Remote-Sensing Data Distribution and Processing in the Cloud at the ASF DAAC

The Alaska Satellite Facility (ASF) Distributed Active Archive Center (DAAC) has been tasked to archive and distribute data from both SENTINEL-1 satellites and from the NASA-ISRO Synthetic Aperture Radar (NISAR) satellite in a cost effective manner. In order to best support processing and distribution of these large data sets for users, the ASF DAAC enhanced our data system in a number of ways that will be detailed in this presentation.The SENTINEL-1 mission comprises a constellation of two polar-orbiting satellites, operating day and night performing C-band Synthetic Aperture Radar (SAR) imaging, enabling them to acquire imagery regardless of the weather. SENTINEL-1A was launched by the European Space Agency (ESA) in April 2014. SENTINEL-1B is scheduled to launch in April 2016.The NISAR satellite is designed to observe and take measurements of some of the planet's most complex processes, including ecosystem disturbances, ice-sheet collapse, and natural hazards such as earthquakes, tsunamis, volcanoes and landslides. NISAR will employ radar imaging, polarimetry, and interferometry techniques using the SweepSAR technology employed for full-resolution wide-swath imaging. NISAR data files are large, making storage and processing a challenge for conventional store and download systems.To effectively process, store, and distribute petabytes of data in a High-performance computing environment, ASF took a long view with regard to technology choices and picked a path of most flexibility and Software re-use. To that end, this Software tools and services presentation will cover Web Object Storage (WOS) and the ability to seamlessly move from local sunk cost hardware to public cloud, such as Amazon Web Services (AWS). A prototype of SENTINEL-1A system that is in AWS, as well as a local hardware solution, will be examined to explain the pros and cons of each. In preparation for NISAR files which will be even larger than SENTINEL-1A, ASF has embarked on a number of cloud initiatives, including processing in the cloud at scale, processing data on-demand, and processing end-user computations on DAAC data in the cloud.