When modelling, considering the influence of all forms of uncertainty is important. This study used Monte Carlo simulation approaches to quantify the influence of positional and thematic uncertainties in landscape maps on metrics and model coefficients based on these maps. First, a brief comparison of simulation approaches, differing in their consideration of spatially correlated thematic error within agricultural fields, was conducted. Results helped to identify an approach under which the output distributions best-represented reference metrics. Second, the influence of both positional and thematic uncertainties on model coefficients were quantified and compared to already-considered forms of uncertainty. Three simulation approaches, differing in how they consider spatially correlated thematic error between fields, were used. Results suggest that the influence of positional and thematic uncertainties was lower. The demonstrated simulation approaches may be useful to studies in similar landscapes, where local reference data are not available, for updating coefficient confidence intervals.