[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2424":3,"related-2424":95},{"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,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":62,"slug":65,"view_count":57,"doi":66,"paper":67,"created_at":94},2424,"Nonlinear relationships between vegetation dynamics and ecosystem services in the Yangtze River Basin: insights from random forest and GTWR analyzes","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17538947.2026.2722459","Understanding the nonlinear relationships between vegetation dynamics and ecosystem services (ES) is essential for sustainable ecosystem management in the Yangtze River Basin (YRB). Using the Google Earth Engine platform, we analyzed NDVI dynamics in the YRB from 2000 to 2020. The InVEST model was used to assess four key ES, including water yield (WY), carbon storage (CS), habitat quality (HQ), and soil retention (SR). Random forest and spatiotemporal geographically weighted regression models were further applied to explore the nonlinear relationships and spatial heterogeneity between NDVI and ES. The results showed that: (1) mean NDVI in the YRB was 0.692, 0.697, and 0.724 in 2000, 2010, and 2020, respectively, exhibiting clear spatiotemporal heterogeneity. (2) WY showed a non-monotonic trajectory of initial increase followed by decline, SR increased markedly, HQ remained relatively stable, and CS showed a slight decrease. (3) CS and SR showed more pronounced positive associations with NDVI, whereas WY and HQ exhibited stronger interval variability or weaker localized effects. (4) NDVI generally had positive local effects on CS, HQ, and SR, while the NDVI–WY relationship exhibited both positive associations and localized negative effects. These findings provide valuable insights for ecological restoration and ecosystem service management in the YRB.","理解植被动态与生态系统服务（ES）之间的非线性关系，对长江流域（YRB）生态系统的可持续管理至关重要。本研究基于Google Earth Engine平台，分析了2000—2020年长江流域NDVI的动态变化，并利用InVEST模型评估了四项关键生态系统服务，包括产水量（WY）、碳储量（CS）、生境质量（HQ）和土壤保持（SR）。进一步采用随机森林模型和时空地理加权回归模型，探讨NDVI与生态系统服务之间的非线性关系及空间异质性。结果表明：（1）长江流域NDVI均值在2000年、2010年和2020年分别为0.692、0.697和0.724，呈现出明显的时空异质性。（2）产水量呈先增后减的非单调变化轨迹，土壤保持显著增加，生境质量相对稳定，碳储量略有下降。（3）碳储量和土壤保持与NDVI的正相关关系更为显著，而产水量和生境质量则表现出更强的区间变异性或较弱的局部效应。（4）NDVI总体上对碳储量、生境质量和土壤保持具有正向局部效应，而NDVI与产水量之间既存在正相关关系，也存在局部负效应。这些发现可为长江流域生态修复与生态系统服务管理提供有价值的参考。",null,"International Journal of Digital Earth","2026-09-12T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,14,8,1,"基于GEE与随机森林、GTWR方法揭示长江流域植被与生态系统服务非线性关系，方法新颖、数据扎实，对流域生态修复有参考价值，但属学术论文、公共政策影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","长江流域","生态系统服务","植被动态","数字地球",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":61},[39,47,55],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":46},"H6",7,true,[],[29,45],"ecosystem service",[29],{"code":48,"score":49,"supported":42,"title":50,"abstract":51,"tags":54},"H2",6,[],[52,53],"土壤","soil",[],{"code":56,"score":57,"supported":14,"title":58,"abstract":59,"tags":60},"I3",2,[],[],[30,27],"农业相关性需要复核",[63,64],"生态系统服务 数字地球 植被动态 长江流域","生态系统服务 数字地球","生态系统服务数字地球植被动态长江流域-2424","10.1080\u002F17538947.2026.2722459",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":84,"oa_url":85,"card":86,"direction":92,"ingested_from":93},"W7212379230",[70,72,74,77,80,82],{"name":71,"orcid":9},"Jinhuang Lin",{"name":73,"orcid":9},"Jixing Huang",{"name":75,"orcid":76},"Yongwu Dai","https:\u002F\u002Forcid.org\u002F0000-0002-8562-5733",{"name":78,"orcid":79},"Guoqing Li","https:\u002F\u002Forcid.org\u002F0000-0003-1767-3052",{"name":81,"orcid":9},"Yuanrui Zang",{"name":83,"orcid":9},"Yan Huang",0,"https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2722459?needAccess=true",{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"用随机森林与GTWR揭示长江流域2000—2020年NDVI与四项生态系统服务的非线性关系。","GEE提取NDVI，InVEST评估四项ES，随机森林与GTWR分析非线性及空间","NDVI与碳储、土壤保持正相关更强，与产水呈先增后减且存在局部负效应。","农业绿色发展与碳","可引入多源遥感与作物物候变量，探究NDVI-ES非线性阈值及跨尺度驱动机制。","农业遥感与作物表型","openalex","2026-09-14T23:30:24.653682Z",{"total":49,"page":22,"page_size":49,"items":96},[97,178,254,315,401,469],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":110,"tags":112,"primary_topic":9,"secondary_topics":116,"classification":117,"search_phrases":151,"slug":154,"view_count":22,"doi":155,"paper":156,"created_at":177},4228,"Mapping satellite based research on climate change impacts and environmental responses through bibliometric analysis","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01090-8","Climate change affects ecosystems, water resources, and human systems worldwide, while satellite remote sensing provides large-scale and repeated observations of environmental change. However, existing bibliometric assessments often examine individual environmental applications, methodological domains, or broader Earth observation research, providing limited understanding of the integrated development of satellite-based climate–environment research. This study addresses this gap by systematically examining the growth, scientific structure, collaboration patterns, and thematic evolution of satellite-based research on climate change impacts and environmental responses from 2001 to 2025. A dataset of 1,112 documents was compiled from the Web of Science Core Collection and Scopus and analyzed using bibliometric indicators, network analysis, Multiple Correspondence Analysis, thematic mapping, and thematic evolution. The results demonstrate sustained growth in the research field, concentration within a relatively small group of core journals, and a semi-centralized collaboration structure involving prominent authors, countries, and institutions. The thematic analysis reveals continuity of established MODIS- and NDVI-based environmental research alongside the emergence of climate-change-oriented, machine-learning, and cloud-based approaches, particularly random forest and Google Earth Engine. The study provides an integrated view of how satellite-based climate–environment research has developed across interconnected ecosystems, hydrological processes, vegetation dynamics, and climate-related environmental responses. The findings can support researchers in identifying major knowledge structures, emerging research directions, collaboration opportunities, and methodological trends, while also highlighting the need to interpret bibliometric patterns within the coverage limitations of WoS- and Scopus-indexed English-language literature.","气候变化影响全球生态系统、水资源和人类系统，而卫星遥感为环境变化提供了大尺度、重复观测。然而，现有文献计量评估多考察单一环境应用、方法学领域或更广泛的地球观测研究，对基于卫星的气候—环境研究综合发展的认识有限。本研究通过系统考察2001—2025年基于卫星的气候变化影响与环境响应研究的增长、科学结构、合作模式及主题演化，弥补这一空白。研究从Web of Science核心合集和Scopus编制了1,112篇文献的数据集，并采用文献计量指标、网络分析、多重对应分析、主题制图和主题演化进行分析。结果表明，该研究领域持续增长，成果集中于少数核心期刊，并呈现涉及知名作者、国家和机构的半集中式合作结构。主题分析揭示了基于MODIS和NDVI的既有环境研究的延续性，同时出现了面向气候变化、机器学习和云端方法的研究方向，尤其是随机森林和Google Earth Engine。本研究提供了关于基于卫星的气候—环境研究如何在相互关联的生态系统、水文过程、植被动态和气候相关环境响应中发展的综合视角。研究结果可帮助研究人员识别主要知识结构、新兴研究方向、合作机会和方法趋势，同时也强调需在WoS和Scopus索引英文文献的覆盖局限内解读文献计量模式。","Discover Environment","2026-10-04T00:00:00Z",64,{"impact":21,"substance":19,"depth":17,"authority":107,"freshness":108,"relevant":22,"comment":109},13,9,"基于1112篇文献的卫星遥感气候变化研究计量分析，方法规范但偏学术综述，对农业信息化有间接参考价值。",[111],{"name":103,"url":100},[113,27,114,115,30],"气候变化","文献计量","Google Earth Engine",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":118},{"candidates":119,"agricultural_evidence":14,"reason":61},[120,127,133,138,145],{"code":121,"score":122,"supported":42,"title":123,"abstract":125,"tags":126},"H7",15,[124],"climate change",[113,124],[113],{"code":128,"score":122,"supported":42,"title":129,"abstract":131,"tags":132},"I7",[130],"bibliometric",[114,130],[114],{"code":56,"score":108,"supported":42,"title":134,"abstract":135,"tags":137},[],[27,136],"remote sensing",[115,30,27],{"code":139,"score":140,"supported":42,"title":141,"abstract":142,"tags":144},"H3",3,[],[143],"水资源",[],{"code":146,"score":140,"supported":42,"title":147,"abstract":148,"tags":150},"I2",[],[149],"机器学习",[],[152,153],"卫星遥感 气候变化 文献计量","文献计量 植被动态 气候变化 遥感","卫星遥感气候变化文献计量-4228","10.1007\u002Fs44274-026-01090-8",{"doi":155,"openalex_id":157,"authors":158,"venue":103,"cited_by_count":84,"oa_url":171,"card":172,"direction":92,"ingested_from":93},"W7219744000",[159,162,165,168],{"name":160,"orcid":161},"Md. Nazmul Islam","https:\u002F\u002Forcid.org\u002F0000-0003-0528-8296",{"name":163,"orcid":164},"Md. Ashiqur Rahman","https:\u002F\u002Forcid.org\u002F0009-0005-1511-497X",{"name":166,"orcid":167},"Md. Shahajada Masud Anowarul Haque","https:\u002F\u002Forcid.org\u002F0000-0001-6415-8665",{"name":169,"orcid":170},"Josiline Phiri Chigwada","https:\u002F\u002Forcid.org\u002F0000-0003-0964-3582","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01090-8.pdf",{"tldr":173,"method":174,"finding":175,"direction":92,"opportunity":176},"用文献计量分析2001-2025年卫星遥感气候变化与环境响应研究的增长、结构与主题演化。","1112篇WoS\u002FScopus文献，用文献计量指标、网络分析、多重对应分析与主题","领域持续增长且期刊集中，MODIS\u002FNDVI研究延续，机器学习与GEE等云方法兴起。","可结合机器学习与云平台，将卫星气候环境响应研究延伸至农业生态系统与作物表型监测。","2026-10-05T23:30:24.867196Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":9,"source_name":184,"source_url":181,"published_at":185,"category":12,"cover_url":9,"hotness":186,"is_selected":14,"score":187,"score_detail":188,"sources":191,"tags":195,"primary_topic":9,"secondary_topics":199,"classification":200,"search_phrases":238,"slug":241,"view_count":57,"doi":242,"paper":243,"created_at":253},4169,"Spatial Patterns and Dynamics of Agricultural Land Use: A Geographical Analysis","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23117515","This study examines the spatial patterns and dynamics of agricultural land use through a geographical perspective, emphasizing the relationship between land use, land cover, and socio-economic as well as environmental factors. The research integrates spatial analysis methods, remote sensing, and GIS techniques to understand the distribution, transformation, and management of agricultural landscapes across different regions. It highlights how factors such as market forces, technological advancements, policy interventions, and environmental constraints influence land-use decisions and transitions. The study also explores agricultural typologies, landscape ecosystems, and regional variations in land-use practices, supported by global case studies including Europe, North America, East Asia, Sub-Saharan Africa, and South America. Furthermore, it discusses the impacts of agricultural land use on biodiversity, soil, water resources, and climate systems, emphasizing sustainability challenges and ecosystem services. The findings underline the importance of integrated land-use planning, governance reforms, and adaptive strategies to promote sustainable agricultural development in the face of climate change and increasing global demand for food.","本研究从地理学视角考察农业土地利用的空间格局与动态变化，重点关注土地利用、土地覆盖与社会经济及环境因素之间的关系。研究综合运用空间分析方法、遥感与GIS技术，理解不同区域农业景观的分布、转型与管理。研究强调市场力量、技术进步、政策干预和环境约束等因素如何影响土地利用决策与转变。研究还探讨了农业类型学、景观生态系统以及土地利用实践的区域差异，并辅以欧洲、北美、东亚、撒哈拉以南非洲和南美洲的全球案例研究。此外，研究讨论了农业土地利用对生物多样性、土壤、水资源和气候系统的影响，强调可持续性挑战与生态系统服务。研究结论强调，面对气候变化和全球粮食需求不断增长，综合土地利用规划、治理改革和适应性策略对于促进农业可持续发展具有重要意义。","Zenodo (CERN European Organization for Nuclear Research)","2026-11-30T00:00:00Z",25,49,{"impact":21,"substance":20,"depth":122,"authority":189,"freshness":84,"relevant":22,"comment":190},12,"综述性论文，方法整合遥感与GIS，但缺乏新数据与具体案例，且发布日期在未来，时效性不足，不建议进入每日精选。",[192,193],{"name":184,"url":181},{"name":184,"url":194},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23117514",[196,27,197,29,198],"可持续农业","GIS","农业土地利用",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":201},{"candidates":202,"agricultural_evidence":42,"reason":237},[203,209,216,220,224,228,232],{"code":40,"score":13,"supported":42,"title":204,"abstract":205,"tags":208},[],[206,29,207],"生物多样性","biodiversity",[29],{"code":210,"score":13,"supported":42,"title":211,"abstract":212,"tags":215},"I4",[],[213,197,214],"空间分析","spatial analysis",[197],{"code":56,"score":41,"supported":42,"title":217,"abstract":218,"tags":219},[],[27,136],[27],{"code":48,"score":49,"supported":42,"title":221,"abstract":222,"tags":223},[],[52,53],[],{"code":121,"score":49,"supported":42,"title":225,"abstract":226,"tags":227},[],[113,124],[],{"code":139,"score":140,"supported":42,"title":229,"abstract":230,"tags":231},[],[143],[],{"code":233,"score":22,"supported":14,"title":234,"abstract":235,"tags":236},"H8",[],[],[196],"多个产业证据相当，需要确认主归属",[239,240],"农业土地利用 空间格局 遥感","农业土地利用 生态系统服务 可持续农业 遥感","农业土地利用空间格局遥感-4169","10.5281\u002Fzenodo.23117515",{"doi":242,"openalex_id":244,"authors":245,"venue":184,"cited_by_count":84,"oa_url":181,"card":248,"direction":92,"ingested_from":93},"W7218853570",[246],{"name":247,"orcid":9},"Anjali Harshavardhan Thorat",{"tldr":249,"method":250,"finding":251,"direction":92,"opportunity":252},"从地理视角分析全球农业土地利用的空间格局与动态变化。","整合空间分析、遥感和GIS技术，结合多区域案例研究。","市场、技术、政策和环境共同驱动土地利用转变，需综合规划与适应性治理。","可结合多源遥感与机器学习，量化政策与市场对土地利用变化的非线性驱动机制。","2026-10-04T23:30:24.369006Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":257,"published_at":261,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":262,"score_detail":263,"sources":266,"tags":268,"primary_topic":9,"secondary_topics":270,"classification":271,"search_phrases":296,"slug":299,"view_count":22,"doi":300,"paper":301,"created_at":314},4097,"Urban Forestry: A Bibliometric Analysis","https:\u002F\u002Fdoi.org\u002F10.58812\u002Fwsnt.v4i03.3145","This study presents a bibliometric analysis of urban forestry research to map its intellectual structure, thematic evolution, and collaboration patterns. A total of 468 Scopus-indexed documents published between 2000 and 2026 were analyzed using VOSviewer through keyword co-occurrence, overlay, and density visualizations, co-authorship analysis at the author, institution, and country levels, and citation analysis. The results show that the field is organized around five interrelated themes: ecological sustainability, management and governance, human–tree interactions, tree planting and canopy management, and greenspace-oriented urbanization. The overlay map indicates a shift from management, policy, and participation topics toward climate change, ecosystem services, biodiversity, carbon sequestration, and remote sensing in recent years. Citation analysis highlights foundational works on the definition, costs, ecosystem benefits, and governance of urban forestry. Co-authorship networks reveal several cohesive research groups connected by a few influential scholars, while institutional collaboration remains limited. At the country level, the United States is the dominant hub, followed by Canada, China, Italy, Germany, the United Kingdom, and Brazil. These findings provide an overview of the development of urban forestry research and indicate directions for future studies.","本研究对城市林业研究进行了文献计量分析，以描绘其知识结构、主题演变与合作模式。研究采用VOSviewer对2000年至2026年间Scopus收录的468篇文献进行分析，通过关键词共现、叠加和密度可视化，作者、机构和国家层面的合著分析，以及引文分析。结果表明，该领域围绕五个相互关联的主题组织：生态可持续性、管理与治理、人与树木的互动、植树与树冠管理，以及面向绿地的城市化。叠加图谱显示，近年来研究重心已从管理、政策和参与议题转向气候变化、生态系统服务、生物多样性、碳固存和遥感。引文分析凸显了关于城市林业的定义、成本、生态系统效益和治理的基础性著作。合著网络揭示了若干由少数有影响力的学者连接的内聚性研究群体，而机构间合作仍然有限。在国家层面，美国是主导中心，其次是加拿大、中国、意大利、德国、英国和巴西。这些发现概述了城市林业研究的发展，并指出了未来研究的方向。","West Science Nature and Technology","2026-09-30T00:00:00Z",56,{"impact":21,"substance":19,"depth":17,"authority":13,"freshness":264,"relevant":22,"comment":265},4,"基于468篇Scopus文献的计量分析，方法规范、结论清晰，但属综述性研究且时效偏旧，公共价值有限。",[267],{"name":260,"url":257},[113,27,114,29,269],"城市林业",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":272},{"candidates":273,"agricultural_evidence":14,"reason":61},[274,278,282,286,290],{"code":128,"score":122,"supported":42,"title":275,"abstract":276,"tags":277},[130],[114,130],[114],{"code":40,"score":13,"supported":42,"title":279,"abstract":280,"tags":281},[],[206,29,207],[29],{"code":121,"score":41,"supported":42,"title":283,"abstract":284,"tags":285},[],[113,124],[113],{"code":56,"score":41,"supported":42,"title":287,"abstract":288,"tags":289},[],[27,136],[27],{"code":291,"score":140,"supported":42,"title":292,"abstract":293,"tags":295},"H4",[],[294],"carbon sequestration",[],[297,298],"城市林业 研究热点","生态系统服务 城市林业 文献计量 气候变化","城市林业研究热点-4097","10.58812\u002Fwsnt.v4i03.3145",{"doi":300,"openalex_id":302,"authors":303,"venue":260,"cited_by_count":84,"oa_url":308,"card":309,"direction":92,"ingested_from":93},"W7214898413",[304,306],{"name":305,"orcid":9},"Loso Judijanto",{"name":307,"orcid":9},"Sintia Permata Sari","https:\u002F\u002Fwsj.westsciences.com\u002Findex.php\u002Fwsnt\u002Farticle\u002Fdownload\u002F3145\u002F3163",{"tldr":310,"method":311,"finding":312,"direction":92,"opportunity":313},"用文献计量分析2000-2026年468篇城市林业文献，梳理主题演变与合作网络。","VOSviewer关键词共现、叠加、密度图及合著与引文分析，Scopus数据。","领域分五大主题，近年转向气候变化、生态系统服务、生物多样性与遥感。","城市林业遥感与碳汇、生态系统服务结合的研究仍稀缺，可切入城市绿地碳计量。","2026-10-03T23:30:37.434360Z",{"id":316,"title":317,"url":318,"summary":319,"summary_zh":320,"content":9,"source_name":321,"source_url":318,"published_at":322,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":323,"score_detail":324,"sources":327,"tags":329,"primary_topic":333,"secondary_topics":337,"classification":338,"search_phrases":360,"slug":363,"view_count":57,"doi":364,"paper":365,"created_at":400},3628,"Identifying ecosystem service drivers to inform land use scenario simulation: An InVEST-PLSR-PLUS framework for the Danjiangkou Reservoir Basin, China","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecolind.2026.115581","The Danjiangkou Reservoir Basin is the core water source of the Middle Route of China's South-to-North Water Diversion Project, yet research in this basin has largely focused on static ecosystem service (ES) assessments, leaving the drivers of ES dynamics and their future trajectories poorly understood. This study develops an integrated InVEST–PLSR–PLUS framework that quantifies the spatiotemporal patterns of water yield, soil conservation, and carbon storage, identifies their dominant drivers objectively via Partial Least Squares Regression (PLSR), and projects land-use dynamics to 2030 using the PLUS model parameterized with PLSR-derived drivers rather than empirical selection. The main findings are as follows. (1) From 2010 to 2020, water yield increased markedly (from 79.22 to 895.38 to 166.69–1000.24 mm year −1 ), soil conservation declined (maximum from 941.60 to 641.91 t ha −1 year −1 ), and carbon storage remained stable (maximum around 20.4 t ha −1 year −1 ); high soil conservation and carbon storage values were concentrated in ecological and economic forests, whereas elevated water yield occurred mainly in farmland, orchards, and built-up land. (2) Vegetation and land-use variables dominated ES dynamics: EVI (VIP = 1.609), NDVI (VIP = 1.583), ecological forest area (VIP = 1.553), economic forest area (VIP = 1.476), and farmland area (VIP = 1.445) exerted strong positive effects; among landscape-pattern metrics, the landscape shape index (LSI; VIP = 1.313) was positively associated with all three services, whereas patch-subdivision metrics (PD, ED, IJI) showed negative associations; climatic and socioeconomic variables contributed only marginally (VIP \u003C 0.8, except GDP at 0.864). (3) Under the natural evolution scenario, ecological forest and orchard areas are projected to decrease by 40.89 km 2 and 20.54 km 2 by 2030, while economic forest (+29.63 km 2 ), water area (+24.10 km 2 ), farmland (+1.68 km 2 ), and built-up land (+5.98 km 2 ) expand. These findings demonstrate that vegetation cover and landscape connectivity fundamentally regulate ES provision, and indicate that ecological forest protection, control landscape fragmentation, and regulate built-up land and orchard expansion to safeguard the ecological security of this strategic water-source region.","丹江口库区是中国南水北调中线工程的核心水源区，但该区域的研究多集中于静态生态系统服务（ES）评估，对其动态变化的驱动机制及未来演变趋势的认识仍十分有限。本研究构建了InVEST–PLSR–PLUS集成框架，量化了产水量、土壤保持和碳储量的时空格局，通过偏最小二乘回归（PLSR）客观识别其主导驱动因子，并利用以PLSR-derived驱动因子而非经验选择参数化的PLUS模型，将土地利用动态模拟至2030年。主要结论如下：（1）2010—2020年，产水量显著增加（从79.22增至895.38，再到166.69~1000.24 mm·a⁻¹），土壤保持量下降（最大值从941.60降至641.91 t·ha⁻¹·a⁻¹），碳储量保持稳定（最大值约为20.4 t·ha⁻¹·a⁻¹）；土壤保持和碳储量的高值集中于生态林和经济林，而产水量的高值主要出现在耕地、果园和建设用地。（2）植被和土地利用变量主导了ES动态变化：EVI（VIP=1.609）、NDVI（VIP=1.583）、生态林面积（VIP=1.553）、经济林面积（VIP=1.476）和耕地面积（VIP=1.445）具有较强正向影响；在景观格局指标中，景观形状指数（LSI；VIP=1.313）与三项服务均呈正相关，而斑块细分指标（PD、ED、IJI）呈负相关；气候和社会经济变量的贡献较小（VIP\u003C0.8，GDP为0.864除外）。（3）在自然演变情景下，预计到2030年生态林和果园面积将分别减少40.89 km²和20.54 km²，而经济林（+29.63 km²）、水域（+24.10 km²）、耕地（+1.68 km²）和建设用地（+5.98 km²）将扩张。研究结果表明，植被覆盖和景观连通性从根本上调控着生态系统服务的供给，并指出应保护生态林、控制景观破碎化、调控建设用地和果园扩张，以保障这一战略性水源区的生态安全。","Ecological Indicators","2026-09-25T00:00:00Z",80,{"impact":17,"substance":325,"depth":19,"authority":20,"freshness":108,"relevant":22,"comment":326},23,"以PLSR驱动PLUS模型预测丹江口库区生态系统服务与土地利用变化，方法有创新、数据扎实，对水源区生态安全与农业空间管控有参考价值。",[328],{"name":321,"url":318},[27,29,330,331,332],"土地利用模拟","水源地保护","丹江口库区",{"code":40,"slug":207,"name":334,"path":335,"parent_code":336},"生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity","H",[],{"status":339,"locked":14,"version":35,"method":36,"evidence":340},"classified",{"candidates":341,"agricultural_evidence":42},[342,346,350,356],{"code":40,"score":122,"supported":42,"title":343,"abstract":344,"tags":345},[45],[29,45],[29],{"code":48,"score":49,"supported":42,"title":347,"abstract":348,"tags":349},[],[52,53],[],{"code":351,"score":140,"supported":42,"title":352,"abstract":353,"tags":355},"B2",[],[354],"orchard",[],{"code":56,"score":22,"supported":14,"title":357,"abstract":358,"tags":359},[],[],[27],[361,362],"丹江口水库 生态系统服务","InVEST PLUS 土地利用模拟","丹江口水库生态系统服务-3628","10.1016\u002Fj.ecolind.2026.115581",{"doi":364,"openalex_id":366,"authors":367,"venue":321,"cited_by_count":84,"oa_url":393,"card":394,"direction":399,"ingested_from":93},"W7214192017",[368,371,374,376,379,381,383,386,389,391],{"name":369,"orcid":370},"Bojun Ma","https:\u002F\u002Forcid.org\u002F0000-0002-7867-3817",{"name":372,"orcid":373},"Kun Sun","https:\u002F\u002Forcid.org\u002F0000-0002-9460-6681",{"name":375,"orcid":9},"Jian Zhang",{"name":377,"orcid":378},"Chucai Peng","https:\u002F\u002Forcid.org\u002F0000-0003-3366-9547",{"name":380,"orcid":9},"孙宝洋",{"name":382,"orcid":9},"Xiaoshuang Wang",{"name":384,"orcid":385},"Jigen Liu","https:\u002F\u002Forcid.org\u002F0009-0006-6798-2864",{"name":387,"orcid":388},"Jinquan Huang","https:\u002F\u002Forcid.org\u002F0000-0001-7372-1915",{"name":390,"orcid":9},"Lin Li",{"name":392,"orcid":9},"Feipeng Ren","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1470160X26009830\u002Fpdf",{"tldr":395,"method":396,"finding":397,"direction":90,"opportunity":398},"构建InVEST-PLSR-PLUS框架，识别丹江口库区生态系统服务驱动因子并模拟2030年土地利用","InVEST模型、偏最小二乘回归（PLSR）与PLUS模型耦合，基于2010-2","植被与土地利用主导生态系统服务，自然演变下生态林和果园将减少，经济林与建设用地扩张。","可将PLSR驱动因子筛选推广至其他水源区，并结合多情景政策模拟优化生态补偿与土地管控。","农业人工智能与决策模型","2026-09-27T23:31:29.684586Z",{"id":402,"title":403,"url":404,"summary":405,"summary_zh":406,"content":9,"source_name":184,"source_url":404,"published_at":407,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":408,"score_detail":409,"sources":412,"tags":414,"primary_topic":419,"secondary_topics":424,"classification":425,"search_phrases":453,"slug":456,"view_count":84,"doi":457,"paper":458,"created_at":468},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","2026-10-21T00:00:00Z",74,{"impact":189,"substance":410,"depth":19,"authority":189,"freshness":13,"relevant":22,"comment":411},22,"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[413],{"name":184,"url":404},[415,416,417,27,418],"智慧农业","农业人工智能","粮食安全","气候分析",{"code":146,"slug":420,"name":421,"path":422,"parent_code":423},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":339,"locked":14,"version":35,"method":36,"evidence":426},{"candidates":427,"agricultural_evidence":42},[428,433,439,444,449],{"code":146,"score":108,"supported":42,"title":429,"abstract":431,"tags":432},[430],"artificial intelligence",[],[416],{"code":434,"score":140,"supported":42,"title":435,"abstract":436,"tags":438},"I5",[],[437],"农业传感",[],{"code":440,"score":22,"supported":14,"title":441,"abstract":442,"tags":443},"A8",[],[],[417],{"code":445,"score":22,"supported":14,"title":446,"abstract":447,"tags":448},"I1",[],[],[415],{"code":56,"score":22,"supported":14,"title":450,"abstract":451,"tags":452},[],[],[27],[454,455],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":457,"openalex_id":459,"authors":460,"venue":184,"cited_by_count":84,"oa_url":404,"card":463,"direction":399,"ingested_from":93},"W7223111766",[461],{"name":462,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":464,"method":465,"finding":466,"direction":92,"opportunity":467},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","2026-10-22T11:21:02.682497Z",{"id":470,"title":471,"url":472,"summary":473,"summary_zh":9,"content":9,"source_name":474,"source_url":472,"published_at":407,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":475,"score_detail":476,"sources":478,"tags":480,"primary_topic":484,"secondary_topics":488,"classification":489,"search_phrases":501,"slug":504,"view_count":84,"doi":505,"paper":506,"created_at":522},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",38,{"impact":21,"substance":49,"depth":21,"authority":49,"freshness":13,"relevant":22,"comment":477},"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。",[479],{"name":474,"url":472},[481,482,27,483],"数字农业","农业信息化","地图评估",{"code":445,"slug":485,"name":486,"path":487,"parent_code":423},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":339,"locked":14,"version":35,"method":36,"evidence":490},{"candidates":491,"agricultural_evidence":42},[492,497],{"code":445,"score":107,"supported":42,"title":493,"abstract":495,"tags":496},[494],"digital agriculture",[494],[482,481],{"code":56,"score":22,"supported":14,"title":498,"abstract":499,"tags":500},[],[],[27],[502,503],"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":505,"openalex_id":507,"authors":508,"venue":474,"cited_by_count":84,"oa_url":472,"card":9,"direction":521,"ingested_from":93},"W7223099987",[509,512,515,518],{"name":510,"orcid":511},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":513,"orcid":514},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":516,"orcid":517},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":519,"orcid":520},"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"]