[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2530":3,"related-2530":84},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":59,"slug":62,"view_count":22,"doi":63,"paper":64,"created_at":83},2530,"Seeing the Green from Above: A Review of Remote Sensing Techniques for Vegetation Cover Discrimination","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189410","This review synthesizes recent regional applications of satellite, unmanned aerial vehicle (UAV), and hyperspectral\u002Fspectroradiometric remote sensing for vegetation cover discrimination, including crop and natural vegetation discrimination, plant disease and pest detection, and yield assessment. The reviewed evidence demonstrates complementary rather than universally superior capabilities among sensing platforms. Satellite observations provide repeated large-area monitoring but remain constrained by spatial resolution, cloud interference, and spectral mixing, whereas UAVs offer very-high-resolution and flexible field-scale observations at the expense of spatial coverage and greater acquisition and processing requirements. Hyperspectral and spectroradiometric approaches provide detailed spectral information for distinguishing subtle vegetation differences, but are limited by data complexity and operational scalability. The quantitative results reported in the reviewed studies illustrate this variability: satellite-based crop discrimination achieved approximately 90% overall accuracy with QuickBird and 81% overall accuracy (κ = 0.74) with Sentinel-2 at a 10 m resolution, while a UAV hyperspectral vegetation classification study achieved 94.5% accuracy. However, these values are not directly comparable because the vegetation targets, sensors, acquisition conditions, and analytical methods differed among studies. Recent evidence also indicates that the phenological timing, spectral band selection, spatial resolution, and representative training data strongly influence the discrimination performance, while the transfer of disease detection models from controlled experiments to operational field conditions remains a major challenge. By integrating evidence from satellite, UAV, and ground-based spectroradiometric approaches, this review provides a comprehensive framework for understanding the complementary capabilities of these technologies for vegetation cover discrimination and highlights their importance for improving vegetation monitoring, precision agriculture, and sustainable ecosystem management.","本文综述了近年来卫星、无人机（UAV）及高光谱\u002F光谱辐射遥感在植被覆盖判别中的区域应用，包括作物与自然植被判别、植物病虫害检测及产量评估。所综述的证据表明，各传感平台之间的能力是互补的，而非普遍存在优劣之分。卫星观测可提供重复的大面积监测，但仍受空间分辨率、云层干扰及光谱混合的限制；无人机则能够提供超高分辨率且灵活的田块尺度观测，但代价是空间覆盖范围有限，且采集与处理要求更高。高光谱与光谱辐射方法可提供精细的光谱信息，用于区分细微的植被差异，但受数据复杂性及业务化可扩展性的制约。所综述研究报告的定量结果体现了这种差异性：基于卫星的作物判别在QuickBird下总体精度约为90%，在10 m分辨率的Sentinel-2下总体精度为81%（κ = 0.74），而一项无人机高光谱植被分类研究达到了94.5%的精度。然而，这些数值之间并不能直接比较，因为各研究中的植被目标、传感器、采集条件及分析方法均不相同。近期证据还表明，物候时序、光谱波段选择、空间分辨率及代表性训练数据对判别性能有显著影响，而将病害检测模型从受控实验推广至实际田间条件仍是一项重大挑战。通过整合来自卫星、无人机及地面光谱辐射方法的证据，本综述为理解这些技术在植被覆盖判别中的互补能力提供了一个综合框架，并强调了其对改进植被监测、精准农业及可持续生态系统管理的重要意义。",null,"Sustainability","2026-09-14T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,8,1,"系统综述卫星、无人机与高光谱遥感在植被识别、病虫害检测与估产中的互补能力，数据翔实、结论审慎，对农业遥感应用有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准农业","遥感","病虫害监测","作物估产",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":52},19,true,[49],"remote sensing",[29,49,51],"hyperspectral",[31,29],{"code":54,"score":21,"supported":47,"title":55,"abstract":56,"tags":58},"I1",[],[28,57],"precision agriculture",[27,28],[60,61],"病虫害监测 作物估产 智慧农业 精准农业","病虫害监测 作物估产","病虫害监测作物估产智慧农业精准农业-2530","10.3390\u002Fsu18189410",{"doi":63,"openalex_id":65,"authors":66,"venue":10,"cited_by_count":75,"oa_url":6,"card":76,"direction":80,"ingested_from":82},"W7212851379",[67,70,73],{"name":68,"orcid":69},"Ghada A. Khdery","https:\u002F\u002Forcid.org\u002F0000-0002-1492-6029",{"name":71,"orcid":72},"Mohamed S. Shokr","https:\u002F\u002Forcid.org\u002F0000-0003-0328-7679",{"name":74,"orcid":9},"Aleksandra O. Utkina",0,{"tldr":77,"method":78,"finding":79,"direction":80,"opportunity":81},"综述卫星、无人机与高光谱遥感在植被覆盖判别中的区域应用与互补能力。","综述卫星、无人机、高光谱\u002F光谱辐射遥感在作物与植被判别中的案例。","各平台能力互补而非普遍优越，物候、波段、分辨率与训练数据显著影响精度。","农业遥感与作物表型","可探索跨平台遥感数据融合与病害检测模型从受控实验到田间业务的迁移。","openalex","2026-09-15T23:30:20.628746Z",{"total":85,"page":22,"page_size":85,"items":86},6,[87,185,264,335,427,494],{"id":88,"title":89,"url":90,"summary":91,"summary_zh":92,"content":9,"source_name":93,"source_url":90,"published_at":94,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":95,"score_detail":96,"sources":99,"tags":101,"primary_topic":104,"secondary_topics":105,"classification":117,"search_phrases":169,"slug":172,"view_count":173,"doi":9,"paper":174,"created_at":184},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":97,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":98},16,"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。",[100],{"name":93,"url":90},[27,28,29,102,103],"盐碱地治理","土壤监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[106,111],{"code":107,"slug":108,"name":109,"path":110,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":112,"slug":113,"name":114,"path":115,"parent_code":116},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":118},{"candidates":119,"agricultural_evidence":47},[120,125,133,139,143,150,157,163],{"code":33,"score":17,"supported":47,"title":121,"abstract":122,"tags":124},[49],[29,49,123],"multispectral",[29],{"code":107,"score":19,"supported":47,"title":126,"abstract":128,"tags":132},[127],"GIS",[129,130,127,131],"地理信息","空间分析","geographic information",[],{"code":112,"score":134,"supported":47,"title":135,"abstract":136,"tags":138},15,[113],[137,113],"土壤",[102],{"code":54,"score":13,"supported":47,"title":140,"abstract":141,"tags":142},[57],[],[27,28],{"code":144,"score":85,"supported":47,"title":145,"abstract":146,"tags":149},"I2",[],[147,148],"机器学习","machine learning",[],{"code":151,"score":152,"supported":47,"title":153,"abstract":154,"tags":156},"H3",3,[],[155],"灌溉",[],{"code":158,"score":152,"supported":47,"title":159,"abstract":160,"tags":162},"H7",[],[161],"干旱",[],{"code":164,"score":152,"supported":47,"title":165,"abstract":166,"tags":168},"I7",[],[167],"methodological framework",[],[170,171],"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":175,"authors":176,"venue":93,"cited_by_count":75,"oa_url":90,"card":179,"direction":80,"ingested_from":82},"W7221843179",[177],{"name":178,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":180,"method":181,"finding":182,"direction":80,"opportunity":183},"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":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":94,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":192,"score_detail":193,"sources":198,"tags":200,"primary_topic":203,"secondary_topics":204,"classification":206,"search_phrases":246,"slug":249,"view_count":22,"doi":250,"paper":251,"created_at":263},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",72,{"impact":194,"substance":195,"depth":19,"authority":20,"freshness":196,"relevant":22,"comment":197},12,21,9,"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。",[199],{"name":191,"url":188},[27,28,29,201,202],"土壤健康","覆盖作物",{"code":112,"slug":113,"name":114,"path":115,"parent_code":116},[205],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":207},{"candidates":208,"agricultural_evidence":47},[209,213,217,221,228,235,241],{"code":112,"score":134,"supported":47,"title":210,"abstract":211,"tags":212},[113],[137,113],[201],{"code":33,"score":194,"supported":47,"title":214,"abstract":215,"tags":216},[49],[29],[29],{"code":54,"score":21,"supported":47,"title":218,"abstract":219,"tags":220},[],[28,57],[27,28],{"code":222,"score":85,"supported":47,"title":223,"abstract":224,"tags":227},"A4",[],[225,226],"油菜","rapeseed",[],{"code":229,"score":85,"supported":47,"title":230,"abstract":231,"tags":234},"H8",[],[232,233],"可持续农业","sustainable agriculture",[],{"code":236,"score":152,"supported":47,"title":237,"abstract":238,"tags":240},"B1",[],[239],"萝卜",[],{"code":242,"score":22,"supported":14,"title":243,"abstract":244,"tags":245},"A7",[],[],[202],[247,248],"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":250,"openalex_id":252,"authors":253,"venue":191,"cited_by_count":75,"oa_url":9,"card":258,"direction":80,"ingested_from":82},"W7221669942",[254,256],{"name":255,"orcid":9},"Ali Mirzakhani Nafchi",{"name":257,"orcid":9},"Salman Mirzaee",{"tldr":259,"method":260,"finding":261,"direction":80,"opportunity":262},"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":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":9,"source_name":270,"source_url":267,"published_at":271,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":272,"score_detail":273,"sources":275,"tags":277,"primary_topic":280,"secondary_topics":286,"classification":288,"search_phrases":309,"slug":312,"view_count":22,"doi":313,"paper":314,"created_at":334},3893,"Modeling the productive potential of coffee trees in the Cerrado of Minas Gerais through hierarchical landscape analysis and remote sensing data","https:\u002F\u002Fdoi.org\u002F10.25186\u002F.v21i.2516","Brazil stands out as the world’s largest producer and exporter of Arabica coffee, one of the world’s most important commodities, which plays a crucial role in the country’s economy. In this context, this study develops a model to classify the productive potential of coffee trees based on hierarchical landscape analysis and coffee plant metrics, applied to different farms in the Córrego Feio watershed, Patrocínio, Minas Gerais. We employed a model based on landscape metrics (slope, water flow, vegetation, and slope shape), inspired by Ross’s (1994) proposal for Environmental Fragility. Each variable was reclassified according to hierarchical levels from the literature and empirical observations of the area. The data were integrated to generate two products: Factor T, which represents the potential of the area based on static soil and relief variables, and Calculated Potential, which combines Factor T with the NDVI vegetation index for a dynamic assessment. The application of the model demonstrated a strong capacity to interpret the qualitative hierarchy of productive potential, supporting the delineation of differentiated management zones. The results indicated that productive potential is more strongly associated with soil and topographic factors than with the vegetation index. It is concluded that the proposed method is a valuable tool for precision agriculture, allowing the delineation of management zones for the application of inputs in a more efficient and sustainable manner, contributing to resource conservation and environmental protection. Key words: Arabica coffee; environmental fragility; hierarchical analysis; vegetation index; watershed.","巴西是全球最大的阿拉比卡咖啡（Arabica coffee）生产国和出口国，阿拉比卡咖啡作为全球最重要的商品之一，在该国经济中发挥着至关重要的作用。在此背景下，本研究开发了一个基于层次景观分析和咖啡植株指标的咖啡树生产潜力分类模型，并将其应用于米纳斯吉拉斯州帕特罗西尼奥市Córrego Feio流域的不同农场。我们采用了一个基于景观指标（坡度、水流、植被和坡形）的模型，其灵感来源于Ross（1994）提出的环境脆弱性方案。每个变量根据文献和该区域经验观察所得的层次等级进行重新分类。数据经过整合生成了两个产品：因子T，代表基于静态土壤和地形变量的区域潜力；以及计算潜力，将因子T与NDVI植被指数相结合以进行动态评估。模型的应用展现出强大的能力，能够解释生产潜力的定性层次，支持差异化经营区的划分。结果表明，生产潜力与土壤和地形因子的关联性强于与植被指数的关联性。研究认为，所提出的方法是一种有价值的精准农业工具，能够以更高效和可持续的方式划定管理区以施用投入品，有助于资源保护和环境保护。关键词：阿拉比卡咖啡；环境脆弱性；层次分析；植被指数；流域。","Coffee Science","2026-09-30T00:00:00Z",70,{"impact":194,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":274},"基于景观分析与NDVI的咖啡生产潜力分级模型，方法新颖、结论可靠，对精准农业管理分区有实用价值，但属区域性研究，影响范围有限。",[276],{"name":270,"url":267},[27,28,29,278,279],"管理分区","咖啡种植",{"code":281,"slug":282,"name":283,"path":284,"parent_code":285},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[287],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":289},{"candidates":290,"agricultural_evidence":47},[291,297,301,305],{"code":281,"score":134,"supported":47,"title":292,"abstract":294,"tags":296},[293],"coffee",[295,293],"咖啡",[279],{"code":33,"score":196,"supported":47,"title":298,"abstract":299,"tags":300},[49],[],[29],{"code":54,"score":21,"supported":47,"title":302,"abstract":303,"tags":304},[],[28,57],[27,28],{"code":112,"score":85,"supported":47,"title":306,"abstract":307,"tags":308},[],[137,113],[],[310,311],"Minas Gerais 咖啡 遥感","Córrego Feio 流域 咖啡 生产潜力","MinasGerais咖啡遥感-3893","10.25186\u002F.v21i.2516",{"doi":313,"openalex_id":315,"authors":316,"venue":270,"cited_by_count":75,"oa_url":267,"card":329,"direction":80,"ingested_from":82},"W7214888407",[317,320,323,326],{"name":318,"orcid":319},"Jorge Eduardo Ferreira Cunha","https:\u002F\u002Forcid.org\u002F0000-0002-3679-6376",{"name":321,"orcid":322},"Mateus Duarte Segismundo","https:\u002F\u002Forcid.org\u002F0009-0001-1289-406X",{"name":324,"orcid":325},"George Deroco Martins","https:\u002F\u002Forcid.org\u002F0000-0001-9738-7325",{"name":327,"orcid":328},"João Vítor Meza Bravo","https:\u002F\u002Forcid.org\u002F0000-0002-5457-3192",{"tldr":330,"method":331,"finding":332,"direction":80,"opportunity":333},"基于景观指标与NDVI构建模型，对巴西塞拉多咖啡树生产潜力进行分级分类。","层次化景观分析（坡度、水流、植被、坡形）结合NDVI，生成因子T与计算潜力。","生产潜力与土壤和地形因素关联强于植被指数，可划分差异化精准管理区。","可引入多时相遥感与机器学习，动态预测咖啡生产潜力并优化管理区划分。","2026-10-01T23:30:28.485534Z",{"id":336,"title":337,"url":338,"summary":339,"summary_zh":340,"content":9,"source_name":341,"source_url":338,"published_at":342,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":343,"score_detail":344,"sources":346,"tags":348,"primary_topic":351,"secondary_topics":352,"classification":358,"search_phrases":390,"slug":393,"view_count":85,"doi":394,"paper":395,"created_at":426},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":194,"substance":17,"depth":97,"authority":20,"freshness":21,"relevant":22,"comment":345},"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。",[347],{"name":341,"url":338},[27,349,28,29,350],"农业人工智能","土壤碳汇",{"code":112,"slug":113,"name":114,"path":115,"parent_code":116},[353,357],{"code":144,"slug":354,"name":355,"path":356,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":359},{"candidates":360,"agricultural_evidence":47},[361,366,371,375,379,386],{"code":112,"score":362,"supported":47,"title":363,"abstract":364,"tags":365},14,[113],[137,113],[],{"code":144,"score":13,"supported":47,"title":367,"abstract":368,"tags":370},[],[147,148,369],"artificial intelligence",[349],{"code":33,"score":13,"supported":47,"title":372,"abstract":373,"tags":374},[],[29,49,51],[29],{"code":54,"score":21,"supported":47,"title":376,"abstract":377,"tags":378},[],[28,57],[27,28],{"code":380,"score":381,"supported":47,"title":382,"abstract":383,"tags":385},"H4",4,[],[384],"carbon sequestration",[350],{"code":158,"score":152,"supported":47,"title":387,"abstract":388,"tags":389},[],[161],[],[391,392],"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":394,"openalex_id":396,"authors":397,"venue":341,"cited_by_count":75,"oa_url":338,"card":421,"direction":80,"ingested_from":82},"W7213561504",[398,401,404,407,410,413,415,417,419],{"name":399,"orcid":400},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":402,"orcid":403},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":405,"orcid":406},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":408,"orcid":409},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":411,"orcid":412},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":414,"orcid":9},"Almaha Jamal",{"name":416,"orcid":9},"Afra Rashed",{"name":418,"orcid":9},"Hamda Yousif",{"name":420,"orcid":9},"Sarah Sadeq",{"tldr":422,"method":423,"finding":424,"direction":80,"opportunity":425},"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":428,"title":429,"url":430,"summary":431,"summary_zh":432,"content":9,"source_name":433,"source_url":430,"published_at":434,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":435,"score_detail":436,"sources":439,"tags":441,"primary_topic":443,"secondary_topics":444,"classification":445,"search_phrases":461,"slug":464,"view_count":173,"doi":465,"paper":466,"created_at":493},2165,"Multi-grained image-text retrieval in farmland remote sensing via multimodal large language models","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116113","Low-altitude farmland remote sensing (FRS) has become an important tool for precision agricultural monitoring. However, existing FRS methods are mainly designed for segmentation with predefined categories, making them insufficient for semantic understanding and unable to support flexible natural-language queries. To address this limitation, we introduce an artificial intelligence-based task, FRS cross-modal retrieval, which aims to retrieve target farmland regions from large-scale remote sensing images using textual descriptions. To support this task, we construct the first Farmland Remote Sensing Cross-Modal Retrieval (FRS-CMR) dataset, containing 67,806 image-text pairs generated through multimodal large language models and expert refinement. Based on FRS-CMR, we propose a Farmland-oriented Multi-Grained Retrieval (FarmMGR) framework for image-text alignment. FarmMGR jointly exploits global scene semantics and local region-level cues to capture both large-scale landscape patterns and fine-grained farmland characteristics. To further improve semantic alignment, we design Prototype-Based Semantic Alignment (PBSA) to alleviate the soft-positive sample problem, Global Semantics Hierarchical Feature Integration (GSHFI) to adaptively fuse global and local representations, and Instance-Level Semantic Relation Modeling (ISRM) to capture relational dependencies among farmland instances. In addition, Memory-Enhanced Cross-Modal Contrastive Learning (MECCL) enlarges the negative sample pool and improves representation discrimination. Extensive experiments on FRS-CMR show that FarmMGR achieves a mean recall of 28.57%, outperforming existing image-text retrieval methods and demonstrating its effectiveness for language-driven farmland remote sensing retrieval.","低空农田遥感(FRS)已成为精准农业监测的重要工具。然而，现有农田遥感方法主要针对预定义类别的分割任务设计，难以进行语义理解，也无法支持灵活的自然语言查询。为解决这一局限，我们引入了一项基于人工智能的任务——农田遥感跨模态检索，旨在利用文本描述从大规模遥感图像中检索目标农田区域。为支撑该任务，我们构建了首个农田遥感跨模态检索(FRS-CMR)数据集，包含67,806个通过多模态大语言模型生成并经专家精炼的图像-文本对。基于FRS-CMR，我们提出了面向农田的多粒度检索(FarmMGR)框架以实现图像-文本对齐。FarmMGR联合利用全局场景语义和局部区域级线索，以同时捕获大尺度景观格局和细粒度农田特征。为进一步提升语义对齐效果，我们设计了基于原型的语义对齐(PBSA)以缓解软正样本问题，全局语义层次特征融合(GSHFI)以自适应融合全局与局部表示，以及实例级语义关系建模(ISRM)以捕获农田实例间的关系依赖。此外，记忆增强跨模态对比学习(MECCL)扩大了负样本池并提升了表示判别能力。在FRS-CMR上的大量实验表明，FarmMGR取得了28.57%的平均召回率，优于现有图像-文本检索方法，证明了其在语言驱动农田遥感检索中的有效性。","Engineering Applications of Artificial Intelligence","2026-09-10T00:00:00Z",81,{"impact":17,"substance":437,"depth":17,"authority":362,"freshness":196,"relevant":22,"comment":438},22,"构建首个农田遥感跨模态检索数据集并提出多粒度对齐框架，方法新颖、数据规模可观，对语言驱动的精准农业监测有实质推动。",[440],{"name":433,"url":430},[27,349,28,29,442],"多模态大模型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":446},{"candidates":447,"agricultural_evidence":47},[448,452,457],{"code":33,"score":134,"supported":47,"title":449,"abstract":450,"tags":451},[49],[29,49],[29],{"code":54,"score":453,"supported":47,"title":454,"abstract":455,"tags":456},5,[],[28],[27,28],{"code":144,"score":453,"supported":47,"title":458,"abstract":459,"tags":460},[],[369],[349,442],[462,463],"农业人工智能 多模态大模型 智慧农业 精准农业","农业人工智能 多模态大模型","农业人工智能多模态大模型智慧农业精准农业-2165","10.1016\u002Fj.engappai.2026.116113",{"doi":465,"openalex_id":467,"authors":468,"venue":433,"cited_by_count":75,"oa_url":430,"card":488,"direction":80,"ingested_from":82},"W7212180335",[469,471,474,476,479,481,483,485],{"name":470,"orcid":9},"KeJian Yu",{"name":472,"orcid":473},"Wentao Ma","https:\u002F\u002Forcid.org\u002F0000-0002-2781-1693",{"name":475,"orcid":9},"Shichao Jin",{"name":477,"orcid":478},"Lu Liu","https:\u002F\u002Forcid.org\u002F0000-0002-3170-9376",{"name":480,"orcid":9},"Yuwei Wang",{"name":482,"orcid":9},"Weiwei Wang",{"name":484,"orcid":9},"Longzhe Quan",{"name":486,"orcid":487},"Lichuan Gu","https:\u002F\u002Forcid.org\u002F0000-0002-3768-8203",{"tldr":489,"method":490,"finding":491,"direction":80,"opportunity":492},"提出农田遥感图文跨模态检索任务，构建数据集并提出FarmMGR框架实现语言驱动检索。","构建67,806图文对FRS-CMR数据集，用多粒度对齐与记忆增强对比学习。","FarmMGR平均召回率28.57%，优于现有图文检索方法，验证语言驱动农田遥感检索可行。","可探索多模态大模型生成数据的噪声校正、跨区域泛化及细粒度农田语义检索基准。","2026-09-11T23:30:30.005240Z",{"id":495,"title":496,"url":497,"summary":498,"summary_zh":499,"content":9,"source_name":500,"source_url":497,"published_at":501,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":502,"score_detail":503,"sources":505,"tags":507,"primary_topic":510,"secondary_topics":511,"classification":512,"search_phrases":530,"slug":533,"view_count":75,"doi":534,"paper":535,"created_at":554},5571,"Temporal Multi-Scale Spatio-Temporal Network (TMST): Polarization–Temporal Feature Selection and Multi-Scale Spatiotemporal Fusion for Crop Mapping","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202068","Accurate and timely crop mapping supports agricultural resource management, yield prediction, and food security decision-making. In this study, we propose the Temporal Multi-scale Spatio-Temporal Network (TMST), a crop mapping method that integrates multi-source remote sensing time series through polarization–temporal feature selection and multi-scale spatiotemporal fusion. TMST combines Sentinel-1 dual-polarization synthetic aperture radar (SAR), Sentinel-2 multispectral data, and vegetation indices. Its Temporal Feature Importance Explainer (TFIExplainer) selects informative polarization–time features, the Multi-Scale Attention Aggregation (MSAA) module enhances spatial representations, and the Spatio-Temporal Feature Encoder (STFE) models long-range spatial dependencies and phenological dynamics; a composite loss improves minority-class and difficult-pixel recognition. On the Xinxiang dataset, TMST achieved an overall accuracy (OA) of 0.8625 and a mean F1-score (mF1) of 0.8329. Module and data-source ablations suggested benefits of the combined modules and multi-source fusion, while a cropland mask reduced non-cropland misclassification. In direct cross-dataset evaluation, models trained on a self-constructed Austrian dataset and tested on the radar-extended Panoptic Agricultural Satellite Time Series (PASTIS-R) dataset showed that TMST outperformed the evaluated baselines under the specified source–target setting. Annual mapping of Xinxiang for 2019–2023, using year-specific samples and independently trained models, provided map-based estimates of regional cropping structures and spatial patterns. Under the evaluated settings, TMST provides an effective approach to crop classification and annually updated crop mapping.","准确的作物制图有助于农业资源管理、产量预测和粮食安全决策。本研究提出了一种时间多尺度时空网络（Temporal Multi-scale Spatio-Temporal Network，TMST），这是一种通过极化—时间特征选择和多尺度时空融合来整合多源遥感时间序列的作物制图方法。TMST结合了Sentinel-1双极化合成孔径雷达（SAR）、Sentinel-2多光谱数据和植被指数。其时间特征重要性解释器（Temporal Feature Importance Explainer，TFIExplainer）用于选择信息量丰富的极化—时间特征，多尺度注意力聚合（Multi-Scale Attention Aggregation，MSAA）模块增强空间表征，时空特征编码器（Spatio-Temporal Feature Encoder，STFE）建模长程空间依赖关系和物候动态；复合损失函数改善了少数类和困难像元的识别。在新乡数据集上，TMST取得了0.8625的总体精度（OA）和0.8329的平均F1分数（mF1）。模块和数据源消融实验表明，组合模块和多源融合具有优势，而耕地掩膜减少了非耕地误分类。在直接的跨数据集评估中，在自行构建的奥地利数据集上训练并在雷达扩展的Panoptic农业卫星时间序列（PASTIS-R）数据集上测试的模型表明，在指定的源—目标设置下，TMST优于所评估的基线方法。使用逐年样本和独立训练的模型对新乡2019—2023年进行年度制图，提供了基于制图的区域种植结构和空间格局估计。在所评估的设置下，TMST为作物分类和年度更新的作物制图提供了一种有效方法。","Agronomy","2026-10-21T00:00:00Z",73,{"impact":194,"substance":18,"depth":19,"authority":362,"freshness":13,"relevant":22,"comment":504},"提出多源遥感融合的作物制图新方法，方法新颖、实验充分，但属细分技术进展，公共影响有限。",[506],{"name":500,"url":497},[27,508,29,509],"作物制图","SAR",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":513},{"candidates":514,"agricultural_evidence":47},[515,519,526],{"code":33,"score":13,"supported":47,"title":516,"abstract":517,"tags":518},[],[29,49,123],[29],{"code":520,"score":85,"supported":47,"title":521,"abstract":522,"tags":525},"A8",[],[523,524],"粮食安全","food security",[],{"code":54,"score":22,"supported":14,"title":527,"abstract":528,"tags":529},[],[],[27],[531,532],"Sentinel-1 Sentinel-2 作物分类","TMST 多尺度时空网络","Sentinel-1Sentinel-2作物分类-5571","10.3390\u002Fagronomy16202068",{"doi":534,"openalex_id":536,"authors":537,"venue":500,"cited_by_count":75,"oa_url":497,"card":549,"direction":80,"ingested_from":82},"W7223117203",[538,541,543,546],{"name":539,"orcid":540},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":542,"orcid":9},"Wentao Lv",{"name":544,"orcid":545},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":547,"orcid":548},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":550,"method":551,"finding":552,"direction":80,"opportunity":553},"提出TMST网络，融合SAR与多光谱时序数据实现作物制图。","Sentinel-1\u002F2时序、极化-时序特征选择、多尺度注意力与时空编码器。","新乡OA达0.8625，多源融合与模块组合有效，跨数据集优于基线。","可探索极化-时序特征选择在跨区域迁移与少样本作物制图中的泛化能力。","2026-10-22T11:20:39.359881Z"]