[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4228":3,"related-4228":103},{"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":73,"slug":76,"view_count":22,"doi":77,"paper":78,"created_at":102},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。",null,"Discover Environment","2026-10-04T00:00:00Z","论文",10,false,64,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,16,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"气候变化","遥感","文献计量","Google Earth Engine","植被动态",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":72},[39,47,53,59,66],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":46},"H7",15,true,[44],"climate change",[27,44],[27],{"code":48,"score":41,"supported":42,"title":49,"abstract":51,"tags":52},"I7",[50],"bibliometric",[29,50],[29],{"code":54,"score":21,"supported":42,"title":55,"abstract":56,"tags":58},"I3",[],[28,57],"remote sensing",[30,31,28],{"code":60,"score":61,"supported":42,"title":62,"abstract":63,"tags":65},"H3",3,[],[64],"水资源",[],{"code":67,"score":61,"supported":42,"title":68,"abstract":69,"tags":71},"I2",[],[70],"机器学习",[],"农业相关性需要复核",[74,75],"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":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":93,"oa_url":94,"card":95,"direction":99,"ingested_from":101},"W7219744000",[81,84,87,90],{"name":82,"orcid":83},"Md. Nazmul Islam","https:\u002F\u002Forcid.org\u002F0000-0003-0528-8296",{"name":85,"orcid":86},"Md. Ashiqur Rahman","https:\u002F\u002Forcid.org\u002F0009-0005-1511-497X",{"name":88,"orcid":89},"Md. Shahajada Masud Anowarul Haque","https:\u002F\u002Forcid.org\u002F0000-0001-6415-8665",{"name":91,"orcid":92},"Josiline Phiri Chigwada","https:\u002F\u002Forcid.org\u002F0000-0003-0964-3582",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01090-8.pdf",{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"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。","农业遥感与作物表型","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。","openalex","2026-10-05T23:30:24.867196Z",{"total":104,"page":22,"page_size":104,"items":105},6,[106,172,233,306,406,459],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":112,"source_url":109,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":114,"score_detail":115,"sources":118,"tags":120,"primary_topic":9,"secondary_topics":123,"classification":124,"search_phrases":153,"slug":156,"view_count":22,"doi":157,"paper":158,"created_at":171},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":17,"substance":18,"depth":19,"authority":13,"freshness":116,"relevant":22,"comment":117},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。",[119],{"name":112,"url":109},[27,28,29,121,122],"生态系统服务","城市林业",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":125},{"candidates":126,"agricultural_evidence":14,"reason":72},[127,131,138,143,147],{"code":48,"score":41,"supported":42,"title":128,"abstract":129,"tags":130},[50],[29,50],[29],{"code":132,"score":13,"supported":42,"title":133,"abstract":134,"tags":137},"H6",[],[135,121,136],"生物多样性","biodiversity",[121],{"code":40,"score":139,"supported":42,"title":140,"abstract":141,"tags":142},7,[],[27,44],[27],{"code":54,"score":139,"supported":42,"title":144,"abstract":145,"tags":146},[],[28,57],[28],{"code":148,"score":61,"supported":42,"title":149,"abstract":150,"tags":152},"H4",[],[151],"carbon sequestration",[],[154,155],"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":157,"openalex_id":159,"authors":160,"venue":112,"cited_by_count":93,"oa_url":165,"card":166,"direction":99,"ingested_from":101},"W7214898413",[161,163],{"name":162,"orcid":9},"Loso Judijanto",{"name":164,"orcid":9},"Sintia Permata Sari","https:\u002F\u002Fwsj.westsciences.com\u002Findex.php\u002Fwsnt\u002Farticle\u002Fdownload\u002F3145\u002F3163",{"tldr":167,"method":168,"finding":169,"direction":99,"opportunity":170},"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":173,"title":174,"url":175,"summary":176,"summary_zh":177,"content":9,"source_name":178,"source_url":175,"published_at":179,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":181,"sources":184,"tags":186,"primary_topic":9,"secondary_topics":190,"classification":191,"search_phrases":202,"slug":205,"view_count":22,"doi":206,"paper":207,"created_at":232},5246,"Spatial divergence of climate–vegetation relationships in Florida: A 43-year multi-method analysis (1982–2024)","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faeb5af","Abstract Attributing long-term vegetation change to climate is difficult in subtropical ecosystems, where temperature and precipitation act simultaneously, are confounded by abrupt disturbances, and where statewide averages can conceal offsetting associations. Here we apply a multi-method convergence-of-evidence framework to a sensor-harmonized 43-year record (1982–2024) of NOAA CDR NDVI, temperature, and precipitation across Florida, which spans cool temperate forests to warm subtropical wetlands. Modified Mann–Kendall tests with false-discovery-rate correction detected monotonic trends; pixel-level correlation, spatial-error regression, and lagged cross-correlation evaluated association strength, direction, and timing; Empirical Orthogonal Function decomposition of deseasonalized anomalies separated persistent climatic signals from episodic disturbance, with the leading mode (58.4% of variance) tracking latitudinal warming and a lower-order mode (PC3) tracking hurricane impacts. Florida warmed by +0.033 °C yr⁻¹, significant in 99.6% of land pixels, while precipitation showed no field-significant trend. The temperature–greenness association was positive across essentially the entire state, marginally stronger in the south; localized negative responses were confined to specific southern wetland and urban-fringe pixels rather than forming a systematic north–south reversal. The state-mean precipitation–NDVI correlation was negative at zero lag and reversed from r = −0.263 to r = +0.102 when incoming shortwave radiation was partialed out, consistent with cloud-associated light suppression rather than water limitation—a phenomenon we term the Water Paradox. SPI\u002FSPEI–NDVI correlations were near zero, and Everglades water levels were largely decoupled from wetland greenness (r = 0.08, n.s.), indicating that inundation acts as a localized threshold modifier rather than a dominant suppressor. Climate–vegetation models for comparable high-rainfall subtropical systems may therefore need to account for spatially divergent temperature sensitivities and treat precipitation as an energy constraint rather than a uniform benefit.","摘要 在亚热带生态系统中，将长期植被变化归因于气候十分困难，因为温度和降水同时起作用，又受到突发干扰的混杂影响，而且全州尺度的平均值可能掩盖相互抵消的关联。本文采用多方法证据汇聚框架，对佛罗里达州1982—2024年共43年经传感器协调的NOAA CDR NDVI、温度和降水记录进行分析，该区域涵盖从冷温带森林到暖亚热带湿地。采用经错误发现率校正的改进Mann–Kendall检验检测单调趋势；通过像元级相关、空间误差回归和滞后互相关评估关联强度、方向和时间；对去季节化异常进行经验正交函数分解，以区分持续性气候信号与偶发干扰，其中主导模态（占总方差58.4%）追踪纬度变暖，低阶模态（PC3）追踪飓风影响。佛罗里达州以+0.033 °C yr⁻¹的速率变暖，在99.6%的陆地像元上显著，而降水未显示出区域显著趋势。温度—绿度关联在全州几乎均为正，南部略强；局地负响应仅限于特定的南部湿地和城市边缘像元，而非形成系统的南北反转。州平均降水—NDVI相关在零滞后时为负，当偏除入射短波辐射后由r = −0.263反转为r = +0.102，这与云相关的光抑制而非水分限制相一致——我们将这一现象称为“水悖论”。SPI\u002FSPEI—NDVI相关接近零，大沼泽地水位与湿地绿度基本解耦（r = 0.08，不显著），表明淹水作为局地阈值调节因子而非主导抑制因子起作用。因此，针对可比的高降雨亚热带系统的气候—植被模型可能需要考虑空间分异的温度敏感性，并将降水视为能量约束而非均一益处。","Environmental Research Communications","2026-10-16T00:00:00Z",70,{"impact":17,"substance":182,"depth":18,"authority":20,"freshness":21,"relevant":22,"comment":183},22,"基于43年NDVI遥感记录的多方法气候-植被关系研究，方法扎实、结论新颖，但属区域基础研究，对国内三农信息化仅有方法借鉴价值。",[185],{"name":178,"url":175},[27,28,187,188,189],"NDVI","植被监测","佛罗里达",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":192},{"candidates":193,"agricultural_evidence":14,"reason":72},[194,198],{"code":54,"score":61,"supported":14,"title":195,"abstract":196,"tags":197},[],[],[187,188,28],{"code":40,"score":22,"supported":14,"title":199,"abstract":200,"tags":201},[],[],[27],[203,204],"Florida NDVI 气候变化","佛罗里达 植被 遥感","FloridaNDVI气候变化-5246","10.1088\u002F2515-7620\u002Faeb5af",{"doi":206,"openalex_id":208,"authors":209,"venue":178,"cited_by_count":93,"oa_url":226,"card":227,"direction":99,"ingested_from":101},"W7222444517",[210,213,216,219,222,224],{"name":211,"orcid":212},"A. Mandal","https:\u002F\u002Forcid.org\u002F0009-0004-0192-7497",{"name":214,"orcid":215},"Aman Kumar Thapa","https:\u002F\u002Forcid.org\u002F0009-0003-8956-6632",{"name":217,"orcid":218},"Madan Thapa Chhetri","https:\u002F\u002Forcid.org\u002F0009-0002-3567-7591",{"name":220,"orcid":221},"Hongbo Su","https:\u002F\u002Forcid.org\u002F0000-0002-2147-3646",{"name":223,"orcid":9},"Yan Yong",{"name":225,"orcid":9},"Fred Bloetscher","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faeb5af\u002Fpdf",{"tldr":228,"method":229,"finding":230,"direction":99,"opportunity":231},"用多方法分析佛罗里达43年气候与植被关系，揭示空间分异。","NOAA CDR NDVI与气候数据，结合趋势检验、回归、EOF分解。","全州变暖显著，温度与绿度正相关；降水因云量抑制光照呈负相关。","高降雨亚热带区需将降水视为能量约束，并关注温度敏感性的空间分异。","2026-10-18T23:30:31.356484Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":241,"score_detail":242,"sources":245,"tags":247,"primary_topic":9,"secondary_topics":251,"classification":252,"search_phrases":274,"slug":277,"view_count":278,"doi":279,"paper":280,"created_at":305},4881,"Mapping the knowledge structure and evolution of artificial intelligence for water reuse and sustainable water management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02389-4","Artificial intelligence (AI) has become a powerful instrument to solve complex problems related to sustainable water resource management because it allows creating advanced, data-driven modelling, prediction, and optimization of water systems. Although the studies in this sphere have increased quickly, the global scientific tendencies have not been thoroughly examined. This research paper presents a bibliometric review of the literature about AI applications in green water management that was published from 2015 to 2025. The Scopus database was searched with a structured search strategy on bibliographic data, and 3,280 peer-reviewed articles were analyzed. VOSviewer was used to perform bibliometric mapping and network visualization to investigate the trends in publications, most active journals, most impactful authors, contributions of institutions, country-level partnerships, and research clusters. The findings indicate that the publishing activity has grown tremendously in the last ten years, which suggests that there is a growing interest in AI-based solutions to water quality monitoring, hydrological prediction, optimization of wastewater treatment, and smart water management system. China, the United States and India became the most productive nations with the University of Chinese Academy of Sciences and Beijing normal university as some of the top contributing institutions. The analysis of the key words co-occurrence (dominant research themes) was based on machine learning, water-quality prediction, hydrological modelling, remote sensing, and decision-support systems. The co-citation analysis has shown the core studies in machine learning algorithms, including random forests, neural networks, and boosting algorithms, which underpin the present studies in this field. Overall, the results depict the growing role of AI technologies to enhance the effectiveness of water resources management and wastewater treatment. This research offers a clear insight into the intellectual framework and the future research perspective of AI-based environmentally sustainable water management which can be of great value to researchers, policymakers, and practitioners who would wish to come up with innovative and data-based water management solutions.","人工智能（AI）已成为解决可持续水资源管理相关复杂问题的有力工具，因为它能够构建先进的数据驱动模型，实现水系统的预测与优化。尽管该领域的研究迅速增加，但全球科学趋势尚未得到充分审视。本文对2015年至2025年间发表的关于人工智能在绿色水资源管理中应用的文献进行了文献计量学综述。采用结构化检索策略对Scopus数据库中的文献数据进行检索，共分析了3，280篇同行评审论文。使用VOSviewer进行文献计量制图和网络可视化，以探究发文趋势、最活跃期刊、最具影响力作者、机构贡献、国家层面合作及研究聚类。研究结果表明，过去十年间发文活动急剧增长，表明基于人工智能的水质监测、水文预测、废水处理优化和智能水管理系统解决方案日益受到关注。中国、美国和印度成为最高产国家，中国科学院和北京师范大学为贡献最多的机构之一。关键词共现分析（主导研究主题）基于机器学习、水质预测、水文建模、遥感和决策支持系统。共被引分析揭示了机器学习算法的核心研究，包括随机森林、神经网络和提升算法，这些构成了该领域当前研究的基础。总体而言，研究结果描绘了人工智能技术在提升水资源管理和废水处理效能方面日益增长的作用。本研究为基于人工智能的环境可持续水资源管理的知识框架和未来研究视角提供了清晰见解，对希望提出创新性和数据驱动型水资源管理方案的研究人员、政策制定者和实践者具有重要价值。","Discover Artificial Intelligence","2026-10-12T00:00:00Z",67,{"impact":243,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":22,"comment":244},12,"基于3280篇文献的AI水资源管理文献计量综述，方法规范、结论有参考价值，但属学术综述类，产业与政策落地性有限。",[246],{"name":239,"url":236},[248,28,29,249,250],"农业人工智能","水资源管理","智慧水务",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":253},{"candidates":254,"agricultural_evidence":14,"reason":72},[255,261,266,270],{"code":67,"score":18,"supported":42,"title":256,"abstract":258,"tags":260},[257],"artificial intelligence",[70,259,257],"machine learning",[248],{"code":60,"score":139,"supported":42,"title":262,"abstract":263,"tags":265},[],[64,264],"water resource",[249],{"code":54,"score":139,"supported":42,"title":267,"abstract":268,"tags":269},[],[28,57],[28],{"code":48,"score":139,"supported":42,"title":271,"abstract":272,"tags":273},[],[29,50],[29],[275,276],"AI 水资源管理 文献计量","人工智能 再生水 可持续水管理","AI水资源管理文献计量-4881",2,"10.1007\u002Fs44163-026-02389-4",{"doi":279,"openalex_id":281,"authors":282,"venue":239,"cited_by_count":93,"oa_url":297,"card":298,"direction":304,"ingested_from":101},"W7221506022",[283,286,289,291,293,295],{"name":284,"orcid":285},"Narendra Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5578-3255",{"name":287,"orcid":288},"Mangey Ram Nagar","https:\u002F\u002Forcid.org\u002F0000-0003-3635-3548",{"name":290,"orcid":9},"Isha Dharsandia",{"name":292,"orcid":9},"Ananya Tiwari",{"name":294,"orcid":9},"Santosh Kumar Mishra",{"name":296,"orcid":9},"Mohit Kumar","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44163-026-02389-4.pdf",{"tldr":299,"method":300,"finding":301,"direction":302,"opportunity":303},"对2015-2025年AI用于水回用与可持续水管理的3280篇文献做文献计量分析，梳理知识结构与演化","Scopus检索3280篇同行评议文献，用VOSviewer做共现与共被引网络可","发文量十年激增，中国、美国、印度领先；主题聚焦机器学习、水质预测、水文建模、遥感与决策支持。","农业人工智能与决策模型","可将AI水管理方法迁移至农业灌溉与面源污染场景，构建农业水-肥-碳协同的智能决策模型。","智慧农业 \u002F 农业物联网","2026-10-14T23:30:10.246538Z",{"id":307,"title":308,"url":309,"summary":310,"summary_zh":311,"content":9,"source_name":312,"source_url":309,"published_at":313,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":241,"score_detail":314,"sources":318,"tags":320,"primary_topic":324,"secondary_topics":329,"classification":330,"search_phrases":368,"slug":371,"view_count":278,"doi":372,"paper":373,"created_at":405},4602,"Phenology-driven multisensor unsupervised crop mapping in data-scarce irrigated agriculture: a case of Adea District in Ethiopia","https:\u002F\u002Fdoi.org\u002F10.2166\u002Fwpt.2026.492","ABSTRACT Accurate irrigation planning, water allocation, and agricultural productivity assessment are frequently constrained in data-scare regions where reliable ground-based crop information and spatial coverage data are limited. This study addresses these gaps using a phenology-driven, multisensor unsupervised crop-mapping framework implemented within Google Earth Engine (GEE). The approach integrates monthly normalized difference vegetation index (NDVI) profiles from Sentinel-2, Sentinel-1VV backscatter, and digital elevation model–derived terrain slope into unsupervised K-means clustering algorithm. Adea District, Ethiopia, served as a demonstration site to evaluate and interpret the phenological signatures of key irrigated crops. The identified crop types were mapped to estimate spatial coverage and compared with the ground truth observations of irrigated wheat and tomato fields. The approach successfully distinguished irrigated wheat and tomato fields with an overall classification accuracy of 91%, a Kappa coefficient of 0.82, and estimated cultivated areas of 666.93 and 812.35 ha, respectively. Combining phenological profiles with multisensor satellite observations significantly enhance unsupervised classification performance in heterogeneous, smallholder-dominated agricultural landscapes. The cloud-based framework offers a computationally scalable and cost-effective solution for crop mapping, and water productivity monitoring in data-scarce agricultural regions.","在数据稀缺地区，可靠的基于地面作物信息和空间覆盖数据往往有限，精准灌溉规划、水资源分配和农业生产力评估因此常受制约。本研究针对这些不足，采用了一种由物候驱动、多传感器协同的无监督作物制图框架，并在Google Earth Engine（GEE）平台上实现。该方法将Sentinel-2的月度归一化植被指数（NDVI）时序、Sentinel-1 VV后向散射以及数字高程模型衍生的地形坡度整合到无监督K均值聚类算法中。以埃塞俄比亚Adea区为示范区，评估和解读主要灌溉作物的物候特征。对识别出的作物类型进行制图以估算空间覆盖范围，并与灌溉小麦和番茄田的地面实测数据进行对比。该方法成功区分了灌溉小麦和番茄田，总体分类精度达91%，Kappa系数为0.82，估算种植面积分别为666.93公顷和812.35公顷。将物候特征与多传感器卫星观测相结合，可显著提升异质性小农主导农业景观中的无监督分类性能。该基于云的框架为数据稀缺农业地区的作物制图和水分生产力监测提供了一种计算可扩展且成本效益高的解决方案。","Water Practice & Technology","2026-10-09T00:00:00Z",{"impact":17,"substance":315,"depth":316,"authority":20,"freshness":21,"relevant":22,"comment":317},20,17,"方法新颖、精度可靠，但属区域案例研究，产业影响有限，可作为技术参考入选。",[319],{"name":312,"url":309},[321,28,322,30,323],"智慧农业","作物识别","灌溉农业",{"code":60,"slug":325,"name":326,"path":327,"parent_code":328},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":331,"locked":14,"version":35,"method":36,"evidence":332},"classified",{"candidates":333,"agricultural_evidence":42},[334,339,346,353,359,363],{"code":60,"score":13,"supported":42,"title":335,"abstract":336,"tags":338},[],[337,64,325],"灌溉",[323],{"code":340,"score":104,"supported":42,"title":341,"abstract":342,"tags":345},"A2",[],[343,344],"小麦","wheat",[],{"code":347,"score":104,"supported":42,"title":348,"abstract":349,"tags":352},"B1",[],[350,351],"番茄","tomato",[],{"code":354,"score":61,"supported":42,"title":355,"abstract":356,"tags":358},"G2",[],[357],"smallholder",[],{"code":54,"score":278,"supported":14,"title":360,"abstract":361,"tags":362},[],[],[30,28],{"code":364,"score":22,"supported":14,"title":365,"abstract":366,"tags":367},"I1",[],[],[321],[369,370],"埃塞俄比亚 Adea 灌溉作物 遥感","Sentinel-2 NDVI 作物分类","埃塞俄比亚Adea灌溉作物遥感-4602","10.2166\u002Fwpt.2026.492",{"doi":372,"openalex_id":374,"authors":375,"venue":312,"cited_by_count":93,"oa_url":309,"card":399,"direction":404,"ingested_from":101},"W7221136167",[376,379,382,385,388,391,394,396],{"name":377,"orcid":378},"Belete Berhanu Kidanewold","https:\u002F\u002Forcid.org\u002F0000-0002-9876-8549",{"name":380,"orcid":381},"Jemal Mohammed Hassen","https:\u002F\u002Forcid.org\u002F0000-0002-7417-173X",{"name":383,"orcid":384},"Rahel Sintayehu Tessema","https:\u002F\u002Forcid.org\u002F0009-0006-2798-1752",{"name":386,"orcid":387},"Saba Kidane","https:\u002F\u002Forcid.org\u002F0009-0002-7403-9939",{"name":389,"orcid":390},"Mulugeta Melese","https:\u002F\u002Forcid.org\u002F0009-0008-3539-910X",{"name":392,"orcid":393},"Tadesse Shimels","https:\u002F\u002Forcid.org\u002F0009-0009-3351-5952",{"name":395,"orcid":9},"Mussie Alemayehu",{"name":397,"orcid":398},"Daneal Fekersillassie","https:\u002F\u002Forcid.org\u002F0009-0006-6194-0903",{"tldr":400,"method":401,"finding":402,"direction":99,"opportunity":403},"基于物候与多传感器数据，在埃塞俄比亚缺数据灌区实现无监督作物制图。","GEE平台融合Sentinel-2 NDVI、Sentinel-1 VV及DEM","成功区分灌溉小麦和番茄，总体精度91%，Kappa 0.82，面积分别为666.93和812.35公","可探索物候特征与多源遥感在缺数据小农灌区的迁移学习及作物水生产力监测。","数字乡村与农业信息化","2026-10-10T23:30:36.165502Z",{"id":407,"title":408,"url":409,"summary":410,"summary_zh":411,"content":9,"source_name":412,"source_url":409,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":413,"sources":415,"tags":417,"primary_topic":9,"secondary_topics":421,"classification":422,"search_phrases":441,"slug":444,"view_count":278,"doi":445,"paper":446,"created_at":458},4101,"Cloud-based remote sensing datasets used in hydrological studies: A review and examples","https:\u002F\u002Fdoi.org\u002F10.53030\u002Ftjags.1885422","Water resources are critically important for the continuity of ecosystems and the sustainability of human activities. However, climate change, population growth, and unplanned and rapid changes in land use are increasingly putting pressure on surface and groundwater resources. This situation necessitates an accurate assessment of the temporal and spatial distribution of water and its effective monitoring at the watershed scale. Traditional hydrological observation methods provide high accuracy but, due to their limited spatial coverage and prohibitive costs, they present significant limitations, particularly in regions with insufficient data. In this context, remote sensing technologies offer an approach that enables the acquisition of parameters represented directly and indirectly in hydrological analyses, covering large areas and providing continuity. This study examines the concept of water balance, which forms the basis of hydrology, within the framework of the hydrological cycle. It evaluates the contributions of different remote sensing datasets to the monitoring and assessment of water resources, as well as the limitations of these approaches, in light of studies in the literature. In addition, a representative study area encompassing the Hazar Lake Basin and the Behramaz Basin was selected, and analyses were conducted using the Google Earth Engine (GEE) platform to demonstrate the applicability of the available datasets.","水资源对于生态系统的延续和人类活动的可持续性至关重要。然而，气候变化、人口增长以及土地利用的无序快速变化正日益对地表水和地下水资源构成压力。这一形势要求对水资源在时间和空间上的分布进行准确评估，并在流域尺度上实施有效监测。传统水文观测方法虽然精度较高，但由于空间覆盖范围有限且成本高昂，存在明显局限性，在数据不足的地区尤为突出。在此背景下，遥感技术提供了一种途径，能够获取水文分析中直接和间接表示的参数，覆盖大范围区域并具有连续性。本研究在水文循环框架内考察了作为水文学基础的水量平衡概念，结合文献中的研究，评估了不同遥感数据集对水资源监测与评估的贡献及其局限性。此外，选取了涵盖哈扎尔湖流域和贝赫拉马兹流域的典型研究区，利用谷歌地球引擎（Google Earth Engine, GEE）平台进行分析，以验证现有数据集的适用性。","OpenAlex",{"impact":243,"substance":18,"depth":19,"authority":243,"freshness":104,"relevant":22,"comment":414},"综述性论文，系统梳理云遥感数据集在水文水资源监测中的应用，并以GEE平台开展案例验证，对农业水资源信息化管理有参考价值，但非突破性成果。",[416],{"name":412,"url":409},[418,28,30,419,420],"农业信息化","水文模型","水资源监测",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":423},{"candidates":424,"agricultural_evidence":14,"reason":72},[425,429,433,437],{"code":54,"score":19,"supported":42,"title":426,"abstract":427,"tags":428},[57],[28,57],[30,28],{"code":40,"score":104,"supported":42,"title":430,"abstract":431,"tags":432},[],[27,44],[],{"code":60,"score":116,"supported":42,"title":434,"abstract":435,"tags":436},[],[64],[419],{"code":364,"score":22,"supported":14,"title":438,"abstract":439,"tags":440},[],[],[418],[442,443],"Hazar Lake Basin 遥感 水文","Google Earth Engine 水资源监测","HazarLakeBasin遥感水文-4101","10.53030\u002Ftjags.1885422",{"doi":445,"openalex_id":447,"authors":448,"venue":9,"cited_by_count":93,"oa_url":452,"card":453,"direction":99,"ingested_from":101},"W7214913155",[449],{"name":450,"orcid":451},"Ceren Sıla Özdemir","https:\u002F\u002Forcid.org\u002F0000-0003-0967-8179","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5701448",{"tldr":454,"method":455,"finding":456,"direction":99,"opportunity":457},"综述云遥感数据集在水文研究中的应用，并以GEE平台示例分析。","文献综述结合Google Earth Engine平台，选取Hazar湖和Beh","云遥感数据集能有效弥补传统水文观测空间覆盖不足，支持流域尺度水资源监测。","可探索云遥感数据集在农业灌溉用水估算与干旱监测中的集成应用与精度验证。","2026-10-03T23:30:37.766386Z",{"id":460,"title":461,"url":462,"summary":463,"summary_zh":9,"content":9,"source_name":464,"source_url":462,"published_at":465,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":466,"score_detail":467,"sources":469,"tags":471,"primary_topic":9,"secondary_topics":474,"classification":475,"search_phrases":491,"slug":494,"view_count":116,"doi":495,"paper":496,"created_at":506},3794,"Assessing Climate Change Impacts on Forest Productivity Using Integrated GIS Remote Sensing Climate and Socioeconomic Methods in Itang Special District Gambella Region Ethiopia","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10789368\u002Fv1","Assessing Climate Change Impacts on Forest Productivity Using Integrated GIS Remote Sensing Climate and Socioeconomic Methods in Itang Special District Gambella Region Ethiopia。Research Square","Research Square","2026-09-28T00:00:00Z",60,{"impact":17,"substance":18,"depth":41,"authority":13,"freshness":21,"relevant":22,"comment":468},"埃塞俄比亚区域尺度的GIS遥感与气候社会经济综合方法研究，方法有参考价值但地域性强、公共影响有限，时效新。",[470],{"name":464,"url":462},[27,28,472,473],"GIS","森林生产力",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":476},{"candidates":477,"agricultural_evidence":14,"reason":72},[478,482,486],{"code":40,"score":243,"supported":42,"title":479,"abstract":480,"tags":481},[44],[44],[27],{"code":54,"score":243,"supported":42,"title":483,"abstract":484,"tags":485},[57],[57],[28],{"code":487,"score":243,"supported":42,"title":488,"abstract":489,"tags":490},"I4",[472],[472],[472],[492,493],"Gambella Itang 森林生产力","GIS 遥感 气候变化","GambellaItang森林生产力-3794","10.21203\u002Frs.3.rs-10789368\u002Fv1",{"doi":495,"openalex_id":497,"authors":498,"venue":464,"cited_by_count":93,"oa_url":505,"card":9,"direction":99,"ingested_from":101},"W7214650564",[499,501,503],{"name":500,"orcid":9},"KOAT MACHAR PUR",{"name":502,"orcid":9},"DAK BANG RUON",{"name":504,"orcid":9},"DUOL NHIAL LUAK","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10789368\u002Flatest.pdf","2026-09-29T23:30:30.100133Z"]