As a crucial component of the solar-terrestrial space environment, the ionosphere's precise monitoring directly impacts the performance of Global Navigation Satellite System (GNSS) navigation and positioning services while also supporting applications such as low-altitude economy and space weather forecasting. With the widespread of GNSS terminals and advancements in hardware performance, the widely distributed crowdsourced GNSS data presents an unprecedented opportunity for ionospheric monitoring research. However, traditional ionospheric monitoring methods rely on continuous phase observations in the time domain, making it challenging to fully exploit the potential of crowdsourced GNSS users in extracting high-precision ionospheric information within short observation windows. Based on the existing Crowdsourced Ionospheric delay correction Map (CIM) constructed through Crowdsourcing RTK technology, this study proposes a novel CIM-PLUS enhancement. By leveraging CIM's intrinsic ionospheric representation that is free of hardware-delay components and integrating Slant Total Electron Content (STEC) measurements from static reference stations, the CIM-PLUS achieves spatial resolution enhanced ionospheric monitoring through the collaboration of dynamic and static GNSS observables. Simulation experiments show that, in a scenario with 100 crowdsourced users, the proposed approach achieves a theoretical enhancement of two orders of magnitude in ray number available for tomography. Static reference network experiments validate that the ionospheric STEC accuracy extracted by the new method is comparable to that of traditional methods. In dynamic monitoring scenarios, the CIM-PLUS expands point-to-point monitoring to point-to-surface monitoring, resulting in an order-of-magnitude improvement in spatial resolution. Finally, we highlight that the development of Crowdsourcing RTK can further drive the ubiquitous application of satellite precise positioning technology by leveraging the demand for high-precision ionospheric monitoring, addressing the limited appeal of existing precise positioning technologies for the public.
Xu et al. (2026) studied this question.