# Variability of Interpolation Errors and Mutual Enhancement of Different Interpolation Methods  
Yunxia He  
School of Geographical Sciences, China West Normal University, Nanchong 637009, China; [EMAIL_ADDRESS] (Y.H. ); [EMAIL_ADDRESS] (H.Y. ); [EMAIL_ADDRESS] (L.  
accuracy. This approach provides a novel pathway for addressing interpolation errors, defined as discrepancies between interpolation-derived estimates and true values, arising from the prediction of unknown data points [3; 4]. The magnitude of these errors depends on factors such as the interpolation method and its parameters. Previous studies have explored strategies to optimize interpolation accuracy. For example, [PERSON] et al. [5] evaluated five interpolation methods to estimate missing point cloud data from typical landforms in the Cold Long Ridge fault zone, identifying the most suitable algorithm. Similarly, [PERSON] [6] analyzed the effects of smoothing factors, search points, and search directions on the interpolation accuracy of digital elevation models (DEMs) using root mean square error (RMSE) metrics. His study defined optimal ranges for interpolation parameters based on discrete point data from diverse landforms. Error similarities among different interpolation methods vary depending on the techniques and data used [7]. Commonly employed interpolation methods [8], such as linear, nearest neighbor, bilinear, and cubic spline interpolation, can be evaluated by comparing their errors with true values. This makes error analysis an essential step in improving interpolation methods. To further refine spatial interpolation accuracy, researchers have conducted extensive studies on interpolation errors. For instance, [PERSON] [9] examined the impact of three popular interpolation techniques--inverse distance weighting (IDW), spline, and kriging--on DEM accuracy across four survey projects. His findings indicate that spline interpolation is best suited for mountainous regions, while IDW or kriging with an exponential variogram model performs better in hilly and plain areas. [PERSON] [10] identified that all elevation errors display spatial positive autocorrelation by analyzing the spatial distribution and characteristics of DEM interpolation errors. [PERSON] et al. [11] compared four interpolation methods using a Gaussian surface as a reference, evaluating each method's accuracy within a specific scale range. This analysis aids in selecting the most suitable interpolation technique for different conditions. [PERSON] [12] applied ten interpolation methods to 10 m sample data from a designated area, interpolating the 10 m DEM to 5 m, 2.5 m, and 1 m resolutions. He then compared the absolute mean differences of each method using survey control points to assess interpolation accuracy at higher resolutions. In meteorological analysis, interpolation methods are vital for processing observation data from various stations, providing more accurate meteorological data for subsequent analyses. [PERSON] [13] compared methods for interpolating DEMs from LiDAR elevation points, as well as easily obtainable DEMs, to evaluate their accuracy in simulations for the North Carolina Floodplain Mapping Program (NCFPMP). This comparison also highlighted the accuracy of the source DEM data. [PERSON] [14] assessed the spatial interpolation accuracy of ordinary kriging, universal kriging, and two inverse distance weighting methods across four variables--surface type, sampling pattern, noise level, and small-scale spatial correlation intensity--using the natural logarithm of the RMSE as a measure of accuracy. The results indicated that both kriging methods outperformed the IDW method across all variables. In hydrological analysis, interpolation enhances data spatial continuity and accuracy, assisting hydrologists in better understanding hydrological processes. [PERSON] et al. [15] analyzed the spatial variability of precipitation in the Laoha River basin, incorporating elevation in their comparison of the IDW, OK, and CK methods.