[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2674":3,"related-2674":106},{"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":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":105},2674,"Bi-decadal drought assessment in Northwestern Algeria: Integrating meteorological and remote sensing indices","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00704-026-06526-y","Abstract Drought is an escalating hazard in arid and semi‑arid regions with significant implications for agriculture, ecosystems, and water resources. This study presents a 2003–2023 integrated assessment of meteorological and vegetation-based drought across northwest Algeria, using the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset and Moderate Resolution Imaging Spectroradiometer (MODIS) remotely‑sensed products. Meteorological drought was quantified with the Standardized Precipitation Index (SPI) approximated using standardized precipitation anomalies, at 3‑, 6‑, and 12‑month timescales, whereas vegetation and thermal stress were assessed with MODIS‑derived Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Temporal trends were evaluated using the Mann–Kendall test and Sen’s slope estimator, and relationships between precipitation and vegetation were examined with Pearson correlation. We identified recurrent drought episodes in 2007–2009, 2011–2012, and a pronounced dry phase from 2020–2023. Mann–Kendall results indicated widespread drying across all SPI timescales, with 56% of the study area showing significant negative trends at SPI‑3 (mean Sen’s slope = − 0.10 yr⁻ 1 ). Vegetation indices mirrored these changes, with VHI showing substantially more degraded area than improvement (4.89% vs 0.51% of the domain), while VCI and TCI responses were spatially heterogeneous. The correlation analysis showed moderate but statistically significant relationships between SPI and VHI at the short-term scale (SPI-3 vs. VHI, r = 0.567, p = 0.007), followed by SPI-6 ( r = 0.509, p = 0.019), indicating that vegetation response is more strongly associated with short- to medium-term precipitation variability. These results demonstrate the value of combining meteorological and satellite vegetation indices for regional drought monitoring and early warning, and underscore an ongoing shift toward increased aridity with implications for water management and agricultural adaptation.","摘要 干旱是干旱和半干旱地区日益加剧的灾害，对农业、生态系统和水资源具有重大影响。本研究利用气候灾害组红外降水与站点数据（CHIRPS）数据集和中分辨率成像光谱仪（MODIS）遥感产品，对阿尔及利亚西北部2003—2023年气象干旱和基于植被的干旱进行了综合评估。气象干旱采用标准化降水指数（SPI）量化，该指数通过标准化降水距平在3个月、6个月和12个月时间尺度上近似计算；植被和热胁迫则通过MODIS衍生的植被状态指数（VCI）、温度状态指数（TCI）和植被健康指数（VHI）进行评估。采用Mann–Kendall检验和Sen斜率估计器评估时间趋势，并使用Pearson相关分析检验降水与植被之间的关系。我们识别出2007—2009年、2011—2012年的反复干旱事件，以及2020—2023年显著的干旱阶段。Mann–Kendall结果表明，所有SPI时间尺度上均呈现大范围干旱化趋势，研究区56%的面积在SPI-3上表现出显著负趋势（Sen斜率均值 = −0.10 yr⁻¹）。植被指数反映了这些变化，VHI显示退化面积远大于改善面积（分别占研究区的4.89%和0.51%），而VCI和TCI的响应在空间上具有异质性。相关分析表明，SPI与VHI在短期尺度上存在中等但统计显著的关系（SPI-3与VHI，r = 0.567，p = 0.007），其次为SPI-6（r = 0.509，p = 0.019），表明植被响应与短期至中期降水变率的关系更为密切。这些结果证明了结合气象指标和卫星植被指数进行区域干旱监测与预警的价值，并凸显了干旱化持续加剧的趋势及其对水资源管理和农业适应的影响。",null,"Theoretical and Applied Climatology","2026-09-15T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,8,1,"基于CHIRPS与MODIS的阿尔及利亚西北部20年干旱综合评估，方法整合气象与遥感指标，结论对区域干旱监测与农业适应有参考价值，但属境外区域研究，国内产业影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业气象","水资源管理","遥感监测","植被指数","干旱预警",{"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},[51,59,65],{"code":33,"score":52,"supported":53,"title":54,"abstract":56,"tags":58},16,true,[55],"drought",[57,55],"干旱",[27,31],{"code":40,"score":20,"supported":53,"title":60,"abstract":62,"tags":64},[61],"remote sensing",[63],"遥感",[30,29],{"code":66,"score":67,"supported":53,"title":68,"abstract":69,"tags":71},"H3",4,[],[70],"水资源",[28],[73,74],"水资源管理 农业气象 干旱预警 植被指数","水资源管理 农业气象","水资源管理农业气象干旱预警植被指数-2674",2,"10.1007\u002Fs00704-026-06526-y",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":97,"oa_url":6,"card":98,"direction":102,"ingested_from":104},"W7213346236",[81,84,87,90,92,95],{"name":82,"orcid":83},"Ramzi Benhizia","https:\u002F\u002Forcid.org\u002F0000-0002-8967-2051",{"name":85,"orcid":86},"Brahim Abdelkebir","https:\u002F\u002Forcid.org\u002F0000-0002-8761-8537",{"name":88,"orcid":89},"Behnam Ata","https:\u002F\u002Forcid.org\u002F0000-0002-9690-7269",{"name":91,"orcid":9},"Mukovhe Vele Singo",{"name":93,"orcid":94},"Kwanele Phinzi","https:\u002F\u002Forcid.org\u002F0000-0003-1865-7011",{"name":96,"orcid":9},"György Szabó",0,{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"整合气象与遥感指数评估阿尔及利亚西北部2003–2023年干旱演变。","CHIRPS降水与MODIS植被指数，SPI、VCI、TCI、VHI，Mann-","全区普遍变干，SPI-3显著负趋势占56%，VHI退化面积远大于改善，短期降水与植被相关性最强。","农业遥感与作物表型","可引入机器学习融合多源遥感与气象数据，构建区域干旱预警与作物适应性决策模型。","openalex","2026-09-16T23:30:30.795285Z",{"total":107,"page":22,"page_size":107,"items":108},6,[109,188,252,317,379,436],{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":9,"source_name":115,"source_url":112,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":122,"tags":124,"primary_topic":126,"secondary_topics":127,"classification":133,"search_phrases":164,"slug":167,"view_count":22,"doi":168,"paper":169,"created_at":187},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":119,"substance":18,"depth":120,"authority":20,"freshness":21,"relevant":22,"comment":121},14,18,"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。",[123],{"name":115,"url":112},[27,28,29,125,31],"Google Earth Engine",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[128,129],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":66,"slug":130,"name":131,"path":132,"parent_code":37},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":46,"locked":14,"version":47,"method":48,"evidence":134},{"candidates":135,"agricultural_evidence":53},[136,140,144,150,157],{"code":33,"score":52,"supported":53,"title":137,"abstract":138,"tags":139},[55],[57,55],[27,31],{"code":40,"score":20,"supported":53,"title":141,"abstract":142,"tags":143},[61],[63],[125,29],{"code":66,"score":13,"supported":53,"title":145,"abstract":146,"tags":149},[],[70,147,148],"water resource","evapotranspiration",[28],{"code":151,"score":107,"supported":53,"title":152,"abstract":153,"tags":156},"H2",[],[154,155],"土壤","soil",[],{"code":158,"score":159,"supported":53,"title":160,"abstract":161,"tags":163},"J1",3,[],[162],"农业规划",[],[165,166],"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":168,"openalex_id":170,"authors":171,"venue":115,"cited_by_count":97,"oa_url":112,"card":182,"direction":102,"ingested_from":104},"W7215092640",[172,175,177,179],{"name":173,"orcid":174},"Hadi Siasar","https:\u002F\u002Forcid.org\u002F0000-0002-9323-4345",{"name":176,"orcid":9},"Mahboube Ebrahimi",{"name":178,"orcid":9},"Mehdi Bahrami",{"name":180,"orcid":181},"Hossein Hamidifar","https:\u002F\u002Forcid.org\u002F0000-0001-9054-0120",{"tldr":183,"method":184,"finding":185,"direction":102,"opportunity":186},"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":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":9,"source_name":194,"source_url":191,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":196,"score_detail":197,"sources":201,"tags":203,"primary_topic":207,"secondary_topics":208,"classification":210,"search_phrases":223,"slug":226,"view_count":97,"doi":227,"paper":228,"created_at":251},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":17,"substance":198,"depth":120,"authority":119,"freshness":199,"relevant":22,"comment":200},22,9,"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。",[202],{"name":194,"url":191},[29,30,204,205,206],"干旱风险","适应策略","农业气候",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[209],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":211},{"candidates":212,"agricultural_evidence":53},[213,217],{"code":40,"score":13,"supported":53,"title":214,"abstract":215,"tags":216},[61],[],[30,29],{"code":33,"score":199,"supported":53,"title":218,"abstract":219,"tags":222},[],[220,57,221,55],"气候变化","climate change",[],[224,225],"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":227,"openalex_id":229,"authors":230,"venue":194,"cited_by_count":97,"oa_url":191,"card":246,"direction":102,"ingested_from":104},"W7221570035",[231,234,237,240,243],{"name":232,"orcid":233},"Bhanwar Vishvendra Raj Singh","https:\u002F\u002Forcid.org\u002F0000-0001-7750-5240",{"name":235,"orcid":236},"Vivek Agarwal","https:\u002F\u002Forcid.org\u002F0000-0003-1185-3802",{"name":238,"orcid":239},"Stuart Marsh","https:\u002F\u002Forcid.org\u002F0000-0002-1168-6760",{"name":241,"orcid":242},"Gajendra Singh Sisodiya","https:\u002F\u002Forcid.org\u002F0009-0006-4118-7491",{"name":244,"orcid":245},"Ajay Sharma","https:\u002F\u002Forcid.org\u002F0009-0001-6077-9875",{"tldr":247,"method":248,"finding":249,"direction":102,"opportunity":250},"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":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":263,"tags":265,"primary_topic":269,"secondary_topics":270,"classification":272,"search_phrases":298,"slug":301,"view_count":97,"doi":302,"paper":303,"created_at":316},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":21,"substance":18,"depth":19,"authority":20,"freshness":199,"relevant":22,"comment":262},"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。",[264],{"name":258,"url":255},[266,267,29,30,268],"粮食安全","防灾减灾","农业干旱",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[271],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":273},{"candidates":274,"agricultural_evidence":53},[275,280,284,291],{"code":33,"score":120,"supported":53,"title":276,"abstract":277,"tags":279},[55],[220,278,57,55],"气候适应",[267],{"code":40,"score":21,"supported":53,"title":281,"abstract":282,"tags":283},[],[63,61],[30,29],{"code":285,"score":286,"supported":53,"title":287,"abstract":288,"tags":290},"A8",7,[],[266,289],"food security",[266],{"code":292,"score":107,"supported":53,"title":293,"abstract":294,"tags":297},"I4",[],[295,296],"空间分析","spatial analysis",[],[299,300],"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":302,"openalex_id":304,"authors":305,"venue":258,"cited_by_count":97,"oa_url":255,"card":311,"direction":102,"ingested_from":104},"W7221455809",[306,308],{"name":307,"orcid":9},"Murad Hedo Haji",{"name":309,"orcid":310},"Karuturi Venkata Suryabhagavan","https:\u002F\u002Forcid.org\u002F0000-0003-2528-9106",{"tldr":312,"method":313,"finding":314,"direction":102,"opportunity":315},"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":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":194,"source_url":320,"published_at":323,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":196,"score_detail":324,"sources":326,"tags":328,"primary_topic":331,"secondary_topics":332,"classification":335,"search_phrases":359,"slug":362,"view_count":22,"doi":363,"paper":364,"created_at":378},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。","2026-10-08T00:00:00Z",{"impact":17,"substance":198,"depth":120,"authority":119,"freshness":199,"relevant":22,"comment":325},"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。",[327],{"name":194,"url":320},[266,27,29,329,330],"西非农业","干旱指数",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[333,334],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":66,"slug":130,"name":131,"path":132,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":336},{"candidates":337,"agricultural_evidence":53},[338,342,347,351,355],{"code":33,"score":52,"supported":53,"title":339,"abstract":340,"tags":341},[55],[57,55],[27,330],{"code":40,"score":343,"supported":53,"title":344,"abstract":345,"tags":346},15,[61],[63,61],[29],{"code":66,"score":119,"supported":53,"title":348,"abstract":349,"tags":350},[148],[70,148],[],{"code":151,"score":107,"supported":53,"title":352,"abstract":353,"tags":354},[],[154,155],[],{"code":285,"score":22,"supported":14,"title":356,"abstract":357,"tags":358},[],[],[266],[360,361],"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":363,"openalex_id":365,"authors":366,"venue":194,"cited_by_count":97,"oa_url":320,"card":373,"direction":102,"ingested_from":104},"W7220963372",[367,370],{"name":368,"orcid":369},"Abdoul-Aziz Bio Sidi D. Bouko","https:\u002F\u002Forcid.org\u002F0009-0003-1179-8046",{"name":371,"orcid":372},"Bing Gao","https:\u002F\u002Forcid.org\u002F0000-0003-0782-8858",{"tldr":374,"method":375,"finding":376,"direction":102,"opportunity":377},"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":380,"title":381,"url":382,"summary":383,"summary_zh":9,"content":9,"source_name":384,"source_url":382,"published_at":385,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":386,"score_detail":387,"sources":389,"tags":391,"primary_topic":394,"secondary_topics":395,"classification":397,"search_phrases":413,"slug":416,"view_count":159,"doi":417,"paper":418,"created_at":435},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":198,"substance":18,"depth":120,"authority":119,"freshness":199,"relevant":22,"comment":388},"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。",[390],{"name":384,"url":382},[266,27,392,29,393],"作物产量","涝渍胁迫",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[396],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":398},{"candidates":399,"agricultural_evidence":53},[400,404,409],{"code":40,"score":20,"supported":53,"title":401,"abstract":402,"tags":403},[61],[61],[392,29],{"code":33,"score":17,"supported":53,"title":405,"abstract":407,"tags":408},[406],"waterlogging",[406],[27],{"code":285,"score":22,"supported":14,"title":410,"abstract":411,"tags":412},[],[],[266],[414,415],"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":417,"openalex_id":419,"authors":420,"venue":384,"cited_by_count":97,"oa_url":382,"card":9,"direction":102,"ingested_from":104},"W7214084384",[421,423,425,427,429,431,433],{"name":422,"orcid":9},"Yi Yang",{"name":424,"orcid":9},"Shuyang Wu",{"name":426,"orcid":9},"Tianqi Ge",{"name":428,"orcid":9},"Weimin Jin",{"name":430,"orcid":9},"Hanqing Wu",{"name":432,"orcid":9},"Bofu Zheng",{"name":434,"orcid":9},"Wei Wan","2026-09-24T23:30:04.275048Z",{"id":437,"title":438,"url":439,"summary":440,"summary_zh":441,"content":9,"source_name":442,"source_url":439,"published_at":443,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":97,"score_detail":444,"sources":446,"tags":448,"primary_topic":451,"secondary_topics":452,"classification":458,"search_phrases":475,"slug":478,"view_count":97,"doi":479,"paper":480,"created_at":492},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":97,"substance":97,"depth":97,"authority":97,"freshness":97,"relevant":97,"comment":445},"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。",[447],{"name":442,"url":439},[267,29,449,450],"洪水风险评估","GIS应用",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[453,457],{"code":292,"slug":454,"name":455,"path":456,"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":459},{"candidates":460,"agricultural_evidence":53},[461,465,471],{"code":40,"score":343,"supported":53,"title":462,"abstract":463,"tags":464},[61],[63,61],[29],{"code":292,"score":119,"supported":53,"title":466,"abstract":468,"tags":470},[467],"GIS",[469,467],"地理信息",[],{"code":33,"score":21,"supported":53,"title":472,"abstract":473,"tags":474},[],[57,55],[449,267],[476,477],"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":479,"openalex_id":481,"authors":482,"venue":442,"cited_by_count":97,"oa_url":486,"card":487,"direction":102,"ingested_from":104},"W7222885988",[483],{"name":484,"orcid":485},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":488,"method":489,"finding":490,"direction":102,"opportunity":491},"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"]