A Flexible Multi-Temporal and Multi-Modal Framework for Sentinel-1 and Sentinel-2 Analysis Ready Data

Abstract

The rich, complementary data provided by Sentinel-1 and Sentinel-2 satellite constellations host considerable potential to transform Earth observation (EO) applications. However, a substantial amount of effort and infrastructure is still required for the generation of analysis-ready data (ARD) from the low-level products provided by the European Space Agency (ESA). Here, a flexible Python framework able to generate a range of consistent ARD aligned with the ESA-recommended processing pipeline is detailed. Sentinel-1 Synthetic Aperture Radar (SAR) data are radiometrically calibrated, speckle-filtered and terrain-corrected, and Sentinel-2 multi-spectral data resampled in order to harmonise the spatial resolution between the two streams and to allow stacking with multiple scene classification masks. The global coverage and flexibility of the framework allows users to define a specific region of interest (ROI) and time window to create geo-referenced Sentinel-1 and Sentinel-2 images, or a combination of both with closest temporal alignment. The framework can be applied to any location and is user-centric and versatile in generating multi-modal and multi-temporal ARD. Finally, the framework handles automatically the inherent challenges in processing Sentinel data, such as boundary regions with missing values within Sentinel-1 and the filtering of Sentinel-2 scenes based on ROI cloud coverage.

Deep Internal Learning for Inpainting of Cloud-Affected Regions in Satellite Imagery

“…The resulting sample counts for both regions are shown in Table 1. The raw data are acquired and preprocessed using the analysis-ready data framework described in [51], as shown in Figure 1. The Sentinel-1 SAR data were radiometrically calibrated, speckle filtered, and terrain corrected.…”

Multi-Modal Convolutional Parameterisation Network for Guided Image Inverse Problems

“…This naturally relies on the assumption that the structure of the scene has not changed significantly compared to the historic data acquisition. The dataset used in [ 16 ] and created using a framework described in [ 23 ], contains pairs of temporally proximate Sentinel-1 and Sentinel-2 images for a period of 2 years and has been employed to evaluate MCPN. More specifically, the clear sky images from Scotland in the year 2020 are used as targets for the inpainting task, and the clear sky images from 2019 are averaged and used as the historical informing prior.…”