[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4504":3,"related-4504":113},{"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":50,"search_phrases":90,"slug":93,"view_count":22,"doi":94,"paper":95,"created_at":112},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。",null,"Remote Sensing","2026-10-08T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,14,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"粮食安全","农业气象","遥感监测","西非农业","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",[39,44],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,64,71,77,85],{"code":33,"score":57,"supported":58,"title":59,"abstract":61,"tags":63},16,true,[60],"drought",[62,60],"干旱",[28,31],{"code":45,"score":65,"supported":58,"title":66,"abstract":68,"tags":70},15,[67],"remote sensing",[69,67],"遥感",[29],{"code":40,"score":20,"supported":58,"title":72,"abstract":74,"tags":76},[73],"evapotranspiration",[75,73],"水资源",[],{"code":78,"score":79,"supported":58,"title":80,"abstract":81,"tags":84},"H2",6,[],[82,83],"土壤","soil",[],{"code":86,"score":22,"supported":14,"title":87,"abstract":88,"tags":89},"A8",[],[],[27],[91,92],"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":94,"openalex_id":96,"authors":97,"venue":10,"cited_by_count":104,"oa_url":6,"card":105,"direction":109,"ingested_from":111},"W7220963372",[98,101],{"name":99,"orcid":100},"Abdoul-Aziz Bio Sidi D. Bouko","https:\u002F\u002Forcid.org\u002F0009-0003-1179-8046",{"name":102,"orcid":103},"Bing Gao","https:\u002F\u002Forcid.org\u002F0000-0003-0782-8858",0,{"tldr":106,"method":107,"finding":108,"direction":109,"opportunity":110},"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。","农业遥感与作物表型","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。","openalex","2026-10-09T23:30:25.838340Z",{"total":79,"page":22,"page_size":79,"items":114},[115,190,265,322,386,479],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":9,"source_name":121,"source_url":118,"published_at":122,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":123,"score_detail":124,"sources":129,"tags":131,"primary_topic":135,"secondary_topics":136,"classification":139,"search_phrases":166,"slug":169,"view_count":22,"doi":170,"paper":171,"created_at":189},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":125,"depth":19,"authority":126,"freshness":127,"relevant":22,"comment":128},21,13,8,"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。",[130],{"name":121,"url":118},[28,132,29,133,134],"水资源管理","Google Earth Engine","干旱预警",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[137,138],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":140},{"candidates":141,"agricultural_evidence":58},[142,146,150,155,159],{"code":33,"score":57,"supported":58,"title":143,"abstract":144,"tags":145},[60],[62,60],[28,134],{"code":45,"score":126,"supported":58,"title":147,"abstract":148,"tags":149},[67],[69],[133,29],{"code":40,"score":13,"supported":58,"title":151,"abstract":152,"tags":154},[],[75,153,73],"water resource",[132],{"code":78,"score":79,"supported":58,"title":156,"abstract":157,"tags":158},[],[82,83],[],{"code":160,"score":161,"supported":58,"title":162,"abstract":163,"tags":165},"J1",3,[],[164],"农业规划",[],[167,168],"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":170,"openalex_id":172,"authors":173,"venue":121,"cited_by_count":104,"oa_url":118,"card":184,"direction":109,"ingested_from":111},"W7215092640",[174,177,179,181],{"name":175,"orcid":176},"Hadi Siasar","https:\u002F\u002Forcid.org\u002F0000-0002-9323-4345",{"name":178,"orcid":9},"Mahboube Ebrahimi",{"name":180,"orcid":9},"Mehdi Bahrami",{"name":182,"orcid":183},"Hossein Hamidifar","https:\u002F\u002Forcid.org\u002F0000-0001-9054-0120",{"tldr":185,"method":186,"finding":187,"direction":109,"opportunity":188},"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":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":203,"tags":205,"primary_topic":210,"secondary_topics":211,"classification":214,"search_phrases":238,"slug":241,"view_count":242,"doi":243,"paper":244,"created_at":264},3551,"Diachronic Assessment of Water Springs in Ribat El Kheir Plateau (Morocco), under Climatic and Anthropogenic Pressure, Using Remote Sensing (1985-2025)","https:\u002F\u002Fdoi.org\u002F10.4028\u002Fp-9o0ojj","The Ribat El Kheir plateau, located in the northern part of the Tabular Middle Atlas (Morocco), is characterized by carbonate formations (Liasic dolomitic limestones) and a dense network of tectonic faults. This study conducts a diachronic assessment of water springs and their interaction with climatic conditions and anthropogenic factors related to the expansion of irrigated agriculture (farms) from 1985 to 2025. A multi-source methodological approach was adopted, combining the inventory and mapping of springs, the application of remote sensing, using Landsat and Sentinel satellite imagery, and the calculation of standard indices, including the Normalized Difference Water Index (NDWI), the Normalized Difference Vegetation Index (NDVI), and the Standardized Precipitation Index (SPI). The results indicate that a combination of geomorphological context primarily influences the spatial distribution of the 101 inventoried springs, the lithological nature of the formations (permeable dolomites), structural features, and climatic factors. However, a significant regression in both the number and flow of these springs has been observed. Concurrently, irrigated fruit agriculture has expanded remarkably over the study period. The SPI analysis further reveals a climatic trend towards increased aridity. These findings highlight the critical and synergistic pressure of climate change and intensive agriculture on water resources in this vulnerable semi-arid region.","位于摩洛哥中阿特拉斯板状山脉北部的里巴特·埃尔·海尔（Ribat El Kheir）高原，以碳酸盐岩地层（里阿斯统白云质灰岩）和密集的构造断裂网络为特征。本研究对1985年至2025年间泉水动态及其与气候条件和灌溉农业（农场）扩张相关人为因素的相互作用进行了历时性评估。研究采用多源方法学途径，结合泉水编录与制图、遥感技术应用（利用Landsat和Sentinel卫星影像）以及标准指数的计算，包括归一化差异水体指数（NDWI）、归一化差异植被指数（NDVI）和标准化降水指数（SPI）。结果表明，101处编录泉水的空间分布主要受地貌背景、地层岩性特征（渗透性白云岩）、构造特征及气候因素的综合影响。然而，观测到这些泉水的数量和流量均出现显著衰退。与此同时，灌溉果树农业在研究期内显著扩张。SPI分析进一步揭示了气候趋于干旱化的趋势。这些发现凸显了气候变化与集约化农业对这一脆弱半干旱地区水资源的临界协同压力。","International journal of engineering research in Africa","2026-09-25T00:00:00Z",67,{"impact":127,"substance":200,"depth":201,"authority":126,"freshness":21,"relevant":22,"comment":202},20,17,"基于Landsat与Sentinel遥感的40年泉水退化评估，方法扎实、数据翔实，但属区域案例研究，对国内三农实践的直接参考价值有限。",[204],{"name":196,"url":193},[206,207,29,208,209],"农业水资源","NDVI","灌溉农业","干旱化",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[212,213],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":215},{"candidates":216,"agricultural_evidence":58},[217,221,227,232],{"code":45,"score":57,"supported":58,"title":218,"abstract":219,"tags":220},[67],[69,67],[207,29],{"code":33,"score":21,"supported":58,"title":222,"abstract":223,"tags":226},[],[224,62,225],"气候变化","climate change",[],{"code":40,"score":127,"supported":58,"title":228,"abstract":229,"tags":231},[],[230,75],"灌溉",[206,208],{"code":233,"score":161,"supported":58,"title":234,"abstract":235,"tags":237},"B2",[],[236],"果树",[],[239,240],"Ribat El Kheir 泉水 遥感","摩洛哥 灌溉农业 水资源","RibatElKheir泉水遥感-3551",2,"10.4028\u002Fp-9o0ojj",{"doi":243,"openalex_id":245,"authors":246,"venue":196,"cited_by_count":104,"oa_url":9,"card":259,"direction":109,"ingested_from":111},"W7214294130",[247,249,250,253,255,256],{"name":248,"orcid":9},"Hamid Achiban",{"name":248,"orcid":9},{"name":251,"orcid":252},"Miloud Afenzar","https:\u002F\u002Forcid.org\u002F0009-0008-0839-0230",{"name":254,"orcid":9},"Hassan Achiban",{"name":254,"orcid":9},{"name":257,"orcid":258},"Nourddine El Gali","https:\u002F\u002Forcid.org\u002F0009-0001-7025-4765",{"tldr":260,"method":261,"finding":262,"direction":109,"opportunity":263},"基于遥感和指数分析，评估摩洛哥Ribat El Kheir高原1985-2025年泉水数量与流量的退","Landsat\u002FSentinel影像、NDWI\u002FNDVI\u002FSPI指数、泉水编目与","泉水数量与流量显著减少，灌溉农业扩张与气候干旱化协同加剧水资源压力。","可结合多源遥感与机器学习，量化灌溉扩张对泉水流量的贡献，并预测未来情景。","2026-09-26T23:30:29.568284Z",{"id":266,"title":267,"url":268,"summary":269,"summary_zh":9,"content":9,"source_name":270,"source_url":268,"published_at":271,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":272,"score_detail":273,"sources":275,"tags":277,"primary_topic":280,"secondary_topics":281,"classification":283,"search_phrases":299,"slug":302,"view_count":161,"doi":303,"paper":304,"created_at":321},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":18,"substance":125,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":274},"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。",[276],{"name":270,"url":268},[27,28,278,29,279],"作物产量","涝渍胁迫",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[282],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":284},{"candidates":285,"agricultural_evidence":58},[286,290,295],{"code":45,"score":126,"supported":58,"title":287,"abstract":288,"tags":289},[67],[67],[278,29],{"code":33,"score":17,"supported":58,"title":291,"abstract":293,"tags":294},[292],"waterlogging",[292],[28],{"code":86,"score":22,"supported":14,"title":296,"abstract":297,"tags":298},[],[],[27],[300,301],"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":303,"openalex_id":305,"authors":306,"venue":270,"cited_by_count":104,"oa_url":268,"card":9,"direction":109,"ingested_from":111},"W7214084384",[307,309,311,313,315,317,319],{"name":308,"orcid":9},"Yi Yang",{"name":310,"orcid":9},"Shuyang Wu",{"name":312,"orcid":9},"Tianqi Ge",{"name":314,"orcid":9},"Weimin Jin",{"name":316,"orcid":9},"Hanqing Wu",{"name":318,"orcid":9},"Bofu Zheng",{"name":320,"orcid":9},"Wei Wan","2026-09-24T23:30:04.275048Z",{"id":323,"title":324,"url":325,"summary":326,"summary_zh":327,"content":9,"source_name":328,"source_url":325,"published_at":329,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":330,"score_detail":331,"sources":333,"tags":335,"primary_topic":339,"secondary_topics":340,"classification":342,"search_phrases":367,"slug":370,"view_count":104,"doi":371,"paper":372,"created_at":385},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":127,"substance":125,"depth":201,"authority":126,"freshness":21,"relevant":22,"comment":332},"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。",[334],{"name":328,"url":325},[27,336,29,337,338],"防灾减灾","植被指数","农业干旱",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[341],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":343},{"candidates":344,"agricultural_evidence":58},[345,350,354,360],{"code":33,"score":19,"supported":58,"title":346,"abstract":347,"tags":349},[60],[224,348,62,60],"气候适应",[336],{"code":45,"score":127,"supported":58,"title":351,"abstract":352,"tags":353},[],[69,67],[337,29],{"code":86,"score":355,"supported":58,"title":356,"abstract":357,"tags":359},7,[],[27,358],"food security",[27],{"code":361,"score":79,"supported":58,"title":362,"abstract":363,"tags":366},"I4",[],[364,365],"空间分析","spatial analysis",[],[368,369],"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":371,"openalex_id":373,"authors":374,"venue":328,"cited_by_count":104,"oa_url":325,"card":380,"direction":109,"ingested_from":111},"W7221455809",[375,377],{"name":376,"orcid":9},"Murad Hedo Haji",{"name":378,"orcid":379},"Karuturi Venkata Suryabhagavan","https:\u002F\u002Forcid.org\u002F0000-0003-2528-9106",{"tldr":381,"method":382,"finding":383,"direction":109,"opportunity":384},"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":387,"title":388,"url":389,"summary":390,"summary_zh":391,"content":9,"source_name":392,"source_url":389,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":393,"score_detail":394,"sources":397,"tags":399,"primary_topic":402,"secondary_topics":403,"classification":410,"search_phrases":454,"slug":457,"view_count":242,"doi":458,"paper":459,"created_at":478},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",81,{"impact":19,"substance":18,"depth":395,"authority":20,"freshness":127,"relevant":22,"comment":396},19,"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[398],{"name":392,"url":389},[400,27,401,29,338],"智慧农业","机器学习",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[404,405],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":406,"slug":407,"name":408,"path":409,"parent_code":49},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":51,"locked":14,"version":52,"method":53,"evidence":411},{"candidates":412,"agricultural_evidence":58},[413,417,421,429,433,437,443,449],{"code":45,"score":65,"supported":58,"title":414,"abstract":415,"tags":416},[67],[69,67],[29],{"code":33,"score":20,"supported":58,"title":418,"abstract":419,"tags":420},[60],[62,60],[],{"code":406,"score":13,"supported":58,"title":422,"abstract":423,"tags":428},[],[424,401,425,426,427],"深度学习","machine learning","deep learning","artificial intelligence",[401],{"code":86,"score":355,"supported":58,"title":430,"abstract":431,"tags":432},[],[27,358],[27],{"code":78,"score":79,"supported":58,"title":434,"abstract":435,"tags":436},[],[82,83],[],{"code":438,"score":161,"supported":58,"title":439,"abstract":440,"tags":442},"D",[],[441],"水产",[],{"code":444,"score":161,"supported":58,"title":445,"abstract":446,"tags":448},"H6",[],[447],"农业生态",[],{"code":450,"score":22,"supported":14,"title":451,"abstract":452,"tags":453},"I1",[],[],[400],[455,456],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575","10.1080\u002F15481603.2026.2738981",{"doi":458,"openalex_id":460,"authors":461,"venue":392,"cited_by_count":104,"oa_url":471,"card":472,"direction":477,"ingested_from":111},"W7221081979",[462,465,468],{"name":463,"orcid":464},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":466,"orcid":467},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":469,"orcid":470},"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":473,"method":474,"finding":475,"direction":109,"opportunity":476},"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":480,"title":481,"url":482,"summary":483,"summary_zh":484,"content":9,"source_name":485,"source_url":482,"published_at":486,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":487,"score_detail":488,"sources":491,"tags":493,"primary_topic":496,"secondary_topics":497,"classification":499,"search_phrases":524,"slug":527,"view_count":104,"doi":528,"paper":529,"created_at":568},4313,"A semi-supervised multi-sensor remote sensing pipeline for mapping irrigation dynamics in ground-truth deficient conflict zones: a case study of the Kakhovka Dam failure","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1920951","The ongoing war in Ukraine has drawn international attention to the impact of conflict on global agricultural production and food security. On 6 June 2023, the Kakhovka Dam was destroyed. The loss of this critical agricultural and energy infrastructure has left most farmers on the East bank of the Dnipro River without irrigation in Kherson and Zaporizhzhia oblasts. This study develops a multi-sensor Earth Observation framework combining high resolution satellite imagery (Sentinel-2, Landsat) with meteorological data (CHIRPS) and water productivity data (FAO WaPOR), using a semi-supervised machine learning pipeline. Using a rigorous stratified random-sampling approach with photointerpretation, we found pre-war irrigated areas between 2019 to 2021 to be on average 187.94 ± 14.43 kHa, approximately 4% of pre-war baseline cropland and 8% of summer season cropland in Kherson and Zaporizhzhia oblasts. In 2022, these numbers contracted to 140.04 ± 17.44 kHa with the expanded occupation of these territories by Russia, seeing an additional decline in 2023 to 74.97 ± 17.84 kHa. Fields in 2023 also exhibited variability in irrigation, ranging from interrupted irrigation owing to the destruction of the Kakhovka reservoir, to those that were sufficiently irrigated with water remaining in the channels. Irrigated areas diminished further in 2024 to 15.95 ± 9.97 kHa, a ∼91% decline from pre-war averages. Based on inspection, formerly irrigated fields before 2023 were either systematically abandoned or converted to winter cropland with lower input requirements by 2024–a pattern that continued into 2025 (total irrigated area 5.76 ± 0.87 kHa). This study demonstrates the power of advanced EO workflows to precisely quantify agricultural transformation and infrastructural failure in ground-truth deficient, high-risk environments.","乌克兰持续不断的战争使国际社会关注冲突对全球农业生产和粮食安全的影响。2023年6月6日，卡霍夫卡大坝被毁。这一关键农业和能源基础设施的丧失，使第聂伯河（Dnipro River）东岸赫尔松州和扎波罗热州的大多数农民失去了灌溉条件。本研究开发了一个多传感器地球观测（Earth Observation）框架，将高分辨率卫星影像（Sentinel-2、Landsat）与气象数据（CHIRPS）和水生产力数据（FAO WaPOR）相结合，采用半监督机器学习流程。通过严格的 stratified random-sampling 方法并结合照片判读，我们发现2019年至2021年战前灌溉面积平均为187.94 ± 14.43 kHa，约占赫尔松州和扎波罗热州战前基线耕地面积的4%，以及夏季作物耕地面积的8%。2022年，随着俄罗斯对这些领土占领的扩大，上述数字缩减至140.04 ± 17.44 kHa，2023年进一步下降至74.97 ± 17.84 kHa。2023年的田块在灌溉方面也表现出差异性，从因卡霍夫卡水库被毁而灌溉中断，到因渠道中仍有存水而得到充分灌溉的情况不等。2024年，灌溉面积进一步减少至15.95 ± 9.97 kHa，较战前平均值下降约91%。根据核查，2023年之前曾受灌溉的田块到2024年要么被系统性弃耕，要么转为投入需求较低的冬季作物用地——这一模式延续至2025年（总灌溉面积为5.76 ± 0.87 kHa）。本研究表明，先进的地球观测工作流程能够在缺乏地面真值、高风险的环境中精确量化农业转型和基础设施失效。","Frontiers in Remote Sensing","2026-10-05T00:00:00Z",87,{"impact":18,"substance":489,"depth":395,"authority":126,"freshness":21,"relevant":22,"comment":490},24,"用半监督多源遥感量化战区灌溉萎缩，方法新颖、数据扎实，对农业遥感与粮食安全研究有参考价值。",[492],{"name":485,"url":482},[27,29,494,495,230],"冲突农业","多源遥感",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[498],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":500},{"candidates":501,"agricultural_evidence":58},[502,506,510,514,518],{"code":40,"score":20,"supported":58,"title":503,"abstract":504,"tags":505},[41],[230,41],[],{"code":45,"score":13,"supported":58,"title":507,"abstract":508,"tags":509},[67],[],[495,29],{"code":86,"score":355,"supported":58,"title":511,"abstract":512,"tags":513},[],[27,358],[27],{"code":406,"score":79,"supported":58,"title":515,"abstract":516,"tags":517},[],[401,425],[],{"code":519,"score":161,"supported":58,"title":520,"abstract":521,"tags":523},"J3",[],[522],"全球农业",[],[525,526],"冲突农业 多源遥感 粮食安全 遥感监测","冲突农业 多源遥感","冲突农业多源遥感粮食安全遥感监测-4313","10.3389\u002Ffrsen.2026.1920951",{"doi":528,"openalex_id":530,"authors":531,"venue":485,"cited_by_count":104,"oa_url":562,"card":563,"direction":109,"ingested_from":111},"W7220438608",[532,535,538,541,544,547,550,552,554,556,559],{"name":533,"orcid":534},"Sheila Baber","https:\u002F\u002Forcid.org\u002F0000-0002-5596-8970",{"name":536,"orcid":537},"Sergii V. Skakun","https:\u002F\u002Forcid.org\u002F0000-0002-9039-0174",{"name":539,"orcid":540},"Yuval Sadeh","https:\u002F\u002Forcid.org\u002F0000-0002-4346-0432",{"name":542,"orcid":543},"Леонід Леонідович Шуміло","https:\u002F\u002Forcid.org\u002F0000-0002-7395-7933",{"name":545,"orcid":546},"Oleksandra Oliinyk","https:\u002F\u002Forcid.org\u002F0000-0002-2392-3938",{"name":548,"orcid":549},"Mehdi Hosseini","https:\u002F\u002Forcid.org\u002F0000-0003-3242-613X",{"name":551,"orcid":9},"Josef Wagner",{"name":553,"orcid":9},"Abdul Qadir Khan",{"name":555,"orcid":9},"Shabarinath Nair",{"name":557,"orcid":558},"Abhishek Kotcharlakota","https:\u002F\u002Forcid.org\u002F0009-0001-1832-4631",{"name":560,"orcid":561},"Inbal Becker‐Reshef","https:\u002F\u002Forcid.org\u002F0000-0002-2160-5151","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1920951\u002Fpdf",{"tldr":564,"method":565,"finding":566,"direction":109,"opportunity":567},"用半监督多源遥感监测卡霍夫卡大坝被毁后灌溉面积从战前18.8万公顷降至2025年0.58万公顷。","Sentinel-2、Landsat、CHIRPS与FAO WaPOR数据结合半","灌溉面积由2019-2021年均187.94 kHa降至2024年15.95 kHa，降幅约91%，","可将该半监督多源框架迁移至其他缺地面真值冲突区，并耦合作物产量与粮食安全评估。","2026-10-06T23:30:20.495894Z"]