Utilising Sentinel-1's orbital stability for efficient pre-processing of sigma nought backscatter

For already more than seven years, the Sentinel-1 C-band Synthetic Aperture Radar (SAR) mission has been providing indispensable information for monitoring bio-geophysical parameters at fine temporal and spatial scales. As many applications require backscatter datacubes as input, enormous amounts of data have to be radiometrically and geometrically corrected to be in a common, Earth-fixed reference system. Pre-processing workflows accomplishing this task have already been established and are implemented in several software suites. However, typically, these workflows are computationally expensive which may lead to prohibitively large costs when generating multi-year Sentinel-1 datacubes for whole continents or the world. In this paper, we discuss existing approaches for generating sigma nought and projected local incidence angle (PLIA) data and present simplifications of the overall workflow relying on the unprecedented orbital stability of Sentinel-1. Propagating orbital deviations through the complete Sentinel-1 pre-processing pipeline helped us to simulate and identify PLIA as a static layer per relative orbit. The outcome of these simulations also provided the necessary information to replace iterative root-finding algorithms for determining the time of closest approach (TCA), i.o.w. the azimuth index, with a linear one - at no expense of radiometric accuracy. All experiments were performed using an in-house developed toolbox named wizsard, which made it possible to speed up Sentinel-1 pre-processing by approximately 4-5 times with respect to the Sentinel Application Platform (SNAP). This could pave the way for producing quality-curated, large-scale backscatter datacubes at continental and global scales in acceptable time frames.

Automated summary

This paper discusses the methods of generating backscatter datacubes using the Sentinel-1 mission, introduces a simplified workflow relying on its orbital stability, and proposes some improvements to speed up processing and reduce computational costs.