[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3179":3,"related-3179":93},{"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":31,"secondary_topics":37,"classification":44,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":92},3179,"Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02541-8","Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image。Journal of the Indian Society of Remote Sensing","多特征与模糊残差LeNet用于基于遥感图像的棉花分类。《印度遥感学会杂志》",null,"Journal of the Indian Society of Remote Sensing","2026-09-21T00:00:00Z","论文",10,false,58,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,14,15,12,9,1,"方法类论文，提出多特征与模糊结合残差LeNet用于遥感棉花分类，专业价值尚可但影响面窄，适合作为技术参考而非每日精选头条。",[25],{"name":10,"url":6},[27,28,29,30],"农业人工智能","棉花","遥感","作物分类",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,57,62],{"code":39,"score":51,"supported":52,"title":53,"abstract":55,"tags":56},16,true,[54],"remote sensing",[29,54],[30,29],{"code":32,"score":19,"supported":52,"title":58,"abstract":60,"tags":61},[59],"cotton",[28,59],[28],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"I2",[],[],[27],[68,69],"Residual LeNet 棉花 遥感分类","农业人工智能 作物分类 棉花 遥感","ResidualLeNet棉花遥感分类-3179",5,"10.1007\u002Fs12524-026-02541-8",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":84,"oa_url":9,"card":85,"direction":89,"ingested_from":91},"W7213944040",[76,78,80,82],{"name":77,"orcid":9},"G. Suresh",{"name":79,"orcid":9},"G. Bhuvaneswari",{"name":81,"orcid":9},"G. Manikandan",{"name":83,"orcid":9},"M. Robinson Joel",0,{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"提出多特征与模糊结合残差LeNet的遥感影像棉花分类方法。","多特征提取、模糊逻辑与残差LeNet网络，用于遥感图像分类。","该方法能有效提升棉花分类精度，验证了深度残差网络结合模糊特征的可行性。","农业遥感与作物表型","可探索多特征与模糊深度学习在其他作物遥感分类中的泛化能力及轻量化部署。","openalex","2026-09-22T23:30:23.811658Z",{"total":94,"page":22,"page_size":94,"items":95},6,[96,151,242,301,383,473],{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":9,"source_name":102,"source_url":99,"published_at":103,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":104,"score_detail":105,"sources":108,"tags":110,"primary_topic":114,"secondary_topics":115,"classification":117,"search_phrases":134,"slug":137,"view_count":138,"doi":139,"paper":140,"created_at":150},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":17,"substance":106,"depth":51,"authority":13,"freshness":21,"relevant":22,"comment":107},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。",[109],{"name":102,"url":99},[111,112,28,29,113],"智慧农业","产量预测","NDVI",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[116],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":118},{"candidates":119,"agricultural_evidence":52},[120,124,129],{"code":32,"score":19,"supported":52,"title":121,"abstract":122,"tags":123},[59],[28,59],[28],{"code":39,"score":125,"supported":52,"title":126,"abstract":127,"tags":128},11,[54],[],[113,112,29],{"code":130,"score":22,"supported":14,"title":131,"abstract":132,"tags":133},"I1",[],[],[111],[135,136],"Mil-Mugan 棉花 遥感","Sentinel-2 NDVI 棉花产量","Mil-Mugan棉花遥感-3838",3,"10.52171\u002Fherald.342",{"doi":139,"openalex_id":141,"authors":142,"venue":102,"cited_by_count":84,"oa_url":99,"card":145,"direction":89,"ingested_from":91},"W7214748696",[143],{"name":144,"orcid":9},"B.H. Babashli",{"tldr":146,"method":147,"finding":148,"direction":89,"opportunity":149},"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":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":165,"tags":167,"primary_topic":171,"secondary_topics":172,"classification":174,"search_phrases":203,"slug":206,"view_count":207,"doi":208,"paper":209,"created_at":241},3014,"Detecting diurnal dynamics of cotton leaf inclination angle under water-salt stress","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115674","Leaf inclination angle (LIA) dynamics act as a rapid response mechanism to abiotic stress, regulating canopy energy balance and water use efficiency. While the adaptive value of diurnal LIA plasticity (e.g., paraheliotropism) is well-recognized in ecology, most current remote sensing algorithms and ecosystem models still treat canopy architecture as static and neglect stress-induced geometric adjustments. Furthermore, the diurnal dynamics of LIA under combined abiotic stresses, such as concurrent water deficit and salinity, still remain poorly understood. Recent advances in unmanned aerial vehicle (UAV) photogrammetry offer a promising approach for capturing LIA dynamics at high spatial and temporal resolution. However, accurately resolving fine scale, dynamic leaf movements in real environments using UAVs remains challenging. To address these gaps, we developed the Constraint-Assisted Point cloud fusion for Leaf scale Analysis (CAPLA), an integrated UAV analytical workflow that combines deep learning with Structure from Motion (SfM). CAPLA employs 2D semantic masks to strictly constrain 3D mesh reconstruction, effectively mitigating motion-induced artifacts. Independent validation against 19 plot level mean leaf angle (MLA) observations collected at 9:30 am and 12:00 pm yielded an R 2 of 0.89 and an RMSE of 0.9°, supporting plot level MLA estimation under the validated acquisition conditions. CAPLA was subsequently applied across five observation times to characterize diurnal canopy structural dynamics. Importantly, repeated measures analysis of the high frequency observations revealed significant effects of irrigation, salinity, and observation time on MLA, together with a significant irrigation × time interaction ( P = 0.0109), indicating that diurnal MLA trajectories differed among irrigation levels. In contrast, neither the irrigation × salinity interaction ( P = 0.8800) nor the irrigation × salinity × time interaction ( P = 0.9086) was significant. Descriptive differences in within-day variability were nevertheless observed among individual treatment combinations, highlighting the value of time-resolved structural monitoring for characterizing canopy responses to combined water and salinity stresses. These findings highlight the complex structural plasticity of canopies under interacting stresses, emphasizing the critical need to transition from static canopy assumptions to dynamic structural monitoring for improving ecosystem models and precision agriculture.","叶片倾角（LIA）动态变化是植物对非生物胁迫的快速响应机制，调控冠层能量平衡与水分利用效率。尽管昼夜LIA可塑性（如避日运动）的适应价值在生态学中已得到广泛认可，但当前大多数遥感算法和生态系统模型仍将冠层结构视为静态，忽略了胁迫诱导的几何调整。此外，在水分亏缺与盐分胁迫等复合非生物胁迫条件下，LIA的昼夜动态变化仍知之甚少。近年来无人机（UAV）摄影测量技术的进展为在高时空分辨率下捕捉LIA动态提供了有前景的方法。然而，利用无人机在真实环境中精确解析精细尺度的动态叶片运动仍具挑战性。为弥补上述不足，我们开发了约束辅助点云融合叶片尺度分析流程（CAPLA），这是一种集成了深度学习与运动恢复结构（SfM）的无人机综合分析工作流。CAPLA利用二维语义掩膜严格约束三维网格重建，有效减轻了运动诱导的伪影。基于上午9：30和中午12：00采集的19个样地水平平均叶倾角（MLA）观测值进行独立验证，结果R²为0.89，RMSE为0.9°，支持在验证采集条件下进行样地水平MLA估算。随后将CAPLA应用于五个观测时段以表征冠层结构的昼夜动态变化。重要的是，对高频观测的重复测量分析揭示了灌溉、盐分和观测时间对MLA的显著影响，以及显著的灌溉×时间交互效应（P = 0.0109），表明不同灌溉水平下MLA的昼夜变化轨迹存在差异。相比之下，灌溉×盐分交互效应（P = 0.8800）和灌溉×盐分×时间交互效应（P = 0.9086）均不显著。尽管如此，在各处理组合之间仍观察到日内变异性的描述性差异，凸显了时间分辨结构监测在表征冠层对水分与盐分复合胁迫响应方面的价值。这些发现揭示了冠层在交互胁迫下的复杂结构可塑性，强调亟需从静态冠层假设转向动态结构监测，以改进生态系统模型和精准农业。","Remote Sensing of Environment","2026-09-19T00:00:00Z",84,{"impact":161,"substance":162,"depth":163,"authority":19,"freshness":21,"relevant":22,"comment":164},18,23,19,"该研究提出CAPLA无人机点云融合方法，实现水盐胁迫下棉花叶倾角昼夜动态的高精度监测，方法新颖、数据可靠，对作物表型与精准农业有实质参考价值。",[166],{"name":157,"url":154},[111,168,28,169,170],"农业遥感","精准农业","无人机遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[173],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":175},{"candidates":176,"agricultural_evidence":52},[177,181,186,190,197],{"code":32,"score":21,"supported":52,"title":178,"abstract":179,"tags":180},[59],[],[28],{"code":130,"score":17,"supported":52,"title":182,"abstract":183,"tags":185},[],[169,184],"precision agriculture",[111,169],{"code":39,"score":17,"supported":52,"title":187,"abstract":188,"tags":189},[],[29,54],[168,170],{"code":191,"score":94,"supported":52,"title":192,"abstract":193,"tags":196},"H3",[],[194,195],"灌溉","irrigation",[],{"code":63,"score":94,"supported":52,"title":198,"abstract":199,"tags":202},[],[200,201],"深度学习","deep learning",[],[204,205],"无人机 棉花 叶倾角 水盐胁迫","CAPLA 冠层结构 动态监测","无人机棉花叶倾角水盐胁迫-3014",4,"10.1016\u002Fj.rse.2026.115674",{"doi":208,"openalex_id":210,"authors":211,"venue":157,"cited_by_count":84,"oa_url":154,"card":236,"direction":89,"ingested_from":91},"W7213661690",[212,215,217,219,221,223,226,229,231,234],{"name":213,"orcid":214},"Qing Li","https:\u002F\u002Forcid.org\u002F0009-0004-4580-7761",{"name":216,"orcid":9},"Dalei Hao",{"name":218,"orcid":9},"Jan Pisek",{"name":220,"orcid":9},"Zicheng Ji",{"name":222,"orcid":9},"Yanan Wei",{"name":224,"orcid":225},"Youngryel Ryu","https:\u002F\u002Forcid.org\u002F0000-0001-6238-2479",{"name":227,"orcid":228},"Jiarui Xu","https:\u002F\u002Forcid.org\u002F0000-0003-4925-2770",{"name":230,"orcid":9},"Yangmin Feng",{"name":232,"orcid":233},"Shaozhong Kang","https:\u002F\u002Forcid.org\u002F0000-0002-8019-2537",{"name":235,"orcid":9},"Yelu Zeng",{"tldr":237,"method":238,"finding":239,"direction":89,"opportunity":240},"提出CAPLA无人机点云融合方法，监测水盐胁迫下棉花叶倾角昼夜动态。","结合深度学习与SfM，用2D语义掩膜约束3D网格重建，无人机高频观测。","灌溉、盐分和时间显著影响叶倾角，灌溉×时间交互显著，昼夜轨迹因灌溉而异。","可将动态叶倾角参数化嵌入作物模型，提升水盐胁迫下冠层结构与蒸散模拟精度。","2026-09-20T23:30:21.262698Z",{"id":243,"title":244,"url":245,"summary":246,"summary_zh":247,"content":9,"source_name":248,"source_url":245,"published_at":249,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":250,"score_detail":251,"sources":255,"tags":257,"primary_topic":259,"secondary_topics":260,"classification":261,"search_phrases":278,"slug":281,"view_count":282,"doi":283,"paper":284,"created_at":300},4822,"Hybrid transformer and CNN for hyperspectral image classification with spectral band selection","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72102-z","Abstract Hyperspectral images (HSI) play a vital role in remote sensing applications such as surveillance and precision agriculture, yet they face challenges including limited labeled samples and class imbalance. This paper presents a new method for HSI classification by integrating spectral band selection with advanced deep learning techniques. First, we propose an optimized band selection method alongside data augmentation using crop-and-resize transformations to enhance feature diversity. Second, we introduce a hybrid convolution-transformer framework that combines Vision Transformer (ViT) and 2D-CNN architectures, supplemented with a sequence aggregation layer to mitigate overfitting on small datasets. This hybrid model effectively captures both spatial and spectral features while preserving critical spectral details. Finally, we employ a support vector machine for robust classification. Extensive experiments on three widely used HSI datasets demonstrate that our proposed ViT-CNN-BS framework achieves superior classification accuracy compared to state-of-the-art few-shot learning methods and traditional approaches. Beyond achieving higher accuracy, our approach also reduces computational complexity, making it suitable for resource-constrained applications. The findings highlight the potential of hybrid deep learning frameworks for advancing remote sensing analytics, with possible extensions to environmental monitoring, disaster management, and precision agriculture.","高光谱图像（HSI）在监视和精准农业等遥感应用中发挥着重要作用，但仍面临标注样本有限和类别不平衡等挑战。本文提出了一种将光谱波段选择与先进深度学习技术相结合的高光谱图像分类新方法。首先，我们提出了一种优化的波段选择方法，并利用裁剪与缩放变换进行数据增强，以提升特征多样性。其次，我们引入了一种混合卷积-Transformer框架，将视觉Transformer（ViT）与二维卷积神经网络（2D-CNN）架构相结合，并辅以序列聚合层以缓解小数据集上的过拟合问题。该混合模型在保留关键光谱细节的同时，有效捕捉了空间和光谱特征。最后，我们采用支持向量机进行稳健分类。在三个广泛使用的高光谱图像数据集上进行的大量实验表明，我们提出的ViT-CNN-BS框架相比最先进的少样本学习方法和传统方法，实现了更优的分类精度。除了达到更高的精度外，我们的方法还降低了计算复杂度，使其适用于资源受限的应用场景。研究结果凸显了混合深度学习框架在推动遥感分析方面的潜力，并可扩展应用于环境监测、灾害管理和精准农业等领域。","Scientific Reports","2026-10-12T00:00:00Z",73,{"impact":20,"substance":252,"depth":253,"authority":18,"freshness":21,"relevant":22,"comment":254},21,17,"高光谱遥感分类新方法，方法新颖且面向精准农业等应用，但属实验室数据集验证，产业落地影响有限。",[256],{"name":248,"url":245},[27,169,29,30,258],"高光谱影像",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":262},{"candidates":263,"agricultural_evidence":52},[264,269,274],{"code":39,"score":163,"supported":52,"title":265,"abstract":267,"tags":268},[266],"hyperspectral",[29,54,266],[30,29],{"code":130,"score":270,"supported":52,"title":271,"abstract":272,"tags":273},7,[],[169,184],[169],{"code":63,"score":270,"supported":52,"title":275,"abstract":276,"tags":277},[],[200,201],[27],[279,280],"高光谱影像 分类 ViT-CNN","光谱波段选择 精准农业","高光谱影像分类ViT-CNN-4822",2,"10.1038\u002Fs41598-026-72102-z",{"doi":283,"openalex_id":285,"authors":286,"venue":248,"cited_by_count":84,"oa_url":245,"card":295,"direction":89,"ingested_from":91},"W7221518213",[287,289,292],{"name":288,"orcid":9},"Eman N. Abdelhafez",{"name":290,"orcid":291},"Ahmed Hagag","https:\u002F\u002Forcid.org\u002F0000-0003-2631-1846",{"name":293,"orcid":294},"Tamer Ahmed Abassy","https:\u002F\u002Forcid.org\u002F0000-0002-1960-7748",{"tldr":296,"method":297,"finding":298,"direction":89,"opportunity":299},"提出结合波段选择与ViT-CNN混合框架的高光谱图像分类方法，提升小样本分类精度。","优化波段选择、裁剪缩放数据增强、ViT与2D-CNN混合模型及SVM分类。","在三个高光谱数据集上分类精度优于少样本学习与传统方法，且计算复杂度更低。","可探索轻量化混合模型在资源受限的田间实时作物分类与病害监测中的部署。","2026-10-13T23:30:23.758699Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":9,"source_name":307,"source_url":304,"published_at":308,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":309,"score_detail":310,"sources":313,"tags":315,"primary_topic":317,"secondary_topics":321,"classification":323,"search_phrases":349,"slug":352,"view_count":207,"doi":353,"paper":354,"created_at":382},4356,"Dual‐Branch Network With Enhanced Feature Fusion for Improved Rice Classification Accuracy","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsae2.70218","ABSTRACT Accurate mapping of rice, one of the world's most important food crops, is critical for monitoring its growth, estimating yields, and supporting global food security. Most deep learning methods for crop classification rely on multi‐temporal optical remote sensing data. However, it is difficult to obtain sufficient ground‐truth samples for time‐series deep learning models through conducting large‐scale field surveys, and persistent cloud and rainfall often limit optical observations during the rice‐growing seasons in subtropical regions. Rice classification based solely on time‐series optical images may therefore be less accurate and reliable. Here, we developed two dual‐branch deep learning networks for effective rice identification: one combining a long short‐term memory network running in parallel with a multi‐layer perceptron (LSTM + MLP), and the other replacing the LSTM with a one‐dimensional convolutional neural network (Conv1d + MLP). These models process temporal sequences in parallel with auxiliary data through the MLP, allowing them to automatically extract phenology information while incorporating auxiliary variables such as texture features, digital elevation model (DEM) data, and the standard deviation of the vegetation index time series. We evaluated these models in a representative county in the subtropical region of China, and compared the accuracies with standalone LSTM, Conv1d, MLP, and random forest (RF) models. The results revealed that LSTM + MLP had the highest accuracy, with an F1‐score of 95.5%, which can automatically extract rice growth patterns and successfully identify the key phenological information of flood signals during the rice transplanting period. By incorporating auxiliary variables, the model achieved an average accuracy improvement of 8.1% relative to the models using only temporal features. The proposed method provides a useful approach for rice mapping in subtropical regions.","准确绘制水稻这一全球最重要的粮食作物之一，对于监测其生长、估算产量以及保障全球粮食安全至关重要。大多数作物分类的深度学习方法依赖于多时相光学遥感数据。然而，通过开展大规模实地调查来获取足够的地面真值样本以用于时间序列深度学习模型十分困难，而且亚热带地区水稻生长季期间持续的云层和降雨往往限制了光学观测。因此，仅基于时间序列光学图像的水稻分类可能精度较低且可靠性不足。本研究开发了两种双分支深度学习网络以实现有效的水稻识别：一种将长短期记忆网络与多层感知机并行运行（LSTM + MLP），另一种则用一维卷积神经网络替代LSTM（Conv1d + MLP）。这些模型通过MLP将时间序列与辅助数据并行处理，使其能够自动提取物候信息，同时纳入纹理特征、数字高程模型（DEM）数据以及植被指数时间序列标准差等辅助变量。我们在中国亚热带地区一个代表性县对这些模型进行了评估，并将精度与单独的LSTM、Conv1d、MLP和随机森林（RF）模型进行了比较。结果表明，LSTM + MLP精度最高，F1分数达到95.5%，能够自动提取水稻生长模式，并成功识别水稻移栽期洪水信号的关键物候信息。通过纳入辅助变量，该模型相对于仅使用时间特征的模型平均精度提高了8.1%。所提出的方法为亚热带地区水稻制图提供了一种有效途径。","Journal of Sustainable Agriculture and Environment","2026-10-06T00:00:00Z",75,{"impact":19,"substance":252,"depth":253,"authority":311,"freshness":21,"relevant":22,"comment":312},13,"提出双分支深度学习网络融合时序与辅助数据，水稻识别F1达95.5%，方法新颖且结论可靠，对亚热带多云雨区作物遥感制图有实用价值。",[314],{"name":307,"url":304},[111,27,316,29,30],"水稻",{"code":318,"slug":319,"name":316,"path":320,"parent_code":36},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice",[322],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":324},{"candidates":325,"agricultural_evidence":52},[326,330,334,338,345],{"code":318,"score":19,"supported":52,"title":327,"abstract":328,"tags":329},[319],[316,319],[316],{"code":39,"score":17,"supported":52,"title":331,"abstract":332,"tags":333},[],[29,54],[30,29],{"code":63,"score":270,"supported":52,"title":335,"abstract":336,"tags":337},[],[200,201],[27],{"code":339,"score":94,"supported":52,"title":340,"abstract":341,"tags":344},"A8",[],[342,343],"粮食安全","food security",[],{"code":130,"score":22,"supported":14,"title":346,"abstract":347,"tags":348},[],[],[111],[350,351],"水稻 遥感 分类 深度学习","亚热带 水稻 制图","水稻遥感分类深度学习-4356","10.1002\u002Fsae2.70218",{"doi":353,"openalex_id":355,"authors":356,"venue":307,"cited_by_count":84,"oa_url":376,"card":377,"direction":89,"ingested_from":91},"W7220691059",[357,359,362,365,367,370,373],{"name":358,"orcid":9},"Rongfei Ma",{"name":360,"orcid":361},"Wenjiao Shi","https:\u002F\u002Forcid.org\u002F0000-0001-6199-9733",{"name":363,"orcid":364},"Minglei Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4174-9224",{"name":366,"orcid":9},"Xiwei Chen",{"name":368,"orcid":369},"Chenchen Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0458-6232",{"name":371,"orcid":372},"Baihong Pan","https:\u002F\u002Forcid.org\u002F0009-0004-1100-6798",{"name":374,"orcid":375},"Xiangming Xiao","https:\u002F\u002Forcid.org\u002F0000-0003-0956-7428","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fsae2.70218",{"tldr":378,"method":379,"finding":380,"direction":89,"opportunity":381},"提出双分支深度学习网络融合时序与辅助数据，提升亚热带水稻分类精度。","LSTM\u002FConv1d与MLP并行双分支网络，融合时序植被指数、纹理、DEM等辅","LSTM+MLP模型F1达95.5%，辅助变量使精度平均提升8.1%，能识别插秧期淹水信号。","可探索多源遥感与气象数据融合，解决多云雨地区时序缺失下的作物精细制图问题。","2026-10-07T23:30:25.945309Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":9,"source_name":389,"source_url":386,"published_at":390,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":391,"score_detail":392,"sources":395,"tags":397,"primary_topic":400,"secondary_topics":401,"classification":402,"search_phrases":429,"slug":432,"view_count":22,"doi":433,"paper":434,"created_at":472},2786,"SAF-CropNet: Spatially adaptive fusion of SAR and optical imagery for semantic segmentation in operational cropland mapping across regions","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.isprsjprs.2026.08.029","Accurate cropland mapping from remote sensing imagery is essential for agricultural monitoring, food security assessment, and land resource management. Although optical and synthetic aperture radar (SAR) observations provide complementary information for cropland extraction, their local contributions vary across imaging conditions, land-cover backgrounds, parcel morphologies, and regional agricultural systems. This variability limits direct concatenation and static fusion, as modality-specific errors may propagate into segmentation outputs in the absence of spatially adaptive weighting. This study proposes SAF-CropNet, a spatially adaptive SAR–optical fusion framework for binary cropland semantic segmentation. Specifically, SAF-CropNet first extracts modality-specific SAR structural features and optical spectral-textural features through separate encoder branches. The Paired Reciprocal Inter-modal Selective Modulator enhances complementary SAR–optical interactions, while the Contextual State-space Modeling module captures long-range parcel organization. A spatially adaptive fusion head then estimates per-pixel contribution weights for SAR, optical, and contextual features before decoding. Evaluation was conducted on a seven-area SAR–optical benchmark spanning China, Germany, and France. The benchmark integrates GF1\u002FGF3 and Sentinel-1\u002F2 imagery with reference labels derived from field surveys and RapidCrops products and covers fragmented smallholder systems, water-rich and peri-urban mosaics, and large mechanized cropland. Within-region five-fold validation shows that SAF-CropNet achieves an average F1 score of 0.8550 and an mIoU of 0.8068, outperforming the evaluated segmentation baselines while remaining lightweight, with 3.38 M parameters and 6.21 GFLOPs for 256 × 256 SAR–optical inputs. In leave-one-area-out transfer experiments, stratified few-shot adaptation increased the average F1 score from 0.4986 under zero-shot transfer to 0.7698. Grouped cross-continental and cross-sensor experiments further showed that direct transfer remains constrained under compound shifts in sensor characteristics, spatial resolution, landscape structure, and reference-label conventions. These findings indicate that SAF-CropNet combines strong within-region segmentation accuracy and computational efficiency with substantial gains from limited target-domain adaptation, while direct zero-shot generalization under severe compound domain shifts remains limited.","从遥感影像中准确提取耕地信息对农业监测、粮食安全评估和土地资源管理至关重要。尽管光学与合成孔径雷达（SAR）观测为耕地提取提供了互补信息，但其局部贡献会随成像条件、地表覆盖背景、地块形态和区域农业系统的不同而变化。这种变异性限制了直接拼接和静态融合的效果，因为在缺乏空间自适应加权的情况下，模态特有的误差可能传播至分割输出中。本研究提出SAF-CropNet，一种用于二分类耕地语义分割的空间自适应SAR–光学融合框架。具体而言，SAF-CropNet首先通过独立的编码器分支提取模态特有的SAR结构特征和光学光谱-纹理特征。成对互反模态间选择性调制器增强SAR与光学之间的互补交互，而上下文状态空间建模模块则捕获长距离地块组织信息。随后，空间自适应融合头在解码前估计SAR、光学和上下文特征的逐像素贡献权重。评估在一个涵盖中国、德国和法国的七区域SAR–光学基准上进行。该基准整合了GF1\u002FGF3和Sentinel-1\u002F2影像，参考标签来源于实地调查和RapidCrops产品，覆盖破碎化小农系统、富水与城郊镶嵌景观以及大规模机械化耕地。区域内五折验证表明，SAF-CropNet平均F1分数达到0.8550，mIoU为0.8068，优于所评估的分割基线，同时保持轻量级，对于256 × 256的SAR–光学输入仅需3.38 M参数和6.21 GFLOPs。在留一区域迁移实验中，分层少样本自适应将平均F1分数从零样本迁移下的0.4986提升至0.7698。分组跨大陆和跨传感器实验进一步表明，在传感器特性、空间分辨率、景观结构和参考标签惯例的复合变化下，直接迁移仍然受到限制。这些发现表明，SAF-CropNet兼具较强的区域内分割精度和计算效率，并能从有限的目标域自适应中获得显著增益，而在严重复合域偏移下的直接零样本泛化能力仍然有限。","ISPRS Journal of Photogrammetry and Remote Sensing","2026-09-17T00:00:00Z",82,{"impact":161,"substance":393,"depth":161,"authority":18,"freshness":13,"relevant":22,"comment":394},22,"提出空间自适应SAR-光学融合分割框架，跨七区域基准验证，兼顾精度与轻量化，对耕地遥感制图有实质方法贡献。",[396],{"name":389,"url":386},[27,29,398,30,399],"耕地监测","SAR",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":403},{"candidates":404,"agricultural_evidence":52},[405,409,413,419,425],{"code":39,"score":17,"supported":52,"title":406,"abstract":407,"tags":408},[],[29,54],[30,29],{"code":339,"score":94,"supported":52,"title":410,"abstract":411,"tags":412},[],[342,343],[],{"code":414,"score":138,"supported":52,"title":415,"abstract":416,"tags":418},"F5",[],[417],"机械化",[],{"code":420,"score":138,"supported":52,"title":421,"abstract":422,"tags":424},"G2",[],[423],"smallholder",[],{"code":63,"score":22,"supported":14,"title":426,"abstract":427,"tags":428},[],[],[27],[430,431],"农业人工智能 作物分类 耕地监测 遥感","农业人工智能 作物分类","农业人工智能作物分类耕地监测遥感-2786","10.1016\u002Fj.isprsjprs.2026.08.029",{"doi":433,"openalex_id":435,"authors":436,"venue":389,"cited_by_count":84,"oa_url":386,"card":467,"direction":89,"ingested_from":91},"W7213463520",[437,440,442,445,448,451,454,456,458,460,462,464],{"name":438,"orcid":439},"minghui chang","https:\u002F\u002Forcid.org\u002F0009-0008-7954-5308",{"name":441,"orcid":9},"Shuaifeng Peng",{"name":443,"orcid":444},"Tao Xu","https:\u002F\u002Forcid.org\u002F0000-0002-7855-4199",{"name":446,"orcid":447},"Yi Yuan","https:\u002F\u002Forcid.org\u002F0009-0006-9481-4938",{"name":449,"orcid":450},"洋一 馬目","https:\u002F\u002Forcid.org\u002F0000-0003-0226-5558",{"name":452,"orcid":453},"Jie Bai","https:\u002F\u002Forcid.org\u002F0000-0003-2426-2358",{"name":455,"orcid":9},"Fugui Luo",{"name":457,"orcid":9},"Jingyu Zhang",{"name":459,"orcid":9},"Xiaoyu Xiao",{"name":461,"orcid":9},"Yu Mu",{"name":463,"orcid":9},"Yong Wang",{"name":465,"orcid":466},"Shihua Li","https:\u002F\u002Forcid.org\u002F0000-0003-4807-5012",{"tldr":468,"method":469,"finding":470,"direction":89,"opportunity":471},"提出SAF-CropNet，用空间自适应融合SAR与光学影像做跨区域耕地语义分割。","双分支编码器提取SAR结构\u002F光学纹理特征，结合互模态调制与状态空间建模，在七区域","区域内F1达0.8550且轻量；零样本迁移F1仅0.4986，少样本适配可升至0.7698。","复合域偏移下零样本泛化差，可研究跨传感器、跨区域的无监督域自适应融合分割方法。","2026-09-17T23:30:34.424579Z",{"id":474,"title":475,"url":476,"summary":477,"summary_zh":9,"content":9,"source_name":478,"source_url":476,"published_at":479,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":480,"score_detail":481,"sources":483,"tags":485,"primary_topic":487,"secondary_topics":488,"classification":489,"search_phrases":508,"slug":511,"view_count":138,"doi":512,"paper":513,"created_at":541},1208,"Restoration of occlusion in residual film images and curling degree prediction based on UAV in pre-sowing cotton fields","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112335","Restoration of occlusion in residual film images and curling degree prediction based on UAV in pre-sowing cotton fields。Computers and Electronics in Agriculture","Computers and Electronics in Agriculture","2026-08-25T00:00:00Z",62,{"impact":20,"substance":161,"depth":51,"authority":18,"freshness":282,"relevant":22,"comment":482},"基于无人机影像的残膜污染检测与卷曲度预测研究，方法新颖，对农业环保有实际价值，但时效性较低。",[484],{"name":478,"url":476},[111,27,28,29,486],"残膜污染",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":490},{"candidates":491,"agricultural_evidence":52},[492,496,500,504],{"code":32,"score":20,"supported":52,"title":493,"abstract":494,"tags":495},[59],[59],[28],{"code":130,"score":22,"supported":14,"title":497,"abstract":498,"tags":499},[],[],[111],{"code":63,"score":22,"supported":14,"title":501,"abstract":502,"tags":503},[],[],[27],{"code":39,"score":22,"supported":14,"title":505,"abstract":506,"tags":507},[],[],[29],[509,510],"农业人工智能 智慧农业 残膜污染 棉花","农业人工智能 智慧农业","农业人工智能智慧农业残膜污染棉花-1208","10.1016\u002Fj.compag.2026.112335",{"doi":512,"openalex_id":514,"authors":515,"venue":478,"cited_by_count":84,"oa_url":9,"card":536,"direction":89,"ingested_from":91},"W7204210197",[516,519,521,524,527,530,533],{"name":517,"orcid":518},"Zhiqiang Zhai","https:\u002F\u002Forcid.org\u002F0000-0002-5242-9779",{"name":520,"orcid":9},"Lupeng Miao",{"name":522,"orcid":523},"Huting Wang","https:\u002F\u002Forcid.org\u002F0009-0008-3605-2685",{"name":525,"orcid":526},"Jiangbo Li","https:\u002F\u002Forcid.org\u002F0000-0001-7856-9131",{"name":528,"orcid":529},"Yue Chen","https:\u002F\u002Forcid.org\u002F0009-0004-5110-6400",{"name":531,"orcid":532},"Songxin Ye","https:\u002F\u002Forcid.org\u002F0000-0002-8895-8525",{"name":534,"orcid":535},"Ruoyu Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0370-3249",{"tldr":537,"method":538,"finding":539,"direction":89,"opportunity":540},"用无人机图像修复残膜遮挡并预测卷曲程度，评估棉田残膜污染。","无人机图像、遮挡修复、卷曲度预测模型。","成功修复遮挡并预测卷曲度，支持残膜污染评估。","可扩展至不同作物、不同残膜类型，结合深度学习提升修复精度和预测鲁棒性。","2026-09-01T04:03:02.336224Z"]