The intensification of global climate change and the increasing frequency of extreme weather events require advanced methods for flood disaster monitoring and management. We propose a cross-sensor framework for robust water body mapping using multitemporal optical imagery, mitigating radiometric discrepancies and temporal drift. By integrating satellite datasets from multiple platforms (Sentinel-2A, Landsat-8, Gaofen-1 (GF-1), and Huanjing-1A (HJ-1A)), we constructed a harmonized remote sensing time series database, developed a relative radiometric normalization approach for heterogeneous imagery, and designed a spatiotemporally invariant feature extraction mechanism. Key innovations in this study include the implementation of a particle swarm optimization-enhanced random forest (PSO-RF) classifier to enhance model robustness and the development of cross-sensor sample transfer strategies to improve the interoperability of training data. To validate the proposed framework, we performed water body extraction using a fusion of multidimensional features, incorporating spectral indices (e.g., the Normalized Difference Water Index (NDWI)) and spatial-textural attributes as reference benchmarks. Comparative validation against traditional NDWI-based methods revealed significant classification accuracy improvements, with Landsat-derived water body extractions exhibiting 0.62%-2.10% increased precision. The postnormalized Sentinel-2A and GF-1 imagery consistently yielded Kappa coefficients above 0.8, and the improvement in classification accuracy ranged from 1% to 2% for heterogeneous images. The proposed framework demonstrated significant operational efficiency and detection accuracy improvements for flood monitoring in arid regions. These advancements provide a solid technical foundation for dynamic water resource monitoring in the context of climate change, offering direct implications for real-time disaster management applications.