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Monitoring the spatial and temporal extents of permanent and temporary bodies of surface water is important for various applications such as water resource management, climate modelling, and biodiversity conservation. Satellite remote sensing is an effective source of information to detect surface water over large areas and document their evolution in time. Recently, the European Space Agency (ESA) launched freely available SAR (Synthetic Aperture Radar) and optical sensors (Sentinel-1 & 2) with high revisiting time and spatial resolution. The objective of this paper is to explore the contribution of multi-temporal and multi-source (passive and active) Sentinel observations for improving the detection and mapping of surface waters by applying decision-level image fusion techniques. The approach is tested over Central Ireland using a time series of 16 Sentinel-1 images and a few Sentinel-2 images for the period 2015–2016. Compared to a mono-date approach, the combination of Sentinel-1 & 2 observations provides better accuracy for mapping permanent surface water. Decision level fusion technique allows mapping temporary surface water (such as flooding) with a high accuracy. It also gives the possibility to monitor their dynamics by providing the probability of occurrence of flooded areas at the pixel level. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]