The deeply dissected topography and diverse climate of the Eastern Allegheny Plateau and Mountains (Major Land Resource Area (MLRA) 127) create challenges for dynamic ecological and pedogenic modeling. Soil organic carbon (SOC), one of the most dynamic soil properties, has been previously modeled using State Soil Geographic (STATSGO2) and Soil Survey Geographic (SSURGO) databases for MLRA 127, estimating mean SOC to a depth of 1 m to be 2.6 and 4.4 kg m^-2, respectively. Previous studies have shown that these approximations underestimate true carbon stock due to unpopulated organic horizons and inconsistencies within the databases. Between 1960 and 2009, the Kellogg Soil Survey Lab (KSSL) sampled and characterized 243 pedons within MLRA 127 based on soil survey needs. Each pedon has a site description and associated chemical and physical lab analyses to support its taxonomic classification. Data mining revealed that 12.6% of these 243 pedons lack organic carbon data for one or more horizons and 49.7% lack bulk density values. Different methods for populating missing bulk density and organic carbon data will be assessed and validated in order to calculate SOC stock. Geographically weighted regression kriging with KSSL pedons and environmental covariates will be used to model SOC stock across MLRA 127. The resulting SOC estimates will be cross-validated with Rapid Carbon Assessment (RaCA) samples and uncertainty will be assessed. The methodology used in this study will serve as the foundation for estimating SOC stock across other MLRAs throughout the United States.  Improving SOC stock estimates across MLRA 127 will enhance the understanding of dynamic soil properties and will provide guidance for better land management practices to benefit biological communities.