Potential evapotranspiration (PET) is a key component in the water exchange between the land surface and the atmosphere and plays a fundamental role in the hydrological cycle. While numerous methods approaches have been developed for estimating PET based on data inputs, evaluating the performance of new methods in comparison with common empirical methods is essential to achieve an optimal PET estimation method under data-limited conditions. In this study, precipitable water vapor (PWV) derived from Global Navigation Satellite Systems (GNSS) is used to estimate PET (GNSS-based PET). The GNSS-based PET is then evaluated along with two temperature-based methods (Hargreaves-Samani and Thornthwaite), two radiation-based methods (Priestley-Taylor and Makkink), and three global gridded PET products, including the ERA5-Land reanalysis model from the European Centre for Medium Range Weather Forecasts (ECMWF), the Global Land Evaporation Amsterdam Model (GLEAM version 4.2a), and the Global Land Data Assimilation System (GLDAS version 2.1). The Penman-Monteith method, endorsed by the Food and Agriculture Organization (FAO), was selected as the benchmark to evaluate the performance of all studied methods. Statistical evaluation results indicated that the GNSS-based method exhibited the highest agreement with the Penman-Monteith method compared to the other approaches. For this method, the root mean square error (RMSE) is 0.31 mm, the mean absolute error (MAE) is 0.27 mm, and the correlation coefficient (CC) is 0.97. These results demonstrate the superiority of the GNSS-based method in estimating PET.