Abstract: Landfalling and offshore typhoons often cause significant casualties and property damage through associated hazards such as torrential rainfall, strong winds, and storm surges. Current understanding of three key scientific challenges, including rapid intensification, sudden track changes, and formation mechanisms, remains inadequate. One of the main reasons is the lack of high spatiotemporal resolution in situ observational data covering both internal typhoon dynamics and environmental factors throughout their complete lifecycle. To address the scarcity of marine typhoon observation data, the unmanned surface vehicle (USV) team from the Institute of Atmospheric Physics, Chinese Academy of Sciences has independently developed two long-endurance semi-submersible unmanned surface vehicles and conducted multiple sea trials. This paper proposes a novel concept of networked unmanned vehicle observation systems to comprehensively obtain meteorological and oceanic multi-element field information during typhoon evolution processes. Using automatically deployed solar powered USV and oil-electric powered USV with sounding equipment, a network observation system of USV is constructed in the South China Sea area where tropical cyclones occur frequently and typhoons pass through the Western Pacific, to conduct long-term typhoon observation experiments. The highly maneuverable solar-powered USVs will acquire multi-element observations data of sea surface meteorology and hydrology, and the oil-electric powered USV equipped with rocket-based sounding technology will obtain profile data of the atmospheric boundary layer within typhoons. The UAV networked observation system will enable real-time in situ observation data collection from both the internal structure of marine tropical cyclones and their ambient environmental fields. Concurrent satellite observation products and reanalysis data will undergo validation and comparative analysis with the collected data, forming a comprehensive observational dataset for multiple South China Sea tropical cyclones. This dataset will provide first-hand data for data assimilation in numerical prediction models and model performance evaluation, ultimately improving the models" capabilities in typhoon track forecasting, intensity estimation, and predictions of torrential rainfall and gale-force winds.