This paper, the second part in a series of two articles, aims at presenting a data-driven modeling strategy for forecasting wildfire spread scenarios based on the assimilation of the observed fire front location and on the sequential correction of model parameters or model state. The objective here is to sequentially update the fire front location in order to provide a more reliable initial condition for further model integration and forecast.