洪涝灾害频发对人民生命财产安全构成严重威胁,快速、精准地监测洪水淹没范围与动态变化是灾害应急响应与评估的关键。针对这一需求,本研究以河北省涿州市2023年7-8月洪灾为例,利用Sentinel-2多时序光学影像,开展洪涝水体的识别与动态变化监测。通过融合光谱反射率与多种水体指数构建综合识别特征,并分别构建人工神经网络（ANN）、随机森林（RF）和支持向量机（SVM）模型进行水体提取。结果表明：RF模型在不同时相的水体识别精度均高于98.84%,Kappa系数大于0.97,且稳定性和效率均优于SVM与ANN模型;基于RF的时间序列监测结果,揭示了涿州洪灾“快速淹没–缓慢消退”的完整过程,退水阶段受地形与排水条件限制,部分区域存在持续性积水现象。本研究结合Sentinel-2数据的易获取性与RF模型的高效性,提供了一套高精度、高效率的洪涝应急监测方案。

Frequent flood disasters pose a serious threat to people's lives and property safety. Rapid and accurate monitoring of flood inundation areas and their dynamic changes is crucial for disaster emergency response and assessment. To meet this demand, this study uses the flood disaster in Zhuozhou City, Hebei Province, from July to August 2023 as a study case. Utilizing Sentinel-2 multi-temporal optical imagery, we conduct the identification and dynamic change monitoring of floodwater bodies. By integrating spectral reflectance with various water body indices, we construct comprehensive identification features and separately developed Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) models for water body extraction. The results indicate that the RF model exhibits a water body identification accuracy exceeding 98.84% across different time phases, with a Kappa coefficient greater than 0.97. Furthermore, its stability and efficiency surpass those of the SVM and ANN models. Based on the time-series monitoring results obtained from RF, the complete process of "rapid inundation - slow recession" during the Zhuozhou flood disaster is revealed. During the recession phase, due to topographical and drainage conditions, some areas experience persistent waterlogging. This study combines the accessibility of Sentinel-2 data with the efficiency of the RF model, offering a high-precision and high-efficiency emergency monitoring solution for flood disasters.