Terrain attributes are commonly used to explain the spatial variability of agronomic, pedologic, and hydrologic variables. The terrain attributes studied here (elevation, slope, aspect, and curvature) are estimated readily from digital elevation models (DEMs), but questions remain about how the accuracy and sample spacing of the elevation data affect the estimated attributes. The main objective of this study was to quantify differences in each terrain attribute due to factors affecting DEM accuracy and grid cell size. Three data sources were compared: (i) real-time kinematic global positioning system (RTKGPS); (ii) satellite-differentially corrected global positioning system (DGPS); and (iii) U.S. Geological Survey (USGS) 30-m DEMs. The GPS data from three undulating agricultural fields in northeastern Colorado were interpolated onto 5-, 10-, 20-, and 30-m grid DEMs. The DGPS and USGS DEMs produced similar elevation differences relative to RTKGPS DEMs, but elevation differences in USGS DEMs were more spatially correlated. Estimates of curvature were highly sensitive to DEM differences and the sensitivity of slope, aspect, and curvature estimates decreased as grid cell size increased. The impacts of DEM accuracy and grid cell size were investigated using correlations between wheat (Triticum aestivum L.) grain yields and estimated terrain attributes. The highest correlation coefficients were obtained using RTKGPS data, and decreasing the sample spacing or grid cell size below 30 m did not consistendy improve the correlations. These analyses on agricultural lands indicate the importance of accurate elevation data for detailed terrain analyses on grid cell sizes of 30 m or less.