[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3012":3,"related-3012":63},{"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":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":62},3012,"Topographic and economic drivers of agricultural–ecological coupling in Gansu Province: spatial differentiation and threshold effects","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecolind.2026.115463","Improved understanding of agricultural–ecological coupling is critical for sustainable development in semi-arid regions. This study integrated the Remote Sensing Ecological Index (RSEI), Agricultural Green Development Index (AGDI), Coupling Coordination Degree (CCD) model, and XGBoost–SHAP to examine the spatiotemporal evolution and nonlinear drivers of agricultural–ecological coupling in Gansu Province from 2010 to 2023 at the county scale. A four-quadrant framework was applied to identify differentiated management types. Results showed that farmland ecological quality exhibited a stepped north–south pattern, improving during 2010–2018 but declining after 2019. Agricultural green development displayed a “high southeast, low northwest” pattern, increasing by 0.0036 yr −1 ( p \u003C 0.01). The coupling coordination degree increased from 0.57 to 0.63, with significant spatial clustering (Moran's I = 0.438–0.528, p \u003C 0.001), characterized by stable “high–high” clusters in southern Gansu and “low–low” clusters in northern Gansu. Relief amplitude (40.9%) and GDP per capita (36.1%) jointly explained 77.0% of coordination variation. Elevation above 2000 m constrained coordination, whereas GDP per capita between 15,000 and 30,000 CNY promoted green transformation, with diminishing effects beyond 40,000 CNY. Severe drought conditions (SPEI \u003C −1.5) caused agricultural–ecological imbalance. Counties were classified into four management types (H H, L–H, L–L, and H–L), requiring differentiated governance strategies. This study provides quantifiable thresholds for improving agricultural–ecological coupling in semi-arid regions.","深入理解农业—生态耦合关系对于半干旱地区的可持续发展至关重要。本研究集成遥感生态指数（RSEI）、农业绿色发展指数（AGDI）、耦合协调度（CCD）模型以及XGBoost–SHAP方法，在县域尺度上考察了2010—2023年甘肃省农业—生态耦合的时空演变与非线性驱动因素，并运用四象限框架识别差异化治理类型。结果表明：耕地生态质量呈南北阶梯状格局，2010—2018年持续改善，2019年后有所下降。农业绿色发展呈“东南高、西北低”的格局，年均增长0.0036（p \u003C 0.01）。耦合协调度由0.57升至0.63，具有显著空间集聚特征（Moran's I = 0.438–0.528，p \u003C 0.001），表现为陇南稳定的“高—高”集聚和陇北的“低—低”集聚。地形起伏度（40.9%）与人均GDP（36.1%）共同解释了77.0%的协调度变异。海拔2000 m以上对协调度形成约束，人均GDP在15000～30000元之间促进绿色转型，超过40000元后效应递减。严重干旱条件（SPEI \u003C −1.5）导致农业—生态失衡。各县域被划分为四种管理类型（H–H、L–H、L–L和H–L），需采取差异化治理策略。本研究为半干旱地区提升农业—生态耦合水平提供了可量化的阈值依据。",null,"Ecological Indicators","2026-09-19T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"基于RSEI、AGDI与XGBoost-SHAP的县域尺度农业生态耦合研究，方法新颖、阈值结论可量化，对半干旱区差异化治理有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业绿色发展","遥感监测","甘肃农业","农业生态耦合","县域治理",[32,33],"甘肃 农业生态耦合 遥感","RSEI AGDI 耦合协调度","甘肃农业生态耦合遥感-3012",0,"10.1016\u002Fj.ecolind.2026.115463",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":55,"direction":59,"ingested_from":61},"W7213602184",[40,42,45,47,50,52],{"name":41,"orcid":9},"Baopeng Xie",{"name":43,"orcid":44},"Yongqiang Wang","https:\u002F\u002Forcid.org\u002F0009-0009-5289-2403",{"name":46,"orcid":9},"Ying Chen",{"name":48,"orcid":49},"Tingting Pei","https:\u002F\u002Forcid.org\u002F0000-0002-2789-4919",{"name":51,"orcid":9},"Wen Wang",{"name":53,"orcid":54},"Jinlong Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-1161-5460",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"基于县域遥感与统计指标，揭示甘肃农业-生态耦合的时空分异及地形经济阈值。","RSEI、AGDI、耦合协调度模型与XGBoost-SHAP，2010–2023","地形起伏与人均GDP解释77%耦合差异，海拔2000米以上及严重干旱抑制协调。","农业遥感与作物表型","可探索多尺度耦合阈值与干旱预警结合，构建差异化治理的智能决策支持系统。","openalex","2026-09-20T23:30:21.067452Z",{"total":64,"page":21,"page_size":64,"items":65},6,[66,130,162,208,231,280],{"id":67,"title":68,"url":69,"summary":70,"summary_zh":71,"content":9,"source_name":72,"source_url":69,"published_at":73,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":78,"tags":80,"search_phrases":85,"slug":88,"view_count":35,"doi":89,"paper":90,"created_at":129},3016,"Afforestation enhances ecosystem stability in the farming-pastoral ecotone of northern China, but its effects are constrained by water availability","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjpe\u002Frtag232","Abstract The farming-pastoral ecotone of northern China (FPENC), a typical semi-agricultural and semi-pastoral ecologically fragile zone, has long faced controversy over afforestation due to its water-limited environment, raising concerns about how afforestation affects this fragile ecosystem. In this study, based on Google Earth Engine and a Random Forest model, we derive afforestation distribution (1985-2020), and further investigated ecosystem stability under afforestation from the perspectives of short-term responses and long-term dynamic changes. The results showed: (1) By 2020, 67,700 km2 had been afforested in FPENC (6.20% of its area). Afforestation survival strongly depended on precipitation, with relatively low retention rates (40%-70%) in the zones with \u003C 400 mm. (2) At short-term timescales, afforestation mitigated ecosystem state deviations caused by extreme drought events and improved surrounding ecosystems within a 150 m buffer. (3) Over long-term timescales, NDVI across the FPENC exhibited a significant increasing trend over the past four decades, accompanied by an overall enhancement in ecosystem recovery capacity. In zones with > 400 mm annual precipitation and with forest cover exceeding 40%, ecosystem recovery capacity increased significantly, showing there might be a potential linkage among afforestation, precipitation, and ecosystem recovery processes. This study revealed despite water scarcity and low tree survival, afforestation under suitable precipitation can effectively enhance regional ecosystem stability.","中国北方农牧交错带（FPENC）是典型的半农半牧生态脆弱区，由于环境水分受限，长期以来在造林问题上存在争议，引发了人们对造林如何影响这一脆弱生态系统的担忧。本研究基于Google Earth Engine和随机森林模型，反演了1985—2020年造林分布，并进一步从短期响应和长期动态变化两个角度研究了造林条件下的生态系统稳定性。结果表明：（1）截至2020年，FPENC已造林67,700 km²，占其面积的6.20%。造林存活强烈依赖降水，在降水量小于400 mm的区域，保存率相对较低（40%—70%）。（2）在短期时间尺度上，造林缓解了极端干旱事件引起的生态系统状态偏离，并改善了150 m缓冲区内周边生态系统。（3）在长期时间尺度上，过去四十年FPENC的NDVI呈显著增加趋势，同时生态系统恢复能力总体增强。在年降水量大于400 mm且森林覆盖率超过40%的区域，生态系统恢复能力显著提高，表明造林、降水与生态系统恢复过程之间可能存在潜在联系。本研究揭示，尽管存在水分短缺和树木存活率低的问题，但在适宜降水条件下，造林能够有效增强区域生态系统稳定性。","Journal of Plant Ecology","2026-09-17T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":76,"relevant":21,"comment":77},8,"基于遥感与随机森林量化北方农牧交错带造林成效及水分约束，数据规模大、结论有新意，对生态修复与农业信息化有参考价值。",[79],{"name":72,"url":69},[81,27,82,83,84],"生态修复","造林","农牧交错带","水资源约束",[86,87],"北方农牧交错带 造林 水资源","Google Earth Engine 造林 遥感","北方农牧交错带造林水资源-3016","10.1093\u002Fjpe\u002Frtag232",{"doi":89,"openalex_id":91,"authors":92,"venue":72,"cited_by_count":35,"oa_url":123,"card":124,"direction":59,"ingested_from":61},"W7213445880",[93,96,99,101,103,105,107,109,111,113,116,118,121],{"name":94,"orcid":95},"Yuchao Luo","https:\u002F\u002Forcid.org\u002F0000-0003-0063-3505",{"name":97,"orcid":98},"Haile Zhao","https:\u002F\u002Forcid.org\u002F0000-0002-5870-062X",{"name":100,"orcid":9},"Qianhe Wang",{"name":102,"orcid":9},"Yi Zhou",{"name":104,"orcid":9},"Xin Chen",{"name":106,"orcid":9},"Yuling Jin",{"name":108,"orcid":9},"Xingjie Yin",{"name":110,"orcid":9},"Guoliang Zhang",{"name":112,"orcid":9},"Haorui Sun",{"name":114,"orcid":115},"Jun Bai","https:\u002F\u002Forcid.org\u002F0000-0002-1408-4271",{"name":117,"orcid":9},"Huiyao Shi",{"name":119,"orcid":120},"Zhihua Pan","https:\u002F\u002Forcid.org\u002F0000-0002-8187-1574",{"name":122,"orcid":9},"Pingli An","https:\u002F\u002Facademic.oup.com\u002Fjpe\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fjpe\u002Frtag232\u002F71179175\u002Frtag232.pdf",{"tldr":125,"method":126,"finding":127,"direction":59,"opportunity":128},"基于GEE和随机森林反演1985-2020年北方农牧交错带造林分布，评估其对生态系统稳定性的影响。","Google Earth Engine遥感数据与随机森林模型，分析造林分布及ND","造林可缓解干旱影响并提升恢复力，但存活率与效果受降水制约，400mm为关键阈值。","可探究不同降水梯度下造林-水分-恢复力耦合机制，优化生态修复的水资源约束阈值。","2026-09-20T23:30:22.592517Z",{"id":131,"title":132,"url":133,"summary":134,"summary_zh":135,"content":9,"source_name":136,"source_url":133,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":35,"score_detail":137,"sources":139,"tags":141,"search_phrases":145,"slug":148,"view_count":35,"doi":149,"paper":150,"created_at":161},3015,"Forecasting Urbanization Dynamics On İstanbul’s European Side Using Deep Learning And Extrapolation Techniques","https:\u002F\u002Fdoi.org\u002F10.2478\u002Fjlecol-2026-0037","Abstract Rapid urbanization in metropolitan regions poses significant environmental, social, and infrastructural challenges, necessitating advanced analytical approaches to monitor and predict urban growth. This study investigates the spatio-temporal dynamics of urbanization on the European side of Istanbul from 2013 to 2024 using Landsat 8 imagery and a deep learning (DL)–based Land Cover Classification model integrated within ArcGIS Pro. The U-Net–based pre-trained model generated 15-class Land Use\u002FLand Cover (LULC) maps, which were validated against the Urban Atlas dataset, resulting in high classification accuracies for forest and water classes (PA: 0.84–0.94; UA: 0.87–0.87) and an overall binary urban\u002Fnon-urban accuracy of 87 %, confirming the robustness of the employed DL approach. Spatio-temporal analyses of LULC data were conducted using both Ordinary Least Squares (OLS) and nonlinear regression functions to examine urban growth trends and project future development for 2025, 2026, and 2027. The results indicate a strong linear increase in urbanized areas across most districts, with total developed area on the European side projected to reach approximately 807 km² by 2027, representing a nearly 50% increase compared to 2013. These findings highlight the significant pressure of urban expansion on natural and agricultural lands and emphasize the need for informed planning strategies. By integrating remote sensing, deep learning, and predictive modeling, this study provides actionable insights for sustainable urban development, offering a replicable framework for monitoring rapid urbanization and supporting policy decisions to mitigate environmental and socio-spatial impacts in rapidly growing metropolitan regions.","摘要 大都市区域的快速城市化带来了显著的环境、社会和基础设施挑战，亟需先进的分析方法来监测和预测城市增长。本研究利用Landsat 8影像和集成于ArcGIS Pro中的基于深度学习（DL）的土地覆盖分类模型，研究了2013年至2024年伊斯坦布尔欧洲一侧城市化的时空动态。基于U-Net的预训练模型生成了15类土地利用\u002F土地覆盖（LULC）地图，并依据Urban Atlas数据集进行了验证，森林和水体类别的分类精度较高（生产者精度PA：0.84–0.94；用户精度UA：0.87–0.87），城市\u002F非城市二分类总体精度达87%，证实了所采用深度学习方法稳健可靠。研究采用普通最小二乘法（OLS）和非线性回归函数对LULC数据进行时空分析，以考察城市增长趋势并预测2025年、2026年和2027年的未来发展。结果表明，大多数区域的城市化面积呈显著线性增长，预计到2027年欧洲一侧的总建成区面积将达到约807 km²，较2013年增长近50%。这些发现凸显了城市扩张对自然和农业用地的巨大压力，并强调了科学规划策略的必要性。通过整合遥感、深度学习和预测建模，本研究为可持续城市发展提供了可操作的见解，为监测快速城市化提供了一个可复制的框架，并支持旨在缓解快速增长的都市区域中环境和社会空间影响的政策决策。","Journal of Landscape Ecology",{"impact":35,"substance":35,"depth":35,"authority":35,"freshness":35,"relevant":35,"comment":138},"研究伊斯坦布尔城市扩张与土地覆盖预测，属城市遥感与景观生态领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[140],{"name":136,"url":133},[142,27,143,144],"深度学习","土地利用","城市扩张",[146,147],"伊斯坦布尔 城市扩张 遥感","U-Net 土地覆盖分类","伊斯坦布尔城市扩张遥感-3015","10.2478\u002Fjlecol-2026-0037",{"doi":149,"openalex_id":151,"authors":152,"venue":136,"cited_by_count":35,"oa_url":133,"card":156,"direction":59,"ingested_from":61},"W7213670913",[153],{"name":154,"orcid":155},"Gizem Dinç","https:\u002F\u002Forcid.org\u002F0000-0003-2406-604X",{"tldr":157,"method":158,"finding":159,"direction":59,"opportunity":160},"用Landsat 8影像和U-Net深度学习模型分析伊斯坦布尔欧洲侧2013-2024年城市化动态并","Landsat 8影像、ArcGIS Pro中U-Net预训练模型生成15类LU","城市面积呈强线性增长，2027年预计达807 km²，较2013年增长近50%，挤压自然与农业用地。","可借鉴该遥感+深度学习+外推框架，研究快速城市化对城郊农业用地与耕地保护的时空影响。","2026-09-20T23:30:21.335156Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":9,"source_name":168,"source_url":165,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":169,"score_detail":170,"sources":175,"tags":177,"search_phrases":182,"slug":185,"view_count":35,"doi":186,"paper":187,"created_at":207},3013,"AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41348-026-01352-w","AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration。Journal of Plant Diseases and Protection","人工智能与遥感在真菌及卵菌病害监测中的应用：现状与生物学整合。《植物病害与保护杂志》","Journal of Plant Diseases and Protection",77,{"impact":17,"substance":171,"depth":172,"authority":173,"freshness":20,"relevant":21,"comment":174},20,17,13,"核心期刊综述，系统梳理AI与遥感在真菌及卵菌病害监测中的进展与生物学整合路径，对智慧农业植保方向有参考价值。",[176],{"name":168,"url":165},[178,179,180,27,181],"智慧农业","农业人工智能","植物病害","病害预警",[183,184],"AI 遥感 真菌病害 监测","植物病害 遥感 预警","AI遥感真菌病害监测-3013","10.1007\u002Fs41348-026-01352-w",{"doi":186,"openalex_id":188,"authors":189,"venue":168,"cited_by_count":35,"oa_url":9,"card":202,"direction":59,"ingested_from":61},"W7213649225",[190,192,194,196,199],{"name":191,"orcid":9},"Biju Vadakkemukadiyil Chellappan",{"name":193,"orcid":9},"C. L. Biji",{"name":195,"orcid":9},"Vanshika Arun Meda",{"name":197,"orcid":198},"Sajad Ali","https:\u002F\u002Forcid.org\u002F0000-0002-3230-1436",{"name":200,"orcid":201},"Sherif Mohamed El‐Ganainy","https:\u002F\u002Forcid.org\u002F0000-0001-5226-4604",{"tldr":203,"method":204,"finding":205,"direction":59,"opportunity":206},"综述AI与遥感在真菌及卵菌病害监测中的现状，强调生物信息整合。","文献综述，整合AI、遥感与病原生物学数据。","AI与遥感结合可提升病害监测，但需融入病原生物学机制。","可研究将病原生活史与遥感时序特征耦合的病害预警模型。","2026-09-20T23:30:21.177583Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":9,"content":213,"source_name":214,"source_url":9,"published_at":11,"category":215,"cover_url":9,"hotness":13,"is_selected":14,"score":216,"score_detail":217,"sources":220,"tags":222,"search_phrases":226,"slug":229,"view_count":35,"doi":9,"paper":9,"created_at":230},2973,"中国农业大学校长陈卫：以科技平台与人才培养双轮驱动未来农业","https:\u002F\u002Fnews.cau.edu.cn\u002Fmtndnew\u002Fb3529eeaa2a145b4995e45108ede7599.htm","中国工程院院士、中国农业大学校长陈卫9-16在WAFI 2026上接受媒体采访，把科研部署归纳为\"加大有组织开展科研\"——生物育种与粮食安全、农业绿色发展、农机装备与数字农业三大类。中国农大近年加紧布局\"神农\"等农业大模型，在智能科学与技术、低空经济与工程等方向做交叉支撑，2026级新生入学后本硕招生均保持高位，研究生推免报名超过1.6万人。","**李晨**\n\n“现代农业已不再是‘面朝黄土背朝天’的传统形态，而是以生物技术、人工智能、智能装备为支撑，以有组织科研和复合型人才培养为保障的系统工程。”9月16日，中国工程院院士、中国农业大学校长陈卫在2026世界农业科技创新大会（WAFI）上接受媒体采访时说。\n\n![Image 1](https:\u002F\u002Fnews.cau.edu.cn\u002Fimages\u002F2026-09\u002Fdd48efc31ca94ffd96f8c36da801885b.jpg)\n\n陈卫接受媒体采访。中国农大供图\n\n面对“十五五”开局与农业强国目标，陈卫把科研部署归纳为“加大有组织开展科研”。第一类是生物育种与粮食安全。他介绍，中国农大在小麦、玉米及畜禽领域布局4个全国重点实验室，并建设玉米、小麦两项生物育种产教融合创新基地；同时设生物育种、生命科学两类“强基班”，培养育种专门人才。\n\n陈卫直言，我国水稻、小麦在部分研究领域可与国际先进水平持平或领先，但玉米、大豆单产仍有差距，中国农大要把国家队作用体现在提质增产上。\n\n第二类是农业绿色发展。陈卫说，过去高产常伴随高化肥、高农药投入，带来土壤板结、残留积累等问题。中国农大成立农业绿色发展研究院，推动有机投入、低残留防控与资源循环利用，推动农业生产方式向绿色转型。与之并行的是营养健康导向——成立营养健康系，把育种目标从“增量”扩展到“提质”，让主粮、蔬菜在产量之外更重口感、营养成分与消费健康。\n\n第三类是农机装备与数字农业。他列举播种、施肥、植保、灌溉、收获全过程的大型农机、无人机、卫星遥感与人工智能应用，指出无人化机械、无人农场是现实方向而非设想。中国农大近年来加紧布局“神农”等农业大模型，并在智能科学与技术、低空经济与工程等方向做交叉支撑。“用AI做作物表型采集与筛选，用装备替代人力，用数据提高决策精度。”陈卫说。\n\n与此同时，现代农业的发展需要现代化的农业人才。陈卫介绍，2026级新生入学后，中国农大本硕招生均保持高位，其中研究生推免报名超过1.6万人。陈卫表示，农业产业形态变化——农业已走向规模化、产业化、一产二产三产融合，需要既懂生物又懂信息、既会科研又能落地的复合人才。\n\n陈卫说，中国农大重视社会服务育人发挥的重要作用。中国农大在黑龙江、吉林、河北、云南、安徽等农业优势区长期布点，通过试验站、观察站、地方研究院、科技小院和教授工作站，让把科研与人才培养一起下沉。他强调，成果转化不只发论文，还要让农民会用；人才培养不只学理论，还要在真实产区理解成本、季节、市场与政策约束。\n\n[科学网2026年9月19日](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571749.shtm)","中国农业大学新闻网","报道",84,{"impact":218,"substance":171,"depth":172,"authority":19,"freshness":20,"relevant":21,"comment":219},24,"中国农大校长在WAFI阐述有组织科研与复合型人才培养布局，涉及生物育种、绿色发展与农业大模型，信息增量与权威性俱佳，值得进入每日精选。",[221],{"name":214,"url":211},[178,179,223,224,26,225],"科技小院","生物育种","人才培养",[227,228],"中国农业大学 陈卫 未来农业","神农大模型 农业人工智能","中国农业大学陈卫未来农业-2973","2026-09-20T00:03:00.260298Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":9,"source_name":237,"source_url":234,"published_at":238,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":239,"score_detail":240,"sources":244,"tags":246,"search_phrases":250,"slug":253,"view_count":35,"doi":254,"paper":255,"created_at":279},2955,"Precision agriculture applied to coffee cultivation in agroforestry systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10457-026-01651-z","Abstract The search for more sustainable agricultural production systems is a goal of both agroforestry systems (AFS) and precision agriculture (PA). In this context, integrating PA technologies into coffee cultivation under AFS presents the potential to increase productive efficiency and sustainability. Thus, the objective of this study was to evaluate whether the variability of environmental conditions in coffee agroforestry systems can be estimated using technologies associated with PA. The studies were conducted on three coffee farms in Coimbra and Araponga, Minas Gerais, Brazil. For management zone delineation, combinations of apparent soil electrical conductivity (ECa), Normalized Difference Vegetation Index (NDVI), and Digital Terrain Model (DTM) were used. Microclimatic variability was evaluated using weather stations, considering different agroforestry management practices and altitude conditions in mountain coffee cultivation. Canopy cover was estimated by digital image processing techniques applied to aerial images obtained by an Unmanned Aerial Vehicle (UAV). The combination ECa + NDVI was efficient in delineating zones with distinct physical and chemical attributes. Agroforestry management practices and altitude influenced temperature and relative humidity. Tree canopy estimation proved viable to support management. It is concluded that PA is strategic for coffee cultivation in AFS, favoring site-specific and more sustainable management.","摘要 寻求更可持续的农业生产系统是农林业系统（AFS）和精准农业（PA）的共同目标。在此背景下，将精准农业技术整合到农林业系统下的咖啡种植中，具有提高生产效率和可持续性的潜力。因此，本研究旨在评估是否可以利用与精准农业相关的技术来估算咖啡农林业系统中环境条件的变异性。研究在巴西米纳斯吉拉斯州科英布拉和阿拉蓬加的三个咖啡农场进行。为划定管理区，采用了表观土壤电导率（ECa）、归一化差异植被指数（NDVI）和数字地形模型（DTM）的组合。利用气象站评估微气候变异性，考虑了山地咖啡种植中不同的农林业管理措施和海拔条件。通过将数字图像处理技术应用于无人机（UAV）获取的航空图像来估算冠层覆盖度。ECa + NDVI的组合能够有效划定具有不同物理和化学属性的区域。农林业管理措施和海拔影响了温度和相对湿度。树木冠层估算被证明可用于支持管理。结论是，精准农业对农林业系统下的咖啡种植具有战略意义，有利于因地制宜且更可持续的管理。","Agroforestry Systems","2026-09-18T00:00:00Z",71,{"impact":241,"substance":242,"depth":172,"authority":173,"freshness":76,"relevant":21,"comment":243},12,21,"巴西咖啡农林复合系统中应用精准农业技术的研究，方法新颖、结论可靠，对智慧农业与遥感应用有参考价值，但属区域性研究，影响范围有限。",[245],{"name":237,"url":234},[178,247,27,248,249],"精准农业","农林复合系统","咖啡种植",[251,252],"巴西 咖啡 农林复合系统 精准农业","无人机 冠层覆盖 NDVI 咖啡","巴西咖啡农林复合系统精准农业-2955","10.1007\u002Fs10457-026-01651-z",{"doi":254,"openalex_id":256,"authors":257,"venue":237,"cited_by_count":35,"oa_url":234,"card":272,"direction":278,"ingested_from":61},"W7213541962",[258,260,263,266,269],{"name":259,"orcid":9},"Wagner Silva dos Santos",{"name":261,"orcid":262},"Francisco de Assis de Carvalho Pinto","https:\u002F\u002Forcid.org\u002F0000-0002-8279-9535",{"name":264,"orcid":265},"André Luiz de Freitas Coelho","https:\u002F\u002Forcid.org\u002F0000-0002-7595-9713",{"name":267,"orcid":268},"Daniel Marçal de Queiroz","https:\u002F\u002Forcid.org\u002F0000-0003-0987-3855",{"name":270,"orcid":271},"Bruno Nery Fernandes Vasconcelos","https:\u002F\u002Forcid.org\u002F0000-0001-6298-9748",{"tldr":273,"method":274,"finding":275,"direction":276,"opportunity":277},"评估精准农业技术能否估算农林复合咖啡系统的环境变异，以支持可持续管理。","用ECa、NDVI、DTM划分管理区，气象站测微气候，无人机图像估树冠覆盖。","ECa+NDVI可有效划分土壤属性差异区，农林管理和海拔影响温湿度，树冠估算可行。","智慧农业 \u002F 农业物联网","可探索多源遥感与物联网融合的实时管理区动态划分，并验证其对咖啡产量与碳汇的长期效应。","数字乡村与农业信息化","2026-09-19T23:30:40.775897Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":9,"source_name":286,"source_url":283,"published_at":287,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":35,"score_detail":288,"sources":290,"tags":292,"search_phrases":295,"slug":298,"view_count":35,"doi":299,"paper":300,"created_at":314},2954,"Quantifying the spatiotemporal dynamics and drivers of urban blue spaces in the Beijing–Tianjin–Hebei region using remote sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.indic.2026.101525","Urban blue spaces are essential for water security, ecological restoration, and urban livability, yet long-term assessments often emphasize changes in area while broader landscape-pattern changes remain less well understood. Using the Beijing–Tianjin–Hebei (BTH) region as a case study, this study examined changes in blue-space area and the Composite Landscape Index (CI) from 2000 to 2023 and compared their associations with demographic, socioeconomic, and environmental factors. Blue-space area declined significantly across the region, with a clear change point in 2009, accompanied by increasing fragmentation and declining structural connectivity. CI also decreased significantly at the regional scale, whereas the city-level trends of blue-space area and CI were not always aligned. Population, vegetation conditions (FVC and NDVI), and urbanization jointly accounted for approximately 84%–87% of the model-based importance for both blue-space area and CI, although their relative importance differed between the two variables. These findings show that blue-space area and CI describe different dimensions of long-term blue-space change, and that regional trends may mask substantial city-level differences. By distinguishing areal change from landscape-pattern change across regional and city scales, this study broadens the perspective of urban blue-space assessment, advances the understanding of its multidimensional dynamics, and provides a basis for long-term monitoring, cross-city comparison, and differentiated assessment in rapidly urbanizing regions.","城市蓝空间对水安全、生态修复和城市宜居性至关重要，但长期评估往往侧重于面积变化，而更广泛的景观格局变化仍认识不足。本研究以京津冀（BTH）地区为案例，考察了2000—2023年蓝空间面积和复合景观指数（Composite Landscape Index，CI）的变化，并比较了二者与人口、社会经济和环境因素的关联。整个区域的蓝空间面积显著下降，并在2009年出现明显突变点，同时伴随破碎化加剧和结构连通性下降。CI在区域尺度上也显著下降，而城市尺度的蓝空间面积与CI趋势并不总是一致。人口、植被状况（FVC和NDVI）和城市化共同解释了蓝空间面积和CI基于模型重要性的约84%—87%，但二者的相对重要性存在差异。这些发现表明，蓝空间面积和CI描述了长期蓝空间变化的不同维度，区域趋势可能掩盖了城市间的显著差异。通过区分区域和城市尺度上的面积变化与景观格局变化，本研究拓宽了城市蓝空间评估的视角，增进了对其多维动态的理解，并为快速城市化地区的长期监测、跨城市比较和差异化评估提供了依据。","Environmental and Sustainability Indicators","2026-09-16T00:00:00Z",{"impact":35,"substance":35,"depth":35,"authority":35,"freshness":35,"relevant":35,"comment":289},"研究京津冀城市蓝空间的时空动态与驱动因素，属城市生态与遥感领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[291],{"name":286,"url":283},[293,27,294],"京津冀","城市蓝空间",[296,297],"京津冀 城市蓝空间 遥感","城市蓝空间 遥感监测 京津冀","京津冀城市蓝空间遥感-2954","10.1016\u002Fj.indic.2026.101525",{"doi":299,"openalex_id":301,"authors":302,"venue":286,"cited_by_count":35,"oa_url":308,"card":309,"direction":59,"ingested_from":61},"W7213412393",[303,305],{"name":304,"orcid":9},"Weina Zhen",{"name":306,"orcid":307},"Donghui Shi","https:\u002F\u002Forcid.org\u002F0000-0001-5301-368X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2665972726004149\u002Fpdf",{"tldr":310,"method":311,"finding":312,"direction":59,"opportunity":313},"基于遥感量化京津冀城市蓝空间2000-2023年面积与景观格局变化及其驱动因素。","遥感提取蓝空间面积与综合景观指数，结合人口、社会经济和植被因子建模。","蓝空间面积显著下降且2009年出现拐点，破碎化加剧，区域趋势掩盖城市间差异。","可延伸至蓝空间与农业灌溉、水安全及城乡水生态协同的多尺度遥感监测研究。","2026-09-19T23:30:37.135491Z"]