[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1312":3,"related-1312":127},{"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":30,"secondary_topics":36,"classification":49,"search_phrases":89,"slug":92,"view_count":21,"doi":93,"paper":94,"created_at":126},1312,"Accurate diagnosis of soil moisture and canopy water content in citrus orchards based on synergistic multi-source remote sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110725","Accurate prediction of soil moisture is essential for precise agricultural irrigation decision-making and regional water resource optimal scheduling. However, regional soil moisture inversion commonly suffers from three major limitations, including overreliance on single remote sensing data sources, fixed canopy parameters adopted in conventional physical models, and severe cloud contamination in the cloudy mountainous areas of Southwest China. In this study, a synergistic multi-source remote sensing framework integrating Landsat-8\u002F9, Sentinel-2, and Sentinel-1A datasets was established to improve the traditional water cloud model by introducing phenology-driven dynamic canopy parameters. Specifically, canopy coverage and canopy water content of late-maturing citrus across different growth stages were accurately retrieved from Landsat-8\u002F9 and Sentinel-2 imagery to optimize the simulation of canopy backscattering signals. The all-weather C-band radar data effectively mitigate the persistent cloud-cover limitations prevalent in the hilly regions of Southwest China. The results indicated that the vegetation water content in large-scale citrus orchards retrieved by optical remote sensing exhibits significant spatial variability, mainly distributed in the range of 1.65–5.71 kg·m −2 and 1.55–5.78 kg·m −2 , respectively. The soil moisture inversion results using multi-temporal Sentinel-1A data in VV polarization outperformed those in VH polarization, with R² ranges of 0.785–0.854 and 0.515–0.759, RMSE ranges of 6.125–8.351% and 6.942–8.412%, respectively. The two polarizations data have the highest inversion accuracy in citrus fruit maturation stage, with R² ranges of 0.811–0.842 and 0.682–0.711, RMSE ranges of 6.125–6.132% and 6.942–7.025%, respectively. This study proposes a novel methodology for high-resolution and precision inversion of surface soil moisture at the regional farmland scale.","土壤水分的精准预测对于精准农业灌溉决策和区域水资源优化调度至关重要。然而，区域土壤水分反演普遍面临三大局限，包括对单一遥感数据源的过度依赖、传统物理模型中采用固定冠层参数，以及中国西南多云山区严重的云污染问题。本研究构建了一种融合Landsat-8\u002F9、Sentinel-2和Sentinel-1A数据集的协同多源遥感框架，通过引入物候驱动的动态冠层参数对传统水云模型进行改进。具体而言，利用Landsat-8\u002F9和Sentinel-2影像精确反演了不同生长阶段晚熟柑橘的冠层覆盖度和冠层含水量，以优化冠层后向散射信号的模拟。全天候C波段雷达数据有效缓解了西南丘陵地区普遍存在的持续云覆盖限制。结果表明，光学遥感反演的大规模柑橘园植被含水量表现出显著的空间变异性，主要分布在1.65–5.71 kg·m⁻²和1.55–5.78 kg·m⁻²范围内。使用多时相Sentinel-1A VV极化的土壤水分反演结果优于VH极化，R²范围分别为0.785–0.854和0.515–0.759，RMSE范围分别为6.125–8.351%和6.942–8.412%。两种极化数据在柑橘果实成熟期反演精度最高，R²范围分别为0.811–0.842和0.682–0.711，RMSE范围分别为6.125–6.132%和6.942–7.025%。本研究提出了一种在区域农田尺度上实现地表土壤水分高分辨率精准反演的新方法。",null,"Agricultural Water Management","2026-08-31T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,3,1,"提出多源遥感协同反演土壤水分的新方法，对丘陵山地精准灌溉有参考价值，但时效性一般。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","遥感","土壤墒情","柑橘",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[37,43],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":50,"locked":14,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":57},[55,62,67,72,82],{"code":31,"score":56,"supported":57,"title":58,"abstract":60,"tags":61},15,true,[59],"citrus",[29,59],[29],{"code":38,"score":56,"supported":57,"title":63,"abstract":64,"tags":66},[39],[65,39],"土壤",[28],{"code":44,"score":56,"supported":57,"title":68,"abstract":70,"tags":71},[69],"remote sensing",[27,69],[27],{"code":73,"score":74,"supported":57,"title":75,"abstract":76,"tags":81},"H3",9,[],[77,78,79,80],"灌溉","水资源","irrigation","water resource",[],{"code":83,"score":84,"supported":57,"title":85,"abstract":86,"tags":88},"I1",4,[],[87],"精准农业",[26],[90,91],"土壤墒情 智慧农业 柑橘 遥感","土壤墒情 智慧农业","土壤墒情智慧农业柑橘遥感-1312","10.1016\u002Fj.agwat.2026.110725",{"doi":93,"openalex_id":95,"authors":96,"venue":10,"cited_by_count":118,"oa_url":6,"card":119,"direction":123,"ingested_from":125},"W7204808715",[97,99,101,103,106,109,112,114,116],{"name":98,"orcid":9},"Zongjun Wu",{"name":100,"orcid":9},"Bin Zhu",{"name":102,"orcid":9},"Ningbo Cui",{"name":104,"orcid":105},"Wei Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-0215-8584",{"name":107,"orcid":108},"Yanjun Wang","https:\u002F\u002Forcid.org\u002F0000-0001-5286-6269",{"name":110,"orcid":111},"Liwen Xing","https:\u002F\u002Forcid.org\u002F0000-0003-0007-3560",{"name":113,"orcid":9},"Quanshan Liu",{"name":115,"orcid":9},"Shenglin Wen",{"name":117,"orcid":9},"Bihang Fan",0,{"tldr":120,"method":121,"finding":122,"direction":123,"opportunity":124},"融合多源遥感数据改进水云模型，实现柑橘园土壤水分和冠层含水量高精度反演。","集成Landsat-8\u002F9、Sentinel-2和Sentinel-1A数据，引","VV极化反演精度优于VH，果实成熟期精度最高，R²达0.842。","农业遥感与作物表型","可探索将动态冠层参数方法应用于其他作物或区域，结合更高分辨率数据提升反演精度。","openalex","2026-09-01T23:30:20.097045Z",{"total":128,"page":21,"page_size":128,"items":129},6,[130,223,299,396,471,551],{"id":131,"title":132,"url":133,"summary":134,"summary_zh":135,"content":9,"source_name":136,"source_url":133,"published_at":137,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":138,"score_detail":139,"sources":146,"tags":148,"primary_topic":151,"secondary_topics":152,"classification":159,"search_phrases":207,"slug":210,"view_count":211,"doi":9,"paper":212,"created_at":222},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":140,"substance":141,"depth":142,"authority":143,"freshness":144,"relevant":21,"comment":145},16,20,17,13,8,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[147],{"name":136,"url":133},[26,87,27,149,150],"盐碱地治理","土壤监测",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},[153,158],{"code":154,"slug":155,"name":156,"path":157,"parent_code":48},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":50,"locked":14,"version":51,"method":52,"evidence":160},{"candidates":161,"agricultural_evidence":57},[162,167,175,179,184,191,195,201],{"code":44,"score":17,"supported":57,"title":163,"abstract":164,"tags":166},[69],[27,69,165],"multispectral",[27],{"code":154,"score":142,"supported":57,"title":168,"abstract":170,"tags":174},[169],"GIS",[171,172,169,173],"地理信息","空间分析","geographic information",[],{"code":38,"score":56,"supported":57,"title":176,"abstract":177,"tags":178},[39],[65,39],[149],{"code":83,"score":13,"supported":57,"title":180,"abstract":182,"tags":183},[181],"precision agriculture",[],[26,87],{"code":185,"score":128,"supported":57,"title":186,"abstract":187,"tags":190},"I2",[],[188,189],"机器学习","machine learning",[],{"code":73,"score":20,"supported":57,"title":192,"abstract":193,"tags":194},[],[77],[],{"code":196,"score":20,"supported":57,"title":197,"abstract":198,"tags":200},"H7",[],[199],"干旱",[],{"code":202,"score":20,"supported":57,"title":203,"abstract":204,"tags":206},"I7",[],[205],"methodological framework",[],[208,209],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",2,{"doi":9,"openalex_id":213,"authors":214,"venue":136,"cited_by_count":118,"oa_url":133,"card":217,"direction":123,"ingested_from":125},"W7221843179",[215],{"name":216,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":218,"method":219,"finding":220,"direction":123,"opportunity":221},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":228,"content":9,"source_name":229,"source_url":226,"published_at":230,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":231,"score_detail":232,"sources":236,"tags":238,"primary_topic":242,"secondary_topics":243,"classification":249,"search_phrases":273,"slug":276,"view_count":118,"doi":277,"paper":278,"created_at":298},5006,"Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101966","Soil salinity estimation across arid agricultural regions is challenging due to pronounced environmental heterogeneity and complex dependencies among multi-source variables. This study proposes a Long-Range Dependency-Enhanced Network (LRD-Net) for regional-scale soil salinity estimation in multiple agricultural oasis regions using multi-source remote sensing and environmental covariates. A dataset comprising 180 field samples from three agricultural oasis regions in Xinjiang, China, was constructed with 25 covariates derived from spectral, topographic, climatic, and soil property data. LRD-Net consists of three stages: a multi-scale feature fusion backbone based on depthwise separable convolutions and cross-scale interaction; a Long-Range Dependency Enhancement Module (LDEM) with an asymmetric dual-branch architecture that captures forward and reverse-aware dependencies through reverse alignment and cross-directional fusion; and multi-scale attention refinement with a regression head for continuous salinity estimation. Under the region-stratified random-split evaluation, LRD-Net achieved an R2 of 0.905, an RMSE of 1.207 g·kg−1, and an MAE of 0.868 g·kg−1. Performance decreased under more spatially independent validation, with an R2 of 0.874 under spatial cross-validation and LORO test R2 values ranging from 0.739 to 0.801, indicating that prediction across spatially and geographically independent samples is more challenging than region-stratified random-split evaluation. The proposed framework effectively integrates multi-source information and captures complementary long-range dependencies, providing a potential approach for regional-scale soil salinity estimation within the studied arid agricultural oasis environments and supporting soil salinization monitoring and sustainable land management in the study region.","干旱农业区域土壤盐分估算面临显著的环境异质性和多源变量间复杂依赖关系的挑战。本研究提出了一种长程依赖增强网络（LRD-Net），利用多源遥感与环境协变量对多个农业绿洲区域进行区域尺度土壤盐分估算。研究构建了包含中国新疆三个农业绿洲区域180个野外样本的数据集，并衍生出25个协变量，涵盖光谱、地形、气候和土壤属性数据。LRD-Net由三个阶段组成：基于深度可分离卷积和跨尺度交互的多尺度特征融合骨干网络；具有非对称双分支架构的长程依赖增强模块（LDEM），通过反向对齐和跨方向融合捕获前向与反向感知依赖关系；以及多尺度注意力细化与回归头，用于连续盐分估算。在区域分层随机划分评估下，LRD-Net取得了R²为0.905、RMSE为1.207 g·kg⁻¹、MAE为0.868 g·kg⁻¹的性能。在空间独立性更强的验证下性能有所下降，空间交叉验证R²为0.874，留一区域测试R²范围为0.739至0.801，表明跨空间和地理独立样本的预测比区域分层随机划分评估更具挑战性。所提出的框架有效整合了多源信息并捕获了互补的长程依赖关系，为研究区干旱农业绿洲环境下的区域尺度土壤盐分估算提供了潜在方法，并支持研究区土壤盐渍化监测与可持续土地管理。","Land","2026-10-14T00:00:00Z",72,{"impact":233,"substance":234,"depth":142,"authority":143,"freshness":74,"relevant":21,"comment":235},12,21,"方法新颖、数据扎实的遥感土壤盐渍化估算研究，对干旱区盐碱地监测与治理有参考价值，但属细分领域学术进展，公共影响有限。",[237],{"name":229,"url":226},[26,239,27,240,241],"农业人工智能","土壤盐渍化","新疆农业",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},[244,245],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":185,"slug":246,"name":247,"path":248,"parent_code":48},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":50,"locked":14,"version":51,"method":52,"evidence":250},{"candidates":251,"agricultural_evidence":57},[252,256,260,265,269],{"code":44,"score":56,"supported":57,"title":253,"abstract":254,"tags":255},[69],[27,69],[27],{"code":38,"score":19,"supported":57,"title":257,"abstract":258,"tags":259},[39],[65,39],[],{"code":185,"score":74,"supported":57,"title":261,"abstract":263,"tags":264},[262],"deep learning",[],[239],{"code":196,"score":20,"supported":57,"title":266,"abstract":267,"tags":268},[],[199],[],{"code":83,"score":21,"supported":14,"title":270,"abstract":271,"tags":272},[],[],[26],[274,275],"新疆 农业绿洲 土壤盐渍化 遥感","LRD-Net 土壤盐分 估算","新疆农业绿洲土壤盐渍化遥感-5006","10.3390\u002Fland15101966",{"doi":277,"openalex_id":279,"authors":280,"venue":229,"cited_by_count":118,"oa_url":226,"card":293,"direction":123,"ingested_from":125},"W7221816183",[281,283,286,288,290],{"name":282,"orcid":9},"Mingjie Qian",{"name":284,"orcid":285},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":287,"orcid":9},"Peng Wang",{"name":289,"orcid":9},"Hangyuan Liu",{"name":291,"orcid":292},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":294,"method":295,"finding":296,"direction":123,"opportunity":297},"提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。","构建180个样点、25个协变量的数据集，设计含长程依赖增强模块的多尺度网络。","区域分层随机划分下R²达0.905，但空间独立验证R²降至0.739-0.874，跨区域预测更难。","可探索跨区域迁移学习与不确定性量化，提升空间独立场景下盐分制图泛化能力。","2026-10-15T23:30:29.667342Z",{"id":300,"title":301,"url":302,"summary":303,"summary_zh":304,"content":9,"source_name":305,"source_url":302,"published_at":230,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":306,"score_detail":307,"sources":309,"tags":311,"primary_topic":314,"secondary_topics":315,"classification":318,"search_phrases":367,"slug":370,"view_count":21,"doi":371,"paper":372,"created_at":395},4988,"Hierarchical cross-modal spatiotemporal attention network for crop yield prediction in complex Himalayan agroecosystems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933033","Introduction Accurate crop yield prediction is essential for precision agriculture, climate-resilient farm management, and timely decision-making. However, yield prediction remains challenging because of complex, nonlinear, and multi-scale interactions among crop physiology, soil conditions, weather variability, and management practices. Conventional statistical, machine learning, and deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, often have limitations in capturing long-range temporal dependencies, hierarchical spatial relationships, heterogeneous data modalities, and predictive uncertainty. Methods This study proposes a Hierarchical Cross-Modal Spatio-Temporal Attention Network (HCM-STAN) for crop yield prediction using a unified attention-based framework for integrating heterogeneous agro-environmental data. The architecture incorporates four principal components: (i) hierarchical spatio-temporal attention operating across tree, block, orchard, and regional scales; (ii) a cross-modal attention module for adaptive fusion of IoT-based soil sensor observations, weather data, plant physiological measurements, and drone-based multispectral imagery; (iii) a soil-aware attention head that explicitly models soil memory, hysteresis effects, and root-zone dynamics; and (iv) an uncertainty-aware prediction module combining Monte Carlo dropout and quantile regression to generate calibrated prediction intervals. The framework was evaluated using a multi-source dataset collected from Himalayan apple orchards during 2023–2026, comprising 1,247 monitored trees across three commercial orchards. Results The proposed HCM-STAN achieved an R 2 of 0.89 for four-week-ahead crop yield prediction, representing a 15.6% improvement over the CNN-LSTM baseline. Furthermore, zero-shot transfer to mango orchards achieved an R 2 of 0.67, corresponding to approximately 75% of the performance obtained under full training. Discussion The results demonstrate that integrating hierarchical spatio-temporal representation learning, cross-modal attention, soil-aware modelling, and uncertainty estimation can effectively address the heterogeneous and dynamic nature of crop yield prediction in complex mountain agroecosystems. The observed zero-shot transfer performance further indicates the potential of HCM-STAN to generalize across crop systems without crop-specific retraining. Overall, the proposed framework provides a promising foundation for reliable, uncertainty-aware yield forecasting and can support precision agriculture, climate-smart farm management, and data-driven decision-making in heterogeneous agroecosystems.","引言 准确的作物产量预测对于精准农业、气候适应性农场管理以及及时决策至关重要。然而，由于作物生理、土壤条件、天气变异和管理措施之间复杂、非线性且多尺度的相互作用，产量预测仍然具有挑战性。传统的统计方法、机器学习以及深度学习方法，包括卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）网络，在捕捉长程时间依赖关系、层次化空间关系、异构数据模态以及预测不确定性方面往往存在局限。方法 本研究提出了一种用于作物产量预测的层次化跨模态时空注意力网络（HCM-STAN），采用统一的基于注意力的框架来整合异构农业环境数据。该架构包含四个主要组成部分：（i）在树、地块、果园和区域尺度上运行的层次化时空注意力；（ii）用于自适应融合基于物联网的土壤传感器观测、天气数据、植物生理测量和无人机多光谱影像的跨模态注意力模块；（iii）显式建模土壤记忆、滞后效应和根区动态的土壤感知注意力头；（iv）结合蒙特卡洛 dropout 和分位数回归以生成校准预测区间的不确定性感知预测模块。该框架使用2023—2026年在喜马拉雅苹果园采集的多源数据集进行了评估，该数据集包含三个商业果园中1，247棵监测树木。结果 所提出的 HCM-STAN 在提前四周作物产量预测中达到了0.89的 R²，相较于 CNN-LSTM 基线提升了15.6%。此外，零样本迁移至芒果园达到了0.67的 R²，相当于完整训练下所获性能的约75%。讨论 结果表明，整合层次化时空表征学习、跨模态注意力、土壤感知建模和不确定性估计，能够有效应对复杂山地农业生态系统中作物产量预测的异构性和动态性。观察到的零样本迁移性能进一步表明，HCM-STAN 具有在无需作物特定再训练的情况下跨作物系统泛化的潜力。总体而言，该 p","Frontiers in Plant Science",81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":74,"relevant":21,"comment":308},"提出分层跨模态时空注意力网络，融合物联网土壤传感、气象、生理与无人机多光谱数据，苹果园四周产量预测R²达0.89并实现芒果园零样本迁移，方法新颖、数据扎实，对智慧农业产量预报有参考价值。",[310],{"name":305,"url":302},[26,239,312,313,27],"产量预测","多模态融合",{"code":185,"slug":246,"name":247,"path":248,"parent_code":48},[316,317],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":14,"version":51,"method":52,"evidence":319},{"candidates":320,"agricultural_evidence":57},[321,327,335,340,345,349,356,362],{"code":185,"score":322,"supported":57,"title":323,"abstract":324,"tags":326},11,[],[325,188,189,262],"深度学习",[239,313],{"code":31,"score":74,"supported":57,"title":328,"abstract":329,"tags":334},[],[330,331,332,333],"苹果","芒果","orchard","apple",[],{"code":44,"score":144,"supported":57,"title":336,"abstract":337,"tags":339},[],[338,165],"无人机多光谱",[312,27],{"code":83,"score":341,"supported":57,"title":342,"abstract":343,"tags":344},7,[],[87,181],[26],{"code":38,"score":128,"supported":57,"title":346,"abstract":347,"tags":348},[],[65,39],[],{"code":350,"score":128,"supported":57,"title":351,"abstract":352,"tags":355},"I5",[],[353,354],"物联网","IoT",[],{"code":357,"score":20,"supported":57,"title":358,"abstract":359,"tags":361},"H6",[],[360],"农业生态",[],{"code":196,"score":20,"supported":57,"title":363,"abstract":364,"tags":366},[],[365],"气候适应",[],[368,369],"喜马拉雅 苹果园 产量预测","HCM-STAN 作物产量预测","喜马拉雅苹果园产量预测-4988","10.3389\u002Ffpls.2026.1933033",{"doi":371,"openalex_id":373,"authors":374,"venue":305,"cited_by_count":118,"oa_url":302,"card":388,"direction":394,"ingested_from":125},"W7221795850",[375,378,380,382,384,386],{"name":376,"orcid":377},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":379,"orcid":9},"Umarfarooque Momin",{"name":381,"orcid":9},"Sudeep Kumar E",{"name":383,"orcid":9},"Itfaq Ahmad Mir",{"name":385,"orcid":9},"Ab Rouf Khan",{"name":387,"orcid":9},"Ghulam Mohammad Mir",{"tldr":389,"method":390,"finding":391,"direction":392,"opportunity":393},"提出分层跨模态时空注意力网络HCM-STAN，融合多源农业数据预测喜马拉雅山区苹果产量。","用IoT土壤、气象、生理和无人机多光谱数据，结合分层时空与跨模态注意力及不确定性","四周前产量预测R²达0.89，比CNN-LSTM提升15.6%，零样本迁移芒果R²为0.67。","农业人工智能与决策模型","可探索跨作物零样本迁移的领域自适应机制，并验证模型在更多山地农业生态系统的泛化能力。","智慧农业 \u002F 农业物联网","2026-10-15T23:30:08.750140Z",{"id":397,"title":398,"url":399,"summary":400,"summary_zh":401,"content":9,"source_name":402,"source_url":399,"published_at":137,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":231,"score_detail":403,"sources":405,"tags":407,"primary_topic":410,"secondary_topics":411,"classification":413,"search_phrases":453,"slug":456,"view_count":21,"doi":457,"paper":458,"created_at":470},4896,"Site-Specific Precision Cover Cropping for Soil Health and Environmental Sustainability Using Remote Sensing and Multi-Criteria Analysis","https:\u002F\u002Fdoi.org\u002F10.21663\u002Feeg-d-26-00041","ABSTRACT Cover crops, which are plants or mixtures of plants grown primarily to enhance and protect soil health, are a key strategy in sustainable agriculture. Traditionally, they have been uniformly planted in a field typically during off-season when the main crops are not in competition. However, advancements in precision agriculture, particularly the integration of remote-sensing technology, demand more efficient seeding strategies based on field variability. The aims of this study were to assess the specific needs of a field in Aurora County, SD, and determine the type and optimal combinations of the cover crop mix. The cover crop species considered in this study included legumes, grasses, and brassicas. The remote-sensing data were extracted from Sentinel-2 satellite imagery. Soil samples were collected from 0 to 30 cm depth, and soil properties were measured. Additionally, topographic maps such as slope and aspect were extracted from light detection and ranging (LiDAR) data. The results of precision cover crop analyses showed that 6.5 percent (7.8 ha), 20.6 percent (24.7 ha), 38.1 percent (45.7 ha), 29.6 percent (35.5 ha), and 5.3 percent (6.3 ha) of the studied field should planted in the following mixtures of crops: rye, radish, and vetch; triticale, turnip, and vetch; triticale, radish, and clover; triticale, rapeseed, and pea; and barley, flax, and clover, respectively. The mixtures significantly improved soil properties by reducing compaction, increasing residue C\u002FN ratio, and enhancing weed suppression compared with the control. The resulting map should be interpreted as a decision-support recommendation rather than a fully validated field prescription.","摘要 覆盖作物（cover crops）是指主要为了提升和保护土壤健康而种植的植物或植物混合体，是可持续农业的一项关键策略。传统上，覆盖作物通常在非主作物竞争的季节以均匀方式播种于田间。然而，精准农业的进步，尤其是遥感技术的整合，要求基于田块变异性制定更高效的播种策略。本研究旨在评估南达科他州奥罗拉县某田块的具体需求，并确定覆盖作物混合物的类型及最优组合。本研究考虑的覆盖作物种类包括豆科、禾本科和十字花科植物。遥感数据提取自Sentinel-2卫星影像。土壤样品采集深度为0至30 cm，并测定了土壤性质。此外，从激光雷达（LiDAR）数据中提取了坡度和坡向等地形图。精准覆盖作物分析结果表明，研究田块中分别有6.5%（7.8 ha）、20.6%（24.7 ha）、38.1%（45.7 ha）、29.6%（35.5 ha）和5.3%（6.3 ha）的面积应种植以下作物混合物：黑麦、萝卜和野豌豆；小黑麦、芜菁和野豌豆；小黑麦、萝卜和三叶草；小黑麦、油菜和豌豆；以及大麦、亚麻和三叶草。与对照相比，这些混合物通过降低紧实度、提高残体碳氮比和增强杂草抑制效果，显著改善了土壤性质。所得图谱应被解读为决策支持建议，而非经过充分验证的田间处方。","Environmental and Engineering Geoscience",{"impact":233,"substance":234,"depth":142,"authority":143,"freshness":74,"relevant":21,"comment":404},"该研究将遥感与多准则分析结合用于精准覆盖作物制图，方法新颖、数据具体，对农业信息化与可持续农业有参考价值，但属单点田间案例，影响范围有限。",[406],{"name":402,"url":399},[26,87,27,408,409],"土壤健康","覆盖作物",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[412],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":14,"version":51,"method":52,"evidence":414},{"candidates":415,"agricultural_evidence":57},[416,420,424,428,435,442,448],{"code":38,"score":56,"supported":57,"title":417,"abstract":418,"tags":419},[39],[65,39],[408],{"code":44,"score":233,"supported":57,"title":421,"abstract":422,"tags":423},[69],[27],[27],{"code":83,"score":144,"supported":57,"title":425,"abstract":426,"tags":427},[],[87,181],[26,87],{"code":429,"score":128,"supported":57,"title":430,"abstract":431,"tags":434},"A4",[],[432,433],"油菜","rapeseed",[],{"code":436,"score":128,"supported":57,"title":437,"abstract":438,"tags":441},"H8",[],[439,440],"可持续农业","sustainable agriculture",[],{"code":443,"score":20,"supported":57,"title":444,"abstract":445,"tags":447},"B1",[],[446],"萝卜",[],{"code":449,"score":21,"supported":14,"title":450,"abstract":451,"tags":452},"A7",[],[],[409],[454,455],"Sentinel-2 遥感 覆盖作物","精准覆盖作物 土壤健康","Sentinel-2遥感覆盖作物-4896","10.21663\u002Feeg-d-26-00041",{"doi":457,"openalex_id":459,"authors":460,"venue":402,"cited_by_count":118,"oa_url":9,"card":465,"direction":123,"ingested_from":125},"W7221669942",[461,463],{"name":462,"orcid":9},"Ali Mirzakhani Nafchi",{"name":464,"orcid":9},"Salman Mirzaee",{"tldr":466,"method":467,"finding":468,"direction":123,"opportunity":469},"利用遥感与多准则分析为南达科他州田块制定精准覆盖作物混播方案。","Sentinel-2影像、LiDAR地形与土壤采样，结合多准则分析。","田块被划分为5个管理区，各推荐不同覆盖作物混播，显著改善土壤紧实度、残体C\u002FN比和抑草效果。","可验证该决策支持地图的田间效果，并融合实时物联网数据优化动态推荐。","2026-10-14T23:30:27.077312Z",{"id":472,"title":473,"url":474,"summary":475,"summary_zh":476,"content":9,"source_name":477,"source_url":474,"published_at":478,"category":12,"cover_url":9,"hotness":13,"is_selected":57,"score":306,"score_detail":479,"sources":481,"tags":483,"primary_topic":485,"secondary_topics":486,"classification":488,"search_phrases":515,"slug":518,"view_count":21,"doi":519,"paper":520,"created_at":550},4345,"HyBEAR: a hyperspectral benchmark for bare soil detection","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-18-7301-2026","Detecting bare soil areas is an important step in the analysis of Earth observation data in a variety of Precision Agriculture (PA) applications focused on quantifying soil properties and assessing soil quality. In this paper, we introduce the HyBEAR benchmark – a novel large-scale collection of high-resolution hyperspectral aerial images (with 2 m ground sampling distance) accompanied by manual bare soil annotations verified by domain experts. Usually, the bare soil detection problem is tackled at the pixel level, meaning that detection methods classify all pixels as either bare soil or background. In contrast to this approach, we provide pixel-level annotations for the entire agricultural parcels (if the parcel is labeled as bare soil, then all pixels within that parcel are labeled accordingly), and aim to support the development of methods that identify entire fields with no vegetation. Commonly, such fields undergo further analysis to determine specific soil parameters and characteristics that are important when planning various PA activities, such as fertilization. The HyBEAR benchmark includes (i) the largest-to-date (108 064 591 pixels, corresponding to 43 225 ha) and most heterogeneous dataset for bare soil detection, as well as (ii) the validation procedure (training-test splits and quality metrics) and a set of baseline results, obtained for a set of machine learning bare soil detection models. From the FULL collection of 1954 images in HyBEAR, which we divided into 5 spatially-disjoint folds, we additionally selected a random, stratified subset (MINI) of the images which may be useful for designing and verifying bare soil detection algorithms. Overall, HyBEAR is a step toward standardizing the way the community builds and confronts bare soil detection algorithms in a thorough, reproducible, and unbiased way. The dataset is publicly available at Zenodo: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897 (Wijata et al., 2025).","在多种精准农业（PA）应用中，裸土区域检测是对地观测数据分析的重要步骤，其重点在于量化土壤性质和评估土壤质量。本文介绍了HyBEAR基准——一个全新的大规模高分辨率高光谱航空影像数据集（地面采样距离为2 m），并附有经领域专家验证的人工裸土标注。通常，裸土检测问题在像素层面进行处理，即检测方法将所有像素分类为裸土或背景。与此方法不同，我们为整个农业地块提供像素级标注（如果地块被标记为裸土，则该地块内的所有像素均被相应标记），旨在支持开发能够识别无植被整块田地的方法。通常，此类田地会经过进一步分析以确定特定的土壤参数和特征，这些在规划施肥等各种精准农业活动时至关重要。HyBEAR基准包含：（i）迄今为止规模最大（108 064 591个像素，对应43 225公顷）且最具异质性的裸土检测数据集；（ii）验证流程（训练-测试划分和质量指标）以及一组基线结果，这些结果是在一组机器学习裸土检测模型上获得的。从HyBEAR中全部1954幅图像（我们将其划分为5个空间不相交的折）中，我们额外选择了一个随机分层子集（MINI），该子集可能有助于设计和验证裸土检测算法。总体而言，HyBEAR朝着以全面、可复现且无偏见的方式标准化社区构建和对比裸土检测算法的方向迈出了一步。该数据集已在Zenodo上公开：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897（Wijata et al., 2025）。","Earth system science data","2026-10-06T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":74,"relevant":21,"comment":480},"发布迄今最大规模高光谱裸土检测基准数据集，含像素级标注与基线结果，对精准农业土壤分析有实质推动。",[482],{"name":477,"url":474},[26,239,484,27,150],"高光谱",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},[487],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":14,"version":51,"method":52,"evidence":489},{"candidates":490,"agricultural_evidence":57},[491,495,500,504,508],{"code":38,"score":19,"supported":57,"title":492,"abstract":493,"tags":494},[39],[65,39],[],{"code":44,"score":143,"supported":57,"title":496,"abstract":498,"tags":499},[497],"hyperspectral",[497],[27,484],{"code":83,"score":341,"supported":57,"title":501,"abstract":502,"tags":503},[],[87,181],[26],{"code":185,"score":341,"supported":57,"title":505,"abstract":506,"tags":507},[],[188,189],[239],{"code":509,"score":128,"supported":57,"title":510,"abstract":511,"tags":514},"F3",[],[512,513],"施肥","fertilization",[],[516,517],"HyBEAR 裸土检测 高光谱","裸土检测 基准数据集 遥感","HyBEAR裸土检测高光谱-4345","10.5194\u002Fessd-18-7301-2026",{"doi":519,"openalex_id":521,"authors":522,"venue":477,"cited_by_count":118,"oa_url":544,"card":545,"direction":394,"ingested_from":125},"W7220738771",[523,526,529,532,535,538,541],{"name":524,"orcid":525},"Agata M. Wijata","https:\u002F\u002Forcid.org\u002F0000-0001-6180-9979",{"name":527,"orcid":528},"Bogdan Ruszczak","https:\u002F\u002Forcid.org\u002F0000-0003-1089-1778",{"name":530,"orcid":531},"Adriana Niepala","https:\u002F\u002Forcid.org\u002F0009-0005-8274-9852",{"name":533,"orcid":534},"Michał Gumiela","https:\u002F\u002Forcid.org\u002F0009-0000-2501-2786",{"name":536,"orcid":537},"Krzysztof Smykała","https:\u002F\u002Forcid.org\u002F0000-0003-1970-5388",{"name":539,"orcid":540},"Nicolas Longépé","https:\u002F\u002Forcid.org\u002F0000-0002-6832-3274",{"name":542,"orcid":543},"Jakub Nalepa","https:\u002F\u002Forcid.org\u002F0000-0002-4026-1569","https:\u002F\u002Fessd.copernicus.org\u002Farticles\u002F18\u002F7301\u002F2026\u002Fessd-18-7301-2026.pdf",{"tldr":546,"method":547,"finding":548,"direction":123,"opportunity":549},"提出HyBEAR高光谱航空影像基准数据集，用于整田块裸土检测。","2米分辨率高光谱航空影像，专家验证的田块级像素标注，含训练测试划分与基线模型。","构建了迄今最大（43225公顷）且异质的裸土检测数据集，并给出可复现的评估流程与基线结果。","可基于该基准探索田块级裸土检测的迁移学习与多光谱\u002F高光谱融合方法，并关联土壤参数反演。","2026-10-07T23:30:09.147409Z",{"id":552,"title":553,"url":554,"summary":555,"summary_zh":9,"content":9,"source_name":556,"source_url":554,"published_at":557,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":558,"score_detail":559,"sources":561,"tags":563,"primary_topic":567,"secondary_topics":572,"classification":575,"search_phrases":599,"slug":602,"view_count":21,"doi":603,"paper":604,"created_at":610},4084,"Integrating Crop Rotation and Remote Sensing to Assess Soil and Water Conservation in Organic Soils of the Everglades Agricultural Area","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.31332.74887","Integrating Crop Rotation and Remote Sensing to Assess Soil and Water Conservation in Organic Soils of the Everglades Agricultural Area。OpenAlex","OpenAlex","2026-10-02T00:00:00Z",68,{"impact":233,"substance":17,"depth":140,"authority":143,"freshness":74,"relevant":21,"comment":560},"该论文将轮作与遥感结合评估有机土壤水土保持，方法有创新且数据可靠，但属区域研究，影响范围有限，时效性强，值得关注。",[562],{"name":556,"url":554},[26,564,27,565,566],"轮作","水土保持","有机土壤",{"code":449,"slug":568,"name":569,"path":570,"parent_code":571},"forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[573,574],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":50,"locked":14,"version":51,"method":52,"evidence":576},{"candidates":577,"agricultural_evidence":57},[578,583,587,591,595],{"code":449,"score":233,"supported":57,"title":579,"abstract":581,"tags":582},[580],"crop rotation",[580],[564],{"code":44,"score":233,"supported":57,"title":584,"abstract":585,"tags":586},[69],[69],[27],{"code":38,"score":322,"supported":57,"title":588,"abstract":589,"tags":590},[39],[39],[],{"code":357,"score":21,"supported":14,"title":592,"abstract":593,"tags":594},[],[],[565],{"code":83,"score":21,"supported":14,"title":596,"abstract":597,"tags":598},[],[],[26],[600,601],"Everglades 农业区 遥感 轮作","有机土壤 水土保持 遥感","Everglades农业区遥感轮作-4084","10.13140\u002Frg.2.2.31332.74887",{"doi":603,"openalex_id":605,"authors":606,"venue":9,"cited_by_count":118,"oa_url":554,"card":9,"direction":123,"ingested_from":125},"W7216417358",[607],{"name":608,"orcid":609},"Bivek Bhusal","https:\u002F\u002Forcid.org\u002F0009-0001-6408-0033","2026-10-03T23:30:32.356375Z"]