This document discusses satellite image processing in a high-performance computing (HPC) environment. It provides three examples of satellite image analysis implemented on the Pan cluster: 1) pre-processing of Landsat images, 2) MODIS land surface temperature analysis of Antarctica, and 3) improving New Zealand's land cover database by identifying small woody patches. The key challenges discussed are the large volumes of satellite imagery data and implementing batch-level parallel processing in Python to efficiently analyze the data using HPC resources.