PYTAF: A Python Tool for Spatially Resampling Earth Observation Data

• Published in: Earth science informatics Vol. 15; no. 3; pp. 1443 - 1448
• Main Authors: Zhao, Guangyu, Yang, Muqun, Gao, Yizhao, Zhan, Yizhe, Lee, H. Joe, Di Girolamo, Larry
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022; Springer Nature B.V
• Subjects: Algorithms; Earth; Earth and Environmental Science; Earth science; Earth Sciences; Earth System Sciences; Information Systems Applications (incl.Internet); Ontology; Resampling; Simulation and Modeling; Software; Software Article; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Supercomputers; Weather forecasting; Resample; Pytaf; Swath; Nearest Neighbor; Grid; Python
• ISSN: 1865-0473; 1865-0481; 1865-0481
• DOI: 10.1007/s12145-020-00461-w

Earth observation data have revolutionized Earth science and significantly enhanced the ability to forecast weather, climate and natural hazards. The storage format of the majority of Earth observation data can be classified into swath, grid or point structures. Earth science studies frequently involve resampling between swath, grid and point data when combining measurements from multiple instruments, which can provide more insights into geophysical processes than using any single instrument alone. As the amount of Earth observation data increases each day, the demand for a high computational efficient tool to resample and fuse Earth observation data has never been greater. We present a software tool, called pytaf, that resamples Earth observation data stored in swath, grid or point structures using a novel block indexing algorithm. This tool is specially designed to process large scale datasets. The core functions of pytaf were implemented in C with OpenMP to enable parallel computations in a shared memory environment. A user-friendly python interface was also built. The tool has been extensively tested on supercomputers and successfully used to resample the data from five instruments on the EOS-Terra platform at a mission-wide scale.