[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4345":3,"related-4345":125},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":85,"slug":88,"view_count":21,"doi":89,"paper":90,"created_at":124},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）。",null,"Earth system science data","2026-10-06T00:00:00Z","论文",10,true,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","高光谱","遥感","土壤监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":45,"locked":46,"version":47,"method":48,"evidence":49},"classified",false,"2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":14},[51,56,62,70,77],{"code":32,"score":19,"supported":14,"title":52,"abstract":53,"tags":55},[33],[54,33],"土壤",[],{"code":39,"score":57,"supported":14,"title":58,"abstract":60,"tags":61},13,[59],"hyperspectral",[59],[29,28],{"code":63,"score":64,"supported":14,"title":65,"abstract":66,"tags":69},"I1",7,[],[67,68],"精准农业","precision agriculture",[26],{"code":71,"score":64,"supported":14,"title":72,"abstract":73,"tags":76},"I2",[],[74,75],"机器学习","machine learning",[27],{"code":78,"score":79,"supported":14,"title":80,"abstract":81,"tags":84},"F3",6,[],[82,83],"施肥","fertilization",[],[86,87],"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":89,"openalex_id":91,"authors":92,"venue":10,"cited_by_count":114,"oa_url":115,"card":116,"direction":122,"ingested_from":123},"W7220738771",[93,96,99,102,105,108,111],{"name":94,"orcid":95},"Agata M. Wijata","https:\u002F\u002Forcid.org\u002F0000-0001-6180-9979",{"name":97,"orcid":98},"Bogdan Ruszczak","https:\u002F\u002Forcid.org\u002F0000-0003-1089-1778",{"name":100,"orcid":101},"Adriana Niepala","https:\u002F\u002Forcid.org\u002F0009-0005-8274-9852",{"name":103,"orcid":104},"Michał Gumiela","https:\u002F\u002Forcid.org\u002F0009-0000-2501-2786",{"name":106,"orcid":107},"Krzysztof Smykała","https:\u002F\u002Forcid.org\u002F0000-0003-1970-5388",{"name":109,"orcid":110},"Nicolas Longépé","https:\u002F\u002Forcid.org\u002F0000-0002-6832-3274",{"name":112,"orcid":113},"Jakub Nalepa","https:\u002F\u002Forcid.org\u002F0000-0002-4026-1569",0,"https:\u002F\u002Fessd.copernicus.org\u002Farticles\u002F18\u002F7301\u002F2026\u002Fessd-18-7301-2026.pdf",{"tldr":117,"method":118,"finding":119,"direction":120,"opportunity":121},"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。","农业遥感与作物表型","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","openalex","2026-10-07T23:30:09.147409Z",{"total":79,"page":21,"page_size":79,"items":126},[127,219,294,381,456,518],{"id":128,"title":129,"url":130,"summary":131,"summary_zh":132,"content":9,"source_name":133,"source_url":130,"published_at":134,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":135,"score_detail":136,"sources":142,"tags":144,"primary_topic":146,"secondary_topics":147,"classification":154,"search_phrases":203,"slug":206,"view_count":207,"doi":9,"paper":208,"created_at":218},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":137,"substance":138,"depth":139,"authority":57,"freshness":140,"relevant":21,"comment":141},16,20,17,8,"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。",[143],{"name":133,"url":130},[26,67,29,145,30],"盐碱地治理",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[148,153],{"code":149,"slug":150,"name":151,"path":152,"parent_code":43},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":46,"version":47,"method":48,"evidence":155},{"candidates":156,"agricultural_evidence":14},[157,163,171,176,180,184,191,197],{"code":39,"score":17,"supported":14,"title":158,"abstract":160,"tags":162},[159],"remote sensing",[29,159,161],"multispectral",[29],{"code":149,"score":139,"supported":14,"title":164,"abstract":166,"tags":170},[165],"GIS",[167,168,165,169],"地理信息","空间分析","geographic information",[],{"code":32,"score":172,"supported":14,"title":173,"abstract":174,"tags":175},15,[33],[54,33],[145],{"code":63,"score":13,"supported":14,"title":177,"abstract":178,"tags":179},[68],[],[26,67],{"code":71,"score":79,"supported":14,"title":181,"abstract":182,"tags":183},[],[74,75],[],{"code":185,"score":186,"supported":14,"title":187,"abstract":188,"tags":190},"H3",3,[],[189],"灌溉",[],{"code":192,"score":186,"supported":14,"title":193,"abstract":194,"tags":196},"H7",[],[195],"干旱",[],{"code":198,"score":186,"supported":14,"title":199,"abstract":200,"tags":202},"I7",[],[201],"methodological framework",[],[204,205],"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":209,"authors":210,"venue":133,"cited_by_count":114,"oa_url":130,"card":213,"direction":120,"ingested_from":123},"W7221843179",[211],{"name":212,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":214,"method":215,"finding":216,"direction":120,"opportunity":217},"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":220,"title":221,"url":222,"summary":223,"summary_zh":224,"content":9,"source_name":225,"source_url":222,"published_at":226,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":227,"score_detail":228,"sources":232,"tags":234,"primary_topic":237,"secondary_topics":238,"classification":244,"search_phrases":268,"slug":271,"view_count":114,"doi":272,"paper":273,"created_at":293},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":229,"substance":230,"depth":139,"authority":57,"freshness":20,"relevant":21,"comment":231},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。",[233],{"name":225,"url":222},[26,27,29,235,236],"土壤盐渍化","新疆农业",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[239,240],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":71,"slug":241,"name":242,"path":243,"parent_code":43},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":45,"locked":46,"version":47,"method":48,"evidence":245},{"candidates":246,"agricultural_evidence":14},[247,251,255,260,264],{"code":39,"score":172,"supported":14,"title":248,"abstract":249,"tags":250},[159],[29,159],[29],{"code":32,"score":19,"supported":14,"title":252,"abstract":253,"tags":254},[33],[54,33],[],{"code":71,"score":20,"supported":14,"title":256,"abstract":258,"tags":259},[257],"deep learning",[],[27],{"code":192,"score":186,"supported":14,"title":261,"abstract":262,"tags":263},[],[195],[],{"code":63,"score":21,"supported":46,"title":265,"abstract":266,"tags":267},[],[],[26],[269,270],"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":272,"openalex_id":274,"authors":275,"venue":225,"cited_by_count":114,"oa_url":222,"card":288,"direction":120,"ingested_from":123},"W7221816183",[276,278,281,283,285],{"name":277,"orcid":9},"Mingjie Qian",{"name":279,"orcid":280},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":282,"orcid":9},"Peng Wang",{"name":284,"orcid":9},"Hangyuan Liu",{"name":286,"orcid":287},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":289,"method":290,"finding":291,"direction":120,"opportunity":292},"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":295,"title":296,"url":297,"summary":298,"summary_zh":299,"content":9,"source_name":300,"source_url":297,"published_at":301,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":302,"score_detail":303,"sources":305,"tags":307,"primary_topic":309,"secondary_topics":310,"classification":313,"search_phrases":344,"slug":347,"view_count":79,"doi":348,"paper":349,"created_at":380},2947,"AI-enabled UAV-based Soil Organic Carbon Mapping in Arid Environments: A Pilot Study Protocol","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315495282260915110324","Introduction Soil organic carbon (SOC) is an important indicator of soil health, agricultural productivity, and carbon sequestration potential. However, accurate and scalable SOC mapping in arid environments is constrained by high spatial heterogeneity and the limitations of conventional soil sampling. This study aims to develop a standardized UAV-enabled framework for high-resolution SOC mapping in arid agricultural environments. Methods A pilot-study protocol integrating UAV-based hyperspectral remote sensing with artificial intelligence and machine learning was developed. The workflow encompasses study-site selection, ground-reference sampling, UAV hyperspectral data acquisition, radiometric and geometric preprocessing, spectral feature extraction and selection, machine-learning model development, validation, uncertainty assessment, and performance evaluation using R 2 , RMSE, and MAE. The protocol also incorporates assessment of environmental confounders, including soil moisture, surface roughness, and crop residues. Results The resulting framework provides a systematic and reproducible workflow for UAV-based SOC estimation, integrating field observations, hyperspectral features, predictive modelling, and uncertainty assessment. It establishes defined procedures for evaluating model robustness and transferability across varying field conditions. Discussion The framework addresses an important methodological gap in UAV-enabled SOC mapping by integrating remote sensing and AI within a standardized pilot-study design. Its emphasis on environmental confounders and uncertainty assessment can improve the reliability and comparability of SOC mapping studies. However, field validation across diverse arid environments remains necessary. Conclusion The proposed protocol provides a practical foundation for reproducible SOC mapping and subsequent field validation, supporting precision agriculture, sustainable soil management, and carbon monitoring, reporting, and verification (MRV) in arid regions.","引言 土壤有机碳（SOC）是衡量土壤健康、农业生产力及碳固存潜力的重要指标。然而，干旱环境中高空间异质性和传统土壤采样的局限性制约了准确且可扩展的SOC制图。本研究旨在开发一个标准化的无人机（UAV）框架，用于干旱农业环境中的高分辨率SOC制图。方法 开发了一套整合无人机高光谱遥感与人工智能及机器学习的试点研究方案。该工作流程涵盖研究地点选择、地面参考采样、无人机高光谱数据采集、辐射与几何预处理、光谱特征提取与选择、机器学习模型开发、验证、不确定性评估，以及使用R²、RMSE和MAE进行的性能评价。该方案还包括对环境混杂因素的评估，包括土壤水分、地表粗糙度和作物残茬。结果 所构建的框架为基于无人机的SOC估算提供了系统且可重复的工作流程，整合了野外观测、高光谱特征、预测建模和不确定性评估。它建立了明确的程序，用于评估模型在不同田间条件下的稳健性和可迁移性。讨论 该框架通过将遥感与人工智能整合于标准化的试点研究设计中，填补了无人机SOC制图领域的重要方法学空白。其对环境混杂因素和不确定性评估的重视，可提高SOC制图研究的可靠性和可比性。然而，仍需在不同干旱环境中进行田间验证。结论 所提出的方案为可重复的SOC制图及后续田间验证提供了实用基础，支持干旱地区的精准农业、可持续土壤管理以及碳监测、报告与核查（MRV）。","The Open Agriculture Journal","2026-09-18T00:00:00Z",67,{"impact":229,"substance":17,"depth":137,"authority":57,"freshness":140,"relevant":21,"comment":304},"提出无人机高光谱结合AI的干旱区土壤有机碳制图标准化方案，方法框架清晰但尚属试点协议、缺乏实地验证，具备一定参考价值。",[306],{"name":300,"url":297},[26,27,67,29,308],"土壤碳汇",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[311,312],{"code":71,"slug":241,"name":242,"path":243,"parent_code":43},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":46,"version":47,"method":48,"evidence":314},{"candidates":315,"agricultural_evidence":14},[316,320,325,329,333,340],{"code":32,"score":19,"supported":14,"title":317,"abstract":318,"tags":319},[33],[54,33],[],{"code":71,"score":13,"supported":14,"title":321,"abstract":322,"tags":324},[],[74,75,323],"artificial intelligence",[27],{"code":39,"score":13,"supported":14,"title":326,"abstract":327,"tags":328},[],[29,159,59],[29],{"code":63,"score":140,"supported":14,"title":330,"abstract":331,"tags":332},[],[67,68],[26,67],{"code":334,"score":335,"supported":14,"title":336,"abstract":337,"tags":339},"H4",4,[],[338],"carbon sequestration",[308],{"code":192,"score":186,"supported":14,"title":341,"abstract":342,"tags":343},[],[195],[],[345,346],"无人机 土壤有机碳 制图","AI 高光谱 干旱农业","无人机土壤有机碳制图-2947","10.2174\u002F0118743315495282260915110324",{"doi":348,"openalex_id":350,"authors":351,"venue":300,"cited_by_count":114,"oa_url":297,"card":375,"direction":120,"ingested_from":123},"W7213561504",[352,355,358,361,364,367,369,371,373],{"name":353,"orcid":354},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":356,"orcid":357},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":359,"orcid":360},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":362,"orcid":363},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":365,"orcid":366},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":368,"orcid":9},"Almaha Jamal",{"name":370,"orcid":9},"Afra Rashed",{"name":372,"orcid":9},"Hamda Yousif",{"name":374,"orcid":9},"Sarah Sadeq",{"tldr":376,"method":377,"finding":378,"direction":120,"opportunity":379},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","2026-09-19T23:30:33.273156Z",{"id":382,"title":383,"url":384,"summary":385,"summary_zh":386,"content":9,"source_name":387,"source_url":384,"published_at":134,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":227,"score_detail":388,"sources":390,"tags":392,"primary_topic":395,"secondary_topics":396,"classification":398,"search_phrases":438,"slug":441,"view_count":21,"doi":442,"paper":443,"created_at":455},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":229,"substance":230,"depth":139,"authority":57,"freshness":20,"relevant":21,"comment":389},"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。",[391],{"name":387,"url":384},[26,67,29,393,394],"土壤健康","覆盖作物",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[397],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":46,"version":47,"method":48,"evidence":399},{"candidates":400,"agricultural_evidence":14},[401,405,409,413,420,427,433],{"code":32,"score":172,"supported":14,"title":402,"abstract":403,"tags":404},[33],[54,33],[393],{"code":39,"score":229,"supported":14,"title":406,"abstract":407,"tags":408},[159],[29],[29],{"code":63,"score":140,"supported":14,"title":410,"abstract":411,"tags":412},[],[67,68],[26,67],{"code":414,"score":79,"supported":14,"title":415,"abstract":416,"tags":419},"A4",[],[417,418],"油菜","rapeseed",[],{"code":421,"score":79,"supported":14,"title":422,"abstract":423,"tags":426},"H8",[],[424,425],"可持续农业","sustainable agriculture",[],{"code":428,"score":186,"supported":14,"title":429,"abstract":430,"tags":432},"B1",[],[431],"萝卜",[],{"code":434,"score":21,"supported":46,"title":435,"abstract":436,"tags":437},"A7",[],[],[394],[439,440],"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":442,"openalex_id":444,"authors":445,"venue":387,"cited_by_count":114,"oa_url":9,"card":450,"direction":120,"ingested_from":123},"W7221669942",[446,448],{"name":447,"orcid":9},"Ali Mirzakhani Nafchi",{"name":449,"orcid":9},"Salman Mirzaee",{"tldr":451,"method":452,"finding":453,"direction":120,"opportunity":454},"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":457,"title":458,"url":459,"summary":460,"summary_zh":9,"content":9,"source_name":461,"source_url":459,"published_at":462,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":463,"score_detail":464,"sources":466,"tags":468,"primary_topic":472,"secondary_topics":477,"classification":480,"search_phrases":506,"slug":509,"view_count":21,"doi":510,"paper":511,"created_at":517},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":229,"substance":17,"depth":137,"authority":57,"freshness":20,"relevant":21,"comment":465},"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。",[467],{"name":461,"url":459},[26,469,29,470,471],"轮作","水土保持","有机土壤",{"code":434,"slug":473,"name":474,"path":475,"parent_code":476},"forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[478,479],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":46,"version":47,"method":48,"evidence":481},{"candidates":482,"agricultural_evidence":14},[483,488,492,497,502],{"code":434,"score":229,"supported":14,"title":484,"abstract":486,"tags":487},[485],"crop rotation",[485],[469],{"code":39,"score":229,"supported":14,"title":489,"abstract":490,"tags":491},[159],[159],[29],{"code":32,"score":493,"supported":14,"title":494,"abstract":495,"tags":496},11,[33],[33],[],{"code":498,"score":21,"supported":46,"title":499,"abstract":500,"tags":501},"H6",[],[],[470],{"code":63,"score":21,"supported":46,"title":503,"abstract":504,"tags":505},[],[],[26],[507,508],"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":510,"openalex_id":512,"authors":513,"venue":9,"cited_by_count":114,"oa_url":459,"card":9,"direction":120,"ingested_from":123},"W7216417358",[514],{"name":515,"orcid":516},"Bivek Bhusal","https:\u002F\u002Forcid.org\u002F0009-0001-6408-0033","2026-10-03T23:30:32.356375Z",{"id":519,"title":520,"url":521,"summary":522,"summary_zh":523,"content":9,"source_name":524,"source_url":521,"published_at":525,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":526,"score_detail":527,"sources":529,"tags":531,"primary_topic":534,"secondary_topics":535,"classification":538,"search_phrases":562,"slug":565,"view_count":207,"doi":566,"paper":567,"created_at":586},3916,"Hyperspectral inversion of soil total nitrogen content using spectral and machine learning","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjb.v55i3.93850","The Agro-pastoral Transitional Ecotone of Northern Shaanxi is an ecologically fragile region. Soil nitrogen is a critical factor influencing crop productivity and regional ecological balance. Rapid and accurate monitoring of farmland soil total nitrogen (TN) content is therefore essential for implementing precision agriculture and ensuring sustainable land use in this area. This study focused on the cultivated soils of Dingbian County, a representative area within this ecotone. Field-collected soil samples underwent TN content determination and laboratory-based hyper spectral reflectance measurement. The original spectral reflectance (R) was first transformed using mathematical methods, including first-derivative (FDR), second-derivative (SDR), and logarithm of the reciprocal's first-derivative (LRFD) analysis, to enhance spectral information and suppress background noise. Subsequently, the successive projections algorithm (SPA) and correlation analysis (CA) were integrated to identify feature wavelengths highly correlated with soil TN from the transformed spectral data. Partial Least Squares Regression (PLSR), Decision Tree (DT), and Random Forest (RF) were developed to estimate soil TN content. Model performance was evaluated and compared using the coefficient of determination (R²), root mean square error (RMSE), and ratio of performance to deviation (RPD). The results indicated that: (1) Spectral derivative transformations effectively highlighted subtle information, with LRFD showing the greatest sensitivity to TN. Models built using LRFD-derived features consistently outperformed those based on original reflectance. (2) Among the machine learning algorithms, the Random Forest (RF) model demonstrated superior predictive performance on the validation set (R² = 0.85, RMSE = 0.11 g\u002Fkg, RPD = 2.15), indicating the highest stability and generalization capability. The PLSR model performed moderately well, while the DT model was prone to over fitting and yielded the lowest accuracy. (3) This study confirms that the integration of hyper spectral technology and machine learning algorithms enables efficient and precise estimation of soil TN content in farmland within the Northern Shaanxi agro-pastoral ecotone, providing a reliable technical framework for soil fertility assessment and precision nutrient management in the region. Bangladesh J. Bot. 55(3): 605-614, 2026 (September)","陕北农牧交错带是生态脆弱区，土壤氮素是影响作物生产力和区域生态平衡的关键因子。快速、准确地监测农田土壤全氮（TN）含量，对于该区实施精准农业和保障土地可持续利用至关重要。本研究以该交错带典型区域定边县的耕作土壤为研究对象，对野外采集的土壤样品进行TN含量测定和实验室高光谱反射率测量。首先采用数学方法对原始光谱反射率（R）进行变换，包括一阶微分（FDR）、二阶微分（SDR）和倒数对数一阶微分（LRFD）分析，以增强光谱信息并抑制背景噪声。随后，将连续投影算法（SPA）与相关分析（CA）相结合，从变换后的光谱数据中筛选出与土壤TN高度相关的特征波长。构建偏最小二乘回归（PLSR）、决策树（DT）和随机森林（RF）模型估算土壤TN含量。采用决定系数（R²）、均方根误差（RMSE）和相对分析误差（RPD）对模型性能进行评价与比较。结果表明：（1）光谱微分变换有效凸显了细微信息，其中LRFD对TN的敏感性最强，基于LRFD衍生特征构建的模型始终优于基于原始反射率的模型。（2）在机器学习算法中，随机森林（RF）模型在验证集上表现出优越的预测性能（R² = 0.85，RMSE = 0.11 g\u002Fkg，RPD = 2.15），具有最高的稳定性和泛化能力。PLSR模型表现中等，而DT模型易于过拟合，精度最低。（3）本研究证实，高光谱技术与机器学习算法相结合能够高效、精确地估算陕北农牧交错带农田土壤TN含量，为该区域土壤肥力评估和精准养分管理提供了可靠的技术框架。Bangladesh J. Bot. 55(3): 605-614, 2026 (September)","Bangladesh Journal of Botany","2026-09-30T00:00:00Z",70,{"impact":229,"substance":138,"depth":139,"authority":57,"freshness":140,"relevant":21,"comment":528},"高光谱结合机器学习反演土壤全氮，方法对比扎实、结论可靠，对精准农业养分管理有参考价值，但属区域性田间研究，影响范围有限。",[530],{"name":524,"url":521},[26,532,74,29,533],"精准施肥","土壤氮素",{"code":71,"slug":241,"name":242,"path":243,"parent_code":43},[536,537],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":46,"version":47,"method":48,"evidence":539},{"candidates":540,"agricultural_evidence":14},[541,545,549,553,557],{"code":71,"score":172,"supported":14,"title":542,"abstract":543,"tags":544},[75],[74,75],[74],{"code":32,"score":19,"supported":14,"title":546,"abstract":547,"tags":548},[33],[54,33],[],{"code":39,"score":20,"supported":14,"title":550,"abstract":551,"tags":552},[59],[],[29],{"code":63,"score":64,"supported":14,"title":554,"abstract":555,"tags":556},[],[67,68],[26],{"code":78,"score":335,"supported":14,"title":558,"abstract":559,"tags":561},[],[560],"养分管理",[532],[563,564],"定边县 土壤全氮 高光谱","陕北农牧交错带 土壤氮素 反演","定边县土壤全氮高光谱-3916","10.3329\u002Fbjb.v55i3.93850",{"doi":566,"openalex_id":568,"authors":569,"venue":524,"cited_by_count":114,"oa_url":521,"card":580,"direction":585,"ingested_from":123},"W7214885868",[570,573,575,577],{"name":571,"orcid":572},"Zhenyu Dong","https:\u002F\u002Forcid.org\u002F0000-0003-0028-3293",{"name":574,"orcid":9},"Ni Wang",{"name":576,"orcid":9},"Jiancang Xie",{"name":578,"orcid":579},"Jungang Luo","https:\u002F\u002Forcid.org\u002F0009-0009-0285-4839",{"tldr":581,"method":582,"finding":583,"direction":120,"opportunity":584},"用高光谱与机器学习反演陕北农牧交错带农田土壤全氮含量。","野外采样测光谱，经FDR\u002FSDR\u002FLRFD变换，SPA+CA选特征波长，建PLS","LRFD变换最敏感，RF模型验证集R²=0.85、RPD=2.15，精度与泛化最优。","可探索多源遥感与深度学习融合，提升生态脆弱区土壤氮素时空反演与制图能力。","农业人工智能与决策模型","2026-10-01T23:30:53.960449Z"]