Wildland fire decision support systems require accurate predictions of wildland fire spread. Fuel moisture content (FMC) is one of the important parameters controlling the rate of spread of wildland fire. However, FMC is a sparsely and relatively infrequently measured surface variable compared to most atmospheric variables. A high resolution, gridded, real-time FMC data set does not currently exist for assimilation into operational wildland fire prediction systems. We use surface observations of live and dead FMC to train machine learning models to estimate FMC based on satellite observations. Machine learning techniques, such as regression trees, random forests, and artificial neural networks, can learn the non-linear relationships between the satellite derived vegetation index predictors and FMC. This allows the methods to be trained on the satellite and surface observations corresponding to the temporally and spatially nearest grid points to the irregularly spaced surface FMC observations. The algorithms are first calibrated on the training data and then applied to a test dataset for Colorado. The results of the test dataset for Colorado show improvements in accuracy for both live and dead FMC estimation compared to persistence and linear regressions. The challenges for managing the different data sources and building the algorithms to achieve the performance necessary for an operational system are discussed. Additionally, we discuss the process of moving the system from the test data of Colorado to the CONUS.