[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4309":3,"related-4309":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":39,"search_phrases":79,"slug":82,"view_count":22,"doi":83,"paper":84,"created_at":103},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。",null,"Climate Services","2026-10-05T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","粮食安全","遥感监测","农业干旱","干旱指数",{"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},[45,53,61,69,74],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":52},15,true,[49],"drought",[51,49],"干旱",[31],{"code":54,"score":55,"supported":47,"title":56,"abstract":57,"tags":60},"I3",7,[],[58,59],"遥感","remote sensing",[29],{"code":62,"score":63,"supported":47,"title":64,"abstract":65,"tags":68},"H2",6,[],[66,67],"土壤","soil",[],{"code":70,"score":22,"supported":14,"title":71,"abstract":72,"tags":73},"A8",[],[],[28],{"code":75,"score":22,"supported":14,"title":76,"abstract":77,"tags":78},"I1",[],[],[27],[80,81],"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":83,"openalex_id":85,"authors":86,"venue":10,"cited_by_count":94,"oa_url":95,"card":96,"direction":100,"ingested_from":102},"W7220385252",[87,89,91],{"name":88,"orcid":9},"Bekele Alemayehu",{"name":90,"orcid":9},"Zenebe Ayele",{"name":92,"orcid":93},"Hamere Yohannes","https:\u002F\u002Forcid.org\u002F0000-0002-6899-5150",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2405880726001056\u002Fpdf",{"tldr":97,"method":98,"finding":99,"direction":100,"opportunity":101},"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%。","农业遥感与作物表型","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。","openalex","2026-10-06T23:30:20.023409Z",{"total":63,"page":22,"page_size":63,"items":105},[106,209,272,341,427,494],{"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":122,"tags":124,"primary_topic":126,"secondary_topics":131,"classification":138,"search_phrases":183,"slug":186,"view_count":187,"doi":188,"paper":189,"created_at":208},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":116,"substance":117,"depth":118,"authority":119,"freshness":120,"relevant":22,"comment":121},18,22,19,14,8,"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。",[123],{"name":112,"url":109},[27,28,125,29,30],"机器学习",{"code":54,"slug":127,"name":128,"path":129,"parent_code":130},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[132,133],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":134,"slug":135,"name":136,"path":137,"parent_code":130},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":40,"locked":14,"version":41,"method":42,"evidence":139},{"candidates":140,"agricultural_evidence":47},[141,145,149,157,162,166,173,179],{"code":54,"score":46,"supported":47,"title":142,"abstract":143,"tags":144},[59],[58,59],[29],{"code":33,"score":119,"supported":47,"title":146,"abstract":147,"tags":148},[49],[51,49],[],{"code":134,"score":13,"supported":47,"title":150,"abstract":151,"tags":156},[],[152,125,153,154,155],"深度学习","machine learning","deep learning","artificial intelligence",[125],{"code":70,"score":55,"supported":47,"title":158,"abstract":159,"tags":161},[],[28,160],"food security",[28],{"code":62,"score":63,"supported":47,"title":163,"abstract":164,"tags":165},[],[66,67],[],{"code":167,"score":168,"supported":47,"title":169,"abstract":170,"tags":172},"D",3,[],[171],"水产",[],{"code":174,"score":168,"supported":47,"title":175,"abstract":176,"tags":178},"H6",[],[177],"农业生态",[],{"code":75,"score":22,"supported":14,"title":180,"abstract":181,"tags":182},[],[],[27],[184,185],"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":188,"openalex_id":190,"authors":191,"venue":112,"cited_by_count":94,"oa_url":201,"card":202,"direction":207,"ingested_from":102},"W7221081979",[192,195,198],{"name":193,"orcid":194},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":196,"orcid":197},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":199,"orcid":200},"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":203,"method":204,"finding":205,"direction":100,"opportunity":206},"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":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":221,"tags":223,"primary_topic":226,"secondary_topics":227,"classification":229,"search_phrases":253,"slug":256,"view_count":94,"doi":257,"paper":258,"created_at":271},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。","Agrosystems Geosciences & Environment","2026-10-11T00:00:00Z",68,{"impact":120,"substance":219,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":220},21,"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。",[222],{"name":215,"url":212},[28,224,29,225,30],"防灾减灾","植被指数",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[228],{"code":54,"slug":127,"name":128,"path":129,"parent_code":130},{"status":40,"locked":14,"version":41,"method":42,"evidence":230},{"candidates":231,"agricultural_evidence":47},[232,238,242,246],{"code":33,"score":116,"supported":47,"title":233,"abstract":234,"tags":237},[49],[235,236,51,49],"气候变化","气候适应",[224],{"code":54,"score":120,"supported":47,"title":239,"abstract":240,"tags":241},[],[58,59],[225,29],{"code":70,"score":55,"supported":47,"title":243,"abstract":244,"tags":245},[],[28,160],[28],{"code":247,"score":63,"supported":47,"title":248,"abstract":249,"tags":252},"I4",[],[250,251],"空间分析","spatial analysis",[],[254,255],"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","10.1002\u002Fagg2.70475",{"doi":257,"openalex_id":259,"authors":260,"venue":215,"cited_by_count":94,"oa_url":212,"card":266,"direction":100,"ingested_from":102},"W7221455809",[261,263],{"name":262,"orcid":9},"Murad Hedo Haji",{"name":264,"orcid":265},"Karuturi Venkata Suryabhagavan","https:\u002F\u002Forcid.org\u002F0000-0003-2528-9106",{"tldr":267,"method":268,"finding":269,"direction":100,"opportunity":270},"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。","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。","2026-10-12T23:30:24.342375Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":9,"source_name":278,"source_url":275,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":279,"score_detail":280,"sources":282,"tags":284,"primary_topic":287,"secondary_topics":288,"classification":295,"search_phrases":321,"slug":324,"view_count":22,"doi":325,"paper":326,"created_at":340},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":117,"depth":116,"authority":119,"freshness":21,"relevant":22,"comment":281},"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。",[283],{"name":278,"url":275},[28,285,29,286,31],"农业气象","西非农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[289,290],{"code":54,"slug":127,"name":128,"path":129,"parent_code":130},{"code":291,"slug":292,"name":293,"path":294,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":40,"locked":14,"version":41,"method":42,"evidence":296},{"candidates":297,"agricultural_evidence":47},[298,303,307,313,317],{"code":33,"score":299,"supported":47,"title":300,"abstract":301,"tags":302},16,[49],[51,49],[285,31],{"code":54,"score":46,"supported":47,"title":304,"abstract":305,"tags":306},[59],[58,59],[29],{"code":291,"score":119,"supported":47,"title":308,"abstract":310,"tags":312},[309],"evapotranspiration",[311,309],"水资源",[],{"code":62,"score":63,"supported":47,"title":314,"abstract":315,"tags":316},[],[66,67],[],{"code":70,"score":22,"supported":14,"title":318,"abstract":319,"tags":320},[],[],[28],[322,323],"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":325,"openalex_id":327,"authors":328,"venue":278,"cited_by_count":94,"oa_url":275,"card":335,"direction":100,"ingested_from":102},"W7220963372",[329,332],{"name":330,"orcid":331},"Abdoul-Aziz Bio Sidi D. Bouko","https:\u002F\u002Forcid.org\u002F0009-0003-1179-8046",{"name":333,"orcid":334},"Bing Gao","https:\u002F\u002Forcid.org\u002F0000-0003-0782-8858",{"tldr":336,"method":337,"finding":338,"direction":100,"opportunity":339},"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":342,"title":343,"url":344,"summary":345,"summary_zh":346,"content":9,"source_name":347,"source_url":344,"published_at":348,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":349,"score_detail":350,"sources":352,"tags":354,"primary_topic":357,"secondary_topics":358,"classification":360,"search_phrases":391,"slug":394,"view_count":168,"doi":395,"paper":396,"created_at":426},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":299,"substance":117,"depth":116,"authority":119,"freshness":21,"relevant":22,"comment":351},"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。",[353],{"name":347,"url":344},[27,355,356,29,30],"作物产量","精准灌溉",{"code":54,"slug":127,"name":128,"path":129,"parent_code":130},[359],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":361},{"candidates":362,"agricultural_evidence":47},[363,367,371,376,383,387],{"code":54,"score":299,"supported":47,"title":364,"abstract":365,"tags":366},[59],[58,59],[355,29],{"code":33,"score":119,"supported":47,"title":368,"abstract":369,"tags":370},[49],[51,49],[],{"code":291,"score":55,"supported":47,"title":372,"abstract":373,"tags":375},[],[374,292],"灌溉",[356],{"code":377,"score":63,"supported":47,"title":378,"abstract":379,"tags":382},"A2",[],[380,381],"小麦","wheat",[],{"code":62,"score":63,"supported":47,"title":384,"abstract":385,"tags":386},[],[66,67],[],{"code":75,"score":22,"supported":14,"title":388,"abstract":389,"tags":390},[],[],[27],[392,393],"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":395,"openalex_id":397,"authors":398,"venue":347,"cited_by_count":94,"oa_url":344,"card":421,"direction":100,"ingested_from":102},"W7214788171",[399,402,405,408,410,413,416,418],{"name":400,"orcid":401},"Youness Ablila","https:\u002F\u002Forcid.org\u002F0009-0006-3081-5990",{"name":403,"orcid":404},"Maria‐José Escorihuela","https:\u002F\u002Forcid.org\u002F0000-0002-7780-7334",{"name":406,"orcid":407},"Abdelhakim Amazirh","https:\u002F\u002Forcid.org\u002F0000-0002-6665-3843",{"name":409,"orcid":9},"Jose Antonio Martinez Casasnovas",{"name":411,"orcid":412},"El Houssaine Bouras","https:\u002F\u002Forcid.org\u002F0000-0002-6973-6644",{"name":414,"orcid":415},"Saïd Khabba","https:\u002F\u002Forcid.org\u002F0000-0003-3309-9935",{"name":417,"orcid":9},"Zaineb Bouswir",{"name":419,"orcid":420},"Salah Er‐Raki","https:\u002F\u002Forcid.org\u002F0000-0002-8595-7949",{"tldr":422,"method":423,"finding":424,"direction":100,"opportunity":425},"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":428,"title":429,"url":430,"summary":431,"summary_zh":432,"content":9,"source_name":433,"source_url":430,"published_at":434,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":435,"score_detail":436,"sources":438,"tags":440,"primary_topic":442,"secondary_topics":443,"classification":445,"search_phrases":473,"slug":476,"view_count":22,"doi":477,"paper":478,"created_at":493},3772,"Integrating Vegetation Health Indices and Machine Learning for Early Prediction of Agricultural Drought","https:\u002F\u002Fdoi.org\u002F10.1080\u002F02626667.2026.2740781","Agricultural drought threatens food security across Nigeria’s Sahel, yet spatially explicit machine-learning-based prediction remains limited in the region. This study developed a spatiotemporal drought monitoring and prediction framework for Yobe State (2000–2025) integrating seven remote-sensing indicators; NDVI, EVI, VCI, TCI, SMI, LST, and rainfall within Google Earth Engine at 3 km resolution. A persistent north–south drought gradient emerged across all indicators, with northern local government areas exhibiting chronic vegetation stress, soil moisture deficit, and elevated surface temperatures throughout the study period. Three machine learning models; LSTM, Random Forest, and XGBoost were trained on Vegetation Health Index-derived drought severity classes. XGBoost achieved the highest predictive performance (accuracy = 86.9%; Kappa = 0.829; RMSE = 2.26), with rainfall and vegetation condition as dominant predictors. All three models converge on the northern LGAs as the highest-risk zone by 2030, supporting targeted early warning deployment and climate-smart agricultural investment across the Sahel.","农业干旱威胁着尼日利亚萨赫勒地区的粮食安全，但该地区基于机器学习且具有空间显式特征的预测研究仍然有限。本研究在Google Earth Engine平台上，以3 km分辨率构建了约贝州（2000—2025年）的时空干旱监测与预测框架，集成了七种遥感指标：归一化植被指数（NDVI）、增强植被指数（EVI）、植被条件指数（VCI）、温度条件指数（TCI）、土壤水分指数（SMI）、地表温度（LST）和降雨量。所有指标均呈现出持续的南北干旱梯度，北部地方政府辖区在整个研究期内表现出长期的植被胁迫、土壤水分亏缺和地表温度升高。研究以植被健康指数（VHI）衍生的干旱严重程度等级为标签，训练了三种机器学习模型：长短期记忆网络（LSTM）、随机森林（Random Forest）和极端梯度提升（XGBoost）。XGBoost取得了最高的预测性能（准确率=86.9%；Kappa=0.829；RMSE=2.26），其中降雨量和植被状况为主要预测因子。三种模型一致预测，到2030年北部地方政府辖区将成为最高风险区，这为萨赫勒地区有针对性的预警部署和气候智慧型农业投资提供了依据。","Hydrological Sciences Journal","2026-09-28T00:00:00Z",80,{"impact":116,"substance":117,"depth":116,"authority":119,"freshness":120,"relevant":22,"comment":437},"基于多源遥感与机器学习构建萨赫勒地区农业干旱早期预警框架，方法扎实、结论明确，对干旱监测预警有参考价值。",[439],{"name":433,"url":430},[27,125,29,30,441],"预警系统",{"code":134,"slug":135,"name":136,"path":137,"parent_code":130},[444],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":446},{"candidates":447,"agricultural_evidence":47},[448,452,456,460,464,469],{"code":134,"score":46,"supported":47,"title":449,"abstract":450,"tags":451},[153],[125,153],[125],{"code":33,"score":119,"supported":47,"title":453,"abstract":454,"tags":455},[49],[51,49],[],{"code":70,"score":63,"supported":47,"title":457,"abstract":458,"tags":459},[],[28,160],[],{"code":62,"score":63,"supported":47,"title":461,"abstract":462,"tags":463},[],[66,67],[],{"code":54,"score":465,"supported":47,"title":466,"abstract":467,"tags":468},4,[],[58],[29],{"code":75,"score":22,"supported":14,"title":470,"abstract":471,"tags":472},[],[],[27],[474,475],"尼日利亚 萨赫勒 农业干旱","XGBoost 植被健康指数 干旱预测","尼日利亚萨赫勒农业干旱-3772","10.1080\u002F02626667.2026.2740781",{"doi":477,"openalex_id":479,"authors":480,"venue":433,"cited_by_count":94,"oa_url":9,"card":488,"direction":207,"ingested_from":102},"W7214639469",[481,484,486],{"name":482,"orcid":483},"Gbolahan S. Badru","https:\u002F\u002Forcid.org\u002F0000-0003-4020-6356",{"name":485,"orcid":9},"Shakirudeen Odunuga",{"name":487,"orcid":9},"Michael Adebisi Adeyemi",{"tldr":489,"method":490,"finding":491,"direction":100,"opportunity":492},"基于多源遥感与机器学习构建尼日利亚萨赫勒地区农业干旱早期预测框架。","GEE整合NDVI、EVI、VCI等7个指标，用LSTM、随机森林、XGBoos","XGBoost预测精度最高（86.9%），北部LGAs为2030年前最高风险区。","可探索多模型集成与迁移学习，将干旱预警框架推广至其他萨赫勒地区并耦合作物产量预测。","2026-09-29T23:30:09.236357Z",{"id":495,"title":496,"url":497,"summary":498,"summary_zh":499,"content":9,"source_name":500,"source_url":497,"published_at":501,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":435,"score_detail":502,"sources":504,"tags":506,"primary_topic":508,"secondary_topics":513,"classification":521,"search_phrases":563,"slug":566,"view_count":120,"doi":567,"paper":568,"created_at":596},3169,"Climate change and the rising threat of Macrophomina phaseolina: implications for food security, food safety, and sustainable crop health management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10725-026-01525-5","Abstract Macrophomina phaseolina is a destructive soil-borne necrotrophic fungal pathogen that causes substantial yield losses in a wide range of economically important crops worldwide. Disease severity is strongly enhanced under drought, high temperature, and salinity stress, conditions that are becoming increasingly prevalent under current climate change scenarios. This review examines the interactions between climate-driven abiotic stress, host physiological regulation, and pathogen aggressiveness, highlighting how stress-induced disruptions in hormonal signalling, reactive oxygen species homeostasis, antioxidant defence systems, and plant metabolism collectively increase susceptibility to M. phaseolina . Recent advances in understanding pathogen virulence mechanisms, plant immune responses, and resistance-associated molecular pathways are synthesised together with emerging evidence from transcriptomics, proteomics, metabolomics, and comparative genomics. The review further evaluates current management strategies, including host resistance, biological control, plant growth-promoting microorganisms, stress priming, and integrated disease management, while discussing their limitations under field conditions. Emerging technologies such as precision agriculture, remote sensing, artificial intelligence-assisted disease forecasting, and multi-omics approaches are highlighted as promising tools for improving early diagnosis, risk prediction, and climate-resilient disease management. By integrating advances in plant physiology, molecular biology, and sustainable crop protection, this review provides a comprehensive framework for understanding M. phaseolina pathogenesis under changing environmental conditions. It identifies key research priorities to improve crop resilience and safeguard global food security.","摘要 菜豆壳球孢（Macrophomina phaseolina）是一种具有破坏性的土传死体营养型真菌病原菌，在全球范围内对多种具有重要经济价值的作物造成严重产量损失。在干旱、高温和盐胁迫条件下，病害严重程度显著加剧，而这些条件在当前气候变化情景下正变得越来越普遍。本文综述了气候驱动的非生物胁迫、寄主生理调控与病原菌致病力之间的相互作用，重点阐述了胁迫诱导的激素信号传导紊乱、活性氧稳态失衡、抗氧化防御系统受损以及植物代谢改变如何共同增加对菜豆壳球孢的易感性。本文综合了病原菌毒力机制、植物免疫反应及抗性相关分子通路方面的最新研究进展，并结合转录组学、蛋白质组学、代谢组学和比较基因组学的新兴证据。本文进一步评估了当前的管理策略，包括寄主抗性、生物防治、植物促生微生物、胁迫 priming 和病害综合管理，同时讨论了这些策略在田间条件下的局限性。精准农业、遥感、人工智能辅助病害预测和多组学方法等新兴技术被重点介绍为改善早期诊断、风险预测和气候韧性病害管理的有前景的工具。通过整合植物生理学、分子生物学和可持续作物保护方面的进展，本文为理解变化环境条件下菜豆壳球孢的致病机制提供了综合框架，并确定了提高作物韧性和保障全球粮食安全的关键研究优先方向。","Plant Growth Regulation","2026-09-22T00:00:00Z",{"impact":116,"substance":18,"depth":116,"authority":119,"freshness":13,"relevant":22,"comment":503},"核心期刊综述，系统梳理气候胁迫下土传病害机制与AI遥感等智慧防控手段，对农业信息化与粮食安全主题有聚合价值。",[505],{"name":500,"url":497},[27,28,507,235,29],"植物病害",{"code":70,"slug":509,"name":510,"path":511,"parent_code":512},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[514,515],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":516,"slug":517,"name":518,"path":519,"parent_code":520},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":40,"locked":14,"version":41,"method":42,"evidence":522},{"candidates":523,"agricultural_evidence":47},[524,529,533,538,544,548,555,559],{"code":33,"score":116,"supported":47,"title":525,"abstract":527,"tags":528},[526],"climate change",[235,51,526,49],[235],{"code":70,"score":46,"supported":47,"title":530,"abstract":531,"tags":532},[160],[28,160],[28],{"code":516,"score":120,"supported":47,"title":534,"abstract":536,"tags":537},[535],"food safety",[],[],{"code":75,"score":55,"supported":47,"title":539,"abstract":540,"tags":543},[],[541,542],"精准农业","precision agriculture",[27],{"code":54,"score":55,"supported":47,"title":545,"abstract":546,"tags":547},[],[58,59],[29],{"code":549,"score":63,"supported":47,"title":550,"abstract":551,"tags":554},"F4",[],[552,553],"生物防治","crop protection",[],{"code":62,"score":168,"supported":47,"title":556,"abstract":557,"tags":558},[],[67],[],{"code":134,"score":168,"supported":47,"title":560,"abstract":561,"tags":562},[],[155],[],[564,565],"Macrophomina phaseolina 病害 防控","气候变暖 土传病害 粮食安全","Macrophominaphaseolina病害防控-3169","10.1007\u002Fs10725-026-01525-5",{"doi":567,"openalex_id":569,"authors":570,"venue":500,"cited_by_count":94,"oa_url":497,"card":590,"direction":100,"ingested_from":102},"W7213972258",[571,573,575,578,581,584,587],{"name":572,"orcid":9},"Sindiswa Khawula",{"name":574,"orcid":9},"Siyabonga Ntshalitshali",{"name":576,"orcid":577},"Arun Gokul","https:\u002F\u002Forcid.org\u002F0000-0003-1575-0632",{"name":579,"orcid":580},"Lee‐Ann Niekerk","https:\u002F\u002Forcid.org\u002F0000-0002-9788-3131",{"name":582,"orcid":583},"Ashwil Klein","https:\u002F\u002Forcid.org\u002F0000-0002-5606-886X",{"name":585,"orcid":586},"Marshall Keyster","https:\u002F\u002Forcid.org\u002F0000-0002-8718-736X",{"name":588,"orcid":589},"Mbukeni Nkomo","https:\u002F\u002Forcid.org\u002F0000-0002-7652-1588",{"tldr":591,"method":592,"finding":593,"direction":594,"opportunity":595},"综述气候变化下干旱高温盐胁迫加剧菜豆壳球孢菌病害的机制与可持续防控策略。","整合转录组、蛋白组、代谢组、比较基因组及精准农业、遥感、AI预测等技术。","气候胁迫破坏激素与ROS平衡降低作物抗性，需多组学与智能技术实现早期预警和抗性管理。","农业人工智能与决策模型","可构建融合多组学与气象遥感的AI病害预警模型，并研发胁迫 priming 与生防协同的田间方案。","2026-09-22T23:30:22.790623Z"]