[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3186":3,"related-3186":99},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":98},3186,"Cross-Regional Classification of Rice Cropping Systems Based on Within-Year Seasonal Composition Using Multimodal Remote-Sensing Time Series","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183241","Rice cropping-intensity products indicate the number of rice-growing seasons within a year but cannot reveal the seasonal compositions that define rice cropping systems. This limitation constrains detailed characterization of regional rice production patterns. To address this gap, this study developed a framework for rice cropping-system classification based on combinations of early-, middle-, and late-season rice cultivation. The framework integrates SAR-assisted reconstruction of cloud-contaminated Sentinel-2 time series using Sentinel-1 observations, Phenology-Constrained Time-Series Splicing (PTS) to construct PTS samples for underrepresented rice cropping-system classes, and an Ordered-Cycle Query Transformer (OCQT), which encodes annual-global and chronologically ordered-cycle representations with learnable season-query tokens for eight-class classification. On the spatial-block validation set, OCQT trained using real samples together with PTS samples achieved an overall accuracy of 0.9040, a macro-F1 of 0.8643, and a region-balanced macro-F1 of 0.8275, outperforming OCQT trained using only real samples. Across all four independent regional test sets, this configuration increased weighted-F1 by an average of 3.98 percentage points, with a maximum gain of 9.88 percentage points. This study advances rice monitoring from cropping intensity to explicit characterization of within-year seasonal composition and provides a framework for detailed agricultural monitoring using multi-source remote sensing and ordered-cycle representation learning.","水稻种植强度产品能够指示一年内的水稻种植季数，但无法揭示界定水稻种植系统的季节组成。这一局限制约了对区域水稻生产格局的精细刻画。为弥补这一不足，本研究构建了一个基于早稻、中稻和晚稻种植组合的水稻种植系统分类框架。该框架集成了以下三个部分：利用Sentinel-1观测对受云污染的Sentinel-2时间序列进行SAR辅助重建；采用物候约束时间序列拼接（Phenology-Constrained Time-Series Splicing, PTS）为样本不足的水稻种植系统类别构建PTS样本；以及有序循环查询Transformer（Ordered-Cycle Query Transformer, OCQT），该模型通过可学习的季节查询令牌编码年度全局表示和时间有序循环表示，以实现八类分类。在空间分块验证集上，使用真实样本与PTS样本共同训练的OCQT达到了0.9040的总体精度、0.8643的宏F1和0.8275的区域均衡宏F1，优于仅使用真实样本训练的OCQT。在全部四个独立区域测试集上，该配置将加权F1平均提升了3.98个百分点，最大提升达9.88个百分点。本研究推动了水稻监测从种植强度向年内季节组成的显式刻画迈进，并为利用多源遥感和有序循环表示学习开展精细农业监测提供了框架。",null,"Remote Sensing","2026-09-20T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"提出融合SAR与光学时序及有序周期Transformer的水稻种植制度分类框架，方法新颖、精度可靠，对农业遥感监测有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","遥感监测","水稻种植","作物分类","多源遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","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},[50,56,62],{"code":32,"score":51,"supported":52,"title":53,"abstract":54,"tags":55},15,true,[33],[34,33],[28],{"code":39,"score":20,"supported":52,"title":57,"abstract":58,"tags":61},[],[59,60],"遥感","remote sensing",[29,30,27],{"code":63,"score":21,"supported":14,"title":64,"abstract":65,"tags":66},"I1",[],[],[26],[68,69],"Sentinel-1 Sentinel-2 水稻","水稻种植制度 遥感分类","Sentinel-1Sentinel-2水稻-3186",5,"10.3390\u002Frs18183241",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":90,"oa_url":6,"card":91,"direction":95,"ingested_from":97},"W7213906853",[76,78,81,83,85,87],{"name":77,"orcid":9},"Jingrou Wang",{"name":79,"orcid":80},"Tingting Lu","https:\u002F\u002Forcid.org\u002F0000-0002-3140-5882",{"name":82,"orcid":9},"Beibei Xue",{"name":84,"orcid":9},"Lu Xu",{"name":86,"orcid":9},"Yanyan Shi",{"name":88,"orcid":89},"Dongping Ming","https:\u002F\u002Forcid.org\u002F0000-0002-3422-7399",0,{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"提出多模态遥感时序框架，实现跨区域水稻种植制度八分类。","Sentinel-1\u002F2融合重建、PTS样本拼接与OCQT有序周期Transfo","结合PTS样本的OCQT精度达0.904，跨区加权F1平均提升3.98个百分点。","农业遥感与作物表型","可将有序周期表示学习扩展至其他多季作物制度识别与跨区域泛化研究。","openalex","2026-09-22T23:30:25.478404Z",{"total":100,"page":21,"page_size":100,"items":101},6,[102,189,259,363,429,505],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":116,"tags":118,"primary_topic":120,"secondary_topics":121,"classification":123,"search_phrases":154,"slug":157,"view_count":158,"doi":159,"paper":160,"created_at":188},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":51,"substance":112,"depth":113,"authority":114,"freshness":20,"relevant":21,"comment":115},21,17,13,"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。",[117],{"name":108,"url":105},[26,119,34,59,29],"农业人工智能",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[122],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":124},{"candidates":125,"agricultural_evidence":52},[126,130,135,143,150],{"code":32,"score":51,"supported":52,"title":127,"abstract":128,"tags":129},[33],[34,33],[34],{"code":39,"score":131,"supported":52,"title":132,"abstract":133,"tags":134},8,[],[59,60],[29,59],{"code":136,"score":137,"supported":52,"title":138,"abstract":139,"tags":142},"I2",7,[],[140,141],"深度学习","deep learning",[119],{"code":144,"score":100,"supported":52,"title":145,"abstract":146,"tags":149},"A8",[],[147,148],"粮食安全","food security",[],{"code":63,"score":21,"supported":14,"title":151,"abstract":152,"tags":153},[],[],[26],[155,156],"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":159,"openalex_id":161,"authors":162,"venue":108,"cited_by_count":90,"oa_url":182,"card":183,"direction":95,"ingested_from":97},"W7220691059",[163,165,168,171,173,176,179],{"name":164,"orcid":9},"Rongfei Ma",{"name":166,"orcid":167},"Wenjiao Shi","https:\u002F\u002Forcid.org\u002F0000-0001-6199-9733",{"name":169,"orcid":170},"Minglei Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4174-9224",{"name":172,"orcid":9},"Xiwei Chen",{"name":174,"orcid":175},"Chenchen Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0458-6232",{"name":177,"orcid":178},"Baihong Pan","https:\u002F\u002Forcid.org\u002F0009-0004-1100-6798",{"name":180,"orcid":181},"Xiangming Xiao","https:\u002F\u002Forcid.org\u002F0000-0003-0956-7428","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fsae2.70218",{"tldr":184,"method":185,"finding":186,"direction":95,"opportunity":187},"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":190,"title":191,"url":192,"summary":193,"summary_zh":9,"content":9,"source_name":194,"source_url":192,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":196,"score_detail":197,"sources":201,"tags":203,"primary_topic":206,"secondary_topics":207,"classification":215,"search_phrases":236,"slug":239,"view_count":232,"doi":240,"paper":241,"created_at":258},3500,"Remote sensing-driven soil fertility modelling and summer rice suitability mapping in a floodplain agroecosystem of Assam","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10333-026-01102-6","Remote sensing-driven soil fertility modelling and summer rice suitability mapping in a floodplain agroecosystem of Assam。Paddy and Water Environment","Paddy and Water Environment","2026-09-24T00:00:00Z",67,{"impact":198,"substance":17,"depth":199,"authority":114,"freshness":131,"relevant":21,"comment":200},12,16,"核心期刊论文，遥感驱动土壤肥力建模与水稻适宜性制图，方法有参考价值但属区域案例，未达每日精选门槛。",[202],{"name":194,"url":192},[26,204,59,205,28],"农业信息化","土壤肥力",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[208,214],{"code":209,"slug":210,"name":211,"path":212,"parent_code":213},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":216},{"candidates":217,"agricultural_evidence":52},[218,223,227,231],{"code":32,"score":51,"supported":52,"title":219,"abstract":220,"tags":222},[33],[33,221],"paddy",[28],{"code":209,"score":198,"supported":52,"title":224,"abstract":225,"tags":226},[210],[210],[205],{"code":39,"score":198,"supported":52,"title":228,"abstract":229,"tags":230},[60],[60],[59],{"code":63,"score":232,"supported":14,"title":233,"abstract":234,"tags":235},2,[],[],[204,26],[237,238],"Assam 洪泛平原 水稻 遥感","土壤肥力 遥感 建模","Assam洪泛平原水稻遥感-3500","10.1007\u002Fs10333-026-01102-6",{"doi":240,"openalex_id":242,"authors":243,"venue":194,"cited_by_count":90,"oa_url":9,"card":9,"direction":95,"ingested_from":97},"W7214202229",[244,246,249,251,254,256],{"name":245,"orcid":9},"Pompy Deka",{"name":247,"orcid":248},"Jwngsar Moshahary","https:\u002F\u002Forcid.org\u002F0000-0002-3409-6691",{"name":250,"orcid":9},"Mrinal Choudhury",{"name":252,"orcid":253},"Perves Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2114-1043",{"name":255,"orcid":9},"Britan Rahman",{"name":257,"orcid":9},"Ranjit Sarma","2026-09-25T23:30:30.923946Z",{"id":260,"title":261,"url":262,"summary":263,"summary_zh":264,"content":9,"source_name":10,"source_url":262,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":265,"score_detail":266,"sources":268,"tags":270,"primary_topic":272,"secondary_topics":273,"classification":279,"search_phrases":329,"slug":332,"view_count":71,"doi":333,"paper":334,"created_at":362},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",79,{"impact":199,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":267},"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。",[269],{"name":10,"url":262},[26,34,271,59,29],"机器学习",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[274,278],{"code":136,"slug":275,"name":276,"path":277,"parent_code":43},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":280},{"candidates":281,"agricultural_evidence":52},[282,286,291,295,302,306,313,319,325],{"code":32,"score":51,"supported":52,"title":283,"abstract":284,"tags":285},[33],[34,33],[34],{"code":136,"score":20,"supported":52,"title":287,"abstract":289,"tags":290},[288],"machine learning",[],[271],{"code":39,"score":131,"supported":52,"title":292,"abstract":293,"tags":294},[],[59,60],[29,59],{"code":296,"score":100,"supported":52,"title":297,"abstract":298,"tags":301},"A7",[],[299,300],"轮作","crop rotation",[],{"code":144,"score":100,"supported":52,"title":303,"abstract":304,"tags":305},[],[147,148],[],{"code":307,"score":308,"supported":52,"title":309,"abstract":310,"tags":312},"A2",3,[],[311],"wheat",[],{"code":314,"score":308,"supported":52,"title":315,"abstract":316,"tags":318},"A4",[],[317],"rapeseed",[],{"code":320,"score":308,"supported":52,"title":321,"abstract":322,"tags":324},"H8",[],[323],"可持续农业",[],{"code":63,"score":21,"supported":14,"title":326,"abstract":327,"tags":328},[],[],[26],[330,331],"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","10.3390\u002Frs18193302",{"doi":333,"openalex_id":335,"authors":336,"venue":10,"cited_by_count":90,"oa_url":262,"card":357,"direction":95,"ingested_from":97},"W7214147197",[337,340,343,346,348,351,354],{"name":338,"orcid":339},"Xuan Li","https:\u002F\u002Forcid.org\u002F0000-0001-5509-2385",{"name":341,"orcid":342},"Lintao Chen","https:\u002F\u002Forcid.org\u002F0009-0000-6558-1289",{"name":344,"orcid":345},"Lin Chen","https:\u002F\u002Forcid.org\u002F0000-0002-9270-1626",{"name":347,"orcid":9},"Chao Su",{"name":349,"orcid":350},"Hoi Leong Lee","https:\u002F\u002Forcid.org\u002F0000-0002-4984-2183",{"name":352,"orcid":353},"Ruci Wang","https:\u002F\u002Forcid.org\u002F0000-0001-7049-7006",{"name":355,"orcid":356},"Xuguang Tang","https:\u002F\u002Forcid.org\u002F0009-0008-3494-8867",{"tldr":358,"method":359,"finding":360,"direction":95,"opportunity":361},"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":364,"title":365,"url":366,"summary":367,"summary_zh":368,"content":9,"source_name":369,"source_url":366,"published_at":370,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":371,"score_detail":372,"sources":374,"tags":376,"primary_topic":378,"secondary_topics":379,"classification":381,"search_phrases":410,"slug":413,"view_count":90,"doi":414,"paper":415,"created_at":428},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":198,"freshness":131,"relevant":21,"comment":373},"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[375],{"name":369,"url":366},[26,119,34,377,59],"产量预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[380],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":382},{"candidates":383,"agricultural_evidence":52},[384,388,392,396,402,406],{"code":32,"score":51,"supported":52,"title":385,"abstract":386,"tags":387},[33],[34,33],[34],{"code":39,"score":131,"supported":52,"title":389,"abstract":390,"tags":391},[],[59,60],[377,59],{"code":144,"score":100,"supported":52,"title":393,"abstract":394,"tags":395},[],[147,148],[],{"code":397,"score":308,"supported":52,"title":398,"abstract":399,"tags":401},"J1",[],[400],"农业规划",[],{"code":63,"score":21,"supported":14,"title":403,"abstract":404,"tags":405},[],[],[26],{"code":136,"score":21,"supported":14,"title":407,"abstract":408,"tags":409},[],[],[119],[411,412],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":414,"openalex_id":416,"authors":417,"venue":369,"cited_by_count":90,"oa_url":366,"card":423,"direction":95,"ingested_from":97},"W7222942064",[418,420],{"name":419,"orcid":9},"Nayana Venkatesh",{"name":421,"orcid":422},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":424,"method":425,"finding":426,"direction":95,"opportunity":427},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z",{"id":430,"title":431,"url":432,"summary":433,"summary_zh":9,"content":9,"source_name":434,"source_url":432,"published_at":435,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":436,"score_detail":437,"sources":440,"tags":442,"primary_topic":444,"secondary_topics":445,"classification":448,"search_phrases":467,"slug":470,"view_count":21,"doi":471,"paper":472,"created_at":504},4500,"Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11163942\u002Fv1","Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice。Research Square","Research Square","2026-10-08T00:00:00Z",74,{"impact":17,"substance":438,"depth":17,"authority":13,"freshness":131,"relevant":21,"comment":439},20,"将无人机遥感与深度学习结合用于水稻白叶枯病早期检测，方法有创新、结论具应用价值，但属预印本且为细分技术进展，适合进入主题聚合而非头条精选。",[441],{"name":434,"url":432},[26,119,34,59,443],"病害检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[446,447],{"code":136,"slug":275,"name":276,"path":277,"parent_code":43},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":449},{"candidates":450,"agricultural_evidence":52},[451,455,459,463],{"code":32,"score":198,"supported":52,"title":452,"abstract":453,"tags":454},[33],[33],[34],{"code":136,"score":198,"supported":52,"title":456,"abstract":457,"tags":458},[141],[141],[119],{"code":39,"score":198,"supported":52,"title":460,"abstract":461,"tags":462},[60],[60],[59],{"code":63,"score":21,"supported":14,"title":464,"abstract":465,"tags":466},[],[],[26],[468,469],"无人机遥感 水稻 白叶枯病","深度学习 水稻病害 早期检测","无人机遥感水稻白叶枯病-4500","10.21203\u002Frs.3.rs-11163942\u002Fv1",{"doi":471,"openalex_id":473,"authors":474,"venue":434,"cited_by_count":90,"oa_url":432,"card":9,"direction":95,"ingested_from":97},"W7221055917",[475,478,481,483,485,488,490,492,495,497,499,501],{"name":476,"orcid":477},"Carlito B. Balingbing","https:\u002F\u002Forcid.org\u002F0000-0002-3672-9253",{"name":479,"orcid":480},"Dmytro Chebotarov","https:\u002F\u002Forcid.org\u002F0000-0003-1351-9453",{"name":482,"orcid":9},"Ian  Carlo Perilla",{"name":484,"orcid":9},"Stephen Klassen",{"name":486,"orcid":487},"Nancy Castilla","https:\u002F\u002Forcid.org\u002F0000-0002-9143-4933",{"name":489,"orcid":9},"Raphael  Gabriel Carvajal",{"name":491,"orcid":9},"Kendrix Ortega",{"name":493,"orcid":494},"Joseph M. Sandro","https:\u002F\u002Forcid.org\u002F0000-0002-8311-2299",{"name":496,"orcid":9},"Nurmi Pangesti",{"name":498,"orcid":9},"Caesar Arloo R. Centeno",{"name":500,"orcid":9},"Arnel Rala",{"name":502,"orcid":503},"Li-Yu  Daisy Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6997-8101","2026-10-09T23:30:18.390549Z",{"id":506,"title":507,"url":508,"summary":509,"summary_zh":510,"content":9,"source_name":511,"source_url":508,"published_at":512,"category":12,"cover_url":9,"hotness":513,"is_selected":14,"score":514,"score_detail":515,"sources":517,"tags":521,"primary_topic":522,"secondary_topics":523,"classification":526,"search_phrases":568,"slug":571,"view_count":90,"doi":572,"paper":573,"created_at":583},4310,"Comprehensive remote sensing and machine learning framework for multi-regional rice yield forecasting under hydroclimatic variability in Sahelian Niger","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23149579","It is crucial to forecast rice yields effectively in order to improve agricultural planning and resilience to climate change in the Sahel, where production is greatly affected by hydroclimatic variability. This research has developed an integrated framework combining remote sensing analysis and machine learning to assess the variability in rice production in Niamey, Dosso, Tillaberi and Diffa, in Niger, over the period 2010–2025. They combined Landsat-derived NDVI and EVI with parameters such as evapotranspiration, precipitation, temperature, humidity, solar radiation, soil moisture, cultivated area, year and season. We processed and harmonised the datasets using Google Earth Engine and Python. We then evaluated four regression algorithms separately, Random Forest (RF), k-nearest neighbours (KNN), support vector regression (SVR) and XGBoost (XGB), for each region. The correlation analysis revealed modest positive links between rice yield and the NDVI (r = 0.27) and the EVI (r = 0.14), while a strong correlation was observed between the NDVI and the EVI (r = 0.80). The vegetation indices showed marked seasonal and interannual variability, generally exhibiting higher values during the wet season. Model performance varied considerably from one region to another","在萨赫勒地区，有效预测水稻产量对于改善农业规划和增强气候变化适应能力至关重要，因为该地区的生产在很大程度上受到水文气候变率的制约。本研究开发了一个集成框架，将遥感分析与机器学习相结合，以评估尼日尔尼亚美、多索、蒂拉贝里和迪法在2010—2025年期间水稻产量的变异性。研究将基于Landsat的归一化植被指数（NDVI）和增强型植被指数（EVI）与蒸散发、降水、温度、湿度、太阳辐射、土壤水分、耕地面积、年份和季节等参数相结合。利用Google Earth Engine和Python对数据集进行了处理和协调。随后，针对每个地区分别评估了四种回归算法：随机森林（RF）、k近邻（KNN）、支持向量回归（SVR）和XGBoost（XGB）。相关分析显示，水稻产量与NDVI（r = 0.27）和EVI（r = 0.14）之间存在弱正相关，而NDVI与EVI之间则观察到强相关（r = 0.80）。植被指数表现出明显的季节性和年际变异性，通常在湿季呈现较高值。模型性能在不同地区之间差异显著。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-05T00:00:00Z",25,72,{"impact":198,"substance":112,"depth":113,"authority":114,"freshness":20,"relevant":21,"comment":516},"基于Landsat植被指数与四种机器学习模型的多区域水稻产量预测研究，方法扎实、数据跨度长，但属区域性学术成果，对国内农业信息化有方法借鉴价值。",[518,519],{"name":511,"url":508},{"name":511,"url":520},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23149580",[26,119,34,377,59],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[524,525],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":136,"slug":275,"name":276,"path":277,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":527},{"candidates":528,"agricultural_evidence":52},[529,533,537,541,546,553,560,564],{"code":39,"score":199,"supported":52,"title":530,"abstract":531,"tags":532},[60],[59,60],[377,59],{"code":32,"score":51,"supported":52,"title":534,"abstract":535,"tags":536},[33],[34,33],[34],{"code":136,"score":51,"supported":52,"title":538,"abstract":539,"tags":540},[288],[271,288],[119],{"code":209,"score":100,"supported":52,"title":542,"abstract":543,"tags":545},[],[544,210],"土壤",[],{"code":547,"score":100,"supported":52,"title":548,"abstract":549,"tags":552},"H3",[],[550,551],"蒸散发","evapotranspiration",[],{"code":554,"score":100,"supported":52,"title":555,"abstract":556,"tags":559},"H7",[],[557,558],"气候变化","climate change",[],{"code":397,"score":308,"supported":52,"title":561,"abstract":562,"tags":563},[],[400],[],{"code":63,"score":21,"supported":14,"title":565,"abstract":566,"tags":567},[],[],[26],[569,570],"尼日尔 水稻 遥感 产量预测","Landsat NDVI 水稻 机器学习","尼日尔水稻遥感产量预测-4310","10.5281\u002Fzenodo.23149579",{"doi":572,"openalex_id":574,"authors":575,"venue":511,"cited_by_count":90,"oa_url":508,"card":578,"direction":95,"ingested_from":97},"W7220391503",[576],{"name":577,"orcid":9},"Battula Vijaya Saradhi Halarou Saidou Souley",{"tldr":579,"method":580,"finding":581,"direction":95,"opportunity":582},"结合遥感植被指数与机器学习，预测尼日尔萨赫勒四地区2010–2025年水稻产量。","Landsat NDVI\u002FEVI 加气象土壤变量，GEE与Python处理，比较","NDVI\u002FEVI与产量仅弱相关，模型精度区域差异大，植被指数呈明显季节与年际波动。","可引入时序深度学习与迁移学习，解决跨区域泛化差及植被指数解释力弱的问题。","2026-10-06T23:30:20.077977Z"]