The uncertainty of a scalar field is essential structuring information for any estimation problem. Establishing the uncertainty in a dense gridded product from sparse or random uncertainty-attributed input data is not, however, routine.\ This manuscript develops an equation that propagates the uncertainty of individual observations, arbitrarily distributed in R^{2}, to a common estimation location at which they can be used to determine the composite uncertainty of the output field. The equation includes the effect of the distance between the observation and estimation locations, the field and horizontal uncertainty of the observation, and user-parameters to control the expected variability in the field as a function of distance. Two computational versions of the equation, a lower cost conservative approach and a higher cost mean-distance approach, are developed and evaluated for computational cost and resulting accuracy in numerical experiments over simulated bathymetric data. The mean-distance approach is more accurate, but more costly; suitable numerical approximations are proposed to control computational costs. A benefit of the work described is flexibility and enhancement for applications of the model, such as the Combined Uncertainty and Bathymetry Estimator (CUBE) algorithm, which is used as a demonstration of the difference between the two versions of the equation.