IN43B-1734
The Synthetic Aperture Radar Science Data Processing Foundry Concept for Earth Science

Thursday, 17 December 2015
Poster Hall (Moscone South)
Paul Alan Rosen1, Hook Hua1, Charles D Norton1 and Michael M Little2, (1)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (2)NASA Headquarters, Earth Science Technology Office, Washington, DC, United States
Abstract:
Since 2008, NASA's Earth Science Technology Office and the Advanced Information Systems Technology Program have invested in two technology evolutions to meet the needs of the community of scientists exploiting the rapidly growing database of international synthetic aperture radar (SAR) data. JPL, working with the science community, has developed the InSAR Scientific Computing Environment (ISCE), a next-generation interferometric SAR processing system that is designed to be flexible and extensible. ISCE currently supports many international space borne data sets but has been primarily focused on geodetic science and applications. A second evolutionary path, the Advanced Rapid Imaging and Analysis (ARIA) science data system, uses ISCE as its core science data processing engine and produces automated science and response products, quality assessments and metadata. The success of this two-front effort has been demonstrated in NASA's ability to respond to recent events with useful disaster support. JPL has enabled high-volume and low latency data production by the re-use of the hybrid cloud computing science data system (HySDS) that runs ARIA, leveraging on-premise cloud computing assets that are able to burst onto the Amazon Web Services (AWS) services as needed. Beyond geodetic applications, needs have emerged to process large volumes of time-series SAR data collected for estimation of biomass and its change, in such campaigns as the upcoming AfriSAR field campaign. ESTO is funding JPL to extend the ISCE-ARIA model to a “SAR Science Data Processing Foundry” to on-ramp new data sources and to produce new science data products to meet the needs of science teams and, in general, science community members. An extension of the ISCE-ARIA model to support on-demand processing will permit PIs to leverage this Foundry to produce data products from accepted data sources when they need them. This paper will describe each of the elements of the SAR SDP Foundry and describe their integration into a new conceptual approach to enable more effective use of SAR instruments.