This paper presents an automated technique, embedded in an online service, 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 but practical and diﬀerent per
1 × 1 degree tile. For each tile, a probability distribution function of a pixel, being covered with water or being dry is established based on a long SAR training dataset. These probability distributions are conditional on the backscatter and the incidence angle. In classiﬁcation mode the probability of water coverage is calculated, conditional on 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 demonstrate its use in ﬂood situations using Envisat ASAR information during the 2011 Thailand ﬂoods. A ﬁrst merge with a NASA near real time water product based on MODIS optical satellite imagery shows excellent agreement between these independent satellite-based water products.