[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4084":3,"related-4084":97},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":50,"search_phrases":82,"slug":85,"view_count":21,"doi":86,"paper":87,"created_at":96},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",null,"OpenAlex","2026-10-02T00:00:00Z","论文",10,false,68,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,16,13,9,1,"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。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","轮作","遥感","水土保持","有机土壤",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":51,"locked":13,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":57},[56,62,67,72,77],{"code":32,"score":16,"supported":57,"title":58,"abstract":60,"tags":61},true,[59],"crop rotation",[59],[27],{"code":45,"score":16,"supported":57,"title":63,"abstract":65,"tags":66},[64],"remote sensing",[64],[28],{"code":39,"score":68,"supported":57,"title":69,"abstract":70,"tags":71},11,[40],[40],[],{"code":73,"score":21,"supported":13,"title":74,"abstract":75,"tags":76},"H6",[],[],[29],{"code":78,"score":21,"supported":13,"title":79,"abstract":80,"tags":81},"I1",[],[],[26],[83,84],"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":86,"openalex_id":88,"authors":89,"venue":8,"cited_by_count":93,"oa_url":6,"card":8,"direction":94,"ingested_from":95},"W7216417358",[90],{"name":91,"orcid":92},"Bivek Bhusal","https:\u002F\u002Forcid.org\u002F0009-0001-6408-0033",0,"农业遥感与作物表型","openalex","2026-10-03T23:30:32.356375Z",{"total":98,"page":21,"page_size":98,"items":99},6,[100,197,290,366,440,522],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":8,"source_name":106,"source_url":103,"published_at":107,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":108,"score_detail":109,"sources":112,"tags":114,"primary_topic":118,"secondary_topics":119,"classification":121,"search_phrases":149,"slug":152,"view_count":93,"doi":153,"paper":154,"created_at":196},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":17,"substance":110,"depth":17,"authority":16,"freshness":20,"relevant":21,"comment":111},22,"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。",[113],{"name":106,"url":103},[26,115,116,28,117],"无人机","育种","紫花苜蓿",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[120],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":13,"version":52,"method":53,"evidence":122},{"candidates":123,"agricultural_evidence":57},[124,131,136,143],{"code":32,"score":125,"supported":57,"title":126,"abstract":128,"tags":130},15,[127],"alfalfa",[129,127],"苜蓿",[117],{"code":45,"score":16,"supported":57,"title":132,"abstract":134,"tags":135},[133],"multispectral",[133],[28],{"code":78,"score":137,"supported":57,"title":138,"abstract":139,"tags":142},7,[],[140,141],"精准农业","precision agriculture",[26],{"code":144,"score":145,"supported":57,"title":146,"abstract":147,"tags":148},"C4",4,[],[129],[117],[150,151],"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":153,"openalex_id":155,"authors":156,"venue":106,"cited_by_count":93,"oa_url":103,"card":191,"direction":94,"ingested_from":95},"W7223083966",[157,160,163,166,169,171,173,176,179,182,185,188],{"name":158,"orcid":159},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":161,"orcid":162},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":164,"orcid":165},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":167,"orcid":168},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":170,"orcid":8},"Julie Hansen",{"name":172,"orcid":8},"Christopher Pierce",{"name":174,"orcid":175},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":177,"orcid":178},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":180,"orcid":181},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":183,"orcid":184},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":186,"orcid":187},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":189,"orcid":190},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":192,"method":193,"finding":194,"direction":94,"opportunity":195},"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":198,"title":199,"url":200,"summary":201,"summary_zh":202,"content":8,"source_name":203,"source_url":200,"published_at":204,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":205,"score_detail":206,"sources":211,"tags":213,"primary_topic":216,"secondary_topics":217,"classification":224,"search_phrases":274,"slug":277,"view_count":278,"doi":8,"paper":279,"created_at":289},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":18,"substance":207,"depth":208,"authority":19,"freshness":209,"relevant":21,"comment":210},20,17,8,"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。",[212],{"name":203,"url":200},[26,140,28,214,215],"盐碱地治理","土壤监测",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[218,223],{"code":219,"slug":220,"name":221,"path":222,"parent_code":49},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":51,"locked":13,"version":52,"method":53,"evidence":225},{"candidates":226,"agricultural_evidence":57},[227,231,239,244,248,255,262,268],{"code":45,"score":17,"supported":57,"title":228,"abstract":229,"tags":230},[64],[28,64,133],[28],{"code":219,"score":208,"supported":57,"title":232,"abstract":234,"tags":238},[233],"GIS",[235,236,233,237],"地理信息","空间分析","geographic information",[],{"code":39,"score":125,"supported":57,"title":240,"abstract":241,"tags":243},[40],[242,40],"土壤",[214],{"code":78,"score":12,"supported":57,"title":245,"abstract":246,"tags":247},[141],[],[26,140],{"code":249,"score":98,"supported":57,"title":250,"abstract":251,"tags":254},"I2",[],[252,253],"机器学习","machine learning",[],{"code":256,"score":257,"supported":57,"title":258,"abstract":259,"tags":261},"H3",3,[],[260],"灌溉",[],{"code":263,"score":257,"supported":57,"title":264,"abstract":265,"tags":267},"H7",[],[266],"干旱",[],{"code":269,"score":257,"supported":57,"title":270,"abstract":271,"tags":273},"I7",[],[272],"methodological framework",[],[275,276],"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":8,"openalex_id":280,"authors":281,"venue":203,"cited_by_count":93,"oa_url":200,"card":284,"direction":94,"ingested_from":95},"W7221843179",[282],{"name":283,"orcid":8},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":285,"method":286,"finding":287,"direction":94,"opportunity":288},"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":291,"title":292,"url":293,"summary":294,"summary_zh":295,"content":8,"source_name":296,"source_url":293,"published_at":297,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":298,"score_detail":299,"sources":302,"tags":304,"primary_topic":308,"secondary_topics":309,"classification":315,"search_phrases":340,"slug":343,"view_count":93,"doi":344,"paper":345,"created_at":365},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":16,"substance":300,"depth":208,"authority":19,"freshness":20,"relevant":21,"comment":301},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。",[303],{"name":296,"url":293},[26,305,28,306,307],"农业人工智能","土壤盐渍化","新疆农业",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[310,311],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":249,"slug":312,"name":313,"path":314,"parent_code":49},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":51,"locked":13,"version":52,"method":53,"evidence":316},{"candidates":317,"agricultural_evidence":57},[318,322,327,332,336],{"code":45,"score":125,"supported":57,"title":319,"abstract":320,"tags":321},[64],[28,64],[28],{"code":39,"score":323,"supported":57,"title":324,"abstract":325,"tags":326},14,[40],[242,40],[],{"code":249,"score":20,"supported":57,"title":328,"abstract":330,"tags":331},[329],"deep learning",[],[305],{"code":263,"score":257,"supported":57,"title":333,"abstract":334,"tags":335},[],[266],[],{"code":78,"score":21,"supported":13,"title":337,"abstract":338,"tags":339},[],[],[26],[341,342],"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":344,"openalex_id":346,"authors":347,"venue":296,"cited_by_count":93,"oa_url":293,"card":360,"direction":94,"ingested_from":95},"W7221816183",[348,350,353,355,357],{"name":349,"orcid":8},"Mingjie Qian",{"name":351,"orcid":352},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":354,"orcid":8},"Peng Wang",{"name":356,"orcid":8},"Hangyuan Liu",{"name":358,"orcid":359},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":361,"method":362,"finding":363,"direction":94,"opportunity":364},"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":367,"title":368,"url":369,"summary":370,"summary_zh":371,"content":8,"source_name":372,"source_url":369,"published_at":204,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":298,"score_detail":373,"sources":375,"tags":377,"primary_topic":380,"secondary_topics":381,"classification":383,"search_phrases":422,"slug":425,"view_count":21,"doi":426,"paper":427,"created_at":439},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":16,"substance":300,"depth":208,"authority":19,"freshness":20,"relevant":21,"comment":374},"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。",[376],{"name":372,"url":369},[26,140,28,378,379],"土壤健康","覆盖作物",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[382],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":13,"version":52,"method":53,"evidence":384},{"candidates":385,"agricultural_evidence":57},[386,390,394,398,405,412,418],{"code":39,"score":125,"supported":57,"title":387,"abstract":388,"tags":389},[40],[242,40],[378],{"code":45,"score":16,"supported":57,"title":391,"abstract":392,"tags":393},[64],[28],[28],{"code":78,"score":209,"supported":57,"title":395,"abstract":396,"tags":397},[],[140,141],[26,140],{"code":399,"score":98,"supported":57,"title":400,"abstract":401,"tags":404},"A4",[],[402,403],"油菜","rapeseed",[],{"code":406,"score":98,"supported":57,"title":407,"abstract":408,"tags":411},"H8",[],[409,410],"可持续农业","sustainable agriculture",[],{"code":413,"score":257,"supported":57,"title":414,"abstract":415,"tags":417},"B1",[],[416],"萝卜",[],{"code":32,"score":21,"supported":13,"title":419,"abstract":420,"tags":421},[],[],[379],[423,424],"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":426,"openalex_id":428,"authors":429,"venue":372,"cited_by_count":93,"oa_url":8,"card":434,"direction":94,"ingested_from":95},"W7221669942",[430,432],{"name":431,"orcid":8},"Ali Mirzakhani Nafchi",{"name":433,"orcid":8},"Salman Mirzaee",{"tldr":435,"method":436,"finding":437,"direction":94,"opportunity":438},"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":441,"title":442,"url":443,"summary":444,"summary_zh":445,"content":8,"source_name":446,"source_url":443,"published_at":447,"category":11,"cover_url":8,"hotness":12,"is_selected":57,"score":448,"score_detail":449,"sources":451,"tags":453,"primary_topic":455,"secondary_topics":456,"classification":458,"search_phrases":485,"slug":488,"view_count":21,"doi":489,"paper":490,"created_at":521},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",81,{"impact":17,"substance":110,"depth":17,"authority":323,"freshness":20,"relevant":21,"comment":450},"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。",[452],{"name":446,"url":443},[26,305,454,28,215],"高光谱",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[457],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":13,"version":52,"method":53,"evidence":459},{"candidates":460,"agricultural_evidence":57},[461,465,470,474,478],{"code":39,"score":323,"supported":57,"title":462,"abstract":463,"tags":464},[40],[242,40],[],{"code":45,"score":19,"supported":57,"title":466,"abstract":468,"tags":469},[467],"hyperspectral",[467],[28,454],{"code":78,"score":137,"supported":57,"title":471,"abstract":472,"tags":473},[],[140,141],[26],{"code":249,"score":137,"supported":57,"title":475,"abstract":476,"tags":477},[],[252,253],[305],{"code":479,"score":98,"supported":57,"title":480,"abstract":481,"tags":484},"F3",[],[482,483],"施肥","fertilization",[],[486,487],"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":489,"openalex_id":491,"authors":492,"venue":446,"cited_by_count":93,"oa_url":514,"card":515,"direction":520,"ingested_from":95},"W7220738771",[493,496,499,502,505,508,511],{"name":494,"orcid":495},"Agata M. Wijata","https:\u002F\u002Forcid.org\u002F0000-0001-6180-9979",{"name":497,"orcid":498},"Bogdan Ruszczak","https:\u002F\u002Forcid.org\u002F0000-0003-1089-1778",{"name":500,"orcid":501},"Adriana Niepala","https:\u002F\u002Forcid.org\u002F0009-0005-8274-9852",{"name":503,"orcid":504},"Michał Gumiela","https:\u002F\u002Forcid.org\u002F0009-0000-2501-2786",{"name":506,"orcid":507},"Krzysztof Smykała","https:\u002F\u002Forcid.org\u002F0000-0003-1970-5388",{"name":509,"orcid":510},"Nicolas Longépé","https:\u002F\u002Forcid.org\u002F0000-0002-6832-3274",{"name":512,"orcid":513},"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":516,"method":517,"finding":518,"direction":94,"opportunity":519},"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。","智慧农业 \u002F 农业物联网","2026-10-07T23:30:09.147409Z",{"id":523,"title":524,"url":525,"summary":526,"summary_zh":527,"content":8,"source_name":528,"source_url":525,"published_at":529,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":530,"score_detail":531,"sources":534,"tags":536,"primary_topic":540,"secondary_topics":541,"classification":543,"search_phrases":559,"slug":562,"view_count":145,"doi":563,"paper":564,"created_at":611},4026,"RTAS: A radiative transfer model for agricultural strip-intercropping structures","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115708","Agricultural strip-intercropping, characterized by alternating tall and short crops, poses substantial challenges for current radiative transfer (RT) modeling due to complex mutual shadowing and heterogeneous canopy optical and structural properties. Most existing geometric-optical models for row crops are restricted to monoculture configurations, unable to account for the asymmetric radiative interactions arising from the coexistence of two distinct crop species. In this study, we developed the Radiative Transfer Model for Agricultural Strip-intercropping Structures (RTAS), a hybrid analytical model integrating geometric-optical (GO) principles and turbid-medium RT theory under periodic boundary conditions. RTAS showed strong agreement with 3D reference simulations from the LESS (Large-scale Remote Sensing Data and Image Simulation Framework) ray-tracing model in both red and near-infrared (NIR) bands ( R 2 ≥ 0.93, RRMSE ≤3.96%) and with multi-angular unmanned aerial vehicle (UAV) observations ( R 2 = 0.94, RMSE ≤0.0252, RRMSE ≤12.44%). Structural sensitivity analysis showed stable performance across the tested intercropping configurations, with the RRMSE remaining below 8%. RTAS also outperformed the Spectral Linear Mixture (SLM) model and GO baseline, whose red-band errors exceeded 14% and reached nearly 27% at large solar zenith angles. Furthermore, component-level analysis revealed a wavelength-dependent difference between geometric visibility and radiative contribution in the NIR band, associated with enhanced multiple scattering and non-negligible contributions from shaded vegetation. RTAS provides an efficient forward simulation framework with potential for future retrieval studies in complex strip-intercropping structures.","农业条带间作以高矮作物交替种植为特征，由于复杂的相互遮蔽以及异质冠层的光学和结构属性，给当前的辐射传输（RT）建模带来了巨大挑战。现有的大多数行播作物几何光学模型仅限于单作配置，无法考虑两种不同作物共存所产生的非对称辐射相互作用。本研究开发了农业条带间作结构辐射传输模型（RTAS），这是一个在周期性边界条件下整合几何光学（GO）原理与浑浊介质辐射传输理论的混合解析模型。RTAS在红光和近红外（NIR）波段均与LESS（大规模遥感数据与图像模拟框架）光线追踪模型的三维参考模拟结果高度一致（R² ≥ 0.93，RRMSE ≤ 3.96%），并与多角度无人机（UAV）观测结果吻合良好（R² = 0.94，RMSE ≤ 0.0252，RRMSE ≤ 12.44%）。结构敏感性分析表明，该模型在测试的间作配置下性能稳定，RRMSE保持在8%以下。RTAS还优于光谱线性混合（SLM）模型和GO基线模型，后两者在红光波段的误差超过14%，在大太阳天顶角时接近27%。此外，组分层面分析揭示了近红外波段中几何可见度与辐射贡献之间存在波长依赖性差异，这与增强的多重散射及阴影植被不可忽略的贡献有关。RTAS提供了一个高效的前向模拟框架，有望用于未来复杂条带间作结构的反演研究。","Remote Sensing of Environment","2026-10-01T00:00:00Z",83,{"impact":17,"substance":110,"depth":532,"authority":125,"freshness":20,"relevant":21,"comment":533},19,"提出面向条带间作结构的混合辐射传输模型RTAS，验证充分、精度可靠，对农业遥感定量反演有实质推进。",[535],{"name":528,"url":525},[26,28,537,538,539],"间作","作物表型","辐射传输模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[542],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":13,"version":52,"method":53,"evidence":544},{"candidates":545,"agricultural_evidence":57},[546,551,555],{"code":32,"score":125,"supported":57,"title":547,"abstract":549,"tags":550},[548],"intercropping",[537,548],[537],{"code":45,"score":209,"supported":57,"title":552,"abstract":553,"tags":554},[],[28,64],[538,28],{"code":78,"score":21,"supported":13,"title":556,"abstract":557,"tags":558},[],[],[26],[560,561],"RTAS 辐射传输模型 间作","条带间作 遥感 冠层","RTAS辐射传输模型间作-4026","10.1016\u002Fj.rse.2026.115708",{"doi":563,"openalex_id":565,"authors":566,"venue":528,"cited_by_count":93,"oa_url":525,"card":606,"direction":94,"ingested_from":95},"W7215043648",[567,570,573,575,578,581,583,585,588,590,593,595,597,600,603],{"name":568,"orcid":569},"Run Zhong","https:\u002F\u002Forcid.org\u002F0009-0003-3067-388X",{"name":571,"orcid":572},"Dalei Hao","https:\u002F\u002Forcid.org\u002F0000-0003-3497-9774",{"name":574,"orcid":8},"Zhihong Li",{"name":576,"orcid":577},"Carlos Camino","https:\u002F\u002Forcid.org\u002F0000-0001-5188-4406",{"name":579,"orcid":580},"Si Gao","https:\u002F\u002Forcid.org\u002F0000-0001-6726-5693",{"name":582,"orcid":8},"Qing Li",{"name":584,"orcid":8},"Yanan Wei",{"name":586,"orcid":587},"Higor  de Queiroz Ribeiro","https:\u002F\u002Forcid.org\u002F0000-0002-8715-3290",{"name":589,"orcid":8},"Hong Sun",{"name":591,"orcid":592},"Feng Yang","https:\u002F\u002Forcid.org\u002F0000-0002-7499-9422",{"name":594,"orcid":8},"Matheus Pinheiro Ferreirad",{"name":596,"orcid":8},"Haoran Liu",{"name":598,"orcid":599},"Renan Caldas Umburanas","https:\u002F\u002Forcid.org\u002F0000-0002-4112-3598",{"name":601,"orcid":602},"Durval Dourado Neto","https:\u002F\u002Forcid.org\u002F0000-0002-8452-8288",{"name":604,"orcid":605},"Yelu Zeng","https:\u002F\u002Forcid.org\u002F0000-0003-4267-1841",{"tldr":607,"method":608,"finding":609,"direction":94,"opportunity":610},"提出RTAS混合辐射传输模型，模拟条带间作农田的反射率并验证其精度。","融合几何光学与浑浊介质理论，用LESS三维模拟和无人机多角度观测验证。","RTAS精度高（R²≥0.93），优于线性混合和几何光学基线，并揭示近红外几何可见度与辐射贡献的差异","可基于RTAS发展条带间作结构参数反演，并耦合多物种冠层异质性提升遥感监测能力。","2026-10-02T23:30:24.513706Z"]