Abstract
Reliable information about the spatial distribution of surface waters is critically important in various scientific disciplines. Synthetic Aperture Radar (SAR) is an effective way to detect floods and monitor water bodies over large areas. Sentinel-1 is a new available SAR and its spatial resolution and short temporal baselines have the potential to facilitate the monitoring of surface water changes, which are dynamic in space and time. While several methods and tools for flood detection and surface water extraction already exist, they often comprise a significant manual user interaction and do not specifically target the exploitation of Sentinel-1 data. The existing methods commonly rely on thresholding at the level of individual pixels, ignoring the correlation among nearby pixels. Thus, in this paper, we propose a fully automatic processing chain for rapid flood and surface water mapping with smooth labeling based on Sentinel-1 amplitude data. The method is applied to three different sites submitted to recent flooding events. The quantitative evaluation shows relevant results with overall accuracies of more than 98% and F-measure values ranging from 0.64 to 0.92. These results are encouraging and the first step to proposing operational image chain processing to help end-users quickly map flooding events or surface waters.
Highlights
Mapping the extent of surface waters is crucial for many applications as waters constitute a resource and a natural hazard during flooding events
It includes five components: (i) preprocessing of raw Sentinel-1 data; (ii) a tiling approach in order to focus on surface water areas automatically; (iii) class modeling with Finite Mixture Models [45] to produce probability maps based oanndesatcacbulrisahcyedaFscFsiligaegussussrrmeem11e.o.nSdSttt.euulddsy;y(aairvree)aabss::ilZZaootnenreeaAAl —f—ilItIrereerlliaannngdd,[,3ZZ6oo]nnfeeoBBr——smEEnnoggollatahnnddlaaabnneddllZZinoognn;eeaCn—dI(tval)yp. ost-processing
This analysis is performed for three tiles of Zone A (Ireland), which are representative for different proportions of land and surface waters (Figure 3)
Summary
Mapping the extent of surface waters is crucial for many applications as waters constitute a resource and a natural hazard during flooding events. With the launch of the X-band TerraSAR-X/TanDEM-X and COSMO-SkyMed (CSK) constellations, higher sensor spatial resolutions (up to 0.24 m for the TerraSAR-X Staring SpotLight mode) or higher revisit times (six days for Sentinel-1, and up to 5 × 20 m in the standard Interferometric Wide—IW—Swath mode), have increased capabilities for estimating flood extent and for flood monitoring in the case that complex, small-scale, and operational scenarios are available. The potential of this new generation X-band data has already been demonstrated by several use cases in flood emergency situations [10,11,12].
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