The Russian wheat aphid, Diuraphis noxia (Kurdjumov), is a pest of small grain crops that has caused hundreds of millions of dollars of damage since it was first reported in the United States in 1986. Much is known about D. noxia population dynamics during the spring and early summer when most of the crop damage occurs, whereas little is known about the system during the overwintering period. Using a spatially explicit model developed from field observations in a wheat/fallow agroecosystem, we sought for predictable variation in overwintering success of D. noxia based on environmental factors such as topography and soil type. Successful modeling of densities of D. noxia would facilitate early control efforts targeting locations where D. noxia successfully overwintered. D. noxia density data were collected over 3 yr at two sites in eastern Colorado. The model incorporates georeferenced data from soil surveys, topography, and satellite imagery as predictor variables. Our approach links an information theoretic approach for model inference and model selection to landscape ecology, allowing for the examination of multiple candidate models and variables within each of the candidate models. Results were used to create trend surface models for D. noxia density in winter wheat agroecosystems. The model has the potential for use in site specific pesticide applications. Using site specific pesticide applications, pesticide inputs could be reduced by an estimated 30%, reducing input costs to the producer, increasing natural enemy refuges, reducing environmental contamination, augmenting pesticide resistance management practices, and reducing exposure of agricultural workers.