# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 5 Discussion  
This paper presents the AUTOWADE tool, which has been conceived to provide the Italian DCP with a product that daily monitors floods and inland waters in a fully automatic way using S1 data. The service implemented by AUTOWADE is tested in pre-operational mode since early 2020 and has been extensively used to monitor the location and the extent of the floods that hit Italy in autumn 2020 (e.g., the events considered in Figures 13 and 14). AUTOWADE utilizes not only S1 data, but also S2 ones generated by a processor described in a previous paper [34], as well as ancillary data provided by the Copernicus Land Monitoring Service. With respect to a previous paper dealing with an automated S1-based flood service [28], AUTOWADE monitors not only floods, but also permanent water bodies and searches for flooded vegetation in the gaps present in the open floodwater map. Moreover, it is tested on a quite large number of case studies. With respect to previous studies on flood mapping from SAR data, this paper considers all the aspects related to the provision of an automated, national flood/water monitoring  
Figure 14: Typical AUTOWADE’s output generated using the S1 acquisition performed on 9 December 2020 (orbit 95, the same considered to produce Figure 5). Panel **(a)** synoptic view; panels **(b,c)**: zoom on two flooded areas. Blue: permanent water; cyan: floodwater. product to an end-user, such as the automatic procurement of satellite data and the delivery of the product to the end-user. As for the algorithmic aspect, a new method is proposed, which does not use an image tile-based approach to determine a threshold that separates water from non-water, as commonly done in the literature (see the Introduction). The BFE approach implemented in AUTOWADE takes advantage of the availability of a reference water mask to identify the edges separating land from open water, also using a clustering method as the ISODATA. Focusing directly on the portion of a SAR image where standing water is present, instead of searching for it by iteratively diving the image in tiles of different sizes, represents a simple method that improves computational efficiency. From this point of view, the application of a despeckling filter, performed in the preprocessing step, is the task with the higher computational burden, as pointed out also in [23], while neither the ISODATA, nor the histogram fitting are very time-consuming. The combined use of an edge-filter and an automatic thresholding method was performed also in [51], where the filter was applied directly on a spectral index, derived from multispectral data, sensitive to the presence of water (Modified Normalized Difference Water Index [52]). In AUTOWADE, considering that SAR images are affected by speckle noise (despite of the application of a despeckling filter, some noise still remains in the images), it has been chosen to preliminarily use a clustering algorithm to make the data more immune to noise, as done in [53]. Regarding accuracy, for permanent water and open floodwater, it has been evaluated considering a set of test cases presenting large differences in terms of total flooded surface, ranging from 280 ha to 9460 ha (Table 2, last column and Table 8, 2 nd column). Even the ratio between the flooded surface and the size of the AOI presents large differences ranging from less than 1% (EMSR411) to about 7% (EMSR429). The maximum discrepancy between CEMS-derived and AUTOWADE-derived maps in terms of total flooded surface is 858 ha (EMSR417; AUTOWADE: 8602 ha, CEMS: 9460 ha, see Table 8). However, in terms of percentage with respect to the size of the AOI, this discrepancy corresponds only to 0.6% (AUTOWADE: 5.8%, CEMS: 6.4%). The maximum percentage difference is 2.2% (EMSR496; AUTOWADE: 5.9%, CEMS: 3.7%). Fairly good results have been obtained for all the case studies, especially considering \(\kappa\) and the F1-score. Almost equal values of the F1-score (0.90-0.91) have been obtained for the biggest flood (EMSR417) and the smallest ones (EMSR359-411).