[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2162":3,"related-2162":84},{"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":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":60,"slug":63,"view_count":20,"doi":64,"paper":65,"created_at":83},2162,"Estimating field-scale net ecosystem exchange by coupling high-resolution remote sensing with a crop carbon model","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agrformet.2026.111459","Estimating field-scale net ecosystem exchange by coupling high-resolution remote sensing with a crop carbon model。Agricultural and Forest Meteorology",null,"Agricultural and Forest Meteorology","2026-09-10T00:00:00Z","论文",10,false,79,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,21,14,8,1,"耦合高分辨率遥感与作物碳模型估算田块尺度净生态系统交换，方法新颖、数据尺度精细，对农业碳汇计量与智慧农业监测有参考价值，但属细分领域学术进展，公共影响有限。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","遥感","作物监测","碳汇核算","农业碳模型",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,55],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},13,true,[47],"remote sensing",[47],[27,26],{"code":51,"score":20,"supported":13,"title":52,"abstract":53,"tags":54},"H4",[],[],[28],{"code":56,"score":20,"supported":13,"title":57,"abstract":58,"tags":59},"I1",[],[],[25],[61,62],"农业碳模型 作物监测 智慧农业 碳汇核算","农业碳模型 作物监测","农业碳模型作物监测智慧农业碳汇核算-2162","10.1016\u002Fj.agrformet.2026.111459",{"doi":64,"openalex_id":66,"authors":67,"venue":9,"cited_by_count":80,"oa_url":8,"card":8,"direction":81,"ingested_from":82},"W7212145680",[68,71,73,75,77],{"name":69,"orcid":70},"Kexin Wang","https:\u002F\u002Forcid.org\u002F0000-0003-0124-0631",{"name":72,"orcid":8},"Li Zhengcan",{"name":74,"orcid":8},"Ruixin Fang",{"name":76,"orcid":8},"Meiling Wang",{"name":78,"orcid":79},"Zhaocong Wu","https:\u002F\u002Forcid.org\u002F0000-0003-2435-5538",0,"农业遥感与作物表型","openalex","2026-09-11T23:30:29.699921Z",{"total":85,"page":20,"page_size":85,"items":86},6,[87,146,224,280,332,394],{"id":88,"title":89,"url":90,"summary":91,"summary_zh":92,"content":8,"source_name":93,"source_url":90,"published_at":94,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":95,"score_detail":96,"sources":103,"tags":105,"primary_topic":108,"secondary_topics":109,"classification":110,"search_phrases":126,"slug":129,"view_count":130,"doi":131,"paper":132,"created_at":145},2952,"Direct quantification of solar-induced chlorophyll fluorescence using compact solar-blind optical radiometers","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115669","Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides a non-invasive, quantitative measure related to plant photosynthetic activity, linking leaf-level physiology to canopy and ecosystem behavior and the global carbon cycle. Current SIF measurements rely on hyperspectral retrievals of the weak fluorescence signal from small changes in Fraunhofer lines or atmospheric absorption features in plant or canopy reflectance spectra. Because this approach is dependent on atmospheric and illumination conditions, it relies on bulky and costly instrumentation, while complex retrieval algorithms demand atmospheric spectroscopy expertise. These limitations restrict widespread proximal SIF remote sensing applications, and contribute to critical observational gaps, highlighting the need for a simplified measurement approach. We introduce a fundamentally different approach to proximal SIF remote sensing: a solar-blind radiometer (SBR) fully resolves a saturated atmospheric O 2 line (ca. 0.01 nm spectral width). Inside this line, SIF is the only natural light source and thus can be measured directly. Calculations show that SBR-SIF instruments can be implemented using a Fabry-Pérot interferometer in double-pass configuration. Plant measurements with our prototype confirm the theoretical calculations and provide direct SIF measurements with a precision of 0.1 mW m -2 sr -1 nm -1 in 3 min, similar or higher than conventional techniques. SBR-SIF is independent of atmospheric and illumination conditions, requires no spectral retrieval or reference measurement, and enables compact, field-deployable instrumentation. Consequently, SBR-SIF enables scalable proximal SIF measurements that can advance our understanding of physiological processes, support validation of satellite observations, and expand SIF applications in ecosystem monitoring and precision agriculture.","太阳诱导叶绿素荧光(SIF)的遥感提供了一种与植物光合活动相关的非侵入式定量测量手段，将叶片尺度的生理过程与冠层和生态系统行为及全球碳循环联系起来。当前的SIF测量依赖于对植物或冠层反射光谱中夫琅禾费线或大气吸收特征微小变化所对应的微弱荧光信号进行高光谱反演。由于该方法依赖于大气和光照条件，需要笨重且昂贵的仪器设备，同时复杂的反演算法要求大气光谱学专业知识。这些局限性限制了近端SIF遥感应用的广泛开展，并造成了关键的观测空白，凸显了发展简化测量方法的必要性。我们提出了一种全新的近端SIF遥感方法：太阳盲辐射计(SBR)能够完全分辨一条饱和的大气O₂吸收线(光谱宽度约0.01 nm)。在该吸收线内，SIF是唯一的自然光源，因此可以被直接测量。计算表明，SBR-SIF仪器可采用双程配置的法布里-珀罗干涉仪实现。使用我们的原型样机进行的植物测量证实了理论计算结果，可在3分钟内提供精度为0.1 mW m⁻² sr⁻¹ nm⁻¹的直接SIF测量，与传统技术相当或更高。SBR-SIF不受大气和光照条件影响，无需光谱反演或参考测量，可实现紧凑、可野外部署的仪器化测量。因此，SBR-SIF能够实现可扩展的近端SIF测量，有望推进我们对生理过程的理解，支持卫星观测的验证，并拓展SIF在生态系统监测和精准农业中的应用。","Remote Sensing of Environment","2026-09-17T00:00:00Z",88,{"impact":97,"substance":98,"depth":99,"authority":100,"freshness":101,"relevant":20,"comment":102},22,23,19,15,9,"提出日盲辐射计直接测量SIF的新方法，摆脱大气与光照条件依赖，实现紧凑可野外部署的仪器，对作物光合监测与卫星验证有实质推动。",[104],{"name":93,"url":90},[25,26,27,106,107],"光合作用","叶绿素荧光",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":111},{"candidates":112,"agricultural_evidence":45},[113,119],{"code":31,"score":114,"supported":45,"title":115,"abstract":116,"tags":118},11,[],[26,47,117],"hyperspectral",[27,26],{"code":56,"score":120,"supported":45,"title":121,"abstract":122,"tags":125},7,[],[123,124],"精准农业","precision agriculture",[25],[127,128],"太阳诱导叶绿素荧光 遥感 仪器","SIF 日盲辐射计 作物监测","太阳诱导叶绿素荧光遥感仪器-2952",2,"10.1016\u002Fj.rse.2026.115669",{"doi":131,"openalex_id":133,"authors":134,"venue":93,"cited_by_count":80,"oa_url":90,"card":140,"direction":81,"ingested_from":82},"W7213500066",[135,137],{"name":136,"orcid":8},"Jonas Kuhn",{"name":138,"orcid":139},"J. Stutz","https:\u002F\u002Forcid.org\u002F0000-0001-6368-7629",{"tldr":141,"method":142,"finding":143,"direction":81,"opportunity":144},"提出太阳盲辐射计直接测量太阳诱导叶绿素荧光，无需光谱反演。","利用法布里-珀罗干涉仪双通配置，全分辨大气氧吸收线，原型实测植物。","原型3分钟精度达0.1 mW m⁻² sr⁻¹ nm⁻¹，且不受大气和光照条件影响。","可开发低成本便携SIF传感器，用于田间作物光合表型与卫星验证。","2026-09-19T23:30:34.736522Z",{"id":147,"title":148,"url":149,"summary":150,"summary_zh":151,"content":8,"source_name":152,"source_url":149,"published_at":153,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":154,"score_detail":155,"sources":157,"tags":159,"primary_topic":162,"secondary_topics":163,"classification":169,"search_phrases":189,"slug":192,"view_count":193,"doi":194,"paper":195,"created_at":223},2660,"Red-Edge Information in Agricultural Remote Sensing: From Spectral Theory to Explainable Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183180","The red-edge (RE) spectral region has become a central component of agricultural remote sensing because it captures physiologically meaningful changes in chlorophyll content, canopy structure and vegetation functioning. The availability of dedicated RE bands on modern multispectral satellites and advances in hyperspectral sensing have stimulated widespread applications for crop monitoring, nutrient assessment, stress detection and yield prediction. However, reported improvements over conventional visible–near-infrared (VIS–NIR) approaches remain highly variable, and the mechanisms governing when and why RE information provides additional value are often poorly synthesised. This review presents a conceptual framework that links the physical and physiological basis of RE reflectance with its condition-dependent agronomic performance and its emerging role within modern machine learning (ML) systems. We first examine how pigment absorption, canopy structure and sensor characteristics jointly determine the representation of RE information from hyperspectral measurements to operational multispectral observations. We then synthesise evidence demonstrating that the agronomic value of RE information is strongly dependent on crop characteristics, phenological stage, environmental conditions and observation geometry, explaining much of the variability reported across previous studies. Finally, we show how recent advances in ML and explainable artificial intelligence have changed the interpretation of RE information. Rather than evaluating RE-derived vegetation indices in isolation, contemporary predictive frameworks integrate RE observations with complementary spectral, climatic, structural and temporal predictors, allowing their physiological contribution to be quantified within multidimensional models. We conclude that future value of RE remote sensing will require not only continued advances in spectral measurement and vegetation index development, but also improved interpretation, transferability and operational integration of physiologically meaningful RE information within explainable, multi-source agricultural monitoring systems.","红边（RE）光谱区已成为农业遥感的核心组成部分，因为它能够捕捉叶绿素含量、冠层结构和植被功能等方面具有生理意义的变化。现代多光谱卫星上专用红边波段的可用性以及高光谱传感技术的进步，推动了其在作物监测、养分评估、胁迫检测和产量预测中的广泛应用。然而，相较于传统可见光—近红外（VIS–NIR）方法所报道的改进效果仍高度可变，而关于红边信息何时以及为何提供额外价值的机制往往缺乏系统梳理。本综述提出了一个概念框架，将红边反射率的物理与生理基础与其条件依赖的农学表现及其在现代机器学习（ML）系统中新兴的作用联系起来。我们首先探讨色素吸收、冠层结构和传感器特性如何共同决定从高光谱测量到业务化多光谱观测中红边信息的表征。随后，我们综合证据表明，红边信息的农学价值强烈依赖于作物特征、物候阶段、环境条件和观测几何，这解释了以往研究中报道的大部分变异性。最后，我们展示了机器学习和可解释人工智能的最新进展如何改变了对红边信息的解读。当代预测框架不再孤立地评估红边衍生的植被指数，而是将红边观测与互补的光谱、气候、结构和时间预测因子相结合，从而在多维模型中量化其生理贡献。我们得出结论：红边遥感的未来价值不仅需要光谱测量和植被指数开发的持续进步，还需要在可解释的多源农业监测系统中改进对具有生理意义的红边信息的解读、可迁移性和业务化整合。","Remote Sensing","2026-09-16T00:00:00Z",82,{"impact":16,"substance":97,"depth":16,"authority":18,"freshness":12,"relevant":20,"comment":156},"系统综述红边遥感从光谱机理到可解释机器学习的农学价值条件，方法框架新颖、结论可靠，对作物监测与产量预测有实质参考意义。",[158],{"name":152,"url":149},[25,160,26,27,161],"农业人工智能","植被指数",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[164],{"code":165,"slug":166,"name":167,"path":168,"parent_code":35},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":38,"locked":13,"version":39,"method":40,"evidence":170},{"candidates":171,"agricultural_evidence":45},[172,178,185],{"code":31,"score":173,"supported":45,"title":174,"abstract":175,"tags":177},20,[47],[26,47,176,117],"multispectral",[27,161,26],{"code":165,"score":16,"supported":45,"title":179,"abstract":181,"tags":184},[180],"machine learning",[182,180,183],"机器学习","artificial intelligence",[160],{"code":56,"score":20,"supported":13,"title":186,"abstract":187,"tags":188},[],[],[25],[190,191],"农业人工智能 作物监测 智慧农业 植被指数","农业人工智能 作物监测","农业人工智能作物监测智慧农业植被指数-2660",4,"10.3390\u002Frs18183180",{"doi":194,"openalex_id":196,"authors":197,"venue":152,"cited_by_count":80,"oa_url":149,"card":218,"direction":81,"ingested_from":82},"W7213344870",[198,201,203,206,209,212,215],{"name":199,"orcid":200},"Ignacio Fuentes","https:\u002F\u002Forcid.org\u002F0000-0001-7066-7482",{"name":202,"orcid":8},"Nikolas Hoskin",{"name":204,"orcid":205},"Patrick Filippi","https:\u002F\u002Forcid.org\u002F0000-0003-3573-084X",{"name":207,"orcid":208},"Abhasha Joshi","https:\u002F\u002Forcid.org\u002F0000-0002-1422-465X",{"name":210,"orcid":211},"Yi Yu","https:\u002F\u002Forcid.org\u002F0000-0002-1140-2713",{"name":213,"orcid":214},"Thomas F. A. Bishop","https:\u002F\u002Forcid.org\u002F0000-0002-6723-7323",{"name":216,"orcid":217},"Dhahi Al-Shammari","https:\u002F\u002Forcid.org\u002F0000-0001-6608-8322",{"tldr":219,"method":220,"finding":221,"direction":81,"opportunity":222},"综述红边遥感在农业中的物理基础、条件依赖性与可解释机器学习中的新角色。","文献综述，整合光谱理论、多光谱\u002F高光谱数据与可解释AI方法。","红边信息的农学价值高度依赖作物、物候、环境与观测几何，需多源融合与可解释建模。","可研究红边信息在不同作物-物候-环境组合下的可迁移性，并构建可解释多源融合模型。","2026-09-16T23:30:28.719858Z",{"id":225,"title":226,"url":227,"summary":228,"summary_zh":229,"content":8,"source_name":230,"source_url":227,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":231,"score_detail":232,"sources":236,"tags":238,"primary_topic":240,"secondary_topics":241,"classification":243,"search_phrases":260,"slug":262,"view_count":263,"doi":264,"paper":265,"created_at":279},2163,"A Hybrid Transfer Learning Framework for Seasonal Classification of Satellite Images","https:\u002F\u002Fdoi.org\u002F10.29109\u002Fgujsc.1958797","Seasonal classification from satellite imagery is an important remote sensing task for monitoring vegetation dynamics, agricultural processes, environmental change, and climate-related spatial patterns. However, developing robust deep learning models for this task is challenging due to limited labeled data, regional variability, and the computational cost of training large-scale networks from scratch. This study proposes a hybrid transfer learning-based framework for seasonal classification using satellite images collected from 81 provinces of Türkiye. A custom dataset was constructed from monthly satellite images, and eight pretrained deep learning architectures were evaluated as feature extractors. The extracted deep representations were classified using seven machine learning algorithms. The experimental results showed that both the choice of pretrained feature extractor and the classifier affect seasonal classification performance. Among models, ConvNeXt combined with the Multi-Layer Perceptron achieved the best performance. Based on the comparative analysis, ConvNeXt, Vision Transformer, and Swin Transformer were selected as the top three feature extractors, while the Multi-Layer Perceptron was selected as the final classifier. The proposed framework provides an effective and computationally practical approach for seasonal classification and offers a promising basis for future environmental monitoring and agricultural remote sensing applications.","基于卫星影像的季节分类是一项重要的遥感任务，可用于监测植被动态、农业过程、环境变化以及与气候相关的空间格局。然而，由于标注数据有限、区域差异以及从零开始训练大规模网络的计算成本，开发用于该任务的稳健深度学习模型具有挑战性。本研究提出了一种基于混合迁移学习的框架，利用从土耳其81个省份收集的卫星影像进行季节分类。研究构建了一个由月度卫星影像组成的自定义数据集，并评估了八种预训练深度学习架构作为特征提取器的效果。提取出的深层表示使用七种机器学习算法进行分类。实验结果表明，预训练特征提取器和分类器的选择均会影响季节分类性能。在各类模型中，ConvNeXt结合多层感知机取得了最佳性能。基于对比分析，ConvNeXt、Vision Transformer和Swin Transformer被选为排名前三的特征提取器，而多层感知机被选为最终分类器。所提出的框架为季节分类提供了一种有效且计算上实用的方法，并为未来环境监测和农业遥感应用提供了有前景的基础。","Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji",66,{"impact":233,"substance":16,"depth":234,"authority":233,"freshness":19,"relevant":20,"comment":235},12,16,"基于土耳其81省卫星影像的迁移学习季节分类框架，方法对比扎实、结论可靠，对农业遥感监测有参考价值，但属学术论文且非国内应用，影响力有限。",[237],{"name":230,"url":227},[25,160,26,27,239],"迁移学习",{"code":165,"slug":166,"name":167,"path":168,"parent_code":35},[242],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":244},{"candidates":245,"agricultural_evidence":45},[246,252,256],{"code":165,"score":114,"supported":45,"title":247,"abstract":248,"tags":251},[],[249,182,180,250],"深度学习","deep learning",[160,239],{"code":31,"score":19,"supported":45,"title":253,"abstract":254,"tags":255},[],[26,47],[27,26],{"code":56,"score":20,"supported":13,"title":257,"abstract":258,"tags":259},[],[],[25],[261,191],"农业人工智能 作物监测 智慧农业 迁移学习","农业人工智能作物监测智慧农业迁移学习-2163",3,"10.29109\u002Fgujsc.1958797",{"doi":264,"openalex_id":266,"authors":267,"venue":230,"cited_by_count":80,"oa_url":227,"card":274,"direction":81,"ingested_from":82},"W7212161719",[268,271],{"name":269,"orcid":270},"Eyyüp YILDIZ","https:\u002F\u002Forcid.org\u002F0000-0002-7051-3368",{"name":272,"orcid":273},"Özge Aslan Yıldız","https:\u002F\u002Forcid.org\u002F0000-0001-7688-9326",{"tldr":275,"method":276,"finding":277,"direction":81,"opportunity":278},"提出混合迁移学习框架，用预训练模型提取特征并结合机器学习分类器实现卫星图像季节分类。","基于土耳其81省月度卫星图像构建数据集，评估8种预训练模型和7种分类器。","ConvNeXt结合多层感知机表现最佳，特征提取器和分类器选择均影响性能。","可探索该框架在作物物候监测、跨区域迁移及多时相农业遥感中的泛化能力。","2026-09-11T23:30:29.840009Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":8,"source_name":286,"source_url":283,"published_at":287,"category":11,"cover_url":8,"hotness":288,"is_selected":13,"score":289,"score_detail":290,"sources":292,"tags":296,"primary_topic":297,"secondary_topics":298,"classification":300,"search_phrases":316,"slug":318,"view_count":130,"doi":319,"paper":320,"created_at":331},1327,"PRECISION AGRICULTURE ANALYTICS USING UAV MULTISPECTRAL IMAGING AND MACHINE LEARNING FOR CROP STRESS DETECTION AND YIELD OPTIMIZATION MODELS","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22111395","PRECISION AGRICULTURE ANALYTICS USING UAV MULTISPECTRAL IMAGING AND MACHINE LEARNING FOR CROP STRESS DETECTION AND YIELD OPTIMIZATION MODELS。International Journal of Engineering Technology Research & Management","利用无人机多光谱成像与机器学习进行作物胁迫检测及产量优化模型的精准农业分析。《国际工程技术研究与管理杂志》","International Journal of Engineering Technology Research & Management","2026-12-21T00:00:00Z",25,30,{"impact":19,"substance":19,"depth":12,"authority":193,"freshness":80,"relevant":20,"comment":291},"论文摘要信息有限，且发表于未来日期，时效性差，但主题相关，内容涉及无人机多光谱与机器学习在作物胁迫检测和产量优化中的应用，有一定技术价值。",[293,294],{"name":286,"url":283},{"name":286,"url":295},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22111394",[25,160,26,27],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[299],{"code":165,"slug":166,"name":167,"path":168,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":301},{"candidates":302,"agricultural_evidence":45},[303,308,312],{"code":31,"score":234,"supported":45,"title":304,"abstract":305,"tags":307},[176],[306,176],"无人机多光谱",[27,26],{"code":56,"score":100,"supported":45,"title":309,"abstract":310,"tags":311},[124],[123,124],[25],{"code":165,"score":100,"supported":45,"title":313,"abstract":314,"tags":315},[180],[182,180],[160],[317,191],"农业人工智能 作物监测 智慧农业 遥感","农业人工智能作物监测智慧农业遥感-1327","10.5281\u002Fzenodo.22111395",{"doi":319,"openalex_id":321,"authors":322,"venue":286,"cited_by_count":80,"oa_url":283,"card":325,"direction":330,"ingested_from":82},"W7204220081",[323],{"name":324,"orcid":8},"Adedayo Oluwaseyi Alawode",{"tldr":326,"method":327,"finding":328,"direction":81,"opportunity":329},"利用无人机多光谱成像与机器学习检测作物胁迫并优化产量。","无人机多光谱成像结合机器学习算法。","该方法可有效检测作物胁迫并优化产量。","可探索多光谱数据与深度学习结合，提高胁迫检测精度，并开发实时决策系统。","农业人工智能与决策模型","2026-09-01T23:30:38.036910Z",{"id":333,"title":334,"url":335,"summary":336,"summary_zh":337,"content":8,"source_name":338,"source_url":335,"published_at":339,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":340,"score_detail":341,"sources":344,"tags":346,"primary_topic":349,"secondary_topics":350,"classification":351,"search_phrases":369,"slug":372,"view_count":80,"doi":373,"paper":374,"created_at":393},5571,"Temporal Multi-Scale Spatio-Temporal Network (TMST): Polarization–Temporal Feature Selection and Multi-Scale Spatiotemporal Fusion for Crop Mapping","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202068","Accurate and timely crop mapping supports agricultural resource management, yield prediction, and food security decision-making. In this study, we propose the Temporal Multi-scale Spatio-Temporal Network (TMST), a crop mapping method that integrates multi-source remote sensing time series through polarization–temporal feature selection and multi-scale spatiotemporal fusion. TMST combines Sentinel-1 dual-polarization synthetic aperture radar (SAR), Sentinel-2 multispectral data, and vegetation indices. Its Temporal Feature Importance Explainer (TFIExplainer) selects informative polarization–time features, the Multi-Scale Attention Aggregation (MSAA) module enhances spatial representations, and the Spatio-Temporal Feature Encoder (STFE) models long-range spatial dependencies and phenological dynamics; a composite loss improves minority-class and difficult-pixel recognition. On the Xinxiang dataset, TMST achieved an overall accuracy (OA) of 0.8625 and a mean F1-score (mF1) of 0.8329. Module and data-source ablations suggested benefits of the combined modules and multi-source fusion, while a cropland mask reduced non-cropland misclassification. In direct cross-dataset evaluation, models trained on a self-constructed Austrian dataset and tested on the radar-extended Panoptic Agricultural Satellite Time Series (PASTIS-R) dataset showed that TMST outperformed the evaluated baselines under the specified source–target setting. Annual mapping of Xinxiang for 2019–2023, using year-specific samples and independently trained models, provided map-based estimates of regional cropping structures and spatial patterns. Under the evaluated settings, TMST provides an effective approach to crop classification and annually updated crop mapping.","准确的作物制图有助于农业资源管理、产量预测和粮食安全决策。本研究提出了一种时间多尺度时空网络（Temporal Multi-scale Spatio-Temporal Network，TMST），这是一种通过极化—时间特征选择和多尺度时空融合来整合多源遥感时间序列的作物制图方法。TMST结合了Sentinel-1双极化合成孔径雷达（SAR）、Sentinel-2多光谱数据和植被指数。其时间特征重要性解释器（Temporal Feature Importance Explainer，TFIExplainer）用于选择信息量丰富的极化—时间特征，多尺度注意力聚合（Multi-Scale Attention Aggregation，MSAA）模块增强空间表征，时空特征编码器（Spatio-Temporal Feature Encoder，STFE）建模长程空间依赖关系和物候动态；复合损失函数改善了少数类和困难像元的识别。在新乡数据集上，TMST取得了0.8625的总体精度（OA）和0.8329的平均F1分数（mF1）。模块和数据源消融实验表明，组合模块和多源融合具有优势，而耕地掩膜减少了非耕地误分类。在直接的跨数据集评估中，在自行构建的奥地利数据集上训练并在雷达扩展的Panoptic农业卫星时间序列（PASTIS-R）数据集上测试的模型表明，在指定的源—目标设置下，TMST优于所评估的基线方法。使用逐年样本和独立训练的模型对新乡2019—2023年进行年度制图，提供了基于制图的区域种植结构和空间格局估计。在所评估的设置下，TMST为作物分类和年度更新的作物制图提供了一种有效方法。","Agronomy","2026-10-21T00:00:00Z",73,{"impact":233,"substance":173,"depth":342,"authority":18,"freshness":12,"relevant":20,"comment":343},17,"提出多源遥感融合的作物制图新方法，方法新颖、实验充分，但属细分技术进展，公共影响有限。",[345],{"name":338,"url":335},[25,347,26,348],"作物制图","SAR",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":352},{"candidates":353,"agricultural_evidence":45},[354,358,365],{"code":31,"score":12,"supported":45,"title":355,"abstract":356,"tags":357},[],[26,47,176],[26],{"code":359,"score":85,"supported":45,"title":360,"abstract":361,"tags":364},"A8",[],[362,363],"粮食安全","food security",[],{"code":56,"score":20,"supported":13,"title":366,"abstract":367,"tags":368},[],[],[25],[370,371],"Sentinel-1 Sentinel-2 作物分类","TMST 多尺度时空网络","Sentinel-1Sentinel-2作物分类-5571","10.3390\u002Fagronomy16202068",{"doi":373,"openalex_id":375,"authors":376,"venue":338,"cited_by_count":80,"oa_url":335,"card":388,"direction":81,"ingested_from":82},"W7223117203",[377,380,382,385],{"name":378,"orcid":379},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":381,"orcid":8},"Wentao Lv",{"name":383,"orcid":384},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":386,"orcid":387},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":389,"method":390,"finding":391,"direction":81,"opportunity":392},"提出TMST网络，融合SAR与多光谱时序数据实现作物制图。","Sentinel-1\u002F2时序、极化-时序特征选择、多尺度注意力与时空编码器。","新乡OA达0.8625，多源融合与模块组合有效，跨数据集优于基线。","可探索极化-时序特征选择在跨区域迁移与少样本作物制图中的泛化能力。","2026-10-22T11:20:39.359881Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":8,"source_name":400,"source_url":397,"published_at":339,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":401,"score_detail":402,"sources":404,"tags":406,"primary_topic":409,"secondary_topics":410,"classification":412,"search_phrases":429,"slug":432,"view_count":80,"doi":433,"paper":434,"created_at":444},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。","Zenodo (CERN European Organization for Nuclear Research)",67,{"impact":19,"substance":173,"depth":342,"authority":233,"freshness":12,"relevant":20,"comment":403},"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[405],{"name":400,"url":397},[25,160,407,26,408],"产量预测","可解释性",{"code":165,"slug":166,"name":167,"path":168,"parent_code":35},[411],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":413},{"candidates":414,"agricultural_evidence":45},[415,421,425],{"code":165,"score":233,"supported":45,"title":416,"abstract":418,"tags":420},[417],"computer vision",[419],"计算机视觉",[160],{"code":31,"score":19,"supported":45,"title":422,"abstract":423,"tags":424},[],[26,47],[407,26],{"code":56,"score":20,"supported":13,"title":426,"abstract":427,"tags":428},[],[],[25],[430,431],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":433,"openalex_id":435,"authors":436,"venue":400,"cited_by_count":80,"oa_url":397,"card":439,"direction":81,"ingested_from":82},"W7223114002",[437],{"name":438,"orcid":8},"Kadiatou Barry",{"tldr":440,"method":441,"finding":442,"direction":81,"opportunity":443},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z"]