Soil moisture (SM) is a key variable in hydrometeorology and climate systems. With the growing interest in capturing fine-scale SM variability for effective hydroclimate applications, spaceborne L-band bistatic radar systems using Global Navigation Satellite System-Reflectometry (GNSS-R) technology hold great potential to meet the demand for high spatiotemporal resolution SM data. Although primarily designed for tropical cyclone monitoring purposes, the first GNSS-R satellite constellation - Cyclone Global Navigation Satellite System (CYGNSS) mission, has demonstrated the benefits of reliably monitoring diurnal SM dynamics through its initial stage of seven-year data record, thanks to its high revisit frequency at sub-daily intervals. Nevertheless, knowledge of SM retrieval from CYGNSS, particularly linked with its distinctive features, remains poorly understood, while numerous existing uncertainties and open issues can restrict its effective SM retrieval and practical applications in the next operating stages. Unlike other review papers, this work aims to bridge this knowledge gap in CYGNSS SM retrieval by highlighting noteworthy design properties based on analyses of its real-world data, while providing a synthesis of recent advances in eliminating external uncertainty factors and improving SM inversion methods. Despite its potential, CYGNSS SM retrieval faces both general and particular challenges arising from common issues in retrieval algorithms for conventional GNSS-R satellites and unique data limitations tied to its technical design. Scientific debates over the contributions of coherent and incoherent components in total CYGNSS signals and accurate partitioning of these two parts are defined as the key algorithm-related challenges to resolve, along with correcting attenuation effects of vegetation and surface roughness. The data-related challenges involve variations in CYGNSS's spatial footprint, temporal frequency, and signal penetration depth across different land surface conditions, inadequate consideration of CYGNSS incidence angle change, excessive dependence on a reference SM dataset for inversion model calibration/training or validation, and computational demands for processing rapid multi-sampling CYGNSS data retrieval. Future research pathways highlight leveraging cuttingedge machine learning/deep learning algorithms to enhance CYGNSS SM data quantity and quality and better interpret its complex interactions with other hydroclimate variables. Assimilating CYGNSS SM data streams into physical models to improve the prediction of related variables and climate extremes also presents a promising prospect.