# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## Materials
### Data Sets  
A time series of 25 Sentinel-1 (S-1) B images with identical orbit configuration (same image geometry) was used to derive the flood-related classes. The Sentinel-1 mission consists of two satellites (Sentinel-1A/B) equipped by C-band (wavelengths [\(\lambda\)] = 5.6 cm) sensors that allow one to monitor the Earth's surface at a repeat frequency of six days. The characteristics of the used images and the acquisition dates are listed in Table 1. Only images with an interval of 12 days could be used for the study area in the analysed period, since only one sensor (Sentinel-1 B) provided both polarizations VV and VH of the Interferometric Wide Swath (IW) mode and was already processed by ESA as Ground Range Detected High Resolution (GRDH) products. The flood event was covered by three S-1 images, whereby the scene acquired on 6 th of April 2017 was used as a flood image for further analysis and classification. This flood scene is chosen due to the temporal proximity of the reference  
Figure 1: Location map of the study area (red rectangle) in Namibia. data. The validation of the developed approach was carried out on the basis of a 27 km\({}^{2}\) large extent in the study area. Figure 2b shows the validation extent of the preprocessed S-1 image at the flooding event (6 April 2017) for VV polarization. The generation of the reference data was carried out by visual interpretation and manual digitalisation of a high-resolution optical Sentinel-2 (S-2) image, which was recorded on 8 April 2017. When the backscatter image and optical data were compered, no changes in the flood extent could be observed within the span of two days. The digitised reference mask is shown in Figure 2b. Urban areas and TFV are both characterized by strong double-bounce and multiple scattering effects. This makes their separation considerably more difficult. For the identification of urban areas, the Global Urban Footprint (GUF) was therefore used as additional information, which was derived on the basis of TerraSAR-X and TanDEM-X SAR data [52]. The spatial resolution of the GUF is 0.4 arcseconds (\(\sim\)12 m). Figure 3a shows the GUF for the study area. In the developed approach, the GUF layer is used as the exclusion layer. Since the focus of the study is on the derivation of TFV areas, urban areas are excluded from the analysis regarding flood and are not considered in the methodology. All regions that lie below an elevation of 20 m above the nearest water network were defined as flood-prone in order to prevent misclassifications in elevated areas. For this purpose, the 'Height above \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Characteristics of the Used Sentinel-1 Data**} & **No.** & **Date** & **No.** & **Date** \\ \hline
**Wavelength** & 5.6 cm & 1 & 2 September 2016 & 14 & 17 February 2017 \\
**Mode** & Interferometric Wide Swath (IW) & 2 & 26 September 2016 & 15 & 1 March 2017 \\
**Polarization** & VV, VH & 3 & 08 October 2016 & 16 & 13 March 2017 \\
**Frequency** & C-Band (GHz) & 4 & 20 October 2016 & 17 & 25 March 2017 \\
**Resolution** & 20 \(\times\) 22 m (ground range and & 5 & 1 November 2016 & 18 & **6 April 2017** \\  & azimuth) & 6 & 13 November 2016 & 19 & 18 April 2017 \\
**Pixel spacing** & 10 \(\times\) 10 m & 7 & 25 November 2016 & 20 & 30 April 2017 \\
**Inc. angle** & 30.4\({}^{\circ}\)-46.2\({}^{\circ}\) & 8 & 7 December 2016 & 21 & 12 May 2017 \\
**Pass direction** & Ascending & 9 & 19 December 2016 & 22 & 24 May 2017 \\
**Relative orbit** & 116 & 10 & 31 December 2016 & 23 & 5 June 2017 \\
**Product level** & Level-1 (Ground Range Detected & 11 & 12 January 2017 & 24 & 29 June 2017 \\  & High Resolution (GRDH)) & 12 & 24 January 2017 & 25 & 23 July 2017 \\  & 13 & 5 February 2017 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics and acquisition dates of the used S-1 satellite data. S-1 scenes are acquired under the same orbit conditions. The scene acquired on 6 April 2017 was used within this study as flood event image.