ground observations exceeded 0.65 for most stages, with the emerged stage improving to 0.47 from 0.052 and 0.357 of the unadjusted and offset-adjusted benchmarks. The average root mean square error (RMSE) was less than 5 days in most cases, representing a reduction of over 40%, with the most substantial improvement at the turning yellow stage, where RMSE dropped from 12.8 days to 2.8 days. A strength of this study lies in its multi-state, multi-decade validation, demonstrating the robustness and temporal consistency of the DPCW method within the major U.S. soybean-growing region. However, a limitation is that the method’s performance may vary with different satellite sensors or crop types, warranting further investigation. The proposed approach is expected to enhance the accuracy of remote sensing-based crop phenology monitoring and offers an effective alternative for calibrating remotely sensed phenological parameters.","准确的大尺度作物物候监测对于优化农业管理至关重要。遥感已被广泛用于估算作物物候阶段，但遥感物候指标与地面观测生育阶段之间常存在时间差异。此外，不同特征化模型衍生的物候参数与田间观测之间存在不同程度的偏差。本研究的主要目标是开发一种新方法，充分利用多种物候参数的偏差模式，以提高大豆物候反演精度。为此，我们利用2000—2020年MODIS NDVI（归一化差异植被指数）时间序列数据，采用基于GU、曲率和导数的方法提取了美国16个州大豆六个关键生育阶段——出苗、开花、结荚、黄化、落叶和收获——的11个物候参数。研究设计的核心是提出一种双物候特征加权（dual-phenological-characteristic weighting，DPCW）方法，该方法利用不同物候参数相对于地面观测生育阶段的偏差特征，通过配对两个不同参数生成复合物候特征。本文的关键创新在于利用双特征加权提高卫星衍生物候指标与田间观测之间的对应关系，为传统物候估算提供了一种替代方案。结果表明，六个生育阶段基于DPCW的最优组合分别为SOS（生长季开始）与GREEN、SOS与POS（生长季峰值）、MATURITY与POS、EOS（生长季结束）与SENES（衰老）、RD（衰退日期）与DD（下降日期）以及EOS与DORM（休眠）。反演得到的转换日期与地面观测之间的决定系数（R²）在大多数阶段超过0.65，其中出苗阶段从基准的0.052和偏移调整后的0.357提高至0.47。大多数情况下平均均方根误差（RMSE）小于5天，降幅超过40%，其中黄化阶段改善最为显著，RMSE从12.8天降至2.8天。本研究的一个优势在于其多州、多年代际验证，证明了DPCW方法在美国主要大豆种植区内的稳健性和时间一致性。","Remote Sensing","2026-09-24T00:00:00Z",81,{"impact":19,"substance":204,"depth":19,"authority":294,"freshness":85,"relevant":22,"comment":295},14,"提出双物候特征加权方法，用MODIS NDVI长时序数据校正大豆物候遥感估算偏差，方法新颖、验证扎实，对农情遥感监测有参考价值。",[297],{"name":290,"url":287},[299,300,28,29,301],"智慧农业","大豆","作物表型",{"code":303,"slug":304,"name":305,"path":306,"parent_code":307},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[309],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":311},{"candidates":312,"agricultural_evidence":47},[313,318,322],{"code":303,"score":20,"supported":47,"title":314,"abstract":316,"tags":317},[315],"soybean",[300,315],[300],{"code":33,"score":85,"supported":47,"title":319,"abstract":320,"tags":321},[],[29,112],[301,28,29],{"code":52,"score":22,"supported":14,"title":323,"abstract":324,"tags":325},[],[],[299],[327,328],"MODIS NDVI 大豆 物候","美国大豆 遥感 物候监测","MODISNDVI大豆物候-3501","10.3390\u002Frs18193300",{"doi":330,"openalex_id":332,"authors":333,"venue":290,"cited_by_count":147,"oa_url":287,"card":344,"direction":190,"ingested_from":192},"W7214203102",[334,336,339,342],{"name":335,"orcid":8},"Qiuxiang Yi",{"name":337,"orcid":338},"Siting Chen","https:\u002F\u002Forcid.org\u002F0000-0003-3468-9320",{"name":340,"orcid":341},"Fumin Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5078-358X",{"name":343,"orcid":8},"Qinyan Zhu",{"tldr":345,"method":346,"finding":347,"direction":190,"opportunity":348},"提出双物候特征加权法，校正MODIS NDVI大豆物候估计与地面观测的时间偏差。","用MODIS NDVI 2000-2020数据，结合GU、曲率、导数三类物候模型","多数生育期R²超0.65，RMSE多小于5天，降幅超40%，转黄期RMSE从12.8天降至2.8天。","可将该加权校正思路迁移到其他作物与多源遥感数据，并探索自适应权重与深度学习融合的物候反演。","2026-09-25T23:30:31.075499Z",{"id":351,"title":352,"url":353,"summary":354,"summary_zh":8,"content":8,"source_name":355,"source_url":353,"published_at":356,"category":80,"cover_url":8,"hotness":13,"is_selected":14,"score":357,"score_detail":358,"sources":360,"tags":362,"primary_topic":366,"secondary_topics":367,"classification":368,"search_phrases":379,"slug":382,"view_count":383,"doi":384,"paper":385,"created_at":401},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","Nature Food","2026-09-10T00:00:00Z",79,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":83,"relevant":22,"comment":359},"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。",[361],{"name":355,"url":353},[27,29,363,364,365],"农业治理","欧盟农业","数据公平",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":369},{"candidates":370,"agricultural_evidence":47},[371,375],{"code":33,"score":53,"supported":47,"title":372,"abstract":373,"tags":374},[112],[112],[29],{"code":52,"score":22,"supported":14,"title":376,"abstract":377,"tags":378},[],[],[27],[380,381],"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":384,"openalex_id":386,"authors":387,"venue":355,"cited_by_count":147,"oa_url":8,"card":8,"direction":190,"ingested_from":192},"W7212112693",[388,390,393,396,398],{"name":389,"orcid":8},"Yeying Zhou",{"name":391,"orcid":392},"Andrea Fiorini","https:\u002F\u002Forcid.org\u002F0000-0002-5601-2954",{"name":394,"orcid":395},"Michele Croci","https:\u002F\u002Forcid.org\u002F0000-0001-7356-2774",{"name":397,"orcid":8},"Xiaotong Tang",{"name":399,"orcid":400},"Davide Cammarano","https:\u002F\u002Forcid.org\u002F0000-0003-0918-550X","2026-09-11T23:30:29.468515Z",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":8,"content":8,"source_name":407,"source_url":8,"published_at":408,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":409,"score_detail":410,"sources":412,"tags":414,"primary_topic":416,"secondary_topics":417,"classification":418,"search_phrases":429,"slug":432,"view_count":59,"doi":8,"paper":8,"created_at":433},1861,"从人跑眼看到卫星巡田——山东省农科院构建天空地一体化遥感数据采集处理体系","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260903\u002F20260903_2\u002Fnmrb_20260903_13395_2_2095256867823521795.html","山东省农业科学院农业信息与经济研究所构建起一套天空地一体化的遥感数据采集处理体系。目标是实现县域2米、5天和省域10米、7天的高频立体监测；2024年开始耗时2年与中国科学院空天信息创新研究院、中国农业科学院等团队合作研发了一款面向县域的大型无人机。","《农民日报》2026年09月03日 第2版","2026-09-03T00:00:00Z",75,{"impact":204,"substance":18,"depth":105,"authority":53,"freshness":383,"relevant":22,"comment":411},"省级农科院构建天空地一体化遥感体系，实现高频立体监测，技术进展显著，具有行业参考价值。",[413],{"name":407,"url":405},[299,415,29,30],"农业监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":419},{"candidates":420,"agricultural_evidence":47},[421,425],{"code":33,"score":53,"supported":47,"title":422,"abstract":423,"tags":424},[29],[29],[29],{"code":52,"score":22,"supported":14,"title":426,"abstract":427,"tags":428},[],[],[299],[430,431],"天空地一体化 农业监测 智慧农业 遥感","天空地一体化 农业监测","天空地一体化农业监测智慧农业遥感-1861","2026-09-08T00:07:13.532365Z",{"id":435,"title":436,"url":437,"summary":438,"summary_zh":8,"content":8,"source_name":439,"source_url":8,"published_at":8,"category":80,"cover_url":8,"hotness":13,"is_selected":14,"score":440,"score_detail":441,"sources":443,"tags":445,"primary_topic":450,"secondary_topics":451,"classification":452,"search_phrases":475,"slug":478,"view_count":383,"doi":8,"paper":8,"created_at":479},341,"对地观测驱动的数字(智慧)农业研究前沿与应用案例","https:\u002F\u002Fcastjournals.cast.org.cn\u002Fjoweb\u002Fzhny\u002FCN\u002FY2026\u002FV8\u002FI2\u002F1","吴炳方、马慧、张淼、潘庆成、张翔、陈水森、邱炳文、徐新刚、刘建红、范锦龙、黄健熙、江佳乐、何昌垂在《智慧农业(中英文)》2026年第8卷第2期发表文章。数字农业的本质在于通过物联网、大数据、人工智能及遥感等信息技术,实现农业生产全过程的数字化映射与智能化管控。文章从对地观测视角系统剖析数字(智慧)农业研究前沿与典型案例:农情智慧监测(全口径作物制图、精准产量预测和灾害监测评估与预警)、数字孪生农场(全要素数字化建模、动态科学指导精准生产)、农业系统韧性(应对气候变化挑战)。结合农情遥感智能监测系统与农业水管理系统两个典型案例,展示数字(智慧)农业的云端服务能力,并分析影响商业化与业务化进程的多重阻力。","智慧农业(中英文) 2026, 8(2): 1-17",76,{"impact":204,"substance":18,"depth":19,"authority":294,"freshness":68,"relevant":22,"comment":442},"核心期刊综述，系统梳理对地观测驱动的智慧农业前沿与应用，具有较高学术价值和行业参考意义，但时效性较低。",[444],{"name":439,"url":437},[27,299,446,447,448,28,449],"数字孪生","智慧农业8","对地观测","农业系统韧性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":453},{"candidates":454,"agricultural_evidence":47},[455,459,464,469],{"code":52,"score":53,"supported":47,"title":456,"abstract":457,"tags":458},[],[299,27,446],[27,446,299],{"code":33,"score":460,"supported":47,"title":461,"abstract":462,"tags":463},4,[],[29],[28],{"code":248,"score":59,"supported":47,"title":465,"abstract":466,"tags":468},[],[467],"气候变化",[],{"code":470,"score":59,"supported":47,"title":471,"abstract":472,"tags":474},"I5",[],[473],"物联网",[],[476,477],"农业系统韧性 智慧农业8 农情监测 对地观测","农业系统韧性 智慧农业8","农业系统韧性智慧农业8农情监测对地观测-341","2026-08-09T23:54:49.621624Z"]