[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4575":3,"related-4575":140},{"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":111,"slug":114,"view_count":115,"doi":116,"paper":117,"created_at":139},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）。",null,"GIScience & Remote Sensing","2026-10-08T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,14,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","粮食安全","机器学习","遥感监测","农业干旱",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[39,45],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,64,70,78,85,93,100,106],{"code":33,"score":57,"supported":58,"title":59,"abstract":61,"tags":63},15,true,[60],"remote sensing",[62,60],"遥感",[30],{"code":40,"score":20,"supported":58,"title":65,"abstract":67,"tags":69},[66],"drought",[68,66],"干旱",[],{"code":46,"score":13,"supported":58,"title":71,"abstract":72,"tags":77},[],[73,29,74,75,76],"深度学习","machine learning","deep learning","artificial intelligence",[29],{"code":79,"score":80,"supported":58,"title":81,"abstract":82,"tags":84},"A8",7,[],[28,83],"food security",[28],{"code":86,"score":87,"supported":58,"title":88,"abstract":89,"tags":92},"H2",6,[],[90,91],"土壤","soil",[],{"code":94,"score":95,"supported":58,"title":96,"abstract":97,"tags":99},"D",3,[],[98],"水产",[],{"code":101,"score":95,"supported":58,"title":102,"abstract":103,"tags":105},"H6",[],[104],"农业生态",[],{"code":107,"score":22,"supported":14,"title":108,"abstract":109,"tags":110},"I1",[],[],[27],[112,113],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575",2,"10.1080\u002F15481603.2026.2738981",{"doi":116,"openalex_id":118,"authors":119,"venue":10,"cited_by_count":129,"oa_url":130,"card":131,"direction":137,"ingested_from":138},"W7221081979",[120,123,126],{"name":121,"orcid":122},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":124,"orcid":125},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":127,"orcid":128},"Olena Dubovyk","https:\u002F\u002Forcid.org\u002F0000-0002-7338-3167",0,"https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2738981?needAccess=true",{"tldr":132,"method":133,"finding":134,"direction":135,"opportunity":136},"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。","农业遥感与作物表型","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","openalex","2026-10-10T23:30:14.933078Z",{"total":87,"page":22,"page_size":87,"items":141},[142,209,275,364,446,524],{"id":143,"title":144,"url":145,"summary":146,"summary_zh":147,"content":9,"source_name":148,"source_url":145,"published_at":149,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":150,"score_detail":151,"sources":153,"tags":155,"primary_topic":157,"secondary_topics":158,"classification":160,"search_phrases":188,"slug":191,"view_count":22,"doi":192,"paper":193,"created_at":208},3772,"Integrating Vegetation Health Indices and Machine Learning for Early Prediction of Agricultural Drought","https:\u002F\u002Fdoi.org\u002F10.1080\u002F02626667.2026.2740781","Agricultural drought threatens food security across Nigeria’s Sahel, yet spatially explicit machine-learning-based prediction remains limited in the region. This study developed a spatiotemporal drought monitoring and prediction framework for Yobe State (2000–2025) integrating seven remote-sensing indicators; NDVI, EVI, VCI, TCI, SMI, LST, and rainfall within Google Earth Engine at 3 km resolution. A persistent north–south drought gradient emerged across all indicators, with northern local government areas exhibiting chronic vegetation stress, soil moisture deficit, and elevated surface temperatures throughout the study period. Three machine learning models; LSTM, Random Forest, and XGBoost were trained on Vegetation Health Index-derived drought severity classes. XGBoost achieved the highest predictive performance (accuracy = 86.9%; Kappa = 0.829; RMSE = 2.26), with rainfall and vegetation condition as dominant predictors. All three models converge on the northern LGAs as the highest-risk zone by 2030, supporting targeted early warning deployment and climate-smart agricultural investment across the Sahel.","农业干旱威胁着尼日利亚萨赫勒地区的粮食安全，但该地区基于机器学习且具有空间显式特征的预测研究仍然有限。本研究在Google Earth Engine平台上，以3 km分辨率构建了约贝州（2000—2025年）的时空干旱监测与预测框架，集成了七种遥感指标：归一化植被指数（NDVI）、增强植被指数（EVI）、植被条件指数（VCI）、温度条件指数（TCI）、土壤水分指数（SMI）、地表温度（LST）和降雨量。所有指标均呈现出持续的南北干旱梯度，北部地方政府辖区在整个研究期内表现出长期的植被胁迫、土壤水分亏缺和地表温度升高。研究以植被健康指数（VHI）衍生的干旱严重程度等级为标签，训练了三种机器学习模型：长短期记忆网络（LSTM）、随机森林（Random Forest）和极端梯度提升（XGBoost）。XGBoost取得了最高的预测性能（准确率=86.9%；Kappa=0.829；RMSE=2.26），其中降雨量和植被状况为主要预测因子。三种模型一致预测，到2030年北部地方政府辖区将成为最高风险区，这为萨赫勒地区有针对性的预警部署和气候智慧型农业投资提供了依据。","Hydrological Sciences Journal","2026-09-28T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":152},"基于多源遥感与机器学习构建萨赫勒地区农业干旱早期预警框架，方法扎实、结论明确，对干旱监测预警有参考价值。",[154],{"name":148,"url":145},[27,29,30,31,156],"预警系统",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},[159],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":161},{"candidates":162,"agricultural_evidence":58},[163,167,171,175,179,184],{"code":46,"score":57,"supported":58,"title":164,"abstract":165,"tags":166},[74],[29,74],[29],{"code":40,"score":20,"supported":58,"title":168,"abstract":169,"tags":170},[66],[68,66],[],{"code":79,"score":87,"supported":58,"title":172,"abstract":173,"tags":174},[],[28,83],[],{"code":86,"score":87,"supported":58,"title":176,"abstract":177,"tags":178},[],[90,91],[],{"code":33,"score":180,"supported":58,"title":181,"abstract":182,"tags":183},4,[],[62],[30],{"code":107,"score":22,"supported":14,"title":185,"abstract":186,"tags":187},[],[],[27],[189,190],"尼日利亚 萨赫勒 农业干旱","XGBoost 植被健康指数 干旱预测","尼日利亚萨赫勒农业干旱-3772","10.1080\u002F02626667.2026.2740781",{"doi":192,"openalex_id":194,"authors":195,"venue":148,"cited_by_count":129,"oa_url":9,"card":203,"direction":137,"ingested_from":138},"W7214639469",[196,199,201],{"name":197,"orcid":198},"Gbolahan S. Badru","https:\u002F\u002Forcid.org\u002F0000-0003-4020-6356",{"name":200,"orcid":9},"Shakirudeen Odunuga",{"name":202,"orcid":9},"Michael Adebisi Adeyemi",{"tldr":204,"method":205,"finding":206,"direction":135,"opportunity":207},"基于多源遥感与机器学习构建尼日利亚萨赫勒地区农业干旱早期预测框架。","GEE整合NDVI、EVI、VCI等7个指标，用LSTM、随机森林、XGBoos","XGBoost预测精度最高（86.9%），北部LGAs为2030年前最高风险区。","可探索多模型集成与迁移学习，将干旱预警框架推广至其他萨赫勒地区并耦合作物产量预测。","2026-09-29T23:30:09.236357Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":224,"tags":226,"primary_topic":229,"secondary_topics":230,"classification":232,"search_phrases":256,"slug":259,"view_count":129,"doi":260,"paper":261,"created_at":274},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":219,"depth":220,"authority":221,"freshness":222,"relevant":22,"comment":223},21,17,13,9,"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。",[225],{"name":215,"url":212},[28,227,30,228,31],"防灾减灾","植被指数",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[231],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":233},{"candidates":234,"agricultural_evidence":58},[235,241,245,249],{"code":40,"score":17,"supported":58,"title":236,"abstract":237,"tags":240},[66],[238,239,68,66],"气候变化","气候适应",[227],{"code":33,"score":21,"supported":58,"title":242,"abstract":243,"tags":244},[],[62,60],[228,30],{"code":79,"score":80,"supported":58,"title":246,"abstract":247,"tags":248},[],[28,83],[28],{"code":250,"score":87,"supported":58,"title":251,"abstract":252,"tags":255},"I4",[],[253,254],"空间分析","spatial analysis",[],[257,258],"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":260,"openalex_id":262,"authors":263,"venue":215,"cited_by_count":129,"oa_url":212,"card":269,"direction":135,"ingested_from":138},"W7221455809",[264,266],{"name":265,"orcid":9},"Murad Hedo Haji",{"name":267,"orcid":268},"Karuturi Venkata Suryabhagavan","https:\u002F\u002Forcid.org\u002F0000-0003-2528-9106",{"tldr":270,"method":271,"finding":272,"direction":135,"opportunity":273},"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":276,"title":277,"url":278,"summary":279,"summary_zh":280,"content":9,"source_name":281,"source_url":278,"published_at":282,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":283,"score_detail":284,"sources":287,"tags":289,"primary_topic":292,"secondary_topics":293,"classification":295,"search_phrases":328,"slug":331,"view_count":95,"doi":332,"paper":333,"created_at":363},3843,"A multi-scale remote sensing approach for assessing drought impacts on rainfed and irrigated cereal yields in Lleida, Spain","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110826","This study evaluates the performance of agricultural drought indices at provincial and field scales, as well as their ability to explain the variability of wheat and barley yields under rainfed and irrigated conditions. The analysis was conducted in the Lleida region (northeastern Spain) from 2010 to 2024 using high-resolution (30 m) multi-source remote sensing data and yield information from 5030 cereal fields. Five drought indices representing vegetative stress, thermal stress, soil moisture conditions, and their combination were evaluated: the Vegetative Condition Index (VCI), the Thermal Condition Index (TCI), the Soil Moisture Condition Index (SMCI), the Vegetative Health Index (VHI), and the Combined Drought Anomaly Index (CDAI). The analysis of drought index–yield relationships reveal a strong dependence on crop type, phenological stage, spatial scale, and water management practices. Barley exhibits greater sensitivity to drought than wheat and responds earlier in the growing season. Correlations are generally weak during early growth stages but become much stronger during the reproductive and maturation phases. Using high spatial resolution data (30 m) significantly enhances the performance of drought indices. Under rainfed conditions, most indices perform well except for the TCI, with the CDAI standing out as the most effective, achieving correlation values of r = 0.85 (p \u003C 0.01) at the provincial scale and a median of r = 0.52, with a maximum of r = 0.95 (p \u003C 0.01) at the field scale. In contrast, drought index–yield correlations in irrigated areas are consistently lower, highlighting the buffering role of irrigation in mitigating drought impacts. In irrigated fields across the study area, yield variability is primarily driven by heat stress rather than water or vegetation stress. Overall, composite drought indices such as the CDAI are particularly well suited for monitoring agricultural drought in rainfed conditions, whereas indices specifically designed for irrigated agriculture remain essential.","本研究在省域和田块尺度上评估了农业干旱指数的表现，以及其在雨养和灌溉条件下解释小麦和大麦产量变异的能力。分析于2010—2024年在西班牙东北部莱里达地区开展，使用了高分辨率（30 m）多源遥感数据和5030块谷类田块的产量信息。研究评估了五个代表植被胁迫、热胁迫、土壤水分状况及其组合的干旱指数：植被状态指数（VCI）、热状态指数（TCI）、土壤水分状态指数（SMCI）、植被健康指数（VHI）和综合干旱异常指数（CDAI）。对干旱指数与产量关系的分析表明，其强烈依赖于作物类型、物候阶段、空间尺度及水分管理方式。大麦对干旱的敏感性高于小麦，且在生长季中响应更早。在生长早期阶段相关性普遍较弱，但在生殖和成熟阶段显著增强。使用高空间分辨率数据（30 m）可显著提升干旱指数的表现。在雨养条件下，除TCI外，大多数指数表现良好，其中CDAI最为有效，在省域尺度上相关系数达到r = 0.85（p \u003C 0.01），在田块尺度上中位数为r = 0.52，最大值为r = 0.95（p \u003C 0.01）。相比之下，灌溉区域的干旱指数与产量相关性 consistently 较低，凸显了灌溉在缓解干旱影响中的缓冲作用。在研究区的灌溉田块中，产量变异主要由热胁迫驱动，而非水分或植被胁迫。总体而言，诸如CDAI之类的复合干旱指数特别适用于监测雨养条件下的农业干旱，而专门为灌溉农业设计的指数仍然不可或缺。","Agricultural Water Management","2026-09-29T00:00:00Z",79,{"impact":285,"substance":18,"depth":17,"authority":20,"freshness":222,"relevant":22,"comment":286},16,"基于30米高分辨率遥感与5030块田块数据，量化五种干旱指数对雨养与灌溉谷物产量的解释力，方法扎实、结论对农业干旱监测与灌溉管理有参考价值。",[288],{"name":281,"url":278},[27,290,291,30,31],"作物产量","精准灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[294],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":296},{"candidates":297,"agricultural_evidence":58},[298,302,306,313,320,324],{"code":33,"score":285,"supported":58,"title":299,"abstract":300,"tags":301},[60],[62,60],[290,30],{"code":40,"score":20,"supported":58,"title":303,"abstract":304,"tags":305},[66],[68,66],[],{"code":307,"score":80,"supported":58,"title":308,"abstract":309,"tags":312},"H3",[],[310,311],"灌溉","irrigation",[291],{"code":314,"score":87,"supported":58,"title":315,"abstract":316,"tags":319},"A2",[],[317,318],"小麦","wheat",[],{"code":86,"score":87,"supported":58,"title":321,"abstract":322,"tags":323},[],[90,91],[],{"code":107,"score":22,"supported":14,"title":325,"abstract":326,"tags":327},[],[],[27],[329,330],"Lleida 干旱 遥感 谷物产量","CDAI 干旱指数 灌溉","Lleida干旱遥感谷物产量-3843","10.1016\u002Fj.agwat.2026.110826",{"doi":332,"openalex_id":334,"authors":335,"venue":281,"cited_by_count":129,"oa_url":278,"card":358,"direction":135,"ingested_from":138},"W7214788171",[336,339,342,345,347,350,353,355],{"name":337,"orcid":338},"Youness Ablila","https:\u002F\u002Forcid.org\u002F0009-0006-3081-5990",{"name":340,"orcid":341},"Maria‐José Escorihuela","https:\u002F\u002Forcid.org\u002F0000-0002-7780-7334",{"name":343,"orcid":344},"Abdelhakim Amazirh","https:\u002F\u002Forcid.org\u002F0000-0002-6665-3843",{"name":346,"orcid":9},"Jose Antonio Martinez Casasnovas",{"name":348,"orcid":349},"El Houssaine Bouras","https:\u002F\u002Forcid.org\u002F0000-0002-6973-6644",{"name":351,"orcid":352},"Saïd Khabba","https:\u002F\u002Forcid.org\u002F0000-0003-3309-9935",{"name":354,"orcid":9},"Zaineb Bouswir",{"name":356,"orcid":357},"Salah Er‐Raki","https:\u002F\u002Forcid.org\u002F0000-0002-8595-7949",{"tldr":359,"method":360,"finding":361,"direction":135,"opportunity":362},"评估多尺度遥感干旱指数对西班牙Lleida地区雨养与灌溉谷物产量的解释能力。","2010-2024年30米多源遥感数据与5030块田产量，比较VCI、TCI、S","CDAI在雨养区表现最佳（省域r=0.85），灌溉区相关性低且产量主要受热胁迫驱动。","可开发针对灌溉农业的专用干旱指数，并融合热胁迫与灌溉管理数据提升产量预测。","2026-09-30T23:30:29.241563Z",{"id":365,"title":366,"url":367,"summary":368,"summary_zh":369,"content":9,"source_name":370,"source_url":367,"published_at":371,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":372,"score_detail":373,"sources":375,"tags":377,"primary_topic":380,"secondary_topics":386,"classification":389,"search_phrases":416,"slug":419,"view_count":95,"doi":420,"paper":421,"created_at":445},3167,"Temporal trend analysis and multi-temporal satellite feature integration for mango orchard acreage estimation using machine learning algorithms approach","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-65828-3","Accurate estimation of horticultural crop acreage is essential for agricultural planning, market forecasting and evidence-based policy formulation. The present study investigated long-term trends in mango cultivation and developed a multi-temporal remote sensing framework for mango orchard acreage estimation in Navsari District using integrated optical, SAR and machine learning approaches. Time-series (temporal) data spanning 23 years (2001–02 to 2023–24) were analyzed using polynomial regression models to evaluate trends in mango area and production. Linear regression best represented area expansion trends (Adj. R² = 0.969), whereas cubic regression better captured production variability (Adj. R² = 0.634), indicating climatic and seasonal influences on productivity. For orchard classification and acreage estimation, multi-temporal Sentinel-2 imagery acquired from November 2023 to March 2024 was processed within a phenology-guided framework. Monthly composites were generated and integrated with Sentinel-1 SAR backscatter data, vegetation indices (NDVI, GNDVI, NDRE, SAVI, EVI and NDMI) and texture metrics derived from Gray Level Co-occurrence Matrix (GLCM) analysis. A comprehensive 72-band feature stack was developed for classification. Four machine learning algorithms, namely Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM) and Multinomial Logistic Regression (MNLR) were evaluated for orchard discrimination. Among the tested models, RF achieved the highest classification performance with an Overall Accuracy of 99.80% and a Kappa coefficient of 0.997, followed by SVM (99.30%), MNLR (98.21%) and XGB (9.20%). The RF model estimated mango orchard area at 36,099.94 ha, showing the closest agreement with official horticultural statistics (34,363 ha) with only 5.05% estimation error. In contrast, SVM and MNLR overestimated orchard extent by 16.03% and 43.37%, respectively. The proposed framework provides a reliable and scalable methodology for operational horticultural monitoring, crop inventory generation and precision agricultural planning in tropical orchard ecosystems.","准确估算园艺作物种植面积对于农业规划、市场预测和循证政策制定至关重要。本研究探讨了芒果种植的长期趋势，并开发了一个多时相遥感框架，结合光学、合成孔径雷达（SAR）和机器学习方法，用于纳夫萨里县芒果园种植面积估算。利用多项式回归模型分析了跨越23年（2001—02年至2023—24年）的时间序列数据，以评估芒果面积和产量的变化趋势。线性回归最能表征面积扩张趋势（调整R² = 0.969），而三次回归更能捕捉产量变异性（调整R² = 0.634），表明气候和季节性因素对生产力具有影响。在果园分类和面积估算方面，基于物候指导框架处理了2023年11月至2024年3月获取的多时相Sentinel-2影像。生成了月度合成影像，并将其与Sentinel-1 SAR后向散射数据、植被指数（NDVI、GNDVI、NDRE、SAVI、EVI和NDMI）以及基于灰度共生矩阵（GLCM）分析提取的纹理指标进行整合。构建了一个包含72个波段的综合特征集用于分类。评估了四种机器学习算法，即随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）和多项逻辑回归（MNLR），用于果园判别。在测试的模型中，RF取得了最高的分类性能，总体精度为99.80%，Kappa系数为0.997，其次是SVM（99.30%）、MNLR（98.21%）和XGB（9.20%）。RF模型估算的芒果园面积为36,099.94公顷，与官方园艺统计数据（34,363公顷）最为接近，估算误差仅为5.05%。相比之下，SVM和MNLR分别高估了果园面积16.03%和43.37%。所提出的框架为热带果园生态系统中的业务化园艺监测、作物清单生成和精准农业规划提供了一种可靠且可扩展的方法。","Scientific Reports","2026-09-22T00:00:00Z",78,{"impact":20,"substance":18,"depth":17,"authority":20,"freshness":13,"relevant":22,"comment":374},"方法扎实、数据规模大且精度高，但属区域性作物遥感估产研究，产业影响有限，可作为技术方法类精选。",[376],{"name":370,"url":367},[27,29,378,30,379],"芒果","作物估产",{"code":381,"slug":382,"name":383,"path":384,"parent_code":385},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[387,388],{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":390},{"candidates":391,"agricultural_evidence":58},[392,397,401,405,410],{"code":381,"score":57,"supported":58,"title":393,"abstract":395,"tags":396},[394],"orchard",[378,394],[378],{"code":46,"score":57,"supported":58,"title":398,"abstract":399,"tags":400},[74],[29,74],[29],{"code":33,"score":21,"supported":58,"title":402,"abstract":403,"tags":404},[],[62,60],[379,30],{"code":107,"score":180,"supported":58,"title":406,"abstract":407,"tags":409},[],[408],"精准农业",[27],{"code":411,"score":95,"supported":58,"title":412,"abstract":413,"tags":415},"J1",[],[414],"农业规划",[],[417,418],"Navsari 芒果 遥感估产","Sentinel-2 芒果 果园面积","Navsari芒果遥感估产-3167","10.1038\u002Fs41598-026-65828-3",{"doi":420,"openalex_id":422,"authors":423,"venue":370,"cited_by_count":129,"oa_url":367,"card":440,"direction":135,"ingested_from":138},"W7213920056",[424,426,429,431,433,436,438],{"name":425,"orcid":9},"V. Raju",{"name":427,"orcid":428},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":430,"orcid":9},"Dr. V. S. Thorat",{"name":432,"orcid":9},"V. T. Shinde",{"name":434,"orcid":435},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":437,"orcid":9},"Alok Shrivastava",{"name":439,"orcid":9},"A. P. Chaudhary",{"tldr":441,"method":442,"finding":443,"direction":135,"opportunity":444},"融合多时相Sentinel-1\u002F2与机器学习，估算印度芒果园面积并分析23年种植趋势。","23年时序回归分析；Sentinel-2月合成+SAR+植被指数+GLCM纹理共","RF精度最高（总体精度99.80%，Kappa 0.997），面积估算误差仅5.05%，优于SVM和","可迁移该多时相SAR-光学特征框架至其他热带果园，并探索深度学习与物候自适应特征优化。","2026-09-22T23:30:22.619289Z",{"id":447,"title":448,"url":449,"summary":450,"summary_zh":451,"content":9,"source_name":452,"source_url":449,"published_at":453,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":454,"score_detail":455,"sources":458,"tags":460,"primary_topic":463,"secondary_topics":464,"classification":466,"search_phrases":507,"slug":510,"view_count":22,"doi":511,"paper":512,"created_at":523},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z",70,{"impact":17,"substance":285,"depth":57,"authority":456,"freshness":222,"relevant":22,"comment":457},12,"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[459],{"name":452,"url":449},[27,461,462,238,30],"农业人工智能","可持续发展",{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},[465],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":467},{"candidates":468,"agricultural_evidence":58},[469,473,478,483,487,494,500],{"code":46,"score":17,"supported":58,"title":470,"abstract":471,"tags":472},[76],[73,29,74,75,76],[461],{"code":40,"score":57,"supported":58,"title":474,"abstract":476,"tags":477},[475],"climate change",[238,475],[238],{"code":107,"score":80,"supported":58,"title":479,"abstract":480,"tags":482},[],[408,481],"precision agriculture",[27],{"code":33,"score":80,"supported":58,"title":484,"abstract":485,"tags":486},[],[62,60],[30],{"code":488,"score":87,"supported":58,"title":489,"abstract":490,"tags":493},"H4",[],[491,492],"碳排放","carbon emission",[],{"code":101,"score":87,"supported":58,"title":495,"abstract":496,"tags":499},[],[497,498],"生物多样性","biodiversity",[],{"code":501,"score":87,"supported":58,"title":502,"abstract":503,"tags":506},"I5",[],[504,505],"物联网","internet of things",[],[508,509],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":511,"openalex_id":513,"authors":514,"venue":452,"cited_by_count":129,"oa_url":449,"card":517,"direction":137,"ingested_from":138},"W7222092855",[515],{"name":516,"orcid":9},"Deepmala Suryavanshi",{"tldr":518,"method":519,"finding":520,"direction":521,"opportunity":522},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","2026-10-16T23:30:15.059867Z",{"id":525,"title":526,"url":527,"summary":528,"summary_zh":529,"content":9,"source_name":530,"source_url":527,"published_at":531,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":532,"score_detail":533,"sources":535,"tags":537,"primary_topic":540,"secondary_topics":541,"classification":544,"search_phrases":567,"slug":570,"view_count":22,"doi":571,"paper":572,"created_at":592},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",77,{"impact":285,"substance":219,"depth":17,"authority":221,"freshness":222,"relevant":22,"comment":534},"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[536],{"name":530,"url":527},[27,538,461,30,539],"无人机","病害预警",{"code":381,"slug":382,"name":383,"path":384,"parent_code":385},[542,543],{"code":46,"slug":47,"name":48,"path":49,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":545},{"candidates":546,"agricultural_evidence":58},[547,554,558,563],{"code":381,"score":220,"supported":58,"title":548,"abstract":550,"tags":553},[549],"apple",[551,552,549],"苹果","葡萄",[],{"code":46,"score":57,"supported":58,"title":555,"abstract":556,"tags":557},[75],[73,75],[461],{"code":33,"score":456,"supported":58,"title":559,"abstract":561,"tags":562},[560],"multispectral",[560],[30],{"code":107,"score":80,"supported":58,"title":564,"abstract":565,"tags":566},[],[408,481],[27],[568,569],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":571,"openalex_id":573,"authors":574,"venue":530,"cited_by_count":129,"oa_url":586,"card":587,"direction":137,"ingested_from":138},"W7221558726",[575,578,581,583],{"name":576,"orcid":577},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":579,"orcid":580},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":582,"orcid":9},"Fatih Sinan Bir",{"name":584,"orcid":585},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":588,"method":589,"finding":590,"direction":135,"opportunity":591},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","2026-10-13T23:30:10.242467Z"]