Field measured estimates of aboveground biomass (AGB) for 15 transects in Bwindi Impenetrable National Park (BINP), Uganda were used to generate a number of prediction models for estimating aboveground biomass (AGB) over the full extent of BINP. AGB estimates were extrapolated from the field data using dual-polarization radar satellite data alone, optical satellite data, and a combination of both. The effectiveness of the dual-polarization radar remote sensing data alone was limited due to the difficulties of geocoding and terrain correction in this mountainous region, producing problems with layover and shadowing. The optical-only method demonstrated that perhaps thermal bands may be more sensitive to biomass in tropical forests than visible bands. The radar and optical combined method, generated using the non-parametric algorithm Random Forest (RF) in R, provided the lowest RMSE error (~120 Mg ha-1). The analysis also demonstrated that a number of radar backscatter variables had greater utility for generating a predictive model of biomass than many optical bands in this mountainous region. The combined optical and radar remote sensing model was used to produce a final AGB map over the full 331 km2 extent of BINP; AGB in BINP was estimated at 89.1 million Mg ± 3.9 million Mg, with a mean carbon density of 44.5 million Mg C ± 60 Mg C ha-1.