Abstract
Abstract. This paper presents an automated technique which ingests orbital synthetic-aperture radar (SAR) imagery and outputs surface water maps in near real time and on a global scale. The service anticipates future open data dissemination of water extent information using the European Space Agency's Sentinel-1 data. The classification methods used are innovative and practical and automatically calibrated to local conditions per 1 × 1° tile. For each tile, a probability distribution function in the range between being covered with water or being dry is established based on a long-term SAR training dataset. These probability distributions are conditional on the backscatter and the incidence angle. In classification mode, the probability of water coverage per pixel of 1 km × 1 km is calculated with the input of the current backscatter – incidence angle combination. The overlap between the probability distributions of a pixel being wet or dry is used as a proxy for the quality of our classification. The service has multiple uses, e.g. for water body dynamics in times of drought or for urgent inundation extent determination during floods. The service generates data systematically: it is not an on-demand service activated only for emergency response, but instead is always up-to-date and available. We validate its use in flood situations using Envisat ASAR information during the 2011 Thailand floods and the Pakistan 2010 floods and perform a first merge with a NASA near real time water product based on MODIS optical satellite imagery. This merge shows good agreement between these independent satellite-based water products.
Highlights
The consequences of inland and coastal flooding can be devastating and flooding needs to be detected and mapped as accurately and quickly as possible, so that appropriate measures can be taken by governments or disaster management agencies, pre-warnings may be issued, and downstream forecasts may be initiated (Carsell et al, 2004; Werner et al, 2005)
In situ networks of hydrological gauges are increasingly being complemented by satellite imagery, which plays an important role in the European Global Monitoring of Environment and Security (GMES; Brachet, 2004) Emergency Response Core Service
30 to 24◦), the flood probability is still higher than 70 %, but decreasing as α decreases. If we interpolate this finding on the misclassification result of the Pakistan floo3d0, it is reconhistogram firmed that the probability threshold is related to the incidence angle
Summary
The consequences of inland and coastal flooding can be devastating and flooding needs to be detected and mapped as accurately and quickly as possible, so that appropriate measures can be taken by governments or disaster management agencies, pre-warnings may be issued, and downstream forecasts may be initiated (Carsell et al, 2004; Werner et al, 2005). In situ networks of hydrological gauges are increasingly being complemented by satellite imagery, which plays an important role in the European Global Monitoring of Environment and Security (GMES; Brachet, 2004) Emergency Response Core Service. That service is meant to provide “Rapid Mapping”: fast retrieval of information from satellite imagery in order to map consequences related to hazards and civil protection. A number of commercial and non-commercial agencies can respond to flood disasters within a short amount of time (fast retrieval). These agencies react on demand, when an emergency response has already begun.
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