In the detection applications of synthetic aperture radar (SAR) data, a crucial problem is developing precise models for clutter statistics. Generalized gamma distribution (GΓD) has been widely applied in many fields of signal processing, and it has been demonstrated to be an appropriate model for describing the statistical behaviors of SAR sea clutter, wherein parameter estimation is a key issue for determining the practical application of GΓD. Work that contains three major aspects is performed in this paper. First, an approximate estimator for GΓD parameters based on the well-known “method-of-log-cumulants” is derived; a theoretical comparison between the approximate estimator and other known estimators is also presented. Second, based on this estimator, a scheme of parameter estimation is further given by comprehensively considering estimation precision, speed, and applicable conditions. The simulation results show that the presented scheme is fast and effective. Third, we assess the fitting performance of GΓD and the proposed scheme using real SAR sea clutter data, and compare the model with generalized-K distribution. The experiments on single-look complex and multilook processing L-band ALOS-PALSAR and C-band RADARSAT-2 SAR data verify the effectiveness of the proposed scheme of GΓD parameter estimation. Moreover, several examples of ship detection in real SAR images testify to the usefulness of the proposed scheme in practical applications.