[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4747":3,"related-4747":104},{"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":45,"search_phrases":82,"slug":85,"view_count":86,"doi":87,"paper":88,"created_at":103},4747,"Spatiotemporal mapping of agricultural drought using geospatial approach in the East Bale Zone, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fagg2.70475","Abstract Agricultural drought significantly threatens food security, livelihoods, and ecosystem stability, especially in regions prone to drought, such as the East Bale zone. This study investigates the spatial and temporal patterns of agricultural drought over 30 years (1993–2024) by combining meteorological station data with satellite‐derived vegetation and thermal indices. To assess meteorological drought, the standardized precipitation index (SPI) was calculated at 3‐, 6‐, and 12‐month intervals. For agricultural and thermal stress analysis, remote sensing‐based indices including the vegetation condition index (VCI), temperature condition index, and vegetation health index (VHI) were utilized. The results indicate that the years 2003, 2008, and 2018 experienced the most severe droughts, as shown consistently across all indices. Spatial analysis revealed that the central, southern, and eastern lowland areas faced the highest frequency and severity of drought, while the northwestern highlands remained comparatively resilient. Cross‐index correlation revealed strong agreement between SPI and VCI ( R 2 = 0.77), and between VHI and VCI ( R 2 = 0.83), supporting the effectiveness of integrated drought monitoring approaches. This research advances the field of drought assessment by enhancing spatial and temporal resolution and validating cross‐index relationships at the subregional level. The results provide critical insights for localized drought early warning systems, climate adaptation strategies, and future research to improve agricultural resilience under changing climatic conditions. Hence, agricultural drought risk mapping serves as a valuable tool for guiding decision‐making in drought monitoring and for mitigating its impacts on agricultural productivity.","摘要 农业干旱严重威胁粮食安全、生计和生态系统稳定性，尤其是在东巴莱区等易旱地区。本研究结合气象站数据与卫星反演的植被和热力指数，探讨了30年（1993—2024年）间农业干旱的时空格局。为评估气象干旱，计算了3个月、6个月和12个月尺度的标准化降水指数（SPI）。在农业与热胁迫分析方面，采用了基于遥感的植被状态指数（VCI）、温度状态指数和植被健康指数（VHI）。结果表明，2003年、2008年和2018年经历了最严重的干旱，各指数均一致反映了这一情况。空间分析显示，中部、南部和东部低地地区干旱发生频率最高、程度最严重，而西北部高地则相对具有较强恢复力。跨指数相关性分析表明，SPI与VCI之间（R²=0.77）以及VHI与VCI之间（R²=0.83）具有高度一致性，支持了综合干旱监测方法的有效性。本研究通过提高时空分辨率并在次区域尺度上验证跨指数关系，推进了干旱评估领域的发展。研究结果为地方性干旱预警系统、气候适应策略以及未来研究提供了重要见解，有助于在气候变化条件下提升农业韧性。因此，农业干旱风险制图为干旱监测决策及其对农业生产影响的减缓提供了有价值的工具。",null,"Agrosystems Geosciences & Environment","2026-10-11T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,21,17,13,9,1,"基于30年气象与遥感指数交叉验证的农业干旱时空制图研究，方法扎实、结论可靠，但属区域性案例，对国内三农信息化仅有方法借鉴价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"粮食安全","防灾减灾","遥感监测","植被指数","农业干旱",{"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",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,61,67,74],{"code":33,"score":52,"supported":53,"title":54,"abstract":56,"tags":60},18,true,[55],"drought",[57,58,59,55],"气候变化","气候适应","干旱",[28],{"code":40,"score":17,"supported":53,"title":62,"abstract":63,"tags":66},[],[64,65],"遥感","remote sensing",[30,29],{"code":68,"score":69,"supported":53,"title":70,"abstract":71,"tags":73},"A8",7,[],[27,72],"food security",[27],{"code":75,"score":76,"supported":53,"title":77,"abstract":78,"tags":81},"I4",6,[],[79,80],"空间分析","spatial analysis",[],[83,84],"埃塞俄比亚 East Bale 农业干旱","SPI VCI 干旱监测","埃塞俄比亚EastBale农业干旱-4747",0,"10.1002\u002Fagg2.70475",{"doi":87,"openalex_id":89,"authors":90,"venue":10,"cited_by_count":86,"oa_url":6,"card":96,"direction":100,"ingested_from":102},"W7221455809",[91,93],{"name":92,"orcid":9},"Murad Hedo Haji",{"name":94,"orcid":95},"Karuturi Venkata Suryabhagavan","https:\u002F\u002Forcid.org\u002F0000-0003-2528-9106",{"tldr":97,"method":98,"finding":99,"direction":100,"opportunity":101},"结合气象站与卫星植被热指数，绘制埃塞俄比亚East Bale区30年农业干旱时空图。","用SPI（3\u002F6\u002F12月）与遥感VCI、TCI、VHI指数做交叉相关分析。","2003、2008、2018年干旱最重，中南部和东部低地最脆弱，SPI与VCI相关性R²=0.77。","农业遥感与作物表型","可在小区域尺度融合多源指数构建干旱预警模型，并验证指数在不同作物区的适用性。","openalex","2026-10-12T23:30:24.342375Z",{"total":76,"page":22,"page_size":76,"items":105},[106,208,265,327,395,481],{"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":120,"tags":122,"primary_topic":125,"secondary_topics":126,"classification":133,"search_phrases":182,"slug":185,"view_count":186,"doi":187,"paper":188,"created_at":207},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",81,{"impact":52,"substance":116,"depth":117,"authority":118,"freshness":17,"relevant":22,"comment":119},22,19,14,"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。",[121],{"name":112,"url":109},[123,27,124,29,31],"智慧农业","机器学习",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[127,128],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":129,"slug":130,"name":131,"path":132,"parent_code":44},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":46,"locked":14,"version":47,"method":48,"evidence":134},{"candidates":135,"agricultural_evidence":53},[136,141,145,153,157,164,171,177],{"code":40,"score":137,"supported":53,"title":138,"abstract":139,"tags":140},15,[65],[64,65],[29],{"code":33,"score":118,"supported":53,"title":142,"abstract":143,"tags":144},[55],[59,55],[],{"code":129,"score":13,"supported":53,"title":146,"abstract":147,"tags":152},[],[148,124,149,150,151],"深度学习","machine learning","deep learning","artificial intelligence",[124],{"code":68,"score":69,"supported":53,"title":154,"abstract":155,"tags":156},[],[27,72],[27],{"code":158,"score":76,"supported":53,"title":159,"abstract":160,"tags":163},"H2",[],[161,162],"土壤","soil",[],{"code":165,"score":166,"supported":53,"title":167,"abstract":168,"tags":170},"D",3,[],[169],"水产",[],{"code":172,"score":166,"supported":53,"title":173,"abstract":174,"tags":176},"H6",[],[175],"农业生态",[],{"code":178,"score":22,"supported":14,"title":179,"abstract":180,"tags":181},"I1",[],[],[123],[183,184],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575",2,"10.1080\u002F15481603.2026.2738981",{"doi":187,"openalex_id":189,"authors":190,"venue":112,"cited_by_count":86,"oa_url":200,"card":201,"direction":206,"ingested_from":102},"W7221081979",[191,194,197],{"name":192,"orcid":193},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":195,"orcid":196},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":198,"orcid":199},"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":202,"method":203,"finding":204,"direction":100,"opportunity":205},"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。","智慧农业 \u002F 农业物联网","2026-10-10T23:30:14.933078Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":9,"source_name":214,"source_url":211,"published_at":215,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":86,"score_detail":216,"sources":218,"tags":220,"primary_topic":223,"secondary_topics":224,"classification":230,"search_phrases":247,"slug":250,"view_count":86,"doi":251,"paper":252,"created_at":264},5326,"Flood Hazard and Vulnerability Assessment of Chitrakoot District, Uttar Pradesh: An Integrated Geographical, GIS and Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.89708","Flood is one of the most important hydro-meteorological hazards affecting human settlements, agriculture, infrastructure and regional economies. Chitrakoot district of Uttar Pradesh presents a distinctive case of hydro-climatic vulnerability because it belongs to the drought-prone Bundelkhand region but simultaneously experiences recurrent riverine and flash-flood conditions during intense monsoon rainfall. The present study assesses the spatial characteristics and determinants of flood hazard and vulnerability in Chitrakoot district using an integrated geographical, GIS and remote sensing approach. The analysis incorporates the Digital Elevation Model (DEM), drainage network, physiography, river proximity, historical flood information, satellite-based flood observations and socio-economic exposure. The district's DEM indicates a marked elevation gradient, approximately from 66 m to 451 m, with relatively higher terrain in the southern part and lower terrain towards the northern Yamuna valley. This topographic configuration facilitates rapid surface runoff from elevated southern terrain towards comparatively low-lying riverine areas. The drainage system is dominated by the Yamuna and Mandakini\u002FPaisuni systems along with Baghain, Gunta, Bardaha and smaller tributaries. The Uttar Pradesh Flood Hazard Atlas identifies 5,114 ha of flood-hazard area in Chitrakoot for 1998–2020, of which 4,129 ha (81%) falls in the very-low hazard category and 985 ha (19%) in the low-hazard category. However, the historical classification does not fully represent the potential consequences of extreme rainfall events. The August 2026 flood provides compelling evidence: the Mandakini at Chitrakoot reached a reported new high flood level of 145.60 m on 29 August 2026, approximately 2.95 m above the previous reported high level, while the water level rose by about 7.5 m within six hours. The study concludes that flood risk in Chitrakoot is spatially concentrated around river corridors, low-elevation areas, floodplains, agricultural land, settlements and tourism infrastructure. A shift from conventional relief-oriented flood management towards integrated floodplain regulation, watershed management, GIS-based risk zonation, real-time monitoring and community-based preparedness is therefore essential.","洪水是影响人类住区、农业、基础设施和区域经济的最重要的水文气象灾害之一。北方邦吉德古德（Chitrakoot）县在水文气候脆弱性方面呈现出独特案例，因为它属于易受干旱影响的本德尔坎德（Bundelkhand）地区，但同时在强季风降雨期间反复遭遇河流洪水和山洪状况。本研究采用地理学、地理信息系统（GIS）和遥感相结合的综合方法，评估吉德古德县洪水灾害与脆弱性的空间特征及其决定因素。分析纳入了数字高程模型（DEM）、河网、地貌、距河流远近、历史洪水信息、基于卫星的洪水观测以及社会经济暴露度。该县的DEM显示出明显的高程梯度，大约从66米到451米，南部地形相对较高，向北部的亚穆纳（Yamuna）河谷地势降低。这种地形格局促使地表径流从较高的南部地形快速流向相对低洼的河流区域。排水系统以亚穆纳河和曼达基尼\u002F派苏尼（Mandakini\u002FPaisuni）水系为主，另有巴盖恩（Baghain）、贡塔（Gunta）、巴尔达哈（Bardaha）及较小支流。北方邦洪水灾害图集确认，1998—2020年吉德古德县洪水灾害面积为5，114公顷，其中4，129公顷（81%）属于极低灾害类别，985公顷（19%）属于低灾害类别。然而，历史分类并不能充分反映极端降雨事件的潜在后果。2026年8月的洪水提供了有力证据：据报道，2026年8月29日吉德古德的曼达基尼河达到145.60米的新高洪水位，比此前报告的高水位高出约2.95米，而水位在六小时内上升了约7.5米。研究得出结论，吉德古德的洪水风险在空间上集中于河流廊道、低海拔地区、洪泛平原、农业用地、住区和旅游基础设施周围。因此，必须从传统的以救济为导向的洪水管理转向洪泛平原综合调控、流域管理、基于GIS的风险区划、实时监测和社区为基础的备灾。","International Journal For Multidisciplinary Research","2026-10-17T00:00:00Z",{"impact":86,"substance":86,"depth":86,"authority":86,"freshness":86,"relevant":86,"comment":217},"该研究聚焦印度北方邦的洪水灾害评估，虽涉及GIS与遥感方法，但与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[219],{"name":214,"url":211},[28,29,221,222],"洪水风险评估","GIS应用",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[225,229],{"code":75,"slug":226,"name":227,"path":228,"parent_code":44},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":231},{"candidates":232,"agricultural_evidence":53},[233,237,243],{"code":40,"score":137,"supported":53,"title":234,"abstract":235,"tags":236},[65],[64,65],[29],{"code":75,"score":118,"supported":53,"title":238,"abstract":240,"tags":242},[239],"GIS",[241,239],"地理信息",[],{"code":33,"score":17,"supported":53,"title":244,"abstract":245,"tags":246},[],[59,55],[221,28],[248,249],"Chitrakoot 洪水风险 GIS","Yamuna 洪水脆弱性 遥感","Chitrakoot洪水风险GIS-5326","10.36948\u002Fijfmr.2026.v08i05.89708",{"doi":251,"openalex_id":253,"authors":254,"venue":214,"cited_by_count":86,"oa_url":258,"card":259,"direction":100,"ingested_from":102},"W7222885988",[255],{"name":256,"orcid":257},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":260,"method":261,"finding":262,"direction":100,"opportunity":263},"用GIS与遥感综合评估Chitrakoot区洪水灾害与脆弱性空间分布。","DEM、河网、历史洪水与卫星观测及社会经济数据叠加分析。","洪水风险集中于河谷低地、农田与居民点，历史分级低估极端降雨风险。","可结合时序遥感与水文模型，构建极端降雨情景下的农田洪涝风险动态制图与预警。","2026-10-19T23:30:24.356743Z",{"id":266,"title":267,"url":268,"summary":269,"summary_zh":270,"content":9,"source_name":271,"source_url":268,"published_at":272,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":273,"score_detail":274,"sources":277,"tags":279,"primary_topic":283,"secondary_topics":284,"classification":286,"search_phrases":298,"slug":301,"view_count":86,"doi":302,"paper":303,"created_at":326},4826,"Multi-Index Remote Sensing Assessment of Agro-Climatic Change and Agricultural Responses in Temperate and Semi-Arid Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18203496","Diverse climatic regions exhibit unique agro-climatic vulnerabilities that are significantly shaped by climate stress. Understanding these distinct vulnerabilities is essential for developing effective policies to mitigate climate change impacts and ensuring sustainable environmental management. This study examines agro-climatic vulnerabilities in Nottinghamshire County (United Kingdom) and Udaipur District (India) from 2000 to 2024, comparing climate change impacts across temperate and semi-arid agricultural systems that represent fundamentally different system behaviours under contrasting climatic conditions. Using multi-temporal Landsat imagery and WorldClim climate data, nine environmental indices were analysed: the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Normalized Difference Water Index (NDWI), Moisture Stress Index (MSI), Land Surface Temperature (LST), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Drought Risk Index (DRI), and Crop Health Index (CHI). The results reveal contrasting regional trajectories that challenge conventional assumptions about climate vulnerability. Udaipur exhibited improved vegetation and moisture conditions across the observation period, whereas Nottinghamshire showed intensifying moisture stress and declining precipitation. Warming was more pronounced in Nottinghamshire, while strong inter-index correlations validated the monitoring approach. These findings indicate that Udaipur benefits from intensified monsoon patterns, requiring flood mitigation and water-storage infrastructure, whereas Nottinghamshire’s rising moisture stress underscores the need for sustained monitoring and adaptive management. This comparative framework provides evidence-based insights for region-specific adaptation strategies essential to sustainable agricultural development across diverse agro-climatic zones.","不同气候区域表现出独特的农业气候脆弱性，而这些脆弱性在很大程度上受到气候胁迫的影响。理解这些不同的脆弱性对于制定有效政策以减缓气候变化影响、确保可持续环境管理至关重要。本研究考察了2000年至2024年间英国诺丁汉郡与印度乌代布尔县的农业气候脆弱性，比较了温带与半干旱农业系统在气候变化影响下的差异，这两类系统在截然不同的气候条件下代表了根本不同的系统行为。研究利用多时相Landsat影像和WorldClim气候数据，分析了九个环境指数：归一化植被指数（NDVI）、植被状态指数（VCI）、归一化水体指数（NDWI）、水分胁迫指数（MSI）、地表温度（LST）、温度状态指数（TCI）、植被健康指数（VHI）、干旱风险指数（DRI）和作物健康指数（CHI）。结果揭示了截然不同的区域变化轨迹，挑战了关于气候脆弱性的传统假设。乌代布尔在观测期内表现出植被和水分条件的改善，而诺丁汉郡则显示出加剧的水分胁迫和降水减少。诺丁汉郡的增温更为显著，而指数间较强的相关性验证了该监测方法。这些发现表明，乌代布尔受益于季风模式的增强，需要防洪和水储存基础设施，而诺丁汉郡不断上升的水分胁迫则凸显了持续监测和适应性管理的必要性。这一比较框架为因地制宜的适应策略提供了循证依据，对跨不同农业气候区的可持续发展至关重要。","Remote Sensing","2026-10-12T00:00:00Z",75,{"impact":275,"substance":116,"depth":52,"authority":118,"freshness":21,"relevant":22,"comment":276},12,"基于多时相遥感与九项环境指数的跨区域对比研究，方法扎实、结论具政策参考价值，但属学术论文，产业影响有限。",[278],{"name":271,"url":268},[29,30,280,281,282],"干旱风险","适应策略","农业气候",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[285],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":287},{"candidates":288,"agricultural_evidence":53},[289,293],{"code":40,"score":13,"supported":53,"title":290,"abstract":291,"tags":292},[65],[],[30,29],{"code":33,"score":21,"supported":53,"title":294,"abstract":295,"tags":297},[],[57,59,296,55],"climate change",[],[299,300],"Landsat 遥感 农业气候","诺丁汉郡 乌代布尔","Landsat遥感农业气候-4826","10.3390\u002Frs18203496",{"doi":302,"openalex_id":304,"authors":305,"venue":271,"cited_by_count":86,"oa_url":268,"card":321,"direction":100,"ingested_from":102},"W7221570035",[306,309,312,315,318],{"name":307,"orcid":308},"Bhanwar Vishvendra Raj Singh","https:\u002F\u002Forcid.org\u002F0000-0001-7750-5240",{"name":310,"orcid":311},"Vivek Agarwal","https:\u002F\u002Forcid.org\u002F0000-0003-1185-3802",{"name":313,"orcid":314},"Stuart Marsh","https:\u002F\u002Forcid.org\u002F0000-0002-1168-6760",{"name":316,"orcid":317},"Gajendra Singh Sisodiya","https:\u002F\u002Forcid.org\u002F0009-0006-4118-7491",{"name":319,"orcid":320},"Ajay Sharma","https:\u002F\u002Forcid.org\u002F0009-0001-6077-9875",{"tldr":322,"method":323,"finding":324,"direction":100,"opportunity":325},"用多时相遥感和九项指数对比英国诺丁汉郡与印度乌代浦2000—2024年农业气候脆弱性。","Landsat多时相影像与WorldClim气候数据，计算NDVI、VCI、ND","乌代浦植被与水分条件改善、季风增强，诺丁汉郡水分胁迫加剧、降水下降且升温更明显。","可拓展多区域长时序遥感指数耦合机器学习，构建跨气候带农业气候脆弱性预警与适应决策模型。","2026-10-13T23:30:24.062929Z",{"id":328,"title":329,"url":330,"summary":331,"summary_zh":332,"content":9,"source_name":271,"source_url":330,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":273,"score_detail":333,"sources":335,"tags":337,"primary_topic":341,"secondary_topics":342,"classification":349,"search_phrases":375,"slug":378,"view_count":22,"doi":379,"paper":380,"created_at":394},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。",{"impact":275,"substance":116,"depth":52,"authority":118,"freshness":21,"relevant":22,"comment":334},"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。",[336],{"name":271,"url":330},[27,338,29,339,340],"农业气象","西非农业","干旱指数",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[343,344],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":345,"slug":346,"name":347,"path":348,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":46,"locked":14,"version":47,"method":48,"evidence":350},{"candidates":351,"agricultural_evidence":53},[352,357,361,367,371],{"code":33,"score":353,"supported":53,"title":354,"abstract":355,"tags":356},16,[55],[59,55],[338,340],{"code":40,"score":137,"supported":53,"title":358,"abstract":359,"tags":360},[65],[64,65],[29],{"code":345,"score":118,"supported":53,"title":362,"abstract":364,"tags":366},[363],"evapotranspiration",[365,363],"水资源",[],{"code":158,"score":76,"supported":53,"title":368,"abstract":369,"tags":370},[],[161,162],[],{"code":68,"score":22,"supported":14,"title":372,"abstract":373,"tags":374},[],[],[27],[376,377],"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":379,"openalex_id":381,"authors":382,"venue":271,"cited_by_count":86,"oa_url":330,"card":389,"direction":100,"ingested_from":102},"W7220963372",[383,386],{"name":384,"orcid":385},"Abdoul-Aziz Bio Sidi D. Bouko","https:\u002F\u002Forcid.org\u002F0009-0003-1179-8046",{"name":387,"orcid":388},"Bing Gao","https:\u002F\u002Forcid.org\u002F0000-0003-0782-8858",{"tldr":390,"method":391,"finding":392,"direction":100,"opportunity":393},"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":396,"title":397,"url":398,"summary":399,"summary_zh":400,"content":9,"source_name":401,"source_url":398,"published_at":402,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":403,"score_detail":404,"sources":406,"tags":408,"primary_topic":411,"secondary_topics":412,"classification":414,"search_phrases":445,"slug":448,"view_count":166,"doi":449,"paper":450,"created_at":480},3843,"A multi-scale remote sensing approach for assessing drought impacts on rainfed and irrigated cereal yields in Lleida, Spain","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110826","This study evaluates the performance of agricultural drought indices at provincial and field scales, as well as their ability to explain the variability of wheat and barley yields under rainfed and irrigated conditions. The analysis was conducted in the Lleida region (northeastern Spain) from 2010 to 2024 using high-resolution (30 m) multi-source remote sensing data and yield information from 5030 cereal fields. Five drought indices representing vegetative stress, thermal stress, soil moisture conditions, and their combination were evaluated: the Vegetative Condition Index (VCI), the Thermal Condition Index (TCI), the Soil Moisture Condition Index (SMCI), the Vegetative Health Index (VHI), and the Combined Drought Anomaly Index (CDAI). The analysis of drought index–yield relationships reveal a strong dependence on crop type, phenological stage, spatial scale, and water management practices. Barley exhibits greater sensitivity to drought than wheat and responds earlier in the growing season. Correlations are generally weak during early growth stages but become much stronger during the reproductive and maturation phases. Using high spatial resolution data (30 m) significantly enhances the performance of drought indices. Under rainfed conditions, most indices perform well except for the TCI, with the CDAI standing out as the most effective, achieving correlation values of r = 0.85 (p \u003C 0.01) at the provincial scale and a median of r = 0.52, with a maximum of r = 0.95 (p \u003C 0.01) at the field scale. In contrast, drought index–yield correlations in irrigated areas are consistently lower, highlighting the buffering role of irrigation in mitigating drought impacts. In irrigated fields across the study area, yield variability is primarily driven by heat stress rather than water or vegetation stress. Overall, composite drought indices such as the CDAI are particularly well suited for monitoring agricultural drought in rainfed conditions, whereas indices specifically designed for irrigated agriculture remain essential.","本研究在省域和田块尺度上评估了农业干旱指数的表现，以及其在雨养和灌溉条件下解释小麦和大麦产量变异的能力。分析于2010—2024年在西班牙东北部莱里达地区开展，使用了高分辨率（30 m）多源遥感数据和5030块谷类田块的产量信息。研究评估了五个代表植被胁迫、热胁迫、土壤水分状况及其组合的干旱指数：植被状态指数（VCI）、热状态指数（TCI）、土壤水分状态指数（SMCI）、植被健康指数（VHI）和综合干旱异常指数（CDAI）。对干旱指数与产量关系的分析表明，其强烈依赖于作物类型、物候阶段、空间尺度及水分管理方式。大麦对干旱的敏感性高于小麦，且在生长季中响应更早。在生长早期阶段相关性普遍较弱，但在生殖和成熟阶段显著增强。使用高空间分辨率数据（30 m）可显著提升干旱指数的表现。在雨养条件下，除TCI外，大多数指数表现良好，其中CDAI最为有效，在省域尺度上相关系数达到r = 0.85（p \u003C 0.01），在田块尺度上中位数为r = 0.52，最大值为r = 0.95（p \u003C 0.01）。相比之下，灌溉区域的干旱指数与产量相关性 consistently 较低，凸显了灌溉在缓解干旱影响中的缓冲作用。在研究区的灌溉田块中，产量变异主要由热胁迫驱动，而非水分或植被胁迫。总体而言，诸如CDAI之类的复合干旱指数特别适用于监测雨养条件下的农业干旱，而专门为灌溉农业设计的指数仍然不可或缺。","Agricultural Water Management","2026-09-29T00:00:00Z",79,{"impact":353,"substance":116,"depth":52,"authority":118,"freshness":21,"relevant":22,"comment":405},"基于30米高分辨率遥感与5030块田块数据，量化五种干旱指数对雨养与灌溉谷物产量的解释力，方法扎实、结论对农业干旱监测与灌溉管理有参考价值。",[407],{"name":401,"url":398},[123,409,410,29,31],"作物产量","精准灌溉",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[413],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":415},{"candidates":416,"agricultural_evidence":53},[417,421,425,430,437,441],{"code":40,"score":353,"supported":53,"title":418,"abstract":419,"tags":420},[65],[64,65],[409,29],{"code":33,"score":118,"supported":53,"title":422,"abstract":423,"tags":424},[55],[59,55],[],{"code":345,"score":69,"supported":53,"title":426,"abstract":427,"tags":429},[],[428,346],"灌溉",[410],{"code":431,"score":76,"supported":53,"title":432,"abstract":433,"tags":436},"A2",[],[434,435],"小麦","wheat",[],{"code":158,"score":76,"supported":53,"title":438,"abstract":439,"tags":440},[],[161,162],[],{"code":178,"score":22,"supported":14,"title":442,"abstract":443,"tags":444},[],[],[123],[446,447],"Lleida 干旱 遥感 谷物产量","CDAI 干旱指数 灌溉","Lleida干旱遥感谷物产量-3843","10.1016\u002Fj.agwat.2026.110826",{"doi":449,"openalex_id":451,"authors":452,"venue":401,"cited_by_count":86,"oa_url":398,"card":475,"direction":100,"ingested_from":102},"W7214788171",[453,456,459,462,464,467,470,472],{"name":454,"orcid":455},"Youness Ablila","https:\u002F\u002Forcid.org\u002F0009-0006-3081-5990",{"name":457,"orcid":458},"Maria‐José Escorihuela","https:\u002F\u002Forcid.org\u002F0000-0002-7780-7334",{"name":460,"orcid":461},"Abdelhakim Amazirh","https:\u002F\u002Forcid.org\u002F0000-0002-6665-3843",{"name":463,"orcid":9},"Jose Antonio Martinez Casasnovas",{"name":465,"orcid":466},"El Houssaine Bouras","https:\u002F\u002Forcid.org\u002F0000-0002-6973-6644",{"name":468,"orcid":469},"Saïd Khabba","https:\u002F\u002Forcid.org\u002F0000-0003-3309-9935",{"name":471,"orcid":9},"Zaineb Bouswir",{"name":473,"orcid":474},"Salah Er‐Raki","https:\u002F\u002Forcid.org\u002F0000-0002-8595-7949",{"tldr":476,"method":477,"finding":478,"direction":100,"opportunity":479},"评估多尺度遥感干旱指数对西班牙Lleida地区雨养与灌溉谷物产量的解释能力。","2010-2024年30米多源遥感数据与5030块田产量，比较VCI、TCI、S","CDAI在雨养区表现最佳（省域r=0.85），灌溉区相关性低且产量主要受热胁迫驱动。","可开发针对灌溉农业的专用干旱指数，并融合热胁迫与灌溉管理数据提升产量预测。","2026-09-30T23:30:29.241563Z",{"id":482,"title":483,"url":484,"summary":485,"summary_zh":9,"content":9,"source_name":486,"source_url":484,"published_at":487,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":488,"score_detail":489,"sources":491,"tags":493,"primary_topic":495,"secondary_topics":496,"classification":498,"search_phrases":514,"slug":517,"view_count":166,"doi":518,"paper":519,"created_at":536},3329,"Nonlinear threshold effects of waterlogging stress on crop productivity across China: A remote sensing-based macro-assessment","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104985","Nonlinear threshold effects of waterlogging stress on crop productivity across China: A remote sensing-based macro-assessment。Agricultural Systems","Agricultural Systems","2026-09-23T00:00:00Z",84,{"impact":116,"substance":18,"depth":52,"authority":118,"freshness":21,"relevant":22,"comment":490},"基于遥感开展全国尺度涝渍胁迫非线性阈值评估，方法新颖、结论具宏观参考价值，值得进入每日精选。",[492],{"name":486,"url":484},[27,338,409,29,494],"涝渍胁迫",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[497],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":499},{"candidates":500,"agricultural_evidence":53},[501,505,510],{"code":40,"score":20,"supported":53,"title":502,"abstract":503,"tags":504},[65],[65],[409,29],{"code":33,"score":275,"supported":53,"title":506,"abstract":508,"tags":509},[507],"waterlogging",[507],[338],{"code":68,"score":22,"supported":14,"title":511,"abstract":512,"tags":513},[],[],[27],[515,516],"全国 涝渍胁迫 作物产量 遥感","作物产量 农业气象 涝渍胁迫 粮食安全","全国涝渍胁迫作物产量遥感-3329","10.1016\u002Fj.agsy.2026.104985",{"doi":518,"openalex_id":520,"authors":521,"venue":486,"cited_by_count":86,"oa_url":484,"card":9,"direction":100,"ingested_from":102},"W7214084384",[522,524,526,528,530,532,534],{"name":523,"orcid":9},"Yi Yang",{"name":525,"orcid":9},"Shuyang Wu",{"name":527,"orcid":9},"Tianqi Ge",{"name":529,"orcid":9},"Weimin Jin",{"name":531,"orcid":9},"Hanqing Wu",{"name":533,"orcid":9},"Bofu Zheng",{"name":535,"orcid":9},"Wei Wan","2026-09-24T23:30:04.275048Z"]