[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3505":3,"related-3505":85},{"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":18,"tags":20,"search_phrases":25,"slug":28,"view_count":15,"doi":29,"paper":30,"created_at":84},3505,"A dataset of high–spatiotemporal–resolution dust and non–dust aerosol mass concentration profiles from ground–based remote sensing in central China (2018–2024)","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-2026-677","Abstract. The atmospheric aerosol composition and vertical structure over eastern China are jointly influenced by mineral dust transport, anthropogenic emissions, and boundary layer processes; however, long–term vertically resolved observations that distinguish dust from non–dust aerosol mass remain limited. Here, we present a quality–controlled dataset of dust, non–dust, and total aerosol mass concentration profiles derived from continuous ground–based polarization lidar observations at the Shouxian National Climate Observatory (32.434° N, 116.793° E) in central China from January 2018 to December 2024. The site lies in a regional transport corridor between the Beijing–Tianjin–Hebei and Yangtze River Delta regions and is therefore well–suited for monitoring aerosol exchange between two major source–receptor systems in eastern China. Raw lidar measurements were screened for clouds, precipitation, and instrumental artifacts, harmonized to 15 min and 7.5 m resolution, and processed with the Fernald inversion and Polarization lidar photometer networking (POLIPHON) framework to retrieve vertically resolved dust and non–dust aerosol mass concentrations from approximately 0.3 to 6 km. Monthly extinction–to–mass conversion factors were constrained using collocated CE–318 sun–photometer observations. The product was evaluated against independent column, profile, and near–surface references, including CE–318 aerosol optical depth, CALIPSO aerosol extinction and depolarization profiles, and surface particulate matter measurements from a colocated Grimm monitor. The comparisons indicate that the dataset reproduces the main temporal variability of column aerosol loading, captures the principal vertical structure of aerosol extinction coefficient, and is statistically consistent with near–surface particulate matter variability. A pointwise retrieval quality index (RQI) ranging from 0 to 100 is provided to facilitate data screening and reuse, with RQI values of 75–100 recommended for quantitative analyses and event studies, 50–75 for routine statistical analyses, and lower values mainly for qualitative interpretation or analyses based on temporal or vertical averaging. This dataset provides a multi–year, high–resolution observational record of aerosol type–resolved mass profiles in central China and is intended to support community reuse in satellite validation, model evaluation, aerosol transport studies, boundary layer research, and data assimilation applications. The dataset is available for free at Mendeley Data (https:\u002F\u002Fdoi.org\u002F10.17632\u002Fkfvpbb3vcs.1; Wang et al., 2026).","摘要。中国东部的大气气溶胶组成与垂直结构受矿物沙尘输送、人为排放和边界层过程的共同影响；然而，能够区分沙尘与非沙尘气溶胶质量的长期垂直分辨观测仍然有限。本文提出了一套经过质量控制的数据集，包含2018年1月至2024年12月在中国中部寿县国家气候观象台（32.434° N，116.793° E）连续地基偏振激光雷达观测反演的沙尘、非沙尘及总气溶胶质量浓度廓线。该站点位于京津冀与长三角地区之间的区域输送通道上，因此非常适合监测中国东部两个主要源–汇系统之间的气溶胶交换。原始激光雷达测量数据经过云、降水和仪器伪影筛查，统一至15分钟和7.5米分辨率，并采用Fernald反演和偏振激光雷达–光度计联网（POLIPHON）框架进行处理，以反演约0.3至6 km高度范围内的垂直分辨沙尘和非沙尘气溶胶质量浓度。利用同址CE–318太阳光度计观测约束了逐月消光–质量转换因子。该产品通过与独立的柱、廓线和近地面参考数据进行评估，包括CE–318气溶胶光学厚度、CALIPSO气溶胶消光和退偏廓线，以及同址Grimm监测仪的地面颗粒物测量。比较结果表明，该数据集再现了柱气溶胶载荷的主要时间变率，捕捉了气溶胶消光系数的主要垂直结构，并与近地面颗粒物变率在统计上一致。提供了0至100的逐点反演质量指数（RQI），以便于数据筛选和再利用，其中RQI值75–100推荐用于定量分析和事件研究，50–75用于常规统计分析，较低值主要用于定性解释或基于时间或垂直平均的分析。该数据集提供了中国中部多年、高分辨率的气溶胶类型分辨质量廓线观测记录，旨在支持社区在卫星验证、模式评估、气溶胶输送研究、边界层研究和数据同化应用中的再利用。",null,"Earth System Science Data Discussions","2026-09-23T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文为大气气溶胶遥感数据集研究，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。",[19],{"name":10,"url":6},[21,22,23,24],"气候变化","大气气溶胶","激光雷达遥感","遥感数据集",[26,27],"寿县国家气候观象台 气溶胶 激光雷达","华中 沙尘 气溶胶 廓线 数据集","寿县国家气候观象台气溶胶激光雷达-3505","10.5194\u002Fessd-2026-677",{"doi":29,"openalex_id":31,"authors":32,"venue":10,"cited_by_count":15,"oa_url":76,"card":77,"direction":81,"ingested_from":83},"W7214075032",[33,36,38,39,41,43,45,47,49,51,53,55,57,59,61,63,66,68,70,73],{"name":34,"orcid":35},"Zhuang Wang","https:\u002F\u002Forcid.org\u002F0000-0001-7032-4500",{"name":37,"orcid":9},"Chengxiao Liu",{"name":37,"orcid":9},{"name":40,"orcid":9},"Yanfeng Huo",{"name":42,"orcid":9},"Xinfeng Lin",{"name":44,"orcid":9},"Shaowei Yan",{"name":46,"orcid":9},"Xiaoyun Sun",{"name":48,"orcid":9},"Jiaqi Chen",{"name":50,"orcid":9},"Nan Ge",{"name":52,"orcid":9},"Guanyin Yang",{"name":54,"orcid":9},"Zhenzhen Hua",{"name":56,"orcid":9},"Hao Zhang",{"name":58,"orcid":9},"Chune Shi",{"name":60,"orcid":9},"Yong Zhu",{"name":62,"orcid":9},"Yizhi Zhu",{"name":64,"orcid":65},"Congzi Xia","https:\u002F\u002Forcid.org\u002F0000-0002-5185-2209",{"name":67,"orcid":9},"Kaidi Zhang",{"name":69,"orcid":9},"Xintong Chen",{"name":71,"orcid":72},"Yuehua Chen","https:\u002F\u002Forcid.org\u002F0000-0003-4709-7623",{"name":74,"orcid":75},"Chengzhi Xing","https:\u002F\u002Forcid.org\u002F0000-0002-0265-2358","https:\u002F\u002Fessd.copernicus.org\u002Fpreprints\u002Fessd-2026-677\u002Fessd-2026-677.pdf",{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"发布中国中部2018–2024年高时空分辨率沙尘与非沙尘气溶胶质量浓度廓线数据集。","地基偏振激光雷达结合Fernald反演与POLIPHON，用CE-318光度计约","数据集能重现柱气溶胶主要时间变化、垂直消光结构，并与近地面颗粒物统计一致，附0–100质量指数。","农业遥感与作物表型","可将该气溶胶廓线数据用于农业区气溶胶-辐射-作物光合作用耦合研究，或与卫星遥感产品交叉验证。","openalex","2026-09-25T23:30:32.574320Z",{"total":86,"page":87,"page_size":86,"items":88},6,1,[89,119,173,214,252,294],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":95,"source_url":92,"published_at":96,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":97,"sources":99,"tags":101,"search_phrases":105,"slug":108,"view_count":15,"doi":109,"paper":110,"created_at":118},3502,"Diagnosis of climate condition variations and their socio-environmental impacts in the Ogou prefecture (Southeast of the Plateaux Region - Togo)","https:\u002F\u002Fdoi.org\u002F10.24214\u002Fjcbps.d.16.4.68398","This study is conducted within the context of increasingly variable climate conditions across the world, which are affecting socioeconomic activities.The study aims to assess climate changes and identify their socio-environmental effects in the Ogou Prefecture.Data from ANAMET and Power NASA (1990-2024) enabled a frequency analysis of climate indices and the five-year spatio-temporal mapping of temperatures.Landsat imagery from 2003 and 2023, as well as MODIS data (temperature and NDVI) from 2000 to 2024 were used to study changes in vegetation cover in relation to temperature.From the survey of 369 participants, socioeconomic impacts have been identified.The results show a decline in rainy seasons, a recurrence of dry spells, and a rise in temperatures along a northwest-southeast gradient, along with values exceeding 26°C.The five-year period from 2015 to 2019 and the period from 2020 to 2024 are the most affected by global warming.A Diachronic analysis of land-cover maps reveals a degradation of vegetation cover; Diagnosis of …Faya Lemou et al.","本研究是在全球气候条件日益多变、并影响社会经济活动的背景下开展的。研究旨在评估气候变化并识别其在奥古省（Ogou Prefecture）产生的社会环境效应。利用ANAMET和Power NASA（1990—2024年）的数据，对气候指数进行了频率分析，并绘制了五年期气温时空分布图。研究还使用了2003年和2023年的Landsat影像，以及2000—2024年的MODIS数据（温度和NDVI），以探讨植被覆盖随温度变化的情况。通过对369名受访者的调查，识别出了社会经济影响。结果表明，雨季缩短、干旱期反复出现，气温沿西北—东南梯度上升，且数值超过26°C。2015—2019年的五年期以及2020—2024年期间受全球变暖影响最为严重。土地覆盖图的历时分析揭示了植被覆盖退化；对……Faya Lemou等人的诊断","Journal of Chemical Biological and Physical Sciences","2026-09-24T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":98},"研究多哥Ogou地区气候变化与社会环境影响，属气候环境科学，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。",[100],{"name":95,"url":92},[21,102,103,104],"遥感监测","植被覆盖","多哥",[106,107],"植被覆盖 气候变化 遥感监测 多哥","植被覆盖 气候变化","植被覆盖气候变化遥感监测多哥-3502","10.24214\u002Fjcbps.d.16.4.68398",{"doi":109,"openalex_id":111,"authors":112,"venue":95,"cited_by_count":15,"oa_url":92,"card":113,"direction":81,"ingested_from":83},"W7214205919",[],{"tldr":114,"method":115,"finding":116,"direction":81,"opportunity":117},"评估多哥Ogou省气候变化及其对社会环境的影响。","用ANAMET\u002FNASA气象数据、Landsat与MODIS影像及369份问卷分","雨季减少、干旱频发、气温升高，植被退化，2015年后变暖加剧。","可结合多源遥感与农户调查，量化气候变暖对西非农业生计的级联影响。","2026-09-25T23:30:31.156223Z",{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":126,"score_detail":127,"sources":134,"tags":136,"search_phrases":141,"slug":144,"view_count":15,"doi":145,"paper":146,"created_at":172},3477,"Ratoon rice under a changing climate: Physiological responses and management strategies","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16473","Ratoon rice has gained attention as a sustainable strategy for maintaining rice productivity under changing climatic conditions, where rising temperatures, irregular rainfall, droughts, floods and other extreme weather events threaten crop productivity and global food security. Ratoon rice has emerged as a resource efficient and climate smart production method as it makes use of the regenerative ability of plants to generate a second harvest from stubble of the main crop. Compared to conventional rice cultivation, ratoon rice requires less labor, reduced inputs and a shorter growing period while improving land, water and nutrient use efficiency. However, the physiological responses of rice plants to changing environmental conditions are important for the success of ratoon crops. Bud regeneration, carbohydrate reserve mobilisation, hormonal regulation, photosynthetic efficiency and source sink relationship play a critical role in determining ratoon establishment and yield under climatic stress. Ratoon rice is more resilient to heat, drought, flooding and other environmental challenges when the appropriate management techniques such as optimal stubble height, balanced nutrition, effective irrigation and plant growth regulators are used. Furthermore, improving soil health through organic amendments and beneficial microbial interactions contributes to greater stress tolerance and sustainable productivity. Ratoon rice production is still limited despite its potential due to automation issues, yield instability and climate related hazards. To enhance the adaptation under shifting climatic conditions, future research should concentrate on creating climate-resilient ratoon cultivars, precision nutrient and water management technologies and decision support systems. This review summarises the physiological response of ratoon rice to climate change and highlights effective management strategies that can enhance productivity, resource use efficiency and sustainability, thereby supporting resilient rice production and long term food security.","再生稻作为一种在气候变化条件下维持水稻生产力的可持续策略而受到关注，气温升高、降雨不规律、干旱、洪涝及其他极端天气事件正威胁作物生产力和全球粮食安全。再生稻已成为一种资源高效且气候智慧型的生产方式，因为它利用植物的再生能力，从主作物的稻桩上获得第二次收获。与传统水稻栽培相比，再生稻所需劳动力更少、投入更低、生育期更短，同时提高了土地、水分和养分利用效率。然而，水稻植株对环境条件变化的生理响应对于再生稻作的成功至关重要。在气候胁迫下，芽再生、碳水化合物储备动员、激素调控、光合效率及源库关系在决定再生稻建成和产量方面发挥着关键作用。当采用适宜的管理技术，如最佳留桩高度、平衡营养、有效灌溉和植物生长调节剂时，再生稻对高温、干旱、洪涝及其他环境挑战具有更强的适应力。此外，通过有机改良和有益微生物互作改善土壤健康，有助于提高抗逆性和可持续生产力。尽管再生稻生产潜力巨大，但由于自动化问题、产量不稳定及气候相关灾害，其发展仍然受限。为增强在不断变化的气候条件下的适应性，未来研究应集中于培育气候韧性再生稻品种、精准养分和水分管理技术及决策支持系统。本综述总结了再生稻对气候变化的生理响应，并重点介绍了能够提高生产力、资源利用效率和可持续性的有效管理策略，从而支持具有韧性的水稻生产和长期粮食安全。","Plant Science Today",73,{"impact":128,"substance":129,"depth":130,"authority":131,"freshness":132,"relevant":87,"comment":133},15,20,17,13,8,"综述系统梳理再生稻应对气候变化的生理机制与管理策略，专业性强但属学术综述，产业即时影响有限。",[135],{"name":125,"url":122},[137,138,139,21,140],"智慧农业","粮食安全","再生稻","水稻栽培",[142,143],"再生稻 气候变化 栽培管理","Plant Science Today 再生稻","再生稻气候变化栽培管理-3477","10.14719\u002Fpst.16473",{"doi":145,"openalex_id":147,"authors":148,"venue":125,"cited_by_count":15,"oa_url":164,"card":165,"direction":171,"ingested_from":83},"W7214098380",[149,152,155,158,161],{"name":150,"orcid":151},"R Abirami","https:\u002F\u002Forcid.org\u002F0009-0002-8075-9908",{"name":153,"orcid":154},"R Vigneshwari","https:\u002F\u002Forcid.org\u002F0000-0002-4417-1034",{"name":156,"orcid":157},"K. Malarkodi","https:\u002F\u002Forcid.org\u002F0000-0002-8974-9389",{"name":159,"orcid":160},"N Sakthivel","https:\u002F\u002Forcid.org\u002F0009-0005-8248-3812",{"name":162,"orcid":163},"Koothan Vanitha","https:\u002F\u002Forcid.org\u002F0000-0001-8516-9275","https:\u002F\u002Fhorizonepublishing.com\u002Fjournals\u002Findex.php\u002FPST\u002Farticle\u002Fdownload\u002F16473\u002F16260",{"tldr":166,"method":167,"finding":168,"direction":169,"opportunity":170},"综述再生稻在气候变化下的生理响应与栽培管理策略，以提升产量与可持续性。","文献综述，聚焦再生芽、碳储备、激素、光合及源库关系等生理机制。","优化留桩高度、水肥与生长调节剂可增强再生稻抗逆性，但自动化与产量稳定性仍受限。","农业绿色发展与碳","可研发气候韧性再生稻品种及精准水肥决策支持系统，并探索微生物互作提升抗逆性。","智慧农业 \u002F 农业物联网","2026-09-25T23:30:11.458343Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":9,"source_name":179,"source_url":176,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":181,"sources":186,"tags":188,"search_phrases":192,"slug":195,"view_count":15,"doi":196,"paper":197,"created_at":213},3353,"Integrating artificial intelligence in climate change and sustainable development: A comprehensive bibliometric review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sftr.2026.102145","The integration of Artificial Intelligence (AI) with climate change and sustainable development (SD) studies has significant potential to enable actionable, data-driven, innovative, and long-term mitigation and adaptation strategies. However, there is a lack of a well-organized, comprehensive review of mapping that integrates AI, climate change, and the SD nexus. This study addresses this notable gap through a bibliometric review that dissects descriptive bibliometric, performance, and conceptual analyses. Utilizing the Web of Science Core Collection, 3291 publications from 2007 to 2025 were analyzed using Bibliometrix and VOSviewer software. The findings indicate a 38.29% annual publication growth rate, and original articles (80%) were the dominant publication type. The Chinese Academy of Sciences was the top contributor, and China and the USA were leaders in research output and international AI-integrated climate and SD research collaborations. Keyword analysis shows “artificial intelligence”, “climate change”, “deep learning”, “machine learning”, and “sustainability” as dominant keywords. Conceptual mapping identified four approaches: macro-level AI-driven digitalization; methodologically driven geospatial and machine-learning-based environmental monitoring; predictive modeling; and AI-driven smart agriculture applications. Thematic mapping reveals a shift from fundamental AI for climate change-focused research (2007–2013) to integrating AI with climate change and SD (2014–2019), and modeling and predicting approaches (2020–2025). Findings highlight substantial geographic asymmetries; data and research gaps persist in climate-vulnerable low-income areas. The extractivism rate for low-income countries was 41%, indicating that nearly half of their publications are led by high-income countries. Recommendations are grounded in theoretical, methodological, practical, and policy considerations, with an emphasis on SDGs 13 and 17. This study provides insights for researchers, practitioners, and policymakers to emphasize policies and technologies and implement a nexus-based framework.","人工智能（Artificial Intelligence, AI）与气候变化及可持续发展（Sustainable Development, SD）研究的融合，在推动可操作、数据驱动、创新性和长期性的减缓与适应策略方面具有巨大潜力。然而，目前缺乏对AI、气候变化与可持续发展三者交叉领域的系统化、综合性文献计量综述。本研究通过文献计量学综述填补了这一显著空白，从描述性文献计量分析、绩效分析和概念分析三个维度进行剖析。利用Web of Science核心合集，采用Bibliometrix和VOSviewer软件对2007年至2025年间的3291篇文献进行了分析。研究结果表明，年度发文增长率为38.29%，原创论文（80%）为主要文献类型。中国科学院是最大的贡献机构，中国和美国在研究产出及AI融合气候与可持续发展研究的国际合作方面处于领先地位。关键词分析显示，“人工智能”“气候变化”“深度学习”“机器学习”和“可持续性”是主导性关键词。概念图谱识别出四种研究路径：宏观层面的AI驱动数字化；方法论驱动的基于地理空间和机器学习的环境监测；预测建模；以及AI驱动的智慧农业应用。主题图谱揭示了研究重心的演变：从气候变化聚焦的基础AI研究（2007—2013年），到AI与气候变化及可持续发展的融合（2014—2019年），再到建模与预测方法（2020—2025年）。研究发现存在显著的地理不对称性；气候脆弱型低收入地区仍存在数据和研究缺口。低收入国家的提取主义率为41%，表明其近半数出版物由高收入国家主导。建议基于理论、方法论、实践和政策层面的考量，并着重关注可持续发展目标13和目标17。本研究为研究人员、实践者和政策制定者提供了洞见，以强调政策与技术的重要性，并实施基于交叉领域的框架。","Sustainable Futures",80,{"impact":182,"substance":183,"depth":182,"authority":131,"freshness":184,"relevant":87,"comment":185},18,22,9,"基于3291篇文献的AI与气候变化及可持续发展文献计量综述，方法规范、数据规模大，对智慧农业与农业AI研究具有参考价值。",[187],{"name":179,"url":176},[137,189,190,21,191],"农业人工智能","可持续发展","文献计量",[193,194],"农业人工智能 可持续发展 文献计量 智慧农业","农业人工智能 可持续发展","农业人工智能可持续发展文献计量智慧农业-3353","10.1016\u002Fj.sftr.2026.102145",{"doi":196,"openalex_id":198,"authors":199,"venue":179,"cited_by_count":15,"oa_url":176,"card":207,"direction":171,"ingested_from":83},"W7214099405",[200,203,205],{"name":201,"orcid":202},"H. B. T. P. Jayathilaka","https:\u002F\u002Forcid.org\u002F0009-0001-0589-6999",{"name":204,"orcid":9},"Shiyan Zhai",{"name":206,"orcid":9},"Yuke Feng",{"tldr":208,"method":209,"finding":210,"direction":211,"opportunity":212},"用文献计量法梳理2007-2025年AI与气候变化及可持续发展交叉研究，揭示主题演化与地域失衡。","Web of Science 3291篇文献，Bibliometrix与VOSv","年增38.29%，主题从基础AI转向建模预测，低收入国家41%论文由高收入国家主导。","农业人工智能与决策模型","可针对气候脆弱低收入地区，构建AI+智慧农业的本地化数据与决策模型，填补研究空白。","2026-09-24T23:30:10.248845Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":9,"source_name":220,"source_url":217,"published_at":11,"category":12,"cover_url":9,"hotness":221,"is_selected":14,"score":222,"score_detail":223,"sources":225,"tags":229,"search_phrases":234,"slug":237,"view_count":15,"doi":238,"paper":239,"created_at":251},3352,"Agricultural Productivity under Climate Change and the Dynamics of Rural Economic Resilience: Adaptation Pathways from Farms to Households and Territories","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22925809","This paper examines the relationship between climate change, agricultural productivity, and rural economic resilience by integrating evidence on climatic pressures, farm-level adaptation, and broader rural transformation. Climate change affects agricultural production through rising temperatures, heat stress, rainfall variability, drought, water scarcity, soil degradation, pests, and other interacting environmental pressures. These effects are transmitted beyond crop yields through farm income, employment, food security, agricultural value chains, and the economic stability of rural territories. The paper shows that agricultural resilience depends on farmers’ capacity to adopt complementary adaptation strategies, including crop diversification, improved soil management, climate-smart agriculture, water-management technologies, climate information, agricultural insurance, and other risk-management mechanisms. At the household level, livelihood diversification, migration, entrepreneurship, access to productive assets, and institutional support can reduce dependence on climate-sensitive agricultural activities. At the territorial level, resilient agricultural value chains, market access, infrastructure, local product valorization, and non-farm employment contribute to absorbing and adapting to climatic disturbances. The analysis therefore approaches resilience as a multidimensional process connecting agricultural productivity, household adaptive capacity, food security, and rural economic transformation. The findings suggest that effective climate adaptation requires coordinated interventions combining productive, financial, institutional, social, and territorial mechanisms rather than isolated technological responses","本文通过整合气候压力、农场层面适应以及更广泛的农村转型等方面的证据，考察了气候变化、农业生产率与农村经济韧性之间的关系。气候变化通过气温上升、热应激、降雨变率、干旱、水资源短缺、土壤退化、病虫害及其他相互作用的环境压力影响农业生产。这些影响超越作物产量，经由农场收入、就业、粮食安全、农业价值链以及农村地域的经济稳定等渠道传导。本文表明，农业韧性取决于农民采取互补性适应策略的能力，包括作物多样化、改善土壤管理、气候智慧型农业、水资源管理技术、气候信息、农业保险及其他风险管理机制。在农户层面，生计多样化、迁移、创业、生产性资产获取以及制度支持能够降低对气候敏感型农业活动的依赖。在地域层面，具有韧性的农业价值链、市场准入、基础设施、本地产品增值以及非农就业有助于吸收和适应气候扰动。因此，本分析将韧性视为一个多维过程，连接农业生产率、农户适应能力、粮食安全与农村经济转型。研究结果表明，有效的气候适应需要将生产性、金融性、制度性、社会性和地域性机制相结合的协调干预，而非孤立的技术应对措施。","Zenodo (CERN European Organization for Nuclear Research)",25,77,{"impact":182,"substance":129,"depth":130,"authority":131,"freshness":184,"relevant":87,"comment":224},"系统梳理气候变化下农业生产力与农村经济韧性的多层次适应路径，结论具政策参考价值，但属综述性论文，非突破性成果。",[226,227],{"name":220,"url":217},{"name":220,"url":228},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22925808",[230,231,232,21,233],"乡村振兴","农业韧性","农业保险","气候智慧农业",[235,236],"气候智慧农业 适应路径","农业韧性 农村经济","气候智慧农业适应路径-3352","10.5281\u002Fzenodo.22925809",{"doi":238,"openalex_id":240,"authors":241,"venue":220,"cited_by_count":15,"oa_url":217,"card":246,"direction":171,"ingested_from":83},"W7214087966",[242,244],{"name":243,"orcid":9},"Naledi Ncube",{"name":245,"orcid":9},"Rudo Marashe",{"tldr":247,"method":248,"finding":249,"direction":169,"opportunity":250},"综述气候变化下农业生产力与农村经济韧性的多维适应路径。","整合气候压力、农场适应与农村转型的文献证据分析。","有效适应需生产、金融、制度、社会与区域机制协同而非单一技术。","可量化农户—区域多层级适应组合的协同效应与韧性阈值。","2026-09-24T23:30:10.179607Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":264,"tags":266,"search_phrases":271,"slug":274,"view_count":15,"doi":275,"paper":276,"created_at":293},3191,"Spatial prediction of soil organic carbon stocks in Sudanese clay soils using regression kriging","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fsjss.2026.16733","Soil organic carbon (SOC) stocks are a critical component of terrestrial carbon pools, influencing soil quality, agricultural productivity, and climate change mitigation. This study aimed to map and improve spatial estimation of SOC stocks in Sudan’s Blue Nile clay soils using regression kriging (RK). The model integrated 554 spatially unique soil profiles with nine environmental covariates: precipitation, temperature, relative humidity, normalized difference vegetation index (NDVI), land use\u002Fcover, bare soil index (BSI), digital elevation model (DEM), LS-factor, and aspect. Spectral indices were derived from Landsat 9 imagery (April 2024), while climate and terrain data were obtained from CHIRPS\u002FWorldClim and SRTM (30 m). RK performance was robust, with spatial cross-validation R 2 = 0.72, RMSE = 8.4 Mg C ha −1 (29% of mean observed stock), and mean bias = −0.8 Mg C ha −1 . Predicted SOC stocks (0–30 cm) ranged from 12.4 to 51.2 Mg C ha −1 (mean 28.6 Mg C ha −1 ). NDVI, clay content, and topographic wetness index were the most influential predictors. Agricultural lands exhibited the highest stocks (51.2 Mg C ha −1 ), while bare lands had the lowest (14.2 Mg C ha −1 ). This study (1) applies spatially explicit validation for SOC mapping in Sudan’s Blue Nile region, (2) harmonizes legacy and contemporary soil data using equivalent soil mass correction, and (3) provides high-resolution SOC maps for climate-resilient agricultural planning. Findings support soil carbon management and climate mitigation in semi-arid regions.","土壤有机碳（SOC）储量是陆地碳库的重要组成部分，影响土壤质量、农业生产力及气候变化减缓。本研究旨在利用回归克里金（RK）方法对苏丹青尼罗河黏土区SOC储量进行制图并改进其空间估算。该模型整合了554个空间独立土壤剖面与9个环境协变量：降水、温度、相对湿度、归一化植被指数（NDVI）、土地利用\u002F覆盖、裸土指数（BSI）、数字高程模型（DEM）、LS因子和坡向。光谱指数源自Landsat 9影像（2024年4月），气候与地形数据分别来自CHIRPS\u002FWorldClim和SRTM（30 m）。RK表现稳健，空间交叉验证R²=0.72，RMSE=8.4 Mg C ha⁻¹（为实测储量均值的29%），平均偏差=−0.8 Mg C ha⁻¹。预测SOC储量（0–30 cm）范围为12.4–51.2 Mg C ha⁻¹（均值28.6 Mg C ha⁻¹）。NDVI、黏粒含量和地形湿度指数是最具影响力的预测因子。农地储量最高（51.2 Mg C ha⁻¹），裸地最低（14.2 Mg C ha⁻¹）。本研究（1）对苏丹青尼罗河地区SOC制图采用空间显式验证，（2）利用等效土壤质量校正协调历史与当代土壤数据，（3）为气候韧性农业规划提供高分辨率SOC图。研究结果支持半干旱地区的土壤碳管理与气候减缓。","Spanish Journal of Soil Science","2026-09-22T00:00:00Z",68,{"impact":132,"substance":262,"depth":130,"authority":131,"freshness":184,"relevant":87,"comment":263},21,"基于554个土壤剖面与多源遥感协变量的回归克里金制图研究，方法规范、验证充分，对半干旱区土壤碳管理与气候适应型农业规划有参考价值，但属区域性学术成果，公共影响有限。",[265],{"name":258,"url":255},[267,21,268,269,270],"农业遥感","遥感","土壤碳汇","数字土壤制图",[272,273],"苏丹青尼罗河 土壤有机碳 回归克里金","Landsat 9 土壤有机碳 空间预测","苏丹青尼罗河土壤有机碳回归克里金-3191","10.3389\u002Fsjss.2026.16733",{"doi":275,"openalex_id":277,"authors":278,"venue":258,"cited_by_count":15,"oa_url":255,"card":287,"direction":292,"ingested_from":83},"W7213971196",[279,281,283,285],{"name":280,"orcid":9},"Faroug A.H. Jadalla",{"name":282,"orcid":9},"Kolapo O. Oluwasemire",{"name":284,"orcid":9},"Abd Elmagid A. Elmobarak",{"name":286,"orcid":9},"Mohammed A. M. Mohammed Zein",{"tldr":288,"method":289,"finding":290,"direction":81,"opportunity":291},"用回归克里金结合多源环境协变量预测苏丹青尼罗河粘土区土壤有机碳储量。","554个土壤剖面与9个环境协变量，Landsat 9、CHIRPS\u002FWorldC","模型R²=0.72，NDVI、粘土含量和地形湿度指数影响最大，农地碳储量最高。","可引入时序遥感与机器学习提升半干旱区SOC动态预测，并耦合农业管理措施评估固碳潜力。","数字乡村与农业信息化","2026-09-22T23:30:31.028178Z",{"id":295,"title":296,"url":297,"summary":298,"summary_zh":299,"content":9,"source_name":300,"source_url":297,"published_at":301,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":126,"score_detail":302,"sources":305,"tags":307,"search_phrases":310,"slug":313,"view_count":15,"doi":314,"paper":315,"created_at":326},3183,"Integrated Remote Sensing and Time Series Analysis for Long-Term Assessment of Vegetation Resilience: Synthesizing Climatic Variables and Land-Use\u002FLand-Cover Trajectories in Al-Ahsa Oasis, Saudi Arabia","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15091758","Continuous monitoring of land-use\u002Fland-cover (LULC) change is essential in the Al-Ahsa Oasis, an arid region characterized by a sensitive and fragile environment. This study is the first to integrate vegetation, climate, and land-use frameworks by combining vegetation indicators, climatic variables, and LULC trajectory classification over a 41-year period (1985–2025). Data from three Landsat sensors (Landsat 5-TM, 7-ETM+, and 8-OLI) were processed to derive vegetation indicators and an LULC trajectory classification, while ERA5-Land and CHIRPS data provided surface temperature and rainfall information, respectively. LULC trajectory classification was implemented using a random forest classifier, generating six LULC trajectory classes: stable barren, stable urban, stable water, urban expansion, transition lands, and stable vegetation. Pearson correlation analysis was used to assess the relationships between the vegetation indicators (normalized difference vegetation index [NDVI] and soil-adjusted vegetation index [SAVI]) and climatic variables (temperature and rainfall) at different time lags (0–3 years) and after detrending the time series. The detrending analysis showed no statistically significant associations between the vegetation indicators (NDVI and SAVI) and the climatic variables (temperature and rainfall) across the four examined lags, whereas the original, non-detrended time series showed positive associations that were mainly attributable to long-term trends rather than to interannual climate–vegetation covariation. The accuracy of LULC trajectory classification was high, with an overall accuracy of 0.939 and a kappa coefficient of 0.924, indicating robust mapping performance. Overall vegetation cover improved over the study period. The degradation occurred in the central part of the oasis (stable urban, urban expansion, and stable water classes, accounting for 4.506%, 5.179%, and 41.773% of each trajectory class, respectively), whereas the recovery was observed in the northern, southern, and eastern parts (stable vegetation and transition lands, accounting for 37.656% and 52.891% of each trajectory class, respectively). These findings provide long-term insights into vegetation resilience in the Al-Ahsa Oasis over 41 years, supporting sustainable urban planning and vegetation and agricultural management.","在环境敏感而脆弱的干旱区——哈萨绿洲，持续监测土地利用\u002F土地覆盖（LULC）变化至关重要。本研究首次在41年（1985—2025年）时间跨度内，通过整合植被指标、气候变量与LULC轨迹分类，构建了植被、气候与土地利用的综合框架。研究处理了三颗Landsat传感器（Landsat 5-TM、7-ETM+和8-OLI）的数据，以提取植被指标并进行LULC轨迹分类，同时分别利用ERA5-Land和CHIRPS数据获取地表温度和降雨信息。LULC轨迹分类采用随机森林分类器实现，生成了六类LULC轨迹：稳定裸地、稳定城市、稳定水体、城市扩张、过渡土地和稳定植被。采用Pearson相关分析评估植被指标（归一化差异植被指数[NDVI]和土壤调节植被指数[SAVI]）与气候变量（温度和降雨）在不同时间滞后（0—3年）及时间序列去趋势后的关系。去趋势分析表明，在四个考察滞后下，植被指标（NDVI和SAVI）与气候变量（温度和降雨）之间均无统计学显著关联，而原始未去趋势时间序列则显示出正相关，这主要归因于长期趋势而非年际气候—植被协变。LULC轨迹分类精度较高，总体精度为0.939，kappa系数为0.924，表明制图性能稳健。研究期间整体植被覆盖有所改善。退化发生在绿洲中部（稳定城市、城市扩张和稳定水体类别，分别占各轨迹类别的4.506%、5.179%和41.773%），而恢复则出现在北部、南部和东部（稳定植被和过渡土地，分别占各轨迹类别的37.656%和52.891%）。这些发现为哈萨绿洲41年来的植被恢复力提供了长期见解，可支持可持续城市规划以及植被与农业管理。","Land","2026-09-20T00:00:00Z",{"impact":303,"substance":183,"depth":182,"authority":131,"freshness":132,"relevant":87,"comment":304},12,"41年长时序遥感与气候变量整合分析，方法扎实、结论可靠，对干旱区绿洲农业可持续管理有参考价值，但属区域性案例研究，公共影响有限。",[306],{"name":300,"url":297},[21,102,308,103,309],"土地利用","绿洲农业",[311,312],"Al-Ahsa Oasis 遥感 植被","Landsat NDVI 土地利用变化","Al-AhsaOasis遥感植被-3183","10.3390\u002Fland15091758",{"doi":314,"openalex_id":316,"authors":317,"venue":300,"cited_by_count":15,"oa_url":297,"card":321,"direction":81,"ingested_from":83},"W7213862324",[318],{"name":319,"orcid":320},"Amal H. Aljaddani","https:\u002F\u002Forcid.org\u002F0000-0003-1171-8416",{"tldr":322,"method":323,"finding":324,"direction":81,"opportunity":325},"整合41年Landsat植被指数、气候变量与LULC轨迹，评估沙特Al-Ahsa绿洲植被恢复力。","Landsat 5\u002F7\u002F8时序、ERA5-Land与CHIRPS气候数据、随机森","去趋势后植被与气候无显著相关，绿洲整体植被改善，中部退化、南北东恢复。","可引入非线性\u002F因果推断与高分辨率数据，区分灌溉、城市化与气候对干旱区绿洲植被恢复力的驱动。","2026-09-22T23:30:25.304168Z"]