## 2 Materials and Methods
### Processing Methods
#### 2.4.1 Ndwi  
Medium-resolution images alone can rarely be used to visualise archaeological sites; hence, to achieve the aims of this research, there was a need for a method able to clearly discriminate between land and water surfaces.  
There are different techniques available to delineate and map water features (for an overview, see refs 48,63), but one of the most widely used in is the Normalised Difference Water Index (NDWI). As argued by [PERSON] et al. ([48], pp. 672, 673), NDWI is a suitable tool for mapping water features, because it is easier to obtain and generally more reliable than single-band classification methods, such as density slicing (on this topic, see also [PERSON] et al.,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Satellite Images** & **Date Range** & **No. Images** \\ \hline Landsat 2 & 1975 & 1 \\ \hline Landsat 5 & 1984–1998/2000/2004–2005/2008–2011 & 54 \\ \hline Landsat 7 & 1999–2012 & 33 \\ \hline Landsat 8 & 2013–2014 & 6 \\ \hline Sentinel-2 & 2015–2019 & 15 \\ \hline Sentinel-2 (Monthly) & 2018 & 36 \\ \hline Planetscope & 2018 & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Temporal coverage and number of satellites images used in this research.  
Figure 7: The workflow adopted for the analysis.  
2012 [62], pp. 421,422; [PERSON] et al., 2013 [63], pp. 5531-5533; [PERSON] et al., 2016 [51], p. 3; for a density slicing approach, [PERSON] at al., 2005 [64]; the same approach was also used by [PERSON] et al., 2019 [19]). Both qualities perfectly fit the scope of this research, and this index was chosen as the one to delineate water masses.  
Two formulae are generally used to obtain the NDWI, by combining the green band with either the near-infrared band (NIR) or the short-wave infrared (SWIR) band of satellite images. These formulas are usually called after their creators, or named NDWI [65] and modified NDWI [66], respectively:  
\[\mathrm{NDWI_{McFeeters}}\frac{(\mathrm{Green})-(\mathrm{NIR})}{(\mathrm{Green} )+(\mathrm{NIR})} \tag{1}\]  
\[\mathrm{NDWI_{Xu}}\frac{(\mathrm{Green})-(\mathrm{SWIR})}{(\mathrm{Green})+( \mathrm{SWIR})} \tag{2}\]  
In the NDWI images, values close to or below an arbitrary threshold (usually 0, but for other examples see [PERSON] et al., 2009 [67]) can be identified as land formations, while values above the threshold can be classified as water. There is no unanimous agreement about which of the two formulas works best for any occasion, and literature about their use mostly shows how the benefit of the two bands might be very situational and tied to the immediate surrounding environment of the water bodies [50; 68; 69; 70; 71; 72]. The formula used here is Equation (2), because it is generally agreed that, unlike Equation (1), it can better distinguish between water and built-up areas without returning false positives [66] (pp. 3026, 3027). One weakness of this formula is, however, that it tends to return positive values when snow is present [51]. The selected study areas do not have dense vegetation or snow around them but can present sparse built-up features close to the lakes; therefore, Equation (2) seemed more appropriate. The NDWI is generally aimed at identifying water bodies, but if the location of an archaeological site is known, then the index can be used to check if that location is under water or not. In fact, on satellite images, the basic difference between an emerged and a submerged archaeological site will be whether pixels can be identified as representing water or not.