A major concern in landscape management and precision agriculture is the variable-rate application of herbicides in order to reduce herbicide treatment load. These applications require a correct assessment and knowledge of the density and potential spatial variability of weed species within fields. This article addresses the issue of incorporating a digital elevation model as secondary spatial information into the mapping of main weed species present in two sunflower crops in Andalusia, Spain. Two prediction methods were used and compared for mapping weed density for precision agriculture. The primary information was obtained from an intensive grid weed density sampling and the secondary spatial information, e.g., elevation from a digital elevation model. The prediction methods were two geostatistical algorithms: ordinary kriging and kriging with an external drift, which takes into account the influence of landscape. Mean squared error was used to evaluate the performance of the map prediction quality. The best prediction method for mapping most of the weed species was kriging with an external drift, with the smallest mean squared error, indicating the highest accuracy. The results showed that kriging with an external drift with elevation reduced the prediction variance compared with ordinary kriging. Maps obtained from these kriged estimates showed that the incorporation of a digital elevation model as secondary exhaustive information can improve the accuracy of predicted weed densities within fields. These results suggest that kriging with an external drift of weed density data with elevation as a secondary exhaustive variable could be used in such situations, and in this way, the accuracy of maps for precision agriculture, which is the preliminary step in a precision agricultural management program, could be improved with little or no additional cost, since a digital elevation model could be obtained as part of other analyses.