Agricultural productivity, ecosystem health, and wetland restoration rely on soil organic carbon (SOC) as vital for microbial activity and plant health. This study assessed: (1) accuracy of topographic-based non-linear models for predicting SOC; and (2) the effect of analytic strategies and soil condition on performance of spectral-based models for predicting SOC. SOC data came from 28 agriculturally converted Delmarva Bays sampled down to 1 meter. R2 was used as an indicator of model performance. For topographic-based modeling, correlation coefficients and condition indices reduced 50 terrain-related values to three datasets of 16, 11, and 7 variables. Five types of non-linear models were examined: Generalized Linear Mondel (GLM) ridge, GLM LASSO, Generalized Additive Model (GAM) non-penalized, GAM cubic splice, and partial least-squares regression. Carbon stocks varied widely, 50 to 219 Mg/ha, with the average around 93 Mg/ha. Topography shared a weak relationship to SOC with most attributes showing a correlation coefficient less than 0.3. GLM ridge and both GAMs achieved moderate accuracy at least once, usually using the 16 or 11 variable datasets. GAMs consistently performed the best. Prior to carbon analysis, hyperspectral signatures were recorded for the topmost soil horizons under different conditions: moist unground, dry unground, and dry ground. Twenty-four math treatment and smoothing technique combinations were run on each hyperspectral dataset. R2 varied greatly within datasets depending on analytic strategy, but all datasets returned an R2 greater than 0.9 at least twice. Moist unground soil models outperformed the others when comparing the best models among datasets.