## 3 Materials and Methods
### Methodology  
For the needs of the study, various sources were used (Figure 2). The first source of information regards the optical and radar Sentinel datasets, acquired over the area of interest. The second source of information considers the Sentinel ready products from the Sentinel Hub service. The third component refers to the crowdsourced vector geodata manually digitized by several users of the area of interest, available at the OpenStreetMap service [35]. The accuracy assessment of the OpenStreetMap service is still debated by researchers while several examples of this service blended with remote sensing can be found in the literature [36; 37; 38]. Finally, the compressed red-green-blue (RGB) high-resolution optical data from the Google Earth platform are used for validation purposes. Figure 1: The selected case study area located in the western part of Cyprus. The various thematic classes from the Corine Land Use Land Cover 2018 dataset [13] over the area of interest are shown in (**a**), while some high-resolution aerial orthophotos provided by the Department of Land and Surveys (scale 1:5000) are shown in (**b**) (archaeological site of Nea Paphos) and (**c**) (historical Centre of Paphos town). (**d**) shows the elevation of the area. Through the Sentinel Hub service, radar and optical Sentinel images were retrieved (see more details below). Beyond the calibrated bands from both sensors, additional products were generated. The NDVI and the RVI were processed using Equations (1) and (2):  
\[\text{NDVI}=(\rho_{\text{NIR}}\ \text{-}\ \rho_{\text{RED}})/(\rho_{\text{NIR}} \ +\ \rho_{\text{RED}}), \tag{1}\]  
\[\text{RVI}=(\text{VV}/(\text{VV}\ +\ \text{VH}))^{0.5}\ (4\ \text{VH})/(\text{VV}\ +\ \text{VH}), \tag{2}\]  
where the \(\rho_{\text{NIR}}\) and \(\rho_{\text{RED}}\) refer to the reflectance value (%) of the near-infrared and red bands of the Sentinel-2 sensor (namely using the band 8 and band 4), while the VV and VH refer to the polarization bands of the Sentinel-1 sensor, it should be mentioned that the estimation of the RVI was based upon a custom script available with the Sentinel-Hub services at [39]. As mentioned in [39], an equivalent to the degree of polarization (DOP) is calculated as VV/(total power), where the latter is estimated as the sum of the VV and the VH polarization of the Sentinel sensor. The DOP is utilized to obtain the depolarized fraction as m = 1 - DOP and it can range between 0 and 1. For pure or elementary targets this value is close to zero, whereas for fully random canopy (at high vegetative growth), it reaches close to 1. This m factor is multiplied with the vegetation depolarization power fraction (4 \(\times\) VH)/(VV + VH). The m factor is modulated with a square root scaling for a better dynamic range of the RVI. From optical and radar vegetation indices, the proportion of vegetation can be retrieved. In our study, two different models were applied for both optical and radar datasets:  
\[\text{PV}_{1}\text{-radar}=(\text{RVI}\ \text{- RVI non-veg.})/(\text{RVI veg}\ \text{- RVI non-veg.}), \tag{3}\]  
\[\text{PV}_{1}\text{-optical}=(\text{NDVI}\ \text{- NDVI non-veg.})/(\text{NDVI veg}\ \text{- NDVI non-veg.}), \tag{4}\]  
\[\text{PV}_{2}\text{-radar}=[(\text{RVI}\ \text{- RVI min})/(\text{RVI max - RVI min})]^{0.5} \tag{5}\]  
\[\text{PV}_{2}\text{-optical}=[(\text{NDVI}\ \text{- NDVI min})/(\text{NDVI max - NDVI min})]^{0.5} \tag{6}\]  
where vegetation index veg (NDVI veg and RVI veg) and non-vegetation index (NDVI non-veg. and RVI non-veg.) represent the vegetated and non-vegetated pixels of the considered index, respectively,  
Figure 2: A schematic representation of the four “layers” of information used in this study: the Earth Observation Sentinel-1 and Sentinel-2 images (**a**), the Sentinel Hub, an Earth Observation big data cloud platform (**b**), crowdsourced geodata from OpenStreetMap (**c**) and the Google Earth platform (**d**).