Abstract
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.
Highlights
Sentinel-1 is a constellation of polar-orbiting radar satellites operated by the European Space Agency (ESA) as part of the European Union’s Copernicus programme
Considering the algorithmic approaches discussed above, we propose to distinguish three broad categories of data processing architectures, namely single-image, dual-image, and data cube processing architectures
In order to either allow a regular re-processing of the historic flood maps, or to support a data cube processing architecture (Section 3.3), an off-line high performance computing (HPC) environment is needed in parallel to the near real-time (NRT) system
Summary
Sentinel-1 is a constellation of polar-orbiting radar satellites operated by the European Space Agency (ESA) as part of the European Union’s Copernicus programme. A key advantage of this systemic approach is that processing lines can be streamlined and made fully automatic Another important advantage is that coverage is maximised, becoming de facto mainly limited by the maximum duty cycle of the SAR instrument which, in the case of IW mode, is about 25 min per 100 min orbit. The high temporal sampling rate is beneficial for the mapping of urban areas (Lisini et al, 2018), forests (Dostalovaet al., 2018), rice (Bazzi et al, 2019) and other land cover types, but even more so for the monitoring of dynamic land surface variables such as soil moisture (BauerMarschallinger et al, 2019), vegetation (Vreugdenhil et al, 2018), snow (Lievens et al, 2019) and dynamic water bodies (Huang et al, 2018) In this contribution we discuss how Sentinel-1 can be used for systematic and fully-automatic monitoring of flood events. The importance of choosing the right system architecture is illustrated in Section 5 by analysing a recent flood event that occurred in Austria in November 2019
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