## 6 Conclusions  
Estimating vegetation proportion through Earth Observation sensors is important, especially when dealing with the monitoring of hazards in the vicinity of archaeological sites. Vegetation cover is usually extracted from optical vegetation indices such as the NDVI. However, with the increasing availability of radar sensors like the Sentinel-1, radar-related vegetation indices have also been introduced in the literature.  
The Sentinel Hub big data cloud platform can provide readily calibrated reflectance optical bands and VV/VH radar polarizations to end-users, and other ready products. Here, we explored the use of  
Figure 13: New pseudo-colour composite on the right-left of the Figure integrating the RVI \(\times\) NDVI vegetation proportion index, and the VV/VH polarizations from Sentinel-1. Vegetated areas are shown with red colour, while buildings with green colour. On the left side, the overall procedure (as explained in the previous sections) for estimating the RVI \(\times\) NDVI index.  
Sentinel-1 and -2 datasets for estimating vegetation proportion, as well as crowdsourced geo-datasets from the OpenStreetMap service, on an area in the western part of Cyprus  
The overall findings indicate that Sentinel-1 and -2 indices can provide a similar pattern only over vegetated areas, which can be further elaborated to estimate temporal changes using integrated optical and radar Sentinel data. The use of the OpenStreetMap data was found very helpful as it allowed us to locate vegetated and non-vegetated areas with high accuracy, which would be difficult to achieve with the medium-resolution Sentinel data. Additionally, satellite images need to be radiometrically calibrated and corrected.  
RVI and NDVI should be elaborated with caution since no direct correlation can be established. However, processed products such as differences of the vegetation proportion estimation can uncover similar patterns between the optical and the radar data. This allows us to fill gaps if no optical data are available. The combination of RVI \(\times\) NDVI can enhance the presence of vegetated areas and can be integrated back to the VV and VH polarization (see Figure 13). The results from these findings can be further utilized so as to support the extraction of vegetation proportion using integrated radar and optical datasets, especially in areas with high cloud-coverage, supporting hazard analysis and risk management studies. In future, the author will explore ways to better adapt these two types of sensors for risk analysis at archaeological sites.  
This article is submitted under the NAVIGATOR project. The project is being co-funded by the Republic of Cyprus and the Structural Funds of the European Union in Cyprus under the Research and Innovation Foundation grant agreement EXCELLENCE/0918/0052 (Copernicus Earth Observation Big Data for Cultural Heritage).  
The author would like to acknowledge the \"CUT Open Access Author Fund\" for covering the open access publication fees of the paper. The author would like to acknowledge the use of the CORINE Land Cover 2018 (C) European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)\", in 2018: \"&\" European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)\"), the Sentinel Hub (Modified Copernicus Sentinel data 2020/Sentinel Hub\"), the OpenStreetMap service (C) OpenStreetMap contributors) and Google Earth platform. Thanks, are also provided to the Eratosthenes Research Centre of the Cyprus University of Technology for its support. The Centre is currently being upgraded through the H2020 Teaming Excelsior project (www.exccelsior2020.eu).  
The author declares no conflict of interest