Comparative Analysis of PyTAF and kd-tree Resampling Methods for Geostationary Satellite Data: Performance and Efficiency Insights

This study compares two resampling methods, the k-dimensional tree (kd-tree) and the Python interface for Terra Advanced Fusion Resample/Reprojection functions (PyTAF), using surface albedo data from the GEO-KOMSAT-2A (GK2A) Advanced Meteorological Imager (AMI) geostationary satellite, with and without consideration of Earth's curvature.Evaluation metrics include Correlation Coefficient (R), Root Mean Square Deviation (RMSD), Relative RMSD (RRMSD), Bias, spatial distribution, and processing time.Additional quantitative analyses were performed based on viewing zenith angle (VZA) intervals, ranging from 0 to 80 at 20 increments.The results showed that both resampling methods exhibited similar performance in terms of quantitative metrics, but differences emerged in processing time and VZA-specific analysis.These differences were primarily attributed to variations in algorithm design.Specifically, as VZA increased, the panoramic effect caused each pixel to cover a larger geographic area, resulting in geometric distortions.Additionally, the influence of reflectance variability between snowcovered and non-snow-covered regions further exacerbated data uncertainty and geometric distortions.These combined factors contributed to reduced accuracy and increased errors during resampling, leading to higher RMSD and RRMSD values.This study provides empirical evidence of the performance differences between the two resampling methods, offering practical insights for selecting the optimal resampling technique based on research objectives and data conditions.