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. Emergency responders require information on the distribution of triggered landslides within two weeks of an earthquake or storm. Useable satellite radar imagery is acquired within days of any such event worldwide. Recently, several landslide detection methods that use these data have been developed, but testing of these methods has been limited in each case to a single event and satellite sensor. Here we systematically test five methods using ALOS-2 and Sentinel-1 data across four triggering events. The best performing method was dependent on the satellite sensor. For three of our four case study events, an initial ALOS-2 image was acquired within 2 weeks, and with these data the ARIA method performs best. Using a single post-event Sentinel-1 image, the best-performing method was the boxcar-sibling method. We also present three new methods which incorporate a second post-event image. While the waiting time for this second post-event image is disadvantageous for emergency response, these methods perform more consistently and on average 10 % better across event and sensor type than the boxcar-sibling and ARIA methods. Thus, our results demonstrate that useful landslide density information can be generated on the timescale of emergency response, and allow us to make recommendations on the best method based on the availability and latency of post-event radar data.

A systematic exploration of satellite radar coherence methods for rapid landslide detection