User-defined parameters (point density, interpolation, and pixel size) have a great influence on the accuracy of digital terrain model (DTM). Therefore, the optimal interpolation method (IM) and appropriate pixel size should be used to create a continuous surface. Pixel size or spatial resolution tends to be a compromise between the number of samples and the size of the study area, whereby input dataset is often devaluated. In this paper, the authors propose a new methodological approach to DTM production (hybrid DTM—HDTM) from an aero-photogrammetry dataset. Two different approaches to DTM production are presented: the usual (UDTM) and the hybrid (HDTM). HDTM is based on restructuring and refining the input dataset by generating contour lines, determining the optimal interpolation method and selecting the appropriate spatial resolution. The goal is to develop a qualitative DTM while minimalizing the devaluation of input data and error propagation. The accuracy of different IMs for UDTM was examined in a comparative analysis of six statistical parameters by applying cross-validation, with the focus on the root mean square error (RMSE) parameter. Four different methods of selecting optimal spatial resolution were tested for the same model. The UDTM and HDTM generated were compared by interior, exterior, and visual accuracy assessment and by the performance success of specific hydrological parameters: (1) flow accumulation—by applying a developed DNA concept and (2) watershed—by calculating volume and 3D surface. The reference value for exterior accuracy assessment was high-resolution airborne LiDAR DTM (LDTM). It was found that ordinary kriging (OK) was the best IM (RMSE, 1.9893 m). The s​