The extent of fires, their periodicity and their impact on terrestrial communities have been a major concern in the last century. Wildfires play an important role in shaping landscapes and as a source of CO2 and particulate matter, contributing to the green house effect and to global warming. Modeling the spatial variability of wildfire extent is therefore an important subject in order to understand and to predict future trends on their effect in landscape changes and global warming. The most common approaches have been through point pattern analysis or by Markov random fields. Those methods have made possible to build risk maps, but for many forest managers knowing the fire size besides the location of the fire is very useful. In this work we use spatial marked point processes to model the fire size of the forest fires observed in Castellón, Spain, during the years 2001-2006. Our modeling approach incorporates spatial covariates as they are useful to model spatial variability and to gain insight about factors related to the presence of forest fires. Such information may be of great utility to predict the spreading of ongoing fires and also to prevent wildfire outburst by controlling risk factors. We describe and take advantage of the Bayesian methodology including Integrated Nested Laplace Approximation (INLA) and Stochastic Partial Differential Equation (SPDE) in the modeling process. We present the results of different models fitted to the data and discuss its usefulness to fire managers and planners.