南水北调中线工程水源区，即丹江口库区及其上游流域，在2025年8—10月遭遇罕见秋汛。针对秋汛期间洪水淹没区域及范围开展遥感监测，并进行重点城市洪水韧性评估。采用Sentinel-1卫星合成孔径雷达（SAR）影像，基于Sentinel-1双极化水体指数（SDWI）提取了水源区秋汛前后水体范围，并使用Sentinel-2光学影像通过U-Net深度学习模型提取的水体范围进行验证，总体精度达到94.6%。基于SDWI的水面面积监测结果表明：淹没区域主要集中在流域中下游，水体扩张面积最大为淅川县，为222.633 0 km²，其次为丹江口市，为104.566 5 km²，白河县水体扩张面积最小，为0.098 4 km²。进一步结合生态环境与经济社会相关指标，基于熵权法-双基点（EWM-TOPSIS）法对城市洪水韧性进行评估。结果表明：面对洪水风险，高韧性城市分布在水源区上游和下游；低韧性城市主要分布在中游；位于湖北省十堰市的茅箭区、张湾区及位于陕西省商洛市的商州区等地区表现为高韧性，位于陕西省安康市的石泉县、紫阳县等地区表现为低韧性；水体扩张面积与人均国内生产总值（GDP）是影响城市洪水韧性水平的关键因素。研究成果可为水源区洪水风险管理提供决策参考，为城市洪水韧性提升提供有效依据。

[Objective] In existing urban flood resilience assessment frameworks，flood stress is typically quantified using precipitation data or hazard characteristics derived from hydrological and hydrodynamic models. This study utilizes Sentinel-1 SAR data to extract urban inundation extents and establishes a comprehensive indicator system to evaluate the flood resilience of cities within the water source area of the Middle Route of the South-to-North Water Diversion Project. This research aims to objectively reflect the actual coping capacities and vulnerabilities of these cities under extreme hydrological events，providing a scientific basis for flood risk management and resilience enhancement in the water source region. [Methods] Based on Sentinel-1 dual-polarization SAR satellite data，the SDWI （Sentinel-1 Dual-Polarized Water Index） for the study area was calculated，and water body was extracted using an empirical threshold. U-Net deep learning model was employed to improve the accuracy of water extraction from optical images. Urban flood resilience was assessed using the PSR （Pressure-State-Response） model from the three dimensions of pressure，state，and response. A total of nine indicators were adopted： water expansion area，river network density，average slope，per capita GDP，population density，green coverage rate of built-up areas，road network density，drainage pipeline density in built-up areas，and hospital accessibility. The EWM-TOPSIS method （Entropy Weight Method - Technique for Order Preference by Similarity to an Ideal Solution） was used to determine the indicator weights and assess urban flood resilience. [Results] Cross-validation between the pre-flood water extraction results and optical imagery achieved an overall accuracy of 94.6%. Based on SAR imagery，the continuous surface water extent dynamics in the water source area during the 2025 autumn flood were obtained for the period from July 15 to November 2. The results indicate that the increase in surface water area in and around the Danjiangkou Reservoir was initially concentrated in the Danjiang section of the reservoir； by September 26，the Hanjiang section also began to expand rapidly，reaching its maximum on October 21，when the reservoir was impounded to its normal storage level of 170 m. The inundated areas in the water source area during the flood period were primarily concentrated in the middle and lower reaches of the basin. Xichuan County exhibited the largest water expansion area （222.633 0 km²），followed by Danjiangkou City （104.566 5 km²），while Baihe County showed the smallest water expansion area （0.098 4 km²）. 2） Urban flood resilience assessment results reveal that water expansion area and per capita GDP had the highest indicator weights，at 0.345 and 0.228，respectively，whereas average slope and road network density had the lowest weights，at 0.015 and 0.013，respectively. Maojian District and Zhangwan District in Shiyan City，Danfeng County and Shangzhou District in Shangluo City，Mian County，Hantai District，and Liuba County in Hanzhong City，as well as Taibai County in Baoji City，exhibited high resilience. Shiquan County，Ziyang County，Langao County，and Ningshan County in Ankang City，Zhashui County in Shangluo City，and Foping County in Hanzhong City exhibited low resilience. [Conclusion] The inundated areas were concentrated in the middle and lower reaches of the water source area，and regions with larger water expansion extents were all located near the reservoir，indicating that the arrival of the autumn flood posed challenges to reservoir flood control regulation. Inundation was more pronounced at the confluences of main streams and tributaries，while areas at higher elevations experienced less inundation； overall，the inundated areas were concentrated in flat terrain. Areas with higher resilience in the water source area were generally distributed in the upper and lower reaches and in city center areas，whereas lower-resilience areas were located in the middle reaches and rural areas. Water expansion area and per capita GDP are important factors influencing urban flood resilience. To enhance the urban flood resilience of the water source area of the middle route，efforts should focus on improving the level of urban economic development and the capacity for reservoir flood control regulation.