## 2 Materials and Methods
### Methodology
[PERSON] [26] reported that the MNDWI produced better results than the Normalized Difference Water Index [35] in enhancing and extracting water from a background that is dominated by built-up land areas. The tasseled cap transformation [73] is a useful tool for compressing spectral data into a few bands associated with physical scene characteristics [30; 31]. In particular, it converts the readings in a set of bands into composite values, using the linear regression method--similar in concept to principal components analysis. The coefficients used to create the TCT are derived statistically and vary among the imaging sensors. The composite values define a new coordinate system with a new set of orthogonal axes in order to represent the brightness of each pixel, the degree of greenness/yellowness of vegetation and the wetness which is correlated to soil moisture, water, and other moist features. The other additional components contain image noise and atmospheric influences, such as clouds, haze, sun angle differences, etc. For L7 data, the tasselled cap transformation produces six output bands: brightness, greenness, wetness, fourth (haze), fifth, sixth. This type of transformation should be applied to atmospherically corrected and calibrated reflectance data rather than applying to raw digital number imagery. The third component relating to the tasseled cap wetness (TCW) has been used as a tool for flooded area delineation. Furthermore, the DVW and RSWIR indices were implemented. The first one functions by subtracting the simple Normalized Difference Water Index (NDWI) from the Normalized Difference Vegetation Index (NDVI), while the second one uses the short wave infrared (SWIR) band in its equation. One of the major advantages of using SAR images, apart from its all-weather capability, lies in its ability to sharply distinguish water from other classes. Water bodies act as a mirror reflecting surface, their response is low (low backscatter coefficient in SAR images) and thus look like a dark area. The land, for its part, gives a much higher amount of radar energy due to the surface roughness and this generates the high contrast between surfaces: soil and water [75]. The S1 SAR image obtained from Sentinel-1C-band comprises an image which was acquired at the early stages of the flood. The processing method sets a threshold value (which was established manually) of radar backscatter, which is followed by a binary algorithm (band math equation) to determine whether a given raster pixel is flooded or not. Figure 3: Filling the wedge-shaped gaps of the L7 image, (**a**) image with gaps, (**b**) corrected image. The data processing of the S1 VV polarization SAR image followed specific steps. The first step is related to the pre-processing of the data. Initially, a subset of the images was made, to reduce the size of the data and make them more easily managed. Then, a calibration of the images was carried out, comparing the instrument's measuring accuracy to a known standard. Finally, a speckle filter (Lee filter with window size 7 by 7) was applied [76]. Speckle appears as a grainy'salt and pepper' texture in an image. This is caused by random constructive and destructive interference from the multiple scattering returns that occur within each resolution cell. Speckle is essentially a form of noise which degrades the quality of an image and may make interpretation (visual or digital) more difficult. Thus, it is generally desirable to reduce speckle before interpretation and analysis. The second step deals with the main process of the data. Thus, a threshold was selected, analyzing first the histogram of the filtered backscatter coefficient. The histogram shows a peak of different magnitude. Low values of the backscatter correspond to the water, and high values correspond to the non-water class. To binarize