洪涝灾害动态监测是流域防汛减灾与水安全保障的重要内容。传统监测方法依赖单一观测手段，在时空连续性、复杂环境适应性及实时响应能力等方面存在着明显的不足。近年来，随着对地观测技术、无人系统、物联网及人工智能的快速发展，融合天基遥感、空基平台与地基传感网络的天-空-地一体化洪涝监测体系已成为智慧水利领域的重要研究方向。系统论述了天基遥感卫星、低空无人机遥感及地基传感网络在洪涝监测中的关键技术进展，重点评述多源异构数据融合与同化、地理人工智能（GeoAI）与数字孪生驱动的实时预演等新兴技术在洪涝感知、动态模拟与辅助决策中的应用模式与研究现状。在此基础上，总结不同技术路径的适用场景、优势与不足，并对未来洪涝监测向实时化、数智化的发展趋势进行了展望。研究成果可为数字孪生水利监测感知、水雨情测报“三道防线”建设等提供技术参考。

[Objective] Traditional monitoring methods for flood disasters primarily rely on single ground-based stations，which often suffer from limited spatial coverage，vulnerability to extreme weather，and delayed response capabilities. This research aims to address these limitations by systematically reviewing the “Space-Air-Ground” integrated monitoring framework. The primary objective is to investigate the synergistic mechanisms and key emerging technologies—such as Multi-source Data Fusion，Geospatial Artificial Intelligence （GeoAI），and Digital Twins—that enable high-precision，real-time，and intelligent flood perception and decision support. [Methods] The study adopts a multi-dimensional perspective to analyze the integrated monitoring architecture consisting of three layers： Space （Satellite clusters），Air （UAVs and aviation platforms），and Ground （IoT sensors and hydrological stations）. It evaluates the synergy between these layers across the Data，Transmission，and Application tiers. Specifically，the research reviews three levels of data fusion： data-level，feature-level （utilizing Cross-modal Transformers），and decision-level. Furthermore，it explores advanced data assimilation techniques，comparing traditional variational and sequential methods with modern Deep Learning-based approaches like PINN （Physics-Informed Neural Networks） and End-to-End Neural Assimilation. The study also classifies GeoAI applications in flood monitoring into four categories： inundation extraction，water level inversion，discharge estimation，and vulnerability assessment. [Results] The integrated framework significantly enhances monitoring reliability by achieving multi-layered spatio-temporal complementarity. Key results include：（1） Synergistic Perception： Ground-based sensors provide high-confidence “truth” data to calibrate satellite and UAV inversions，while satellite signals can trigger UAV swarms for targeted detailed inspections in a “discovery-tracking-verification” loop. （2） GeoAI Advancement： Modern models like Segment Anything Model （SAM） and Bitemporal Image Transformer （BIT） have improved water body segmentation accuracy under complex conditions，such as urban shadows or cloud cover，by utilizing cross-modal feature reconstruction. （3） Digital Twin Evolution： The technology has evolved from geometric visualization （L1） and physical mechanism simulation （L2） to logic-driven intelligent prediction （L3）. The integration of Reduced-Order Models （ROM） and GeoAI allows for “second-level” decision feedback by shifting computational burdens to the pre-training phase. （4） Operational Efficacy： Pilot applications in the Changjiang and Pearl River basins demonstrate that digital twin systems can support the “Four Pre-s” （Forecasting，Warning，Rehearsal，and Planning），providing critical technical support during major flood events. [Conclusions] This research concludes that the “Space-Air-Ground” integrated network represents the future of intelligent flood disaster management. The study’s innovation lies in proposing a vertically integrated “Perception-Model-Intelligence” architecture that moves beyond simple multi-source observation to a hybrid synergistic mechanism driven by both physical laws and data. However，several challenges remain：（1） technical barriers in high-precision cross-modal spatio-temporal alignment；（2） the lack of physical interpretability in “black-box” GeoAI models； and （3） the need for deeper coupling between digital twin systems and actual flood control business workflows. Future research should focus on：（1） Edge Intelligence： Developing lightweight algorithms for on-orbit real-time data processing. （2） Physics-Driven Sensing： Embedding hydrological mechanisms into neural networks to ensure consistency with physical laws. （3） Standardization： Establishing unified technical standards and interface specifications for digital twin flood systems to enhance their engineering applicability and cross-regional generalization.