Baseline uncertainties in biomass burning emission models resulting from spatial error in satellite active fire location data

Global and regional-scale estimates of biomass burning emissions rely on satellite data products with varying spatial resolution. In heterogeneous landscapes, moderate-resolution fire location data may not capture fine-scale variation in fuel type, leading to both random and systematic error in emissions. Using 120-meter land cover data for the Amazon basin, we estimate the probability of accurate classification of individual fires at 88% for MODIS and 74% for GOES. Classification error limits the ability of emission models to reproduce patterns of emissions, and can only be reduced by innovative use of current satellite data or ancillary data. Emissions biases caused by spatial error vary with regional distribution of fire types. For regional-scale studies in the Amazon Basin, we estimate emissions biases of +3% to +19% for MODIS and +6% to +39% for GOES. The difference between these two systems is an important consideration for multi-sensor fire applications. Citation: Hyer, E. J., and J. S. Reid (2009), Baseline uncertainties in biomass burning emission models resulting from spatial error in satellite active fire location data, Geophys. Res. Lett., 36, L05802, doi: 10.1029/2008GL036767.