Stability analysis of unsaturated landslide deposits requires reliable estimates of soil moisture and pore water pressure. However, modeled soil moisture and pore water pressure contain substantial uncertainties due to imperfect information on soil hydraulic properties. Due to the relatively high dimensionality, commonly used parameter optimization strategies can be significantly affected by equifinality problems. This study investigates the effectiveness of reducing parameter estimation dimensionality using soil pedo-transfer functions. Specifically, we first estimated soil hydraulic parameters using the traditional Generalized Likelihood Uncertainty Estimation (GLUE) method, with parameters randomly drawn from the entire space (refer to as GLUE-random). In a second strategy, we use the Rosetta 3 pedotransfer function to constrain soil hydraulic parameters (refer to as GLUE-Rosetta). The two methods were tested in a typical landslide deposit with in-situ measured soil moisture dynamics for inverse modeling. The GLUE-random estimated soil hydraulic parameters contained substantial uncertainties –resulting in poorly constrained soil water retention curves (SWCC) and hydraulic conductivity functions (HCF). As a result, the uncertainty bands of pore water pressure and slope stability can cross values with several orders of magnitudes. In contrast, GLUE-Rosetta provided well-constrained SWCC and HCF, which significantly reduce the uncertainties in pore water pressure and slope stability estimates. These results suggest that the Rosetta 3 pedotransfer function can significantly improve the reliability of soil hydraulic parameters by reducing the dimensionality of the optimization problem and high-quality prior information of soil hydraulic properties. In conclusion, Rosetta 3 can enhance the reliability of soil parameters estimates and the reliability of subsurface hydrology, which may benefit the development of landslide early-warning systems.