[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2324":3},{"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,"view_count":31,"doi":32,"paper":33,"created_at":61},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",0,"10.3389\u002Ffeart.2026.1914670",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":6,"card":54,"direction":58,"ingested_from":60},"W7212247745",[36,39,42,45,48,51],{"name":37,"orcid":38},"Rachid Amiha","https:\u002F\u002Forcid.org\u002F0000-0002-9863-6685",{"name":40,"orcid":41},"Belkacem Kabbachi","https:\u002F\u002Forcid.org\u002F0000-0003-3802-1256",{"name":43,"orcid":44},"Mohamed Ait Haddou","https:\u002F\u002Forcid.org\u002F0000-0002-0587-1230",{"name":46,"orcid":47},"Youssef Bouchriti","https:\u002F\u002Forcid.org\u002F0000-0002-8566-1451",{"name":49,"orcid":50},"Hicham Gougueni","https:\u002F\u002Forcid.org\u002F0000-0001-5340-7566",{"name":52,"orcid":53},"Mustapha Ikirri","https:\u002F\u002Forcid.org\u002F0000-0002-9971-6249",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"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"]