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Article

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

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Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
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Department of Chemical and Process Engineering, University of Strathclyde, Glasgow G1 1XJ, UK
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Computing Systems Laboratory, National Technical University of Athens, 157 80 Athens, Greece
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Author to whom correspondence should be addressed.
Academic Editor: Gregory Giuliani
Remote Sens. 2022, 14(5), 1120; https://doi.org/10.3390/rs14051120
Received: 31 January 2022 / Revised: 16 February 2022 / Accepted: 21 February 2022 / Published: 24 February 2022
(This article belongs to the Special Issue Sentinel Analysis Ready Data (Sentinel ARD))
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. View Full-Text
Keywords: Sentinel-1; Sentinel-2; analysis-ready data; multi-modal; multi-temporal Sentinel-1; Sentinel-2; analysis-ready data; multi-modal; multi-temporal
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MDPI and ACS Style

Upadhyay, P.; Czerkawski, M.; Davison, C.; Cardona, J.; Macdonald, M.; Andonovic, I.; Michie, C.; Atkinson, R.; Papadopoulou, N.; Nikas, K.; Tachtatzis, C. A Flexible Multi-Temporal and Multi-Modal Framework for Sentinel-1 and Sentinel-2 Analysis Ready Data. Remote Sens. 2022, 14, 1120. https://doi.org/10.3390/rs14051120

AMA Style

Upadhyay P, Czerkawski M, Davison C, Cardona J, Macdonald M, Andonovic I, Michie C, Atkinson R, Papadopoulou N, Nikas K, Tachtatzis C. A Flexible Multi-Temporal and Multi-Modal Framework for Sentinel-1 and Sentinel-2 Analysis Ready Data. Remote Sensing. 2022; 14(5):1120. https://doi.org/10.3390/rs14051120

Chicago/Turabian Style

Upadhyay, Priti, Mikolaj Czerkawski, Christopher Davison, Javier Cardona, Malcolm Macdonald, Ivan Andonovic, Craig Michie, Robert Atkinson, Nikela Papadopoulou, Konstantinos Nikas, and Christos Tachtatzis. 2022. "A Flexible Multi-Temporal and Multi-Modal Framework for Sentinel-1 and Sentinel-2 Analysis Ready Data" Remote Sensing 14, no. 5: 1120. https://doi.org/10.3390/rs14051120

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