**1. Introduction**

Human societies are currently facing global warming, and national governments are working to find solutions against the climate crisis, by promoting the installation of renewable energy sources (RESs) to replace fossil fuels. This energy transition could avoid the emissions of GHG (greenhouse gases) [1,2] and reduce the use of carbon fuel reserves [3]—minimizing at the same time geopolitical conflicts to access oil and gas sources [4]. Additionally, this green energy transition is creating new challenges in different sectors and consequentially new job opportunities in economic, technological, and environmental topics [5], together with other kinds of economic and social benefits [6]. With the aim of reducing carbon exploitation, many power energy converters have been designed and installed, considering the specific contexts of each analyzed area. Depending on the sources of power extracted from seas and oceans, two different categories should be defined: "blue energy" (BE) and "marine renewable energies" (MREs). Blue energy, such as salinity gradients, thermal differences and MREs including sea surface waves, sea current, tides, wind, geothermal and solar. Assessing the renewable energy sources (RESs) availability is important in developing short and long-term planning [7,8]. In this regard, wind energy could be one of the safest and most reliable sources of renewable energy [9]. To use these renewable sources, many aspects must be examined. Among these, it is fundamental to assess the exact amount of power for each type of energy converter. This has given more attention to the development of new offshore solutions, such as wind turbines with larger rotors, deep water foundation and floating platforms [10]. Northern and central European countries (ECs) have a long history of designing, developing and installing offshore wind farms [11], since the installation of first prototypes of bottom fixed and floating offshore wind farm in Baltic Sea and Scotland pilot park [12]. Nearly 90% of the world's MREs are in Europe. However, the proportion of the Mediterranean in the use of this vast resource is extremely low [13].

Among those with a feasible amount of wind energy source, the best suitable sites for offshore wind should be selected mainly according to the optimal combination of water depth and distance to the shore [14]. Water depth is a key factor in better understanding the dynamics of the marine environment, in predicting tides, currents and waves and planning offshore facilities and infrastructure such as wind turbines. Selecting suitable places with the optimal combination of water depth and distance to the beach is a complex task and should be carefully examined. The coefficient of water depth in the desired area is the basic parameter in the type of wind farm installation, mainly due to the maintenance and installation cost increase. In addition, the distance from the desired point to the power grid is very important, which increases due to the greater distance, which leads to more use of cables and batteries. Floating platforms could help in this regard which is the current trend to move considering deeper waters. Furthermore, there are more crucial factors/limitations influencing offshore wind (OW) applications, such as ships sea routes for marine transportation, migrating birds, economic activities (e.g., fisheries areas), environmental constraints (marine protected areas) and landscaping view. To combine economic sustainability and public acceptance, the concept of floating marine turbines operating away from coastal areas has also begun and expanding in the Mediterranean Sea [14]. Together with the growth of onshore wind farms installations, there is an expectation for significantly increasing the new wind farms in offshore marine areas, mainly thanks to floating platform technologies [15]. The dramatic growth of new technologies has led to an immediate revolution in the use of new offshore wind farms, statistically, with the wind farm sector in continental Europe showing an annual growth of 101% in 2017 [16].

In general, the installation capacity of wind farms in 2019 for European countries (ECs) is 21.1 GW and there is an expectation that in 2020, total OW energy production could reach 25 GWh, and by 2030, it could reach to 70 GW in o ffshore installation capacity [17,18]. Totally, ECs have 4149 grid-connected wind turbines and 81 o ffshore wind farms installed, which are used only in 10 countries of northern Europe. According to data from 2017, about 50% of o ffshore wind farms in continental Europe were installed in UK (which is 53% of the net 3.15 GWh of installed capacity in Europe). By 2024, Europe's total installed capacity is expected to reach 29.8 GW, expanding at an annual growth rate of 12% [19]. On the other hand, despite all these considerations, there are still no OW farms installed in the Mediterranean Sea, mainly for the water depth that usually does not allow the installation of bottom fixed wind turbines; anyway there are attractive hot spots for future developments of OW in the Mediterranean [20].

The first step in installing wind farms is to evaluate wind sources in focused areas or hot spots. Traditional methods, such as cub anemometers, are frequently used to measure wind sources, by installing calibrated calipers on tall masts. It should be noted that the height of the wind gauges is directly related to the height of the installed wind turbines. Due to the significant growth of technology, the height of wind turbines is constantly increasing. Consequentially, taller masts are needed, increasing the installation costs and the operation and maintenance e fforts. In addition to all the above consideration, it should be noted that natural and human obstacles are the most important factors for the installation of anemometers. The fewer natural and human factors in the area, the fewer wind gauges are needed. However, if there are natural and human obstacles such as cities, more wind gauges are needed [21]. On the other hand, due to the significant development of on-site remote sensing sector, LiDAR (light detection and ranging) and SODAR (sonic detection and ranging) tools could be applied. Goit et al. [22], explained the though reconstruction from LiDAR-measured radial wind speed to wind speed vector is a challenge, LiDAR-based wind speed measurements are undergoing a significant increase in interest for wind energy application. Here, the study employed the scanning of Doppler LiDAR for assessment and comparison. First, the evaluation of the e ffect of carrier-to-noise-ratio (CNR) and data available on the quality of scanning LiDAR measurements was done. Then, it was proposed to reconstruct the wind fields from plan-position indicator (PPI) and range height indicator (RHI) scans of LiDAR-measured line of sight velocities. It was observed that an internal boundary layer with strong shear could be developed from the coastline. Lastly, PPI scan was involved to measure the flow field around a wind turbine and validate wake models. Chaurasiya et al. [23] investigated how to increase the confidence of RS technique to compute Weibull parameters at higher heights for assessment of wind energy resource. It is known that RS techniques are gaining attention worldwide for their comprehensive assessment of wind source in flat, complex, and mountainous terrain. The 10 min average time series wind speed data for the period of one month (in September 2014) were recorded simultaneously at 80 m and 100 m using the cup anemometer installed in the proximity of a 120 m meteorological mast, second wind triton SODAR (sound detection and ranging) and continuous wave wind LiDAR (light detection and ranging). Nine di fferent methods have been implemented to analyze and obtain Weibull parameters on the data obtained from the measurements. Totally, there is an expectation that the outcome of this study could encourage the deployment of remote sensing techniques.

However, this equipment is very expensive and needs to be installed in a study area for more than one year to ge<sup>t</sup> enough data; on the other hand, high maintenance and repair costs are required [24,25]. Consequentially, it is very important to develop new methods that can help to identify suitable areas faster and economically. Satellites are the only tool that can measure the average, minimum and maximum wind speed in a study area (hot spot areas) in the shortest possible time. It should be noted that the reasons for the popularity of these data in the research and academic communities can be mentioned as follows: (i) This data are generally available for free (open access), (ii) They can cover a period of more than 40 years. Due to the fact that it is not possible to install ground wind gauges (such as, cub anemometers) in di fferent areas, due to its high cost, satellites are the only device that can cover areas for more than a year, which is an important factor in assessing wind resources [21]. Satellite technologies for observing, reporting, and evaluating RES potential have led to a revolution in the installation of energy converters in new locations that have not previously been considered. In addition, due to the increase in surveys to identify industrial wind at an altitude of more than 100 m, various atlases were generated as an outcome. It takes long time to install anemometers on-site to measure industrial winds at higher altitudes which can be done with satellite data. However, to bring out the best options and strategies, all aspects of SWOT (strengths, weaknesses, opportunities and strengths, weaknesses, opportunities and threats) must be considered. However, all aspects of SWOT need to be considered to point out the best options and strategies [26]. A SWOT analysis can be used to achieve this goal. This type of analysis, derived from an interdisciplinary approach, can identify barriers and factors influencing the development of marine renewables.

Pisacane et al. [27] explained the current prospects for the exploitation of power plants in the Mediterranean Sea, outlining and discussing challenges, opportunities, and limitations for the deployment of power converters. It was stated that blue energy conversion technologies are now ready to be fully deployed in the device farms. Goffetti et al. [26] described a SWOT analysis of strategic plans for marine renewable energy technologies to minimize and maximize inefficiencies and energy production. SWOT analyses have been used for the navigation of renewable energy technologies and identifying key or hot spots points in various sectors including social, economic, legal, technological, and environmental. Nikolaidis et al. [28] investigated the status of marine renewable energy potential in the Mediterranean Sea and especially around the island of Cyprus. According to their study, OW energy in the Mediterranean Sea is the prominent outlet followed by marine biomasses. On the other hand, they explained that the main physical parameter for developing marine renewable energy projects around that islands is bathymetry. Azzellino et al. [29], using a spatial planning approach, explained the feasibility of installation by choosing a best location for wind turbine generators (WTGs) and wave energy converters (WECs). They proposed a quantitative spatial approach to identify potential sites of interest for the development of marine renewables with an effective perspective, by considering and minimizing potential environmental impacts. The obtained results showed that the Tyrrhenian coastline surrounding the island of Elba, the Northern and Western Sardinian coasts and the Adriatic Sea and Ionian coastal waters, were the most suitable sites for installing marine energy converters. Moreover, further studies about SWOT analysis locations are available.

In the light of the above considerations, the main aim of this research is to develop for the first time a framework on SWOT analysis for investigating the strengths, weaknesses, opportunities and threat of remote sensing (RS) techniques to measure potential power from OW installations. In particular, the present study aimed at elaborating a SWOT analysis for assessing the wind energy potential with RS techniques in the biggest islands of the Mediterranean Sea: Sicily and Sardinia. This study raises a better understanding of how to use RS technology to replace traditional wind measurement tools. The results of the SWOT analysis are expected: (i) to highlight satellites' ability to measure marine renewables; (ii) to identify pros and cons of using these techniques.

#### *1.1. Synthetic Aperture Radar (SAR) Satellites*

The first European Space Agency (ESA) satellite was launched in July 1991 and called the European remote sensing satellite (ERS-1) [30], which is a C band (5.3 GHz) microwave instrumentation (AMI) satellite [31]. The ESA has recently designed, developed, and launched new satellites so-called Sentinel family providing free data after a simple registration. These satellites could be used to conduct research on various parameters of RESs, for example wind speed, wind direction, wave height, tidal, thermal ocean water and ocean depth measurement. Several SAR satellites and scatterometer, such as QuikSCAT, OSCAT, ASCAT-A, ASCAT-B and Sentinel have already in use for this purpose. Sentinel family which is a group of satellites orbiting around the earth with varying revisiting time for observing land, ocean and atmosphere from space and then for providing us the data free of charge anytime around the year (24/7 and 365 days). One of the most ambitious and world's largest earth observation program in existence today is the Copernicus. Previously known as GMES (global monitoring for environment and security), aiming to tackle environmental challenges with a fleet of autonomous

satellites. Starting from global warming to land use change and the atmosphere, Copernicus gather earth data from space and in the center of it all is its Sentinel family of satellites. Satellites are largely used for di fferent kind of applications [32].

Karagali et al. [33], explained the characterization of the near-surface winds over the northern European shelf seas using satellite data, including the inter- and intra- annual variability for resource assessment purposes. Comparison of mean winds from QuikSCAT with reanalysis fields from the WRF (weather research and forecasting) model and in situ data from FINO-1 o ffshore research mast was carried out. By this study, the applicability of satellite observations as the means to provide useful information for selecting areas performing higher resolution model runs or for mast installations. It is observed that there were biases ranging mostly between 0.6 and −0.6 ms<sup>−</sup><sup>1</sup> with a standard deviation of 1.8–2.8 ms<sup>−</sup>1. The combined analyses of inter- and intra- annual indices and the wind speed and direction distributions allow the identification of three subdomains with similar intraannual variability. High-resolution satellite SAR wind fields were depicted to observe the local characteristics from the long-term QuikSCAT wind rose distributions. The WRF reanalysis dataset misses seasonal features observed for QuikSCAT and at FINO-1 winds.

Bentamy et al. [34] considered some more specific areas for studying and assessing the o ffshore wind power potential. To achieve their objective, requirement was given on wind speed and direction with enough spatial and temporal sampling under all weather conditions during day and night. For more than 12 years of remotely sensed consistent data that were retrieved from ASCAT and QuikSCAT scatterometer estimations, were used to find conventional moments associated with wind distribution parameters and then the latter comparable to wind observations from meteorological stations. Further improvisation was carried out by combining in situ and scatterometer wind information. Wind statistical results were used to study the spatial and temporal patterns of the wind power. Also, they depicted the main parameters characterizing wind power potential such as variability, mean, maximum energy, wind speed and intraannual exhibit seasonal features and interannual variability and then found the di fferences between the wind power estimated for northern and for southern Brittany. Signell et al. [35] detailed spatial structure of jets using the in-situ observations that were carried out on northeast Bora wind events in the Adriatic Sea during the winter. For this, high resolution spaceborne RADARSAT 1 SAR images collected during the active Bora period of the year 2003, created a series of high-resolution maps of 300 m dimension. Along with the previous observations on Bora winds, in this study, it was understood that along the Italian coast, several images show a wide (20–30 km) band of northwesterly winds that abruptly change to northeasterly Bora winds further the o ffshore. It was concluded by meteorological model that northwesterly winds are consistent with those of a barrier jet forming along Italian Apennine mountain chain.
