## 4 Discussion
### Using MODIS Products in Fuel Studies  
Our approach showed that MODIS products can be very effective for estimating surface fuel load changes in grasslands and shrublands, and for portraying the actual fuel changes that take place within and between years. Thus, there is much potential for using MODIS to provide more accurate and detailed assessments of fuel load in these grass and shrub areas than the static fuel load estimates provided by the existing LANDFIRE products.  
We believe that we can greatly improve upon the current approach. First, we foresee being able to improve upon the spatial resolution of the NDVI data used. While we used 250-m resolution MODIS data in this analysis, the spatial scale of these datasets could be refined using the spatial and temporal adaptive reflectance fusion model (STARFM) [55; 56]. This model enables melting of high-temporal-resolution MODIS data with high-spatial-resolution Landsat data (30 m). This model has been applied successfully in numerous studies [55; 57; 58]. The STARFM-type modeling would enable generation of Landsat-like spatial data while retaining the temporal qualities of MODIS in the shrubland grass-dominated systems that we are investigating. We envision developing Landsat-like NDVI data for the spring time frame annually using STARFM. While we typically have ample numbers of high quality MODIS datasets during the spring season, only a limited number of Landsat datasets are available at this time due to cloud cover and the relatively infrequent revisit cycle of Landsat (16 days). The merging of MODIS and Landsat data would provide the fire applications community with high spatial resolution NDVI and biomass data during the critical high-growth phase in these shrub and grasslands when acquisitions of high-resolution Landsat data are rare.  
Second, the MODIS NPP is a global product and its generation includes biome type information, daily meteorological data, and the FPAR/LAI information at global level. This brings some uncertainties for regional studies. As an example, if we are interested in deriving an accurate NPP product for the western United States' shrublands and grasslands, we may find it beneficial to use regional information from the western United States such as the National Land Cover Database (NLCD) [59]. Being Landsat-based, the NLCD has higher spatial resolution and represents land cover in United States with more precision and accuracy than the MODIS land cover product. Integrating NLCD information into the NPP calculation will be an option for future improvement. We also believe that we can improve the Biome-BGC model so that it better captures the live and dead fuel load in the region. The uncertainty of the model parameters can be reduced by calibrating the parameters with local observations of the vegetation characteristics, such as the aboveground live biomass and litter biomass.  
The approach presented in this study used information from general studies of the shrubland/grassland ecosystem, and it is acknowledged that the results have not been validated with field data. More extensive experimentation using accurate field information at the regional and local scale is required to derive a solid predictive model for fire spread prediction in shrubland/grassland ecosystems in the western USA. As shown in this study, incorporating dynamic fuel information can greatly alter the fire model predictions. Such efforts will need to be considered by scientists and decision makers who use these fire models. With the integration of observations from existing Earth observation products, such as MODIS products, we can greatly enhance the fire model performance to support fire risk assessment and planning in large regions.