[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3248":3,"related-3248":85},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":21,"tags":23,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":70,"slug":73,"view_count":52,"doi":8,"paper":74,"created_at":84},3248,"Crop recommendation in precision agriculture: a systematic literature review of methods, trends, and challenges（精准农业中的作物推荐：方法、趋势与挑战系统综述）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37471110","MDPI 发表精准农业作物推荐方法系统综述：从183项研究中严格筛选129篇发表于2020-2026年的文章，使用PRISMA协议进行综合分析。研究表明集成学习方法（特别是随机森林和XGBoost）在各种农业数据集的预测性能上具有强大能力；支持向量机、决策树、k近邻等传统ML方法仍被广泛使用；同时CNN和LSTM被用于遥感和时间相关农业分析。最常用的数据集来源是Kaggle，典型输入包括土壤养分（NPK）、土壤pH、天气条件和NDVI、EVI等卫星指数。研究主要研究空白：有限的实时部署、低多数据源集成、低跨区域验证、低模型可解释性。研究表明可扩展、可解释的AI系统对农业实际应用具有重要意义。",null,"MDPI","2026-09-22T00:00:00Z","论文",10,false,81,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":12,"relevant":19,"comment":20},18,22,13,1,"基于PRISMA的129篇文献系统综述，梳理作物推荐主流方法与四大研究空白，对农业AI落地有参考价值。",[22],{"name":9,"url":6},[24,25,26,27,28],"智慧农业","农业人工智能","机器学习","作物推荐","遥感监测",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":37,"locked":13,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":45},[42,51,57,64],{"code":43,"score":44,"supported":45,"title":46,"abstract":49,"tags":50},"I1",20,true,[47,48],"精准农业","precision agriculture",[47],[24],{"code":30,"score":52,"supported":45,"title":53,"abstract":54,"tags":56},4,[],[55],"遥感",[28],{"code":58,"score":59,"supported":45,"title":60,"abstract":61,"tags":63},"H2",3,[],[62],"土壤",[],{"code":65,"score":66,"supported":13,"title":67,"abstract":68,"tags":69},"I2",2,[],[],[25,26],[71,72],"精准农业 作物推荐 系统综述","XGBoost 随机森林 作物推荐","精准农业作物推荐系统综述-3248",{"doi":8,"openalex_id":8,"authors":75,"venue":8,"cited_by_count":76,"oa_url":8,"card":77,"direction":81,"ingested_from":83},[],0,{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"系统综述129篇2020-2026年文献，梳理精准农业作物推荐的方法、趋势与挑战。","PRISMA协议系统综述，分析183项研究筛选出的129篇文献。","集成学习（随机森林、XGBoost）表现最强，主要空白为实时部署、多源集成、跨区域验证与可解释性。","农业人工智能与决策模型","可探索可解释、可跨区域泛化的实时作物推荐系统，并融合多源遥感与物联网数据。","agent","2026-09-23T00:04:33.331160Z",{"total":86,"page":19,"page_size":86,"items":87},6,[88,177,275,338,413,498],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":8,"source_name":94,"source_url":91,"published_at":95,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":96,"score_detail":97,"sources":102,"tags":104,"primary_topic":107,"secondary_topics":113,"classification":119,"search_phrases":148,"slug":151,"view_count":19,"doi":152,"paper":153,"created_at":176},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":98,"substance":99,"depth":16,"authority":18,"freshness":100,"relevant":19,"comment":101},16,21,9,"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。",[103],{"name":94,"url":91},[24,105,25,28,106],"无人机","病害预警",{"code":108,"slug":109,"name":110,"path":111,"parent_code":112},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[114,118],{"code":65,"slug":115,"name":116,"path":117,"parent_code":34},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":120},{"candidates":121,"agricultural_evidence":45},[122,130,137,143],{"code":108,"score":123,"supported":45,"title":124,"abstract":126,"tags":129},17,[125],"apple",[127,128,125],"苹果","葡萄",[],{"code":65,"score":131,"supported":45,"title":132,"abstract":134,"tags":136},15,[133],"deep learning",[135,133],"深度学习",[25],{"code":30,"score":138,"supported":45,"title":139,"abstract":141,"tags":142},12,[140],"multispectral",[140],[28],{"code":43,"score":144,"supported":45,"title":145,"abstract":146,"tags":147},7,[],[47,48],[24],[149,150],"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":152,"openalex_id":154,"authors":155,"venue":94,"cited_by_count":76,"oa_url":167,"card":168,"direction":174,"ingested_from":175},"W7221558726",[156,159,162,164],{"name":157,"orcid":158},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":160,"orcid":161},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":163,"orcid":8},"Fatih Sinan Bir",{"name":165,"orcid":166},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":169,"method":170,"finding":171,"direction":172,"opportunity":173},"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。","农业遥感与作物表型","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。","智慧农业 \u002F 农业物联网","openalex","2026-10-13T23:30:10.242467Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":8,"source_name":183,"source_url":180,"published_at":184,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":185,"sources":190,"tags":192,"primary_topic":195,"secondary_topics":196,"classification":204,"search_phrases":251,"slug":254,"view_count":66,"doi":255,"paper":256,"created_at":274},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",{"impact":16,"substance":17,"depth":186,"authority":187,"freshness":188,"relevant":19,"comment":189},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。",[191],{"name":183,"url":180},[24,193,26,28,194],"粮食安全","农业干旱",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[197,203],{"code":198,"slug":199,"name":200,"path":201,"parent_code":202},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":65,"slug":115,"name":116,"path":117,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":205},{"candidates":206,"agricultural_evidence":45},[207,212,218,224,230,235,241,247],{"code":30,"score":131,"supported":45,"title":208,"abstract":210,"tags":211},[209],"remote sensing",[55,209],[28],{"code":198,"score":187,"supported":45,"title":213,"abstract":215,"tags":217},[214],"drought",[216,214],"干旱",[],{"code":65,"score":12,"supported":45,"title":219,"abstract":220,"tags":223},[],[135,26,221,133,222],"machine learning","artificial intelligence",[26],{"code":225,"score":144,"supported":45,"title":226,"abstract":227,"tags":229},"A8",[],[193,228],"food security",[193],{"code":58,"score":86,"supported":45,"title":231,"abstract":232,"tags":234},[],[62,233],"soil",[],{"code":236,"score":59,"supported":45,"title":237,"abstract":238,"tags":240},"D",[],[239],"水产",[],{"code":242,"score":59,"supported":45,"title":243,"abstract":244,"tags":246},"H6",[],[245],"农业生态",[],{"code":43,"score":19,"supported":13,"title":248,"abstract":249,"tags":250},[],[],[24],[252,253],"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":255,"openalex_id":257,"authors":258,"venue":183,"cited_by_count":76,"oa_url":268,"card":269,"direction":174,"ingested_from":175},"W7221081979",[259,262,265],{"name":260,"orcid":261},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":263,"orcid":264},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":266,"orcid":267},"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":270,"method":271,"finding":272,"direction":172,"opportunity":273},"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":276,"title":277,"url":278,"summary":279,"summary_zh":280,"content":8,"source_name":281,"source_url":278,"published_at":282,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":283,"score_detail":284,"sources":286,"tags":288,"primary_topic":291,"secondary_topics":292,"classification":294,"search_phrases":313,"slug":316,"view_count":59,"doi":317,"paper":318,"created_at":337},3550,"Asymmetric Vegetation Responses to Flood Exposure in the Chi River Basin: A Multi-Temporal Remote Sensing and Machine Learning Investigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsym18101606","Flooding and drought alternate in many tropical floodplains, but how vegetation responds to both hazards over multiple years remains poorly quantified. We examined vegetation–flood associations in the Chi River Basin, Northeast Thailand, drawing on Sentinel-2 imagery from 2020, 2023, and 2024, flood records from 2017, 2018, 2021, and 2022, and machine learning methods across 230,911 point-based spatial units. Nine forms of asymmetry emerged from the analysis. The most striking was recovery asymmetry: areas that flooded at least twice between 2017 and 2022 gained vegetation during 2020–2023 (mean NDVI change = +0.0270), whereas areas with little or no flood exposure lost vegetation (mean change = −0.0430). This gap was statistically significant (Cohen’s d = 0.5586, p \u003C 0.001) and suggests that repeated flooding may buffer vegetation against subsequent drought. Agricultural land declined less (−0.0316) than forest (−0.0937; ANOVA F = 1717.7, p \u003C 0.001). Baseline vegetation condition, measured as NDVI in 2020, contributed 32.9% to model importance, more than twice the contribution of elevation (14.5%). Spectral indices together accounted for 76.6% of importance, compared with 23.5% for topographic variables. The 2023 El Niño year produced the largest difference between high-flood and low-flood areas (+0.0364); because only one year per ENSO phase was available, we treat this as a case-based comparison rather than a general ENSO response. Threshold analysis identified two distinct values: an operational cut-off at NDVI = 0.05 (overall accuracy 82.67%) and an ecological transition around 0.25–0.30. Spatial clustering was weak but significant (Moran’s I = 0.2179, p \u003C 0.001). Spatial block cross-validation gave lower accuracy (0.597) than random cross-validation (0.627), pointing to spatial autocorrelation in the data. High-flood areas had 1.67 times the vulnerability index of low-flood areas (0.4306 vs. 0.2573). These patterns support differentiated management: elevation-based zoning, warning systems calibrated to local flood regimes, and focused interventions at hotspots.","在许多热带洪泛平原，洪水与干旱交替发生，但植被如何在多年间同时响应这两种灾害，仍缺乏充分的定量研究。我们以泰国东北部栖河（Chi River）流域为研究区，考察植被与洪水的关联，数据来源包括2020年、2023年和2024年的Sentinel-2影像，2017年、2018年、2021年和2022年的洪水记录，并采用机器学习方法，覆盖230 911个基于点的空间单元。分析中出现了九种不对称形式。其中最显著的是恢复不对称：2017—2022年间至少发生两次洪水的区域，在2020—2023年间植被增加（NDVI平均变化=+0.0270），而洪水暴露很少或没有洪水暴露的区域则植被减少（平均变化=−0.0430）。这一差异具有统计显著性（Cohen’s d=0.5586，p\u003C0.001），表明反复洪水可能缓冲植被对随后干旱的响应。农田的下降幅度（−0.0316）小于森林（−0.0937；ANOVA F=1717.7，p\u003C0.001）。以2020年NDVI衡量的基线植被状况对模型重要性的贡献为32.9%，是海拔贡献（14.5%）的两倍多。光谱指数合计占重要性的76.6%，而地形变量占23.5%。2023年厄尔尼诺年（El Niño year）高洪水区与低洪水区之间的差异最大（+0.0364）；由于每个ENSO相位仅有1年数据，我们将其视为基于个案的比较，而非一般性的ENSO响应。阈值分析识别出两个不同的值：操作性截断值为NDVI=0.05（总体精度82.67%），生态过渡值约为0.25~0.30。空间聚类较弱但显著（Moran’s I=0.2179，p\u003C0.001）。空间分块交叉验证的精度（0.597）低于随机交叉验证（0.627），表明数据中存在空间自相关。高洪水区的脆弱性指数是低洪水区的1.67倍（0.4306对0.2573）。这些格局支持差异化治理：基于海拔的分区、根据当地洪水情势校准的预警系统，以及在热点区域的重点干预。","Symmetry","2026-09-25T00:00:00Z",70,{"impact":188,"substance":17,"depth":16,"authority":18,"freshness":100,"relevant":19,"comment":285},"基于Sentinel-2与机器学习的多时相洪涝-植被响应研究，数据规模大、结论具体，对农业遥感与灾害风险管理有参考价值，但属区域案例研究，公共影响有限。",[287],{"name":281,"url":278},[24,25,28,289,290],"洪涝灾害","植被恢复",{"code":65,"slug":115,"name":116,"path":117,"parent_code":34},[293],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":295},{"candidates":296,"agricultural_evidence":45},[297,301,305,309],{"code":65,"score":131,"supported":45,"title":298,"abstract":299,"tags":300},[221],[26,221],[25],{"code":30,"score":12,"supported":45,"title":302,"abstract":303,"tags":304},[209],[],[290,28],{"code":198,"score":144,"supported":45,"title":306,"abstract":307,"tags":308},[],[216,214],[289],{"code":43,"score":19,"supported":13,"title":310,"abstract":311,"tags":312},[],[],[24],[314,315],"湄公河支流 流域 遥感 洪水","Sentinel-2 NDVI 洪涝 植被","湄公河支流流域遥感洪水-3550","10.3390\u002Fsym18101606",{"doi":317,"openalex_id":319,"authors":320,"venue":281,"cited_by_count":76,"oa_url":278,"card":332,"direction":172,"ingested_from":175},"W7214283372",[321,323,326,329],{"name":322,"orcid":8},"Jiradech Majandang",{"name":324,"orcid":325},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":327,"orcid":328},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":330,"orcid":331},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",{"tldr":333,"method":334,"finding":335,"direction":172,"opportunity":336},"利用多时相遥感和机器学习揭示泰国湄公河支流流域植被对洪水的非对称响应。","Sentinel-2影像、洪水记录与机器学习，分析23万个空间单元。","重复洪水区植被增加，低洪水区减少，表明洪水可缓冲干旱影响。","可探索洪水-干旱交替下植被恢复机制，并开发基于阈值的差异化预警系统。","2026-09-26T23:30:29.514361Z",{"id":339,"title":340,"url":341,"summary":342,"summary_zh":343,"content":8,"source_name":344,"source_url":341,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":345,"score_detail":346,"sources":348,"tags":350,"primary_topic":353,"secondary_topics":354,"classification":357,"search_phrases":383,"slug":386,"view_count":59,"doi":387,"paper":388,"created_at":412},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",78,{"impact":187,"substance":17,"depth":16,"authority":187,"freshness":12,"relevant":19,"comment":347},"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。",[349],{"name":344,"url":341},[24,26,351,28,352],"芒果","作物估产",{"code":108,"slug":109,"name":110,"path":111,"parent_code":112},[355,356],{"code":65,"slug":115,"name":116,"path":117,"parent_code":34},{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":358},{"candidates":359,"agricultural_evidence":45},[360,365,369,373,377],{"code":108,"score":131,"supported":45,"title":361,"abstract":363,"tags":364},[362],"orchard",[351,362],[351],{"code":65,"score":131,"supported":45,"title":366,"abstract":367,"tags":368},[221],[26,221],[26],{"code":30,"score":188,"supported":45,"title":370,"abstract":371,"tags":372},[],[55,209],[352,28],{"code":43,"score":52,"supported":45,"title":374,"abstract":375,"tags":376},[],[47],[24],{"code":378,"score":59,"supported":45,"title":379,"abstract":380,"tags":382},"J1",[],[381],"农业规划",[],[384,385],"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":387,"openalex_id":389,"authors":390,"venue":344,"cited_by_count":76,"oa_url":341,"card":407,"direction":172,"ingested_from":175},"W7213920056",[391,393,396,398,400,403,405],{"name":392,"orcid":8},"V. Raju",{"name":394,"orcid":395},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":397,"orcid":8},"Dr. V. S. Thorat",{"name":399,"orcid":8},"V. T. Shinde",{"name":401,"orcid":402},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":404,"orcid":8},"Alok Shrivastava",{"name":406,"orcid":8},"A. P. Chaudhary",{"tldr":408,"method":409,"finding":410,"direction":172,"opportunity":411},"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":414,"title":415,"url":416,"summary":417,"summary_zh":418,"content":8,"source_name":419,"source_url":416,"published_at":420,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":96,"score_detail":421,"sources":423,"tags":425,"primary_topic":427,"secondary_topics":428,"classification":435,"search_phrases":472,"slug":475,"view_count":52,"doi":476,"paper":477,"created_at":497},2659,"Application of Remote Sensing and Machine Learning in Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189472","Global agriculture is undergoing a period of profound transformation, driven by the need to increase food production in a context characterized by climate change, the degradation of natural resources and increasing pressure on agricultural ecosystems [...]","全球农业正经历一场深刻变革，其驱动力是在气候变化、自然资源退化以及农业生态系统压力日益增大的背景下提高粮食产量的需求。[...]","Sustainability","2026-09-16T00:00:00Z",{"impact":16,"substance":44,"depth":123,"authority":18,"freshness":100,"relevant":19,"comment":422},"发表于核心期刊的遥感与机器学习综述，方法视角新颖、时效性强，对智慧农业技术路线有参考价值，值得进入每日精选。",[424],{"name":419,"url":416},[24,25,26,426,55],"可持续农业",{"code":65,"slug":115,"name":116,"path":117,"parent_code":34},[429,434],{"code":430,"slug":431,"name":432,"path":433,"parent_code":202},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":436},{"candidates":437,"agricultural_evidence":45},[438,442,447,451,457,464,468],{"code":65,"score":12,"supported":45,"title":439,"abstract":440,"tags":441},[221],[],[25,26],{"code":430,"score":100,"supported":45,"title":443,"abstract":445,"tags":446},[444],"sustainable agriculture",[],[426],{"code":30,"score":100,"supported":45,"title":448,"abstract":449,"tags":450},[209],[],[55],{"code":198,"score":86,"supported":45,"title":452,"abstract":453,"tags":456},[],[454,455],"气候变化","climate change",[],{"code":458,"score":86,"supported":45,"title":459,"abstract":460,"tags":463},"J3",[],[461,462],"全球农业","global agriculture",[],{"code":242,"score":59,"supported":45,"title":465,"abstract":466,"tags":467},[],[245],[],{"code":43,"score":19,"supported":13,"title":469,"abstract":470,"tags":471},[],[],[24],[473,474],"农业人工智能 可持续农业 智慧农业 机器学习","农业人工智能 可持续农业","农业人工智能可持续农业智慧农业机器学习-2659","10.3390\u002Fsu18189472",{"doi":476,"openalex_id":478,"authors":479,"venue":419,"cited_by_count":76,"oa_url":416,"card":492,"direction":172,"ingested_from":175},"W7213301703",[480,483,486,489],{"name":481,"orcid":482},"Mihai Valentin Herbei","https:\u002F\u002Forcid.org\u002F0000-0002-3884-3658",{"name":484,"orcid":485},"Ana-Cornelia Badea","https:\u002F\u002Forcid.org\u002F0000-0003-4521-5403",{"name":487,"orcid":488},"Aleksandar Ristić","https:\u002F\u002Forcid.org\u002F0000-0003-0979-3345",{"name":490,"orcid":491},"Paul Sestraș","https:\u002F\u002Forcid.org\u002F0000-0002-8554-0924",{"tldr":493,"method":494,"finding":495,"direction":172,"opportunity":496},"综述遥感与机器学习在可持续农业中的应用现状与前景。","综述遥感数据与机器学习方法在农业中的应用。","遥感结合机器学习可提升农业监测与可持续管理能力。","可探索多源遥感与可解释机器学习融合，用于小农户精准决策与碳核算。","2026-09-16T23:30:28.640661Z",{"id":499,"title":500,"url":501,"summary":502,"summary_zh":503,"content":8,"source_name":504,"source_url":501,"published_at":505,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":506,"score_detail":507,"sources":509,"tags":511,"primary_topic":514,"secondary_topics":515,"classification":516,"search_phrases":537,"slug":540,"view_count":66,"doi":541,"paper":542,"created_at":571},2536,"Mapping Native Grass Cover with Random Forest Models: Sentinel-2 Versus Sentinel-2 Combined with Sentinel-1 SAR-Derived GLCM Texture Metrics","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183150","Temperate native grasslands in southeastern Australia have been extensively cleared for agriculture, and the remaining patches are under growing pressure from further land use change, climate variability, and invasive species. Mapping and monitoring their distribution and the cover of native and exotic grasses are critical for their conservation and management. Field-based methods are not always scalable or time-effective, and this study aimed to develop a scalable method to map and monitor the fractional cover-class maps of native C3 and native C4 grass cover as a component of remnant native grasslands on the western outskirts of Melbourne, Victoria, Australia. Field-based reference data for training and validation of random forest machine learning models were collected across multiple sites in 2021. Sentinel-2 optical spectral bands and vegetation indices were used as the primary input data, and Sentinel-1 Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture metrics were assessed for their capacity to improve the model. Results show that random forest models trained on Sentinel-2 data without GLCM texture information derived from Sentinel-1 SAR data provided a moderate overall accuracy (C3: 59.1%, C4: 78.1%). Class-specific metrics showed that reliability was highest for better represented lower-cover classes, particularly the 6–25% native C3 class and the 0–5% native C4 class, while higher-cover classes were less reliable because of the limited number of training and validation samples. Grass cover fractions were modelled well for sparse to moderate grass cover, but dense grass cover was not modelled accurately, probably due to limited high-cover samples in the training dataset. Model performance was not improved by the inclusion of Sentinel-1 SAR-derived GLCM texture metrics, indicating that C-band VH-polarised SAR is not sensitive to the fine-scale structural heterogeneity that characterises native grassland ecosystems. Sparse native C3 and C4 grasses could be mapped most reliably in the lower-cover classes as a component of grasslands with optical remote sensing, and the method developed here can now be applied to enable evidence-based management of grasslands, biodiversity conservation and the monitoring of grassland composition in the WGR and elsewhere. Higher-resolution structural datasets and more sophisticated machine learning approaches may be required to accurately predict native C3 and C4 grass cover fractions in denser grasslands.","澳大利亚东南部的温带原生草原已被大面积开垦用于农业，残余斑块正面临土地利用进一步变化、气候变率和入侵物种日益增大的压力。对其分布以及原生和外来草类覆盖度进行制图和监测，对于草原的保护和管理至关重要。基于实地调查的方法并非总是可扩展或省时的，本研究旨在开发一种可扩展的方法，以制图和监测作为残余原生草原组成部分的原生C3和原生C4草类覆盖度的分数覆盖等级图，研究区位于澳大利亚维多利亚州墨尔本西郊。用于训练和验证随机森林机器学习模型的实地参考数据于2021年在多个样点采集。研究以Sentinel-2光学光谱波段和植被指数作为主要输入数据，并评估了Sentinel-1合成孔径雷达（SAR）衍生的灰度共生矩阵（GLCM）纹理指标对提升模型性能的能力。结果表明，仅使用Sentinel-2数据（不含Sentinel-1 SAR衍生的GLCM纹理信息）训练的随机森林模型提供了中等的总体精度（C3：59.1%，C4：78.1%）。分类别指标显示，对于代表性较好的低覆盖度类别，可靠性最高，尤其是6–25%原生C3类别和0–5%原生C4类别，而较高覆盖度类别的可靠性较低，原因是训练和验证样本数量有限。草类覆盖度分数在稀疏至中等草类覆盖条件下建模效果良好，但茂密草类覆盖未能准确建模，可能是由于训练数据集中高覆盖度样本有限。纳入Sentinel-1 SAR衍生的GLCM纹理指标并未改善模型性能，表明C波段VH极化SAR对原生草原生态系统所特有的精细尺度结构异质性不敏感。作为草原的组成部分，稀疏的原生C3和C4草类在低覆盖度类别中利用光学遥感可最可靠地制图，本研究开发的方法现可应用于西维多利亚草原（WGR）及其他地区，以实现基于证据的草原管理、生物多样性保护和草原组成监测。准确预测原生C3和C4草类覆盖度分数可能需要更高分辨率的结构数据集和更先进的机器学习方法。","Remote Sensing","2026-09-13T00:00:00Z",71,{"impact":138,"substance":44,"depth":123,"authority":187,"freshness":188,"relevant":19,"comment":508},"方法新颖、结论明确（SAR纹理未提升精度），对草地遥感监测有参考价值，但属细分领域研究，影响范围有限。",[510],{"name":504,"url":501},[24,26,28,512,513],"草原生态","植被覆盖",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":517},{"candidates":518,"agricultural_evidence":45},[519,523,529,533],{"code":30,"score":188,"supported":45,"title":520,"abstract":521,"tags":522},[],[55,209],[513,28],{"code":242,"score":144,"supported":45,"title":524,"abstract":525,"tags":528},[],[526,527],"生物多样性","biodiversity",[512],{"code":65,"score":144,"supported":45,"title":530,"abstract":531,"tags":532},[],[26,221],[26],{"code":43,"score":19,"supported":13,"title":534,"abstract":535,"tags":536},[],[],[24],[538,539],"智慧农业 机器学习 植被覆盖 草原生态","智慧农业 机器学习","智慧农业机器学习植被覆盖草原生态-2536","10.3390\u002Frs18183150",{"doi":541,"openalex_id":543,"authors":544,"venue":504,"cited_by_count":76,"oa_url":501,"card":566,"direction":172,"ingested_from":175},"W7212561645",[545,548,551,554,556,558,561,563],{"name":546,"orcid":547},"Sabah Sabaghy","https:\u002F\u002Forcid.org\u002F0000-0002-9453-8922",{"name":549,"orcid":550},"M. Abuzar","https:\u002F\u002Forcid.org\u002F0000-0002-6101-1307",{"name":552,"orcid":553},"Steve J. Sinclair","https:\u002F\u002Forcid.org\u002F0000-0002-4282-1021",{"name":555,"orcid":8},"Tony Dugdale",{"name":557,"orcid":8},"Vanessa Hutchins",{"name":559,"orcid":560},"Yogendra K. Karna","https:\u002F\u002Forcid.org\u002F0000-0002-2120-4710",{"name":562,"orcid":8},"Jonathan Wilson",{"name":564,"orcid":565},"Kathryn Sheffield","https:\u002F\u002Forcid.org\u002F0000-0003-2624-9739",{"tldr":567,"method":568,"finding":569,"direction":172,"opportunity":570},"用随机森林结合Sentinel-2与Sentinel-1纹理特征，绘制澳洲原生草地C3\u002FC4草覆盖度","2021年野外样点训练随机森林，Sentinel-2光谱与植被指数为主，Sent","仅用Sentinel-2精度中等（C3 59.1%、C4 78.1%），加入SAR纹理未提升，高覆盖","高覆盖度草地样本不足且C波段SAR不敏感，可探索高分辨率结构数据与深度模型提升密草覆盖反演。","2026-09-15T23:30:21.287053Z"]