[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1209":3,"related-1209":106},{"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":30,"secondary_topics":36,"classification":37,"search_phrases":72,"slug":75,"view_count":63,"doi":76,"paper":77,"created_at":105},1209,"Integrating soil and canopy sensing to map and relate variability in tart cherry orchards","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10436-4","Abstract Purpose Evaluate how soil and canopy sensing can map within-block variability in tart cherry orchards and identify indicators robust enough for repeatable management decisions. Methods Soil apparent electrical conductivity (ECa) was mapped in spring 2022 across four commercial tart cherry blocks (8.5–10.5 ha; approximately 3,500 trees per block), followed by canopy sensing in 2023–2024. Canopy structure was measured using unmanned aerial vehicle (UAV) photogrammetry and mobile terrestrial laser scanning (MTLS) using light detection and ranging (LiDAR), and canopy density using mobile ceptometry. Spatial layers were aligned to per-tree grid cells. An August 2025 campaign compared UAV- and LiDAR-derived tree height with ground-truthed height. Results Soil-to-canopy relationships were weak to moderate but consistent within blocks ( r = 0.10–0.40), with strength and direction varying by site conditions. Canopy density was more strongly associated with UAV-derived volume than height. UAV-derived 90th-percentile height best predicted ground-truthed height ( R ² = 0.89; RMSE = 0.34 m), whereas LiDAR showed a weaker relationship and greater error ( R ² = 0.70; RMSE = 0.52 m). Cross-sensor agreement was moderate to strong ( r = 0.41–0.65). Per-tree rankings were stable between years for UAV height and volume. Conclusion Whole-block sensing revealed persistent spatial patterns that could support management-zone delineation. UAV photogrammetry provided accurate canopy metrics, MTLS offered measurements suited to routine orchard operations, ceptometry added seasonal canopy-density information, and ECa provided soil context. Occasional ECa mapping combined with strategically timed UAV surveys and other sensors as needed could reduce redundant sensing while supporting fertilizer evaluation, pruning, and labor allocation.",null,"Precision Agriculture","2026-08-29T00:00:00Z","论文",10,false,70,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},15,22,18,12,3,1,"研究整合土壤与冠层传感技术，为果园精准管理提供新方法，数据详实，结论可靠。",[24],{"name":9,"url":6},[26,27,28,29],"智慧农业","精准农业","遥感","果园管理",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,49,55,61,67],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":48},11,true,[32],[32],[],{"code":50,"score":20,"supported":45,"title":51,"abstract":52,"tags":54},"B2",[],[53],"orchard",[],{"code":56,"score":20,"supported":45,"title":57,"abstract":58,"tags":60},"F3",[],[59],"fertilizer",[],{"code":62,"score":63,"supported":13,"title":64,"abstract":65,"tags":66},"I1",2,[],[],[26,27],{"code":68,"score":21,"supported":13,"title":69,"abstract":70,"tags":71},"I3",[],[],[28],[73,74],"智慧农业 果园管理 精准农业 遥感","智慧农业 果园管理","智慧农业果园管理精准农业遥感-1209","10.1007\u002Fs11119-026-10436-4",{"doi":76,"openalex_id":78,"authors":79,"venue":9,"cited_by_count":96,"oa_url":97,"card":98,"direction":102,"ingested_from":104},"W7204650341",[80,82,85,88,91,93],{"name":81,"orcid":8},"Kurt Wedegaertner",{"name":83,"orcid":84},"Brent Black","https:\u002F\u002Forcid.org\u002F0000-0003-0394-9206",{"name":86,"orcid":87},"Anderson Luiz dos Santos Safre","https:\u002F\u002Forcid.org\u002F0000-0001-8334-5976",{"name":89,"orcid":90},"Alfonso F. Torres‐Rua","https:\u002F\u002Forcid.org\u002F0000-0002-2238-9550",{"name":92,"orcid":8},"Grant Cardon",{"name":94,"orcid":95},"Matt Yost","https:\u002F\u002Forcid.org\u002F0000-0001-5012-8481",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs11119-026-10436-4.pdf",{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"整合土壤与冠层传感技术，绘制酸樱桃园地块内变异图，评估其用于管理决策的可靠性。","结合土壤电导率、无人机摄影测量、LiDAR和冠层密度计，按树网格分析空间关系。","土壤-冠层关系弱至中等但稳定，无人机高度预测精度高，冠层密度与体积相关性强。","智慧农业 \u002F 农业物联网","可探索多传感器数据融合与机器学习，优化果园管理分区和精准施肥决策。","openalex","2026-09-01T04:03:03.646173Z",{"total":107,"page":21,"page_size":107,"items":108},6,[109,210,288,351,442,492],{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":8,"source_name":115,"source_url":112,"published_at":116,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":117,"score_detail":118,"sources":125,"tags":127,"primary_topic":130,"secondary_topics":135,"classification":142,"search_phrases":194,"slug":197,"view_count":63,"doi":8,"paper":198,"created_at":209},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":119,"substance":120,"depth":121,"authority":122,"freshness":123,"relevant":21,"comment":124},16,20,17,13,8,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[126],{"name":115,"url":112},[26,27,28,128,129],"盐碱地治理","土壤监测",{"code":68,"slug":131,"name":132,"path":133,"parent_code":134},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[136,141],{"code":137,"slug":138,"name":139,"path":140,"parent_code":134},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":143},{"candidates":144,"agricultural_evidence":45},[145,151,159,164,169,176,182,188],{"code":68,"score":18,"supported":45,"title":146,"abstract":148,"tags":150},[147],"remote sensing",[28,147,149],"multispectral",[28],{"code":137,"score":121,"supported":45,"title":152,"abstract":154,"tags":158},[153],"GIS",[155,156,153,157],"地理信息","空间分析","geographic information",[],{"code":31,"score":16,"supported":45,"title":160,"abstract":161,"tags":163},[32],[162,32],"土壤",[128],{"code":62,"score":12,"supported":45,"title":165,"abstract":167,"tags":168},[166],"precision agriculture",[],[26,27],{"code":170,"score":107,"supported":45,"title":171,"abstract":172,"tags":175},"I2",[],[173,174],"机器学习","machine learning",[],{"code":177,"score":20,"supported":45,"title":178,"abstract":179,"tags":181},"H3",[],[180],"灌溉",[],{"code":183,"score":20,"supported":45,"title":184,"abstract":185,"tags":187},"H7",[],[186],"干旱",[],{"code":189,"score":20,"supported":45,"title":190,"abstract":191,"tags":193},"I7",[],[192],"methodological framework",[],[195,196],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":8,"openalex_id":199,"authors":200,"venue":115,"cited_by_count":96,"oa_url":112,"card":203,"direction":207,"ingested_from":104},"W7221843179",[201],{"name":202,"orcid":8},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":204,"method":205,"finding":206,"direction":207,"opportunity":208},"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。","农业遥感与作物表型","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":211,"title":212,"url":213,"summary":214,"summary_zh":215,"content":8,"source_name":216,"source_url":213,"published_at":116,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":217,"score_detail":218,"sources":222,"tags":224,"primary_topic":227,"secondary_topics":228,"classification":230,"search_phrases":270,"slug":273,"view_count":21,"doi":274,"paper":275,"created_at":287},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":19,"substance":219,"depth":121,"authority":122,"freshness":220,"relevant":21,"comment":221},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。",[223],{"name":216,"url":213},[26,27,28,225,226],"土壤健康","覆盖作物",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[229],{"code":68,"slug":131,"name":132,"path":133,"parent_code":134},{"status":38,"locked":13,"version":39,"method":40,"evidence":231},{"candidates":232,"agricultural_evidence":45},[233,237,241,245,252,259,265],{"code":31,"score":16,"supported":45,"title":234,"abstract":235,"tags":236},[32],[162,32],[225],{"code":68,"score":19,"supported":45,"title":238,"abstract":239,"tags":240},[147],[28],[28],{"code":62,"score":123,"supported":45,"title":242,"abstract":243,"tags":244},[],[27,166],[26,27],{"code":246,"score":107,"supported":45,"title":247,"abstract":248,"tags":251},"A4",[],[249,250],"油菜","rapeseed",[],{"code":253,"score":107,"supported":45,"title":254,"abstract":255,"tags":258},"H8",[],[256,257],"可持续农业","sustainable agriculture",[],{"code":260,"score":20,"supported":45,"title":261,"abstract":262,"tags":264},"B1",[],[263],"萝卜",[],{"code":266,"score":21,"supported":13,"title":267,"abstract":268,"tags":269},"A7",[],[],[226],[271,272],"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":274,"openalex_id":276,"authors":277,"venue":216,"cited_by_count":96,"oa_url":8,"card":282,"direction":207,"ingested_from":104},"W7221669942",[278,280],{"name":279,"orcid":8},"Ali Mirzakhani Nafchi",{"name":281,"orcid":8},"Salman Mirzaee",{"tldr":283,"method":284,"finding":285,"direction":207,"opportunity":286},"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":289,"title":290,"url":291,"summary":292,"summary_zh":8,"content":8,"source_name":293,"source_url":8,"published_at":294,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":295,"score_detail":296,"sources":299,"tags":301,"primary_topic":304,"secondary_topics":308,"classification":310,"search_phrases":338,"slug":341,"view_count":63,"doi":8,"paper":342,"created_at":350},3121,"Optimization of Farmland Management Zoning in the Black Soil Region: A Climate Adaptability Assessment Considering Crop Growth Response and Topographic Characteristics（黑土区农田管理分区优化：考虑作物生长响应与地形特征的气候适应性评估）","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3260","吉林农业大学 Han Yongqi 等联合中科院东北地理与农业生态研究所、东北农业大学在《Remote Sensing》18(18): 3260 发表论文（2026-09-21 发表）。针对精准农业管理分区对单日期影像依赖难以捕捉年际作物环境变化问题，研究评估 29 个特征组合（融合 Sentinel-2 多光谱、PCA、NDVI 和 DEM 数据）在黑土区友谊农场干旱、湿润和融合场景下的气候适应性。实施异构空间注意力网络（HSAN）和 K-means 聚类，以变异系数（CV）评估稳定性与适应性。结果显示 HSAN 在多源融合下优于 K-means，CV 分别为 11.303-14.774% 与 14.823-16.011%；多期 NDVI 数据是主导因素，相对 CV 减少 34.850-53.701%；DEM 贡献有限；PCA 增强稳定性；多期融合在极端气候年份提升分区生态一致性与适用性。","MDPI Remote Sensing","2026-09-21T00:00:00Z",79,{"impact":16,"substance":17,"depth":18,"authority":297,"freshness":12,"relevant":21,"comment":298},14,"黑土区精准农业管理分区研究，方法新颖、数据扎实，对农业遥感应用有参考价值。",[300],{"name":293,"url":291},[26,27,302,28,303],"黑土区","管理分区",{"code":183,"slug":305,"name":306,"path":307,"parent_code":35},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[309],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":311},{"candidates":312,"agricultural_evidence":45},[313,318,322,327,332],{"code":183,"score":297,"supported":45,"title":314,"abstract":316,"tags":317},[315],"气候适应",[315,186],[],{"code":31,"score":123,"supported":45,"title":319,"abstract":320,"tags":321},[32],[],[],{"code":62,"score":323,"supported":45,"title":324,"abstract":325,"tags":326},5,[],[27],[26,27],{"code":68,"score":328,"supported":45,"title":329,"abstract":330,"tags":331},4,[],[147],[28],{"code":333,"score":20,"supported":45,"title":334,"abstract":335,"tags":337},"H6",[],[336],"农业生态",[],[339,340],"黑土区 管理分区 遥感","Sentinel-2 黑土区 气候适应性","黑土区管理分区遥感-3121",{"doi":8,"openalex_id":8,"authors":343,"venue":8,"cited_by_count":96,"oa_url":8,"card":344,"direction":207,"ingested_from":349},[],{"tldr":345,"method":346,"finding":347,"direction":207,"opportunity":348},"评估黑土区多源遥感特征组合在干旱湿润场景下的农田管理分区气候适应性。","融合Sentinel-2多光谱、NDVI、PCA与DEM，用HSAN和K-mea","HSAN优于K-means，多期NDVI主导稳定性提升，DEM贡献有限，PCA增强稳定性。","可探索多期时序特征与深度聚类在极端气候下的跨区域迁移及分区决策落地。","agent","2026-09-22T00:05:38.189091Z",{"id":352,"title":353,"url":354,"summary":355,"summary_zh":356,"content":8,"source_name":357,"source_url":354,"published_at":358,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":359,"score_detail":360,"sources":362,"tags":364,"primary_topic":367,"secondary_topics":368,"classification":374,"search_phrases":405,"slug":408,"view_count":107,"doi":409,"paper":410,"created_at":441},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":19,"substance":18,"depth":119,"authority":122,"freshness":123,"relevant":21,"comment":361},"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。",[363],{"name":357,"url":354},[26,365,27,28,366],"农业人工智能","土壤碳汇",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[369,373],{"code":170,"slug":370,"name":371,"path":372,"parent_code":134},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":68,"slug":131,"name":132,"path":133,"parent_code":134},{"status":38,"locked":13,"version":39,"method":40,"evidence":375},{"candidates":376,"agricultural_evidence":45},[377,381,386,391,395,401],{"code":31,"score":297,"supported":45,"title":378,"abstract":379,"tags":380},[32],[162,32],[],{"code":170,"score":12,"supported":45,"title":382,"abstract":383,"tags":385},[],[173,174,384],"artificial intelligence",[365],{"code":68,"score":12,"supported":45,"title":387,"abstract":388,"tags":390},[],[28,147,389],"hyperspectral",[28],{"code":62,"score":123,"supported":45,"title":392,"abstract":393,"tags":394},[],[27,166],[26,27],{"code":396,"score":328,"supported":45,"title":397,"abstract":398,"tags":400},"H4",[],[399],"carbon sequestration",[366],{"code":183,"score":20,"supported":45,"title":402,"abstract":403,"tags":404},[],[186],[],[406,407],"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":409,"openalex_id":411,"authors":412,"venue":357,"cited_by_count":96,"oa_url":354,"card":436,"direction":207,"ingested_from":104},"W7213561504",[413,416,419,422,425,428,430,432,434],{"name":414,"orcid":415},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":417,"orcid":418},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":420,"orcid":421},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":423,"orcid":424},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":426,"orcid":427},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":429,"orcid":8},"Almaha Jamal",{"name":431,"orcid":8},"Afra Rashed",{"name":433,"orcid":8},"Hamda Yousif",{"name":435,"orcid":8},"Sarah Sadeq",{"tldr":437,"method":438,"finding":439,"direction":207,"opportunity":440},"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":443,"title":444,"url":445,"summary":446,"summary_zh":8,"content":8,"source_name":447,"source_url":445,"published_at":448,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":449,"score_detail":450,"sources":452,"tags":454,"primary_topic":456,"secondary_topics":457,"classification":458,"search_phrases":474,"slug":477,"view_count":63,"doi":478,"paper":479,"created_at":491},2300,"Smart Agriculture Optimization in Off-Grid Terrains via Satellite- Coupled Soil Sensing Framework","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10720368\u002Fv1","Smart Agriculture Optimization in Off-Grid Terrains via Satellite- Coupled Soil Sensing Framework。Research Square","Research Square","2026-09-11T00:00:00Z",57,{"impact":19,"substance":16,"depth":297,"authority":123,"freshness":123,"relevant":21,"comment":451},"预印本论文提出卫星耦合土壤感知框架，面向离网地形智慧农业优化，方法有创新但尚缺同行评议，属细分领域进展。",[453],{"name":447,"url":445},[26,27,28,129,455],"卫星遥感",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":459},{"candidates":460,"agricultural_evidence":45},[461,466,470],{"code":62,"score":122,"supported":45,"title":462,"abstract":464,"tags":465},[463],"smart agriculture",[463],[26,27],{"code":31,"score":44,"supported":45,"title":467,"abstract":468,"tags":469},[32],[32],[],{"code":68,"score":63,"supported":13,"title":471,"abstract":472,"tags":473},[],[],[455,28],[475,476],"卫星遥感 土壤监测 智慧农业 精准农业","卫星遥感 土壤监测","卫星遥感土壤监测智慧农业精准农业-2300","10.21203\u002Frs.3.rs-10720368\u002Fv1",{"doi":478,"openalex_id":480,"authors":481,"venue":447,"cited_by_count":96,"oa_url":445,"card":8,"direction":102,"ingested_from":104},"W7212267038",[482,484,486,489],{"name":483,"orcid":8},"Masood Ahmad",{"name":485,"orcid":8},"Shahid Kamal",{"name":487,"orcid":488},"Fasee Ullah","https:\u002F\u002Forcid.org\u002F0000-0001-6167-5253",{"name":490,"orcid":8},"Ishtiaq Wahid","2026-09-13T23:30:09.626390Z",{"id":493,"title":494,"url":495,"summary":496,"summary_zh":8,"content":8,"source_name":9,"source_url":495,"published_at":497,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":498,"sources":500,"tags":502,"primary_topic":505,"secondary_topics":510,"classification":512,"search_phrases":531,"slug":534,"view_count":63,"doi":535,"paper":536,"created_at":547},1211,"A map-based decision-support framework for variable-depth seeding of fodder maize integrating proximal soil sensing and multi-temporal satellite NDVI data","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10437-3","A map-based decision-support framework for variable-depth seeding of fodder maize integrating proximal soil sensing and multi-temporal satellite NDVI data。Precision Agriculture","2026-08-25T00:00:00Z",{"impact":16,"substance":120,"depth":18,"authority":16,"freshness":63,"relevant":21,"comment":499},"论文提出结合土壤传感与卫星NDVI的变量播种决策框架，方法新颖，数据可靠，但时效性较低。",[501],{"name":9,"url":495},[26,503,27,28,504],"玉米","土壤传感",{"code":506,"slug":507,"name":503,"path":508,"parent_code":509},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[511],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":513},{"candidates":514,"agricultural_evidence":45},[515,519,523,527],{"code":506,"score":19,"supported":45,"title":516,"abstract":517,"tags":518},[507],[507],[503],{"code":31,"score":44,"supported":45,"title":520,"abstract":521,"tags":522},[32],[32],[],{"code":62,"score":323,"supported":45,"title":524,"abstract":525,"tags":526},[],[166],[26,27],{"code":68,"score":21,"supported":13,"title":528,"abstract":529,"tags":530},[],[],[28],[532,533],"土壤传感 智慧农业 精准农业 玉米","土壤传感 智慧农业","土壤传感智慧农业精准农业玉米-1211","10.1007\u002Fs11119-026-10437-3",{"doi":535,"openalex_id":537,"authors":538,"venue":9,"cited_by_count":96,"oa_url":8,"card":8,"direction":8,"ingested_from":104},"W7204189193",[539,541,544],{"name":540,"orcid":8},"Jialu Sun",{"name":542,"orcid":543},"Manuel Vázquez-Arellano","https:\u002F\u002Forcid.org\u002F0000-0001-7820-5143",{"name":545,"orcid":546},"Abdul Mounem Mouazen","https:\u002F\u002Forcid.org\u002F0000-0002-0354-0067","2026-09-01T04:03:03.736293Z"]