[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4583":3,"related-4583":81},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":80},4583,"An Efficient Parcel-Center Sampling Strategy for Parcel-Scale Crop Classification Using Multiresolution Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18203463","Parcel-scale crop classification is fundamental to agricultural remote sensing and precision agriculture. The integration of detailed parcel information with medium-spatial-resolution remote sensing data has become an important technical approach to parcel-scale crop classification. For medium-spatial-resolution imagery, the mismatch between parcel boundaries and pixel size makes the parcel–pixel matching strategy a critical step in constructing parcel-level classification features. Existing full-pixels, pixel-center inclusion, and area-weighted strategies generally require identifying pixels associated with each parcel, determining spatial relationships, and performing statistical aggregation. When applied to thousands of parcels across multiple acquisition dates, repeated spatial overlay and multipixel aggregation can become a computational bottleneck in parcel-level feature construction. To address this issue, and assuming sufficient within-parcel feature consistency, a single pixel corresponding to an interior center point or a local window around that point is used to construct parcel-level classification features, thereby reducing the number of pixels involved in spatial matching and feature aggregation. Pringsewu Regency, Lampung Province, Indonesia, which has a highly fragmented parcel pattern, was selected as a case study. Sentinel-2, Landsat-8, Sentinel-1, and topographic data were integrated for parcel-scale crop classification. The results show that the parcel-center sampling strategy substantially reduced the cost of parcel-level feature construction under both the Sentinel-2 and Landsat-8 configurations. Compared with the three multipixel strategies, under the experimental conditions of this study, PCS achieved speedups of approximately 5.2–13.3-fold under Sentinel-2 and 7.3–17.1-fold under Landsat-8. Relative to the full-pixels strategy, OA increased by 0.55 percentage points under the Sentinel-2 configuration and decreased by 1.30 percentage points under the Landsat-8 configuration. Across parcel scales, the parcel-center sampling strategy generally maintained classification performance similar to that of the conventional multipixel strategies, and the resulting spatial patterns of the major crops were broadly consistent. Using a parcel-center pixel or local window can therefore retain sufficient classification information while substantially reducing the cost of parcel-level feature construction. This approach provides a simplified solution for efficient parcel-scale crop classification mapping in fragmented agricultural landscapes using medium-resolution imagery.","地块尺度作物分类是农业遥感和精准农业的基础。将详细的地块信息与中空间分辨率遥感数据相结合，已成为地块尺度作物分类的重要技术途径。对于中空间分辨率影像而言，地块边界与像元大小之间的不匹配使得地块—像元匹配策略成为构建地块级分类特征的关键步骤。现有的全像元策略、像元中心包含策略和面积加权策略通常需要识别与每个地块相关的像元、确定空间关系并进行统计聚合。当应用于多个获取日期下的数千个地块时，重复的空间叠加和多像元聚合可能成为地块级特征构建中的计算瓶颈。为解决这一问题，在假设地块内部特征具有足够一致性的前提下，采用与内部中心点相对应的单个像元或该点周围的局部窗口来构建地块级分类特征，从而减少参与空间匹配和特征聚合的像元数量。选取地块格局高度破碎的印度尼西亚楠榜省普林塞乌县作为案例研究区，集成Sentinel-2、Landsat-8、Sentinel-1和地形数据用于地块尺度作物分类。结果表明，在Sentinel-2和Landsat-8两种配置下，地块中心采样策略均大幅降低了地块级特征构建的成本。与三种多像元策略相比，在本研究的实验条件下，PCS在Sentinel-2下实现了约5.2至13.3倍的加速，在Landsat-8下实现了约7.3至17.1倍的加速。相对于全像元策略，总体精度（OA）在Sentinel-2配置下提高了0.55个百分点，在Landsat-8配置下降低了1.30个百分点。在不同地块尺度下，地块中心采样策略总体上保持了与传统多像元策略相似的分类性能，且主要作物的空间格局基本一致。因此，使用地块中心像元或局部窗口能够在保留足够分类信息的同时，大幅降低地块级特征构建的成本。该方法提供了一种简化的解决方案",null,"Remote Sensing","2026-10-09T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,8,1,"提出地块中心采样策略，在破碎地块区大幅提速且精度基本持平，方法新颖、结论可靠，对农业遥感工程化有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","Sentinel-2","遥感","作物分类","地块尺度",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,51],{"code":33,"score":19,"supported":46,"title":47,"abstract":49,"tags":50},true,[48],"remote sensing",[29,48],[28,30,29],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":58},"I1",7,[],[56,57],"精准农业","precision agriculture",[27],[60,61],"Sentinel-2 地块尺度 作物分类","Pringsewu 遥感 作物制图","Sentinel-2地块尺度作物分类-4583",0,"10.3390\u002Frs18203463",{"doi":64,"openalex_id":66,"authors":67,"venue":10,"cited_by_count":63,"oa_url":6,"card":73,"direction":77,"ingested_from":79},"W7221068088",[68,71],{"name":69,"orcid":70},"Zhihua Wang","https:\u002F\u002Forcid.org\u002F0000-0002-6776-2910",{"name":72,"orcid":9},"Qin Yang",{"tldr":74,"method":75,"finding":76,"direction":77,"opportunity":78},"提出地块中心采样策略，用单像素或局部窗口构建地块级特征，大幅加速地块尺度作物分类。","以印尼Pringsewu为案例，融合Sentinel-2、Landsat-8、S","中心采样较全像素等策略提速5.2–17.1倍，精度基本相当，主要作物空间格局一致。","农业遥感与作物表型","可探索中心采样在破碎地块、多时相与不同分辨率下的自适应窗口选择及精度-效率权衡机制。","openalex","2026-10-10T23:30:27.757031Z",{"total":82,"page":22,"page_size":82,"items":83},6,[84,173,254,360,420,471],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":9,"source_name":90,"source_url":87,"published_at":91,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":92,"score_detail":93,"sources":97,"tags":99,"primary_topic":102,"secondary_topics":107,"classification":109,"search_phrases":138,"slug":141,"view_count":142,"doi":143,"paper":144,"created_at":172},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":94,"substance":18,"depth":19,"authority":20,"freshness":95,"relevant":22,"comment":96},15,9,"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。",[98],{"name":90,"url":87},[27,100,101,29,30],"农业人工智能","水稻",{"code":103,"slug":104,"name":101,"path":105,"parent_code":106},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[108],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":110},{"candidates":111,"agricultural_evidence":46},[112,116,120,127,134],{"code":103,"score":94,"supported":46,"title":113,"abstract":114,"tags":115},[104],[101,104],[101],{"code":33,"score":21,"supported":46,"title":117,"abstract":118,"tags":119},[],[29,48],[30,29],{"code":121,"score":53,"supported":46,"title":122,"abstract":123,"tags":126},"I2",[],[124,125],"深度学习","deep learning",[100],{"code":128,"score":82,"supported":46,"title":129,"abstract":130,"tags":133},"A8",[],[131,132],"粮食安全","food security",[],{"code":52,"score":22,"supported":14,"title":135,"abstract":136,"tags":137},[],[],[27],[139,140],"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":143,"openalex_id":145,"authors":146,"venue":90,"cited_by_count":63,"oa_url":166,"card":167,"direction":77,"ingested_from":79},"W7220691059",[147,149,152,155,157,160,163],{"name":148,"orcid":9},"Rongfei Ma",{"name":150,"orcid":151},"Wenjiao Shi","https:\u002F\u002Forcid.org\u002F0000-0001-6199-9733",{"name":153,"orcid":154},"Minglei Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4174-9224",{"name":156,"orcid":9},"Xiwei Chen",{"name":158,"orcid":159},"Chenchen Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0458-6232",{"name":161,"orcid":162},"Baihong Pan","https:\u002F\u002Forcid.org\u002F0009-0004-1100-6798",{"name":164,"orcid":165},"Xiangming Xiao","https:\u002F\u002Forcid.org\u002F0000-0003-0956-7428","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fsae2.70218",{"tldr":168,"method":169,"finding":170,"direction":77,"opportunity":171},"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":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":9,"source_name":179,"source_url":176,"published_at":180,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":181,"score_detail":182,"sources":187,"tags":189,"primary_topic":192,"secondary_topics":193,"classification":194,"search_phrases":225,"slug":228,"view_count":142,"doi":229,"paper":230,"created_at":253},4025,"Remote estimation of plastic distribution in California agriculture using Sentinel-2 imagery: a case study","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1927646","Global application of agricultural plastic mulch is increasing. While these films offer benefits for crop production, they also are a large source of plastic waste and pollution in both terrestrial and aquatic systems. The current scale of global mulch application and its deleterious impacts are unclear. Current estimates of agricultural plastic use remain limited by field-specific growing patterns and data limitations. Satellite spectroscopy may offer a means to reduce this uncertainty and model agricultural plastic use on large spatial and temporal scales, though temporally variable crop canopy growth and spectrally diverse plastic targets complicate this approach. To build toward improved plasticulture estimates, this study presents an interpretable, spectral-based classification of bulk plastic use in a California agricultural site using Sentinel-2 imagery. In situ reflectance measurements of soil and white, green, and black plastic mulch endmembers were collected using a Spectra Vista Corporation HR-1024i Spectroradiometer from 350–2500 nm. A combination of field observations and high spatial resolution NearMap imagery (n = 416 spectra) were compiled and divided into 60% training and 40% testing data using a stratified sampling approach. Informed by field reflectance data, mixture analyses, and Sentinel-2 reflectance imagery, an empirical decision tree classification was parameterized to classify dark and bright plastic debris from soil and crop (accuracy = 0.982, kappa coefficient = 0.973, macro-averaged F1-score = 0.967). Modeled agricultural plastic distributions were then retrieved through a time series of Sentinel-2 imagery over Santa Maria, CA, from April 2023 to January 2025, elucidating patterns in plastic application and crop canopy growth without the need for prior knowledge of field growing and management patterns. These results are encouraging for the utility of optical remote sensing in modeling plastic use patterns and identifying contamination hotspots throughout California and, potentially, similarly mulched agroecological systems.","全球农业塑料地膜的应用日益增加。尽管这些薄膜为作物生产带来了益处，但它们同时也是陆地和水生系统中塑料废弃物和污染的重要来源。目前全球地膜应用的规模及其有害影响尚不明确。当前对农业塑料使用量的估算仍受限于特定田块的种植模式和数据局限性。卫星光谱学可能为减少这种不确定性并在大时空尺度上模拟农业塑料使用提供一种手段，尽管作物冠层的时序变化和光谱多样的塑料目标使这一方法变得复杂。为推进对塑料农业（plasticulture）估算的改进，本研究利用Sentinel-2影像，对加利福尼亚州一个农业站点的大宗塑料使用进行了可解释的、基于光谱的分类。使用Spectra Vista Corporation HR-1024i光谱辐射仪在350–2500 nm范围内采集了土壤以及白色、绿色和黑色塑料地膜端元的原位反射率测量数据。结合野外观测和高空间分辨率NearMap影像（n = 416条光谱），采用分层抽样方法将数据分为60%训练集和40%测试集。在野外反射率数据、混合分析及Sentinel-2反射率影像的指导下，参数化了一棵经验决策树分类器，以从土壤和作物中分类暗色和亮色塑料残膜（精度 = 0.982，kappa系数 = 0.973，宏平均F1分数 = 0.967）。随后通过2023年4月至2025年1月期间覆盖加利福尼亚州圣玛丽亚的Sentinel-2影像时间序列，反演了模拟的农业塑料分布，阐明了塑料施用和作物冠层生长的模式，而无需事先了解田块种植和管理模式。这些结果令人鼓舞，表明光学遥感在模拟加利福尼亚州乃至其他类似覆膜农业生态系统的塑料使用模式和识别污染热点方面具有实用价值。","Frontiers in Remote Sensing","2026-10-01T00:00:00Z",78,{"impact":183,"substance":184,"depth":185,"authority":20,"freshness":95,"relevant":22,"comment":186},16,22,18,"该研究利用Sentinel-2影像实现农膜分布遥感估算，方法新颖、精度高，对农业面源污染监测有参考价值，但属区域案例，影响范围有限。",[188],{"name":179,"url":176},[27,28,29,190,191],"农膜污染","加州农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":195},{"candidates":196,"agricultural_evidence":46},[197,201,208,215,221],{"code":33,"score":21,"supported":46,"title":198,"abstract":199,"tags":200},[],[29,48],[28,29],{"code":202,"score":82,"supported":46,"title":203,"abstract":204,"tags":207},"H2",[],[205,206],"土壤","soil",[],{"code":209,"score":210,"supported":46,"title":211,"abstract":212,"tags":214},"H6",3,[],[213],"农业生态",[],{"code":216,"score":210,"supported":46,"title":217,"abstract":218,"tags":220},"J3",[],[219],"全球农业",[],{"code":52,"score":22,"supported":14,"title":222,"abstract":223,"tags":224},[],[],[27],[226,227],"Sentinel-2 农膜 遥感","加州 农业塑料 遥感监测","Sentinel-2农膜遥感-4025","10.3389\u002Ffrsen.2026.1927646",{"doi":229,"openalex_id":231,"authors":232,"venue":179,"cited_by_count":63,"oa_url":176,"card":248,"direction":77,"ingested_from":79},"W7215027304",[233,235,237,240,242,245],{"name":234,"orcid":9},"Eva Scrivner",{"name":236,"orcid":9},"Dexter Hamilton",{"name":238,"orcid":239},"Daniel J. Sousa","https:\u002F\u002Forcid.org\u002F0000-0002-1632-1955",{"name":241,"orcid":9},"Margot Mattson",{"name":243,"orcid":244},"Nicholas E. Williams","https:\u002F\u002Forcid.org\u002F0000-0003-4013-9296",{"name":246,"orcid":247},"Seeta A. Sistla","https:\u002F\u002Forcid.org\u002F0000-0002-6345-462X",{"tldr":249,"method":250,"finding":251,"direction":77,"opportunity":252},"利用Sentinel-2影像和实地光谱，分类并制图加州农业塑料覆盖分布。","Sentinel-2影像、实地光谱与决策树分类，结合高分辨率影像验证。","决策树分类精度达0.982，可无需先验知识反演塑料覆盖时空格局。","可扩展至全球地膜污染热点监测，并融合多源遥感提升复杂作物区塑料识别。","2026-10-02T23:30:24.457168Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":10,"source_url":257,"published_at":260,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":261,"score_detail":262,"sources":265,"tags":267,"primary_topic":269,"secondary_topics":270,"classification":276,"search_phrases":325,"slug":328,"view_count":329,"doi":330,"paper":331,"created_at":359},3494,"Rice Cropping Pattern Mapping Using GEE-Based Sentinel-1\u002F2 Time-Series Imagery and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193302","Accurate mapping of rice cropping patterns is fundamental to sustainable agricultural management and regional food security assessment. In this study, we developed a high-precision mapping framework for major rice cropping patterns in the Yangtze River Delta region, China, using the Google Earth Engine (GEE) cloud platform. A multi-temporal and multi-source feature set was constructed by integrating Sentinel-1 radar backscatter (VV\u002FVH polarization), Sentinel-2 optical indices, including the normalized difference vegetation index (NDVI) and land surface water index (LSWI), and topographic factors (DEM and slope), to classify three predominant cropping systems, namely, wheat–rice rotation, double rice, and rapeseed–rice rotation. A total of 548 field-surveyed sample points collected during the 2024–2025 growing season were used for model training and validation. Three classical classifiers—Random Forest (RF), A Gradient Boosting Tree (GBTREE), and a Support Vector Machine (SVM)—were systematically compared. The ablation experiment demonstrated that the fusion of Sentinel-1 and Sentinel-2 outperformed both the Sentinel-1-only and Sentinel-2-only configurations across all three classifiers. Among them, the GBTREE achieved the highest overall accuracy (93.8%), Kappa coefficient (0.87), and macro-average F1 score (88.3%) in this specific experiment. Notably, it also performed best on the more challenging double-rice class. The SHapley Additive exPlanations (SHAP)-based feature importance analysis revealed that multi-temporal NDVI phenological features were the primary drivers of classification accuracy, while radar backscatter and water indices provided essential complementary information, and topographic factors served as spatial constraints at the regional scale. The spatial distribution derived from the GBTREE classification exhibited clear patterns: wheat–rice rotation dominated the northern plains (northern Jiangsu, northern Anhui, and the Hangjiahu Plain); double rice was concentrated in the southern Zhejiang hills and scattered valley plains; and rapeseed–rice rotation showed a scattered, mosaic distribution. Overall, this study demonstrates that integrating multi-source remote sensing data on the GEE platform with the GBTREE classifier enables effective and scalable high-precision mapping of rice cropping patterns in complex agricultural landscapes. This approach provides a reliable technical foundation for regional agricultural structure analysis, crop rotation assessment, and sustainable agricultural monitoring.","准确绘制水稻种植模式图是实现可持续农业管理和区域粮食安全评估的基础。本研究基于Google Earth Engine（GEE）云平台，构建了长江三角洲地区主要水稻种植模式的高精度制图框架。通过整合Sentinel-1雷达后向散射（VV\u002FVH极化）、Sentinel-2光学指数（包括归一化差异植被指数NDVI和地表水体指数LSWI）以及地形因子（DEM和坡度），构建了多时相、多源特征集，用于分类三种主要种植制度，即麦–稻轮作、双季稻和油–稻轮作。利用2024—2025年生长季采集的548个实地调查样点进行模型训练与验证。系统比较了三种经典分类器——随机森林（RF）、梯度提升树（GBTREE）和支持向量机（SVM）。消融实验表明，Sentinel-1与Sentinel-2的融合在三种分类器中均优于仅使用Sentinel-1或仅使用Sentinel-2的配置。其中，GBTREE在本实验中取得了最高的总体精度（93.8%）、Kappa系数（0.87）和宏平均F1分数（88.3%）。值得注意的是，其在更具挑战性的双季稻类别上同样表现最佳。基于SHapley加法解释（SHAP）的特征重要性分析表明，多时相NDVI物候特征是分类精度的主要驱动因素，雷达后向散射和水体指数提供了必要的补充信息，而地形因子则在区域尺度上起到空间约束作用。基于GBTREE分类得到的空间分布呈现出清晰的格局：麦–稻轮作主导北部平原（苏北、皖北和杭嘉湖平原）；双季稻集中于浙南丘陵和零散的河谷平原；油–稻轮作则呈零散镶嵌状分布。总体而言，本研究表明，在GEE平台上整合多源遥感数据与GBTREE分类器，能够对复杂农业景观中的水稻种植模式进行有效且可扩展的高精度制图。该方法为区域农业结构分析和作物","2026-09-24T00:00:00Z",79,{"impact":183,"substance":184,"depth":185,"authority":263,"freshness":95,"relevant":22,"comment":264},14,"基于GEE与Sentinel-1\u002F2时序影像结合机器学习实现长三角水稻种植模式高精度制图，方法扎实、结论可靠，对农业遥感监测有参考价值。",[266],{"name":10,"url":257},[27,101,268,29,30],"机器学习",{"code":103,"slug":104,"name":101,"path":105,"parent_code":106},[271,275],{"code":121,"slug":272,"name":273,"path":274,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":277},{"candidates":278,"agricultural_evidence":46},[279,283,288,292,299,303,309,315,321],{"code":103,"score":94,"supported":46,"title":280,"abstract":281,"tags":282},[104],[101,104],[101],{"code":121,"score":95,"supported":46,"title":284,"abstract":286,"tags":287},[285],"machine learning",[],[268],{"code":33,"score":21,"supported":46,"title":289,"abstract":290,"tags":291},[],[29,48],[30,29],{"code":293,"score":82,"supported":46,"title":294,"abstract":295,"tags":298},"A7",[],[296,297],"轮作","crop rotation",[],{"code":128,"score":82,"supported":46,"title":300,"abstract":301,"tags":302},[],[131,132],[],{"code":304,"score":210,"supported":46,"title":305,"abstract":306,"tags":308},"A2",[],[307],"wheat",[],{"code":310,"score":210,"supported":46,"title":311,"abstract":312,"tags":314},"A4",[],[313],"rapeseed",[],{"code":316,"score":210,"supported":46,"title":317,"abstract":318,"tags":320},"H8",[],[319],"可持续农业",[],{"code":52,"score":22,"supported":14,"title":322,"abstract":323,"tags":324},[],[],[27],[326,327],"长三角 水稻 种植模式 遥感","Sentinel-1 Sentinel-2 水稻制图","长三角水稻种植模式遥感-3494",5,"10.3390\u002Frs18193302",{"doi":330,"openalex_id":332,"authors":333,"venue":10,"cited_by_count":63,"oa_url":257,"card":354,"direction":77,"ingested_from":79},"W7214147197",[334,337,340,343,345,348,351],{"name":335,"orcid":336},"Xuan Li","https:\u002F\u002Forcid.org\u002F0000-0001-5509-2385",{"name":338,"orcid":339},"Lintao Chen","https:\u002F\u002Forcid.org\u002F0009-0000-6558-1289",{"name":341,"orcid":342},"Lin Chen","https:\u002F\u002Forcid.org\u002F0000-0002-9270-1626",{"name":344,"orcid":9},"Chao Su",{"name":346,"orcid":347},"Hoi Leong Lee","https:\u002F\u002Forcid.org\u002F0000-0002-4984-2183",{"name":349,"orcid":350},"Ruci Wang","https:\u002F\u002Forcid.org\u002F0000-0001-7049-7006",{"name":352,"orcid":353},"Xuguang Tang","https:\u002F\u002Forcid.org\u002F0009-0008-3494-8867",{"tldr":355,"method":356,"finding":357,"direction":77,"opportunity":358},"基于GEE融合Sentinel-1\u002F2时序与地形特征，用机器学习高精度制图长三角水稻种植模式。","GEE平台、Sentinel-1\u002F2时序特征、DEM、548个实地样本、RF\u002FG","GBTREE精度最高（总体93.8%、Kappa 0.87），双季稻识别最好；NDVI物候特征贡献最","可探索样本稀缺区迁移学习与多作物轮作模式泛化制图，并耦合产量与碳核算。","2026-09-25T23:30:30.511522Z",{"id":361,"title":362,"url":363,"summary":364,"summary_zh":365,"content":9,"source_name":366,"source_url":363,"published_at":367,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":368,"score_detail":369,"sources":372,"tags":374,"primary_topic":377,"secondary_topics":378,"classification":379,"search_phrases":395,"slug":398,"view_count":63,"doi":399,"paper":400,"created_at":419},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":17,"substance":370,"depth":19,"authority":263,"freshness":13,"relevant":22,"comment":371},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。",[373],{"name":366,"url":363},[27,375,29,376],"作物制图","SAR",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":380},{"candidates":381,"agricultural_evidence":46},[382,387,391],{"code":33,"score":13,"supported":46,"title":383,"abstract":384,"tags":386},[],[29,48,385],"multispectral",[29],{"code":128,"score":82,"supported":46,"title":388,"abstract":389,"tags":390},[],[131,132],[],{"code":52,"score":22,"supported":14,"title":392,"abstract":393,"tags":394},[],[],[27],[396,397],"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":399,"openalex_id":401,"authors":402,"venue":366,"cited_by_count":63,"oa_url":363,"card":414,"direction":77,"ingested_from":79},"W7223117203",[403,406,408,411],{"name":404,"orcid":405},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":407,"orcid":9},"Wentao Lv",{"name":409,"orcid":410},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":412,"orcid":413},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":415,"method":416,"finding":417,"direction":77,"opportunity":418},"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":421,"title":422,"url":423,"summary":424,"summary_zh":425,"content":9,"source_name":426,"source_url":423,"published_at":367,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":427,"score_detail":428,"sources":430,"tags":432,"primary_topic":435,"secondary_topics":436,"classification":438,"search_phrases":455,"slug":458,"view_count":63,"doi":459,"paper":460,"created_at":470},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":21,"substance":370,"depth":19,"authority":17,"freshness":13,"relevant":22,"comment":429},"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。",[431],{"name":426,"url":423},[27,100,433,29,434],"产量预测","可解释性",{"code":121,"slug":272,"name":273,"path":274,"parent_code":37},[437],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":439},{"candidates":440,"agricultural_evidence":46},[441,447,451],{"code":121,"score":17,"supported":46,"title":442,"abstract":444,"tags":446},[443],"computer vision",[445],"计算机视觉",[100],{"code":33,"score":21,"supported":46,"title":448,"abstract":449,"tags":450},[],[29,48],[433,29],{"code":52,"score":22,"supported":14,"title":452,"abstract":453,"tags":454},[],[],[27],[456,457],"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":459,"openalex_id":461,"authors":462,"venue":426,"cited_by_count":63,"oa_url":423,"card":465,"direction":77,"ingested_from":79},"W7223114002",[463],{"name":464,"orcid":9},"Kadiatou Barry",{"tldr":466,"method":467,"finding":468,"direction":77,"opportunity":469},"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":472,"title":473,"url":474,"summary":475,"summary_zh":476,"content":9,"source_name":477,"source_url":474,"published_at":478,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":261,"score_detail":479,"sources":481,"tags":483,"primary_topic":487,"secondary_topics":491,"classification":493,"search_phrases":515,"slug":518,"view_count":63,"doi":519,"paper":520,"created_at":562},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",{"impact":185,"substance":184,"depth":185,"authority":17,"freshness":95,"relevant":22,"comment":480},"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[482],{"name":477,"url":474},[27,484,485,29,486],"无人机","育种","紫花苜蓿",{"code":293,"slug":488,"name":489,"path":490,"parent_code":106},"forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping",[492],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":494},{"candidates":495,"agricultural_evidence":46},[496,502,506,510],{"code":293,"score":94,"supported":46,"title":497,"abstract":499,"tags":501},[498],"alfalfa",[500,498],"苜蓿",[486],{"code":33,"score":17,"supported":46,"title":503,"abstract":504,"tags":505},[385],[385],[29],{"code":52,"score":53,"supported":46,"title":507,"abstract":508,"tags":509},[],[56,57],[27],{"code":511,"score":142,"supported":46,"title":512,"abstract":513,"tags":514},"C4",[],[500],[486],[516,517],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":519,"openalex_id":521,"authors":522,"venue":477,"cited_by_count":63,"oa_url":474,"card":557,"direction":77,"ingested_from":79},"W7223083966",[523,526,529,532,535,537,539,542,545,548,551,554],{"name":524,"orcid":525},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":527,"orcid":528},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":530,"orcid":531},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":533,"orcid":534},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":536,"orcid":9},"Julie Hansen",{"name":538,"orcid":9},"Christopher Pierce",{"name":540,"orcid":541},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":543,"orcid":544},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":546,"orcid":547},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":549,"orcid":550},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":552,"orcid":553},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":555,"orcid":556},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":558,"method":559,"finding":560,"direction":77,"opportunity":561},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z"]