Advances in sensor technology with higher spatial, spectral and temporal resolutions are revolutionizing the way remote sensing data are collected, managed and processed. Latest-generation instruments for Earth and planetary observation are now producing a nearly-continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly introduced new processing challenges. In particular, many current and future applications of remote sensing in Earth and space sciences require the incorporation of high performance computing techniques and practices to address applications with high societal impact such as retrieval of Earth and planetary atmospheres, monitoring of natural disasters including earthquakes and floods, or tracking of man-induced hazards such as wild-land and forest fires, oil spills and other types of chemical contamination. Many of these applications require timely responses for swift decisions which depend upon (near) real-time performance of algorithm analysis. These systems and applications can greatly benefit from high performance computing techniques and practices to speed up data processing, either after the data has been collected and transmitted to a ground station on Earth, or during the data collection procedure onboard the sensor, in real-time fashion. In recent years, GPUs have evolved into highly parallel many-core processors with tremendous computing power and high memory bandwidth to offer two to three orders of magnitude speedup over the CPUs. Intel's Xeon Phi coprocessors are also delivering promising computational performance. Parallel and distributed computing facilities and algorithms have become indispensable tools to tackle the issues of processing massive remote sensing data.