Meeting the Big Synthetic Aperture Radar Data Processing Challenge: Introducing the Software for Earth Big Data Processing, Prediction Modeling, and Organization Cloud-Scaling Solution
Josef Kellndorfer, Oliver Cartus, Sean Helfrich · IEEE Geoscience and Remote Sensing Magazine · 2025
With the launch of the National Aeronautics and Space Administration (NASA) and Indian Space Research Organisation (ISRO) synthetic aperture radar mission (NISAR), available open access Earth observation data reach a new milestone with an estimated data rate of ∼70 TB of NISAR science data produced daily. These additional data create many opportunities within the science and user community for new and more in-depth analyses, yet at the same time, most organizations lack the budget, resources, and skills to analyze all this data on-premise. This article introduces concepts on how synthetic aperture radar (SAR) processing software from commercial and open source providers can be coupled with highly scalable cloud processing techniques to digest SAR data at various input levels (from raw to value-added products). Included in this article are examples of the global-scale processing ofSentinel-1data for InSAR coherence estimation, the input to global biomass modeling, and the routine operational processing of SAR time-series data for national and international near real-time flood mapping. The cloud-scaling Software for Earth big data Processing, Prediction modeling, and Organization (SEPPO), which is used to meet the processing challenges utilizing Amazon Web Services (AWS) for large volume and operational SAR data processing in complex workflows, is introduced.