Derivation of land-surface backscatter signatures from SAR time series for flood and wetland delineation

Remote sensing plays an important role for mapping and monitoring of floods and wetlands. Synthetic aperture radars (SAR) are of special importance due to their ability to penetrate cloud cover and their high sensitivity to the occurrence of surface water and variations in soil moisture. During flood events this sensitivity typically leads to deviations from backscatter behaviour that is observed under non-flooded conditions. These alterations are typically mapped using change detection techniques in which the changes between a preand a post-flood SAR image are quantified and classified. However, a number of open research questions exist with respect to automatic detection of flood-induced backscatter changes: (i) in change detection, selection of a suitable pre-flood reference image is often not trivial. This is especially the case when user intervention should be minimised, a constraint often imposed by the requirements of automatic processing chains. (ii) Numerous studies have demonstrated the importance of seasonality in backscatter from different land-surface types, which is typically not addressed explicitly in change detection methodologies. (iii) A number of confounding factors exist that contribute to the overall uncertainty of the delineated flood extents, such as ambiguous radar signatures of different land-cover types and the contribution of speckle. Traditionally, binary flood maps include no information on the expected uncertainty introduced by these factors whereas more recently, flood mapping methodologies have been proposed that result in fuzzy or probabilistic flood maps. The overall objective of this thesis is to address these open questions by introducing and evaluating a novel change detection framework for mapping flood-induced deviations from long-term backscattering behaviour. For this purpose, backscatter signatures were derived by characterising seasonality using harmonic analysis of ENVISAT Advanced SAR (ASAR) Wide Swath time series spanning multiple years (> 50 scenes). The estimated harmonic model parameters were analysed and discussed for complex wetland areas in the context of ancillary data on hydrological and biophysical processes. The model residuals were used as a measure of the flood-induced deviation from average seasonal backscattering behaviour. Finally, a Bayesian approach was applied for deriving probabilistic flood maps from the residuals. The results show that harmonic analysis of SAR time series can be effectively used for deriving seasonal signatures characteristic of different landsurface types. Moreover, the applicability of the model parameters for classifying different wetland backscattering classes is demonstrated. The use of the model residuals for delineating areas that were inundated during non-seasonal events indicates that seasonal harmonic model estimates can serve as a reliable estimate of the seasonal backscattering behaviour a land surface would display under non-flooded conditions. The probabilistic flood mapping approach is shown to provide reliable maps and can be used for characterising uncertainty caused by some of the aforementioned factors. In conclusion, the proposed approach provides a framework for characterising seasonal backscattering behaviour and detecting flood-induced backscatter changes in SAR time series. An outlook to using the proposed method in the context of novel SAR missions such as Sentinel-1 is provided.