ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
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Volume V-3-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 641–648, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-641-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 641–648, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-641-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Aug 2020

03 Aug 2020

DATA PROCESSING ARCHITECTURES FOR MONITORING FLOODS USING SENTINEL-1

W. Wagner1,2, V. Freeman3, S. Cao1, P. Matgen4, M. Chini4, P. Salamon5, N. McCormick5, S. Martinis6, B. Bauer-Marschallinger1, C. Navacchi1, M. Schramm1, C. Reimer2, and C. Briese2 W. Wagner et al.
  • 1Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria
  • 2EODC Earth Observation Data Centre, 1040 Vienna, Austria
  • 3Spire Global Luxembourg, 33 rue Sainte Zithe, 2763, Luxembourg
  • 4Luxembourg Institute of Science and Technology, 4362 Esch-sur-Alzette, Luxembourg
  • 5European Commission, Joint Research Centre (JRC), Ispra, Italy
  • 6Deutsches Zentrum f¨ur Luft- und Raumfahrt, 82234 Wessling, Germany

Keywords: SAR, Sentinel-1, Floods, Water Bodies, Data Cubes, Big Data, Model Calibration, Change Detection

Abstract. Synthetic Aperture Radar (SAR) images acquired by Earth observation satellites often constitute the only source of information for monitoring the progression of flood events over larger regions. Particularly attractive are the SAR data acquired by the Copernicus Sentinel-1 satellites because they are free and open, and combine a short revisit time with a good spatial and radiometric resolution. In this contribution, we discuss how a Sentinel-1 data processing system should be designed to optimally benefit from the dense Sentinel-1 time series and advanced algorithms such as change detection or machine learning methods. This was one of the questions addressed by an expert group tasked by the Joint Research Centre of the European Commission to investigate the feasibility of an automated, global, satellite-based flood monitoring product for the Copernicus Emergency Management Service. Drawing from the expert group report, we distinguish three broad categories of data processing architectures, namely single-image, dual-image, and data cube processing architectures. While the latter architecture is the most demanding in terms of large storage and compute capacities, it is also the most promising to derive high-quality Sentinel-1 flood maps comprised not just of the flood mask but also of data fields describing the retrieval uncertainty and masks showing where Sentinel-1 cannot detect floods due to physical reasons. Therefore, we recommend to use data cube processing architectures and showcase the use of the Austrian Data Cube for monitoring a small-scale flood event that occurred in Austria in November 2019.