[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3919":3,"related-3919":124},{"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":83,"doi":84,"paper":85,"created_at":123},3919,"Meteorological to agricultural drought propagation and vegetation response across China","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ejrh.2026.104022","Study region This study covers mainland China, spanning the Eastern Monsoon Region, the Northwest Arid and Semi-Arid Region, and the Qinghai-Tibet Alpine Region. Study focus Meteorological drought does not translate linearly into agricultural drought, and lagged vegetation responses remain poorly constrained. We combined Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), satellite solar-induced chlorophyll fluorescence (SIF), and drought diagnostics based on the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSMI) to quantify meteorological-to-agricultural drought propagation, drought duration and severity, and vegetation response memory across China. We examined long-term trends and propagation lags, evaluated cumulative and delayed vegetation responses using correlation and wavelet coherence analyses, and used machine-learning attribution to identify the dominant controls on vegetation variability. New hydrological insights for the region Agricultural drought lasts longer and is more severe than the meteorological drought that triggers it, with a duration approximately one month longer, reflecting the drought amplification effect within the soil-vegetation system. Drought propagation time varies significantly across China, with a median lag of 3–5 months, and the longest lags occur in inland semi-arid regions. Vegetation exhibits a pronounced drought memory effect: NDVI and SIF responses lag approximately 5 months behind drought onset. Compared with NDVI, SIF is more sensitive to short-term water stress. In most parts of China, temperature and evapotranspiration dominate vegetation dynamics, while hydrological factors exert a stronger influence in the humid southeastern regions.","研究区域 本研究涵盖中国大陆，横跨东部季风区、西北干旱半干旱区以及青藏高寒区。研究重点 气象干旱并非线性转化为农业干旱，植被的滞后响应仍缺乏充分约束。我们结合中分辨率成像光谱仪（MODIS）归一化植被指数（NDVI）、卫星太阳诱导叶绿素荧光（SIF），以及基于标准化降水蒸散指数（SPEI）和标准化土壤湿度指数（SSMI）的干旱诊断，量化了中国气象干旱向农业干旱的传播过程、干旱历时与严重程度，以及植被响应记忆。我们检验了长期趋势与传播滞后，利用相关分析和小波相干分析评估了植被的累积响应与延迟响应，并采用机器学习归因方法识别植被变异的主导控制因子。该区域的新水文学认识 农业干旱比触发它的气象干旱持续时间更长、程度更严重，历时约长一个月，反映了土壤—植被系统内的干旱放大效应。干旱传播时间在中国各地差异显著，中位滞后为3—5个月，最长滞后出现在内陆半干旱区。植被表现出显著的干旱记忆效应：NDVI和SIF的响应滞后于干旱发生约5个月。与NDVI相比，SIF对短期水分胁迫更为敏感。在中国大部分地区，温度和蒸散主导植被动态，而在湿润的东南部地区，水文因素的影响更强。",null,"Journal of Hydrology Regional Studies","2026-09-29T00:00:00Z","论文",10,true,85,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,18,14,8,1,"基于多源遥感与机器学习量化全国气象到农业干旱传导及植被滞后响应，方法新颖、结论可靠，对农业旱情监测预警有直接参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"机器学习","农业气象","遥感监测","干旱预警","植被响应",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":40,"locked":41,"version":42,"method":43,"evidence":44},"classified",false,"2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":14},[46,53,61,67,74],{"code":33,"score":47,"supported":14,"title":48,"abstract":50,"tags":52},16,[49],"drought",[51,49],"干旱",[28,30],{"code":54,"score":55,"supported":14,"title":56,"abstract":57,"tags":60},"H2",6,[],[58,59],"土壤","soil",[],{"code":62,"score":63,"supported":14,"title":64,"abstract":65,"tags":66},"I2",4,[],[27],[27],{"code":68,"score":69,"supported":14,"title":70,"abstract":71,"tags":73},"H3",3,[],[72],"evapotranspiration",[],{"code":75,"score":22,"supported":41,"title":76,"abstract":77,"tags":78},"I3",[],[],[29],[80,81],"MODIS NDVI SIF 干旱","SPEI SSMI 农业干旱","MODISNDVISIF干旱-3919",7,"10.1016\u002Fj.ejrh.2026.104022",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":114,"oa_url":6,"card":115,"direction":121,"ingested_from":122},"W7214733729",[88,91,94,96,99,102,104,106,108,110,112],{"name":89,"orcid":90},"Dong Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6924-0748",{"name":92,"orcid":93},"Zihan Ma","https:\u002F\u002Forcid.org\u002F0009-0009-2854-2622",{"name":95,"orcid":9},"Liangliang Zhang",{"name":97,"orcid":98},"Nan Sun","https:\u002F\u002Forcid.org\u002F0009-0008-4472-3573",{"name":100,"orcid":101},"Muhammad Abrar Faiz","https:\u002F\u002Forcid.org\u002F0000-0003-0626-0308",{"name":103,"orcid":9},"Mo Li",{"name":105,"orcid":9},"Xiaochen Qi",{"name":107,"orcid":9},"Tianxiao Li",{"name":109,"orcid":9},"Song Cui",{"name":111,"orcid":9},"Zhaoqiang Zhou",{"name":113,"orcid":9},"Muhammad Imran Khan",0,{"tldr":116,"method":117,"finding":118,"direction":119,"opportunity":120},"量化中国气象干旱向农业干旱的传播及植被滞后响应。","用MODIS NDVI、SIF、SPEI、SSMI及机器学习归因分析。","农业干旱比气象干旱长约1个月，植被响应滞后约5个月，SIF更敏感。","农业遥感与作物表型","可探索不同气候区干旱传播阈值与SIF早期预警指标，构建区域化干旱记忆模型。","农业人工智能与决策模型","openalex","2026-10-01T23:30:55.666679Z",{"total":55,"page":22,"page_size":55,"items":125},[126,208,274,334,429,492],{"id":127,"title":128,"url":129,"summary":130,"summary_zh":131,"content":9,"source_name":132,"source_url":129,"published_at":133,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":134,"score_detail":135,"sources":139,"tags":141,"primary_topic":144,"secondary_topics":145,"classification":155,"search_phrases":184,"slug":187,"view_count":22,"doi":188,"paper":189,"created_at":207},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":136,"depth":19,"authority":137,"freshness":21,"relevant":22,"comment":138},21,13,"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。",[140],{"name":132,"url":129},[28,142,29,143,30],"水资源管理","Google Earth Engine",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[146,151],{"code":75,"slug":147,"name":148,"path":149,"parent_code":150},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":68,"slug":152,"name":153,"path":154,"parent_code":37},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":40,"locked":41,"version":42,"method":43,"evidence":156},{"candidates":157,"agricultural_evidence":14},[158,162,168,174,178],{"code":33,"score":47,"supported":14,"title":159,"abstract":160,"tags":161},[49],[51,49],[28,30],{"code":75,"score":137,"supported":14,"title":163,"abstract":165,"tags":167},[164],"remote sensing",[166],"遥感",[143,29],{"code":68,"score":13,"supported":14,"title":169,"abstract":170,"tags":173},[],[171,172,72],"水资源","water resource",[142],{"code":54,"score":55,"supported":14,"title":175,"abstract":176,"tags":177},[],[58,59],[],{"code":179,"score":69,"supported":14,"title":180,"abstract":181,"tags":183},"J1",[],[182],"农业规划",[],[185,186],"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":188,"openalex_id":190,"authors":191,"venue":132,"cited_by_count":114,"oa_url":129,"card":202,"direction":119,"ingested_from":122},"W7215092640",[192,195,197,199],{"name":193,"orcid":194},"Hadi Siasar","https:\u002F\u002Forcid.org\u002F0000-0002-9323-4345",{"name":196,"orcid":9},"Mahboube Ebrahimi",{"name":198,"orcid":9},"Mehdi Bahrami",{"name":200,"orcid":201},"Hossein Hamidifar","https:\u002F\u002Forcid.org\u002F0000-0001-9054-0120",{"tldr":203,"method":204,"finding":205,"direction":119,"opportunity":206},"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":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":9,"source_name":214,"source_url":211,"published_at":215,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":216,"score_detail":217,"sources":221,"tags":223,"primary_topic":225,"secondary_topics":226,"classification":228,"search_phrases":243,"slug":246,"view_count":247,"doi":248,"paper":249,"created_at":273},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。","Theoretical and Applied Climatology","2026-09-15T00:00:00Z",71,{"impact":218,"substance":136,"depth":219,"authority":137,"freshness":21,"relevant":22,"comment":220},12,17,"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。",[222],{"name":214,"url":211},[28,142,29,224,30],"植被指数",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[227],{"code":75,"slug":147,"name":148,"path":149,"parent_code":150},{"status":40,"locked":41,"version":42,"method":43,"evidence":229},{"candidates":230,"agricultural_evidence":14},[231,235,239],{"code":33,"score":47,"supported":14,"title":232,"abstract":233,"tags":234},[49],[51,49],[28,30],{"code":75,"score":137,"supported":14,"title":236,"abstract":237,"tags":238},[164],[166],[224,29],{"code":68,"score":63,"supported":14,"title":240,"abstract":241,"tags":242},[],[171],[142],[244,245],"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":248,"openalex_id":250,"authors":251,"venue":214,"cited_by_count":114,"oa_url":211,"card":268,"direction":119,"ingested_from":122},"W7213346236",[252,255,258,261,263,266],{"name":253,"orcid":254},"Ramzi Benhizia","https:\u002F\u002Forcid.org\u002F0000-0002-8967-2051",{"name":256,"orcid":257},"Brahim Abdelkebir","https:\u002F\u002Forcid.org\u002F0000-0002-8761-8537",{"name":259,"orcid":260},"Behnam Ata","https:\u002F\u002Forcid.org\u002F0000-0002-9690-7269",{"name":262,"orcid":9},"Mukovhe Vele Singo",{"name":264,"orcid":265},"Kwanele Phinzi","https:\u002F\u002Forcid.org\u002F0000-0003-1865-7011",{"name":267,"orcid":9},"György Szabó",{"tldr":269,"method":270,"finding":271,"direction":119,"opportunity":272},"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。","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。","2026-09-16T23:30:30.795285Z",{"id":275,"title":276,"url":277,"summary":278,"summary_zh":279,"content":9,"source_name":280,"source_url":277,"published_at":281,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":282,"score_detail":283,"sources":286,"tags":288,"primary_topic":292,"secondary_topics":293,"classification":294,"search_phrases":315,"slug":318,"view_count":22,"doi":319,"paper":320,"created_at":333},5087,"Method for ten-day assessment of the contribution of agrometeorological conditions to expected winter grain crop productivity based on satellite and ground-based information","https:\u002F\u002Fdoi.org\u002F10.37162\u002F2618-9631-2026-3-167-183","The paper presents a developed and tested method for 10-day assessment of the contribution of agrometeorological factors to winter grain crop productivity. The method is based on an integrated analysis of satellite spectral indices and ground-based meteorological data for the North Caucasus, Volga and Central Black Earth regions. Satellite data from MODIS (Terra satellite) and VIIRS (SUOMI NPP satellite) radiometers were used to calculate the NDVI, VCI, and VCNI vegetation indices. Ground-based information includes meteorological and agrometeorological indicators reflecting temperature and humidity conditions. The algorithm and software modules developed within the study allow for automated tracking of 10-day changes in agrometeorological conditions and quantitative ranking of factors based on their contribution to the expected winter grain crop yield, providing an information basis for informed planning of agricultural measures.","本文提出并验证了一种用于每旬评估农业气象因素对冬粮作物生产力贡献的方法。该方法基于对北高加索、伏尔加河沿岸和中央黑土区卫星光谱指数与地面气象数据的综合分析。利用MODIS（Terra卫星）和VIIRS（SUOMI NPP卫星）辐射计的卫星数据计算NDVI、VCI和VCNI植被指数。地面信息包括反映温湿状况的气象和农业气象指标。研究开发的算法和软件模块能够自动跟踪农业气象条件的每旬变化，并根据各因素对预期冬粮作物产量的贡献进行定量排序，为农业措施的合理规划提供信息依据。","Hydrometeorological research and forecasting","2026-10-15T00:00:00Z",70,{"impact":218,"substance":284,"depth":219,"authority":137,"freshness":21,"relevant":22,"comment":285},20,"方法新颖、数据扎实的区域性遥感估产研究，对农业信息化有参考价值，但属细分领域学术成果，公共影响有限。",[287],{"name":280,"url":277},[289,28,29,290,291],"智慧农业","冬小麦","作物估产",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":295},{"candidates":296,"agricultural_evidence":14},[297,301,305,310],{"code":33,"score":63,"supported":14,"title":298,"abstract":299,"tags":300},[],[28],[28],{"code":75,"score":247,"supported":41,"title":302,"abstract":303,"tags":304},[],[],[291,29],{"code":306,"score":22,"supported":41,"title":307,"abstract":308,"tags":309},"A2",[],[],[290],{"code":311,"score":22,"supported":41,"title":312,"abstract":313,"tags":314},"I1",[],[],[289],[316,317],"MODIS VIIRS 冬小麦 遥感估产","农业气象 冬谷类作物 产量评估","MODISVIIRS冬小麦遥感估产-5087","10.37162\u002F2618-9631-2026-3-167-183",{"doi":319,"openalex_id":321,"authors":322,"venue":280,"cited_by_count":114,"oa_url":277,"card":328,"direction":119,"ingested_from":122},"W7221991562",[323,326],{"name":324,"orcid":325},"A. D. Kleshchenko","https:\u002F\u002Forcid.org\u002F0000-0002-4200-6668",{"name":327,"orcid":9},"O. V. Savitskaya",{"tldr":329,"method":330,"finding":331,"direction":119,"opportunity":332},"提出一种基于卫星与地面数据每十天评估农业气象因子对冬粮作物产量贡献的方法。","用MODIS和VIIRS计算NDVI、VCI、VCNI，结合气象与农业气象指标做","可自动追踪十天农业气象变化并量化排序各因子对预期产量的贡献。","可扩展至多作物多区域，融合机器学习提升因子贡献动态预测精度。","2026-10-16T23:30:26.817010Z",{"id":335,"title":336,"url":337,"summary":338,"summary_zh":339,"content":9,"source_name":340,"source_url":337,"published_at":341,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":342,"score_detail":343,"sources":346,"tags":348,"primary_topic":351,"secondary_topics":352,"classification":358,"search_phrases":404,"slug":407,"view_count":247,"doi":408,"paper":409,"created_at":428},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":19,"substance":17,"depth":344,"authority":20,"freshness":21,"relevant":22,"comment":345},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。",[347],{"name":340,"url":337},[289,349,27,29,350],"粮食安全","农业干旱",{"code":75,"slug":147,"name":148,"path":149,"parent_code":150},[353,354],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":62,"slug":355,"name":356,"path":357,"parent_code":150},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":40,"locked":41,"version":42,"method":43,"evidence":359},{"candidates":360,"agricultural_evidence":14},[361,366,370,378,384,388,394,400],{"code":75,"score":362,"supported":14,"title":363,"abstract":364,"tags":365},15,[164],[166,164],[29],{"code":33,"score":20,"supported":14,"title":367,"abstract":368,"tags":369},[49],[51,49],[],{"code":62,"score":13,"supported":14,"title":371,"abstract":372,"tags":377},[],[373,27,374,375,376],"深度学习","machine learning","deep learning","artificial intelligence",[27],{"code":379,"score":83,"supported":14,"title":380,"abstract":381,"tags":383},"A8",[],[349,382],"food security",[349],{"code":54,"score":55,"supported":14,"title":385,"abstract":386,"tags":387},[],[58,59],[],{"code":389,"score":69,"supported":14,"title":390,"abstract":391,"tags":393},"D",[],[392],"水产",[],{"code":395,"score":69,"supported":14,"title":396,"abstract":397,"tags":399},"H6",[],[398],"农业生态",[],{"code":311,"score":22,"supported":41,"title":401,"abstract":402,"tags":403},[],[],[289],[405,406],"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":408,"openalex_id":410,"authors":411,"venue":340,"cited_by_count":114,"oa_url":421,"card":422,"direction":427,"ingested_from":122},"W7221081979",[412,415,418],{"name":413,"orcid":414},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":416,"orcid":417},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":419,"orcid":420},"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":423,"method":424,"finding":425,"direction":119,"opportunity":426},"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":430,"title":431,"url":432,"summary":433,"summary_zh":434,"content":9,"source_name":435,"source_url":432,"published_at":341,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":436,"score_detail":437,"sources":440,"tags":442,"primary_topic":445,"secondary_topics":446,"classification":449,"search_phrases":472,"slug":475,"view_count":22,"doi":476,"paper":477,"created_at":491},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":218,"substance":17,"depth":19,"authority":20,"freshness":438,"relevant":22,"comment":439},9,"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。",[441],{"name":435,"url":432},[349,28,29,443,444],"西非农业","干旱指数",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[447,448],{"code":75,"slug":147,"name":148,"path":149,"parent_code":150},{"code":68,"slug":152,"name":153,"path":154,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":450},{"candidates":451,"agricultural_evidence":14},[452,456,460,464,468],{"code":33,"score":47,"supported":14,"title":453,"abstract":454,"tags":455},[49],[51,49],[28,444],{"code":75,"score":362,"supported":14,"title":457,"abstract":458,"tags":459},[164],[166,164],[29],{"code":68,"score":20,"supported":14,"title":461,"abstract":462,"tags":463},[72],[171,72],[],{"code":54,"score":55,"supported":14,"title":465,"abstract":466,"tags":467},[],[58,59],[],{"code":379,"score":22,"supported":41,"title":469,"abstract":470,"tags":471},[],[],[349],[473,474],"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":476,"openalex_id":478,"authors":479,"venue":435,"cited_by_count":114,"oa_url":432,"card":486,"direction":119,"ingested_from":122},"W7220963372",[480,483],{"name":481,"orcid":482},"Abdoul-Aziz Bio Sidi D. Bouko","https:\u002F\u002Forcid.org\u002F0009-0003-1179-8046",{"name":484,"orcid":485},"Bing Gao","https:\u002F\u002Forcid.org\u002F0000-0003-0782-8858",{"tldr":487,"method":488,"finding":489,"direction":119,"opportunity":490},"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":493,"title":494,"url":495,"summary":496,"summary_zh":497,"content":9,"source_name":498,"source_url":495,"published_at":499,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":500,"score_detail":501,"sources":503,"tags":505,"primary_topic":509,"secondary_topics":512,"classification":515,"search_phrases":540,"slug":543,"view_count":247,"doi":544,"paper":545,"created_at":572},4172,"Machine learning reveals distinct drivers of soil organic carbon across the mountain-oasis-desert system of northwest China","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13021-026-00522-5","The mountain-oasis-desert system (MODS) is a distinctive landscape pattern shaped by interactions between geomorphological configurations and hydrological processes in arid regions. However, most soil organic carbon (SOC) mapping studies treat arid landscapes as homogeneous units, leaving limited quantitative evidence on how SOC patterns and environmental associations differ among MODS subsystems. As a critical ecosystem highly sensitive to climate change and human activities, understanding these responses is essential for regional soil carbon management and ecological restoration. In this study, we developed an ecosystem-based modeling framework in the Hexi Region (HXR) to construct separate SOC prediction models for different subsystems, aiming to investigate the spatiotemporal dynamics of SOC stocks and identify their key driving factors across MODS. The results show that incorporating remote sensing (RS) variables significantly improved model performance, increasing the mean R 2 from 0.51 to 0.58. Among the tested models, XGBoost achieved the best performance, providing an effective tool for SOC mapping in arid regions. Shapley additive explanations (SHAP) analysis of the best-performing XGBoost models identified distinct leading predictors across the MODS, including surface shortwave radiation (SRAD) in mountain and desert ecosystems and the difference vegetation index (DVI) in oasis ecosystems. Furthermore, the partial least squares path model (PLS-PM) revealed contrasting influences of climate change and human activities across ecological subsystems. Climatic factors exerted the strongest influence in the mountainous subsystem, whereas in the oasis subsystem intensive human management became the dominant driver of soil carbon cycling. From 2001 to 2023, SOC stocks in oasis regions increased significantly at a rate of 0.17 t C ha⁻ 1 year⁻ 1 , potentially associated with land-use change and agricultural management. Approximately 19.3% of the study area experienced significant SOC stocks declines. By resolving subsystem-specific SOC patterns and environmental associations, this study advances ecosystem-aware SOC assessment in arid regions. The resulting framework also provides a basis for differentiated carbon management across MODS.","山地-绿洲-荒漠系统（MODS）是干旱区地貌格局与水文过程相互作用形成的独特景观格局。然而，大多数土壤有机碳（SOC）制图研究将干旱区景观视为均质单元，关于MODS各子系统间SOC格局及其环境关联差异的定量证据仍然有限。作为对气候变化和人类活动高度敏感的关键生态系统，理解这些响应对于区域土壤碳管理和生态恢复至关重要。本研究在河西地区（HXR）构建了基于生态系统的建模框架，针对不同子系统分别建立SOC预测模型，旨在探究MODS中SOC储量的时空动态并识别其关键驱动因子。结果表明，引入遥感（RS）变量显著提升了模型性能，平均R²从0.51提高至0.58。在测试的模型中，XGBoost表现最佳，为干旱区SOC制图提供了有效工具。对最优XGBoost模型的SHAP（Shapley加法解释）分析识别出MODS各子系统的不同主导预测因子，其中山地和荒漠生态系统为地表短波辐射（SRAD），绿洲生态系统为差值植被指数（DVI）。此外，偏最小二乘路径模型（PLS-PM）揭示了气候变化和人类活动在各生态子系统中的差异化影响。气候因子在山地子系统中影响最强，而在绿洲子系统中，集约化人类管理成为土壤碳循环的主导驱动因素。2001年至2023年，绿洲区域SOC储量以0.17 t C ha⁻¹ year⁻¹的速率显著增加，这可能与土地利用变化和农业管理有关。约19.3%的研究区域经历了SOC储量的显著下降。通过解析子系统特定的SOC格局及其环境关联，本研究推进了干旱区生态系统感知型SOC评估，所构建的框架也为MODS差异化碳管理提供了依据。","Carbon Balance and Management","2026-10-03T00:00:00Z",79,{"impact":47,"substance":17,"depth":19,"authority":20,"freshness":438,"relevant":22,"comment":502},"基于XGBoost与SHAP的干旱区SOC分异驱动研究，方法新颖、数据翔实，对区域碳管理与生态修复有参考价值。",[504],{"name":498,"url":495},[27,506,507,29,508],"土壤有机碳","生态修复","干旱区农业",{"code":54,"slug":59,"name":510,"path":511,"parent_code":37},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[513,514],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":62,"slug":355,"name":356,"path":357,"parent_code":150},{"status":40,"locked":41,"version":42,"method":43,"evidence":516},{"candidates":517,"agricultural_evidence":14},[518,522,528,532,536],{"code":54,"score":362,"supported":14,"title":519,"abstract":520,"tags":521},[59],[58,59],[506],{"code":33,"score":13,"supported":14,"title":523,"abstract":524,"tags":527},[],[525,51,526],"气候变化","climate change",[508],{"code":62,"score":438,"supported":14,"title":529,"abstract":530,"tags":531},[374],[],[27],{"code":75,"score":83,"supported":14,"title":533,"abstract":534,"tags":535},[],[166,164],[29],{"code":395,"score":22,"supported":41,"title":537,"abstract":538,"tags":539},[],[],[507],[541,542],"河西地区 土壤有机碳 机器学习","山地-绿洲-荒漠 遥感 碳储量","河西地区土壤有机碳机器学习-4172","10.1186\u002Fs13021-026-00522-5",{"doi":544,"openalex_id":546,"authors":547,"venue":498,"cited_by_count":114,"oa_url":495,"card":567,"direction":119,"ingested_from":122},"W7219075005",[548,550,553,555,558,560,563,565],{"name":549,"orcid":9},"Y. Chen",{"name":551,"orcid":552},"Haiyang Xi","https:\u002F\u002Forcid.org\u002F0000-0003-3158-8161",{"name":554,"orcid":9},"Meng Zhu",{"name":556,"orcid":557},"Wenju Cheng","https:\u002F\u002Forcid.org\u002F0000-0002-1822-5668",{"name":559,"orcid":9},"Yulian Hao",{"name":561,"orcid":562},"Linbo Qu","https:\u002F\u002Forcid.org\u002F0009-0007-4669-8407",{"name":564,"orcid":9},"Zhehui Wang",{"name":566,"orcid":9},"Bin Wang",{"tldr":568,"method":569,"finding":570,"direction":119,"opportunity":571},"构建基于生态系统的机器学习框架，预测西北荒漠-绿洲-山地系统土壤有机碳并识别各子系统驱动因子。","XGBoost、SHAP、PLS-PM，结合遥感变量与2001-2023年多源数","遥感变量提升模型R²至0.58；山地和沙漠由辐射主导，绿洲由人类管理主导，绿洲碳储量年增0.17 t","可延伸研究绿洲农业管理措施对土壤碳的长期影响，并开发子系统自适应的碳监测与决策模型。","2026-10-04T23:30:24.631460Z"]