[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5241":3,"related-5241":74},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":52,"slug":55,"view_count":56,"doi":57,"paper":58,"created_at":73},5241,"Assessing Nutritional Status in Super-High-Density Olive Orchards: Integrating Diagnostic Models and NDVI-Based Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11156833\u002Fv1","Assessing Nutritional Status in Super-High-Density Olive Orchards: Integrating Diagnostic Models and NDVI-Based Remote Sensing。Research Square",null,"Research Square","2026-10-16T00:00:00Z","论文",10,false,72,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,18,17,12,9,1,"该研究将诊断模型与NDVI遥感结合用于超密植油橄榄园营养评估，方法有创新、对智慧果园管理有参考价值，但属预印本且应用面偏窄，可入精选但非头条。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","遥感","NDVI","果园管理","油橄榄",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":13,"reason":51},[38,46],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":45},"I3",13,true,[43],"remote sensing",[43],[28,27],{"code":47,"score":21,"supported":13,"title":48,"abstract":49,"tags":50},"I1",[],[],[26],"农业相关性需要复核",[53,54],"超密植 油橄榄 营养诊断","NDVI 遥感 果园","超密植油橄榄营养诊断-5241",0,"10.21203\u002Frs.3.rs-11156833\u002Fv1",{"doi":57,"openalex_id":59,"authors":60,"venue":9,"cited_by_count":56,"oa_url":70,"card":8,"direction":71,"ingested_from":72},"W7222710807",[61,64,67],{"name":62,"orcid":63},"Mahrokh Sharifmand","https:\u002F\u002Forcid.org\u002F0009-0006-8010-5348",{"name":65,"orcid":66},"Mehdi Taheri","https:\u002F\u002Forcid.org\u002F0000-0002-9228-763X",{"name":68,"orcid":69},"Ebrahim Sepehr","https:\u002F\u002Forcid.org\u002F0000-0001-5843-0669","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11156833\u002Flatest.pdf","智慧农业 \u002F 农业物联网","openalex","2026-10-18T23:30:11.167282Z",{"total":75,"page":21,"page_size":75,"items":76},6,[77,164,237,297,369,455],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":82,"content":8,"source_name":83,"source_url":80,"published_at":84,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":85,"sources":88,"tags":90,"primary_topic":8,"secondary_topics":93,"classification":94,"search_phrases":141,"slug":144,"view_count":56,"doi":145,"paper":146,"created_at":163},4824,"Robust site-specific radiometric calibration of PlanetScope NDVI using UAS-based reference data for precision agriculture","https:\u002F\u002Fdoi.org\u002F10.3897\u002Fjbgs.e185817","Index images provide opportunities for monitoring phenotypic characteristics such as plant height and disease symptoms, as well as the detection of pest presence. This allows for targeted application of agrochemicals and the implementation of preventive measures, leading to increased yields and reduced environmental impact. The study focuses on the radiometric inconsistencies between satellite-based imagery and high-resolution unmanned aerial system (UAS) imagery. While PlanetScope provides high temporal resolution, its spectral response often deviates from ground-truth UAS flight missions due to varying atmospheric conditions and sensor specifications. A robust site-specific calibration workflow using Quantile-Based Piecewise Linear Normalization and residual-based outlier mitigation was used. The method was tested across three different crop types: barley, sunflower and maize. Initial results showed significant systematic offset. Following the application of the proposed robust normalization model, the root mean square error (RMSE) was reduced by up to 39%, while maintaining high coefficients of determination. The study demonstrates that site-specific non-linear calibration is essential for reliable crop health monitoring, particularly in heterogeneous fields where soil background and canopy shadows significantly affect the spectral signature satellite data, enabling accurate multi-temporal analysis and better-informed decisions for variable-rate fertilization and yield prediction.","指数影像为监测株高、病害症状等表型特征以及检测害虫存在提供了机会。这使得农药的精准施用和预防措施的实施成为可能，从而提高产量并减少环境影响。本研究聚焦于卫星影像与高分辨率无人机系统（UAS）影像之间的辐射不一致性问题。尽管PlanetScope具有高时间分辨率，但由于大气条件和传感器规格的差异，其光谱响应往往偏离地面实测的UAS飞行任务数据。本研究采用了一种稳健的站点特异性校准工作流程，使用基于分位数的分段线性归一化与基于残差的异常值抑制方法。该方法在三种不同作物类型上进行了测试：大麦、向日葵和玉米。初步结果显示出显著的系统性偏移。在应用所提出的稳健归一化模型后，均方根误差（RMSE）最多降低了39%，同时保持了较高的决定系数。研究表明，站点特异性非线性校准对于可靠的作物健康监测至关重要，尤其是在土壤背景和冠层阴影显著影响卫星数据光谱特征的异质田块中，该方法能够实现准确的多时相分析，并为变量施肥和产量预测提供更明智的决策依据。","Journal of the Bulgarian Geographical Society","2026-10-12T00:00:00Z",{"impact":19,"substance":86,"depth":18,"authority":40,"freshness":20,"relevant":21,"comment":87},21,"该论文提出基于UAS参考数据的PlanetScope NDVI站点特异性辐射定标方法，RMSE降低39%，对精准农业多时相监测有实用价值，但属细分方法研究，影响范围有限。",[89],{"name":83,"url":80},[26,91,27,28,92],"精准农业","作物监测",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":95},{"candidates":96,"agricultural_evidence":41,"reason":140},[97,102,109,116,123,130,136],{"code":47,"score":12,"supported":41,"title":98,"abstract":100,"tags":101},[99],"precision agriculture",[],[26,91],{"code":103,"score":75,"supported":41,"title":104,"abstract":105,"tags":108},"A3",[],[106,107],"玉米","maize",[],{"code":110,"score":75,"supported":41,"title":111,"abstract":112,"tags":115},"F3",[],[113,114],"施肥","fertilization",[],{"code":117,"score":75,"supported":41,"title":118,"abstract":119,"tags":122},"H2",[],[120,121],"土壤","soil",[],{"code":124,"score":125,"supported":41,"title":126,"abstract":127,"tags":129},"A4",3,[],[128],"向日葵",[],{"code":131,"score":125,"supported":41,"title":132,"abstract":133,"tags":135},"F4",[],[134],"农药",[],{"code":39,"score":125,"supported":13,"title":137,"abstract":138,"tags":139},[],[],[28,92,27],"多个产业证据相当，需要确认主归属",[142,143],"PlanetScope NDVI 辐射定标","UAS 无人机 遥感 精准农业","PlanetScopeNDVI辐射定标-4824","10.3897\u002Fjbgs.e185817",{"doi":145,"openalex_id":147,"authors":148,"venue":83,"cited_by_count":56,"oa_url":156,"card":157,"direction":161,"ingested_from":72},"W7221533152",[149,151,154],{"name":150,"orcid":8},"Yana Lipiyska",{"name":152,"orcid":153},"Tsvetelina Atansova-Evdenova","https:\u002F\u002Forcid.org\u002F0009-0000-5669-6439",{"name":155,"orcid":8},"A. S. Yordanov","https:\u002F\u002Fjbgs.arphahub.com\u002Farticle\u002F185817\u002Fdownload\u002Fpdf\u002F",{"tldr":158,"method":159,"finding":160,"direction":161,"opportunity":162},"提出基于无人机参考数据的站点特异性辐射校准方法，提升PlanetScope NDVI精度。","分位数分段线性归一化与残差异常值抑制，测试大麦、向日葵和玉米。","校准后RMSE降低达39%，保持高决定系数，证明站点非线性校准必要性。","农业遥感与作物表型","可探索多时相、多作物通用校准模型，结合土壤背景与冠层阴影动态校正。","2026-10-13T23:30:23.916508Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":8,"source_name":170,"source_url":167,"published_at":171,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":172,"score_detail":173,"sources":177,"tags":179,"primary_topic":182,"secondary_topics":187,"classification":188,"search_phrases":214,"slug":217,"view_count":218,"doi":219,"paper":220,"created_at":236},4497,"A SwiGLU enhanced ConvNeXt framework for multispectral Sentinel-2 land use and land cover classification","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-67985-x","Land use and land cover (LCLU) classification from multispectral satellite imagery is important for applications such as urban planning, environmental monitoring, agriculture, and disaster management. Existing deep learning based approaches have improved LCLU; however, most of them utilize only the red, green, and blue (RGB) bands of multispectral sentinel-2 imagery, thereby underutilizing the complementary spectral information available in the near-infrared (NIR), short-wave infrared (SWIR), and derived spectral indices such as normalized difference built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). In this work, we propose a convolutional neural network (CNN), SwigNeXT, based on a modified ConvNeXt architecture for the classification of multispectral Sentinel-2 imagery using spectral band combinations which are RGB, NIR–Red–Green, SWIR–NIR–Red, and derived index combination NDBI–NDVI–NDWI rather than using only the RGB. The SwigNeXT incorporates SwiGLU-based gated feature modulation to improve spectral spatial feature representation. The final classification is obtained by ensembling the predictions of the three spectral band combinations and the derived index combination. Experimental results show that the proposed ensemble framework achieved an overall accuracy of 99.19% with a Kappa coefficient ( \\(\\kappa \\) ) of 0.9909, outperforming conventional CNNs, transfer learning based CNNs and transformer based approaches. A gradient based SHapley Additive exPlanations framework was further integrated to analyse spatial and spectral feature contributions. The proposed framework was further validated on Sentinel-2 imagery from the Delhi–NCR region, to examine its applicability under geographically distinct real world imaging conditions.","多光谱卫星影像的土地利用与土地覆盖（LCLU）分类对城市规划、环境监测、农业和灾害管理等应用具有重要意义。现有的基于深度学习的方法改进了LCLU分类；然而，大多数方法仅利用多光谱Sentinel-2影像的红、绿、蓝（RGB）波段，从而未能充分利用近红外（NIR）、短波红外（SWIR）以及归一化差异建成区指数（NDBI）、归一化差异植被指数（NDVI）和归一化差异水体指数（NDWI）等衍生光谱指数中可用的互补光谱信息。在本研究中，我们提出了一种基于改进ConvNeXt架构的卷积神经网络（CNN）——SwigNeXT，用于多光谱Sentinel-2影像分类，采用RGB、NIR–Red–Green、SWIR–NIR–Red等光谱波段组合以及NDBI–NDVI–NDWI衍生指数组合，而非仅使用RGB。SwigNeXT引入了基于SwiGLU的门控特征调制，以改进光谱空间特征表示。最终分类通过集成三种光谱波段组合和衍生指数组合的预测结果获得。实验结果表明，所提出的集成框架达到了99.19%的总体精度，Kappa系数（\\(\\kappa\\)）为0.9909，优于传统CNN、基于迁移学习的CNN以及基于Transformer的方法。进一步集成了基于梯度的SHapley加性解释框架，以分析空间和光谱特征贡献。所提出的框架在德里–NCR地区的Sentinel-2影像上进一步验证，以检验其在地理上不同的真实成像条件下的适用性。","Scientific Reports","2026-10-07T00:00:00Z",81,{"impact":17,"substance":174,"depth":17,"authority":175,"freshness":20,"relevant":21,"comment":176},22,14,"提出融合SwiGLU门控与多光谱波段组合的ConvNeXt集成框架，在Sentinel-2土地利用分类上达99.19%精度，方法新颖且对农业遥感监测有实用价值。",[178],{"name":170,"url":167},[26,180,27,28,181],"农业人工智能","土地利用",{"code":39,"slug":183,"name":184,"path":185,"parent_code":186},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":189,"locked":13,"version":34,"method":35,"evidence":190},"classified",{"candidates":191,"agricultural_evidence":41},[192,197,205,210],{"code":39,"score":40,"supported":41,"title":193,"abstract":195,"tags":196},[194],"multispectral",[194],[28,27],{"code":198,"score":199,"supported":41,"title":200,"abstract":201,"tags":204},"I2",7,[],[202,203],"深度学习","deep learning",[180],{"code":206,"score":21,"supported":13,"title":207,"abstract":208,"tags":209},"H6",[],[],[181],{"code":47,"score":21,"supported":13,"title":211,"abstract":212,"tags":213},[],[],[26],[215,216],"Sentinel-2 多光谱 土地覆盖分类","ConvNeXt SwiGLU 遥感分类","Sentinel-2多光谱土地覆盖分类-4497",2,"10.1038\u002Fs41598-026-67985-x",{"doi":219,"openalex_id":221,"authors":222,"venue":170,"cited_by_count":56,"oa_url":230,"card":231,"direction":71,"ingested_from":72},"W7220869344",[223,225,227],{"name":224,"orcid":8},"Neeraj Kumar",{"name":226,"orcid":8},"Navin Rajpal",{"name":228,"orcid":229},"Pramod Kumar Soni","https:\u002F\u002Forcid.org\u002F0000-0003-0632-5044","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67985-x_reference.pdf",{"tldr":232,"method":233,"finding":234,"direction":161,"opportunity":235},"提出SwiGLU增强的ConvNeXt框架，融合多光谱波段与指数集成分类Sentinel-2土地利用","改进ConvNeXt加SwiGLU门控，用RGB、NIR、SWIR及NDVI\u002FN","集成框架总体精度99.19%、Kappa 0.9909，优于CNN、迁移学习和Transformer","可探索多光谱指数组合与门控机制在作物精细分类及跨区域迁移中的泛化能力。","2026-10-09T23:30:12.234854Z",{"id":238,"title":239,"url":240,"summary":241,"summary_zh":242,"content":8,"source_name":243,"source_url":240,"published_at":244,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":245,"score_detail":246,"sources":250,"tags":252,"primary_topic":255,"secondary_topics":261,"classification":263,"search_phrases":281,"slug":284,"view_count":125,"doi":285,"paper":286,"created_at":296},3838,"Forecasting Cotton Productivity in the Mil-Mugan Economic Region using Climate Variables and Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.52171\u002Fherald.342","Satellite imagery is considered as one of the most valuable and functional data sources for evaluating and forecasting production in agricultural systems. This study aims to forecast productivity in cotton fields within the Mil-Mugan economic region by incorporating vegetation indices and climatic variables. NDVI indicators were derived from Sentinel-2 data, temperature (T) was obtained from ERA5-Land data, and precipitation (R) was collected from the CHIRPS database. The data were analyzed using Google Earth Engine, ArcGIS Pro, and Python environments, consolidating them into an integrated temporal-spatial dimension. The correlation and regression analysis assessed the link among NDVI, temperature, precipitation, and productivity, leading to the development of a multivariate model. The findings derived from the model indicate that NDVI is the primary factor influencing productivity. The model developed for one region was implemented in the other three locations, and the correlation between the actual and predicted indicators was assessed using R² and RMSE metrics. The results indicate that the model's forecast accuracy fluctuates within the 90% range, considered appropriate for predicting regional cotton yield. The study's findings are significant for enhancing operational monitoring and planning of agrotechnical decisions with satellite data.","卫星影像被认为是评估和预测农业系统产量最有价值且最实用的数据来源之一。本研究旨在通过整合植被指数与气候变量，预测米尔-穆甘经济区棉田的生产力。NDVI指标源自Sentinel-2数据，温度（T）取自ERA5-Land数据，降水（R）来自CHIRPS数据库。数据在Google Earth Engine、ArcGIS Pro和Python环境中进行分析，并将其整合为统一的时空维度。通过相关性与回归分析评估了NDVI、温度、降水与生产力之间的关系，进而构建了多变量模型。模型结果表明，NDVI是影响生产力的主要因素。将为某一区域开发的模型应用于其他三个地点，并使用R²和RMSE指标评估实际值与预测值之间的相关性。结果表明，模型的预测精度在90%范围内波动，被认为适用于预测区域棉花产量。本研究结果对于利用卫星数据加强农艺决策的业务化监测与规划具有重要意义。","Herald of the Azerbaijan Engineering Academy","2026-09-29T00:00:00Z",63,{"impact":247,"substance":248,"depth":16,"authority":12,"freshness":20,"relevant":21,"comment":249},8,20,"基于Sentinel-2与气候变量的棉花产量预测模型，方法扎实、精度达90%，但属区域案例研究，公共影响有限。",[251],{"name":243,"url":240},[26,253,254,27,28],"产量预测","棉花",{"code":256,"slug":257,"name":258,"path":259,"parent_code":260},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[262],{"code":39,"slug":183,"name":184,"path":185,"parent_code":186},{"status":189,"locked":13,"version":34,"method":35,"evidence":264},{"candidates":265,"agricultural_evidence":41},[266,272,277],{"code":256,"score":267,"supported":41,"title":268,"abstract":270,"tags":271},15,[269],"cotton",[254,269],[254],{"code":39,"score":273,"supported":41,"title":274,"abstract":275,"tags":276},11,[43],[],[28,253,27],{"code":47,"score":21,"supported":13,"title":278,"abstract":279,"tags":280},[],[],[26],[282,283],"Mil-Mugan 棉花 遥感","Sentinel-2 NDVI 棉花产量","Mil-Mugan棉花遥感-3838","10.52171\u002Fherald.342",{"doi":285,"openalex_id":287,"authors":288,"venue":243,"cited_by_count":56,"oa_url":240,"card":291,"direction":161,"ingested_from":72},"W7214748696",[289],{"name":290,"orcid":8},"B.H. Babashli",{"tldr":292,"method":293,"finding":294,"direction":161,"opportunity":295},"结合Sentinel-2 NDVI与气候变量，构建多元模型预测米尔-穆甘地区棉花产量。","Sentinel-2 NDVI、ERA5-Land温度、CHIRPS降水，GEE","NDVI是产量主控因子，模型跨区预测精度约90%，可用于区域估产。","可引入多时相NDVI与机器学习，提升跨区域迁移性和极端气候下的估产鲁棒性。","2026-09-30T23:30:27.786044Z",{"id":298,"title":299,"url":300,"summary":301,"summary_zh":8,"content":8,"source_name":9,"source_url":300,"published_at":302,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":303,"score_detail":304,"sources":306,"tags":308,"primary_topic":311,"secondary_topics":317,"classification":319,"search_phrases":335,"slug":338,"view_count":339,"doi":340,"paper":341,"created_at":368},3619,"Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10767247\u002Fv1","Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee。Research Square","2026-09-24T00:00:00Z",51,{"impact":247,"substance":175,"depth":267,"authority":75,"freshness":247,"relevant":21,"comment":305},"咖啡园NDVI主动传感器与无人机多光谱对比研究，方法有参考价值但属细分作物技术验证，影响面有限。",[307],{"name":9,"url":300},[26,309,27,28,310],"无人机","咖啡种植",{"code":312,"slug":313,"name":314,"path":315,"parent_code":316},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[318],{"code":39,"slug":183,"name":184,"path":185,"parent_code":186},{"status":189,"locked":13,"version":34,"method":35,"evidence":320},{"candidates":321,"agricultural_evidence":41},[322,326,331],{"code":39,"score":40,"supported":41,"title":323,"abstract":324,"tags":325},[194],[194],[28,27],{"code":312,"score":19,"supported":41,"title":327,"abstract":329,"tags":330},[328],"coffee",[328],[310],{"code":47,"score":21,"supported":13,"title":332,"abstract":333,"tags":334},[],[],[26],[336,337],"咖啡 NDVI 无人机 多光谱","Research Square 咖啡 遥感","咖啡NDVI无人机多光谱-3619",5,"10.21203\u002Frs.3.rs-10767247\u002Fv1",{"doi":340,"openalex_id":342,"authors":343,"venue":9,"cited_by_count":56,"oa_url":367,"card":8,"direction":161,"ingested_from":72},"W7214220732",[344,346,348,350,352,355,357,359,362,365],{"name":345,"orcid":8},"Gabriel de Morais Campos",{"name":347,"orcid":8},"Aline Bhering Silva",{"name":349,"orcid":8},"Cileimar Aparecida da Silva",{"name":351,"orcid":8},"Vanda Maria Salles Andrade",{"name":353,"orcid":354},"Thaline M. Pimenta","https:\u002F\u002Forcid.org\u002F0000-0002-5002-177X",{"name":356,"orcid":8},"Andressa Barcellos Silva",{"name":358,"orcid":8},"Marco Thúlio Gonçalves Vieira",{"name":360,"orcid":361},"Daniel Marçal de Queiroz","https:\u002F\u002Forcid.org\u002F0000-0003-0987-3855",{"name":363,"orcid":364},"Fábio Daniel Tancredi","https:\u002F\u002Forcid.org\u002F0000-0002-7619-2200",{"name":366,"orcid":8},"Flora Maria Melo Villar","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10767247\u002Flatest.pdf","2026-09-27T23:30:34.698079Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":8,"source_name":375,"source_url":372,"published_at":376,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":377,"score_detail":378,"sources":380,"tags":382,"primary_topic":385,"secondary_topics":390,"classification":402,"search_phrases":431,"slug":434,"view_count":435,"doi":436,"paper":437,"created_at":454},3607,"Analysis of drought carry-over effect on apple trees using the Leafiness-LiDAR index","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104603","Apple trees ( Malus × domestica Borkh.) are widely grown in Mediterranean regions, where drought-induced irrigation restrictions can impair canopy development and productivity. This study assessed the Leafiness-LiDAR Index (LLI), derived from terrestrial Light Detection and Ranging (LiDAR) point clouds, as a proxy for Leaf Area Index (LAI) and as a non-destructive indicator of canopy response and recovery following water deficit. An ‘Opal®’ apple orchard was monitored over three growing seasons (2023–2025), encompassing one drought year and two recovery years. In 2023, full irrigation (FI; 480 mm) was compared with deficit irrigation (DI; 300 mm; 37.5% less water) under two planting densities (0.5 and 1.0 m tree spacing). Mixed-effects models revealed significant Year × Irrigation interactions for LAI (p = 0.006) and LLI (p \u003C 0.001), but not for yield (p = 0.654), suggesting a partial decoupling between structural and productive recovery. During the drought year, DI reduced LLI by 37% and yield by 47%. Following irrigation restoration, LLI differences persisted, particularly in 2024, indicating lasting structural effects of water deficit. Higher planting density promoted canopy recovery and increased yield. LLI proved to be a valuable, non-destructive estimator of LAI for detecting drought legacy effects and tracking canopy recovery, supporting precision orchard management under Mediterranean water scarcity.","苹果树（Malus × domestica Borkh.）广泛种植于地中海地区，该地区因干旱导致的灌溉限制会损害树冠发育和生产力。本研究评估了基于地面激光雷达（LiDAR）点云衍生的叶量-激光雷达指数（Leafiness-LiDAR Index, LLI）作为叶面积指数（Leaf Area Index, LAI）的替代指标，以及作为水分亏缺后树冠响应与恢复的非破坏性指标的可行性。对一个‘Opal®’苹果园进行了三个生长季（2023—2025年）的监测，涵盖一个干旱年和两个恢复年。2023年，在两种种植密度（株距0.5 m和1.0 m）下，比较了充分灌溉（FI；480 mm）与亏缺灌溉（DI；300 mm；减少37.5%水量）。混合效应模型显示，LAI（p = 0.006）和LLI（p \u003C 0.001）存在显著的年×灌溉交互作用，而产量（p = 0.654）则无此交互作用，表明结构与产量恢复之间存在部分脱耦。在干旱年，DI使LLI降低37%，产量降低47%。灌溉恢复后，LLI差异持续存在，尤其在2024年，表明水分亏缺具有持久的结构性影响。较高的种植密度促进了树冠恢复并提高了产量。LLI被证明是一种有价值的、非破坏性的LAI估算指标，可用于检测干旱遗留效应并追踪树冠恢复，为地中海水资源短缺条件下的精准果园管理提供支持。","Biosystems Engineering","2026-09-26T00:00:00Z",77,{"impact":267,"substance":174,"depth":17,"authority":175,"freshness":247,"relevant":21,"comment":379},"地中海苹果园三年试验证实LiDAR叶量指数可无损监测干旱遗留效应与冠层恢复，方法新颖、数据扎实，对果园精准灌溉有参考价值。",[381],{"name":375,"url":372},[26,383,27,29,384],"苹果","精准灌溉",{"code":386,"slug":387,"name":388,"path":389,"parent_code":316},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit",[391,397],{"code":392,"slug":393,"name":394,"path":395,"parent_code":396},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":398,"slug":399,"name":400,"path":401,"parent_code":396},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":189,"locked":13,"version":34,"method":35,"evidence":403},{"candidates":404,"agricultural_evidence":41},[405,411,417,423,427],{"code":386,"score":17,"supported":41,"title":406,"abstract":408,"tags":410},[407],"apple",[388,383,409,407],"orchard",[383],{"code":392,"score":175,"supported":41,"title":412,"abstract":414,"tags":416},[413],"drought",[415,413],"干旱",[],{"code":398,"score":12,"supported":41,"title":418,"abstract":419,"tags":422},[],[420,421,399],"灌溉","水资源",[384],{"code":47,"score":21,"supported":13,"title":424,"abstract":425,"tags":426},[],[],[26],{"code":39,"score":21,"supported":13,"title":428,"abstract":429,"tags":430},[],[],[27],[432,433],"LiDAR 苹果园 干旱","Leafiness-LiDAR Index 苹果","LiDAR苹果园干旱-3607",4,"10.1016\u002Fj.biosystemseng.2026.104603",{"doi":436,"openalex_id":438,"authors":439,"venue":375,"cited_by_count":56,"oa_url":372,"card":449,"direction":161,"ingested_from":72},"W7214458453",[440,443,446],{"name":441,"orcid":442},"Leire Sandonís-Pozo","https:\u002F\u002Forcid.org\u002F0000-0003-2472-0259",{"name":444,"orcid":445},"José Antonio Martínez-Casasnovas","https:\u002F\u002Forcid.org\u002F0000-0003-1480-3632",{"name":447,"orcid":448},"Miquel Pascual","https:\u002F\u002Forcid.org\u002F0000-0002-3329-4207",{"tldr":450,"method":451,"finding":452,"direction":161,"opportunity":453},"用LiDAR叶量指数追踪苹果园干旱遗留效应与冠层恢复。","三年果园试验，地面LiDAR点云提取LLI，混合效应模型分析。","亏缺灌溉使LLI降37%、产量降47%，复水后结构差异仍持续。","可探索LLI与多源遥感融合，构建果园干旱遗留效应早期预警与精准补灌决策模型。","2026-09-27T23:30:09.907348Z",{"id":456,"title":457,"url":458,"summary":459,"summary_zh":460,"content":8,"source_name":461,"source_url":458,"published_at":462,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":463,"score_detail":464,"sources":466,"tags":468,"primary_topic":469,"secondary_topics":471,"classification":473,"search_phrases":495,"slug":498,"view_count":125,"doi":499,"paper":500,"created_at":518},3177,"A comparative analysis on maize yield prediction using sentinel 2A and Landsat 8 satellite image in Sundarganj, Gaibandha, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjar.v51i1.92530","Maize is an important cereal crops in Bangladesh. Over the last two decades, its cultivation has increased promisingly, especially in the Northern part of Bangladesh. The effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. The traditional ground-based measurement techniques suffer from inefficiencies, and there exists a need for a reliable, precise, and effective method for estimating regional crop yields. This study used remote sensing (RS) techniques for forecasting pre-harvest maize yield to improve the management system. Currently, the normalized difference vegetation index (NDVI) is widely used to predict crop yield including maize. However, the present study used Landsat 8 (~ 30 m) and Sentinel 2A (~ 10 m) high resolution data for 2018-2019 and 2019-2020 to predict maize yield based on the year 2020-2021 at Sundarganj Upazila in Gaibandha district. The single cloud free image acquisition date based on maximum NDVI for both satellite images was used for each maize growing period to develop a yield prediction model. A regression model was performed between NDVI values and 20 farmers field-level maize yields. The absolute mean error of prediction was about 10.30% for Landsat 8 and 6.70% for Sentinel 2A compared to the actual maize yield during 2020-2021. The study revealed that NDVI data extracted from Sentinel 2A high resolution satellite images can be successfully used to predict the maize yield with appreciable accuracy. Finally, this study has demonstrated the efficacy of combining multi-temporal remote sensing data for accurate maize yield estimation, aiding agricultural authorities and production enterprises in the timely formulation and refinement of cropping strategies and management policies for the ongoing season. Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","玉米是孟加拉国重要的谷类作物。过去二十年间，其种植面积增长显著，尤其是在孟加拉国北部地区。在区域尺度上有效估算作物产量对于促进农业部门决策、进而保障粮食安全具有重要意义。传统的地面测量技术效率低下，亟需一种可靠、精确且有效的区域作物产量估算方法。本研究采用遥感（RS）技术预测收获前玉米产量，以改进管理体系。目前，归一化植被指数（NDVI）被广泛用于预测包括玉米在内的作物产量。然而，本研究利用Landsat 8（约30 m）和Sentinel 2A（约10 m）高分辨率数据，基于2018—2019年和2019—2020年的数据，对盖班达县孙达尔甘杰乌帕齐拉2020—2021年的玉米产量进行预测。在每个玉米生长期，选取两颗卫星影像中NDVI最大值对应的单幅无云影像获取日期，用于建立产量预测模型。对NDVI值与20户农民田块级玉米产量进行回归建模。与2020—2021年实际玉米产量相比，Landsat 8的绝对平均预测误差约为10.30%，Sentinel 2A约为6.70%。研究表明，利用Sentinel 2A高分辨率卫星影像提取的NDVI数据可成功用于预测玉米产量，且精度令人满意。最后，本研究证明了结合多时相遥感数据进行准确玉米产量估算的有效性，有助于农业主管部门和生产企业在当季及时制定和完善种植策略与管理政策。Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","Bangladesh Journal of Agricultural Research","2026-09-21T00:00:00Z",65,{"impact":247,"substance":248,"depth":16,"authority":40,"freshness":247,"relevant":21,"comment":465},"基于Sentinel 2A与Landsat 8的玉米遥感估产对比研究，方法清晰、误差数据具体，对遥感估产有参考价值，但属区域小尺度研究，公共影响有限。",[467],{"name":461,"url":458},[26,253,106,27,28],{"code":103,"slug":107,"name":106,"path":470,"parent_code":260},"\u002Ftopic\u002Ffield-crops\u002Fmaize",[472],{"code":39,"slug":183,"name":184,"path":185,"parent_code":186},{"status":189,"locked":13,"version":34,"method":35,"evidence":474},{"candidates":475,"agricultural_evidence":41},[476,480,484,491],{"code":103,"score":267,"supported":41,"title":477,"abstract":478,"tags":479},[107],[106,107],[106],{"code":39,"score":20,"supported":41,"title":481,"abstract":482,"tags":483},[],[27,43],[28,253,27],{"code":485,"score":75,"supported":41,"title":486,"abstract":487,"tags":490},"A8",[],[488,489],"粮食安全","grain security",[],{"code":47,"score":21,"supported":13,"title":492,"abstract":493,"tags":494},[],[],[26],[496,497],"Sentinel 2A Landsat 8 玉米产量预测","孟加拉国 Sundarganj 玉米遥感估产","Sentinel2ALandsat8玉米产量预测-3177","10.3329\u002Fbjar.v51i1.92530",{"doi":499,"openalex_id":501,"authors":502,"venue":461,"cited_by_count":56,"oa_url":458,"card":513,"direction":161,"ingested_from":72},"W7213918280",[503,505,507,509,511],{"name":504,"orcid":8},"N Mohammad",{"name":506,"orcid":8},"MA Islam",{"name":508,"orcid":8},"MG Mahboob",{"name":510,"orcid":8},"MM Rahman",{"name":512,"orcid":8},"I Ahmed",{"tldr":514,"method":515,"finding":516,"direction":161,"opportunity":517},"用Sentinel 2A与Landsat 8的NDVI回归模型预测孟加拉国玉米产量并比较精度。","基于最大NDVI单期影像与20个农户地块产量做回归，比较两种卫星。","Sentinel 2A预测绝对平均误差6.70%，优于Landsat 8的10.30%。","可探索多时相NDVI与机器学习融合，提升小农户尺度玉米估产精度与迁移性。","2026-09-22T23:30:23.551878Z"]