When assimilating soil moisture data for the early warning of rainfall-induced landslides, the accuracy of inversely estimated parameters and the modeling of landslide hydrology largely depend on the initialization of soil hydraulic parameters. This study integrates the Particle Batch Smoother (PBS) data assimilation method with the initialized soil hydraulic parameters using the Rosetta pedotransfer function (Rosetta method)，and evaluates the combined PBS-Rosetta approach through synthetic numerical examples and real-case applications. The results demonstrate that PBS-Rosetta can obtain accurate posterior parameters and improve the simulation of landslide hydrology. In the application to the Yindongzi Gully landslide case in Dujiangyan, Sichuan Province, PBS-Rosetta successfully provided an accurate early warning for the debris flow disaster that occurred on June 26, 2018. Given its critical role in accurately estimating soil hydraulic parameters and improving landslide hydrological simulations, PBS-Rosetta is recommended for rainfall-induced landslide warning systems.