[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1210":3,"related-1210":115},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":35,"classification":42,"search_phrases":78,"slug":81,"view_count":20,"doi":82,"paper":83,"created_at":114},1210,"Assessing rice genotype dependency in remote sensing: challenges in nitrogen discrimination and yield forecasting","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10432-8","Abstract Background Rice ( Oryza sativa L.) is a staple crop that accounts for 8% of the global primary crop production. This sector faces an environmental challenge driven by climate change, rising water scarcity, and the need for more resilient and adaptive production systems. Although Remote Sensing (RS) offers solutions for optimizing inputs, most current models are calibrated for specific varieties, limiting their application across diverse cultivars. This study evaluates the transferability of RS models for monitoring nitrogen (N) fertilization status and predicting yield across a highly heterogeneous dataset of rice genotypes, locations and seasons. Materials and methods Six field trials were conducted across three locations in Spain (Valencia and Tarragona) during seasons 2022 and 2023. The study analysed over 170 cultivars, including commercial Japonica and Indica varieties, and a selection of 170 non-commercialized-under development varieties, both subjected to low (100 kg N\u002Fha) and high (200 kg N\u002Fha) fertilization regimes. Multispectral UAV imagery (MAIA S2) was normalized using Accumulated Growing Degree Days (GDD) to align phenological stages across sites. Random Forest (RF) classifiers were employed to analyse the capacity of RS to identify whether rice paddies are under- or over-fertilized. The transferability of N models between rice genotypes was also assessed. Furthermore, the previously established MS3 + yield regression model, originally developed for the JSendra variety, was evaluated against a multi-variety dataset. Results Random Forest classifiers effectively discriminated between nitrogen application rates across diverse genotypes, with several sites exceeding an 85% validation accuracy. A consistent trend emerges when analysing spectral importance: visible (VIS) bands take importance during the early season stages, whereas near-infrared (NIR) and red-edge (RE) reflectance provide critical diagnostic information throughout the entire crop cycle. Notably, 82% of the evaluated varieties demonstrated a high compatibility with the global model. Conversely, the yield model showed limited transferability between varieties. While it performed poorly on the global dataset, it successfully predicted yields for 48.8% of commercial varieties (residues within ± 1 tons per hectare), specifically those with genetic and structural similarities to the training variety. Conclusion The study concludes that N-status monitoring via RS classifiers is robust across varying rice genetics, whereas yield prediction models exhibit strong genotype dependency.",null,"Precision Agriculture","2026-08-29T00:00:00Z","论文",10,false,75,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,3,1,"研究揭示遥感氮素监测跨基因型稳健，但产量预测依赖品种，对精准农业实践有重要参考。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","水稻","产量预测","遥感","氮素监测",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[36],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":43,"locked":13,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":50},[48,56,61,67,73],{"code":37,"score":49,"supported":50,"title":51,"abstract":53,"tags":55},16,true,[52],"remote sensing",[52,54],"multispectral",[27,28],{"code":31,"score":57,"supported":50,"title":58,"abstract":59,"tags":60},12,[32],[32],[26],{"code":62,"score":19,"supported":50,"title":63,"abstract":64,"tags":66},"F3",[],[65],"fertilization",[],{"code":68,"score":19,"supported":50,"title":69,"abstract":70,"tags":72},"H7",[],[71],"climate change",[],{"code":74,"score":20,"supported":13,"title":75,"abstract":76,"tags":77},"I1",[],[],[25],[79,80],"产量预测 智慧农业 氮素监测 水稻","产量预测 智慧农业","产量预测智慧农业氮素监测水稻-1210","10.1007\u002Fs11119-026-10432-8",{"doi":82,"openalex_id":84,"authors":85,"venue":9,"cited_by_count":104,"oa_url":105,"card":106,"direction":112,"ingested_from":113},"W7204667424",[86,88,91,93,95,97,100,102],{"name":87,"orcid":8},"Fàtima Della-Bellver",{"name":89,"orcid":90},"B. Franch","https:\u002F\u002Forcid.org\u002F0000-0003-0593-7874",{"name":92,"orcid":8},"Javier Tarín-Mestre",{"name":94,"orcid":8},"César José Guerrero-Benavent",{"name":96,"orcid":8},"Concha Domingo",{"name":98,"orcid":99},"Mar Català Forner","https:\u002F\u002Forcid.org\u002F0000-0002-1026-7097",{"name":101,"orcid":8},"Karen Marti-Jerez",{"name":103,"orcid":8},"Luis Marqués",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs11119-026-10432-8.pdf",{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"评估遥感模型在不同水稻基因型间的可转移性，用于氮肥监测和产量预测。","多站点田间试验，无人机多光谱影像，GDD归一化，随机森林分类器，产量回归模型。","氮监测模型跨基因型有效，但产量模型转移性有限，仅对相似品种有效。","农业遥感与作物表型","可研究开发基于基因型特征的通用产量预测模型，或结合机器学习提升跨品种适应性。","智慧农业 \u002F 农业物联网","openalex","2026-09-01T04:03:03.696050Z",{"total":116,"page":20,"page_size":116,"items":117},6,[118,190,278,355,432,533],{"id":119,"title":120,"url":121,"summary":122,"summary_zh":123,"content":8,"source_name":124,"source_url":121,"published_at":125,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":126,"score_detail":127,"sources":130,"tags":132,"primary_topic":134,"secondary_topics":135,"classification":137,"search_phrases":171,"slug":174,"view_count":104,"doi":175,"paper":176,"created_at":189},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":16,"substance":17,"depth":16,"authority":57,"freshness":128,"relevant":20,"comment":129},8,"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。",[131],{"name":124,"url":121},[25,133,26,27,28],"农业人工智能",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[136],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":43,"locked":13,"version":44,"method":45,"evidence":138},{"candidates":139,"agricultural_evidence":50},[140,145,149,156,162,166],{"code":31,"score":141,"supported":50,"title":142,"abstract":143,"tags":144},15,[32],[26,32],[26],{"code":37,"score":128,"supported":50,"title":146,"abstract":147,"tags":148},[],[28,52],[27,28],{"code":150,"score":116,"supported":50,"title":151,"abstract":152,"tags":155},"A8",[],[153,154],"粮食安全","food security",[],{"code":157,"score":19,"supported":50,"title":158,"abstract":159,"tags":161},"J1",[],[160],"农业规划",[],{"code":74,"score":20,"supported":13,"title":163,"abstract":164,"tags":165},[],[],[25],{"code":167,"score":20,"supported":13,"title":168,"abstract":169,"tags":170},"I2",[],[],[133],[172,173],"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":175,"openalex_id":177,"authors":178,"venue":124,"cited_by_count":104,"oa_url":121,"card":184,"direction":110,"ingested_from":113},"W7222942064",[179,181],{"name":180,"orcid":8},"Nayana Venkatesh",{"name":182,"orcid":183},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":185,"method":186,"finding":187,"direction":110,"opportunity":188},"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":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":8,"source_name":196,"source_url":193,"published_at":197,"category":11,"cover_url":8,"hotness":198,"is_selected":13,"score":199,"score_detail":200,"sources":206,"tags":210,"primary_topic":211,"secondary_topics":212,"classification":218,"search_phrases":262,"slug":265,"view_count":104,"doi":266,"paper":267,"created_at":277},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":57,"substance":201,"depth":202,"authority":203,"freshness":204,"relevant":20,"comment":205},21,17,13,9,"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。",[207,208],{"name":196,"url":193},{"name":196,"url":209},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23149580",[25,133,26,27,28],{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[213,214],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"code":167,"slug":215,"name":216,"path":217,"parent_code":41},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":43,"locked":13,"version":44,"method":45,"evidence":219},{"candidates":220,"agricultural_evidence":50},[221,225,229,235,242,249,254,258],{"code":37,"score":49,"supported":50,"title":222,"abstract":223,"tags":224},[52],[28,52],[27,28],{"code":31,"score":141,"supported":50,"title":226,"abstract":227,"tags":228},[32],[26,32],[26],{"code":167,"score":141,"supported":50,"title":230,"abstract":232,"tags":234},[231],"machine learning",[233,231],"机器学习",[133],{"code":236,"score":116,"supported":50,"title":237,"abstract":238,"tags":241},"H2",[],[239,240],"土壤","soil",[],{"code":243,"score":116,"supported":50,"title":244,"abstract":245,"tags":248},"H3",[],[246,247],"蒸散发","evapotranspiration",[],{"code":68,"score":116,"supported":50,"title":250,"abstract":251,"tags":253},[],[252,71],"气候变化",[],{"code":157,"score":19,"supported":50,"title":255,"abstract":256,"tags":257},[],[160],[],{"code":74,"score":20,"supported":13,"title":259,"abstract":260,"tags":261},[],[],[25],[263,264],"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":266,"openalex_id":268,"authors":269,"venue":196,"cited_by_count":104,"oa_url":193,"card":272,"direction":110,"ingested_from":113},"W7220391503",[270],{"name":271,"orcid":8},"Battula Vijaya Saradhi Halarou Saidou Souley",{"tldr":273,"method":274,"finding":275,"direction":110,"opportunity":276},"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",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":8,"content":8,"source_name":283,"source_url":281,"published_at":284,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":285,"score_detail":286,"sources":289,"tags":291,"primary_topic":293,"secondary_topics":294,"classification":297,"search_phrases":317,"slug":320,"view_count":20,"doi":321,"paper":322,"created_at":354},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":16,"substance":287,"depth":16,"authority":12,"freshness":128,"relevant":20,"comment":288},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。",[290],{"name":283,"url":281},[25,133,26,28,292],"病害检测",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[295,296],{"code":167,"slug":215,"name":216,"path":217,"parent_code":41},{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":43,"locked":13,"version":44,"method":45,"evidence":298},{"candidates":299,"agricultural_evidence":50},[300,304,309,313],{"code":31,"score":57,"supported":50,"title":301,"abstract":302,"tags":303},[32],[32],[26],{"code":167,"score":57,"supported":50,"title":305,"abstract":307,"tags":308},[306],"deep learning",[306],[133],{"code":37,"score":57,"supported":50,"title":310,"abstract":311,"tags":312},[52],[52],[28],{"code":74,"score":20,"supported":13,"title":314,"abstract":315,"tags":316},[],[],[25],[318,319],"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":321,"openalex_id":323,"authors":324,"venue":283,"cited_by_count":104,"oa_url":281,"card":8,"direction":110,"ingested_from":113},"W7221055917",[325,328,331,333,335,338,340,342,345,347,349,351],{"name":326,"orcid":327},"Carlito B. Balingbing","https:\u002F\u002Forcid.org\u002F0000-0002-3672-9253",{"name":329,"orcid":330},"Dmytro Chebotarov","https:\u002F\u002Forcid.org\u002F0000-0003-1351-9453",{"name":332,"orcid":8},"Ian  Carlo Perilla",{"name":334,"orcid":8},"Stephen Klassen",{"name":336,"orcid":337},"Nancy Castilla","https:\u002F\u002Forcid.org\u002F0000-0002-9143-4933",{"name":339,"orcid":8},"Raphael  Gabriel Carvajal",{"name":341,"orcid":8},"Kendrix Ortega",{"name":343,"orcid":344},"Joseph M. Sandro","https:\u002F\u002Forcid.org\u002F0000-0002-8311-2299",{"name":346,"orcid":8},"Nurmi Pangesti",{"name":348,"orcid":8},"Caesar Arloo R. Centeno",{"name":350,"orcid":8},"Arnel Rala",{"name":352,"orcid":353},"Li-Yu  Daisy Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6997-8101","2026-10-09T23:30:18.390549Z",{"id":356,"title":357,"url":358,"summary":359,"summary_zh":360,"content":8,"source_name":361,"source_url":358,"published_at":362,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":363,"sources":365,"tags":367,"primary_topic":369,"secondary_topics":370,"classification":372,"search_phrases":397,"slug":400,"view_count":401,"doi":402,"paper":403,"created_at":431},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",{"impact":141,"substance":201,"depth":202,"authority":203,"freshness":204,"relevant":20,"comment":364},"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。",[366],{"name":361,"url":358},[25,133,26,28,368],"作物分类",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[371],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":43,"locked":13,"version":44,"method":45,"evidence":373},{"candidates":374,"agricultural_evidence":50},[375,379,383,389,393],{"code":31,"score":141,"supported":50,"title":376,"abstract":377,"tags":378},[32],[26,32],[26],{"code":37,"score":128,"supported":50,"title":380,"abstract":381,"tags":382},[],[28,52],[368,28],{"code":167,"score":384,"supported":50,"title":385,"abstract":386,"tags":388},7,[],[387,306],"深度学习",[133],{"code":150,"score":116,"supported":50,"title":390,"abstract":391,"tags":392},[],[153,154],[],{"code":74,"score":20,"supported":13,"title":394,"abstract":395,"tags":396},[],[],[25],[398,399],"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":402,"openalex_id":404,"authors":405,"venue":361,"cited_by_count":104,"oa_url":425,"card":426,"direction":110,"ingested_from":113},"W7220691059",[406,408,411,414,416,419,422],{"name":407,"orcid":8},"Rongfei Ma",{"name":409,"orcid":410},"Wenjiao Shi","https:\u002F\u002Forcid.org\u002F0000-0001-6199-9733",{"name":412,"orcid":413},"Minglei Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4174-9224",{"name":415,"orcid":8},"Xiwei Chen",{"name":417,"orcid":418},"Chenchen Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0458-6232",{"name":420,"orcid":421},"Baihong Pan","https:\u002F\u002Forcid.org\u002F0009-0004-1100-6798",{"name":423,"orcid":424},"Xiangming Xiao","https:\u002F\u002Forcid.org\u002F0000-0003-0956-7428","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fsae2.70218",{"tldr":427,"method":428,"finding":429,"direction":110,"opportunity":430},"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":433,"title":434,"url":435,"summary":436,"summary_zh":437,"content":8,"source_name":438,"source_url":435,"published_at":439,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":440,"score_detail":441,"sources":443,"tags":445,"primary_topic":446,"secondary_topics":447,"classification":450,"search_phrases":498,"slug":501,"view_count":502,"doi":503,"paper":504,"created_at":532},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","Remote Sensing","2026-09-24T00:00:00Z",79,{"impact":49,"substance":17,"depth":16,"authority":18,"freshness":204,"relevant":20,"comment":442},"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。",[444],{"name":438,"url":435},[25,26,233,28,368],{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[448,449],{"code":167,"slug":215,"name":216,"path":217,"parent_code":41},{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":43,"locked":13,"version":44,"method":45,"evidence":451},{"candidates":452,"agricultural_evidence":50},[453,457,461,465,472,476,482,488,494],{"code":31,"score":141,"supported":50,"title":454,"abstract":455,"tags":456},[32],[26,32],[26],{"code":167,"score":204,"supported":50,"title":458,"abstract":459,"tags":460},[231],[],[233],{"code":37,"score":128,"supported":50,"title":462,"abstract":463,"tags":464},[],[28,52],[368,28],{"code":466,"score":116,"supported":50,"title":467,"abstract":468,"tags":471},"A7",[],[469,470],"轮作","crop rotation",[],{"code":150,"score":116,"supported":50,"title":473,"abstract":474,"tags":475},[],[153,154],[],{"code":477,"score":19,"supported":50,"title":478,"abstract":479,"tags":481},"A2",[],[480],"wheat",[],{"code":483,"score":19,"supported":50,"title":484,"abstract":485,"tags":487},"A4",[],[486],"rapeseed",[],{"code":489,"score":19,"supported":50,"title":490,"abstract":491,"tags":493},"H8",[],[492],"可持续农业",[],{"code":74,"score":20,"supported":13,"title":495,"abstract":496,"tags":497},[],[],[25],[499,500],"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":503,"openalex_id":505,"authors":506,"venue":438,"cited_by_count":104,"oa_url":435,"card":527,"direction":110,"ingested_from":113},"W7214147197",[507,510,513,516,518,521,524],{"name":508,"orcid":509},"Xuan Li","https:\u002F\u002Forcid.org\u002F0000-0001-5509-2385",{"name":511,"orcid":512},"Lintao Chen","https:\u002F\u002Forcid.org\u002F0009-0000-6558-1289",{"name":514,"orcid":515},"Lin Chen","https:\u002F\u002Forcid.org\u002F0000-0002-9270-1626",{"name":517,"orcid":8},"Chao Su",{"name":519,"orcid":520},"Hoi Leong Lee","https:\u002F\u002Forcid.org\u002F0000-0002-4984-2183",{"name":522,"orcid":523},"Ruci Wang","https:\u002F\u002Forcid.org\u002F0000-0001-7049-7006",{"name":525,"orcid":526},"Xuguang Tang","https:\u002F\u002Forcid.org\u002F0009-0008-3494-8867",{"tldr":528,"method":529,"finding":530,"direction":110,"opportunity":531},"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":534,"title":535,"url":536,"summary":537,"summary_zh":538,"content":8,"source_name":539,"source_url":536,"published_at":540,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":199,"score_detail":541,"sources":543,"tags":545,"primary_topic":548,"secondary_topics":549,"classification":556,"search_phrases":592,"slug":595,"view_count":104,"doi":596,"paper":597,"created_at":615},3168,"Spatial and Temporal Analysis of Rice Yield Using the DSSAT Model in the Kommamuru Canal Command Area, Guntur (Dist), Andhra Pradesh, India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104524","Rice is the predominant crop cultivated in the Kommamuru Canal Command Area of Guntur district, Andhra Pradesh. Rice productivity is strongly influenced by irrigation water availability and climatic conditions. The present study aimed to analyse the spatial and temporal variability of rice productivity using the DSSAT-CERES Rice model integrated with Sentinel-2 remote sensing data during the kharif seasons of 2022, 2023 and 2024. Rice-cultivated areas were mapped using multi-temporal Sentinel-2 imagery, while weather, soil, crop management and cultivar data were used as inputs for DSSAT simulations. The model was calibrated and validated using field observations and yield data collected from representative locations within the command area. Simulated rice yields showed considerable variation among mandals and across years, ranging from 1,560.58 to 4,111.06 kg ha⁻¹. The highest productivity was observed in 2024 due to favourable rainfall distribution, adequate canal water supply and improved crop growth conditions, whereas relatively lower productivity was recorded in 2023 owing to moisture stress and irregular irrigation availability. Spatial yield maps generated through the integration of DSSAT outputs and GIS-based rice maps identified high- and low-productivity zones across the command area. The study demonstrated the usefulness of combining crop simulation modelling and remote sensing techniques for rice yield assessment, irrigation planning, yield forecasting and sustainable water resource management in canal command areas. The study confirmed that the DSSAT-CERES Rice model effectively simulated rice productivity in the Kommamuru Canal Command Area during 2022-2024. Overall, the study provides a reliable framework for sustainable rice production and efficient water management in canal command areas and can be applied for future agricultural planning and decision-making. The validation results showed low percentage deviation values ranging from 0.94% to 4.39%, indicating good agreement between simulated and actual farmer yields. Low percentage deviation values between simulated and actual yields indicated good model accuracy and reliability for yield prediction. The integration of DSSAT, GIS and remote sensing techniques was useful for sustainable rice production planning and efficient irrigation management.","水稻是安得拉邦贡土尔县科马穆鲁灌区种植的主要作物。水稻生产力受灌溉可用水量和气候条件的强烈影响。本研究旨在利用DSSAT-CERES水稻模型结合Sentinel-2遥感数据，分析2022年、2023年和2024年kharif季水稻生产力的时空变异性。利用多时相Sentinel-2影像绘制水稻种植区，同时将气象、土壤、作物管理和品种数据作为DSSAT模拟的输入。利用从灌区内代表性地点收集的田间观测数据和产量数据对模型进行校准和验证。模拟水稻产量在不同mandal和年份间表现出相当大的变异，范围为1,560.58至4,111.06 kg ha⁻¹。2024年由于有利的降雨分布、充足的渠水供应和改善的作物生长条件，生产力最高；而2023年由于水分胁迫和不规律的灌溉可用性，生产力相对较低。通过整合DSSAT输出和基于GIS的水稻分布图生成的空间产量图，识别出灌区内的高产区和低产区。研究表明，将作物模拟模型与遥感技术相结合，对于灌区水稻产量评估、灌溉规划、产量预测和可持续水资源管理具有实用价值。研究证实，DSSAT-CERES水稻模型有效模拟了2022-2024年科马穆鲁灌区的水稻生产力。总体而言，本研究为灌区可持续水稻生产和高效水资源管理提供了可靠的框架，可应用于未来的农业规划和决策。验证结果显示，百分比偏差值较低，范围为0.94%至4.39%，表明模拟产量与实际农民产量之间具有良好的一致性。模拟产量与实际产量之间较低的百分比偏差值表明模型在产量预测方面具有良好的准确性和可靠性。DSSAT、GIS和遥感技术的整合有助于可持续水稻生产规划和高效灌溉管理。","Journal of Experimental Agriculture International","2026-09-22T00:00:00Z",{"impact":57,"substance":287,"depth":202,"authority":203,"freshness":12,"relevant":20,"comment":542},"印度区域尺度的DSSAT与Sentinel-2融合估产研究，方法成熟、验证可靠，对遥感估产与灌溉管理有参考价值，但属区域性案例，公共影响有限。",[544],{"name":539,"url":536},[26,27,28,546,547],"作物模型","灌溉管理",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[550,555],{"code":243,"slug":551,"name":552,"path":553,"parent_code":554},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":43,"locked":13,"version":44,"method":45,"evidence":557},{"candidates":558,"agricultural_evidence":50},[559,563,570,574,578,584,588],{"code":31,"score":141,"supported":50,"title":560,"abstract":561,"tags":562},[32],[26,32],[26],{"code":243,"score":12,"supported":50,"title":564,"abstract":565,"tags":569},[],[566,567,551,568],"灌溉","水资源","water resource",[547],{"code":37,"score":128,"supported":50,"title":571,"abstract":572,"tags":573},[],[28,52],[27,28],{"code":236,"score":116,"supported":50,"title":575,"abstract":576,"tags":577},[],[239,240],[],{"code":579,"score":19,"supported":50,"title":580,"abstract":581,"tags":583},"I4",[],[582],"GIS",[],{"code":157,"score":19,"supported":50,"title":585,"abstract":586,"tags":587},[],[160],[],{"code":167,"score":20,"supported":13,"title":589,"abstract":590,"tags":591},[],[],[546],[593,594],"DSSAT CERES Rice 水稻","Sentinel-2 水稻 遥感估产","DSSATCERESRice水稻-3168","10.9734\u002Fjeai\u002F2026\u002Fv48i104524",{"doi":596,"openalex_id":598,"authors":599,"venue":539,"cited_by_count":104,"oa_url":536,"card":610,"direction":110,"ingested_from":113},"W7213966772",[600,602,604,606,608],{"name":601,"orcid":8},"Rana Prathap",{"name":603,"orcid":8},"G. Ravi Babu",{"name":605,"orcid":8},"V. Muthayya Chowdary",{"name":607,"orcid":8},"K.Krupavathi",{"name":609,"orcid":8},"K. Chandrasekhar",{"tldr":611,"method":612,"finding":613,"direction":110,"opportunity":614},"用DSSAT-CERES水稻模型结合Sentinel-2遥感，分析印度Guntur灌区2022-20","DSSAT-CERES水稻模型、Sentinel-2多时相影像、GIS空间制图与","模拟产量1560-4111 kg\u002Fha，2024年最高、2023年因水分胁迫最低，验证偏差仅0.94","可引入机器学习同化遥感与作物模型，提升灌区尺度产量预报精度并支撑灌溉决策。","2026-09-22T23:30:22.711640Z"]