[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3888":3,"related-3888":117},{"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":87,"slug":90,"view_count":70,"doi":91,"paper":92,"created_at":116},3888,"Spatiotemporal shifts in composite weather stress across US croplands","https:\u002F\u002Fdoi.org\u002F10.1088\u002F1748-9326\u002Faead01","Abstract Weather stressors influence crop production, farm management, and long-term agricultural suitability across the United States. These stressors vary across agricultural regions. While numerous weather indices exist, a generalized metric tailored to a region’s specific agricultural context remains lacking. Here, we introduce a multi-stressor framework that integrates 32 weather-based stress indicators representing compound drought, flood, extreme heat, cold, and thermal accumulation events across the Contiguous United States from 1980 to 2023. The framework applies an Agro-Ecological Zones (AEZ)-specific weighted averaging scheme that minimizes index redundancy while assigning region-specific importance to individual stressors. We then use a spatiotemporal analog tracking approach to characterize shifts in weather stress relative to a historical baseline period. This approach produces two composite metrics - weather stress divergence and weather stress similarity - quantifying deviations of current weather stress from, and spatial correspondence with, the historical baseline, respectively. To evaluate relevance, we examine their association with county-scale detrended yield anomalies for corn, soybeans, and wheat. Yield anomalies are negatively associated with weather stress divergence, with higher values indicating greater departures from historical conditions, and positively associated with weather stress similarity in a subset of counties. Across CONUS croplands, more than 50% experienced increasing weather stress divergence, primarily driven by drought and extreme heat, while weather stress similarity declined across many croplands. The most pronounced spatial analog shifts are concentrated in eastern agricultural regions, highlighting a growing loss of correspondence between current weather regimes and historical baselines. Although technological advances and adaptive management have contributed to long-term yield improvements, compound weather stress metrics explain measurable components of interannual yield variability. The proposed framework provides a systematic, region-specific approach to benchmark compound weather stress exposure and support agricultural risk assessment and planning from farm to national scales.","天气胁迫因子影响着美国各地的作物生产、农场管理及长期农业适宜性。这些胁迫因子在不同农业区域间存在差异。尽管已有众多天气指数，但仍缺乏一种针对特定区域农业情境的通用度量指标。在此，我们提出一个多胁迫因子框架，整合了1980年至2023年美国本土48州（Contiguous United States, CONUS）代表复合干旱、洪水、极端高温、低温及热量累积事件的32项天气胁迫指标。该框架采用农业生态区（Agro-Ecological Zones, AEZ）特定的加权平均方案，在最小化指标冗余的同时赋予各胁迫因子区域特定的重要性。随后，我们采用时空类比追踪方法（spatiotemporal analog tracking approach）刻画天气胁迫相对于历史基准期的变化。该方法产生两个综合指标——天气胁迫偏离度（weather stress divergence）和天气胁迫相似度（weather stress similarity），分别量化当前天气胁迫相对于历史基准的偏离程度及其与历史基准的空间对应关系。为评估其相关性，我们考察了这两个指标与县级尺度玉米、大豆和小麦去趋势产量异常之间的关联。产量异常与天气胁迫偏离度呈负相关（偏离度越高表明与历史条件的差异越大），在部分县中与天气胁迫相似度呈正相关。在美国本土48州的农田中，超过50%的区域经历了天气胁迫偏离度的增加，主要由干旱和极端高温驱动，而许多农田的天气胁迫相似度下降。最显著的空间类比偏移集中在中东部农业区，凸显出当前天气状况与历史基准之间的对应关系日益丧失。尽管技术进步和适应性管理促进了长期产量提升，复合天气胁迫指标仍能解释年际产量变异性中可测量的部分。所提出的框架提供了一种系统性、区域特定的方法来基准评估复合天气胁迫暴露程度，并支持从农场到国家尺度的农业风险评估与规划。",null,"Environmental Research Letters","2026-09-28T00:00:00Z","论文",10,true,86,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,19,14,8,1,"该研究构建了覆盖美国本土1980-2023年32项天气胁迫指标的多胁迫框架，方法新颖、数据规模大，对农业风险区划与气候适应性规划具有参考价值，但属美国区域研究，对国内三农信息化直接指导意义有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业气象","作物产量","遥感监测","复合天气胁迫","农业风险评估",{"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":41,"version":42,"method":43,"evidence":44},"classified",false,"2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":14},[46,53,61,68,75,81],{"code":33,"score":47,"supported":14,"title":48,"abstract":49,"tags":52},7,[],[50,51],"干旱","drought",[27],{"code":54,"score":55,"supported":14,"title":56,"abstract":57,"tags":60},"A2",6,[],[58,59],"小麦","wheat",[],{"code":62,"score":55,"supported":14,"title":63,"abstract":64,"tags":67},"A3",[],[65,66],"玉米","corn",[],{"code":69,"score":70,"supported":14,"title":71,"abstract":72,"tags":74},"A4",3,[],[73],"大豆",[],{"code":76,"score":70,"supported":14,"title":77,"abstract":78,"tags":80},"H6",[],[79],"农业生态",[],{"code":82,"score":83,"supported":41,"title":84,"abstract":85,"tags":86},"I3",2,[],[],[28,29],[88,89],"美国农田 复合天气胁迫","玉米 大豆 小麦 产量异常","美国农田复合天气胁迫-3888","10.1088\u002F1748-9326\u002Faead01",{"doi":91,"openalex_id":93,"authors":94,"venue":10,"cited_by_count":106,"oa_url":107,"card":108,"direction":114,"ingested_from":115},"W7161382464",[95,98,101,104],{"name":96,"orcid":97},"Srishti Vishwakarma","https:\u002F\u002Forcid.org\u002F0000-0003-1674-4197",{"name":99,"orcid":100},"Moetasim Ashfaq","https:\u002F\u002Forcid.org\u002F0000-0003-4106-3027",{"name":102,"orcid":103},"Rebecca Ann Efroymson","https:\u002F\u002Forcid.org\u002F0000-0002-3190-880X",{"name":105,"orcid":9},"Peter E. Thornton",0,"https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F1748-9326\u002Faead01\u002Fpdf",{"tldr":109,"method":110,"finding":111,"direction":112,"opportunity":113},"构建多胁迫天气指标框架，量化1980-2023年美国农田复合天气胁迫的时空偏移。","整合32个天气胁迫指标，按农业生态区加权，用时空类比追踪对比历史基线。","超50%农田天气胁迫偏离度上升，主要由干旱和极端高温驱动，与玉米大豆小麦产量异常相关。","农业遥感与作物表型","可将该复合胁迫框架与作物模型或遥感表型结合，评估区域适应能力与品种布局优化。","智慧农业 \u002F 农业物联网","openalex","2026-10-01T23:30:12.444553Z",{"total":55,"page":22,"page_size":55,"items":118},[119,187,248,323,394,477],{"id":120,"title":121,"url":122,"summary":123,"summary_zh":9,"content":9,"source_name":124,"source_url":122,"published_at":125,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":126,"score_detail":127,"sources":132,"tags":134,"primary_topic":137,"secondary_topics":142,"classification":144,"search_phrases":164,"slug":167,"view_count":70,"doi":168,"paper":169,"created_at":186},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":17,"substance":128,"depth":129,"authority":20,"freshness":130,"relevant":22,"comment":131},21,18,9,"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。",[133],{"name":124,"url":122},[135,27,28,29,136],"粮食安全","涝渍胁迫",{"code":82,"slug":138,"name":139,"path":140,"parent_code":141},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[143],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":145},{"candidates":146,"agricultural_evidence":14},[147,153,159],{"code":82,"score":148,"supported":14,"title":149,"abstract":151,"tags":152},13,[150],"remote sensing",[150],[28,29],{"code":33,"score":154,"supported":14,"title":155,"abstract":157,"tags":158},12,[156],"waterlogging",[156],[27],{"code":160,"score":22,"supported":41,"title":161,"abstract":162,"tags":163},"A8",[],[],[135],[165,166],"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":168,"openalex_id":170,"authors":171,"venue":124,"cited_by_count":106,"oa_url":122,"card":9,"direction":112,"ingested_from":115},"W7214084384",[172,174,176,178,180,182,184],{"name":173,"orcid":9},"Yi Yang",{"name":175,"orcid":9},"Shuyang Wu",{"name":177,"orcid":9},"Tianqi Ge",{"name":179,"orcid":9},"Weimin Jin",{"name":181,"orcid":9},"Hanqing Wu",{"name":183,"orcid":9},"Bofu Zheng",{"name":185,"orcid":9},"Wei Wan","2026-09-24T23:30:04.275048Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":9,"source_name":193,"source_url":190,"published_at":194,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":195,"score_detail":196,"sources":200,"tags":202,"primary_topic":206,"secondary_topics":207,"classification":208,"search_phrases":229,"slug":232,"view_count":22,"doi":233,"paper":234,"created_at":247},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。","Hydrometeorological research and forecasting","2026-10-15T00:00:00Z",70,{"impact":154,"substance":197,"depth":198,"authority":148,"freshness":21,"relevant":22,"comment":199},20,17,"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。",[201],{"name":193,"url":190},[203,27,29,204,205],"智慧农业","冬小麦","作物估产",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":209},{"candidates":210,"agricultural_evidence":14},[211,216,220,224],{"code":33,"score":212,"supported":14,"title":213,"abstract":214,"tags":215},4,[],[27],[27],{"code":82,"score":83,"supported":41,"title":217,"abstract":218,"tags":219},[],[],[205,29],{"code":54,"score":22,"supported":41,"title":221,"abstract":222,"tags":223},[],[],[204],{"code":225,"score":22,"supported":41,"title":226,"abstract":227,"tags":228},"I1",[],[],[203],[230,231],"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":233,"openalex_id":235,"authors":236,"venue":193,"cited_by_count":106,"oa_url":190,"card":242,"direction":112,"ingested_from":115},"W7221991562",[237,240],{"name":238,"orcid":239},"A. D. Kleshchenko","https:\u002F\u002Forcid.org\u002F0000-0002-4200-6668",{"name":241,"orcid":9},"O. V. Savitskaya",{"tldr":243,"method":244,"finding":245,"direction":112,"opportunity":246},"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。","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。","2026-10-16T23:30:26.817010Z",{"id":249,"title":250,"url":251,"summary":252,"summary_zh":253,"content":9,"source_name":254,"source_url":251,"published_at":255,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":256,"score_detail":257,"sources":259,"tags":261,"primary_topic":264,"secondary_topics":265,"classification":272,"search_phrases":303,"slug":306,"view_count":22,"doi":307,"paper":308,"created_at":322},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","2026-10-08T00:00:00Z",75,{"impact":154,"substance":17,"depth":129,"authority":20,"freshness":130,"relevant":22,"comment":258},"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。",[260],{"name":254,"url":251},[135,27,29,262,263],"西非农业","干旱指数",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[266,267],{"code":82,"slug":138,"name":139,"path":140,"parent_code":141},{"code":268,"slug":269,"name":270,"path":271,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":40,"locked":41,"version":42,"method":43,"evidence":273},{"candidates":274,"agricultural_evidence":14},[275,280,286,292,299],{"code":33,"score":276,"supported":14,"title":277,"abstract":278,"tags":279},16,[51],[50,51],[27,263],{"code":82,"score":281,"supported":14,"title":282,"abstract":283,"tags":285},15,[150],[284,150],"遥感",[29],{"code":268,"score":20,"supported":14,"title":287,"abstract":289,"tags":291},[288],"evapotranspiration",[290,288],"水资源",[],{"code":293,"score":55,"supported":14,"title":294,"abstract":295,"tags":298},"H2",[],[296,297],"土壤","soil",[],{"code":160,"score":22,"supported":41,"title":300,"abstract":301,"tags":302},[],[],[135],[304,305],"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":307,"openalex_id":309,"authors":310,"venue":254,"cited_by_count":106,"oa_url":251,"card":317,"direction":112,"ingested_from":115},"W7220963372",[311,314],{"name":312,"orcid":313},"Abdoul-Aziz Bio Sidi D. Bouko","https:\u002F\u002Forcid.org\u002F0009-0003-1179-8046",{"name":315,"orcid":316},"Bing Gao","https:\u002F\u002Forcid.org\u002F0000-0003-0782-8858",{"tldr":318,"method":319,"finding":320,"direction":112,"opportunity":321},"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":324,"title":325,"url":326,"summary":327,"summary_zh":328,"content":9,"source_name":329,"source_url":326,"published_at":330,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":331,"score_detail":332,"sources":334,"tags":336,"primary_topic":340,"secondary_topics":341,"classification":344,"search_phrases":370,"slug":373,"view_count":22,"doi":374,"paper":375,"created_at":393},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":20,"substance":128,"depth":129,"authority":148,"freshness":21,"relevant":22,"comment":333},"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。",[335],{"name":329,"url":326},[27,337,29,338,339],"水资源管理","Google Earth Engine","干旱预警",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[342,343],{"code":82,"slug":138,"name":139,"path":140,"parent_code":141},{"code":268,"slug":269,"name":270,"path":271,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":345},{"candidates":346,"agricultural_evidence":14},[347,351,355,360,364],{"code":33,"score":276,"supported":14,"title":348,"abstract":349,"tags":350},[51],[50,51],[27,339],{"code":82,"score":148,"supported":14,"title":352,"abstract":353,"tags":354},[150],[284],[338,29],{"code":268,"score":13,"supported":14,"title":356,"abstract":357,"tags":359},[],[290,358,288],"water resource",[337],{"code":293,"score":55,"supported":14,"title":361,"abstract":362,"tags":363},[],[296,297],[],{"code":365,"score":70,"supported":14,"title":366,"abstract":367,"tags":369},"J1",[],[368],"农业规划",[],[371,372],"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":374,"openalex_id":376,"authors":377,"venue":329,"cited_by_count":106,"oa_url":326,"card":388,"direction":112,"ingested_from":115},"W7215092640",[378,381,383,385],{"name":379,"orcid":380},"Hadi Siasar","https:\u002F\u002Forcid.org\u002F0000-0002-9323-4345",{"name":382,"orcid":9},"Mahboube Ebrahimi",{"name":384,"orcid":9},"Mehdi Bahrami",{"name":386,"orcid":387},"Hossein Hamidifar","https:\u002F\u002Forcid.org\u002F0000-0001-9054-0120",{"tldr":389,"method":390,"finding":391,"direction":112,"opportunity":392},"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",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":9,"source_name":400,"source_url":397,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":402,"score_detail":403,"sources":405,"tags":407,"primary_topic":410,"secondary_topics":411,"classification":412,"search_phrases":436,"slug":439,"view_count":47,"doi":440,"paper":441,"created_at":476},3919,"Meteorological to agricultural drought propagation and vegetation response across China","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ejrh.2026.104022","Study region This study covers mainland China, spanning the Eastern Monsoon Region, the Northwest Arid and Semi-Arid Region, and the Qinghai-Tibet Alpine Region. Study focus Meteorological drought does not translate linearly into agricultural drought, and lagged vegetation responses remain poorly constrained. We combined Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), satellite solar-induced chlorophyll fluorescence (SIF), and drought diagnostics based on the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSMI) to quantify meteorological-to-agricultural drought propagation, drought duration and severity, and vegetation response memory across China. We examined long-term trends and propagation lags, evaluated cumulative and delayed vegetation responses using correlation and wavelet coherence analyses, and used machine-learning attribution to identify the dominant controls on vegetation variability. New hydrological insights for the region Agricultural drought lasts longer and is more severe than the meteorological drought that triggers it, with a duration approximately one month longer, reflecting the drought amplification effect within the soil-vegetation system. Drought propagation time varies significantly across China, with a median lag of 3–5 months, and the longest lags occur in inland semi-arid regions. Vegetation exhibits a pronounced drought memory effect: NDVI and SIF responses lag approximately 5 months behind drought onset. Compared with NDVI, SIF is more sensitive to short-term water stress. In most parts of China, temperature and evapotranspiration dominate vegetation dynamics, while hydrological factors exert a stronger influence in the humid southeastern regions.","研究区域 本研究涵盖中国大陆，横跨东部季风区、西北干旱半干旱区以及青藏高寒区。研究重点 气象干旱并非线性转化为农业干旱，植被的滞后响应仍缺乏充分约束。我们结合中分辨率成像光谱仪（MODIS）归一化植被指数（NDVI）、卫星太阳诱导叶绿素荧光（SIF），以及基于标准化降水蒸散指数（SPEI）和标准化土壤湿度指数（SSMI）的干旱诊断，量化了中国气象干旱向农业干旱的传播过程、干旱历时与严重程度，以及植被响应记忆。我们检验了长期趋势与传播滞后，利用相关分析和小波相干分析评估了植被的累积响应与延迟响应，并采用机器学习归因方法识别植被变异的主导控制因子。该区域的新水文学认识 农业干旱比触发它的气象干旱持续时间更长、程度更严重，历时约长一个月，反映了土壤—植被系统内的干旱放大效应。干旱传播时间在中国各地差异显著，中位滞后为3—5个月，最长滞后出现在内陆半干旱区。植被表现出显著的干旱记忆效应：NDVI和SIF的响应滞后于干旱发生约5个月。与NDVI相比，SIF对短期水分胁迫更为敏感。在中国大部分地区，温度和蒸散主导植被动态，而在湿润的东南部地区，水文因素的影响更强。","Journal of Hydrology Regional Studies","2026-09-29T00:00:00Z",85,{"impact":17,"substance":18,"depth":129,"authority":20,"freshness":21,"relevant":22,"comment":404},"基于多源遥感与机器学习量化全国气象到农业干旱传导及植被滞后响应，方法新颖、结论可靠，对农业旱情监测预警有直接参考价值。",[406],{"name":400,"url":397},[408,27,29,339,409],"机器学习","植被响应",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":413},{"candidates":414,"agricultural_evidence":14},[415,419,423,428,432],{"code":33,"score":276,"supported":14,"title":416,"abstract":417,"tags":418},[51],[50,51],[27,339],{"code":293,"score":55,"supported":14,"title":420,"abstract":421,"tags":422},[],[296,297],[],{"code":424,"score":212,"supported":14,"title":425,"abstract":426,"tags":427},"I2",[],[408],[408],{"code":268,"score":70,"supported":14,"title":429,"abstract":430,"tags":431},[],[288],[],{"code":82,"score":22,"supported":41,"title":433,"abstract":434,"tags":435},[],[],[29],[437,438],"MODIS NDVI SIF 干旱","SPEI SSMI 农业干旱","MODISNDVISIF干旱-3919","10.1016\u002Fj.ejrh.2026.104022",{"doi":440,"openalex_id":442,"authors":443,"venue":400,"cited_by_count":106,"oa_url":397,"card":470,"direction":475,"ingested_from":115},"W7214733729",[444,447,450,452,455,458,460,462,464,466,468],{"name":445,"orcid":446},"Dong Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6924-0748",{"name":448,"orcid":449},"Zihan Ma","https:\u002F\u002Forcid.org\u002F0009-0009-2854-2622",{"name":451,"orcid":9},"Liangliang Zhang",{"name":453,"orcid":454},"Nan Sun","https:\u002F\u002Forcid.org\u002F0009-0008-4472-3573",{"name":456,"orcid":457},"Muhammad Abrar Faiz","https:\u002F\u002Forcid.org\u002F0000-0003-0626-0308",{"name":459,"orcid":9},"Mo Li",{"name":461,"orcid":9},"Xiaochen Qi",{"name":463,"orcid":9},"Tianxiao Li",{"name":465,"orcid":9},"Song Cui",{"name":467,"orcid":9},"Zhaoqiang Zhou",{"name":469,"orcid":9},"Muhammad Imran Khan",{"tldr":471,"method":472,"finding":473,"direction":112,"opportunity":474},"量化中国气象干旱向农业干旱的传播及植被滞后响应。","用MODIS NDVI、SIF、SPEI、SSMI及机器学习归因分析。","农业干旱比气象干旱长约1个月，植被响应滞后约5个月，SIF更敏感。","可探索不同气候区干旱传播阈值与SIF早期预警指标，构建区域化干旱记忆模型。","农业人工智能与决策模型","2026-10-01T23:30:55.666679Z",{"id":478,"title":479,"url":480,"summary":481,"summary_zh":482,"content":9,"source_name":483,"source_url":480,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":484,"score_detail":485,"sources":487,"tags":489,"primary_topic":492,"secondary_topics":493,"classification":495,"search_phrases":523,"slug":526,"view_count":70,"doi":527,"paper":528,"created_at":558},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",79,{"impact":276,"substance":17,"depth":129,"authority":20,"freshness":130,"relevant":22,"comment":486},"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。",[488],{"name":483,"url":480},[203,28,490,29,491],"精准灌溉","农业干旱",{"code":82,"slug":138,"name":139,"path":140,"parent_code":141},[494],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":496},{"candidates":497,"agricultural_evidence":14},[498,502,506,511,515,519],{"code":82,"score":276,"supported":14,"title":499,"abstract":500,"tags":501},[150],[284,150],[28,29],{"code":33,"score":20,"supported":14,"title":503,"abstract":504,"tags":505},[51],[50,51],[],{"code":268,"score":47,"supported":14,"title":507,"abstract":508,"tags":510},[],[509,269],"灌溉",[490],{"code":54,"score":55,"supported":14,"title":512,"abstract":513,"tags":514},[],[58,59],[],{"code":293,"score":55,"supported":14,"title":516,"abstract":517,"tags":518},[],[296,297],[],{"code":225,"score":22,"supported":41,"title":520,"abstract":521,"tags":522},[],[],[203],[524,525],"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":527,"openalex_id":529,"authors":530,"venue":483,"cited_by_count":106,"oa_url":480,"card":553,"direction":112,"ingested_from":115},"W7214788171",[531,534,537,540,542,545,548,550],{"name":532,"orcid":533},"Youness Ablila","https:\u002F\u002Forcid.org\u002F0009-0006-3081-5990",{"name":535,"orcid":536},"Maria‐José Escorihuela","https:\u002F\u002Forcid.org\u002F0000-0002-7780-7334",{"name":538,"orcid":539},"Abdelhakim Amazirh","https:\u002F\u002Forcid.org\u002F0000-0002-6665-3843",{"name":541,"orcid":9},"Jose Antonio Martinez Casasnovas",{"name":543,"orcid":544},"El Houssaine Bouras","https:\u002F\u002Forcid.org\u002F0000-0002-6973-6644",{"name":546,"orcid":547},"Saïd Khabba","https:\u002F\u002Forcid.org\u002F0000-0003-3309-9935",{"name":549,"orcid":9},"Zaineb Bouswir",{"name":551,"orcid":552},"Salah Er‐Raki","https:\u002F\u002Forcid.org\u002F0000-0002-8595-7949",{"tldr":554,"method":555,"finding":556,"direction":112,"opportunity":557},"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"]