[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2799":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":54},2799,"Multi-Temporal Assessment of Bimodal Monsoon Flood Dynamics and Agricultural Exposure Using Integrated Sentinel-1 SAR and Sentinel-2 Optical Data in Punjab, Pakistan","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fgeohazards7040114","Floods in monsoon-dominated river basins exhibit high spatio-temporal variability, necessitating high-resolution, multi-sensor approaches for reliable monitoring and impact assessment. In flood-prone agricultural regions, continuous monitoring using optical remote sensing is frequently hindered by dense monsoon cloud cover. This study establishes a comprehensive multi-sensor framework within the Google Earth Engine (GEE) to examine the spatio-temporal dynamics and land surface impacts of the 2025 monsoon floods in Punjab, Pakistan. Flood inundation mapping was executed using a 12-day Sentinel-1 Synthetic Aperture Radar (SAR) time series via a dual-threshold change detection methodology. Concurrently, Sentinel-2 imagery facilitated the derivation of land use\u002Fland cover (LULC) changes and vegetation dynamics using a Random Forest classifier, achieving overall accuracy of 93% (pre-flood), 91% (during flood), and 94% (post-flood). These accuracy levels were consistent across all three phases despite spectral confusion between water, saturated soil, and vegetation during peak inundation, indicating consistent classification performance under monsoon conditions. The analysis revealed a distinct bimodal flooding regime, characterized by an early monsoon peak in July–August and a more severe late monsoon peak in August-September. The cumulative maximum flood extent reached 9495.33 km2, with peak single-date inundation reaching 5449 km2. Mapped cropland declined by 6.7% (8181 km2) during peak flooding, with 3.9% (4796 km2) remaining non-cropland by the end of the observation period; 5892 km2 of pre-flood cropland was identified as inundated through spatial intersection. In addition, the Normalized Difference Vegetation Index (NDVI) declined by 28.6%, from 0.28 to 0.20, indicating a substantial reduction in vegetation greenness. Spatial consistency was checked with the United Nations Satellite Centre (UNOSAT) and the Food and Agriculture Organization (FAO), independently collected data showing moderate spatial agreement. The proposed framework is highly scalable for continuous flood monitoring, offering critical insights for disaster management and climate adaptation planning in monsoon regions plagued by data scarcity and persistent cloudiness. The approach is particularly relevant for near-real-time operational monitoring, given its reliance on freely available Sentinel data and cloud-based processing that requires no specialized ground infrastructure.","在季风主导的流域，洪水表现出高度的时空变异性，因此需要高分辨率、多传感器方法来进行可靠监测和影响评估。在易受洪水影响的农业区域，利用光学遥感进行连续监测常常受到季风期密集云层的阻碍。本研究在Google Earth Engine（GEE）中建立了一个综合多传感器框架，以考察2025年巴基斯坦旁遮普省季风洪水的时空动态及其对地表的影 响。利用12天Sentinel-1合成孔径雷达（SAR）时间序列，通过双阈值变化检测方法进行洪水淹没制图。同时，利用Sentinel-2影像，通过随机森林分类器提取土地利用\u002F土地覆盖（LULC）变化和植被动态，总体精度分别达到93%（洪水前）、91%（洪水期间）和94%（洪水后）。尽管在淹没峰值期水体、饱和土壤和植被之间存在光谱混淆，这三个阶段的精度水平仍保持一致，表明在季风条件下分类性能稳定。分析揭示出明显的双峰洪水情势，其特征是7—8月季风早期峰值和8—9月更为严重的季风晚期峰值。累计最大洪水范围达到9495.33 km²，单日最大淹没面积达到5449 km²。在洪水峰值期，制图耕地减少6.7%（8181 km²），到观测期末仍有3.9%（4796 km²）为非耕地；通过空间交集识别出5892 km²的洪水前耕地被淹没。此外，归一化植被指数（NDVI）下降28.6%，从0.28降至0.20，表明植被绿度显著降低。利用联合国卫星中心（UNOSAT）和联合国粮食及农业组织（FAO）独立收集的数据进行了空间一致性检验，结果显示具有中等空间一致性。所提出的框架对于连续洪水监测具有高度可扩展性，为受数据稀缺和持续多云困扰的季风地区的灾害管理和气候适应规划提供了关键见解。鉴于该方法依赖免费可用的Sentinel数据和无需专门地面基础设施的云端处理，它尤其适用于近实时业务化监测。",null,"GeoHazards","2026-09-16T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于Sentinel-1\u002F2多源遥感与GEE平台的洪水-农业暴露评估，方法可迁移、数据详实，对农业灾害遥感监测有参考价值，但属区域性案例研究，非国内三农政策或产业级事件。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","遥感监测","洪涝灾害","作物受灾评估","巴基斯坦",0,"10.3390\u002Fgeohazards7040114",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":6,"card":47,"direction":51,"ingested_from":53},"W7213432591",[36,38,41,44],{"name":37,"orcid":9},"Nida Khursheed",{"name":39,"orcid":40},"Asif Sajjad","https:\u002F\u002Forcid.org\u002F0000-0003-1921-8213",{"name":42,"orcid":43},"Mazhar Iqbal","https:\u002F\u002Forcid.org\u002F0000-0001-5891-9798",{"name":45,"orcid":46},"Rana Waqar Aslam","https:\u002F\u002Forcid.org\u002F0000-0002-8711-8700",{"tldr":48,"method":49,"finding":50,"direction":51,"opportunity":52},"基于GEE融合Sentinel-1\u002F2监测2025年巴基斯坦旁遮普季风洪水双峰动态及农田暴露。","GEE平台、Sentinel-1 SAR双阈值变化检测、Sentinel-2随机","洪水呈7-8月与8-9月双峰，最大淹没9495平方公里，耕地减少6.7%，NDVI降28.6%。","农业遥感与作物表型","可延伸至多云区近实时洪涝-作物损失耦合评估与灾后恢复监测模型构建。","openalex","2026-09-17T23:30:37.558541Z"]