[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3617":3,"related-3617":56},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":55},3617,"Applications and challenges of geospatial technologies in veterinary medicine","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44338-026-00259-y","Geospatial technologies are increasingly recognized as critical assets in veterinary medicine, providing robust frameworks for disease surveillance, risk mapping, and informed decision‑making. The objective of this narrative review is to summarize the role of Geographic Information Systems, Global Positioning System, and Remote Sensing in advancing veterinary medicine by synthesizing current applications, identifying prevailing challenges, and exploring future prospects. Collectively, these tools enable monitoring of disease dynamics, identification of epidemiological hotspots, outbreak prediction, and integration of spatial and epidemiological datasets to guide targeted interventions. For instance, GIS‑based mapping of Rift Valley fever outbreaks in East Africa has enabled rapid identification of epidemiological hotspots and guided vaccination campaigns, while RS‑derived vegetation indices have successfully predicted tick distribution patterns in southern Africa, improving targeted control strategies. Their utility extends beyond livestock health, contributing significantly to wildlife disease monitoring, zoonotic risk assessment, food safety assurance, and broader veterinary decision processes. Despite such successes, adoption remains limited, hindered by technical\u002Finfrastructure barriers, poor data quality, and ethical and policy challenges. In Ethiopia, recent surveys indicate that fewer than 20% of veterinary institutions actively use GIS platforms for routine surveillance, and integration of RS into national veterinary services is still limited to pilot projects. This gap underscores the urgent need for national geospatial data repositories, standardized protocols, and capacity‑building initiatives to ensure equitable access and sustainable utilization. Future opportunities lie in harnessing advances in Artificial Intelligence, Machine Learning, and cloud‑based GIS platforms, alongside global collaboration and training programs, to strengthen veterinary epidemiology and surveillance systems. Addressing these challenges will be essential for the sustainable integration of geospatial technologies into veterinary practice, thereby enhancing disease control, safeguarding public health, and supporting livestock productivity.","地理空间技术在兽医学中日益被视为关键资产，为疾病监测、风险制图和知情决策提供了稳健框架。本叙述性综述旨在总结地理信息系统（Geographic Information Systems, GIS）、全球定位系统（Global Positioning System, GPS）和遥感（Remote Sensing, RS）在推动兽医学发展中的作用，通过综合当前应用、识别主要挑战并探讨未来前景。总体而言，这些工具有助于监测疾病动态、识别流行病学热点、预测疫情，并整合空间与流行病学数据集以指导针对性干预。例如，基于GIS的东非裂谷热疫情制图能够快速识别流行病学热点并指导疫苗接种活动，而遥感衍生的植被指数已成功预测南部非洲蜱虫分布模式，改善了针对性控制策略。其效用不仅限于家畜健康，还对野生动物疾病监测、人兽共患病风险评估、食品安全保障及更广泛的兽医决策过程作出重要贡献。尽管取得了这些成功，其应用仍然有限，受到技术\u002F基础设施障碍、数据质量不佳以及伦理和政策挑战的制约。在埃塞俄比亚，近期调查表明，不到20%的兽医机构在常规监测中积极使用GIS平台，而遥感纳入国家兽医服务仍局限于试点项目。这一差距凸显了建立国家地理空间数据存储库、标准化方案和能力建设举措的迫切需求，以确保公平获取和可持续利用。未来的机遇在于利用人工智能、机器学习和基于云的GIS平台的进步，以及全球合作和培训项目，来加强兽医流行病学和监测系统。应对这些挑战对于将地理空间技术可持续地整合到兽医实践中至关重要，从而增强疾病控制、保障公共卫生并支持家畜生产力。",null,"Discover Animals","2026-09-24T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,12,8,1,"综述系统梳理GIS、GPS与遥感在兽医领域的应用与瓶颈，含东非裂谷热、南部非洲蜱虫分布及埃塞俄比亚使用率不足20%等实证数据，对农业信息化与动物疫病防控有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"动物疫病防控","遥感监测","人兽共患病","兽医信息化","地理信息系统",[33,34],"GIS 兽医 疾病监测","遥感 蜱虫 分布 预测","GIS兽医疾病监测-3617",0,"10.1007\u002Fs44338-026-00259-y",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":46,"card":47,"direction":53,"ingested_from":54},"W7214225608",[41,44],{"name":42,"orcid":43},"Assaye Wollelie Fentie","https:\u002F\u002Forcid.org\u002F0009-0005-9099-9819",{"name":45,"orcid":9},"Daniel Haile Asefa","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44338-026-00259-y.pdf",{"tldr":48,"method":49,"finding":50,"direction":51,"opportunity":52},"综述地理空间技术在兽医领域的应用、挑战与前景，聚焦疾病监测与风险制图。","叙述性综述，整合GIS、GPS、遥感在兽医流行病学中的应用案例与调查数据。","GIS\u002FRS能有效预测疫情和病媒分布，但应用受限于基础设施、数据质量及政策，如埃塞俄比亚使用率不足2","农业遥感与作物表型","可探索AI\u002F机器学习与云GIS结合，构建兽医领域标准化地理空间数据平台与预警系统。","智慧农业 \u002F 农业物联网","openalex","2026-09-27T23:30:26.015992Z",{"total":57,"page":22,"page_size":57,"items":58},6,[59,93,139,179,203,242],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":9,"source_name":65,"source_url":62,"published_at":66,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":36,"score_detail":67,"sources":69,"tags":71,"search_phrases":74,"slug":77,"view_count":22,"doi":78,"paper":79,"created_at":92},2438,"The Ecology, Identification and Control of Bunyaviruses from a One Health Perspective","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.0941.v1","Zoonotic viral diseases continue to pose major challenges to global health, particularly as environmental, social and economic changes increasingly influence interactions among humans, animals, vectors, and ecosystems. This review explores Rift Valley fever virus (RVFV), Crimean-Congo hemorrhagic fever virus (CCHFV), and hantaviruses (HTVs) within the framework of One Health, emphasizing the ecological and epidemiological factors that govern their emergence and transmission. Despite their distinct transmission cycles, these viruses share important determinants of zoonotic risk. RVFV is primarily maintained through interactions among mosquitoes, livestock, wildlife, and humans; CCHFV is sustained within complex tick–vertebrate systems involving Hyalomma ticks, domestic animals, and wildlife; while HTVs are predominantly maintained through persistent infections in mammalian reservoir hosts, particularly rodents. Climate variability, habitat alteration, agricultural expansion, livestock movement, urbanization, and other anthropogenic pressures can modify these transmission systems and create new opportunities for pathogen spillover. Effective prevention and control therefore require integrated surveillance that encompasses human disease, animal populations, vectors or wildlife reservoirs, and environmental conditions. Advances in molecular diagnostics, genomic epidemiology, ecological modeling, remote sensing, and climate-based forecasting provide valuable opportunities for improving early detection and outbreak preparedness. Ultimately, the experiences of RVFV, CCHFV, and HTVs that reducing the burden of zoonotic viral diseases requires sustained collaboration among human and veterinary health professionals, ecologists, entomologists, environmental scientists, and public health authorities. A coordinated One Health approach is consequently essential for anticipating emerging threats, strengthening preparedness, and protecting human, animal, and ecosystem health.","人兽共患病毒病持续对全球健康构成重大挑战，尤其是当环境、社会和经济变化日益影响人类、动物、媒介和生态系统之间的相互作用时。本综述在“同一健康”（One Health）框架下探讨了裂谷热病毒（RVFV）、克里米亚-刚果出血热病毒（CCHFV）和汉坦病毒（HTVs），重点关注决定其出现和传播的生态学与流行病学因素。尽管这些病毒的传播循环各不相同，但它们共享重要的人兽共患风险决定因素。RVFV主要通过蚊虫、家畜、野生动物和人类之间的相互作用维持传播；CCHFV在涉及璃眼蜱属蜱虫、家畜和野生动物的复杂蜱-脊椎动物系统中持续循环；而HTVs主要通过哺乳动物储存宿主（尤其是啮齿动物）的持续性感染维持。气候变异性、栖息地改变、农业扩张、家畜流动、城市化及其他人为压力可改变这些传播系统，并为病原体外溢创造新的机会。因此，有效的预防和控制需要涵盖人类疾病、动物种群、媒介或野生动物储存宿主以及环境条件的综合监测。分子诊断、基因组流行病学、生态建模、遥感和基于气候的预测方面的进展为改善早期发现和疫情准备提供了宝贵机遇。最终，RVFV、CCHFV和HTVs的经验表明，减轻人兽共患病毒病的负担需要人类与兽医卫生专业人员、生态学家、昆虫学家、环境科学家和公共卫生当局之间的持续合作。因此，协调的“同一健康”方法对于预判新发威胁、加强准备以及保护人类、动物和生态系统健康至关重要。","Preprints.org","2026-09-11T00:00:00Z",{"impact":36,"substance":36,"depth":36,"authority":36,"freshness":36,"relevant":36,"comment":68},"该文为公共卫生领域的人兽共患病毒综述，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[70],{"name":65,"url":62},[27,72,29,73],"One Health","虫媒传染病",[75,76],"动物疫病防控 人兽共患病 虫媒传染病 One Health","动物疫病防控 人兽共患病","动物疫病防控人兽共患病虫媒传染病OneHealth-2438","10.20944\u002Fpreprints202609.0941.v1",{"doi":78,"openalex_id":80,"authors":81,"venue":65,"cited_by_count":36,"oa_url":62,"card":86,"direction":51,"ingested_from":54},"W7212458598",[82,84],{"name":83,"orcid":9},"Daniel Desmecht",{"name":85,"orcid":9},"Hani Boshra",{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"从One Health视角综述裂谷热、克里米亚-刚果出血热和汉坦病毒的生态、鉴定与防控。","文献综述，整合分子诊断、基因组流行病学、生态建模、遥感与气候预测。","三种人兽共患病毒溢出风险受气候、栖息地改变、农业扩张和牲畜流动等驱动，需跨部门联合监测。","农业绿色发展与碳","可探索农业扩张与牲畜流动如何量化驱动病毒溢出，并构建遥感与气候耦合的早期预警模型。","2026-09-14T23:30:27.836713Z",{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":107,"tags":109,"search_phrases":114,"slug":117,"view_count":36,"doi":118,"paper":119,"created_at":138},3622,"Assessment and projection of mangrove blue carbon sink along the Guangdong coastline: integrating remote sensing and modeling techniques","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecolind.2026.115559","Mangrove ecosystems along the Guangdong coastline serve as a vital blue carbon sink, yet their carbon sequestration potential under changing climatic conditions requires rigorous assessment and forward-looking evaluation. This study develops an integrated framework combining remote sensing techniques and spatial modeling approaches to quantify historical changes and project future trajectories of mangrove blue carbon stocks in Guangdong Province, China. Time-series satellite imagery was used to quantify changes in mangrove extent and associated carbon storage over the period 1994 to 2024. Field-validated sediment core data, coupled with the InVEST and CA-Markov models, were utilized to assess historical carbon storage and forecast future challenges along the coastline. Our results reveal substantial spatial variability in mangrove carbon storage across Guangdong. A non-significant increase in mangrove area was observed during the first decade of the study period (1994–2004). In contrast, a significant area expansion of 10.71% occurred between 2014 and 2024, with projections indicating a further increase of 19.45% by 2034. The eastern coast of Guangdong exhibited greater potential for mangrove expansion than the western coast. Based on the projected mangrove extent and the adopted carbon-density framework, the InVEST model estimated a potential 24.62% increase in mangrove carbon storage by 2034 relative to 2024. By explicitly integrating spatiotemporal remote sensing analysis with predictive land-use and carbon storage modeling, this study advances a scalable and transferable framework for assessing mangrove blue carbon dynamics under climate and development pressures. The findings provide critical evidence to inform adaptive coastal management, strengthen Guangdong's carbon neutrality goals, and support nature-based solutions for climate mitigation. Overall, this integrated approach provides a robust scientific basis for coastal spatial planning and offers actionable insights for conserving and enhancing mangrove blue carbon resources along this ecologically and economically important coastline.","广东沿海的红树林生态系统是重要的蓝碳汇，但其在气候变化条件下的碳封存潜力亟需严谨评估与前瞻性预测。本研究构建了一个集成遥感技术与空间建模方法的框架，用以量化广东省红树林蓝碳储量的历史变化并预测其未来演变轨迹。利用时间序列卫星影像量化了1994年至2024年间红树林面积及相关碳储量的变化。结合实地验证的沉积柱数据，并运用InVEST模型和CA-Markov模型，评估了历史碳储量并预测了沿海地区未来面临的挑战。结果表明，广东红树林碳储量存在显著的空间异质性。在研究期的第一个十年（1994—2004年），红树林面积呈非显著性增加。相比之下，2014年至2024年间出现了10.71%的显著面积扩张，预测表明到2034年将进一步增加19.45%。广东东岸的红树林扩张潜力大于西岸。基于预测的红树林面积和所采用的碳密度框架，InVEST模型估算到2034年红树林碳储量较2024年可能增加24.62%。通过明确整合时空遥感分析与土地利用及碳储量预测建模，本研究提出了一个可扩展、可迁移的框架，用于评估气候与发展压力下红树林蓝碳动态。研究结果为适应性海岸管理提供了关键证据，有助于强化广东的碳中和目标，并支持基于自然的气候减缓方案。总体而言，这一集成方法为海岸空间规划提供了坚实的科学依据，并为保护和提升这一具有重要生态和经济价值海岸线的红树林蓝碳资源提供了可操作的见解。","Ecological Indicators","2026-09-25T00:00:00Z",81,{"impact":17,"substance":103,"depth":104,"authority":105,"freshness":21,"relevant":22,"comment":106},22,19,14,"融合遥感与InVEST\u002FCA-Markov模型评估并预测广东红树林蓝碳储量，方法可迁移、数据翔实，对沿海生态管理与碳中和工作有参考价值。",[108],{"name":99,"url":96},[28,110,111,112,113],"碳汇核算","蓝碳","红树林","海岸带管理",[115,116],"广东 红树林 蓝碳","InVEST CA-Markov 碳储量","广东红树林蓝碳-3622","10.1016\u002Fj.ecolind.2026.115559",{"doi":118,"openalex_id":120,"authors":121,"venue":99,"cited_by_count":36,"oa_url":132,"card":133,"direction":51,"ingested_from":54},"W7214279727",[122,125,128,130],{"name":123,"orcid":124},"Hafiz Hassan Javed","https:\u002F\u002Forcid.org\u002F0000-0003-0776-8701",{"name":126,"orcid":127},"You‐Shao Wang","https:\u002F\u002Forcid.org\u002F0000-0001-9565-2666",{"name":129,"orcid":9},"Hao Cheng",{"name":131,"orcid":9},"ABD Ullah","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1470160X26009611\u002Fpdf",{"tldr":134,"method":135,"finding":136,"direction":51,"opportunity":137},"融合遥感与模型评估并预测广东红树林蓝碳储量的历史变化与未来趋势。","时间序列卫星影像、沉积柱数据、InVEST与CA-Markov模型。","2014-2024年红树林面积增10.71%，2034年碳储量或较2024年增24.62%，东岸潜力","可延伸至多气候情景下红树林碳汇预测及与碳交易、海岸带规划耦合研究。","2026-09-27T23:30:46.163566Z",{"id":140,"title":141,"url":142,"summary":143,"summary_zh":144,"content":9,"source_name":145,"source_url":142,"published_at":146,"category":12,"cover_url":9,"hotness":147,"is_selected":14,"score":148,"score_detail":149,"sources":152,"tags":156,"search_phrases":161,"slug":164,"view_count":36,"doi":165,"paper":166,"created_at":178},3616,"Integrated Machine Learning for Agriculture, Environment and Health-Adjacent Systems: Solar Cell Screening, Reservoir Monitoring and IoT-Driven Advisory Across Interdisciplinary Contexts","https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.34001991","This paper explores the convergence of precision agriculture and environmental monitoring by taking advantage of overlapping physical sensor infrastructure. The authors present three interdisciplinary machine learning studies built upon a shared edge-inference architecture optimized for low-latency execution on a Raspberry Pi 4. First, the framework accelerates agrivoltaic deployments by combining density functional theory with gradient boosting regressors to screen perovskite solar cell efficiencies without physical fabrication cycles. Second, it introduces a hybrid 1D-CNN and random forest model to detect water level anomalies in Bangladesh's Kaptai Reservoir, reducing RMSE by 35% over a 40-year validation window. Third, it details an IoT agricultural platform (\"AgriLM\") that pairs ResNet-50 pest detection with an instruction-tuned LLM layer to deliver natural language, climate-adaptive management advice to smallholder farmers, optimizing cross-system resource constraints.","本文探讨了通过利用重叠的物理传感器基础设施，实现精准农业与环境监测的融合。作者提出了三项跨学科机器学习研究，均构建于一个共享的边缘推理架构之上，该架构针对树莓派4（Raspberry Pi 4）上的低延迟执行进行了优化。首先，该框架将密度泛函理论与梯度提升回归器相结合，无需物理制造周期即可筛选钙钛矿太阳能电池效率，从而加速农业光伏部署。其次，该框架引入了一种混合一维卷积神经网络（1D-CNN）与随机森林模型，用于检测孟加拉国卡普泰水库（Kaptai Reservoir）的水位异常，在40年验证窗口内将均方根误差（RMSE）降低了35%。第三，该框架详细介绍了物联网农业平台（“AgriLM”），该平台将ResNet-50害虫检测与指令微调的大语言模型（LLM）层相结合，为小农户提供自然语言、气候适应性的管理建议，并优化跨系统资源约束。","Figshare","2026-09-26T00:00:00Z",25,70,{"impact":20,"substance":103,"depth":17,"authority":150,"freshness":150,"relevant":22,"comment":151},9,"跨学科机器学习论文，方法新颖、数据扎实，但偏学术预印本，产业落地影响有限。",[153,154],{"name":145,"url":142},{"name":145,"url":155},"https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.34001991.v1",[157,158,159,28,160],"智慧农业","农业人工智能","物联网","光伏农业",[162,163],"Kaptai Reservoir 水位监测","AgriLM 物联网农业平台","KaptaiReservoir水位监测-3616","10.6084\u002Fm9.figshare.34001991",{"doi":165,"openalex_id":167,"authors":168,"venue":145,"cited_by_count":36,"oa_url":142,"card":173,"direction":53,"ingested_from":54},"W7214485392",[169,171],{"name":170,"orcid":9},"Alepera Rasaed",{"name":172,"orcid":9},"Minaya Aleguja",{"tldr":174,"method":175,"finding":176,"direction":53,"opportunity":177},"用共享边缘推理架构整合机器学习，实现太阳能电池筛选、水库监测与IoT农业咨询。","Raspberry Pi 4边缘推理，DFT+梯度提升、1D-CNN+随机森林、","混合模型将水库水位异常检测RMSE降低35%，AgriLM可提供自然语言气候适应建议。","可探索多任务边缘模型在农业-环境-健康跨域场景下的能效与泛化性权衡。","2026-09-27T23:30:24.718444Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":9,"content":184,"source_name":185,"source_url":9,"published_at":100,"category":186,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":187,"sources":191,"tags":193,"search_phrases":198,"slug":201,"view_count":36,"doi":9,"paper":9,"created_at":202},3586,"黑龙江省农科院科技包联现场观摩会举办——'松粳22'优质食味水稻新品种示范田实测亩产557.5公斤","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588143.html","9月22日，黑龙江省农科院科技包联哈尔滨市现场观摩会暨乔府大院第十一届开镰节开幕式上，试验田新品种展示区块18个水稻品种分区规整排布，其中9个是黑龙江省农科院自主选育品种。黑龙江省农科院生物技术研究所（五常水稻研究所）副所长、研究员闫平介绍，新品种'松粳22'150亩示范田按照14.5%标准含水率折算，平均亩产达到557.5公斤，在优质食味水稻中属于上乘水平，抗倒伏能力显著优于传统优质稻。秸秆基质育苗示范点位展示秸秆基质育苗技术，今年在哈尔滨18个点位示范。","![Image 1](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Fshare_logo.jpg)\n\n[![Image 2](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Flogo.png)](http:\u002F\u002Fwww.stdaily.com\u002F)\n\n*   [时政](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fszxw\u002Fnode_357.html)\n*   [热点](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Frdxw\u002Fnode_327.html)\n*   [政务](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fzwxw\u002Fnode_362.html)\n*   [深瞳](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fstbd\u002Fnode_434.html)\n*   [访谈](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Ffangtan\u002Fnode_441.html)\n*   [视频](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fspxw\u002Fnode_701.html)\n*   [国际](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgjxw\u002Fnode_381.html)\n*   [地方](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fdfxw\u002Fnode_368.html)\n*   [专题](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fztxw\u002Fnode_335.html)\n*   [English](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002FEnglish\u002Findex.html)\n*   [滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)\n\n[![Image 3](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Ffdj.png)](https:\u002F\u002Fsearch.stdaily.com:8888\u002Ffounder\u002FNewSearchServlet.do?siteID=1)[登录](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html?url=https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588143.html)[-](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html)\n\n 所在位置： [中国科技网首页](https:\u002F\u002Fwww.stdaily.com\u002Findex.html)>[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)> 正文 \n\n# 乡村行 看振兴丨科技赋能丰产 定制深耕良田——黑龙江省农科院科技包联现场观摩会举办\n\n 2026-09-25 12:39:56 来源: 科技日报 点击数：\n\n![Image 4](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimg\u002F20240301dzv.png)0\n\n[](javascript:; \"新浪微博\")[](javascript:; \"微信\")\n\n**科技日报记者 朱虹**\n\n金色稻海漫过黑龙江五常的黑土地，稻浪深处，开镰号令响起。随着乔府大院董事长乔文志一声“开镰”，嘉宾们手持镰刀踏入金黄稻田，割下了今秋第一束稻穗。\n\n9月22日，黑龙江省农科院科技包联哈尔滨市现场观摩会暨乔府大院第十一届开镰节开幕式上，传统农耕与现代科技撞个满怀，一场丰收开镰，也是一次看得见摸得着的农业科技大考。\n\n在试验田新品种展示区块，18个水稻品种分区规整排布，其中9个是黑龙江省农科院自主选育品种。该院生物技术研究所（五常水稻研究所）副所长、研究员闫平站在田垄之间，伸手托起沉甸甸的“松粳22”稻穗，招呼围观嘉宾观察茎秆与籽粒状态。现场发布前，实收测产数据显示，150亩示范田按照14.5%标准含水率折算，松粳22平均亩产达到557.5公斤，在优质食味水稻中属于上乘水平。\n\n“传统稻花香大米口感出众，可茎秆偏软，遇上风雨天气极易大面积倒伏。”闫平指着成片挺立的稻株讲解道，“今天展示的新品种，抗倒伏能力显著优于传统优质稻。”\n\n一句“抗倒伏”，点破了好米难产的旧账。传统优质稻往往“娇贵”——品质好却秆软易倒、产量不稳，种高端米像押宝。新品种把“好吃”和“抗倒”拧在一起：抗倒伏，意味着风雨天不趴窝、机械收割进得去、规模种植稳得住，高端米供给才算有了“压舱石”。从“靠天吃饭”到“凭种稳产”，良种攻关为高端稻米产业蹚出一条新路。\n\n米食品鉴区，新米出锅，香气四溢，米粒莹润油亮。定制客户捧着一碗米饭感慨：“一碗好米的根基就在种子，科研选育的新品种，让老百姓既吃得饱，更吃得好。”\n\n观摩队伍在试验田里走走停停。秸秆基质育苗示范点位旁，黑龙江省农科院耕作栽培研究所作物栽培与装备研究室主任董文军被团团围住，他弯腰捧起一把棕褐色基质，讲起秸秆基质育苗的门道。\n\n“原料就是经多年沤熟的秸秆有机肥，配上三成秸秆打包回收时附带的表层土，育苗完全不用再挖黑土。”董文军介绍，杀菌剂、控旺剂都已预配好，农户拿到手直接铺苗床，取土、晒土、混拌这些累活全省了。“今年在哈尔滨18个点位示范，秧苗长势不比传统育苗土差，产量持平就可以得到推广，前期试验发现不同的配方基质比当地基质和黑土能增产5%到15%。”董文军说。秸秆从田间收走、发酵成基质，秧苗育成再回归水田。这条闭环，把曾经的“田间负担”变成“黑土养料”，围观的种植大户连连点头，当场追问推广条件。\n\n智慧农业贯穿作物生长每一环。黑龙江省农科院农业遥感与信息研究所副所长张有智介绍：“无人机图像结合人工智能模型算法，实现作物的长势情况和杂草分布反演识别，自动生成施肥处方图和施药处方图，直接推送到无人机飞手账号进行作业。作业精度能到厘米级。”今年试验，这套技术实现减肥6%、减药17%左右。\n\n黑龙江省农科院耕作栽培研究所所长李柱刚告诉科技日报记者，2023年黑龙江省农科院建立科技包联服务机制以来，哈尔滨已建成粮食单产提升示范田、科技服务田28个，示范推广面积25800亩。\n\n“为龙头企业提供高端定制服务，是科技包联的重头戏。”黑龙江省农科院副院长焦少杰站在田埂上说，“从品种定向选育、栽培技术集成，到植保方案、加工品鉴，全程‘一对一’定制，让高端米从‘卖原粮’升级为‘卖标准、卖品质’。”他介绍，黑龙江省农科院已深化科技包联13个市（地）服务机制，推动“百项技术、千个基地、万名人才”增粮示范落地。\n\n责任编辑：王倩\n\n相关稿件：\n\n![Image 5](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Fpinglun.png)网友评论\n\n评论\n\n最热评论\n\n没有更多评论了\n\n[热点](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Frdxw\u002Fnode_327.html)\n\n[![Image 6](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F26\u002F588575_33202b28-b632-4676-bc9e-c4e3a4a74a61_802x5353422954copy.png)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588575.html)\n\n[古老颜料为钙钛矿光伏提出稳定性新解](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588575.html)\n\n[![Image 7](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F26\u002F588535_fd615e80-b5e0-48e0-8090-66b870bc474ecopy.jpg)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588535.html)\n\n[四创“世界第一” 石沱长江大桥公路桥合龙](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588535.html)\n\n[![Image 8](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F26\u002F588546_4331e545-c470-4f9c-974b-efa355998578_935x6230736666copy.jpg)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588546.html)\n\n[火车票预约购票试点服务日期范围调整为开车前第30天至17天](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588546.html)\n\n[![Image 9](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F25\u002F588308_t1_1X0X600X337_513a7353-1ba4-4b1b-9142-a1359a1c6a38_1069x7125514627copy.jpeg)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588308.html)\n\n[北冰洋考察记丨我国在加拿大海盆北部高纬冰区发现极鳕幼鱼](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588308.html)\n\n[封面新闻](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Ffmxw\u002Fnode_334.html)\n\n*   [![Image 10](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2024-09\u002F10\u002F227211_t0_0X0X532X399_f37403ac-57e6-47f7-91e3-ba6e5e28a64d.jpg)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2024-09\u002F10\u002Fcontent_227211.html) \n[封面新闻丨第40个教师节，致敬这些良师益友](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2024-09\u002F10\u002Fcontent_227211.html)\n\n*   [![Image 11](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2024-09\u002F09\u002F226617_t0_0X3X515X389_c0de85fe-9e16-4131-98ff-9f011359aeb7.png)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2024-09\u002F09\u002Fcontent_226617.html) \n[亮点纷呈 氛围感拉满！2024年国家网络安全宣传周开启](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2024-09\u002F09\u002Fcontent_226617.html)\n\n*   [![Image 12](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2024-08\u002F29\u002F221287_t0_35X3X554X392_6088faa3-97fd-4574-bd90-3340514ec6c0.jpg)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2024-08\u002F29\u002Fcontent_221287.html) \n[封面新闻丨共赴十年之约 2024数博会引领数字经济发展新潮流](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2024-08\u002F29\u002Fcontent_221287.html)\n\n*   [![Image 13](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2024-08\u002F15\u002F214881_t1_11X0X583X322_f39b4a42-0f79-4c79-937c-02aa2572567c.JPG)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2024-08\u002F15\u002Fcontent_214881.html) \n[全国生态日丨我国生态环境和质量持续改善](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2024-08\u002F15\u002Fcontent_214881.html)\n\n[精彩视频](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fspxw\u002Fnode_701.html)\n\n*   [![Image 14](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F26\u002F588619_b1c8d85f-f732-47e4-b0b8-6790b0626cf4_800x5332954762copy.png)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588619.html) \n[气球上切豆腐！数贸会上机器人秀“绝活儿”](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588619.html)\n\n*   [![Image 15](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F26\u002F588597_38284776-f13a-4f45-8f27-1f0636acb8ee.jpg)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588597.html) \n[科普一下丨秋天氛围感天花板！桂花为啥这么香？](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588597.html)\n\n*   [![Image 16](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F26\u002F588410_t1_2X96X248X234_96fff04b-7d27-4a10-8113-c29a5b039e41.jpg)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588410.html) \n[世赛现场有“霸王餐”吃？](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588410.html)\n\n*   [![Image 17](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F26\u002F588405_e66f49a5-73ef-445d-87a6-bb1379aa57bc.png)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588405.html) \n[当人工智能开始“做菜”，烹饪世界冠军：AI还抢不走饭碗](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F26\u002Fcontent_588405.html)\n\n[专题报道](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fztxw\u002Fnode_335.html)\n\n*   [科技新观察](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2025-06\u002F05\u002Fcontent_350698.html)\n*   [创新故事](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fzhuantiji\u002Fztcxgs.html)\n*   [科普一下](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fzhuantiji\u002Fztkpyx.html)\n*   [庆祝中国共产党成立105周年](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-06\u002F12\u002Fcontent_531282.html)\n\n![Image 18](https:\u002F\u002Fwww.stdaily.com\u002Fresource\u002FtemplateRes\u002F\u002F202403\u002F03\u002F228\u002Fimages\u002Fcode_img.png)\n\n### 友情链接\n\n*   [中国政府网](https:\u002F\u002Fwww.gov.cn\u002F)\n*   [国家发展和改革委员会](http:\u002F\u002Fwww.ndrc.gov.cn\u002F)\n*   [教育部](http:\u002F\u002Fwww.moe.gov.cn\u002F)\n*   [科学技术部](http:\u002F\u002Fwww.most.gov.cn\u002Findex.html)\n*   [工业和信息化部](https:\u002F\u002Fwww.miit.gov.cn\u002F)\n*   [公安部](https:\u002F\u002Fwww.mps.gov.cn\u002F)\n*   [民政部](http:\u002F\u002Fwww.mca.gov.cn\u002F)\n*   [司法部](http:\u002F\u002Fwww.moj.gov.cn\u002F)\n*   [自然资源部](http:\u002F\u002Fwww.mnr.gov.cn\u002F)\n*   [生态环境部](https:\u002F\u002Fwww.mee.gov.cn\u002F)\n*   [住房和城乡建设部](https:\u002F\u002Fwww.mohurd.gov.cn\u002F)\n*   [交通运输部](https:\u002F\u002Fwww.mot.gov.cn\u002F)\n*   [水利部](http:\u002F\u002Fwww.mwr.gov.cn\u002F)\n*   [农业农村部](http:\u002F\u002Fwww.moa.gov.cn\u002F)\n*   [国家卫生健康委员会](http:\u002F\u002Fwww.nhc.gov.cn\u002F)\n*   [国家市场监督管理总局](https:\u002F\u002Fwww.samr.gov.cn\u002F)\n*   [国家广播电视总局](http:\u002F\u002Fwww.nrta.gov.cn\u002F)\n*   [国家体育总局](https:\u002F\u002Fwww.sport.gov.cn\u002F)\n*   [国家互联网信息办公室](http:\u002F\u002Fwww.cac.gov.cn\u002F)\n*   [国务院新闻办公室](http:\u002F\u002Fwww.scio.gov.cn\u002Findex.htm)\n*   [中国科学院](https:\u002F\u002Fwww.cas.cn\u002F)\n*   [中国社会科学院](http:\u002F\u002Fwww.cass.cn\u002F)\n*   [中国工程院](https:\u002F\u002Fwww.cae.cn\u002F)\n*   [国家文物局](http:\u002F\u002Fwww.ncha.gov.cn\u002F)\n*   [国家知识产权局](https:\u002F\u002Fwww.cnipa.gov.cn\u002F)\n*   [人民网](http:\u002F\u002Fwww.people.com.cn\u002F)\n*   [新华网](http:\u002F\u002Fwww.news.cn\u002F)\n*   [中国网](http:\u002F\u002Fwww.china.com.cn\u002F)\n*   [国际在线](http:\u002F\u002Fwww.cri.cn\u002F)\n*   [中国日报网](https:\u002F\u002Fcn.chinadaily.com.cn\u002F)\n*   [央视网](https:\u002F\u002Fwww.cctv.com\u002F)\n*   [中国青年网](https:\u002F\u002Fwww.youth.cn\u002Findex.htm)\n*   [中国经济网](http:\u002F\u002Fwww.ce.cn\u002F)\n*   [中国台湾网](http:\u002F\u002Fwww.taiwan.cn\u002F)\n*   [中国西藏网](http:\u002F\u002Fwww.tibet.cn\u002F)\n*   [央广网](http:\u002F\u002Fwww.cnr.cn\u002F)\n*   [光明网](https:\u002F\u002Fwww.gmw.cn\u002F)\n*   [中国军网](http:\u002F\u002Fwww.81.cn\u002F)\n*   [中国新闻网](https:\u002F\u002Fwww.chinanews.com.cn\u002F)\n*   [人民政协网](http:\u002F\u002Fwww.rmzxb.com.cn\u002Findex.shtml)\n*   [法治网](http:\u002F\u002Fwww.legaldaily.com.cn\u002F)\n\n*   [科技日报社概况](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002F2019-07\u002F12\u002Fcontent_1790748.html)\n*   [科技日报概况](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002F2022-01\u002F26\u002Fcontent_1790749.html)\n*   [报社领导](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002F2017-07\u002F17\u002Fcontent_1790744.html)\n*   [关于中国科技网](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002F2022-03\u002F12\u002Fcontent_1790745.html)\n*   [联系我们](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002F2016-09\u002F02\u002Fcontent_1790743.html)\n*   [公示公告](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fnode_10787.html)\n*   [广告刊例](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002F2025-12\u002F26\u002Fcontent_453856.html)\n*   [科技日报社公开招聘公告](https:\u002F\u002Fwww.stdaily.com\u002Fnode_355.html)\n*   [互联网新闻信息服务许可证](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002F2022-06\u002F21\u002Fcontent_1790750.html)\n*   [信息网络传播视听节目许可证](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002F2021-12\u002F10\u002Fcontent_1790742.html)\n*   [举报平台](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002F2021-09\u002F10\u002Fcontent_1790741.html)\n*   [版权声明](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002F2017-01\u002F01\u002Fcontent_1790740.html)\n\nCopyright © Science and Technology Daily, All Rights Reserved 科技日报社 中国科技网 版权所有\n*   [京ICP备 06005116 号](https:\u002F\u002Fbeian.miit.gov.cn\u002F)\n*   [违法和不良信息举报电话：010-58884152](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002F2016-12\u002F05\u002Fcontent_1790739.html)\n*   [京公网安备11010802036145号](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=11010802036145)\n\n[![Image 19](https:\u002F\u002Fwww.stdaily.com\u002Fresource\u002FtemplateRes\u002F\u002F202408\u002F19\u002F345\u002Fimages\u002Fclose.png)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588143.html)\n\n#### 抱歉，您使用的浏览器版本过低或开启了浏览器兼容模式，这会影响您正常浏览本网页\n\n### 您可以进行以下操作:\n\n## 1.将浏览器切换回极速模式\n\n![Image 20](https:\u002F\u002Fwww.stdaily.com\u002Fresource\u002FtemplateRes\u002F\u002F202408\u002F19\u002F345\u002Fimages\u002F360tip.png)\n\n## 2.点击下面图标升级或更换您的浏览器\n\n[![Image 21](https:\u002F\u002Fwww.stdaily.com\u002Fresource\u002FtemplateRes\u002F\u002F202408\u002F19\u002F345\u002Fimages\u002Ffirefox.jpg)](http:\u002F\u002Fwww.firefox.com.cn\u002Fdownload\u002F)[![Image 22](https:\u002F\u002Fwww.stdaily.com\u002Fresource\u002FtemplateRes\u002F\u002F202408\u002F19\u002F345\u002Fimages\u002Fchrome.jpg)](http:\u002F\u002Fwww.chromeliulanqi.com\u002F)\n\n## 3.暂不升级，继续浏览\n\n[继续浏览](javascript:;)","科技日报 2026-09-25","报道",{"impact":17,"substance":18,"depth":188,"authority":189,"freshness":21,"relevant":22,"comment":190},16,13,"省级科研机构科技包联现场会，含新品种实测产量、秸秆基质育苗与遥感处方图等实质数据，对种业与智慧农业主题有聚合价值。",[192],{"name":185,"url":182},[157,194,28,195,196,197],"种业振兴","水稻新品种","科技包联","秸秆基质育苗",[199,200],"黑龙江省农科院 松粳22 水稻","五常 科技包联 观摩会","黑龙江省农科院松粳22水稻-3586","2026-09-27T00:05:16.125922Z",{"id":204,"title":205,"url":206,"summary":207,"summary_zh":208,"content":9,"source_name":209,"source_url":206,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":210,"score_detail":211,"sources":214,"tags":216,"search_phrases":221,"slug":224,"view_count":36,"doi":225,"paper":226,"created_at":241},3568,"Interpretable multi-index framework for extracting dry-season built-up areas: benchmarking machine learning with Sentinel-2","https:\u002F\u002Fdoi.org\u002F10.4995\u002Fraet.2027.25885","Spectral similarity between built-up surfaces and exposed dry soil significantly constrains built-up area extraction during dry seasons. Although machine-learning techniques can address this challenge by modeling complex spectral relationships, they generally require sufficiently large and representative labeled training datasets. This study therefore investigates a less data-demanding, rule-based multi-index approach for dry-season built-up mapping, aiming to reduce reliance on large labeled training datasets while maintaining effective classification performance. Sentinel-2A imagery acquired in April 2024 was analyzed, and temporal validation was conducted for 2020, 2022, and 2024. Among the tested combinations, the Built-up Area Extraction Index (BAEI), Dry BareSoil Index (DBSI), and Normalized Difference Vegetation Index (NDVI) achieved the highest performance, yielding 95 % overall accuracy and a Kappa coefficient of 0.89. This represents a substantial improvement over singleindex BAEI (80 % overall accuracy; Kappa 0.55), increasing the built-up user’s accuracy from 61 % to 92 %. Multitemporal validation confirmed that the optimized BAEI–DBSI–NDVI decision rules remained stable across the 2020–2024 dry-season images without recalibration, achieving 93–96 % overall accuracy and a built-up F1-score of 84–93 %. Spatial analysis demonstrated robust performance across peripheral and agricultural zones, with moderate variability in dense urban cores due to spectral heterogeneity. To evaluate the operational robustness of the proposed framework, it was benchmarked against machine-learning classifiers, specifically Support Vector Machine and Random Forest (RF). Under the specific conditions of this study, the proposed rule-based method (95 % accuracy) marginally outperformed both SVM (93 %) and RF (92 %), while offering greater transparency and reducing dependency on large, labeled training datasets. Furthermore, feature importance analysis confirmed the critical role of these selected indices in resolving spectral confusion. These findings suggest that the proposed framework offers a transparent and computationally efficient approach for dry-season urban monitoring in tropical coastal environments similar to Visakhapatnam.","建成表面与裸露干土之间的光谱相似性显著制约了旱季建成区提取。尽管机器学习技术可通过建模复杂光谱关系来应对这一挑战，但其通常需要足够大且具有代表性的标记训练数据集。因此，本研究探讨了一种对数据需求较低、基于规则的多指数方法用于旱季建成区制图，旨在减少对大规模标记训练数据集的依赖，同时保持有效的分类性能。研究分析了2024年4月获取的Sentinel-2A影像，并对2020年、2022年和2024年进行了时间验证。在测试的组合中，建成区提取指数（BAEI）、干裸土指数（DBSI）和归一化差异植被指数（NDVI）表现最佳，总体精度达95%，Kappa系数为0.89。相较于单一指数BAEI（总体精度80%；Kappa 0.55），这一结果有显著提升，建成区用户精度从61%提高至92%。多时相验证证实，优化后的BAEI–DBSI–NDVI决策规则在2020—2024年旱季影像上无需重新校准即可保持稳定，总体精度达93%—96%，建成区F1分数为84%—93%。空间分析表明，该方法在外围和农业区域表现稳健，而在密集城市核心区由于光谱异质性存在中等程度变异。为评估所提框架的业务化稳健性，将其与机器学习分类器进行了基准比较，具体为支持向量机（SVM）和随机森林（RF）。在本研究的特定条件下，所提出的基于规则的方法（95%精度）略优于SVM（93%）和RF（92%），同时具有更高的透明性并减少了对大规模标记训练数据集的依赖。此外，特征重要性分析证实了所选指数在解决光谱混淆方面的关键作用。这些发现表明，所提框架为类似维沙卡帕特南的热带沿海环境旱季城市监测提供了一种透明且计算高效的方法。","Revista de teledetección: Revista de la Asociación Española de Teledetección",72,{"impact":20,"substance":212,"depth":19,"authority":189,"freshness":150,"relevant":22,"comment":213},21,"该研究提出可解释的多指数规则框架，在旱季建成区提取上以更少标注数据达到95%精度并优于SVM\u002FRF，方法新颖、验证充分，对农业遥感与乡村土地利用监测有参考价值。",[215],{"name":209,"url":206},[217,218,219,28,220],"Sentinel-2","机器学习","NDVI","建成区提取",[222,223],"Sentinel-2 建成区提取","BAEI DBSI NDVI 旱季","Sentinel-2建成区提取-3568","10.4995\u002Fraet.2027.25885",{"doi":225,"openalex_id":227,"authors":228,"venue":209,"cited_by_count":36,"oa_url":206,"card":235,"direction":240,"ingested_from":54},"W7214385607",[229,232],{"name":230,"orcid":231},"Sarah Sejari","https:\u002F\u002Forcid.org\u002F0009-0002-7300-9084",{"name":233,"orcid":234},"Vazeer Mahammood","https:\u002F\u002Forcid.org\u002F0000-0003-1667-5232",{"tldr":236,"method":237,"finding":238,"direction":51,"opportunity":239},"提出可解释多指数规则框架，用Sentinel-2提取旱季建成区，性能优于机器学习。","Sentinel-2A影像，BAEI、DBSI、NDVI组合规则，对比SVM与随","BAEI-DBSI-NDVI规则达95%精度、Kappa 0.89，优于单指数及SVM\u002FRF且无需重","可探索该规则框架向其他气候带与传感器迁移，并结合少量样本的半监督学习提升城市核心区精度。","农业人工智能与决策模型","2026-09-26T23:30:53.200163Z",{"id":243,"title":244,"url":245,"summary":246,"summary_zh":247,"content":9,"source_name":248,"source_url":245,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":249,"score_detail":250,"sources":252,"tags":254,"search_phrases":258,"slug":261,"view_count":36,"doi":262,"paper":263,"created_at":278},3558,"Meteorolojik Kuraklığın Vejetasyon Sağlığı Üzerindeki Mekânsal ve Zamansal Etkilerinin SPI ve NDVI ile Analizi: Gediz Havzası Örneği","https:\u002F\u002Fdoi.org\u002F10.48123\u002Frsgis.1858019","Kuraklık, özellikle yarı kurak iklim koşullarına sahip havzalarda su kaynakları, tarımsal üretim ve ekosistem sürdürülebilirliği üzerinde belirleyici etkiler oluşturan başlıca doğal afetlerden biridir. Bu bağlamda, kuraklığın bitki örtüsü üzerindeki mekânsal ve zamansal etkilerinin bütüncül yaklaşımlarla değerlendirilmesi büyük önem taşımaktadır. Bu çalışmada, meteorolojik kuraklığın vejetasyon sağlığı üzerindeki mekânsal ve zamansal etkileri, Standartlaştırılmış Yağış İndeksi (SPI) ve Normalize Fark Bitki Örtüsü İndeksi (NDVI) kullanılarak Gediz Havzası örneğinde analiz edilmiştir. SPI analizleri 1970–2023 yılları arasındaki uzun dönem yağış verilerine dayanırken, NDVI verileri 2000–2023 dönemine ait MODIS uydu görüntülerinden elde edilmiştir. İki veri seti arasındaki ilişkiler, ortak dönem olan 2000–2023 yılları için incelenmiştir. Elde edilen bulgular, meteorolojik kuraklık ile vejetasyon sağlığı arasındaki ilişkinin mevsimsel olarak değişkenlik gösterdiğini ortaya koymaktadır. En güçlü ilişki yaz mevsiminde gözlenmiş (r ≈ 0.70) ve bu durum vejetasyonun yağış eksikliklerine en duyarlı olduğu dönemin yaz ayları olduğunu göstermiştir. Buna karşılık, kış (r ≈ 0.05) ve ilkbahar (r ≈ 0.02) mevsimlerinde ilişki oldukça zayıf bulunmuştur. Belirlenen kurak yıllarda (2004, 2008 ve 2022) NDVI değerlerinde belirgin düşüşler gözlenmiştir. Sonuç olarak, vejetasyonun kuraklığa verdiği tepkinin yıl boyunca homojen olmadığı, mevsimsel iklim koşulları ve diğer çevresel faktörlere bağlı olarak değiştiği belirlenmiştir.","干旱是主要自然灾害之一，尤其在具有半干旱气候条件的流域中，对水资源、农业生产和生态系统可持续性产生决定性影响。在此背景下，以整体性方法评估干旱对植被的时空影响具有重要意义。本研究以盖迪兹流域为例，利用标准化降水指数（SPI）和归一化植被指数（NDVI）分析了气象干旱对植被健康的时空影响。SPI分析基于1970—2023年的长期降水数据，而NDVI数据则来自2000—2023年期间的MODIS卫星影像。两个数据集之间的关系在共同时段2000—2023年进行了分析。研究结果表明，气象干旱与植被健康之间的关系呈现季节性变化。最显著的关系出现在夏季（r ≈ 0.70），这表明夏季是植被对降水亏缺最为敏感的时期。相比之下，冬季（r ≈ 0.05）和春季（r ≈ 0.02）的关系则非常微弱。在确定的干旱年份（2004年、2008年和2022年），NDVI值出现了明显下降。综上，植被对干旱的响应在全年并非均匀一致，而是随季节性气候条件及其他环境因素而变化。","Turkish Journal of Remote Sensing and GIS",71,{"impact":20,"substance":212,"depth":19,"authority":189,"freshness":21,"relevant":22,"comment":251},"基于SPI与NDVI长时序数据揭示气象干旱对植被健康影响的季节性差异，方法规范、结论可靠，对农业干旱遥感监测有参考价值，但属区域案例研究，公共影响有限。",[253],{"name":248,"url":245},[219,28,255,256,257],"干旱监测","植被指数","SPI",[259,260],"Gediz Havzası SPI NDVI","气象干旱 植被健康 遥感","GedizHavzasıSPINDVI-3558","10.48123\u002Frsgis.1858019",{"doi":262,"openalex_id":264,"authors":265,"venue":248,"cited_by_count":36,"oa_url":272,"card":273,"direction":51,"ingested_from":54},"W7214166880",[266,269],{"name":267,"orcid":268},"Kemal Yurddaş","https:\u002F\u002Forcid.org\u002F0000-0003-4691-4038",{"name":270,"orcid":271},"Murat Karabulut","https:\u002F\u002Forcid.org\u002F0000-0002-1456-6908","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F5578303",{"tldr":274,"method":275,"finding":276,"direction":51,"opportunity":277},"用SPI和NDVI分析土耳其盖迪兹流域气象干旱对植被健康的时空影响。","基于1970-2023年降水SPI与2000-2023年MODIS NDVI，做","干旱与植被关系随季节变化，夏季最强(r≈0.70)，冬春极弱；干旱年NDVI明显下降。","可引入滞后效应与多尺度SPI，结合土壤水分和灌溉数据，提升干旱对植被影响的预测能力。","2026-09-26T23:30:31.357310Z"]