[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-InSAR":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":7,"page_size":8,"items":9},"InSAR","tag",[4],1,100,[10],{"id":11,"title":12,"url":13,"summary":14,"summary_zh":15,"content":16,"source_name":17,"source_url":13,"published_at":18,"category":19,"cover_url":16,"hotness":20,"is_selected":21,"score":22,"score_detail":23,"sources":30,"tags":32,"search_phrases":37,"slug":40,"view_count":41,"doi":42,"paper":43,"created_at":67},3181,"Estimating groundwater abstraction for irrigation in a data-scarce semi-arid region using optical and InSAR time-series analysis: the Tensift catchment, Morocco","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrwa.2026.1917387","In Morocco, the expansion of irrigated agriculture has intensified groundwater abstraction and increased pressure on overexploited aquifer systems. Moreover, the substantial lack of pumping measurement data represents a critical limitation for water resources management. To address this gap, the present study quantifies groundwater abstraction at the perimeter scale during agricultural season (2015–2016) in the Tensift catchment, using an integrated methodology combining optical and radar remote sensing (Sentinel-1,-2) with field-based observations. The results show that the total groundwater abstraction reached 4.13 × 10 6 m 3 with a monthly average of 344,515 m 3 . During the April–August period, groundwater supplied 36.1% of crop water requirements, while dam releases contributed 63.9%. These estimates were validated against pumping records from monitoring wells, where field measurements quantified groundwater and dam release contributions of 45% and 55%, respectively. The water balance assessment indicated a total agricultural water supply of 18.8 × 10 6 m 3 .yr −1 , consistent with crop water requirements estimated at 17.8 × 10 6 m 3 .yr −1 . InSAR-derived land subsidence analysis revealed marked spatial heterogeneity, with maximum subsidence rates reaching 6.5 mm.yr −1 , concentrated in the northern sector and isolated central clusters. Overlap analysis showed a co-occurrence rate of 70.6% between high-abstraction and subsidence zones. The spatial Pearson correlation between land subsidence and groundwater abstraction ranges from 0.39 to 0.71. This study demonstrates that the proposed approach provides a robust and scalable framework for groundwater abstraction monitoring in data-scarce environments.","在摩洛哥，灌溉农业的扩张加剧了地下水开采，并增加了对过度开采含水层系统的压力。此外，抽水计量数据的大量缺失是水资源管理的一个关键限制。为弥补这一空白，本研究采用结合光学与雷达遥感（Sentinel-1、-2）及实地观测的综合方法，量化了Tensift流域农业季（2015—2016年）周边尺度的地下水开采量。结果表明，地下水总开采量达4.13 × 10⁶ m³，月均344,515 m³。在4月至8月期间，地下水满足了作物需水量的36.1%，而水库放水贡献了63.9%。这些估算结果经监测井抽水记录验证，实地测量量化地下水与水库放水的贡献分别为45%和55%。水量平衡评估表明，农业总供水量为18.8 × 10⁶ m³·yr⁻¹，与估算的作物需水量17.8 × 10⁶ m³·yr⁻¹一致。基于InSAR的地面沉降分析揭示了显著的空间异质性，最大沉降速率达6.5 mm·yr⁻¹，集中于北部区域和孤立的中部簇群。重叠分析显示，高开采区与沉降区的共现率为70.6%。地面沉降与地下水开采之间的空间Pearson相关系数范围为0.39至0.71。本研究表明，所提出的方法为数据稀缺环境下的地下水开采监测提供了一个稳健且可扩展的框架。",null,"Frontiers in Water","2026-09-21T00:00:00Z","论文",10,false,78,{"impact":24,"substance":25,"depth":26,"authority":27,"freshness":28,"relevant":7,"comment":29},16,22,18,13,9,"结合光学与InSAR遥感估算数据稀缺区灌溉地下水开采量，方法新颖、结论可靠，对农业水资源信息化管理有参考价值。",[31],{"name":17,"url":13},[33,34,35,36,4],"农业水资源","遥感监测","半干旱农业","地下水灌溉",[38,39],"Tensift 流域 地下水 灌溉","Sentinel-1 Sentinel-2 灌溉 遥感","Tensift流域地下水灌溉-3181",0,"10.3389\u002Ffrwa.2026.1917387",{"doi":42,"openalex_id":44,"authors":45,"venue":17,"cited_by_count":41,"oa_url":13,"card":60,"direction":64,"ingested_from":66},"W7213955905",[46,48,51,53,56,58],{"name":47,"orcid":16},"Youssef Hajhouji",{"name":49,"orcid":50},"Abdelhakim Amazirh","https:\u002F\u002Forcid.org\u002F0000-0002-6665-3843",{"name":52,"orcid":16},"Wassim Mohamed Baba",{"name":54,"orcid":55},"Marieme Seif-Ennasr","https:\u002F\u002Forcid.org\u002F0009-0005-8205-265X",{"name":57,"orcid":16},"M Serraj",{"name":59,"orcid":16},"Salah Er-Raki",{"tldr":61,"method":62,"finding":63,"direction":64,"opportunity":65},"结合光学与InSAR遥感估算摩洛哥半干旱区灌溉地下水开采量。","Sentinel-1\u002F2时序遥感、实地观测与InSAR沉降分析。","地下水满足36.1%作物需水，高开采区与沉降区重叠达70.6%。","农业遥感与作物表型","可迁移至其他数据稀缺区，融合多源遥感与机器学习提升开采量估算精度。","openalex","2026-09-22T23:30:24.043679Z"]