Many applications of synthetic aperture radar (SAR) backscatter data require analysis-ready data (ARD) as input, which can be derived from Level-1 SAR images by applying e.g. geocoding, radiometric calibration, multi-looking, and imaging noise adjustments. Several publications [1, 5] present guidelines on how to weave these operations together using well-known SAR toolboxes like SNAP (https://step.esa.int/main/snap-8-0-released/), GAMMA (https://www.gamma-rs.ch/software), or ISCE2 (https://github.com/isce-framework/isce2) to create a preprocessing workflow with calibrated and georeferenced backscatter datacubes as output. In addition to backscatter data, most of these software packages also allow to produce certain auxiliary layers like projected local incidence angle (PLIA) data or shadow/layover masks, which are indispensable for enhancing the quality and interpretability of ARD data. Producing all these layers for every individual scene is very time consuming and may become a bottleneck for services which demand efficient, worldwide preprocessing in near-real-time. Geocoding, which creates a link between orbit and ground geometry, claims most of the preprocessing time. The ground geometry—usually represented by a digital terrain model—is assumed static, whereas orbit data is dynamically updated with the latest vectors for every scene. Assessing the stability of orbit trajectories could thus help to significantly improve the overall preprocessing performance. With orbital tube diameters of around 500m (1σ), Sentinel-1’s predecessor C-band SAR missions ENVISAT and ERS-1/2 had already proven their value for manifold interferometric SAR (InSAR) applications [4]. In this respect, the Sentinel-1 constellation goes one step further. Both platforms, currently Sentinel-1A and Sentinel-1B, share the same orbital plane, and revolve the earth in an orbital tube with a diameter of only 100m (RMS) [2]. Hitherto, studies have only analysed the role of Sentinel-1’s orbital tube in terms of certain InSAR parameters [3], but did not contemplate on the potential of the satellite’s orbit stability over time when generating SAR backscatter data. In this conference contribution, we address the question how Sentinel-1’s orbit fluctuations propagate into different SAR layers and how Sentinel-1’s preprocessing can be made more efficient—without reducing the quality of the output. On the basis of four years of Sentinel-1 orbit data, we aim to quantify the impact of the orbital tube to state whether a layer can be declared as static or not. First results indicate a steady behaviour of PLIA over time, thus paving the way to establish it as a static layer per relative orbit. Such knowledge can be incorporated into the design of performant workflows, allowing to save costly resources, in particular when conducting Sentinel-1 preprocessing on a global scale. [1] Federico Filipponi. “Sentinel-1 GRD preprocessing workflow”. In: Multidisciplinary Digital Publishing Institute Proceedings. Vol. 18. 1. 2019, p. 11. [2] Dirk Geudtner et al. “Sentinel-1 system capabilities and applications”. In: 2014 IEEE Geoscience and Remote Sensing Symposium. IEEE. 2014, pp. 1457–1460. [3] Pau Prats-Iraola et al. “Role of the orbital tube in interferometric spaceborne SAR missions”. In: IEEE Geoscience and Remote Sensing Letters 12.7 (2015), pp. 1486–1490. [4] Fabio Rocca. “Diameters of the orbital tubes in long-term interferometric SAR surveys”. In: IEEE Geoscience and Remote Sensing Letters 1.3 (2004), pp. 224–227. [5] John Truckenbrodt et al. “Towards Sentinel-1 SAR analysis-ready data: A best practices assessment on preparing backscatter data for the cube”. In: Data 4.3 (2019), p. 93.