[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4092":3,"related-4092":99},{"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":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":98},4092,"Integrating multi-sensor optical and SAR data with machine learning ensembles for chickpea area estimation in the Krishna zone of Andhra Pradesh","https:\u002F\u002Fdoi.org\u002F10.33545\u002F2618060x.2026.v9.i10sa.6760","This study estimated chickpea (Cicer arietinum L.) cultivation area using geospatial technologies by combining optical and microwave remote sensing data in the Krishna zone of Andhra Pradesh during the rabi season of 2025-2026. The study employed 143 georeferenced ground truth observations for crop classification using Landsat-9 OLI, Sentinel-2 MSI, and Sentinel-1 SAR data. Two different workflows were used to assess chickpea area estimation, a Maximum Likelihood Classification (MLC) in ENVI software and an advanced machine learning ensemble classifier that combined Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) algorithms applied to a 21 band multitemporal feature stack created in Google Earth Engine (GEE) and processed in R software. MLC technique estimated a total chickpea area of 87,004 ha with an overall accuracy of 88.23% ( κ=0.74 κ=0.74), the machine learning ensemble classifier significantly outperformed MLC, identifying a total area of 1,33,216 ha with a higher overall accuracy of 94.11% ( κ=0.88 κ=0.88). The multi-sensor ensemble approach significantly improved the spatial representation of chickpea distribution across the six districts of the Krishna Zone, according to a comparative analysis with Department of Agriculture (DoA) statistics. This highlights the potential of machine learning ensemble classification frameworks for reliable regional crop monitoring.","本研究利用地理空间技术，结合光学与微波遥感数据，对安得拉邦克里希纳地区2025—2026年rabi季鹰嘴豆（Cicer arietinum L.）种植面积进行了估算。研究采用143个地理参考地面真值观测数据，基于Landsat-9 OLI、Sentinel-2 MSI和Sentinel-1 SAR数据进行作物分类。采用两种不同工作流程评估鹰嘴豆面积估算：一种是在ENVI软件中进行的最大似然分类（MLC），另一种是先进的机器学习集成分类器，该分类器将随机森林（RF）、支持向量机（SVM）和梯度提升机（GBM）算法相结合，应用于在Google Earth Engine（GEE）中创建的21波段多时相特征堆栈，并在R软件中进行处理。MLC技术估算鹰嘴豆总面积为87,004 ha，总体精度为88.23%（κ=0.74）；机器学习集成分类器显著优于MLC，识别总面积为1,33,216 ha，总体精度更高，达94.11%（κ=0.88）。与农业部（DoA）统计数据相比，多传感器集成方法显著改善了克里希纳地区六个区鹰嘴豆分布的空间表征。这凸显了机器学习集成分类框架在可靠区域作物监测方面的潜力。",null,"International Journal of Research in Agronomy","2026-10-01T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"多源光学与SAR遥感结合机器学习集成分类估算鹰嘴豆面积，方法新颖、精度提升明显，对区域作物监测有参考价值，但属区域性研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","机器学习","遥感监测","作物面积估算","鹰嘴豆",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,60],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},15,true,[49],"machine learning",[28,49],[28],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":59},"I3",7,[],[57,58],"遥感","remote sensing",[29],{"code":61,"score":22,"supported":14,"title":62,"abstract":63,"tags":64},"I1",[],[],[27],[66,67],"作物面积估算 智慧农业 机器学习 遥感监测","作物面积估算 智慧农业","作物面积估算智慧农业机器学习遥感监测-4092",4,"10.33545\u002F2618060x.2026.v9.i10sa.6760",{"doi":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":90,"oa_url":9,"card":91,"direction":95,"ingested_from":97},"W7215997699",[74,76,78,80,82,84,86,88],{"name":75,"orcid":9},"A Harshitha",{"name":77,"orcid":9},"Sunil M Kumar",{"name":79,"orcid":9},"AS Bharathi",{"name":81,"orcid":9},"TV Sridhar",{"name":83,"orcid":9},"IB Ramarao",{"name":85,"orcid":9},"Y Srilatha Ch. Vani",{"name":87,"orcid":9},"AK Mrudula",{"name":89,"orcid":9},"AD Grace",0,{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"融合光学与SAR多时相数据，用机器学习集成分类器估算安得拉邦鹰嘴豆种植面积。","Landsat-9、Sentinel-2与Sentinel-1数据，GEE构建2","集成分类器精度94.11%（κ=0.88），优于最大似然分类的88.23%，面积估算更接近农业部门统","农业遥感与作物表型","可探索多源遥感与集成学习在更多小宗作物、跨区域和跨年份的迁移能力与不确定性评估。","openalex","2026-10-03T23:30:35.319788Z",{"total":100,"page":22,"page_size":100,"items":101},6,[102,191,295,361,438,520],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":114,"tags":116,"primary_topic":119,"secondary_topics":125,"classification":133,"search_phrases":167,"slug":170,"view_count":90,"doi":171,"paper":172,"created_at":190},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":112,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":113},16,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[115],{"name":108,"url":105},[27,28,29,117,118],"土壤墒情","冬小麦",{"code":120,"slug":121,"name":122,"path":123,"parent_code":124},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[126,132],{"code":127,"slug":128,"name":129,"path":130,"parent_code":131},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":134},{"candidates":135,"agricultural_evidence":47},[136,142,147,151,156,163],{"code":120,"score":137,"supported":47,"title":138,"abstract":140,"tags":141},26,[139,121],"winter wheat",[122,139,121],[118],{"code":127,"score":46,"supported":47,"title":143,"abstract":144,"tags":146},[128],[145,128],"土壤",[117],{"code":33,"score":46,"supported":47,"title":148,"abstract":149,"tags":150},[49],[28,49],[28],{"code":53,"score":13,"supported":47,"title":152,"abstract":153,"tags":155},[],[57,58,154],"multispectral",[29],{"code":157,"score":100,"supported":47,"title":158,"abstract":159,"tags":162},"H3",[],[160,161],"灌溉","irrigation",[],{"code":61,"score":69,"supported":47,"title":164,"abstract":165,"tags":166},[],[27],[27],[168,169],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":171,"openalex_id":173,"authors":174,"venue":108,"cited_by_count":90,"oa_url":105,"card":184,"direction":189,"ingested_from":97},"W7223156204",[175,178,180,182],{"name":176,"orcid":177},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":179,"orcid":9},"Yi Zhang",{"name":181,"orcid":9},"WEI Lei",{"name":183,"orcid":9},"Chengcai Zhang",{"tldr":185,"method":186,"finding":187,"direction":95,"opportunity":188},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":192,"title":193,"url":194,"summary":195,"summary_zh":196,"content":9,"source_name":197,"source_url":194,"published_at":198,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":199,"score_detail":200,"sources":207,"tags":209,"primary_topic":212,"secondary_topics":216,"classification":223,"search_phrases":270,"slug":273,"view_count":274,"doi":275,"paper":276,"created_at":294},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":201,"substance":202,"depth":203,"authority":204,"freshness":205,"relevant":22,"comment":206},18,22,19,14,8,"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[208],{"name":197,"url":194},[27,210,28,29,211],"粮食安全","农业干旱",{"code":53,"slug":213,"name":214,"path":215,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[217,222],{"code":218,"slug":219,"name":220,"path":221,"parent_code":131},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":224},{"candidates":225,"agricultural_evidence":47},[226,230,236,243,249,253,260,266],{"code":53,"score":46,"supported":47,"title":227,"abstract":228,"tags":229},[58],[57,58],[29],{"code":218,"score":204,"supported":47,"title":231,"abstract":233,"tags":235},[232],"drought",[234,232],"干旱",[],{"code":33,"score":13,"supported":47,"title":237,"abstract":238,"tags":242},[],[239,28,49,240,241],"深度学习","deep learning","artificial intelligence",[28],{"code":244,"score":54,"supported":47,"title":245,"abstract":246,"tags":248},"A8",[],[210,247],"food security",[210],{"code":127,"score":100,"supported":47,"title":250,"abstract":251,"tags":252},[],[145,128],[],{"code":254,"score":255,"supported":47,"title":256,"abstract":257,"tags":259},"D",3,[],[258],"水产",[],{"code":261,"score":255,"supported":47,"title":262,"abstract":263,"tags":265},"H6",[],[264],"农业生态",[],{"code":61,"score":22,"supported":14,"title":267,"abstract":268,"tags":269},[],[],[27],[271,272],"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":275,"openalex_id":277,"authors":278,"venue":197,"cited_by_count":90,"oa_url":288,"card":289,"direction":189,"ingested_from":97},"W7221081979",[279,282,285],{"name":280,"orcid":281},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":283,"orcid":284},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":286,"orcid":287},"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":290,"method":291,"finding":292,"direction":95,"opportunity":293},"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。","2026-10-10T23:30:14.933078Z",{"id":296,"title":297,"url":298,"summary":299,"summary_zh":300,"content":9,"source_name":301,"source_url":298,"published_at":302,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":303,"score_detail":304,"sources":306,"tags":308,"primary_topic":310,"secondary_topics":311,"classification":313,"search_phrases":340,"slug":343,"view_count":22,"doi":344,"paper":345,"created_at":360},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":201,"substance":202,"depth":201,"authority":204,"freshness":205,"relevant":22,"comment":305},"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。",[307],{"name":301,"url":298},[27,28,29,211,309],"预警系统",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[312],{"code":218,"slug":219,"name":220,"path":221,"parent_code":131},{"status":40,"locked":14,"version":41,"method":42,"evidence":314},{"candidates":315,"agricultural_evidence":47},[316,320,324,328,332,336],{"code":33,"score":46,"supported":47,"title":317,"abstract":318,"tags":319},[49],[28,49],[28],{"code":218,"score":204,"supported":47,"title":321,"abstract":322,"tags":323},[232],[234,232],[],{"code":244,"score":100,"supported":47,"title":325,"abstract":326,"tags":327},[],[210,247],[],{"code":127,"score":100,"supported":47,"title":329,"abstract":330,"tags":331},[],[145,128],[],{"code":53,"score":69,"supported":47,"title":333,"abstract":334,"tags":335},[],[57],[29],{"code":61,"score":22,"supported":14,"title":337,"abstract":338,"tags":339},[],[],[27],[341,342],"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":344,"openalex_id":346,"authors":347,"venue":301,"cited_by_count":90,"oa_url":9,"card":355,"direction":189,"ingested_from":97},"W7214639469",[348,351,353],{"name":349,"orcid":350},"Gbolahan S. Badru","https:\u002F\u002Forcid.org\u002F0000-0003-4020-6356",{"name":352,"orcid":9},"Shakirudeen Odunuga",{"name":354,"orcid":9},"Michael Adebisi Adeyemi",{"tldr":356,"method":357,"finding":358,"direction":95,"opportunity":359},"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":362,"title":363,"url":364,"summary":365,"summary_zh":366,"content":9,"source_name":367,"source_url":364,"published_at":368,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":369,"score_detail":370,"sources":372,"tags":374,"primary_topic":377,"secondary_topics":382,"classification":384,"search_phrases":404,"slug":407,"view_count":255,"doi":408,"paper":409,"created_at":437},3485,"Detection of Red Crown Rot of Soybean in Illinois Fields Using High-Resolution Satellite Imagery and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.22.753594","Red crown rot (RCR), caused by Calonectria ilicicola, is an emerging soybean disease in the U.S. Midwest for which scalable approaches to characterize within-field disease distribution are lacking. This study evaluated high-resolution PlanetScope satellite imagery for mapping RCR-affected soybean canopies across 15 commercial fields in Illinois surveyed during the 2024 and 2025 growing seasons. A total of 2,921 georeferenced canopy plots were classified as asymptomatic or RCR-affected and paired with six multispectral bands and seven vegetation indices. Spectral differences between classes were evaluated using linear mixed-effects models, and seven machine-learning classifiers representing linear, tree-based, neural-network, kernel, and probabilistic approaches were compared using spatially independent leave-one-field-out cross-validation. RCR-affected canopies exhibited increased reflectance in the visible and red-edge regions, reduced near-infrared reflectance, and lower vegetation-index values relative to asymptomatic canopies. All classifiers showed strong discrimination, with ROC-AUC values ranging from 0.963 to 0.982. Regularized logistic regression achieved the highest overall performance, with an accuracy of 0.945, balanced accuracy of 0.945, F1-score of 0.948, and ROC-AUC of 0.982 at the optimized decision threshold. Permutation analysis identified EVI, NDVI, and red reflectance as the most influential predictors across representative model architectures. Satellite-derived probability and classification maps generally corresponded with symptomatic canopy patterns observed in high-resolution UAV imagery, although mixed pixels reduced precision near disease-patch boundaries. These results demonstrate the potential of high-resolution satellite imagery for within-field mapping of RCR-associated canopy symptoms across independent commercial soybean fields.","由冬青丽赤壳菌（Calonectria ilicicola）引起的红冠腐病（RCR）是美国中西部一种新发大豆病害，目前尚缺乏可规模化表征田块内病害分布的方法。本研究评估了高分辨率PlanetScope卫星影像在2024年和2025年生长季对伊利诺伊州15块商业田块中受RCR影响的大豆冠层的制图能力。共有2,921个地理参考冠层样区被分类为无症状或受RCR影响，并与6个多光谱波段和7个植被指数配对。采用线性混合效应模型评估类别间的光谱差异，并通过空间独立的留一田块交叉验证比较了7种机器学习分类器，涵盖线性、基于树、神经网络、核函数和概率方法。与无症状冠层相比，受RCR影响的冠层在可见光和红边区域反射率增加，近红外反射率降低，植被指数值较低。所有分类器均表现出较强的判别能力，ROC-AUC值范围为0.963至0.982。正则化逻辑回归在优化决策阈值下取得了最高的整体性能，准确率为0.945，平衡准确率为0.945，F1分数为0.948，ROC-AUC为0.982。置换分析表明，在代表性模型架构中，EVI、NDVI和红光反射率是最具影响力的预测变量。卫星衍生的概率图和分类图总体上与高分辨率无人机影像中观察到的症状冠层模式一致，尽管混合像元降低了病害斑块边界附近的精度。这些结果证明了高分辨率卫星影像在独立商业大豆田块中对RCR相关冠层症状进行田块内制图的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-09-23T00:00:00Z",76,{"impact":112,"substance":202,"depth":201,"authority":17,"freshness":205,"relevant":22,"comment":371},"基于高分辨率卫星影像与机器学习实现大豆红冠腐病田间制图，方法扎实、数据规模可观，对作物病害遥感监测有参考价值。",[373],{"name":367,"url":364},[27,28,375,29,376],"大豆","病害识别",{"code":378,"slug":379,"name":380,"path":381,"parent_code":124},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[383],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":385},{"candidates":386,"agricultural_evidence":47},[387,392,396,400],{"code":378,"score":46,"supported":47,"title":388,"abstract":390,"tags":391},[389],"soybean",[375,389],[375],{"code":33,"score":20,"supported":47,"title":393,"abstract":394,"tags":395},[49],[28],[28,376],{"code":53,"score":69,"supported":47,"title":397,"abstract":398,"tags":399},[],[154],[29],{"code":61,"score":22,"supported":14,"title":401,"abstract":402,"tags":403},[],[],[27],[405,406],"伊利诺伊 大豆 红冠腐病 卫星遥感","PlanetScope 大豆 病害 机器学习","伊利诺伊大豆红冠腐病卫星遥感-3485","10.64898\u002F2026.09.22.753594",{"doi":408,"openalex_id":410,"authors":411,"venue":367,"cited_by_count":90,"oa_url":431,"card":432,"direction":95,"ingested_from":97},"W7214126630",[412,415,418,420,422,425,428],{"name":413,"orcid":414},"Bruno Daniel Pugliese","https:\u002F\u002Forcid.org\u002F0009-0000-4795-4248",{"name":416,"orcid":417},"Juan Andrés Paredes","https:\u002F\u002Forcid.org\u002F0000-0002-3967-0197",{"name":419,"orcid":9},"Andres Fabian Ruiz",{"name":421,"orcid":9},"Norman Denis Bowman",{"name":423,"orcid":424},"Elhan S. Ersoz","https:\u002F\u002Forcid.org\u002F0000-0001-6930-6946",{"name":426,"orcid":427},"Nicolas Federico Martin","https:\u002F\u002Forcid.org\u002F0000-0002-1587-665X",{"name":429,"orcid":430},"Boris X. Camiletti","https:\u002F\u002Forcid.org\u002F0000-0002-1492-7988","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F09\u002F23\u002F2026.09.22.753594.full.pdf",{"tldr":433,"method":434,"finding":435,"direction":95,"opportunity":436},"利用高分辨率卫星影像和机器学习在大豆田中检测红冠腐病。","PlanetScope多光谱影像、植被指数与7种机器学习分类器，留一田块交叉验证","各分类器ROC-AUC达0.963-0.982，正则化逻辑回归最优，EVI、NDVI和红波段最重要。","可探索多时相卫星影像与无人机融合，提升病害斑块边界混合像元识别精度。","2026-09-25T23:30:22.839600Z",{"id":439,"title":440,"url":441,"summary":442,"summary_zh":443,"content":9,"source_name":444,"source_url":441,"published_at":445,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":446,"score_detail":447,"sources":449,"tags":451,"primary_topic":454,"secondary_topics":460,"classification":463,"search_phrases":490,"slug":493,"view_count":255,"doi":494,"paper":495,"created_at":519},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":204,"substance":202,"depth":201,"authority":204,"freshness":13,"relevant":22,"comment":448},"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。",[450],{"name":444,"url":441},[27,28,452,29,453],"芒果","作物估产",{"code":455,"slug":456,"name":457,"path":458,"parent_code":459},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[461,462],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":53,"slug":213,"name":214,"path":215,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":464},{"candidates":465,"agricultural_evidence":47},[466,471,475,479,484],{"code":455,"score":46,"supported":47,"title":467,"abstract":469,"tags":470},[468],"orchard",[452,468],[452],{"code":33,"score":46,"supported":47,"title":472,"abstract":473,"tags":474},[49],[28,49],[28],{"code":53,"score":205,"supported":47,"title":476,"abstract":477,"tags":478},[],[57,58],[453,29],{"code":61,"score":69,"supported":47,"title":480,"abstract":481,"tags":483},[],[482],"精准农业",[27],{"code":485,"score":255,"supported":47,"title":486,"abstract":487,"tags":489},"J1",[],[488],"农业规划",[],[491,492],"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":494,"openalex_id":496,"authors":497,"venue":444,"cited_by_count":90,"oa_url":441,"card":514,"direction":95,"ingested_from":97},"W7213920056",[498,500,503,505,507,510,512],{"name":499,"orcid":9},"V. Raju",{"name":501,"orcid":502},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":504,"orcid":9},"Dr. V. S. Thorat",{"name":506,"orcid":9},"V. T. Shinde",{"name":508,"orcid":509},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":511,"orcid":9},"Alok Shrivastava",{"name":513,"orcid":9},"A. P. Chaudhary",{"tldr":515,"method":516,"finding":517,"direction":95,"opportunity":518},"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":521,"title":522,"url":523,"summary":524,"summary_zh":525,"content":9,"source_name":526,"source_url":523,"published_at":527,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":90,"score_detail":528,"sources":530,"tags":532,"primary_topic":535,"secondary_topics":536,"classification":542,"search_phrases":564,"slug":567,"view_count":90,"doi":568,"paper":569,"created_at":586},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":90,"substance":90,"depth":90,"authority":90,"freshness":90,"relevant":90,"comment":529},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[531],{"name":526,"url":523},[28,29,533,534],"土地利用","城市扩张",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[537],{"code":538,"slug":539,"name":540,"path":541,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":40,"locked":14,"version":41,"method":42,"evidence":543},{"candidates":544,"agricultural_evidence":47},[545,549,556,560],{"code":33,"score":46,"supported":47,"title":546,"abstract":547,"tags":548},[49],[28,49],[28],{"code":538,"score":21,"supported":47,"title":550,"abstract":551,"tags":555},[],[552,553,554],"空间分析","GIS","spatial analysis",[],{"code":53,"score":54,"supported":47,"title":557,"abstract":558,"tags":559},[],[57,58],[29],{"code":261,"score":22,"supported":14,"title":561,"abstract":562,"tags":563},[],[],[533],[565,566],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":568,"openalex_id":570,"authors":571,"venue":526,"cited_by_count":90,"oa_url":523,"card":581,"direction":95,"ingested_from":97},"W7223022155",[572,575,577,579],{"name":573,"orcid":574},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":576,"orcid":9},"Goanar Jal Dup",{"name":578,"orcid":9},"Weyesa Merga Chemeda",{"name":580,"orcid":9},"Shankar Karuppannan",{"tldr":582,"method":583,"finding":584,"direction":95,"opportunity":585},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z"]