# Automated Rain Detection by Dual-Polarization Sentinel-1 Data
## 4 Rain Detection
### Application of Dual-Pol Filter on Different Rain Types  
The application of the dual-pol filter on the three cases in Figure 2 is shown in Figure 12, where the filters generated with VV (400 m), VH (800 m) and dual-polarizations channels are included. Here the blue and purple contours indicate the base reflectivity of 20 dBZ and 40 dBZ as given by the NEXRAD weather radar. In these cases, the dual-pol filter clearly shows a better agreement with NEXRAD data, in particular as reflectivity is larger than 40 dBZ. As shown for the first case, it seems that the VH filter is more sensitive to noise than the VV and dual-pol filters as demonstrated by the subswath jump and the modulation in the azimuthal direction on the left-hand side of the swath at low incidence angle. Furthermore, VV filters seem to capture signatures that do not correspond to high base reflectivity values such as the wind gust front on the first case and the area with low backscattered signal observed on the second case. The third case is more complex. VH and VV signatures have opposite signs and only the VH filter (positive rain signature) can capture the rain signature in the south area. The areas with the strongest rain rate are captured by all three filters. These three cases illustrate the capabilities of the dual-polarization for filtering rain signatures. Figure 12: The first and second columns show the Koch filters generated with VV (400 m) and VH (800 m) channels respectively for the three cases in Figure 2. The last column shows the proposed dual-pol filters correspondingly. The blue and purple contours mark the base reflectivity of 20 dBZ and 40 dBZ respectively. Tropical cyclones or strong storms such as extra-tropical cyclones, polar lows and medicanes are other phenomena in which the question of discriminating between rain and background sea state remains. Figure 13a,b show S-1 images for both VV and VH polarizations in the case of Irma tropical cyclone, when the eye was over Florida. Figure 13c is the corresponding base reflectivity as measured by the ground-based radar from NEXRAD network. Here the wind speed is larger than 30 m/s (larger than in our present study) and the base reflectivity is larger than 48 dBZ. As observed in Figure 13f, when applied to such an extreme case, the dual-pol filter can detect the main areas impacted by rain and certainly helps improve the quality of wind estimates in such complex situations. However, further analysis remains to be done for a proper assessment of our method in those extreme situations. As shown in Figure 10, the detection rate is not 100%, especially for low to medium rain rates and high wind speeds. Indeed, our approach can only detect areas in SAR images where the backscattered signal is significantly affected by rain. Figure 14 illustrates such a case. Here the wind as given by ECMWF is about 9 m/s with two main areas exhibiting rain rates larger than 10 mm/h, but occurring at different incidence angles in the image. The first one (within the purple box) has a stronger rain rate (RR \(\in\) (14,16) mm/h) and corresponds to larger incidence angles (\(\theta\in(41^{\circ},46^{\circ})\)) than the second one (within the yellow box) (RR \(\in\) (10,14) mm/h; \(\theta\in(38^{\circ},41^{\circ})\)). As observed, for the second area, the rain impact on the roughness is much less (see the green transect). Consequently, it is not captured by our detection algorithm. In this case, such a difference between the two signatures probably results from the difference in incidence angle and rain rate. This case also illustrates that our method does not only detect areas impacted by rain but also by non-wind related features that produce inhomogeneity in the SAR image. Indeed, areas with low backscattered values associated with low winds, such as in the lee of the island (middle) or close to the coast (south part of the area) are detected by our filter. This was already mentioned in the previous study by [PERSON] [25] in which the detection is focused on edges and textural features.