[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5087":3,"related-5087":89},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":67,"slug":70,"view_count":22,"doi":71,"paper":72,"created_at":88},5087,"Method for ten-day assessment of the contribution of agrometeorological conditions to expected winter grain crop productivity based on satellite and ground-based information","https:\u002F\u002Fdoi.org\u002F10.37162\u002F2618-9631-2026-3-167-183","The paper presents a developed and tested method for 10-day assessment of the contribution of agrometeorological factors to winter grain crop productivity. The method is based on an integrated analysis of satellite spectral indices and ground-based meteorological data for the North Caucasus, Volga and Central Black Earth regions. Satellite data from MODIS (Terra satellite) and VIIRS (SUOMI NPP satellite) radiometers were used to calculate the NDVI, VCI, and VCNI vegetation indices. Ground-based information includes meteorological and agrometeorological indicators reflecting temperature and humidity conditions. The algorithm and software modules developed within the study allow for automated tracking of 10-day changes in agrometeorological conditions and quantitative ranking of factors based on their contribution to the expected winter grain crop yield, providing an information basis for informed planning of agricultural measures.","本文提出并验证了一种用于每旬评估农业气象因素对冬粮作物生产力贡献的方法。该方法基于对北高加索、伏尔加河沿岸和中央黑土区卫星光谱指数与地面气象数据的综合分析。利用MODIS（Terra卫星）和VIIRS（SUOMI NPP卫星）辐射计的卫星数据计算NDVI、VCI和VCNI植被指数。地面信息包括反映温湿状况的气象和农业气象指标。研究开发的算法和软件模块能够自动跟踪农业气象条件的每旬变化，并根据各因素对预期冬粮作物产量的贡献进行定量排序，为农业措施的合理规划提供信息依据。",null,"Hydrometeorological research and forecasting","2026-10-15T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"方法新颖、数据扎实的区域性遥感估产研究，对农业信息化有参考价值，但属细分领域学术成果，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业气象","遥感监测","冬小麦","作物估产",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,51,57,62],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":50},4,true,[],[28],[28],{"code":52,"score":53,"supported":14,"title":54,"abstract":55,"tags":56},"I3",2,[],[],[31,29],{"code":58,"score":22,"supported":14,"title":59,"abstract":60,"tags":61},"A2",[],[],[30],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"I1",[],[],[27],[68,69],"MODIS VIIRS 冬小麦 遥感估产","农业气象 冬谷类作物 产量评估","MODISVIIRS冬小麦遥感估产-5087","10.37162\u002F2618-9631-2026-3-167-183",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":80,"oa_url":6,"card":81,"direction":85,"ingested_from":87},"W7221991562",[75,78],{"name":76,"orcid":77},"A. D. Kleshchenko","https:\u002F\u002Forcid.org\u002F0000-0002-4200-6668",{"name":79,"orcid":9},"O. V. Savitskaya",0,{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"提出一种基于卫星与地面数据每十天评估农业气象因子对冬粮作物产量贡献的方法。","用MODIS和VIIRS计算NDVI、VCI、VCNI，结合气象与农业气象指标做","可自动追踪十天农业气象变化并量化排序各因子对预期产量的贡献。","农业遥感与作物表型","可扩展至多作物多区域，融合机器学习提升因子贡献动态预测精度。","openalex","2026-10-16T23:30:26.817010Z",{"total":90,"page":22,"page_size":90,"items":91},6,[92,188,258,351,419,480],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":9,"source_name":98,"source_url":95,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":99,"sources":105,"tags":107,"primary_topic":111,"secondary_topics":117,"classification":119,"search_phrases":170,"slug":173,"view_count":22,"doi":174,"paper":175,"created_at":187},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)",{"impact":100,"substance":101,"depth":102,"authority":17,"freshness":103,"relevant":22,"comment":104},18,16,15,9,"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[106],{"name":98,"url":95},[27,108,109,110,29],"农业人工智能","可持续发展","气候变化",{"code":112,"slug":113,"name":114,"path":115,"parent_code":116},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[118],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":120},{"candidates":121,"agricultural_evidence":47},[122,131,136,143,149,156,163],{"code":112,"score":100,"supported":47,"title":123,"abstract":125,"tags":130},[124],"artificial intelligence",[126,127,128,129,124],"深度学习","机器学习","machine learning","deep learning",[108],{"code":33,"score":102,"supported":47,"title":132,"abstract":134,"tags":135},[133],"climate change",[110,133],[110],{"code":63,"score":137,"supported":47,"title":138,"abstract":139,"tags":142},7,[],[140,141],"精准农业","precision agriculture",[27],{"code":52,"score":137,"supported":47,"title":144,"abstract":145,"tags":148},[],[146,147],"遥感","remote sensing",[29],{"code":150,"score":90,"supported":47,"title":151,"abstract":152,"tags":155},"H4",[],[153,154],"碳排放","carbon emission",[],{"code":157,"score":90,"supported":47,"title":158,"abstract":159,"tags":162},"H6",[],[160,161],"生物多样性","biodiversity",[],{"code":164,"score":90,"supported":47,"title":165,"abstract":166,"tags":169},"I5",[],[167,168],"物联网","internet of things",[],[171,172],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":174,"openalex_id":176,"authors":177,"venue":98,"cited_by_count":80,"oa_url":95,"card":180,"direction":186,"ingested_from":87},"W7222092855",[178],{"name":179,"orcid":9},"Deepmala Suryavanshi",{"tldr":181,"method":182,"finding":183,"direction":184,"opportunity":185},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","智慧农业 \u002F 农业物联网","2026-10-16T23:30:15.059867Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":9,"content":9,"source_name":193,"source_url":191,"published_at":194,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":200,"tags":202,"primary_topic":206,"secondary_topics":212,"classification":214,"search_phrases":234,"slug":237,"view_count":53,"doi":238,"paper":239,"created_at":257},4650,"A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105011","A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact。Agricultural Systems","Agricultural Systems","2026-10-11T00:00:00Z",81,{"impact":100,"substance":197,"depth":100,"authority":198,"freshness":13,"relevant":22,"comment":199},21,14,"核心期刊提出的面向过程的夏玉米干旱逐日动态监测框架，方法新颖、面向农业信息化应用，时效性强，值得进入每日精选。",[201],{"name":193,"url":191},[27,203,204,28,205],"农业遥感","夏玉米","干旱监测",{"code":207,"slug":208,"name":209,"path":210,"parent_code":211},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[213],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":215},{"candidates":216,"agricultural_evidence":47},[217,222,226,230],{"code":33,"score":20,"supported":47,"title":218,"abstract":220,"tags":221},[219],"drought",[219],[28,205],{"code":207,"score":17,"supported":47,"title":223,"abstract":224,"tags":225},[208],[208],[204],{"code":63,"score":22,"supported":14,"title":227,"abstract":228,"tags":229},[],[],[27],{"code":52,"score":22,"supported":14,"title":231,"abstract":232,"tags":233},[],[],[203],[235,236],"夏玉米 干旱 动态监测","气象胁迫 农业影响 监测框架","夏玉米干旱动态监测-4650","10.1016\u002Fj.agsy.2026.105011",{"doi":238,"openalex_id":240,"authors":241,"venue":193,"cited_by_count":80,"oa_url":191,"card":9,"direction":85,"ingested_from":87},"W7221308782",[242,244,246,248,251,253,255],{"name":243,"orcid":9},"Yuanda Zhang",{"name":245,"orcid":9},"Peijuan Wang",{"name":247,"orcid":9},"Jun Zhu",{"name":249,"orcid":250},"Yang Li","https:\u002F\u002Forcid.org\u002F0000-0001-9021-0201",{"name":252,"orcid":9},"Dianchen Han",{"name":254,"orcid":9},"Dingrong Wu",{"name":256,"orcid":9},"Jianping Guo","2026-10-11T23:30:03.734429Z",{"id":259,"title":260,"url":261,"summary":262,"summary_zh":263,"content":9,"source_name":264,"source_url":261,"published_at":265,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":266,"sources":270,"tags":272,"primary_topic":275,"secondary_topics":279,"classification":282,"search_phrases":327,"slug":330,"view_count":53,"doi":331,"paper":332,"created_at":350},4575,"A review of remote sensing for agricultural drought: technologies, indices, modelling advances and future prospects","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2738981","Agricultural drought is a complex hazard that jeopardises food security and progress toward the Sustainable Development Goals. We systematically reviewed peer-reviewed English-language literature published between 2015 and 2025, focusing on Remote Sensing (RS)-driven agricultural drought monitoring and prediction. Eligible studies were identified from the Web of Science and Scopus databases (last searched in August 2025); review articles, duplicates, and irrelevant studies were excluded, resulting in 181 articles. This review critically examined the evolution of sensors, drought indices, modelling approaches, and emerging technologies used to assess agricultural drought across diverse agro-ecological regions. Specifically, we (1) systematically evaluated the use of optical, thermal, and microwave RS platforms, along with satellite-based, model-based, and reanalysis products for agricultural drought monitoring; (2) reviewed widely used RS-based drought indices, including single indices grouped into nine categories, as well as composite indices; (3) reviewed software or programming languages applied; (4) assessed the progression of modelling approaches from traditional statistical and time-series methods to machine learning (ML) and deep learning (DL) frameworks; and (5) examined validation strategies applied to agricultural drought assessment. The analysis reveals a growing research interest in this topic, with a strong geographic concentration in Asia, particularly China. Optical and thermal platforms (e.g. MODIS and Landsat) dominate the literature (~54%) due to their accessibility, followed by satellite-derived precipitation products (17%), while microwave soil-moisture missions remain underutilized (~7%). Regarding RS-based indices, most studies employed more than one index: 44% analysed multiple individual indices, 51% used composite indices (either alone or in combination with individual indices), and only 5% relied on a single index. Vegetation indices, such as NDVI, remain workhorses; however, their sensitivity to soil background effects and canopy saturation has driven increased adoption of thermal composites (e.g. VHI), as well as adaptive weighting schemes and principal component analysis-based approaches. Moreover, computational tools used in drought research can be broadly grouped into geospatial and RS platforms for large-scale data processing and analysis (e.g. QGIS, GEE), and general-purpose programming languages (e.g. R, Python) for model development and advanced analytics. Regarding the methodology, statistical and time-series approaches account for 64% of the reviewed studies, followed by ML models (20%) and DL methods (15%), while explainable artificial intelligence (XAI) remains nascent. Techniques, such as random forests, support vector machines, and CNN and LSTM architectures, generally outperform traditional models but require large training datasets and raise concerns related to interpretability and ethics. In terms of validation, hydro-climatic indices such as SPI and SPEI are most commonly used (32%), followed by crop yield and yield anomaly data (19%). This review further identifies key research gaps across sensors, indices, and modelling approaches and proposes a roadmap for developing equitable, interpretable, and resilient agricultural drought monitoring systems. Emerging opportunities include context-aware and interpretable AI, multi-sensor and multi-scale data fusion across optical, thermal, and microwave domains, integration of physiologically meaningful indicators such as sun-induced chlorophyll fluorescence (SIF), robust uncertainty quantification, and the development of hybrid and physics-informed modelling frameworks.","农业干旱是一种复杂的灾害，威胁粮食安全和可持续发展目标的实现。我们系统综述了2015年至2025年间发表的同行评议英文文献，聚焦于遥感（RS）驱动的农业干旱监测与预测。符合条件的研究通过Web of Science和Scopus数据库检索确定（最后检索时间为2025年8月）；综述文章、重复文献及不相关研究被排除，最终纳入181篇文献。本综述批判性地审视了用于评估不同农业生态区农业干旱的传感器、干旱指数、建模方法及新兴技术的演变。具体而言，我们（1）系统评估了光学、热红外和微波遥感平台，以及基于卫星、基于模型和再分析产品在农业干旱监测中的应用；（2）综述了广泛使用的基于遥感的干旱指数，包括归为九类的单一指数以及复合指数；（3）综述了所应用的软件或编程语言；（4）评估了建模方法从传统统计和时间序列方法向机器学习（ML）和深度学习（DL）框架的演进；（5）考察了应用于农业干旱评估的验证策略。分析显示，该主题的研究兴趣日益增长，地理上高度集中于亚洲，尤其是中国。光学和热红外平台（如MODIS和Landsat）因其可获取性在文献中占主导地位（约54%），其次是卫星降水产品（17%），而微波土壤水分任务仍未得到充分利用（约7%）。在基于遥感的指数方面，大多数研究采用了不止一种指数：44%分析了多个单一指数，51%使用了复合指数（单独使用或与单一指数结合使用），仅5%依赖单一指数。植被指数（如NDVI）仍是主力工具；然而，其对土壤背景效应和冠层饱和的敏感性推动了热红外复合指数（如VHI）以及自适应权重方案和基于主成分分析的方法得到更多采用。此外，干旱研究中使用的计算工具可大致分为用于大规模数据处理和分析的地理空间与遥感平台（如QGIS、GEE），以及用于模型开发的通用编程语言（如R、Python）。","GIScience & Remote Sensing","2026-10-08T00:00:00Z",{"impact":100,"substance":267,"depth":268,"authority":198,"freshness":21,"relevant":22,"comment":269},22,19,"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[271],{"name":264,"url":261},[27,273,127,29,274],"粮食安全","农业干旱",{"code":52,"slug":276,"name":277,"path":278,"parent_code":116},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[280,281],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":112,"slug":113,"name":114,"path":115,"parent_code":116},{"status":40,"locked":14,"version":41,"method":42,"evidence":283},{"candidates":284,"agricultural_evidence":47},[285,289,294,298,304,311,318,323],{"code":52,"score":102,"supported":47,"title":286,"abstract":287,"tags":288},[147],[146,147],[29],{"code":33,"score":198,"supported":47,"title":290,"abstract":291,"tags":293},[219],[292,219],"干旱",[],{"code":112,"score":13,"supported":47,"title":295,"abstract":296,"tags":297},[],[126,127,128,129,124],[127],{"code":299,"score":137,"supported":47,"title":300,"abstract":301,"tags":303},"A8",[],[273,302],"food security",[273],{"code":305,"score":90,"supported":47,"title":306,"abstract":307,"tags":310},"H2",[],[308,309],"土壤","soil",[],{"code":312,"score":313,"supported":47,"title":314,"abstract":315,"tags":317},"D",3,[],[316],"水产",[],{"code":157,"score":313,"supported":47,"title":319,"abstract":320,"tags":322},[],[321],"农业生态",[],{"code":63,"score":22,"supported":14,"title":324,"abstract":325,"tags":326},[],[],[27],[328,329],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575","10.1080\u002F15481603.2026.2738981",{"doi":331,"openalex_id":333,"authors":334,"venue":264,"cited_by_count":80,"oa_url":344,"card":345,"direction":186,"ingested_from":87},"W7221081979",[335,338,341],{"name":336,"orcid":337},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":339,"orcid":340},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":342,"orcid":343},"Olena Dubovyk","https:\u002F\u002Forcid.org\u002F0000-0002-7338-3167","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2738981?needAccess=true",{"tldr":346,"method":347,"finding":348,"direction":85,"opportunity":349},"系统综述2015-2025年遥感农业干旱监测与预测的传感器、指数、建模及验证进展。","基于Web of Science与Scopus的181篇文献系统综述，涵盖光学\u002F","光学热红外平台占主导，微波土壤水分利用不足；复合指数与机器学习建模成为趋势。","微波土壤水分数据融合与深度学习干旱预测模型在数据稀缺区验证是潜在研究空白。","2026-10-10T23:30:14.933078Z",{"id":352,"title":353,"url":354,"summary":355,"summary_zh":356,"content":9,"source_name":357,"source_url":354,"published_at":265,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":358,"score_detail":359,"sources":361,"tags":363,"primary_topic":366,"secondary_topics":367,"classification":374,"search_phrases":399,"slug":402,"view_count":22,"doi":403,"paper":404,"created_at":418},4504,"Development of the Potential Evapotranspiration-Vegetation-Precipitation Drought Index (PETVPDI) and Its Application in West Africa Using Remote Sensing Datasets","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193435","Drought remains one of the most severe climate-related hazards affecting water resources, ecosystems, and agricultural productivity in West Africa (WA). Traditional drought indices often rely on individual hydroclimatic or vegetation variables and may not fully represent the coupled effects of precipitation supply, atmospheric evaporative demand, and vegetation response. To address this limitation, this study develops and applies a composite drought indicator, the Potential Evapotranspiration–Vegetation–Precipitation Drought Index (PETVPDI), using multi-source remote sensing datasets over WA for 2001–2022. PETVPDI integrates precipitation (P), potential evapotranspiration (PET), and the normalized difference vegetation index (NDVI) within a three-dimensional Euclidean-distance framework. The index was evaluated against GLDAS soil moisture (SM), ESA CCI SM, gross primary productivity (GPP), and net primary productivity (NPP), and compared with four conventional remote-sensing drought indices (cRSDI): VCI, VHI, TVDI, and TVPDI. Deseasonalized anomaly correlations, used as the primary validation evidence to reduce the influence of the shared monsoon seasonal cycle, show that PETVPDI achieved the highest numerical correlations among the evaluated indices with GLDAS SM (R = 0.556, p \u003C 0.001), ESA CCI SM (R = 0.513, p \u003C 0.001), and GPP (R = 0.483, p \u003C 0.001). Steiger’s tests for dependent overlapping correlations show that the PETVPDI–TVPDI difference was statistically significant for raw GLDAS SM and deseasonalized ESA CCI SM, but not for the other evaluated comparisons. Spatial PETVPDI patterns represent each pixel’s relative position within its own 2001–2022 hydroclimatic and vegetation range and therefore should not be interpreted as direct differences in absolute climatic aridity among locations. Long-term trends are spatially heterogeneous across WA. Overall, PETVPDI provides an integrated framework for monitoring relative drought–wetness variability and offers complementary information for drought assessment and early-warning applications in data-scarce regions.","干旱仍然是影响西非（WA）水资源、生态系统和农业生产力最严重的气候相关灾害之一。传统干旱指数通常依赖单一的水文气候或植被变量，可能无法充分反映降水供给、大气蒸发需求和植被响应之间的耦合效应。为解决这一局限，本研究利用2001—2022年西非多源遥感数据集，开发并应用了一种复合干旱指标——潜在蒸散–植被–降水干旱指数（PETVPDI）。PETVPDI在三维欧氏距离框架内整合了降水（P）、潜在蒸散（PET）和归一化差异植被指数（NDVI）。该指数以GLDAS土壤湿度（SM）、ESA CCI SM、总初级生产力（GPP）和净初级生产力（NPP）进行评估，并与四种传统遥感干旱指数（cRSDI）：VCI、VHI、TVDI和TVPDI进行比较。以去季节化异常相关性作为主要验证依据，以减少共同季风季节循环的影响，结果表明PETVPDI在所评估的指数中与GLDAS SM（R = 0.556，p \u003C 0.001）、ESA CCI SM（R = 0.513，p \u003C 0.001）和GPP（R = 0.483，p \u003C 0.001）的数值相关性最高。针对相依重叠相关性的Steiger检验表明，PETVPDI–TVPDI差异在原始GLDAS SM和去季节化ESA CCI SM上具有统计学显著性，但在其他评估比较中不显著。PETVPDI的空间格局表示每个像元在其自身2001—2022年水文气候和植被范围内的相对位置，因此不应被解释为地点之间绝对气候干燥度的直接差异。长期趋势在西非呈现空间异质性。总体而言，PETVPDI为监测相对干旱–湿润变率提供了一个集成框架，并为数据稀缺地区的干旱评估和预警应用提供了补充信息。","Remote Sensing",75,{"impact":17,"substance":267,"depth":100,"authority":198,"freshness":103,"relevant":22,"comment":360},"提出融合降水、蒸散与植被的复合干旱指数，方法新颖、验证扎实，对数据稀缺区农业干旱预警有参考价值，但区域局限于西非，国内应用关联度有限。",[362],{"name":357,"url":354},[273,28,29,364,365],"西非农业","干旱指数",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[368,369],{"code":52,"slug":276,"name":277,"path":278,"parent_code":116},{"code":370,"slug":371,"name":372,"path":373,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":40,"locked":14,"version":41,"method":42,"evidence":375},{"candidates":376,"agricultural_evidence":47},[377,381,385,391,395],{"code":33,"score":101,"supported":47,"title":378,"abstract":379,"tags":380},[219],[292,219],[28,365],{"code":52,"score":102,"supported":47,"title":382,"abstract":383,"tags":384},[147],[146,147],[29],{"code":370,"score":198,"supported":47,"title":386,"abstract":388,"tags":390},[387],"evapotranspiration",[389,387],"水资源",[],{"code":305,"score":90,"supported":47,"title":392,"abstract":393,"tags":394},[],[308,309],[],{"code":299,"score":22,"supported":14,"title":396,"abstract":397,"tags":398},[],[],[273],[400,401],"PETVPDI 干旱指数 西非","遥感 干旱监测 NDVI","PETVPDI干旱指数西非-4504","10.3390\u002Frs18193435",{"doi":403,"openalex_id":405,"authors":406,"venue":357,"cited_by_count":80,"oa_url":354,"card":413,"direction":85,"ingested_from":87},"W7220963372",[407,410],{"name":408,"orcid":409},"Abdoul-Aziz Bio Sidi D. Bouko","https:\u002F\u002Forcid.org\u002F0009-0003-1179-8046",{"name":411,"orcid":412},"Bing Gao","https:\u002F\u002Forcid.org\u002F0000-0003-0782-8858",{"tldr":414,"method":415,"finding":416,"direction":85,"opportunity":417},"构建融合蒸散、植被与降水的复合干旱指数PETVPDI，并用于西非2001—2022年干旱监测。","多源遥感数据，三维欧氏距离整合P、PET、NDVI，与土壤湿度、GPP\u002FNPP及","PETVPDI与土壤湿度和GPP的相关性最高，能更好反映干旱-湿润相对变化，但趋势空间异质。","可将该复合指数迁移至其他数据稀缺区，并结合作物产量或灌溉需水做农业干旱预警与适应性研究。","2026-10-09T23:30:25.838340Z",{"id":420,"title":421,"url":422,"summary":423,"summary_zh":424,"content":9,"source_name":425,"source_url":422,"published_at":426,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":427,"score_detail":428,"sources":430,"tags":432,"primary_topic":433,"secondary_topics":434,"classification":435,"search_phrases":458,"slug":461,"view_count":22,"doi":462,"paper":463,"created_at":479},4309,"Assessing agricultural drought intensification and spatiotemporal pattern in the central Rift Valley using multi-source indices","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cliser.2026.100737","Drought is a complex and multifaceted phenomenon that includes meteorological, agricultural, hydrological, and socio-economic components. Both single and composite drought indices were used in this study to assess agricultural drought in the East Shewa Zone of Central Ethiopia. The Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Soil Moisture Condition Index (SMCI) were used as single indices. To improve drought characterization, three composite indices—the Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI), and Optimized Vegetation Dryness Index (OVDI)—were created using empirical weighting and limited optimization techniques. The association of both single and composite indices with the multi-timescale Standardized Precipitation Index (SPI), a widely used meteorological drought measure, was evaluated using in-sample correlation analysis. The results showed that the composite indices (OVDI, VHI, and SDCI) exhibited stronger associations with the multi-timescale SPI than the individual indices, although the strength of these associations varied across seasons and SPI timescales. Among the single indices, the PCI showed the strongest in-sample correlation with the short-term SPI ( r = 0.764 with SPI-1). In contrast, the VCI and SMCI were more strongly correlated with longer-term SPI scales, with SMCI showing in-sample correlations of ( r = 0.577 with SPI-3) and ( r = 0.467 with SPI-6). Spatiotemporal analysis indicated an increase in drought severity, with the area classified as severely dry expanding from 1% to 11% between 2002 and 2015. The findings provide insights into the use of remote sensing indices for drought monitoring and may contribute to the development of improved drought monitoring and early warning approaches in the region.","干旱是一种复杂且多层面的现象，包括气象、农业、水文和社会经济等组成部分。本研究使用单一和复合干旱指数评估埃塞俄比亚中部东谢瓦区的农业干旱。采用降水条件指数（PCI）、温度条件指数（TCI）、植被条件指数（VCI）和土壤水分条件指数（SMCI）作为单一指数。为改进干旱表征，利用经验加权和有限优化技术构建了三个复合指数——尺度化干旱条件指数（SDCI）、植被健康指数（VHI）和优化植被干燥度指数（OVDI）。采用样本内相关分析评估了单一和复合指数与多时间尺度标准化降水指数（SPI）——一种广泛使用的气象干旱指标——的关联。结果表明，复合指数（OVDI、VHI和SDCI）与多时间尺度SPI的关联强于单一指数，尽管这些关联的强度因季节和SPI时间尺度而异。在单一指数中，PCI与短期SPI的样本内相关性最强（与SPI-1的r = 0.764）。相比之下，VCI和SMCI与较长期SPI尺度的相关性更强，SMCI的样本内相关性为（与SPI-3的r = 0.577）和（与SPI-6的r = 0.467）。时空分析表明干旱严重程度增加，2002年至2015年间被归类为严重干旱的面积从1%扩大至11%。研究结果为利用遥感指数进行干旱监测提供了见解，并可能有助于该地区改进干旱监测和预警方法的发展。","Climate Services","2026-10-05T00:00:00Z",71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":103,"relevant":22,"comment":429},"基于多源遥感指数评估埃塞俄比亚中部裂谷农业干旱加剧趋势，方法组合与时空结论具参考价值，但属区域案例研究，公共影响有限。",[431],{"name":425,"url":422},[27,273,29,274,365],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":436},{"candidates":437,"agricultural_evidence":47},[438,442,446,450,454],{"code":33,"score":102,"supported":47,"title":439,"abstract":440,"tags":441},[219],[292,219],[365],{"code":52,"score":137,"supported":47,"title":443,"abstract":444,"tags":445},[],[146,147],[29],{"code":305,"score":90,"supported":47,"title":447,"abstract":448,"tags":449},[],[308,309],[],{"code":299,"score":22,"supported":14,"title":451,"abstract":452,"tags":453},[],[],[273],{"code":63,"score":22,"supported":14,"title":455,"abstract":456,"tags":457},[],[],[27],[459,460],"埃塞俄比亚 中部裂谷 农业干旱","遥感 干旱指数 SPI","埃塞俄比亚中部裂谷农业干旱-4309","10.1016\u002Fj.cliser.2026.100737",{"doi":462,"openalex_id":464,"authors":465,"venue":425,"cited_by_count":80,"oa_url":473,"card":474,"direction":85,"ingested_from":87},"W7220385252",[466,468,470],{"name":467,"orcid":9},"Bekele Alemayehu",{"name":469,"orcid":9},"Zenebe Ayele",{"name":471,"orcid":472},"Hamere Yohannes","https:\u002F\u002Forcid.org\u002F0000-0002-6899-5150","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2405880726001056\u002Fpdf",{"tldr":475,"method":476,"finding":477,"direction":85,"opportunity":478},"用多源遥感指数评估埃塞俄比亚中部裂谷农业干旱加剧及时空格局。","PCI、TCI、VCI、SMCI及SDCI、VHI、OVDI复合指数，结合SPI","复合指数与SPI相关性更强，2002-2015年重旱面积从1%增至11%。","可探索多源指数优化加权与机器学习融合，提升区域干旱预警精度。","2026-10-06T23:30:20.023409Z",{"id":481,"title":482,"url":483,"summary":484,"summary_zh":485,"content":9,"source_name":486,"source_url":483,"published_at":487,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":488,"score_detail":489,"sources":491,"tags":493,"primary_topic":497,"secondary_topics":498,"classification":501,"search_phrases":527,"slug":530,"view_count":22,"doi":531,"paper":532,"created_at":550},4030,"Enhancing Drought Monitoring Using Integrated Remote Sensing and Ground-Based Indices","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jastp.2026.106987","ABSTRACT Accurate and timely drought monitoring is essential for sustainable water resource management, particularly in semi-arid regions where meteorological observations are sparse and spatially heterogeneous. This study presents an integrated evaluation of drought conditions by applying established individual and composite drought indices within a unified multi-source framework that combines ground-based meteorological data with satellite-derived datasets, including MODIS land surface products and GPM\u002FTRMM precipitation estimates, and is implemented on the Google Earth Engine platform. A set of drought indicators representing vegetation condition, thermal stress, precipitation deficit, and soil moisture variability was analyzed alongside five composite indices to provide a multi-dimensional assessment of drought dynamics. The analysis was conducted across monthly, seasonal, and annual timescales for Tehran, Iran, over the period 2000–2022. In addition, trend significance was assessed using the Mann–Kendall test and Sen’s slope estimator to quantify long-term changes in drought behavior. The results indicate that composite indices provide a more coherent representation of drought evolution and are more effective in capturing the interaction among key climatic drivers. In particular, the analysis reveals a transition from precipitation-driven droughts (e.g., 2008 and 2016) to increasingly temperature-driven drought conditions (e.g., 2022), associated with elevated potential evapotranspiration and thermal stress. This highlights the growing importance of atmospheric evaporative demand in intensifying drought severity under warming conditions. From an applied perspective, the proposed framework supports drought early warning, spatial risk assessment, and informed decision-making in water resource management and agricultural planning. However, the reliability of the results depends on data quality, spatial resolution, and the representation of key drought drivers and should therefore be interpreted with these limitations in mind. The study demonstrates that the integrated application of multi-source data and multi-scale drought indices provides a robust basis for understanding and monitoring drought dynamics in semi-arid urban environments.","准确的干旱监测对于可持续水资源管理至关重要，尤其是在气象观测稀疏且空间异质性强的半干旱地区。本研究在统一的多源框架内，应用已有的单一和综合干旱指数对干旱状况进行综合评估，该框架将地面气象数据与卫星遥感数据集（包括MODIS地表产品和GPM\u002FTRMM降水估算）相结合，并在Google Earth Engine平台上实现。研究分析了表征植被状况、热胁迫、降水亏缺和土壤湿度变率的干旱指标集以及五种综合指数，以对干旱动态进行多维评估。分析在月、季和年尺度上针对伊朗德黑兰2000—2022年期间开展。此外，采用Mann–Kendall检验和Sen斜率估计器评估趋势显著性，以量化干旱行为的长期变化。结果表明，综合指数能更一致地表征干旱演变，且更能有效捕捉关键气候驱动因子之间的相互作用。具体而言，分析揭示了从降水驱动型干旱（如2008年和2016年）向日益增强的温度驱动型干旱（如2022年）的转变，这与潜在蒸散和热胁迫升高有关。这凸显了在变暖条件下大气蒸发需求在加剧干旱严重程度方面日益重要的作用。从应用角度看，所提出的框架可支持干旱预警、空间风险评估以及水资源管理和农业规划中的知情决策。然而，结果的可靠性取决于数据质量、空间分辨率以及关键干旱驱动因子的表征，因此在解读时应考虑这些局限性。本研究表明，多源数据与多尺度干旱指数的综合应用为理解和监测半干旱城市环境中的干旱动态提供了稳健基础。","Journal of Atmospheric and Solar-Terrestrial Physics","2026-10-01T00:00:00Z",74,{"impact":198,"substance":197,"depth":100,"authority":20,"freshness":21,"relevant":22,"comment":490},"多源遥感与地面指数融合的干旱监测框架，方法扎实、结论有新意，但属区域案例研究，产业影响有限。",[492],{"name":486,"url":483},[28,494,29,495,496],"水资源管理","Google Earth Engine","干旱预警",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[499,500],{"code":52,"slug":276,"name":277,"path":278,"parent_code":116},{"code":370,"slug":371,"name":372,"path":373,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":502},{"candidates":503,"agricultural_evidence":47},[504,508,512,517,521],{"code":33,"score":101,"supported":47,"title":505,"abstract":506,"tags":507},[219],[292,219],[28,496],{"code":52,"score":20,"supported":47,"title":509,"abstract":510,"tags":511},[147],[146],[495,29],{"code":370,"score":13,"supported":47,"title":513,"abstract":514,"tags":516},[],[389,515,387],"water resource",[494],{"code":305,"score":90,"supported":47,"title":518,"abstract":519,"tags":520},[],[308,309],[],{"code":522,"score":313,"supported":47,"title":523,"abstract":524,"tags":526},"J1",[],[525],"农业规划",[],[528,529],"MODIS GPM 干旱指数","德黑兰 干旱监测 遥感","MODISGPM干旱指数-4030","10.1016\u002Fj.jastp.2026.106987",{"doi":531,"openalex_id":533,"authors":534,"venue":486,"cited_by_count":80,"oa_url":483,"card":545,"direction":85,"ingested_from":87},"W7215092640",[535,538,540,542],{"name":536,"orcid":537},"Hadi Siasar","https:\u002F\u002Forcid.org\u002F0000-0002-9323-4345",{"name":539,"orcid":9},"Mahboube Ebrahimi",{"name":541,"orcid":9},"Mehdi Bahrami",{"name":543,"orcid":544},"Hossein Hamidifar","https:\u002F\u002Forcid.org\u002F0000-0001-9054-0120",{"tldr":546,"method":547,"finding":548,"direction":85,"opportunity":549},"整合遥感与地面数据，在GEE上评估德黑兰2000–2022年多尺度干旱动态。","MODIS、GPM\u002FTRMM与气象数据，构建5种复合干旱指数，Mann-Kend","复合指数更一致；干旱由降水驱动转向温度驱动，蒸散需求加剧干旱。","可引入机器学习融合多源指数，提升半干旱区干旱预警的时空精度与驱动归因。","2026-10-02T23:30:24.756475Z"]