[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4755":3,"related-4755":82},{"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":23,"tags":25,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":57,"slug":60,"view_count":61,"doi":62,"paper":63,"created_at":81},4755,"Comparison of environmental, remote sensing, and combined variables for predicting and mapping soil organic matter in the Huang-Huai-Hai Plain","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11368-026-04577-2","Comparison of environmental, remote sensing, and combined variables for predicting and mapping soil organic matter in the Huang-Huai-Hai Plain。Journal of Soils and Sediments",null,"Journal of Soils and Sediments","2026-10-10T00:00:00Z","论文",10,false,67,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,16,13,8,1,"核心期刊论文，比较环境与遥感变量预测土壤有机质，方法有参考价值但属细分领域研究，公共影响有限。",[24],{"name":9,"url":6},[26,27,28,29],"数字农业","遥感监测","土壤有机质","黄淮海平原",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":13,"reason":56},[37,44,51],{"code":38,"score":16,"supported":39,"title":40,"abstract":42,"tags":43},"I3",true,[41],"remote sensing",[41],[27],{"code":45,"score":46,"supported":39,"title":47,"abstract":49,"tags":50},"H2",11,[48],"soil",[48],[],{"code":52,"score":21,"supported":13,"title":53,"abstract":54,"tags":55},"I1",[],[],[26],"农业相关性需要复核",[58,59],"黄淮海平原 土壤有机质 遥感","土壤有机质 黄淮海平原 数字农业 遥感监测","黄淮海平原土壤有机质遥感-4755",0,"10.1007\u002Fs11368-026-04577-2",{"doi":62,"openalex_id":64,"authors":65,"venue":9,"cited_by_count":61,"oa_url":8,"card":8,"direction":79,"ingested_from":80},"W7221367130",[66,68,70,72,75,77],{"name":67,"orcid":8},"Fangfang Zhang",{"name":69,"orcid":8},"Ya Liu",{"name":71,"orcid":8},"Yali Luo",{"name":73,"orcid":74},"Le Tang","https:\u002F\u002Forcid.org\u002F0000-0001-9170-4369",{"name":76,"orcid":8},"Xueliang Feng",{"name":78,"orcid":8},"Xianzhang Pan","农业遥感与作物表型","openalex","2026-10-12T23:30:25.993454Z",{"total":83,"page":21,"page_size":83,"items":84},6,[85,159,240,309,363,417],{"id":86,"title":87,"url":88,"summary":89,"summary_zh":90,"content":8,"source_name":91,"source_url":88,"published_at":92,"category":11,"cover_url":8,"hotness":93,"is_selected":13,"score":94,"score_detail":95,"sources":99,"tags":104,"primary_topic":108,"secondary_topics":114,"classification":115,"search_phrases":141,"slug":144,"view_count":137,"doi":145,"paper":146,"created_at":158},5073,"INCREASING REGIONAL ADDED VALUE THROUGH THE PROCESSING OF AGRICULTURAL PRODUCTS IN THE REPUBLIC OF KARAKALPAKSTAN","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23383466","This article examines the economic and environmental significance of using digital innovations to develop green agriculture in the Republic of Karakalpakstan. It analyzes challenges related to water scarcity, soil salinity, production costs, and the efficiency of natural resource utilization. The article highlights the potential of digital monitoring, smart irrigation, remote sensing, and data-driven management to improve agricultural production outcomes.","本文探讨了在卡拉卡尔帕克斯坦共和国利用数字创新发展绿色农业的经济与环境意义。文章分析了与水资源短缺、土壤盐渍化、生产成本以及自然资源利用效率相关的挑战。文章强调了数字监测、智能灌溉、遥感以及数据驱动管理在改善农业生产成效方面的潜力。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z",25,63,{"impact":16,"substance":18,"depth":96,"authority":16,"freshness":97,"relevant":21,"comment":98},14,9,"聚焦乌兹别克斯坦卡拉卡尔帕克斯坦地区数字农业与盐碱地治理，属区域案例研究，对国内智慧农业与盐碱地改良有参考价值，但影响层级与信息增量有限。",[100,101],{"name":91,"url":88},{"name":102,"url":103},"SAMARALI TA’LIM VA BARQAROR INNOVATSIYALAR JURNALI","https:\u002F\u002Finnovativepublication.uz\u002Findex.php\u002Fjelsi\u002Farticle\u002Fview\u002F6404",[26,105,106,107,27],"智慧农业","智能灌溉","盐碱地治理",{"code":109,"slug":110,"name":111,"path":112,"parent_code":113},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":116,"locked":13,"version":33,"method":34,"evidence":117},"classified",{"candidates":118,"agricultural_evidence":39},[119,125,131,136],{"code":109,"score":12,"supported":39,"title":120,"abstract":121,"tags":124},[],[122,123,110],"灌溉","水资源",[106],{"code":45,"score":126,"supported":39,"title":127,"abstract":128,"tags":130},7,[],[129,48],"土壤",[107],{"code":38,"score":126,"supported":39,"title":132,"abstract":133,"tags":135},[],[134,41],"遥感",[27],{"code":52,"score":137,"supported":13,"title":138,"abstract":139,"tags":140},2,[],[],[26,105],[142,143],"卡拉卡尔帕克斯坦 数字农业","咸海地区 盐碱地 智能灌溉","卡拉卡尔帕克斯坦数字农业-5073","10.5281\u002Fzenodo.23383466",{"doi":145,"openalex_id":147,"authors":148,"venue":91,"cited_by_count":61,"oa_url":88,"card":151,"direction":157,"ingested_from":80},"W7221970206",[149],{"name":150,"orcid":8},"Sholpan Eshimova",{"tldr":152,"method":153,"finding":154,"direction":155,"opportunity":156},"研究卡拉卡尔帕克斯坦通过农产品加工与数字创新提升区域附加值。","分析水资源短缺、土壤盐渍化等挑战，评估数字监测、智能灌溉、遥感和数据管理潜力。","数字创新可提高农业生产效率并促进绿色农业发展。","农业绿色发展与碳","可量化数字技术对盐碱地农业附加值及资源效率的贡献，探索适配干旱区的智慧农业模式。","智慧农业 \u002F 农业物联网","2026-10-16T23:30:07.817464Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":8,"source_name":165,"source_url":162,"published_at":166,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":167,"score_detail":168,"sources":171,"tags":173,"primary_topic":177,"secondary_topics":183,"classification":192,"search_phrases":212,"slug":215,"view_count":137,"doi":216,"paper":217,"created_at":239},3792,"Estimation of soil organic carbon under rice-fallow system using Sentinel-2 derivatives","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.11182","The evaluation of soil properties under a century-old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of North-east (NE) India in the Eastern Himalaya faces multiple challenges in conventional studies of soil properties. Alternatively, remote sensing offers a viable solution. An attempt was made to study the soil organic carbon (SOC) under century old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of Bhoirymbong, Meghalaya, NE India in Eastern Himalaya using remote sensing indices derived from Sentinel-2. The objective was to develop a suitable regression model for rice-fallow systems across different hill slopes in northeastern India in the Eastern Himalaya. A total of 100 composite soil samples from the 0–15 cm depth were collected from rice-fallow areas on various slopes (nearly level: 0–3 %, gentle: 3–8 %, moderate: 8–15 %, steep: 15–30 % and very steep: > 30 %) during November 2020. Each composite sample was prepared from 10 randomly collected soil samples within a 10 × 10 m area and analysed for SOC using standard protocols in the analytical laboratory of School of Natural Resource Management, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal. Nearly synchronised, cloud-free (\u003C 10 % cloud cover) Sentinel-2 data consistent with soil sampling period were accessed from the US Geological Survey (USGS) official website to derive 15 indices. Results showed that brightness index (BI), BI2 and the soil bareness index (SBI) were the most influential indices for SOC. The highest and lowest SOC values were found in gently undulating topography (2–5 % slope) and rolling topography (10–15 % slope), at 2.86 % and 1.17 %, respectively. After rice crop harvesting, the red-NIR-based indices did not show a significant difference, whereas the green-NIR-based indices were significantly different at p \u003C 0.05. Hence, it is concluded that green-NIR-based indices are the most suitable for SOC prediction in NE India in the Eastern Himalaya.","在印度东北部东喜马拉雅地区高度脆弱的地形（即山坡和低地）下，对百年传统水稻休耕实践下的土壤性质进行评估，在常规土壤性质研究中面临多重挑战。遥感则提供了一种可行的替代方案。本研究尝试利用Sentinel-2衍生的遥感指数，研究印度东北部东喜马拉雅地区梅加拉亚邦Bhoirymbong在高度脆弱地形（即山坡和低地）下百年传统水稻休耕实践中的土壤有机碳（SOC）。目标是建立一个适用于东喜马拉雅地区印度东北部不同山坡水稻休耕系统的合适回归模型。2020年11月，在不同坡度（近水平：0–3%、缓坡：3–8%、中坡：8–15%、陡坡：15–30%和极陡坡：>30%）的水稻休耕区采集了100个0–15 cm深度的复合土壤样品。每个复合样品由10 × 10 m区域内随机采集的10个土壤样品制备而成，并在Central Agricultural University, Imphal农业科学研究生院自然资源管理学院分析实验室采用标准方法分析SOC。从美国地质调查局（USGS）官方网站获取与土壤采样期基本同步、无云（云量\u003C10%）的Sentinel-2数据，以衍生15个指数。结果表明，亮度指数（BI）、BI2和土壤裸露指数（SBI）是对SOC影响最大的指数。SOC最高值和最低值分别出现在缓起伏地形（2–5%坡度）和波状地形（10–15%坡度），分别为2.86%和1.17%。水稻收获后，基于红-近红外的指数未显示出显著差异，而基于绿-近红外的指数在p \u003C 0.05水平上差异显著。因此，得出结论：基于绿-近红外的指数最适合东喜马拉雅地区印度东北部的SOC预测。","Plant Science Today","2026-09-28T00:00:00Z",69,{"impact":16,"substance":169,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":170},20,"基于Sentinel-2遥感指数估算稻作休耕系统土壤有机碳，方法有新意但属区域案例研究，对国内智慧农业遥感应用有参考价值。",[172],{"name":165,"url":162},[26,174,175,27,176],"Sentinel-2","土壤有机碳","水稻休耕",{"code":178,"slug":179,"name":180,"path":181,"parent_code":182},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[184,187],{"code":45,"slug":48,"name":185,"path":186,"parent_code":113},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":38,"slug":188,"name":189,"path":190,"parent_code":191},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":116,"locked":13,"version":33,"method":34,"evidence":193},{"candidates":194,"agricultural_evidence":39},[195,200,204,208],{"code":45,"score":196,"supported":39,"title":197,"abstract":198,"tags":199},15,[48],[129,48],[175],{"code":178,"score":96,"supported":39,"title":201,"abstract":202,"tags":203},[179],[180,179],[],{"code":38,"score":20,"supported":39,"title":205,"abstract":206,"tags":207},[],[134,41],[174,27],{"code":52,"score":21,"supported":13,"title":209,"abstract":210,"tags":211},[],[],[26],[213,214],"印度东北部 水稻休耕 土壤有机碳","Sentinel-2 遥感 土壤有机碳","印度东北部水稻休耕土壤有机碳-3792","10.14719\u002Fpst.11182",{"doi":216,"openalex_id":218,"authors":219,"venue":165,"cited_by_count":61,"oa_url":162,"card":234,"direction":79,"ingested_from":80},"W7214617000",[220,223,225,228,231],{"name":221,"orcid":222},"Janaki Singh Naorem","https:\u002F\u002Forcid.org\u002F0000-0001-9534-0339",{"name":224,"orcid":8},"D Priya",{"name":226,"orcid":227},"Deepak Kumar Dwivedi","https:\u002F\u002Forcid.org\u002F0009-0003-5408-887X",{"name":229,"orcid":230},"J S Rajkumar","https:\u002F\u002Forcid.org\u002F0009-0003-7863-277X",{"name":232,"orcid":233},"Tamang Basant","https:\u002F\u002Forcid.org\u002F0000-0001-6856-7298",{"tldr":235,"method":236,"finding":237,"direction":79,"opportunity":238},"用Sentinel-2指数估算印度东北部稻作休耕系统不同坡度的土壤有机碳。","采集100个0-15cm土样，结合Sentinel-2的15个遥感指数建立回归模","绿光-近红外指数最适合预测SOC，缓坡SOC最高（2.86%），陡坡最低（1.17%）。","可探索多时相Sentinel-2与地形因子耦合，提升复杂地形区SOC制图精度与普适性。","2026-09-29T23:30:28.690009Z",{"id":241,"title":242,"url":243,"summary":244,"summary_zh":245,"content":8,"source_name":91,"source_url":243,"published_at":246,"category":11,"cover_url":8,"hotness":93,"is_selected":13,"score":247,"score_detail":248,"sources":250,"tags":254,"primary_topic":258,"secondary_topics":259,"classification":260,"search_phrases":290,"slug":293,"view_count":294,"doi":295,"paper":296,"created_at":308},3171,"WATER RESOURCES – AN IMPORTANT FACTOR IN HUMAN LIFE AND SUSTAINABLE DEVELOPMENT","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22882203","This thesis discusses the importance of water resources for human life, agriculture, industry and environmental sustainability. Today, climate change, population growth, urbanization and increasing water demand in different economic sectors have made the rational and efficient use of water resources an increasingly important issue. In Central Asia, particularly in Uzbekistan, limited water availability requires improving irrigation efficiency, reducing water losses and expanding modern water-saving technologies. The study examines the main areas of water use, protection of water quality, water conservation in agriculture, wastewater reuse, groundwater protection and the application of digital technologies in water management. Under the conditions of Uzbekistan, drip and sprinkler irrigation, modernization of irrigation canals, automated water accounting, remote sensing and geographic information systems can contribute significantly to improving the efficiency and sustainability of water resources management.","本论文探讨了水资源对人类生活、农业、工业及环境可持续性的重要意义。当今，气候变化、人口增长、城市化以及各经济部门用水需求的不断增加，使水资源的合理高效利用成为日益重要的议题。在中亚地区，尤其是乌兹别克斯坦，水资源可利用量有限，亟需提高灌溉效率、减少水量损失并推广现代节水技术。本研究考察了水资源利用的主要领域、水质保护、农业节水、废水回用、地下水保护以及数字技术在水管理中的应用。在乌兹别克斯坦的条件下，滴灌与喷灌、灌溉渠道现代化改造、自动化水量核算、遥感与地理信息系统可显著提升水资源管理的效率与可持续性。","2026-09-21T00:00:00Z",60,{"impact":16,"substance":18,"depth":96,"authority":12,"freshness":20,"relevant":21,"comment":249},"聚焦乌兹别克斯坦水资源与数字灌溉技术，属农业信息化范畴，但为区域性综述论文，公共影响与信源权威度一般。",[251,252],{"name":91,"url":243},{"name":91,"url":253},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22882204",[26,255,256,27,257],"节水灌溉","水资源管理","中亚农业",{"code":109,"slug":110,"name":111,"path":112,"parent_code":113},[],{"status":116,"locked":13,"version":33,"method":34,"evidence":261},{"candidates":262,"agricultural_evidence":39},[263,268,272,279,286],{"code":109,"score":46,"supported":39,"title":264,"abstract":265,"tags":267},[],[122,123,266,110],"农业节水",[256,255],{"code":38,"score":126,"supported":39,"title":269,"abstract":270,"tags":271},[],[134,41],[27],{"code":273,"score":83,"supported":39,"title":274,"abstract":275,"tags":278},"H7",[],[276,277],"气候变化","climate change",[],{"code":280,"score":83,"supported":39,"title":281,"abstract":282,"tags":285},"I4",[],[283,284],"地理信息","geographic information",[],{"code":52,"score":21,"supported":13,"title":287,"abstract":288,"tags":289},[],[],[26],[291,292],"乌兹别克斯坦 节水灌溉","遥感 GIS 水资源管理","乌兹别克斯坦节水灌溉-3171",5,"10.5281\u002Fzenodo.22882203",{"doi":295,"openalex_id":297,"authors":298,"venue":91,"cited_by_count":61,"oa_url":243,"card":303,"direction":79,"ingested_from":80},"W7213866152",[299,301],{"name":300,"orcid":8},"ERGASHALIYEV MUXAMMADAZIZ",{"name":302,"orcid":8},"G'aniyev Samandar",{"tldr":304,"method":305,"finding":306,"direction":155,"opportunity":307},"探讨水资源对人类生活与可持续发展的重要性，并以乌兹别克斯坦为例分析节水灌溉与数字化管理。","文献综述与案例分析，涉及滴灌、喷灌、遥感、GIS和自动化水核算。","在乌兹别克斯坦，滴灌、渠道现代化、遥感与GIS可显著提升水资源管理效率与可持续性。","可结合遥感与物联网，量化中亚干旱区节水灌溉对作物产量与碳排放的协同效应。","2026-09-22T23:30:23.028018Z",{"id":310,"title":311,"url":312,"summary":313,"summary_zh":8,"content":8,"source_name":314,"source_url":312,"published_at":315,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":316,"score_detail":317,"sources":319,"tags":321,"primary_topic":324,"secondary_topics":328,"classification":329,"search_phrases":341,"slug":344,"view_count":61,"doi":345,"paper":346,"created_at":362},5579,"A Composite Field-Evaluation Framework for Digital Agriculture Maps","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11283264\u002Fv1","A Composite Field-Evaluation Framework for Digital Agriculture Maps。Research Square","Research Square","2026-10-21T00:00:00Z",38,{"impact":20,"substance":83,"depth":20,"authority":83,"freshness":12,"relevant":21,"comment":318},"预印本提出数字农业地图的田间复合评估框架，方法有一定新意，但摘要信息极少、未经同行评审，公共影响有限，暂不宜进入每日精选。",[320],{"name":314,"url":312},[26,322,134,323],"农业信息化","地图评估",{"code":52,"slug":325,"name":326,"path":327,"parent_code":191},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":116,"locked":13,"version":33,"method":34,"evidence":330},{"candidates":331,"agricultural_evidence":39},[332,337],{"code":52,"score":19,"supported":39,"title":333,"abstract":335,"tags":336},[334],"digital agriculture",[334],[322,26],{"code":38,"score":21,"supported":13,"title":338,"abstract":339,"tags":340},[],[],[134],[342,343],"Research Square 数字农业","农业信息化 地图评估 数字农业 遥感","ResearchSquare数字农业-5579","10.21203\u002Frs.3.rs-11283264\u002Fv1",{"doi":345,"openalex_id":347,"authors":348,"venue":314,"cited_by_count":61,"oa_url":312,"card":8,"direction":361,"ingested_from":80},"W7223099987",[349,352,355,358],{"name":350,"orcid":351},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":353,"orcid":354},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":356,"orcid":357},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":359,"orcid":360},"Louis Longchamps","https:\u002F\u002Forcid.org\u002F0000-0002-4761-6094","数字乡村与农业信息化","2026-10-22T11:20:47.463814Z",{"id":364,"title":365,"url":366,"summary":367,"summary_zh":368,"content":8,"source_name":369,"source_url":366,"published_at":315,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":370,"score_detail":371,"sources":374,"tags":376,"primary_topic":381,"secondary_topics":382,"classification":383,"search_phrases":399,"slug":402,"view_count":61,"doi":403,"paper":404,"created_at":416},5578,"Unlocking digital agriculture through new quality productive forces: nonlinear thresholds and spatial spillovers in China","https:\u002F\u002Fdoi.org\u002F10.1057\u002Fs41599-026-09510-z","New quality productive forces (NQPF) critically enable agricultural digitisation. Using China as a case study, this paper employs panel data from 284 prefecture-level cities in China from 2011 to 2022. The results show that NQPF has a significant promoting effect on agricultural digital transformation and exhibits positive spatial spillover effects, driving not only local transformation but also the development of surrounding regions. Labour skills and innovation capacity play mediating roles in this process. Notably, the enabling effect of NQPF on agricultural digitisation displays significant regional heterogeneity. Threshold analysis indicates that its promoting effect follows a non-linear pattern, characterised by a double threshold effect. Therefore, the development of NQPF should be accelerated, with emphasis placed on regional coordination and talent cultivation, to promote the sustained and in-depth advancement of agricultural digital transformation in accordance with local conditions.","新质生产力（New Quality Productive Forces, NQPF）对农业数字化具有关键赋能作用。本文以中国为案例，采用2011—2022年中国284个地级市的面板数据。结果表明，新质生产力对农业数字化转型具有显著促进作用，并呈现正向空间溢出效应，不仅推动本地转型，也带动周边地区发展。劳动技能与创新能力在这一过程中发挥中介作用。值得注意的是，新质生产力对农业数字化的赋能效应表现出显著的区域异质性。门槛分析表明，其促进作用呈非线性特征，表现为双重门槛效应。因此，应加快新质生产力发展，注重区域协调与人才培养，因地制宜推动农业数字化转型持续深入推进。","Humanities and Social Sciences Communications",81,{"impact":17,"substance":372,"depth":17,"authority":96,"freshness":97,"relevant":21,"comment":373},22,"基于284个地级市面板数据的实证研究，揭示新质生产力对农业数字化的非线性门槛与空间溢出效应，方法规范、结论有政策参考价值，值得进入每日精选。",[375],{"name":369,"url":366},[26,377,378,379,380],"新质生产力","空间溢出","区域协调","农业数字化转型",{"code":52,"slug":325,"name":326,"path":327,"parent_code":191},[],{"status":116,"locked":13,"version":33,"method":34,"evidence":384},{"candidates":385,"agricultural_evidence":39},[386,390,394],{"code":52,"score":12,"supported":39,"title":387,"abstract":388,"tags":389},[334],[],[380,26],{"code":280,"score":21,"supported":13,"title":391,"abstract":392,"tags":393},[],[],[378],{"code":395,"score":21,"supported":13,"title":396,"abstract":397,"tags":398},"J2",[],[],[377],[400,401],"新质生产力 农业数字化","地级市 面板数据","新质生产力农业数字化-5578","10.1057\u002Fs41599-026-09510-z",{"doi":403,"openalex_id":405,"authors":406,"venue":369,"cited_by_count":61,"oa_url":366,"card":411,"direction":361,"ingested_from":80},"W7221723385",[407,409],{"name":408,"orcid":8},"Qingqing Huang",{"name":410,"orcid":8},"Wenjing Guo",{"tldr":412,"method":413,"finding":414,"direction":361,"opportunity":415},"基于中国284个地级市面板数据，检验新质生产力对农业数字化的促进、空间溢出与门槛效应。","2011—2022年284个地级市面板数据，空间计量与门槛回归，中介效应检验。","新质生产力显著促进农业数字化并具正向空间溢出，存在双门槛非线性效应与区域异质性。","可深入探究新质生产力影响农业数字化的微观机制，以及不同区域门槛条件下的差异化政策路径。","2026-10-22T11:20:47.354744Z",{"id":418,"title":419,"url":420,"summary":421,"summary_zh":422,"content":8,"source_name":423,"source_url":420,"published_at":424,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":425,"score_detail":426,"sources":429,"tags":431,"primary_topic":8,"secondary_topics":436,"classification":437,"search_phrases":467,"slug":470,"view_count":61,"doi":471,"paper":472,"created_at":496},5577,"Assessing Land Use and Land Cover Change in the Jamanxim River Region (Brazilian Amazon) from 2015 to 2025 Using CBERS-4\u002FPAN Imagery","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i4.269068","Land use and land cover (LULC) changes in the Amazon are among the main drivers of biodiversity loss, ecosystem degradation, and the intensification of climate change. This study aimed to analyze the multitemporal dynamics of LULC around the Jamanxim River, Pará, Brazilian Amazon, between 2015 and 2025, using high-resolution imagery from the CBERS-4\u002FPAN satellite. The study area covers 492.74 km², including part of the Jamanxim National Forest, a conservation unit under strong pressure from agricultural expansion and logging activities. The methodology included geometric correction of the images, Gram–Schmidt pansharpening, and supervised classification with the Random Forest algorithm, validated with historical imagery from Google Earth Pro. Thematic maps of land use and cover were generated, and performance metrics such as overall accuracy, omission and commission errors, and F1-scores were assessed. The results indicated a significant reduction in forest cover, from 412.28 km² in 2015 to 355.47 km² in 2025 (–13.78%). Non-vegetated areas increased by 132.47%, while agricultural areas expanded by 54.97%. The forest and water body classes showed high accuracy (F1-score > 0.95), while confusion persisted between agricultural and bare soil classes. Overall accuracy remained above 90% across all years, with the highest performance recorded in 2020 (96.31%). These results highlight the potential of CBERS-4\u002FPAN imagery for monitoring multitemporal changes in tropical forest landscapes. At the same time, they reveal the intensification of deforestation and the growing threats to conservation in the Amazon, reinforcing the importance of remote sensing for environmental management and policy support.","亚马逊地区的土地利用与土地覆盖（LULC）变化是生物多样性丧失、生态系统退化以及气候变化加剧的主要驱动因素之一。本研究旨在利用CBERS-4\u002FPAN卫星的高分辨率影像，分析2015年至2025年间巴西亚马孙州雅曼辛河（Jamanxim River）周边土地利用与土地覆盖的多时相动态。研究区面积为492.74 km²，包括雅曼辛国家森林（Jamanxim National Forest）的一部分，该保护区正面临农业扩张和采伐活动的强烈压力。研究方法包括影像几何校正、Gram–Schmidt全色锐化以及基于随机森林（Random Forest）算法的监督分类，并利用Google Earth Pro的历史影像进行验证。研究生成了土地利用与覆盖专题图，并评估了总体精度、漏分误差与错分误差以及F1分数等性能指标。结果表明，森林覆盖显著减少，从2015年的412.28 km²降至2025年的355.47 km²（–13.78%）。无植被区域增加了132.47%，农业区域扩张了54.97%。森林和水体类别表现出较高精度（F1分数 > 0.95），而农业类别与裸土类别之间仍存在混淆。各年份总体精度均保持在90%以上，其中2020年性能最高（96.31%）。这些结果凸显了CBERS-4\u002FPAN影像在监测热带森林景观多时相变化方面的潜力，同时揭示了亚马逊地区森林砍伐的加剧以及保护面临的日益增长的威胁，进一步强调了遥感在环境管理和政策支持中的重要性。","Journal of Hyperspectral Remote Sensing","2026-10-20T00:00:00Z",78,{"impact":17,"substance":372,"depth":427,"authority":16,"freshness":97,"relevant":21,"comment":428},17,"该研究利用CBERS-4\u002FPAN影像量化亚马逊保护区十年间森林减少13.78%，方法规范、结论可靠，对遥感监测与生态保护有参考价值，但地域性强、与国内三农直接关联有限。",[430],{"name":423,"url":420},[27,432,433,434,435],"土地利用","森林保护","亚马逊","CBERS-4",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":438},{"candidates":439,"agricultural_evidence":39,"reason":466},[440,447,451,455,460],{"code":441,"score":126,"supported":39,"title":442,"abstract":443,"tags":446},"H6",[],[444,445],"生物多样性","biodiversity",[432],{"code":38,"score":126,"supported":39,"title":448,"abstract":449,"tags":450},[],[134,41],[27],{"code":273,"score":83,"supported":39,"title":452,"abstract":453,"tags":454},[],[276,277],[],{"code":45,"score":456,"supported":39,"title":457,"abstract":458,"tags":459},3,[],[48],[],{"code":461,"score":456,"supported":39,"title":462,"abstract":463,"tags":465},"I7",[],[464],"研究方法",[],"多个产业证据相当，需要确认主归属",[468,469],"CBERS-4 亚马逊 土地利用","Jamanxim 森林覆盖 遥感","CBERS-4亚马逊土地利用-5577","10.29150\u002Fjhrs.v16i4.269068",{"doi":471,"openalex_id":473,"authors":474,"venue":423,"cited_by_count":61,"oa_url":420,"card":491,"direction":79,"ingested_from":80},"W7223141286",[475,478,480,483,486,489],{"name":476,"orcid":477},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":479,"orcid":8},"Gabriel Henrique Santos Silva",{"name":481,"orcid":482},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":484,"orcid":485},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":487,"orcid":488},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":490,"orcid":8},"Sylvana Melo dos Santos",{"tldr":492,"method":493,"finding":494,"direction":79,"opportunity":495},"用CBERS-4\u002FPAN影像分析亚马逊Jamanxim河流域2015-2025年土地利用变化。","几何校正、Gram-Schmidt融合、随机森林监督分类，Google Eart","森林覆盖减少13.78%，农业扩张54.97%，裸地增132.47%，总体精度超90%。","可探索中巴地球资源卫星在热带森林退化监测中的业务化应用与多源数据融合。","2026-10-22T11:20:42.901761Z"]