[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3020":3,"related-3020":61},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":60},3020,"Spatial variability of soil fertility indicators using GIS for sustainable land management in Banaskantha district western India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44378-026-00320-y","Soil fertility assessment is fundamental for sustainable land management and precision agriculture, particularly in semi-arid regions where soil properties exhibit considerable spatial variability. This study investigated the spatial distribution of major soil fertility indicators across Banaskantha district, Gujarat, India, using a systematic 15 × 15 km grid-based sampling approach integrated with Geographic Information System (GIS) analysis and multivariate statistics. A total of 46 geo-referenced composite soil samples were collected from agricultural fields representing all fourteen talukas and analysed for soil moisture, pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (AvN), available phosphorus (AvP), and available potassium (AvK). GIS-based Inverse Distance Weighting (IDW) interpolation was employed to visualize the regional distribution of soil properties, while Pearson correlation analysis and Principal Component Analysis (PCA) were used to examine relationships among soil fertility indicators and identify the major factors influencing soil variability. The results revealed substantial spatial heterogeneity across the district, with alkaline and saline soils predominantly occurring in the western region, whereas comparatively higher soil moisture and organic carbon were observed in the eastern and northern areas. Available phosphorus was generally deficient throughout the district, while available nitrogen and potassium exhibited pronounced spatial variation associated with parent material, land use, and agricultural management practices. PCA identified salinity-related factors and organic matter–nutrient interactions as the principal contributors to soil fertility variability. The generated spatial distribution maps provide valuable baseline information for identifying nutrient-deficient zones and understanding regional patterns of soil fertility. Although the proposed nutrient management strategies require further validation through field-based agronomic studies, the integration of laboratory soil analysis, GIS, and multivariate statistical techniques provides an effective framework for regional soil fertility assessment and supports informed decision-making for sustainable land management in semi-arid environments.","土壤肥力评估是可持续土地管理和精准农业的基础，尤其是在土壤属性表现出显著空间变异性的半干旱地区。本研究采用基于15 × 15 km网格的系统采样方法，结合地理信息系统（GIS）分析和多元统计方法，调查了印度古吉拉特邦巴纳斯坎塔县主要土壤肥力指标的空间分布。共从代表全部十四个乡的农田中采集了46个地理参照复合土壤样品，并分析了土壤水分、pH、电导率（EC）、有机碳（OC）、有效氮（AvN）、有效磷（AvP）和有效钾（AvK）。采用基于GIS的反距离加权（IDW）插值法可视化土壤属性的区域分布，同时运用Pearson相关分析和主成分分析（PCA）考察土壤肥力指标之间的关系，并识别影响土壤变异性的主要因素。结果表明，该县土壤存在显著的空间异质性，碱性和盐渍化土壤主要分布在西部地区，而东部和北部地区土壤水分和有机碳相对较高。有效磷在整个县域普遍缺乏，而有效氮和有效钾则表现出与母质、土地利用和农业管理措施相关的显著空间变异。PCA识别出盐分相关因子和有机质—养分相互作用是土壤肥力变异性的主要贡献因素。所生成的空间分布图为识别养分缺乏区和理解土壤肥力区域格局提供了有价值的基线信息。尽管所提出的养分管理策略需要通过田间农艺研究进一步验证，但实验室土壤分析、GIS和多元统计技术的整合为区域土壤肥力评估提供了有效框架，并为半干旱环境下可持续土地管理的科学决策提供了支持。",null,"Discover Soil.","2026-09-19T00:00:00Z","论文",10,false,65,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,16,12,9,1,"基于GIS与多元统计的区域土壤肥力空间变异研究，方法规范、数据翔实，对精准农业与可持续土地管理有参考价值，但属区域性案例，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"精准农业","可持续农业","遥感","GIS","土壤肥力",[33,34],"Banaskantha 土壤肥力 GIS","印度 半干旱区 土壤养分","Banaskantha土壤肥力GIS-3020",0,"10.1007\u002Fs44378-026-00320-y",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":52,"direction":58,"ingested_from":59},"W7213587271",[41,44,46,48,50],{"name":42,"orcid":43},"Mukesh P. Chaudhari","https:\u002F\u002Forcid.org\u002F0009-0000-2187-2375",{"name":45,"orcid":9},"Ruchi Nair",{"name":47,"orcid":9},"Pratik Chavda",{"name":49,"orcid":9},"Dharmik Patel",{"name":51,"orcid":9},"Divya Mishra",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"用GIS与多元统计评估印度半干旱区Banaskantha土壤肥力指标的空间变异。","15×15 km网格采样46个样点，结合IDW插值、Pearson相关与PCA分","西部土壤偏碱盐化，东部北部有机碳较高，全区分区有效磷普遍缺乏。","农业遥感与作物表型","可结合遥感植被指数与机器学习提升半干旱区土壤肥力制图精度并验证养分管理策略。","农业人工智能与决策模型","openalex","2026-09-20T23:30:43.716518Z",{"total":62,"page":22,"page_size":62,"items":63},6,[64,97,150,193,241,278],{"id":65,"title":66,"url":67,"summary":68,"summary_zh":9,"content":9,"source_name":69,"source_url":9,"published_at":70,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":71,"score_detail":72,"sources":78,"tags":80,"search_phrases":84,"slug":87,"view_count":36,"doi":9,"paper":88,"created_at":96},3001,"UAV多光谱不同空间分辨率匹配春小麦多性状监测","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1811","天津师范大学张程程等联合天津市农科院农业资源与环境研究所，从原生0.07 m四波段UAV多光谱影像通过像素聚合重采样生成14种空间分辨率（0.07-3.03 m），耦合PROSAIL辐射传输模型与随机森林评估尺度依赖反演性能。研究揭示了叶面积指数（LAI）、叶绿素含量（Cab）和冠层水分含量（Cw）反演精度对空间分辨率的非单调响应，提出物候阶段自适应分辨率策略并开发Heterogeneity-Scale Game Model（HSGM）刻画最优聚合尺度形成机制。","MDPI Agronomy 16(18):1811","2026-09-15T00:00:00Z",74,{"impact":73,"substance":74,"depth":75,"authority":76,"freshness":62,"relevant":22,"comment":77},15,22,18,13,"方法新颖、数据扎实的作物遥感反演研究，对精准农业变量施药与无人机监测有参考价值，但属细分领域学术进展，公共影响有限。",[79],{"name":69,"url":67},[81,27,29,82,83],"智慧农业","作物表型","春小麦",[85,86],"天津师范大学 春小麦 多光谱","UAV 多光谱 空间分辨率","天津师范大学春小麦多光谱-3001",{"doi":9,"openalex_id":9,"authors":89,"venue":9,"cited_by_count":36,"oa_url":9,"card":90,"direction":56,"ingested_from":95},[],{"tldr":91,"method":92,"finding":93,"direction":56,"opportunity":94},"用无人机多光谱重采样14种分辨率，结合PROSAIL与随机森林，研究春小麦多性状反演的空间尺度效应。","UAV四波段多光谱像素聚合重采样，耦合PROSAIL模型与随机森林反演LAI、C","反演精度对空间分辨率呈非单调响应，提出物候自适应分辨率策略与HSGM模型。","可探索不同作物与物候下最优分辨率普适规律，并将尺度自适应策略嵌入实时无人机监测系统。","agent","2026-09-20T00:03:08.168753Z",{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":108,"tags":110,"search_phrases":113,"slug":116,"view_count":36,"doi":117,"paper":118,"created_at":149},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":20,"substance":75,"depth":19,"authority":76,"freshness":17,"relevant":22,"comment":107},"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。",[109],{"name":103,"url":100},[81,111,27,29,112],"农业人工智能","土壤碳汇",[114,115],"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":117,"openalex_id":119,"authors":120,"venue":103,"cited_by_count":36,"oa_url":100,"card":144,"direction":56,"ingested_from":59},"W7213561504",[121,124,127,130,133,136,138,140,142],{"name":122,"orcid":123},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":125,"orcid":126},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":128,"orcid":129},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":131,"orcid":132},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":134,"orcid":135},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":137,"orcid":9},"Almaha Jamal",{"name":139,"orcid":9},"Afra Rashed",{"name":141,"orcid":9},"Hamda Yousif",{"name":143,"orcid":9},"Sarah Sadeq",{"tldr":145,"method":146,"finding":147,"direction":56,"opportunity":148},"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":151,"title":152,"url":153,"summary":154,"summary_zh":155,"content":9,"source_name":156,"source_url":153,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":157,"score_detail":158,"sources":162,"tags":164,"search_phrases":167,"slug":170,"view_count":36,"doi":171,"paper":172,"created_at":192},2942,"Deep learning-driven multisource remote sensing image fusion: Advances, challenges, and future directions","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116319","Multisource remote sensing image fusion has become an important solution to a long-standing limitation in Earth observation: individual sensors rarely provide high spatial detail, rich spectral information, reliable structural sensitivity, and frequent temporal coverage at the same time. This review examines how deep learning and artificial intelligence are being used to integrate multispectral, hyperspectral, panchromatic, optical, and synthetic aperture radar imagery for more reliable interpretation of complex ground scenes. It provides a technical synthesis of convolutional neural networks, autoencoders, generative adversarial networks, transformer architectures, diffusion models, and hybrid model driven approaches, with attention to their fusion mechanisms, reconstruction behavior, computational demand, and suitability for operational use. Applications include land cover mapping, precision agriculture, environmental monitoring, urban analysis, disaster assessment, and defense related interpretation. Rather than treating each fusion task separately, this review connects sensor heterogeneity, spatial and spectral resolution trade offs, radiometric correction, geometric correction, registration, noise reduction, and fusion level design within a single framework. The analysis indicates that convolutional models remain effective for stable local detail recovery, adversarial learning can improve visual sharpness but may introduce spectral distortion, transformer models better capture long range spatial and spectral relationships, and diffusion models offer refined reconstruction at greater computational cost. The review further identifies open challenges involving misregistration, spectral bias, limited labeled data, weak generalization across sensors, high memory requirements, and limited interpretability. Future progress should prioritize sensor aware learning, self supervised training, uncertainty aware evaluation, lightweight deployment, and application oriented benchmarks to improve reliability in operational Earth observation.","多源遥感图像融合已成为解决地球观测领域一个长期局限的重要方案：单一传感器很少能够同时提供高空间细节、丰富光谱信息、可靠的结构敏感性以及频繁的时间覆盖。本文综述了如何利用深度学习和人工智能整合多光谱、高光谱、全色、光学和合成孔径雷达（synthetic aperture radar, SAR）影像，以更可靠地解译复杂地表场景。文章对卷积神经网络、自编码器、生成对抗网络、Transformer架构、扩散模型以及混合模型驱动方法进行了技术综合，重点关注其融合机制、重建行为、计算需求以及业务化适用性。应用领域包括土地覆盖制图、精准农业、环境监测、城市分析、灾害评估和国防相关解译。本文并非将每种融合任务分开处理，而是在一个统一框架内将传感器异质性、空间与光谱分辨率权衡、辐射校正、几何校正、配准、降噪和融合层级设计联系起来。分析表明，卷积模型在稳定的局部细节恢复方面仍然有效，对抗学习可以提升视觉锐度但可能引入光谱失真，Transformer模型能更好地捕捉长程空间与光谱关系，而扩散模型以更高的计算成本提供精细重建。本文进一步指出了涉及配准误差、光谱偏差、标注数据有限、跨传感器泛化能力弱、高内存需求以及可解释性有限等开放挑战。未来的进展应优先关注传感器感知学习、自监督训练、不确定性感知评估、轻量化部署以及面向应用的基准测试，以提高业务化地球观测的可靠性。","Engineering Applications of Artificial Intelligence",82,{"impact":75,"substance":74,"depth":159,"authority":160,"freshness":21,"relevant":22,"comment":161},19,14,"发表于核心期刊的综述，系统梳理深度学习多源遥感融合的方法、应用与挑战，对农业遥感与精准农业有直接参考价值，时效性强，值得进入每日精选。",[163],{"name":156,"url":153},[111,165,27,29,166],"深度学习","多源数据融合",[168,169],"多源遥感 图像融合 深度学习","农业人工智能 多源数据融合 深度学习 精准农业","多源遥感图像融合深度学习-2942","10.1016\u002Fj.engappai.2026.116319",{"doi":171,"openalex_id":173,"authors":174,"venue":156,"cited_by_count":36,"oa_url":153,"card":187,"direction":56,"ingested_from":59},"W7213544281",[175,178,181,184],{"name":176,"orcid":177},"Shahid Karim","https:\u002F\u002Forcid.org\u002F0000-0001-9986-5052",{"name":179,"orcid":180},"Akeel Qadir","https:\u002F\u002Forcid.org\u002F0000-0003-0358-6505",{"name":182,"orcid":183},"Asif Ali Laghari","https:\u002F\u002Forcid.org\u002F0000-0001-5831-5943",{"name":185,"orcid":186},"Irfana Bibi","https:\u002F\u002Forcid.org\u002F0000-0003-2794-504X",{"tldr":188,"method":189,"finding":190,"direction":56,"opportunity":191},"综述深度学习多源遥感图像融合方法、挑战与未来方向。","综述CNN、GAN、Transformer、扩散模型等融合机制与重建行为。","CNN擅局部细节，GAN易谱失真，Transformer长程关系强，扩散模型精度高但算力大。","面向农业的轻量、自监督、不确定性感知融合与基准数据集构建。","2026-09-19T23:30:32.767384Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":200,"score_detail":201,"sources":204,"tags":206,"search_phrases":208,"slug":211,"view_count":36,"doi":212,"paper":213,"created_at":240},2908,"An information-driven air–ground collaborative framework for UAV-based tillage defect identification and re-tillage path optimization","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112447","An information-driven air–ground collaborative framework for UAV-based tillage defect identification and re-tillage path optimization。Computers and Electronics in Agriculture","一种信息驱动的空地协同框架，用于基于无人机耕作缺陷识别与再耕作路径优化。《农业计算机与电子》","Computers and Electronics in Agriculture",79,{"impact":75,"substance":202,"depth":75,"authority":160,"freshness":17,"relevant":22,"comment":203},21,"发表于农业信息领域核心期刊，提出空地协同的无人机耕地缺陷识别与再耕路径优化框架，方法新颖且面向精准农业实际需求，具备较高参考价值。",[205],{"name":199,"url":196},[81,111,207,27,29],"农业无人机",[209,210],"无人机 耕地缺陷 识别","再耕路径 优化","无人机耕地缺陷识别-2908","10.1016\u002Fj.compag.2026.112447",{"doi":212,"openalex_id":214,"authors":215,"venue":199,"cited_by_count":36,"oa_url":9,"card":234,"direction":238,"ingested_from":59},"W7213547466",[216,218,220,222,225,227,229,232],{"name":217,"orcid":9},"Chenshuo Xie",{"name":219,"orcid":9},"Yejun Zhu",{"name":221,"orcid":9},"Dongfang Li",{"name":223,"orcid":224},"Maohua Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-5213-1035",{"name":226,"orcid":9},"Le Yang",{"name":228,"orcid":9},"Yuxuan Wan",{"name":230,"orcid":231},"Weihua Wei","https:\u002F\u002Forcid.org\u002F0000-0001-5333-4707",{"name":233,"orcid":9},"Mingfeng Wang",{"tldr":235,"method":236,"finding":237,"direction":238,"opportunity":239},"提出空地协同框架，用无人机识别耕整缺陷并优化补耕路径。","无人机遥感影像与信息驱动算法，识别缺陷并规划重耕路径。","框架能有效识别耕整缺陷并生成优化补耕路径，提升作业质量。","智慧农业 \u002F 农业物联网","可探索多机协同与实时动态重规划，结合土壤传感器提升缺陷识别精度。","2026-09-19T23:30:02.081036Z",{"id":242,"title":243,"url":244,"summary":245,"summary_zh":246,"content":9,"source_name":247,"source_url":244,"published_at":248,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":36,"score_detail":249,"sources":251,"tags":253,"search_phrases":255,"slug":258,"view_count":36,"doi":259,"paper":260,"created_at":277},2793,"Delineation of groundwater potential zones using analytical hierarchy process and GIS in Bhilangana Block of Tehri Garhwal District, Uttarakhand, India","https:\u002F\u002Fdoi.org\u002F10.33765\u002Fthate.16.4.4","Groundwater is one of the most reliable freshwater resources globally, but overexploitation has led to declining water tables, deteriorating water quality, and increased water scarcity. These problems have raised serious questions about how sustainable groundwater management can be achieved. In recent times, geospatial techniques and the Analytical Hierarchy Process (AHP) have been increasingly applied to assess groundwater availability. This research aims to delineate groundwater potential zones in the Bhilangana Block using the above-mentioned methods. Initially, remote sensing data were employed to generate thematic layers, including geomorphology, geology, slope, elevation, drainage density, lineament density, soil, rainfall, and land use\u002Fland cover. Subsequently, these layers were integrated using a multi-criteria evaluation approach, and weights were assigned using AHP-based weighted overlay analysis. The resulting groundwater potential zones were classified into five categories: very low and low (over 45 %), moderate (35.05 %), high (16.66 %), and very high (3.81 %). The findings of this research offer insights into groundwater occurrence in the study area, which could be helpful for sustainable groundwater management and long-term planning of water resources.","地下水是全球最可靠的淡水资源之一，但过度开采已导致地下水位下降、水质恶化以及水资源短缺加剧。这些问题引发了关于如何实现可持续地下水管理的严肃思考。近年来，地理空间技术和层次分析法（Analytical Hierarchy Process, AHP）越来越多地被应用于地下水可获得性评估。本研究旨在利用上述方法圈定Bhilangana区块的地下水潜力区。首先，利用遥感数据生成专题图层，包括地貌、地质、坡度、高程、河网密度、线性构造密度、土壤、降雨以及土地利用\u002F土地覆盖。随后，采用多准则评价方法整合这些图层，并利用基于AHP的加权叠加分析分配权重。所得到的地下水潜力区被划分为五类：极低和低（超过45 %）、中等（35.05 %）、高（16.66 %）以及极高（3.81 %）。本研究结果有助于认识研究区地下水的赋存情况，可为可持续地下水管理和水资源长期规划提供参考。","The holistic approach to environment","2026-09-16T00:00:00Z",{"impact":36,"substance":36,"depth":36,"authority":36,"freshness":36,"relevant":36,"comment":250},"该论文研究印度北阿坎德邦地下水潜力区划，属水文地质与遥感应用领域，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[252],{"name":247,"url":244},[29,254,30],"水资源管理",[256,257],"水资源管理 遥感 GIS","水资源管理 遥感","水资源管理遥感GIS-2793","10.33765\u002Fthate.16.4.4",{"doi":259,"openalex_id":261,"authors":262,"venue":247,"cited_by_count":36,"oa_url":271,"card":272,"direction":56,"ingested_from":59},"W7213317885",[263,266,269],{"name":264,"orcid":265},"Siddharth Rana","https:\u002F\u002Forcid.org\u002F0009-0006-5304-3233",{"name":267,"orcid":268},"Gaurav Gaurav","https:\u002F\u002Forcid.org\u002F0000-0001-8857-5797",{"name":270,"orcid":9},"Mohan Singh Panwar","https:\u002F\u002Fcasopis.hrcpo.com\u002Fwp-content\u002Fuploads\u002F2026\u002F09\u002FRana-et-al_HAE_16_2026_4.pdf",{"tldr":273,"method":274,"finding":275,"direction":56,"opportunity":276},"用AHP与GIS遥感数据划分印度Bhilangana区块地下水潜力区。","遥感专题图层+AHP加权叠加多准则评价。","潜力区以低和极低为主（超45%），极高仅3.81%。","可结合灌溉需求与作物分布，将地下水潜力图用于农业用水优化与井位选址。","2026-09-17T23:30:36.203631Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":9,"source_name":284,"source_url":281,"published_at":248,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":285,"score_detail":286,"sources":289,"tags":291,"search_phrases":295,"slug":298,"view_count":36,"doi":299,"paper":300,"created_at":321},2679,"Space Science and Technology for Sustainable Development and Climate Resilience in Africa: A Review of Emerging Applications and Challenges","https:\u002F\u002Fdoi.org\u002F10.62292\u002Fnjp.v35(s).2026.731","This study undertakes a systematic review to examine the role of space science and technology in addressing Africa’s pressing challenges in agriculture, water management, disaster resilience, urbanization, and digital inclusion. Evaluating how Earth Observation, satellite navigation, and communication technologies contribute to achieving the Sustainable Development Goals (SDGs) being its main objective. The review draws on secondary sources, including peer‑reviewed journals, space agency reports, and global datasets, with content analysis employed to identify thematic applications and emerging trends. The analysis highlights major themes: precision agriculture, groundwater mapping, urban growth monitoring, disaster early warning systems, climate adaptation, natural resource conservation, and digital inclusion. Case studies from Kenya, Nigeria, Ethiopia, Rwanda, and Mozambique demonstrate measurable benefits, including strengthened food security, improved water resource management, guided infrastructure planning, and enhanced resilience to climate‑induced hazards. Institutional initiatives by the National Space Research and Development Agency (NASRDA) in Nigeria and the African Space Agency further illustrate Africa’s growing commitment to harnessing space technologies for socio‑economic transformation. Despite these advances, more still needs to be done to meet the international standard. Persistent barriers affecting Africa include limited funding, dependence on external datasets, and shortages of technical expertise, which constrain the scalability and sustainability of interventions. Future directions should prioritize investment in indigenous satellite missions, expansion of STEM education, and regional cooperation to build capacity and ensure data sovereignty. Overall, the review underscores that space science offers Africa a strategic pathway to resilience, innovation, and inclusive growth, positioning the continent to accelerate progress toward the SDGs and secure a sustainable future.","本研究开展系统性综述，考察空间科学技术在应对非洲农业、水资源管理、灾害韧性、城市化及数字包容等紧迫挑战中的作用。其主要目标是评估地球观测、卫星导航与通信技术如何助力实现可持续发展目标（SDGs）。本综述基于二手资料，包括同行评审期刊、航天机构报告及全球数据集，并采用内容分析法识别主题性应用与新兴趋势。分析凸显了若干主要主题：精准农业、地下水测绘、城市扩张监测、灾害预警系统、气候适应、自然资源保护及数字包容。来自肯尼亚、尼日利亚、埃塞俄比亚、卢旺达和莫桑比克的案例研究展示了可衡量的效益，包括增强粮食安全、改善水资源管理、指导基础设施规划以及提升对气候诱发灾害的韧性。尼日利亚国家空间研究与发展局（NASRDA）及非洲航天局的机构性举措进一步表明，非洲日益致力于利用空间技术推动社会经济转型。尽管取得了这些进展，但要达到国际标准仍需付出更多努力。影响非洲的持续障碍包括资金有限、依赖外部数据集以及技术专长短缺，这些因素制约了干预措施的可扩展性与可持续性。未来方向应优先投资于本土卫星任务、扩大STEM教育以及区域合作，以建设能力并确保数据主权。总体而言，本综述强调，空间科学为非洲提供了一条通往韧性、创新与包容性增长的战略路径，使该大陆能够加速实现可持续发展目标的进程并确保可持续的未来。","Nigerian Journal of Physics",78,{"impact":75,"substance":18,"depth":287,"authority":76,"freshness":13,"relevant":22,"comment":288},17,"系统综述空间科技在非洲农业、水资源与灾害韧性中的应用与瓶颈，含多国案例与机构实践，对农业遥感与数字乡村议题有参考价值，但属域外经验、非国内政策或产业突破。",[290],{"name":284,"url":281},[292,27,293,29,294],"数字乡村","非洲农业","气候韧性",[296,297],"数字乡村 气候韧性 精准农业 非洲农业","数字乡村 气候韧性","数字乡村气候韧性精准农业非洲农业-2679","10.62292\u002Fnjp.v35(s).2026.731",{"doi":299,"openalex_id":301,"authors":302,"venue":284,"cited_by_count":36,"oa_url":281,"card":315,"direction":320,"ingested_from":59},"W7213235414",[303,305,307,309,311,313],{"name":304,"orcid":9},"Rufus Oladosu Olakunle",{"name":306,"orcid":9},"Abdulrahman Adegbite",{"name":308,"orcid":9},"Harry Obimba",{"name":310,"orcid":9},"Tosin Awotoye",{"name":312,"orcid":9},"Linda Abegunde",{"name":314,"orcid":9},"Jamiu Taiwo Aileru",{"tldr":316,"method":317,"finding":318,"direction":56,"opportunity":319},"系统综述空间科技在非洲农业、水资源、减灾等领域应用及挑战。","系统综述二手文献与机构报告，内容分析提炼主题与案例。","空间科技显著提升非洲粮食安全与水管理，但受资金、数据依赖和人才短缺制约。","可研究非洲本土卫星数据主权与低成本遥感在精准农业中的适配机制。","数字乡村与农业信息化","2026-09-16T23:30:36.955820Z"]