## 3 Test Case and Dataset  
Hurricane Harvey hit Texas in August and September 2017, causing major damage in large areas of Houston, the fourth largest city in the United States. In a four-day period, unprecedented amounts of rainfall accumulated over Eastern Texas and Louisiana, following the Category 4 hurricane Harvey's first landfall on 25 August 2017 on San Jose Island, Texas. After hitting the Texas mainland on the same day, Harvey weakened to a tropical storm and its speed reduced. As it was only progressing slowly, the amount of rainfall in the region became extremely high and the volume of water took days to drain through rivers, causing widespread catastrophic flooding, most notably in the Houston metropolitan area. The local National Weather Service office in Houston observed daily rainfall accumulations of 370 mm and 4018 mm on August 26 and 27, respectively. A total precipitation of 1539 mm in the city of Nederland in southeast Texas, makes Hurricane Harvey the wettest tropical cyclone on record in the United States [43]. As a result, the Houston bayous burst their banks for several days, which led to substantial large-scale flooding, especially to the north and east of the Houston area. The total economic loss from the event amounted to about US$125 billion, with more than 30,000 people evacuated from their homes, more than 17,000 rescues prompted and 106 confirmed deaths in the United States. Hurricane Harvey ties with Hurricane Katrina (2005) as the costliest tropical cyclone on record (Kurz, 2017). Thanks to the systematic acquisition of EO images of this area, the amount of data acquired during Harvey (as part of the activation of the International Charter Space and Major Disasters) has reached an unprecedented level. Here we make use of S-1 images acquired over the metropolitan area of Houston on 30 August 2017, when the flooding was still ongoing and close to its peak, as well as images acquired before the event. On August 30, S-1 operated in both SM and IW mode, making it possible to test the proposed algorithm for two different spatial resolutions, 5 m and 20 m, respectively. For both acquisition modes, images acquired before the catastrophic event started are also available. The IW image covers the entire Houston area, whereas the SM ones, with a smaller swath, cover the central and eastern part of the city. All images are available in the Single Look Complex (SLC) mode (see Figure 2). With respect to SAR intensity, we use a pair of images consisting of a pre-flood, denoted also as'reference image', and a post-flood image. Table 1 provides details about the exact acquisition time of each image and the corresponding characteristics. The following pre-processing steps are applied to each image: multi-looking, terrain correction using a DEM, radiometric calibration, and geocoding. The multi-looking window is 4 \(\times\) 1 in case of IW images, to get a square pixel of 20 m and the higher possible spatial resolution. For the SM mode, we do not perform the multi-looking because the pixel is already square, 5 \(\times\) 5 m, and we want to preserve the spatial resolution. The Refined Lee speckle filter is also applied to reduce the noise level of the intensity images. In addition to the SAR intensity, we also compute \(\rho\) maps using images acquired before and during the flood event, as \(\rho_{\text{pre-IW}}\), \(\rho_{\text{pre-SM}}\) and \(\rho_{\text{co-IW}}\), \(\rho_{\text{co-SM}}\). Firstly, the SAR interferogram is generated from the SLC image pairs and then \(\rho\) is estimated by using a moving window of 9 \(\times\) 9 pixels. All \(\rho\)s are then geocoded and resampled over the same spatial grid of the intensity images. All details are summarized in Table 2.  
Figure 2: S-1 SM and IW footprints (blue rectangles). The common area, between IW and SM images, is delimited in green. The three red boxes are the different test cases discussed in Section 4.