The isotopic composition of surface water vapor flux (δ) is a quantity frequently used to investigate the local and regional water cycle. In this study, the δ determined with the Keeling method was evaluated against the flux-gradient method and the Craig-Gordon model prediction. Previous studies have shown that the choice of regression fitting methods can bias the δ intercept results and precision of the Keeling method. Here, the Keeling method was applied to high-frequency (0.2 to 1 Hz) data measured at a cropland and a lake site to test different regression methods. Results show that the Keeling method with the York’s solution (YS) and the ordinary least squares (OLS) regression produced robust estimates of δ when compared with the flux-gradient method. Increasing concentration range reduced the standard error of estimate but did not bring obvious improvement to the bias error for the YS and OLS regression. The Keeling result was better using data from two sampling heights than only one. There was evidence that the Keeling method with the OLS regression slightly outperformed the flux-gradient method during periods with small vertical vapor gradient. Results also show that the Keeling method with the geometric mean regression gave highly biased estimate of δ for the types of isotope ratio infrared spectroscopy analyzer deployed in this study. These results can inform δ calculations and future experimental designs.