[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2324":3,"related-2324":109},{"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":74,"slug":77,"view_count":78,"doi":79,"paper":80,"created_at":108},2324,"Inferring groundwater overdraft in data-scarce arid agro-ecosystems: a semi-empirical remote sensing framework applied to the Elfeija watershed, Morocco","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffeart.2026.1914670","Introduction Small-scale irrigated agriculture in arid regions relies heavily on unmetered groundwater, creating an “invisible pumping” threat to aquifer sustainability. Monitoring these diffuse withdrawals remains a critical challenge for water governance. This study proposes a synergistic multi-sensor remote sensing workflow to infer groundwater abstraction and assess its impact on local water stress using a semi-empirical thermal-phenological model. Methods We leveraged high-resolution vegetation phenology (NDVI) from Sentinel-2 and thermal data (LST) from Landsat 8\u002F9 over the Elfeija watershed, Morocco (2020–2024). By isolating the thermal-phenological anomaly (ΔLST) between irrigated plots and natural reference areas, we translated the surface cooling effect into evapotranspiration fluxes and pumping volumes. To ensure physical plausibility and avoid circularity, the approach was constrained by independent bottom-up agronomic benchmarks alongside a multi-tier cross-comparison against global models (MOD16, ERA5-Land, GLEAM), and a rigorous Monte Carlo uncertainty propagation integrating both parametric and recharge uncertainties. Results The analysis reveals a distinct seasonal signature of irrigation. Probabilistic modeling indicates that the inferred annual pumping for 2022 (central estimates: 5.85–6.79 Mm 3 ) strongly suggests a state of severe overdraft, with a high probability (P > 80%) of exceeding the estimated renewable aquifer recharge (4.70 Mm 3 ). Furthermore, the framework successfully disentangled climatic triggers from anthropogenic forcing, capturing an anomalous, water-intensive late-season agricultural cycle in 2024. Discussion While the lack of in-situ piezometric data limits absolute ground-truthing, the multi-source convergence (global models and local infrastructure data) robustly brackets the overdraft scenario. The proposed semi-empirical inference workflow provides a scalable, cost-effective framework for data-scarce regions, offering river basin agencies a proactive tool to identify over-extraction hotspots and negotiate sustainable water quotas.","引言 干旱地区的小规模灌溉农业严重依赖未计量的地下水，对含水层可持续性构成了“隐形抽水”威胁。监测这些分散取水仍是水治理面临的关键挑战。本研究提出一种多传感器协同遥感工作流程，利用半经验热-物候模型推断地下水开采量并评估其对局部水资源压力的影响。方法 我们利用Sentinel-2的高分辨率植被物候（NDVI）和Landsat 8\u002F9的热数据（LST），覆盖摩洛哥Elfeija流域（2020—2024年）。通过分离灌溉地块与自然参照区之间的热-物候异常（ΔLST），我们将地表冷却效应转化为蒸散发通量和抽水量。为确保物理合理性和避免循环论证，该方法受到独立自下而上的农学基准约束，并与全球模型（MOD16、ERA5-Land、GLEAM）进行多层级交叉比较，同时通过严格的蒙特卡洛不确定性传播整合参数不确定性和补给不确定性。结果 分析揭示了灌溉明显的季节性特征。概率建模表明，2022年推断的年抽水量（中心估计值：5.85—6.79 Mm³）强烈提示存在严重超采状态，超过估计可再生含水层补给量（4.70 Mm³）的概率较高（P > 80%）。此外，该框架成功地将气候触发因素与人为强迫区分开来，捕捉到2024年异常的、高耗水的晚季农业周期。讨论 尽管缺乏原位测压数据限制了绝对的地面验证，但多源汇聚（全球模型和本地基础设施数据）稳健地界定了超采情景。所提出的半经验推断工作流程为数据稀缺地区提供了可扩展、成本效益高的框架，为流域机构提供了主动识别过度开采热点并协商可持续用水配额的工具。",null,"Frontiers in Earth Science","2026-09-11T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"提出多源遥感半经验框架推断数据稀缺区地下水超采，方法新颖、结论可靠，对农业水资源治理有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"水资源管理","遥感监测","智慧灌溉","地下水超采","干旱农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,58,67],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":57},15,true,[54],"remote sensing",[56,54],"遥感",[27],{"code":39,"score":59,"supported":52,"title":60,"abstract":61,"tags":66},11,[],[62,63,64,40,65],"灌溉","水资源","蒸散发","evapotranspiration",[28,26],{"code":68,"score":69,"supported":52,"title":70,"abstract":71,"tags":73},"H7",3,[],[72],"干旱",[],[75,76],"地下水超采 水资源管理 干旱农业 智慧灌溉","地下水超采 水资源管理","地下水超采水资源管理干旱农业智慧灌溉-2324",0,"10.3389\u002Ffeart.2026.1914670",{"doi":79,"openalex_id":81,"authors":82,"venue":10,"cited_by_count":78,"oa_url":6,"card":101,"direction":105,"ingested_from":107},"W7212247745",[83,86,89,92,95,98],{"name":84,"orcid":85},"Rachid Amiha","https:\u002F\u002Forcid.org\u002F0000-0002-9863-6685",{"name":87,"orcid":88},"Belkacem Kabbachi","https:\u002F\u002Forcid.org\u002F0000-0003-3802-1256",{"name":90,"orcid":91},"Mohamed Ait Haddou","https:\u002F\u002Forcid.org\u002F0000-0002-0587-1230",{"name":93,"orcid":94},"Youssef Bouchriti","https:\u002F\u002Forcid.org\u002F0000-0002-8566-1451",{"name":96,"orcid":97},"Hicham Gougueni","https:\u002F\u002Forcid.org\u002F0000-0001-5340-7566",{"name":99,"orcid":100},"Mustapha Ikirri","https:\u002F\u002Forcid.org\u002F0000-0002-9971-6249",{"tldr":102,"method":103,"finding":104,"direction":105,"opportunity":106},"提出半经验遥感框架，在摩洛哥缺水灌区推断地下水超采量。","Sentinel-2 NDVI与Landsat 8\u002F9 LST构建热-物候异常，","2022年抽水量5.85–6.79 Mm³，超补给量概率超80%，呈严重超采。","农业遥感与作物表型","可迁移至其他数据稀缺干旱区，融合多源遥感与用水配额决策支持。","openalex","2026-09-13T23:30:23.143553Z",{"total":110,"page":21,"page_size":110,"items":111},6,[112,193,270,342,406,500],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":9,"source_name":118,"source_url":115,"published_at":119,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":125,"tags":127,"primary_topic":131,"secondary_topics":135,"classification":138,"search_phrases":169,"slug":172,"view_count":21,"doi":173,"paper":174,"created_at":192},4030,"Enhancing Drought Monitoring Using Integrated Remote Sensing and Ground-Based Indices","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jastp.2026.106987","ABSTRACT Accurate and timely drought monitoring is essential for sustainable water resource management, particularly in semi-arid regions where meteorological observations are sparse and spatially heterogeneous. This study presents an integrated evaluation of drought conditions by applying established individual and composite drought indices within a unified multi-source framework that combines ground-based meteorological data with satellite-derived datasets, including MODIS land surface products and GPM\u002FTRMM precipitation estimates, and is implemented on the Google Earth Engine platform. A set of drought indicators representing vegetation condition, thermal stress, precipitation deficit, and soil moisture variability was analyzed alongside five composite indices to provide a multi-dimensional assessment of drought dynamics. The analysis was conducted across monthly, seasonal, and annual timescales for Tehran, Iran, over the period 2000–2022. In addition, trend significance was assessed using the Mann–Kendall test and Sen’s slope estimator to quantify long-term changes in drought behavior. The results indicate that composite indices provide a more coherent representation of drought evolution and are more effective in capturing the interaction among key climatic drivers. In particular, the analysis reveals a transition from precipitation-driven droughts (e.g., 2008 and 2016) to increasingly temperature-driven drought conditions (e.g., 2022), associated with elevated potential evapotranspiration and thermal stress. This highlights the growing importance of atmospheric evaporative demand in intensifying drought severity under warming conditions. From an applied perspective, the proposed framework supports drought early warning, spatial risk assessment, and informed decision-making in water resource management and agricultural planning. However, the reliability of the results depends on data quality, spatial resolution, and the representation of key drought drivers and should therefore be interpreted with these limitations in mind. The study demonstrates that the integrated application of multi-source data and multi-scale drought indices provides a robust basis for understanding and monitoring drought dynamics in semi-arid urban environments.","准确的干旱监测对于可持续水资源管理至关重要，尤其是在气象观测稀疏且空间异质性强的半干旱地区。本研究在统一的多源框架内，应用已有的单一和综合干旱指数对干旱状况进行综合评估，该框架将地面气象数据与卫星遥感数据集（包括MODIS地表产品和GPM\u002FTRMM降水估算）相结合，并在Google Earth Engine平台上实现。研究分析了表征植被状况、热胁迫、降水亏缺和土壤湿度变率的干旱指标集以及五种综合指数，以对干旱动态进行多维评估。分析在月、季和年尺度上针对伊朗德黑兰2000—2022年期间开展。此外，采用Mann–Kendall检验和Sen斜率估计器评估趋势显著性，以量化干旱行为的长期变化。结果表明，综合指数能更一致地表征干旱演变，且更能有效捕捉关键气候驱动因子之间的相互作用。具体而言，分析揭示了从降水驱动型干旱（如2008年和2016年）向日益增强的温度驱动型干旱（如2022年）的转变，这与潜在蒸散和热胁迫升高有关。这凸显了在变暖条件下大气蒸发需求在加剧干旱严重程度方面日益重要的作用。从应用角度看，所提出的框架可支持干旱预警、空间风险评估以及水资源管理和农业规划中的知情决策。然而，结果的可靠性取决于数据质量、空间分辨率以及关键干旱驱动因子的表征，因此在解读时应考虑这些局限性。本研究表明，多源数据与多尺度干旱指数的综合应用为理解和监测半干旱城市环境中的干旱动态提供了稳健基础。","Journal of Atmospheric and Solar-Terrestrial Physics","2026-10-01T00:00:00Z",74,{"impact":122,"substance":123,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":124},14,21,"多源遥感与地面指数融合的干旱监测框架，方法扎实、结论有新意，但属区域案例研究，产业影响有限。",[126],{"name":118,"url":115},[128,26,27,129,130],"农业气象","Google Earth Engine","干旱预警",{"code":68,"slug":132,"name":133,"path":134,"parent_code":43},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[136,137],{"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":14,"version":46,"method":47,"evidence":139},{"candidates":140,"agricultural_evidence":52},[141,147,151,156,163],{"code":68,"score":142,"supported":52,"title":143,"abstract":145,"tags":146},16,[144],"drought",[72,144],[128,130],{"code":32,"score":19,"supported":52,"title":148,"abstract":149,"tags":150},[54],[56],[129,27],{"code":39,"score":13,"supported":52,"title":152,"abstract":153,"tags":155},[],[63,154,65],"water resource",[26],{"code":157,"score":110,"supported":52,"title":158,"abstract":159,"tags":162},"H2",[],[160,161],"土壤","soil",[],{"code":164,"score":69,"supported":52,"title":165,"abstract":166,"tags":168},"J1",[],[167],"农业规划",[],[170,171],"MODIS GPM 干旱指数","德黑兰 干旱监测 遥感","MODISGPM干旱指数-4030","10.1016\u002Fj.jastp.2026.106987",{"doi":173,"openalex_id":175,"authors":176,"venue":118,"cited_by_count":78,"oa_url":115,"card":187,"direction":105,"ingested_from":107},"W7215092640",[177,180,182,184],{"name":178,"orcid":179},"Hadi Siasar","https:\u002F\u002Forcid.org\u002F0000-0002-9323-4345",{"name":181,"orcid":9},"Mahboube Ebrahimi",{"name":183,"orcid":9},"Mehdi Bahrami",{"name":185,"orcid":186},"Hossein Hamidifar","https:\u002F\u002Forcid.org\u002F0000-0001-9054-0120",{"tldr":188,"method":189,"finding":190,"direction":105,"opportunity":191},"整合遥感与地面数据，在GEE上评估德黑兰2000–2022年多尺度干旱动态。","MODIS、GPM\u002FTRMM与气象数据，构建5种复合干旱指数，Mann-Kend","复合指数更一致；干旱由降水驱动转向温度驱动，蒸散需求加剧干旱。","可引入机器学习融合多源指数，提升半干旱区干旱预警的时空精度与驱动归因。","2026-10-02T23:30:24.756475Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":200,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":202,"sources":207,"tags":209,"primary_topic":213,"secondary_topics":214,"classification":221,"search_phrases":253,"slug":256,"view_count":78,"doi":257,"paper":258,"created_at":269},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",75,{"impact":17,"substance":203,"depth":142,"authority":204,"freshness":205,"relevant":21,"comment":206},20,12,9,"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[208],{"name":199,"url":196},[210,211,56,28,212],"农业人工智能","农业物联网","节水农业",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[215,220],{"code":216,"slug":217,"name":218,"path":219,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":222},{"candidates":223,"agricultural_evidence":52},[224,229,236,240,246],{"code":39,"score":225,"supported":52,"title":226,"abstract":227,"tags":228},19,[40],[62,63,40],[28,212],{"code":216,"score":17,"supported":52,"title":230,"abstract":232,"tags":235},[231],"IoT",[233,234,231],"物联网","internet of things",[211],{"code":32,"score":51,"supported":52,"title":237,"abstract":238,"tags":239},[54],[56,54],[56],{"code":241,"score":204,"supported":52,"title":242,"abstract":244,"tags":245},"I2",[243],"artificial intelligence",[243],[210],{"code":247,"score":110,"supported":52,"title":248,"abstract":249,"tags":252},"H8",[],[250,251],"可持续农业","sustainable agriculture",[],[254,255],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":257,"openalex_id":259,"authors":260,"venue":199,"cited_by_count":78,"oa_url":196,"card":263,"direction":267,"ingested_from":107},"W7223090389",[261],{"name":262,"orcid":9},"Patil",{"tldr":264,"method":265,"finding":266,"direction":267,"opportunity":268},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","智慧农业 \u002F 农业物联网","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":9,"source_name":276,"source_url":273,"published_at":277,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":278,"score_detail":279,"sources":282,"tags":284,"primary_topic":288,"secondary_topics":293,"classification":296,"search_phrases":321,"slug":324,"view_count":78,"doi":325,"paper":326,"created_at":341},4753,"Mapping groundwater potential zones using GIS, remote sensing and analytical hierarchy process (AHP) in Pader and Kitgum Districts, Uganda","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04943-7","Groundwater is a crucial source of freshwater in semi-arid regions, particularly in water-stressed areas, such as northern Uganda, where the surface water is scarce and unreliable precipitation patterns changes. This province lacks sufficient clean water for human consumption and only 20% of the water for agriculture extracted from the ground due to inaccessibility and insufficient information regarding the groundwater. With regard to these difficulties in water that are faced by the local communities, it has not been yet studied the distribution of groundwater in Kitgum and Pader districts. Therefore, the result of this research contributes preliminary groundwater investigation for contacting this indispensable source of water. The study employed a geospatial approach integrated with multi-criteria analysis to identify groundwater potential zones in Kitgum and Pader districts of Acholi subregion. The Utilization of Geographic Information Systems (GIS), remote sensing, and the Analytic Hierarchy Process (AHP) and six thematic layers; rainfall, drainage density, slope, soil type, geology, and land use\u002Fland cover were selected based on their hydrogeological influence and the accessibility of the data. After standardizing, and assigning normalized weights,a weighted overlay analysis in ArcGIS Pro version 3.1.0 was used to synthesize these layers into a comprehensive groundwater potential zonation map. The result categorized the study area into five zones; very low, low, medium, high, and very high potential. The analysis revealed that Kitgum District is predominantly characterized by very low to low groundwater potential zones of 67.24% when it is combined both, constrained by factors such as higher drainage density, and steeper slopes. Conversely, Pader District showed favorable conditions, with 85.5% of its area classified as high to very high groundwater potential, attributed to gentle slopes, and higher rainfall, that enhance infiltration capacity. Validation against yield data from 18 existing boreholes demonstrated a 75.0% alignment between the model and actual well performance, confirming the model’s reliability at acceptable result. This study demonstrated the effectiveness of GIS-based and AHP as a cost-effective tool for preliminary groundwater exploration in data-scarce environments. The resulting groundwater potential map provides a vital scientific foundation for sustainable water resource management, enabling targeted borehole siting, informed aquifer development, and resilience-building strategies to enhance water security for communities and livelihoods in the Acholi sub-region.","地下水是半干旱地区重要的淡水来源，尤其是在乌干达北部等水资源紧张地区，那里地表水稀缺且降水模式变化不可靠。该地区缺乏足够的清洁饮用水供人类消费，由于地下水难以获取且信息不足，仅有20%的农业用水来自地下开采。鉴于当地社区面临这些水资源困难，基特古姆和帕德尔地区的地下水分布尚未得到研究。因此，本研究结果为接触这一不可或缺的水源提供了初步的地下水调查。本研究采用地理空间方法与多准则分析相结合，识别阿乔利次区域基特古姆和帕德尔地区的地下水潜力区。利用地理信息系统（GIS）、遥感技术和层次分析法（AHP），基于其水文地质影响和数据可获取性，选取了六个主题图层：降雨量、河网密度、坡度、土壤类型、地质和土地利用\u002F土地覆盖。经过标准化和归一化权重赋值后，使用ArcGIS Pro 3.1.0版本中的加权叠加分析将这些图层综合成一张全面的地下水潜力分区图。结果将研究区划分为五个区域：极低、低、中等、高和极高潜力。分析表明，基特古姆区主要以极低至低地下水潜力区为主，两者合计占67.24%，受较高河网密度和较陡坡度等因素制约。相反，帕德尔区表现出有利条件，其85.5%的面积被归类为高至极高地下水潜力，这归因于平缓的坡度和较高的降雨量，增强了入渗能力。利用18口现有钻孔的出水量数据进行验证，模型与实际井表现之间的一致率达到75.0%，证实了模型在可接受结果范围内的可靠性。本研究证明了基于GIS和AHP的方法作为数据稀缺环境中初步地下水勘探的成本效益工具的有效性。所生成的地下水潜力图为可持续水资源管理提供了重要的科学基础，有助于有针对性地选址钻孔，为知情决策提供依据。","Discover Sustainability","2026-10-10T00:00:00Z",67,{"impact":20,"substance":203,"depth":280,"authority":19,"freshness":205,"relevant":21,"comment":281},17,"基于GIS、遥感与AHP的乌干达北部地下水潜力制图研究，方法成熟、验证可靠，对缺水地区农业水资源规划有参考价值，但属区域性初步研究，公共影响有限。",[283],{"name":276,"url":273},[56,26,285,286,287],"GIS","地下水潜力","乌干达农业",{"code":289,"slug":290,"name":291,"path":292,"parent_code":36},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[294,295],{"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":14,"version":46,"method":47,"evidence":297},{"candidates":298,"agricultural_evidence":52},[299,305,309,313,317],{"code":289,"score":17,"supported":52,"title":300,"abstract":301,"tags":304},[285],[302,285,303],"地理信息","geographic information",[285],{"code":32,"score":51,"supported":52,"title":306,"abstract":307,"tags":308},[54],[56,54],[56],{"code":39,"score":20,"supported":52,"title":310,"abstract":311,"tags":312},[],[63,154],[286,26],{"code":157,"score":110,"supported":52,"title":314,"abstract":315,"tags":316},[],[160,161],[],{"code":68,"score":69,"supported":52,"title":318,"abstract":319,"tags":320},[],[72],[],[322,323],"乌干达 Kitgum Pader 地下水","GIS AHP 地下水潜力","乌干达KitgumPader地下水-4753","10.1007\u002Fs43621-026-04943-7",{"doi":325,"openalex_id":327,"authors":328,"venue":276,"cited_by_count":78,"oa_url":273,"card":336,"direction":105,"ingested_from":107},"W7221339030",[329,332,334],{"name":330,"orcid":331},"Ahmed Abdirizak Dirie","https:\u002F\u002Forcid.org\u002F0009-0002-8109-0724",{"name":333,"orcid":9},"Abdulkadir Ahmed Mohamed",{"name":335,"orcid":9},"Ambiga Kannapiran",{"tldr":337,"method":338,"finding":339,"direction":105,"opportunity":340},"用GIS、遥感和AHP制图评估乌干达两区地下水潜力并验证。","GIS与遥感结合AHP，加权叠加6个主题图层，用18口井数据验证。","Kitgum区67.24%为低潜力，Pader区85.5%为高潜力，模型与井数据吻合75%。","可将该地下水潜力制图方法迁移到其他数据稀缺区，并结合农业灌溉需求优化井位。","2026-10-12T23:30:24.761508Z",{"id":343,"title":344,"url":345,"summary":346,"summary_zh":347,"content":9,"source_name":348,"source_url":345,"published_at":349,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":350,"sources":352,"tags":354,"primary_topic":358,"secondary_topics":359,"classification":362,"search_phrases":386,"slug":389,"view_count":21,"doi":390,"paper":391,"created_at":405},4504,"Development of the Potential Evapotranspiration-Vegetation-Precipitation Drought Index (PETVPDI) and Its Application in West Africa Using Remote Sensing Datasets","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193435","Drought remains one of the most severe climate-related hazards affecting water resources, ecosystems, and agricultural productivity in West Africa (WA). Traditional drought indices often rely on individual hydroclimatic or vegetation variables and may not fully represent the coupled effects of precipitation supply, atmospheric evaporative demand, and vegetation response. To address this limitation, this study develops and applies a composite drought indicator, the Potential Evapotranspiration–Vegetation–Precipitation Drought Index (PETVPDI), using multi-source remote sensing datasets over WA for 2001–2022. PETVPDI integrates precipitation (P), potential evapotranspiration (PET), and the normalized difference vegetation index (NDVI) within a three-dimensional Euclidean-distance framework. The index was evaluated against GLDAS soil moisture (SM), ESA CCI SM, gross primary productivity (GPP), and net primary productivity (NPP), and compared with four conventional remote-sensing drought indices (cRSDI): VCI, VHI, TVDI, and TVPDI. Deseasonalized anomaly correlations, used as the primary validation evidence to reduce the influence of the shared monsoon seasonal cycle, show that PETVPDI achieved the highest numerical correlations among the evaluated indices with GLDAS SM (R = 0.556, p \u003C 0.001), ESA CCI SM (R = 0.513, p \u003C 0.001), and GPP (R = 0.483, p \u003C 0.001). Steiger’s tests for dependent overlapping correlations show that the PETVPDI–TVPDI difference was statistically significant for raw GLDAS SM and deseasonalized ESA CCI SM, but not for the other evaluated comparisons. Spatial PETVPDI patterns represent each pixel’s relative position within its own 2001–2022 hydroclimatic and vegetation range and therefore should not be interpreted as direct differences in absolute climatic aridity among locations. Long-term trends are spatially heterogeneous across WA. Overall, PETVPDI provides an integrated framework for monitoring relative drought–wetness variability and offers complementary information for drought assessment and early-warning applications in data-scarce regions.","干旱仍然是影响西非（WA）水资源、生态系统和农业生产力最严重的气候相关灾害之一。传统干旱指数通常依赖单一的水文气候或植被变量，可能无法充分反映降水供给、大气蒸发需求和植被响应之间的耦合效应。为解决这一局限，本研究利用2001—2022年西非多源遥感数据集，开发并应用了一种复合干旱指标——潜在蒸散–植被–降水干旱指数（PETVPDI）。PETVPDI在三维欧氏距离框架内整合了降水（P）、潜在蒸散（PET）和归一化差异植被指数（NDVI）。该指数以GLDAS土壤湿度（SM）、ESA CCI SM、总初级生产力（GPP）和净初级生产力（NPP）进行评估，并与四种传统遥感干旱指数（cRSDI）：VCI、VHI、TVDI和TVPDI进行比较。以去季节化异常相关性作为主要验证依据，以减少共同季风季节循环的影响，结果表明PETVPDI在所评估的指数中与GLDAS SM（R = 0.556，p \u003C 0.001）、ESA CCI SM（R = 0.513，p \u003C 0.001）和GPP（R = 0.483，p \u003C 0.001）的数值相关性最高。针对相依重叠相关性的Steiger检验表明，PETVPDI–TVPDI差异在原始GLDAS SM和去季节化ESA CCI SM上具有统计学显著性，但在其他评估比较中不显著。PETVPDI的空间格局表示每个像元在其自身2001—2022年水文气候和植被范围内的相对位置，因此不应被解释为地点之间绝对气候干燥度的直接差异。长期趋势在西非呈现空间异质性。总体而言，PETVPDI为监测相对干旱–湿润变率提供了一个集成框架，并为数据稀缺地区的干旱评估和预警应用提供了补充信息。","Remote Sensing","2026-10-08T00:00:00Z",{"impact":204,"substance":18,"depth":17,"authority":122,"freshness":205,"relevant":21,"comment":351},"提出融合降水、蒸散与植被的复合干旱指数，方法新颖、验证扎实，对数据稀缺区农业干旱预警有参考价值，但区域局限于西非，国内应用关联度有限。",[353],{"name":348,"url":345},[355,128,27,356,357],"粮食安全","西非农业","干旱指数",{"code":68,"slug":132,"name":133,"path":134,"parent_code":43},[360,361],{"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":14,"version":46,"method":47,"evidence":363},{"candidates":364,"agricultural_evidence":52},[365,369,373,377,381],{"code":68,"score":142,"supported":52,"title":366,"abstract":367,"tags":368},[144],[72,144],[128,357],{"code":32,"score":51,"supported":52,"title":370,"abstract":371,"tags":372},[54],[56,54],[27],{"code":39,"score":122,"supported":52,"title":374,"abstract":375,"tags":376},[65],[63,65],[],{"code":157,"score":110,"supported":52,"title":378,"abstract":379,"tags":380},[],[160,161],[],{"code":382,"score":21,"supported":14,"title":383,"abstract":384,"tags":385},"A8",[],[],[355],[387,388],"PETVPDI 干旱指数 西非","遥感 干旱监测 NDVI","PETVPDI干旱指数西非-4504","10.3390\u002Frs18193435",{"doi":390,"openalex_id":392,"authors":393,"venue":348,"cited_by_count":78,"oa_url":345,"card":400,"direction":105,"ingested_from":107},"W7220963372",[394,397],{"name":395,"orcid":396},"Abdoul-Aziz Bio Sidi D. Bouko","https:\u002F\u002Forcid.org\u002F0009-0003-1179-8046",{"name":398,"orcid":399},"Bing Gao","https:\u002F\u002Forcid.org\u002F0000-0003-0782-8858",{"tldr":401,"method":402,"finding":403,"direction":105,"opportunity":404},"构建融合蒸散、植被与降水的复合干旱指数PETVPDI，并用于西非2001—2022年干旱监测。","多源遥感数据，三维欧氏距离整合P、PET、NDVI，与土壤湿度、GPP\u002FNPP及","PETVPDI与土壤湿度和GPP的相关性最高，能更好反映干旱-湿润相对变化，但趋势空间异质。","可将该复合指数迁移至其他数据稀缺区，并结合作物产量或灌溉需水做农业干旱预警与适应性研究。","2026-10-09T23:30:25.838340Z",{"id":407,"title":408,"url":409,"summary":410,"summary_zh":411,"content":9,"source_name":412,"source_url":409,"published_at":413,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":414,"score_detail":415,"sources":418,"tags":420,"primary_topic":423,"secondary_topics":424,"classification":426,"search_phrases":455,"slug":458,"view_count":78,"doi":459,"paper":460,"created_at":499},4313,"A semi-supervised multi-sensor remote sensing pipeline for mapping irrigation dynamics in ground-truth deficient conflict zones: a case study of the Kakhovka Dam failure","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1920951","The ongoing war in Ukraine has drawn international attention to the impact of conflict on global agricultural production and food security. On 6 June 2023, the Kakhovka Dam was destroyed. The loss of this critical agricultural and energy infrastructure has left most farmers on the East bank of the Dnipro River without irrigation in Kherson and Zaporizhzhia oblasts. This study develops a multi-sensor Earth Observation framework combining high resolution satellite imagery (Sentinel-2, Landsat) with meteorological data (CHIRPS) and water productivity data (FAO WaPOR), using a semi-supervised machine learning pipeline. Using a rigorous stratified random-sampling approach with photointerpretation, we found pre-war irrigated areas between 2019 to 2021 to be on average 187.94 ± 14.43 kHa, approximately 4% of pre-war baseline cropland and 8% of summer season cropland in Kherson and Zaporizhzhia oblasts. In 2022, these numbers contracted to 140.04 ± 17.44 kHa with the expanded occupation of these territories by Russia, seeing an additional decline in 2023 to 74.97 ± 17.84 kHa. Fields in 2023 also exhibited variability in irrigation, ranging from interrupted irrigation owing to the destruction of the Kakhovka reservoir, to those that were sufficiently irrigated with water remaining in the channels. Irrigated areas diminished further in 2024 to 15.95 ± 9.97 kHa, a ∼91% decline from pre-war averages. Based on inspection, formerly irrigated fields before 2023 were either systematically abandoned or converted to winter cropland with lower input requirements by 2024–a pattern that continued into 2025 (total irrigated area 5.76 ± 0.87 kHa). This study demonstrates the power of advanced EO workflows to precisely quantify agricultural transformation and infrastructural failure in ground-truth deficient, high-risk environments.","乌克兰持续不断的战争使国际社会关注冲突对全球农业生产和粮食安全的影响。2023年6月6日，卡霍夫卡大坝被毁。这一关键农业和能源基础设施的丧失，使第聂伯河（Dnipro River）东岸赫尔松州和扎波罗热州的大多数农民失去了灌溉条件。本研究开发了一个多传感器地球观测（Earth Observation）框架，将高分辨率卫星影像（Sentinel-2、Landsat）与气象数据（CHIRPS）和水生产力数据（FAO WaPOR）相结合，采用半监督机器学习流程。通过严格的 stratified random-sampling 方法并结合照片判读，我们发现2019年至2021年战前灌溉面积平均为187.94 ± 14.43 kHa，约占赫尔松州和扎波罗热州战前基线耕地面积的4%，以及夏季作物耕地面积的8%。2022年，随着俄罗斯对这些领土占领的扩大，上述数字缩减至140.04 ± 17.44 kHa，2023年进一步下降至74.97 ± 17.84 kHa。2023年的田块在灌溉方面也表现出差异性，从因卡霍夫卡水库被毁而灌溉中断，到因渠道中仍有存水而得到充分灌溉的情况不等。2024年，灌溉面积进一步减少至15.95 ± 9.97 kHa，较战前平均值下降约91%。根据核查，2023年之前曾受灌溉的田块到2024年要么被系统性弃耕，要么转为投入需求较低的冬季作物用地——这一模式延续至2025年（总灌溉面积为5.76 ± 0.87 kHa）。本研究表明，先进的地球观测工作流程能够在缺乏地面真值、高风险的环境中精确量化农业转型和基础设施失效。","Frontiers in Remote Sensing","2026-10-05T00:00:00Z",87,{"impact":18,"substance":416,"depth":225,"authority":19,"freshness":205,"relevant":21,"comment":417},24,"用半监督多源遥感量化战区灌溉萎缩，方法新颖、数据扎实，对农业遥感与粮食安全研究有参考价值。",[419],{"name":412,"url":409},[355,27,421,422,62],"冲突农业","多源遥感",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[425],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":427},{"candidates":428,"agricultural_evidence":52},[429,433,437,443,449],{"code":39,"score":122,"supported":52,"title":430,"abstract":431,"tags":432},[40],[62,40],[],{"code":32,"score":13,"supported":52,"title":434,"abstract":435,"tags":436},[54],[],[422,27],{"code":382,"score":438,"supported":52,"title":439,"abstract":440,"tags":442},7,[],[355,441],"food security",[355],{"code":241,"score":110,"supported":52,"title":444,"abstract":445,"tags":448},[],[446,447],"机器学习","machine learning",[],{"code":450,"score":69,"supported":52,"title":451,"abstract":452,"tags":454},"J3",[],[453],"全球农业",[],[456,457],"冲突农业 多源遥感 粮食安全 遥感监测","冲突农业 多源遥感","冲突农业多源遥感粮食安全遥感监测-4313","10.3389\u002Ffrsen.2026.1920951",{"doi":459,"openalex_id":461,"authors":462,"venue":412,"cited_by_count":78,"oa_url":493,"card":494,"direction":105,"ingested_from":107},"W7220438608",[463,466,469,472,475,478,481,483,485,487,490],{"name":464,"orcid":465},"Sheila Baber","https:\u002F\u002Forcid.org\u002F0000-0002-5596-8970",{"name":467,"orcid":468},"Sergii V. Skakun","https:\u002F\u002Forcid.org\u002F0000-0002-9039-0174",{"name":470,"orcid":471},"Yuval Sadeh","https:\u002F\u002Forcid.org\u002F0000-0002-4346-0432",{"name":473,"orcid":474},"Леонід Леонідович Шуміло","https:\u002F\u002Forcid.org\u002F0000-0002-7395-7933",{"name":476,"orcid":477},"Oleksandra Oliinyk","https:\u002F\u002Forcid.org\u002F0000-0002-2392-3938",{"name":479,"orcid":480},"Mehdi Hosseini","https:\u002F\u002Forcid.org\u002F0000-0003-3242-613X",{"name":482,"orcid":9},"Josef Wagner",{"name":484,"orcid":9},"Abdul Qadir Khan",{"name":486,"orcid":9},"Shabarinath Nair",{"name":488,"orcid":489},"Abhishek Kotcharlakota","https:\u002F\u002Forcid.org\u002F0009-0001-1832-4631",{"name":491,"orcid":492},"Inbal Becker‐Reshef","https:\u002F\u002Forcid.org\u002F0000-0002-2160-5151","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1920951\u002Fpdf",{"tldr":495,"method":496,"finding":497,"direction":105,"opportunity":498},"用半监督多源遥感监测卡霍夫卡大坝被毁后灌溉面积从战前18.8万公顷降至2025年0.58万公顷。","Sentinel-2、Landsat、CHIRPS与FAO WaPOR数据结合半","灌溉面积由2019-2021年均187.94 kHa降至2024年15.95 kHa，降幅约91%，","可将该半监督多源框架迁移至其他缺地面真值冲突区，并耦合作物产量与粮食安全评估。","2026-10-06T23:30:20.495894Z",{"id":501,"title":502,"url":503,"summary":504,"summary_zh":505,"content":9,"source_name":506,"source_url":503,"published_at":507,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":508,"sources":510,"tags":512,"primary_topic":517,"secondary_topics":520,"classification":523,"search_phrases":543,"slug":546,"view_count":69,"doi":547,"paper":548,"created_at":565},3845,"Problems of soil degradation assessment using spectral indices from space images","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71433-1","This article presents an integrated method of spectral indices for assessing soil degradation using Sentinel-2 Earth Remote Sensing (ERS) data. Unlike approaches based on individual indices, the method combines indicators of vegetation, humidity, thermal state, and salinity, increasing the accuracy of mapping saline and degraded lands while minimizing errors caused by variations in natural conditions. The application of the method in the Tashkent region of Uzbekistan (41.1465°N, 69.3009°E) showed that the category of “extremely high” and “high” degradation accounts for 3.29% and 8.28% of pixels, “medium” and “low” for 9.4% and 9.52%, while the remaining 69.51% belong to other category (class) of land cover, confirming the concentration of degradation foci in lowland areas. Comparison with 2020 showed a notable increase in the share of critical degradation (growth from 10.46% to 11.57%), while the share of the “extremely high” category increased from 2.80% to 3.29%, indicating a shift toward more severe conditions that aligns with official regional salinization statistics. Analysis of meteorological data (2013–2025) revealed an inverse relationship between temperature and precipitation, confirming the climatic nature of salinization; four stages of salinization were identified—latent, progressive, severe, and complete degradation and the main components method (PCA) was applied to reduce the size of the data. These results correspond to the expansion of irrigated lands in Uzbekistan from 3.5 to 8.5 million hectares and the associated increase in groundwater levels. Consequently, this method is not the final means of delimiting degraded lands, but represents a quick, inexpensive preliminary check before field surveys: it marks potentially degraded areas for priority ground surveys, supplementing official surveys and government statistics rather than replacing them. The method is applicable in agriculture, land cadastre, and environmental monitoring as an auxiliary tool for climate adaptation and water resource management.","本文提出了一种利用Sentinel-2地球遥感（ERS）数据评估土壤退化的光谱指数集成方法。与基于单一指数的方法不同，该方法综合了植被、湿度、热状态和盐分指标，提高了盐碱化和退化土地制图的精度，同时最大限度地减少了自然条件变化引起的误差。该方法在乌兹别克斯坦塔什干地区（北纬41.1465°，东经69.3009°）的应用表明，“极高”和“高”退化类别分别占像元的3.29%和8.28%，“中等”和“低”分别占9.4%和9.52%，其余69.51%属于其他土地覆盖类别（等级），证实了退化热点集中在低地地区。与2020年相比，临界退化比例显著增加（从10.46%增至11.57%），而“极高”类别比例从2.80%增至3.29%，表明退化状况趋于严重，这与官方区域盐渍化统计数据一致。气象数据（2013—2025年）分析揭示了气温与降水之间的反比关系，证实了盐渍化的气候成因；识别出盐渍化的四个阶段——潜伏期、进展期、严重期和完全退化期，并应用主成分分析方法（PCA）对数据进行了降维处理。这些结果与乌兹别克斯坦灌溉面积从350万公顷扩展至850万公顷以及相关的地下水位上升相吻合。因此，该方法并非划定退化土地的最终手段，而是实地调查前一种快速、低成本的初步筛查：它标记出潜在退化区域以供优先开展地面调查，是对官方调查和政府统计的补充而非替代。该方法适用于农业、土地地籍和环境监测领域，可作为气候适应和水资源管理的辅助工具。","Scientific Reports","2026-09-29T00:00:00Z",{"impact":142,"substance":18,"depth":17,"authority":122,"freshness":205,"relevant":21,"comment":509},"基于Sentinel-2多光谱指数的土壤退化综合评估方法，方法新颖、数据翔实，对盐渍化遥感监测与耕地保护有参考价值。",[511],{"name":506,"url":503},[513,27,514,515,516],"Sentinel-2","土壤退化","盐渍化","土地退化评估",{"code":157,"slug":161,"name":518,"path":519,"parent_code":43},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[521,522],{"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":14,"version":46,"method":47,"evidence":524},{"candidates":525,"agricultural_evidence":52},[526,530,534,538],{"code":157,"score":51,"supported":52,"title":527,"abstract":528,"tags":529},[161],[160,161],[515],{"code":39,"score":205,"supported":52,"title":531,"abstract":532,"tags":533},[],[62,63,154],[],{"code":32,"score":20,"supported":52,"title":535,"abstract":536,"tags":537},[],[56,54],[513,27],{"code":68,"score":69,"supported":52,"title":539,"abstract":540,"tags":542},[],[541],"气候适应",[],[544,545],"乌兹别克斯坦 塔什干 土壤盐渍化","Sentinel-2 光谱指数 土壤退化","乌兹别克斯坦塔什干土壤盐渍化-3845","10.1038\u002Fs41598-026-71433-1",{"doi":547,"openalex_id":549,"authors":550,"venue":506,"cited_by_count":78,"oa_url":559,"card":560,"direction":105,"ingested_from":107},"W7214801255",[551,554,557],{"name":552,"orcid":553},"Z. Z. Shamsiev","https:\u002F\u002Forcid.org\u002F0000-0002-0323-9741",{"name":555,"orcid":556},"L. F. Sulyukova","https:\u002F\u002Forcid.org\u002F0000-0002-7150-4359",{"name":558,"orcid":9},"Rasul Shamsiev","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71433-1_reference.pdf",{"tldr":561,"method":562,"finding":563,"direction":105,"opportunity":564},"提出融合多光谱指数的Sentinel-2土壤退化评估方法，在乌兹别克斯坦验证并识别盐渍化阶段。","Sentinel-2影像多指数融合（植被、湿度、热、盐分）结合PCA与气象数据分","退化集中于低地，2020年以来极重度退化占比由2.80%升至3.29%，与官方盐渍化统计一致。","可将该快速预筛方法与地面调查、机器学习结合，构建多区域可迁移的退化早期预警与制图框架。","2026-09-30T23:30:29.386707Z"]