Improving flood monitoring through advanced modeling of Sentinel-1 multi-temporal stacks

Multi-temporal remotely sensed data are a precious source of information for high spatial and temporal resolution flood mapping. We present a methodology for flood mapping through processing of long time series of Sentinel-1 SAR data, as well as ancillary information. A Bayesian framework is adopted to derive probabilistic maps of the presence of flood waters, through modeling of backscatter time series, based on the assumption that floods represent impulsive temporal anomalies. We illustrate some results over the Basilicata Region, in Southern Italy, recurrently subject to floods.

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