[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2786":3,"related-2786":123},{"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":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":76,"slug":79,"view_count":20,"doi":80,"paper":81,"created_at":122},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。",null,"ISPRS Journal of Photogrammetry and Remote Sensing","2026-09-17T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":21},18,22,14,1,"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。",[23],{"name":10,"url":6},[25,26,27,28,29],"农业人工智能","遥感","耕地监测","作物分类","SAR",{"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":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,58,65,71],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":49},8,true,[],[26,48],"remote sensing",[28,26],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":57},"A8",6,[],[55,56],"粮食安全","food security",[],{"code":59,"score":60,"supported":45,"title":61,"abstract":62,"tags":64},"F5",3,[],[63],"机械化",[],{"code":66,"score":60,"supported":45,"title":67,"abstract":68,"tags":70},"G2",[],[69],"smallholder",[],{"code":72,"score":20,"supported":14,"title":73,"abstract":74,"tags":75},"I2",[],[],[25],[77,78],"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":80,"openalex_id":82,"authors":83,"venue":10,"cited_by_count":114,"oa_url":6,"card":115,"direction":119,"ingested_from":121},"W7213463520",[84,87,89,92,95,98,101,103,105,107,109,111],{"name":85,"orcid":86},"minghui chang","https:\u002F\u002Forcid.org\u002F0009-0008-7954-5308",{"name":88,"orcid":9},"Shuaifeng Peng",{"name":90,"orcid":91},"Tao Xu","https:\u002F\u002Forcid.org\u002F0000-0002-7855-4199",{"name":93,"orcid":94},"Yi Yuan","https:\u002F\u002Forcid.org\u002F0009-0006-9481-4938",{"name":96,"orcid":97},"洋一 馬目","https:\u002F\u002Forcid.org\u002F0000-0003-0226-5558",{"name":99,"orcid":100},"Jie Bai","https:\u002F\u002Forcid.org\u002F0000-0003-2426-2358",{"name":102,"orcid":9},"Fugui Luo",{"name":104,"orcid":9},"Jingyu Zhang",{"name":106,"orcid":9},"Xiaoyu Xiao",{"name":108,"orcid":9},"Yu Mu",{"name":110,"orcid":9},"Yong Wang",{"name":112,"orcid":113},"Shihua Li","https:\u002F\u002Forcid.org\u002F0000-0003-4807-5012",0,{"tldr":116,"method":117,"finding":118,"direction":119,"opportunity":120},"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。","农业遥感与作物表型","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。","openalex","2026-09-17T23:30:34.424579Z",{"total":52,"page":20,"page_size":52,"items":124},[125,192,275,337,395,449],{"id":126,"title":127,"url":128,"summary":129,"summary_zh":130,"content":9,"source_name":131,"source_url":128,"published_at":132,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":133,"score_detail":134,"sources":140,"tags":142,"primary_topic":145,"secondary_topics":146,"classification":147,"search_phrases":169,"slug":172,"view_count":173,"doi":174,"paper":175,"created_at":191},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":135,"substance":136,"depth":137,"authority":19,"freshness":138,"relevant":20,"comment":139},12,21,17,9,"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。",[141],{"name":131,"url":128},[25,143,26,28,144],"精准农业","高光谱影像",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":148},{"candidates":149,"agricultural_evidence":45},[150,156,163],{"code":31,"score":151,"supported":45,"title":152,"abstract":154,"tags":155},19,[153],"hyperspectral",[26,48,153],[28,26],{"code":157,"score":158,"supported":45,"title":159,"abstract":160,"tags":162},"I1",7,[],[143,161],"precision agriculture",[143],{"code":72,"score":158,"supported":45,"title":164,"abstract":165,"tags":168},[],[166,167],"深度学习","deep learning",[25],[170,171],"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":174,"openalex_id":176,"authors":177,"venue":131,"cited_by_count":114,"oa_url":128,"card":186,"direction":119,"ingested_from":121},"W7221518213",[178,180,183],{"name":179,"orcid":9},"Eman N. Abdelhafez",{"name":181,"orcid":182},"Ahmed Hagag","https:\u002F\u002Forcid.org\u002F0000-0003-2631-1846",{"name":184,"orcid":185},"Tamer Ahmed Abassy","https:\u002F\u002Forcid.org\u002F0000-0002-1960-7748",{"tldr":187,"method":188,"finding":189,"direction":119,"opportunity":190},"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":193,"title":194,"url":195,"summary":196,"summary_zh":197,"content":9,"source_name":198,"source_url":195,"published_at":199,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":200,"score_detail":201,"sources":205,"tags":207,"primary_topic":210,"secondary_topics":215,"classification":217,"search_phrases":240,"slug":243,"view_count":244,"doi":245,"paper":246,"created_at":274},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":202,"substance":136,"depth":137,"authority":203,"freshness":138,"relevant":20,"comment":204},15,13,"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。",[206],{"name":198,"url":195},[208,25,209,26,28],"智慧农业","水稻",{"code":211,"slug":212,"name":209,"path":213,"parent_code":214},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[216],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":218},{"candidates":219,"agricultural_evidence":45},[220,224,228,232,236],{"code":211,"score":202,"supported":45,"title":221,"abstract":222,"tags":223},[212],[209,212],[209],{"code":31,"score":44,"supported":45,"title":225,"abstract":226,"tags":227},[],[26,48],[28,26],{"code":72,"score":158,"supported":45,"title":229,"abstract":230,"tags":231},[],[166,167],[25],{"code":51,"score":52,"supported":45,"title":233,"abstract":234,"tags":235},[],[55,56],[],{"code":157,"score":20,"supported":14,"title":237,"abstract":238,"tags":239},[],[],[208],[241,242],"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",4,"10.1002\u002Fsae2.70218",{"doi":245,"openalex_id":247,"authors":248,"venue":198,"cited_by_count":114,"oa_url":268,"card":269,"direction":119,"ingested_from":121},"W7220691059",[249,251,254,257,259,262,265],{"name":250,"orcid":9},"Rongfei Ma",{"name":252,"orcid":253},"Wenjiao Shi","https:\u002F\u002Forcid.org\u002F0000-0001-6199-9733",{"name":255,"orcid":256},"Minglei Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4174-9224",{"name":258,"orcid":9},"Xiwei Chen",{"name":260,"orcid":261},"Chenchen Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0458-6232",{"name":263,"orcid":264},"Baihong Pan","https:\u002F\u002Forcid.org\u002F0009-0004-1100-6798",{"name":266,"orcid":267},"Xiangming Xiao","https:\u002F\u002Forcid.org\u002F0000-0003-0956-7428","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fsae2.70218",{"tldr":270,"method":271,"finding":272,"direction":119,"opportunity":273},"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":276,"title":277,"url":278,"summary":279,"summary_zh":280,"content":9,"source_name":281,"source_url":278,"published_at":282,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":283,"score_detail":284,"sources":286,"tags":288,"primary_topic":290,"secondary_topics":295,"classification":297,"search_phrases":314,"slug":317,"view_count":318,"doi":319,"paper":320,"created_at":336},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》","Journal of the Indian Society of Remote Sensing","2026-09-21T00:00:00Z",58,{"impact":44,"substance":19,"depth":202,"authority":135,"freshness":138,"relevant":20,"comment":285},"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。",[287],{"name":281,"url":278},[25,289,26,28],"棉花",{"code":291,"slug":292,"name":293,"path":294,"parent_code":214},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops",[296],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":298},{"candidates":299,"agricultural_evidence":45},[300,305,310],{"code":31,"score":301,"supported":45,"title":302,"abstract":303,"tags":304},16,[48],[26,48],[28,26],{"code":291,"score":202,"supported":45,"title":306,"abstract":308,"tags":309},[307],"cotton",[289,307],[289],{"code":72,"score":20,"supported":14,"title":311,"abstract":312,"tags":313},[],[],[25],[315,316],"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":319,"openalex_id":321,"authors":322,"venue":281,"cited_by_count":114,"oa_url":9,"card":331,"direction":119,"ingested_from":121},"W7213944040",[323,325,327,329],{"name":324,"orcid":9},"G. Suresh",{"name":326,"orcid":9},"G. Bhuvaneswari",{"name":328,"orcid":9},"G. Manikandan",{"name":330,"orcid":9},"M. Robinson Joel",{"tldr":332,"method":333,"finding":334,"direction":119,"opportunity":335},"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。","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。","2026-09-22T23:30:23.811658Z",{"id":338,"title":339,"url":340,"summary":341,"summary_zh":342,"content":9,"source_name":343,"source_url":340,"published_at":344,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":133,"score_detail":345,"sources":348,"tags":350,"primary_topic":352,"secondary_topics":353,"classification":354,"search_phrases":370,"slug":373,"view_count":114,"doi":374,"paper":375,"created_at":394},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",{"impact":135,"substance":346,"depth":137,"authority":19,"freshness":13,"relevant":20,"comment":347},20,"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。",[349],{"name":343,"url":340},[208,351,26,29],"作物制图",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":355},{"candidates":356,"agricultural_evidence":45},[357,362,366],{"code":31,"score":13,"supported":45,"title":358,"abstract":359,"tags":361},[],[26,48,360],"multispectral",[26],{"code":51,"score":52,"supported":45,"title":363,"abstract":364,"tags":365},[],[55,56],[],{"code":157,"score":20,"supported":14,"title":367,"abstract":368,"tags":369},[],[],[208],[371,372],"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":374,"openalex_id":376,"authors":377,"venue":343,"cited_by_count":114,"oa_url":340,"card":389,"direction":119,"ingested_from":121},"W7223117203",[378,381,383,386],{"name":379,"orcid":380},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":382,"orcid":9},"Wentao Lv",{"name":384,"orcid":385},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":387,"orcid":388},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":390,"method":391,"finding":392,"direction":119,"opportunity":393},"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":396,"title":397,"url":398,"summary":399,"summary_zh":400,"content":9,"source_name":401,"source_url":398,"published_at":344,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":402,"score_detail":403,"sources":405,"tags":407,"primary_topic":410,"secondary_topics":414,"classification":416,"search_phrases":433,"slug":436,"view_count":114,"doi":437,"paper":438,"created_at":448},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":44,"substance":346,"depth":137,"authority":135,"freshness":13,"relevant":20,"comment":404},"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。",[406],{"name":401,"url":398},[208,25,408,26,409],"产量预测","可解释性",{"code":72,"slug":411,"name":412,"path":413,"parent_code":35},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[415],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":417},{"candidates":418,"agricultural_evidence":45},[419,425,429],{"code":72,"score":135,"supported":45,"title":420,"abstract":422,"tags":424},[421],"computer vision",[423],"计算机视觉",[25],{"code":31,"score":44,"supported":45,"title":426,"abstract":427,"tags":428},[],[26,48],[408,26],{"code":157,"score":20,"supported":14,"title":430,"abstract":431,"tags":432},[],[],[208],[434,435],"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":437,"openalex_id":439,"authors":440,"venue":401,"cited_by_count":114,"oa_url":398,"card":443,"direction":119,"ingested_from":121},"W7223114002",[441],{"name":442,"orcid":9},"Kadiatou Barry",{"tldr":444,"method":445,"finding":446,"direction":119,"opportunity":447},"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",{"id":450,"title":451,"url":452,"summary":453,"summary_zh":454,"content":9,"source_name":401,"source_url":452,"published_at":344,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":200,"score_detail":455,"sources":457,"tags":459,"primary_topic":463,"secondary_topics":469,"classification":476,"search_phrases":508,"slug":511,"view_count":114,"doi":512,"paper":513,"created_at":524},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",{"impact":17,"substance":346,"depth":301,"authority":135,"freshness":138,"relevant":20,"comment":456},"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[458],{"name":401,"url":452},[25,460,26,461,462],"农业物联网","智慧灌溉","节水农业",{"code":464,"slug":465,"name":466,"path":467,"parent_code":468},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[470,475],{"code":471,"slug":472,"name":473,"path":474,"parent_code":35},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":477},{"candidates":478,"agricultural_evidence":45},[479,485,492,496,501],{"code":464,"score":151,"supported":45,"title":480,"abstract":481,"tags":484},[465],[482,483,465],"灌溉","水资源",[461,462],{"code":471,"score":17,"supported":45,"title":486,"abstract":488,"tags":491},[487],"IoT",[489,490,487],"物联网","internet of things",[460],{"code":31,"score":202,"supported":45,"title":493,"abstract":494,"tags":495},[48],[26,48],[26],{"code":72,"score":135,"supported":45,"title":497,"abstract":499,"tags":500},[498],"artificial intelligence",[498],[25],{"code":502,"score":52,"supported":45,"title":503,"abstract":504,"tags":507},"H8",[],[505,506],"可持续农业","sustainable agriculture",[],[509,510],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":512,"openalex_id":514,"authors":515,"venue":401,"cited_by_count":114,"oa_url":452,"card":518,"direction":522,"ingested_from":121},"W7223090389",[516],{"name":517,"orcid":9},"Patil",{"tldr":519,"method":520,"finding":521,"direction":522,"opportunity":523},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","智慧农业 \u002F 农业物联网","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z"]