Missing data in long-term eddy covariance measurements of latent heat flux produce errors in the estimation of evapotranspiration and the water budget. Because no standard method of gap filling has been widely accepted, identification of optimal filling methods for gaps is crucial for determining total evapotranspiration. In this study we evaluate the application of a Kalman filter for filling gaps in latent heat flux data collected from an agricultural research station. The filtering approach was compared with several gap-filling methods including mean diurnal variation, multiple regressions, 2-week average Priestley–Taylor coefficient, and multiple imputation. The results demonstrated that a Kalman filtering approach developed using the relationship between latent heat flux, available energy, and vapour pressure deficit provides a closer approximation of the original data and introduces smaller errors than the other methods evaluated. Evaluation of the Kalman filter approach demonstrates the efficiency of this technique in replacing data in both small and large gaps of up to several days.