[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1731":3,"related-1731":90},{"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":18,"tags":20,"primary_topic":22,"secondary_topics":28,"classification":29,"search_phrases":64,"slug":65,"view_count":15,"doi":66,"paper":67,"created_at":89},1731,"Direct estimation of wildfire emissions at high latitudes from combined polar orbiter FRP and Sentinel-5P CO data","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-23-6095-2026","Abstract. High Latitude (HL) landscape fires are an important source of greenhouse gases and aerosols, with growing significance under rapid anthropogenic climate change-induced warming. Current fire emission inventories are mostly ‘bottom-up’ in nature; combining, or relying on linear regressions between, satellite remote sensing data and process-based model outputs. However, these methods rely on uncertainties surrounding fuel load and combustion completeness. Here, we adapt the ‘top-down’ Fire Radiative Energy Emission (FREM) approach for HL fires (HLFREM), linking Fire Radiative Energy (FRE) directly to emissions via coefficients derived solely from satellite observations. We derive biome-specific emission coefficients by combining Fire Radiative Power (FRP) from GFAS v1.4 with TROPOMI Total Column Carbon Monoxide plume observations, for the HL’s four most fire-prone biomes; Deciduous and Evergreen Needleleaf Forests, Grasslands, and Shrublands. By applying these coefficients to daily GFAS v1.2 FRE totals (2003–2024), we estimate CO and total carbon emissions across the HL using HLFREM. HLFREM-derived CO emissions generally agree with other widely used inventories (GFAS v1.2, FEERv1.0-GFASv1.2, and GFEDv4.1s) in forested biomes, with annual average differences of -32 % to -43 % for Deciduous Needleleaf Forests, and -28 % to -43 % for Evergreen Needleleaf forests. For Shrublands and Grassland biomes, HLFREM estimates are 31–43 % and 61–80 % lower respectively. Total carbon emissions, using Emission Factors, were found to show consistent patterns with CO across all biomes. Our results represent the first HL fire emissions dataset based only on satellite data of a major carbon containing gas (CO) emitted by fires and the rate of fire radiative energy release.","摘要：高纬度（HL）景观火灾是温室气体和气溶胶的重要来源，在人为气候变化引起的快速变暖背景下，其重要性日益凸显。当前的火灾排放清单大多属于“自下而上”性质，即结合或依赖于卫星遥感数据与基于过程的模型输出之间的线性回归关系。然而，这些方法受制于燃料负荷和燃烧完全性方面的不确定性。在此，我们针对高纬度火灾（HLFREM）改进了“自上而下”的火灾辐射能排放（FREM）方法，通过仅由卫星观测推导的系数，将火灾辐射能（FRE）直接与排放联系起来。我们结合GFAS v1.4的火灾辐射功率（FRP）与TROPOMI一氧化碳总柱羽流观测数据，为高纬度地区四种最易发生火灾的生物群系（落叶针叶林、常绿针叶林、草地和灌木地）推导出生物群系特定的排放系数。通过将这些系数应用于2003—2024年每日GFAS v1.2火灾辐射能总量，我们利用HLFREM估算了高纬度地区的CO和总碳排放量。HLFREM推导的CO排放量在森林生物群系中与其他广泛使用的清单（GFAS v1.2、FEERv1.0-GFASv1.2和GFEDv4.1s）总体一致，其中落叶针叶林的年均差异为-32%至-43%，常绿针叶林为-28%至-43%。对于灌木地和草地生物群系，HLFREM的估算值分别低31%—43%和61%—80%。使用排放因子计算的总碳排放量在所有生物群系中均显示出与CO一致的模式。我们的结果代表了首个仅基于卫星数据的高纬度火灾排放数据集，这些数据涉及火灾排放的主要含碳气体（CO）以及火灾辐射能释放速率。",null,"Biogeosciences","2026-09-04T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦高纬度野火排放估算，与三农、农业信息化等主题无关。",[19],{"name":10,"url":6},[21],"遥感",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":30,"locked":14,"version":31,"method":32,"evidence":33},"classified","2026-10-22.2","rules",{"candidates":34,"agricultural_evidence":37},[35,42,50,57],{"code":23,"score":36,"supported":37,"title":38,"abstract":39,"tags":41},7,true,[],[21,40],"remote sensing",[21],{"code":43,"score":44,"supported":37,"title":45,"abstract":46,"tags":49},"B7",6,[],[47,48],"温室","greenhouse",[],{"code":51,"score":44,"supported":37,"title":52,"abstract":53,"tags":56},"H7",[],[54,55],"气候变化","climate change",[],{"code":58,"score":59,"supported":37,"title":60,"abstract":61,"tags":63},"H4",3,[],[62],"碳排放",[],[],"Directestimationofwildfireemissionsathig-1731","10.5194\u002Fbg-23-6095-2026",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":81,"oa_url":6,"card":82,"direction":86,"ingested_from":88},"W4415657389",[70,73,76,79],{"name":71,"orcid":72},"William Maslanka","https:\u002F\u002Forcid.org\u002F0000-0002-1777-733X",{"name":74,"orcid":75},"Martin J. Wooster","https:\u002F\u002Forcid.org\u002F0000-0001-6375-7949",{"name":77,"orcid":78},"Zixia Liu","https:\u002F\u002Forcid.org\u002F0000-0003-3550-0163",{"name":80,"orcid":9},"Jiangping He",1,{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"提出基于卫星观测的高纬度火灾排放估算方法HLFREM，直接关联辐射能与CO排放。","结合GFAS FRP与TROPOMI CO柱浓度，推导生物群落特定排放系数，应用","HLFREM在森林生物群落与现有清单一致，但灌木和草地排放估计显著偏低。","农业遥感与作物表型","可扩展至其他区域或气体，改进非森林生物群落排放系数，或结合更高分辨率数据提升精度。","openalex","2026-09-05T23:30:27.137884Z",{"total":44,"page":81,"page_size":44,"items":91},[92,160,217,319,398,475],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":9,"source_name":98,"source_url":95,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":107,"tags":109,"primary_topic":113,"secondary_topics":114,"classification":115,"search_phrases":135,"slug":138,"view_count":15,"doi":139,"paper":140,"created_at":159},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":102,"substance":103,"depth":104,"authority":105,"freshness":13,"relevant":81,"comment":106},12,20,17,14,"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。",[108],{"name":98,"url":95},[110,111,21,112],"智慧农业","作物制图","SAR",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[],{"status":30,"locked":14,"version":31,"method":32,"evidence":116},{"candidates":117,"agricultural_evidence":37},[118,123,130],{"code":23,"score":13,"supported":37,"title":119,"abstract":120,"tags":122},[],[21,40,121],"multispectral",[21],{"code":124,"score":44,"supported":37,"title":125,"abstract":126,"tags":129},"A8",[],[127,128],"粮食安全","food security",[],{"code":131,"score":81,"supported":14,"title":132,"abstract":133,"tags":134},"I1",[],[],[110],[136,137],"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":139,"openalex_id":141,"authors":142,"venue":98,"cited_by_count":15,"oa_url":95,"card":154,"direction":86,"ingested_from":88},"W7223117203",[143,146,148,151],{"name":144,"orcid":145},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":147,"orcid":9},"Wentao Lv",{"name":149,"orcid":150},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":152,"orcid":153},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":155,"method":156,"finding":157,"direction":86,"opportunity":158},"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":161,"title":162,"url":163,"summary":164,"summary_zh":165,"content":9,"source_name":166,"source_url":163,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":167,"score_detail":168,"sources":171,"tags":173,"primary_topic":177,"secondary_topics":182,"classification":184,"search_phrases":201,"slug":204,"view_count":15,"doi":205,"paper":206,"created_at":216},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":169,"substance":103,"depth":104,"authority":102,"freshness":13,"relevant":81,"comment":170},8,"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。",[172],{"name":166,"url":163},[110,174,175,21,176],"农业人工智能","产量预测","可解释性",{"code":178,"slug":179,"name":180,"path":181,"parent_code":27},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[183],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":185},{"candidates":186,"agricultural_evidence":37},[187,193,197],{"code":178,"score":102,"supported":37,"title":188,"abstract":190,"tags":192},[189],"computer vision",[191],"计算机视觉",[174],{"code":23,"score":169,"supported":37,"title":194,"abstract":195,"tags":196},[],[21,40],[175,21],{"code":131,"score":81,"supported":14,"title":198,"abstract":199,"tags":200},[],[],[110],[202,203],"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":205,"openalex_id":207,"authors":208,"venue":166,"cited_by_count":15,"oa_url":163,"card":211,"direction":86,"ingested_from":88},"W7223114002",[209],{"name":210,"orcid":9},"Kadiatou Barry",{"tldr":212,"method":213,"finding":214,"direction":86,"opportunity":215},"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":218,"title":219,"url":220,"summary":221,"summary_zh":222,"content":9,"source_name":223,"source_url":220,"published_at":224,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":225,"score_detail":226,"sources":231,"tags":233,"primary_topic":237,"secondary_topics":243,"classification":245,"search_phrases":271,"slug":274,"view_count":15,"doi":275,"paper":276,"created_at":318},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",79,{"impact":227,"substance":228,"depth":227,"authority":102,"freshness":229,"relevant":81,"comment":230},18,22,9,"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。",[232],{"name":223,"url":220},[110,234,235,21,236],"无人机","育种","紫花苜蓿",{"code":238,"slug":239,"name":240,"path":241,"parent_code":242},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[244],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":246},{"candidates":247,"agricultural_evidence":37},[248,255,259,265],{"code":238,"score":249,"supported":37,"title":250,"abstract":252,"tags":254},15,[251],"alfalfa",[253,251],"苜蓿",[236],{"code":23,"score":102,"supported":37,"title":256,"abstract":257,"tags":258},[121],[121],[21],{"code":131,"score":36,"supported":37,"title":260,"abstract":261,"tags":264},[],[262,263],"精准农业","precision agriculture",[110],{"code":266,"score":267,"supported":37,"title":268,"abstract":269,"tags":270},"C4",4,[],[253],[236],[272,273],"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":275,"openalex_id":277,"authors":278,"venue":223,"cited_by_count":15,"oa_url":220,"card":313,"direction":86,"ingested_from":88},"W7223083966",[279,282,285,288,291,293,295,298,301,304,307,310],{"name":280,"orcid":281},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":283,"orcid":284},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":286,"orcid":287},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":289,"orcid":290},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":292,"orcid":9},"Julie Hansen",{"name":294,"orcid":9},"Christopher Pierce",{"name":296,"orcid":297},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":299,"orcid":300},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":302,"orcid":303},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":305,"orcid":306},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":308,"orcid":309},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":311,"orcid":312},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":314,"method":315,"finding":316,"direction":86,"opportunity":317},"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",{"id":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":9,"source_name":166,"source_url":322,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":325,"score_detail":326,"sources":329,"tags":331,"primary_topic":335,"secondary_topics":341,"classification":348,"search_phrases":381,"slug":384,"view_count":15,"doi":385,"paper":386,"created_at":397},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":227,"substance":103,"depth":327,"authority":102,"freshness":229,"relevant":81,"comment":328},16,"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[330],{"name":166,"url":322},[174,332,21,333,334],"农业物联网","智慧灌溉","节水农业",{"code":336,"slug":337,"name":338,"path":339,"parent_code":340},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[342,347],{"code":343,"slug":344,"name":345,"path":346,"parent_code":27},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":349},{"candidates":350,"agricultural_evidence":37},[351,358,365,369,374],{"code":336,"score":352,"supported":37,"title":353,"abstract":354,"tags":357},19,[337],[355,356,337],"灌溉","水资源",[333,334],{"code":343,"score":227,"supported":37,"title":359,"abstract":361,"tags":364},[360],"IoT",[362,363,360],"物联网","internet of things",[332],{"code":23,"score":249,"supported":37,"title":366,"abstract":367,"tags":368},[40],[21,40],[21],{"code":178,"score":102,"supported":37,"title":370,"abstract":372,"tags":373},[371],"artificial intelligence",[371],[174],{"code":375,"score":44,"supported":37,"title":376,"abstract":377,"tags":380},"H8",[],[378,379],"可持续农业","sustainable agriculture",[],[382,383],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":385,"openalex_id":387,"authors":388,"venue":166,"cited_by_count":15,"oa_url":322,"card":391,"direction":395,"ingested_from":88},"W7223090389",[389],{"name":390,"orcid":9},"Patil",{"tldr":392,"method":393,"finding":394,"direction":395,"opportunity":396},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","智慧农业 \u002F 农业物联网","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":403,"content":9,"source_name":404,"source_url":401,"published_at":405,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":406,"score_detail":407,"sources":410,"tags":412,"primary_topic":417,"secondary_topics":418,"classification":419,"search_phrases":453,"slug":456,"view_count":15,"doi":457,"paper":458,"created_at":474},5395,"Strengthening Flood Resilience in Data-Sparse Agricultural Regions: A Planning Approach for Coastal Guyana","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23451582","Abstract. Coastal Guyana concentrates most of the country’s population and agricultural production in low-lying polder landscapes that are increasingly exposed to flooding from intense rainfall, sea-level rise, and deterioration of drainage and irrigation infrastructure. Engineering design and resilience planning in these settings are often constrained by limited hydrometric monitoring (discharge, water levels), incomplete documented drainage network, and restricted access to institutional datasets. This paper presents the value of using a low-information framework for flood resilience planning and preliminary hydraulic design in data-sparse agricultural regions, using the 364 km² part of Guyana’s Mahaica–Mahaicony catchment as a case study. The framework integrates (i) rainfall frequency analysis from long-term gauge records, (ii) remote sensing and global datasets (DEM and land-use\u002Fland-cover) to delineate sub-basins and screen flood impact, (iii) simplified runoff and volume-based drainage calculations consistent with rice-field storage and saturated soils, and (iv) structured stakeholder engagement to identify flood-prone areas, operational constraints, and feasible interventions. Six-sub-basins are used for developing a routing strategy to distribute runoff volumes and to explicitly incorporate upstream detention in the wetlands, thereby reducing peak flows conveyed northward through constrained canal corridors. High-level drainage canals, dedicated conveyance paths for critical sub-basins, and dual-purpose drains supporting both drainage and irrigation storage are used to determine preliminary cross-section designs, which are then assessed with 1D hydraulic modelling under tidal boundary conditions. The approach supports phased investment planning that improves flood safety, protects livelihoods, and increases agricultural reliability while aligning with national adaptation priorities.","摘要：圭亚那沿海地区集中了全国大部分人口和农业生产，地处低洼圩田景观，日益面临强降雨、海平面上升以及排水灌溉基础设施老化所带来的洪涝风险。在此类地区，工程设计与韧性规划往往受限于水文监测（流量、水位）资料不足、排水管网记录不完整以及机构数据集获取受限。本文以圭亚那马海卡—马海科尼（Mahaica–Mahaicony）流域364 km²的区域为案例，展示了在数据稀疏的农业地区采用低信息框架进行洪涝韧性规划与初步水力设计的价值。该框架整合了：(i) 基于长期测站记录的降雨频率分析；(ii) 利用遥感与全球数据集（DEM及土地利用\u002F土地覆盖）划分子流域并筛查洪涝影响；(iii) 与稻田蓄水和饱和土壤条件相适应的简化径流与基于水量的排水计算；(iv) 结构化利益相关方参与，以识别易涝区域、运行约束和可行干预措施。研究将流域划分为六个子流域，用于制定汇流路径策略，以分配径流量并明确纳入上游湿地滞蓄，从而削减通过受限渠廊向北输送的峰值流量。基于高位排水渠、关键子流域专用输水通道以及兼顾排水与灌溉蓄水的两用沟渠，确定初步断面设计，并在潮汐边界条件下采用一维水动力模型进行评估。该方法支持分阶段投资规划，在提升防洪安全、保护生计和提高农业可靠性的同时，与国家适应优先事项相衔接。","Geo-Eco-Marina","2026-10-18T00:00:00Z",70,{"impact":102,"substance":103,"depth":104,"authority":408,"freshness":169,"relevant":81,"comment":409},13,"该论文提出一种低信息条件下的防洪韧性规划框架，方法新颖，但案例位于圭亚那，对我国三农信息化的直接参考价值有限，因此不宜进入每日精选。",[411],{"name":404,"url":401},[413,21,414,415,416],"数字乡村","气候适应","防洪韧性","灌排设施",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[],{"status":30,"locked":14,"version":31,"method":32,"evidence":420},{"candidates":421,"agricultural_evidence":37},[422,426,433,437,442,448],{"code":23,"score":36,"supported":37,"title":423,"abstract":424,"tags":425},[],[21,40],[21],{"code":427,"score":44,"supported":37,"title":428,"abstract":429,"tags":432},"A1",[],[430,431],"稻田","rice",[],{"code":336,"score":44,"supported":37,"title":434,"abstract":435,"tags":436},[],[355,337],[],{"code":51,"score":267,"supported":37,"title":438,"abstract":439,"tags":441},[],[440],"洪涝",[414],{"code":443,"score":59,"supported":37,"title":444,"abstract":445,"tags":447},"H2",[],[446],"土壤",[],{"code":449,"score":81,"supported":14,"title":450,"abstract":451,"tags":452},"I6",[],[],[413],[454,455],"Guyana Mahaica Mahaicony 洪水","沿海稻田 排水规划","GuyanaMahaicaMahaicony洪水-5395","10.5281\u002Fzenodo.23451582",{"doi":457,"openalex_id":459,"authors":460,"venue":404,"cited_by_count":15,"oa_url":401,"card":468,"direction":86,"ingested_from":88},"W7222952224",[461,463,465],{"name":462,"orcid":9},"Omesh Persaud",{"name":464,"orcid":9},"Colis Allen",{"name":466,"orcid":467},"Ioana Popescu","https:\u002F\u002Forcid.org\u002F0000-0002-1009-5424",{"tldr":469,"method":470,"finding":471,"direction":472,"opportunity":473},"提出低信息框架，为数据稀缺农业区规划洪水韧性并做初步水力设计。","降雨频率分析、遥感DEM与土地利用、简化径流计算、利益相关者参与及一维水力模型。","上游湿地滞蓄与分区排水可降低峰值流量，支撑分阶段投资规划。","农业绿色发展与碳","可探索低监测区遥感与参与式数据融合的洪水韧性规划方法及作物淹水风险量化。","2026-10-20T23:30:19.345049Z",{"id":476,"title":477,"url":478,"summary":479,"summary_zh":480,"content":9,"source_name":481,"source_url":478,"published_at":405,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":482,"score_detail":483,"sources":485,"tags":487,"primary_topic":489,"secondary_topics":491,"classification":493,"search_phrases":522,"slug":525,"view_count":15,"doi":526,"paper":527,"created_at":540},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",78,{"impact":227,"substance":228,"depth":227,"authority":102,"freshness":169,"relevant":81,"comment":484},"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。",[486],{"name":481,"url":478},[110,174,488,175,21],"水稻",{"code":427,"slug":431,"name":488,"path":490,"parent_code":242},"\u002Ftopic\u002Ffield-crops\u002Frice",[492],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":494},{"candidates":495,"agricultural_evidence":37},[496,500,504,508,514,518],{"code":427,"score":249,"supported":37,"title":497,"abstract":498,"tags":499},[431],[488,431],[488],{"code":23,"score":169,"supported":37,"title":501,"abstract":502,"tags":503},[],[21,40],[175,21],{"code":124,"score":44,"supported":37,"title":505,"abstract":506,"tags":507},[],[127,128],[],{"code":509,"score":59,"supported":37,"title":510,"abstract":511,"tags":513},"J1",[],[512],"农业规划",[],{"code":131,"score":81,"supported":14,"title":515,"abstract":516,"tags":517},[],[],[110],{"code":178,"score":81,"supported":14,"title":519,"abstract":520,"tags":521},[],[],[174],[523,524],"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":526,"openalex_id":528,"authors":529,"venue":481,"cited_by_count":15,"oa_url":478,"card":535,"direction":86,"ingested_from":88},"W7222942064",[530,532],{"name":531,"orcid":9},"Nayana Venkatesh",{"name":533,"orcid":534},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":536,"method":537,"finding":538,"direction":86,"opportunity":539},"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"]